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"=================================================================\n", "CAPTION PREP: Stream once, save all chunks\n", "=================================================================\n", " Target: 36,000,000 captions\n", " Chunks: 72 × 500,000\n", " Existing: 24 chunks\n", " Missing: 48 chunks\n", " First missing chunk: 24 (will skip existing chunks in stream)\n", "\n", " Streaming CC12M (no dedup, raw collection)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "4c753f56924b400b87aecde6d9540c6e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "train.jsonl.gz: 0%| | 0.00/3.58G [00:00 10:\n", " existing_chunks.append(c)\n", " elif fname in repo_table:\n", " existing_chunks.append(c)\n", " else:\n", " missing_chunks.append(c)\n", "\n", "print(f\" Existing: {len(existing_chunks)} chunks\")\n", "print(f\" Missing: {len(missing_chunks)} chunks\")\n", "\n", "if not missing_chunks:\n", " print(\" All caption chunks exist! Nothing to do.\")\n", " print(\" Uploading any local chunks not yet in repo...\")\n", " for c in range(N_CHUNKS):\n", " fname = f\"captions_{c:03d}.json\"\n", " local = os.path.join(CACHE_DIR, fname)\n", " if os.path.exists(local) and fname not in repo_table:\n", " api.upload_file(path_or_fileobj=local, path_in_repo=fname,\n", " repo_id=DATASET_REPO, repo_type=\"dataset\")\n", " print(f\" ✓ {fname}\")\n", " print(\"DONE\")\n", " exit()\n", "\n", "first_missing = missing_chunks[0]\n", "print(f\" First missing chunk: {first_missing} (will skip existing chunks in stream)\")\n", "\n", "# ── Stream + chunk directly to disk ──\n", "print(f\"\\n Streaming CC12M (no dedup, raw collection)...\")\n", "from datasets import load_dataset\n", "ds = load_dataset(\"CaptionEmporium/conceptual-captions-cc12m-llavanext\",\n", " split=\"train\", streaming=False)\n", "\n", "current_chunk = []\n", "current_chunk_id = 0\n", "valid_count = 0\n", "chunks_saved = 0\n", "\n", "for row in tqdm(ds, desc=\" Streaming\"):\n", " for key in [\"caption_llava\", \"caption_llava_short\", \"caption\"]:\n", " cap = row.get(key, \"\")\n", " if not isinstance(cap, str) or len(cap) == 0:\n", " continue\n", "\n", " # Skip already-existing chunks by counting\n", " if current_chunk_id in existing_chunks:\n", " valid_count += 1\n", " if valid_count >= CHUNK_SIZE:\n", " valid_count = 0\n", " current_chunk_id += 1\n", " continue\n", "\n", " current_chunk.append(cap)\n", "\n", " # Chunk full?\n", " if len(current_chunk) >= CHUNK_SIZE:\n", " fname = f\"captions_{current_chunk_id:03d}.json\"\n", " local = os.path.join(CACHE_DIR, fname)\n", " with open(local, \"w\") as f:\n", " json.dump(current_chunk, f)\n", "\n", " # Upload immediately\n", " if fname not in repo_table:\n", " api.upload_file(path_or_fileobj=local, path_in_repo=fname,\n", " repo_id=DATASET_REPO, repo_type=\"dataset\")\n", " repo_table[fname] = os.path.getsize(local)\n", "\n", " chunks_saved += 1\n", " print(f\"\\n ✓ captions_{current_chunk_id:03d}.json \"\n", " f\"({len(current_chunk):,}) [saved: {chunks_saved}]\")\n", "\n", " current_chunk = []\n", " current_chunk_id += 1\n", "\n", " # Done?\n", " if current_chunk_id >= N_CHUNKS:\n", " break\n", "\n", " if current_chunk_id >= N_CHUNKS:\n", " break\n", "\n", "# Save partial last chunk if any\n", "if current_chunk and current_chunk_id < N_CHUNKS:\n", " fname = f\"captions_{current_chunk_id:03d}.json\"\n", " local = os.path.join(CACHE_DIR, fname)\n", " with open(local, \"w\") as f:\n", " json.dump(current_chunk, f)\n", " if fname not in repo_table:\n", " api.upload_file(path_or_fileobj=local, path_in_repo=fname,\n", " repo_id=DATASET_REPO, repo_type=\"dataset\")\n", " chunks_saved += 1\n", " print(f\"\\n ✓ captions_{current_chunk_id:03d}.json ({len(current_chunk):,}) [partial]\")\n", "\n", "# Upload manifest\n", "manifest = {\n", " \"total_captions\": N_TOTAL,\n", " \"chunk_size\": CHUNK_SIZE,\n", " \"n_chunks\": N_CHUNKS,\n", " \"caption_fields\": [\"caption_llava\", \"caption_llava_short\", \"caption\"],\n", " \"experts\": [\"bert\", \"modern\", \"roberta\", \"albert\", \"distil\"],\n", "}\n", "mpath = os.path.join(CACHE_DIR, \"captions_manifest.json\")\n", "with open(mpath, \"w\") as f:\n", " json.dump(manifest, f, indent=2)\n", "api.upload_file(path_or_fileobj=mpath, path_in_repo=\"captions_manifest.json\",\n", " repo_id=DATASET_REPO, repo_type=\"dataset\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(f\"DONE: {chunks_saved} chunks saved + uploaded\")\n", "print(f\" Repo: https://huggingface.co/datasets/{DATASET_REPO}\")\n", "print(f\"{'='*65}\")" ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# EXTRACT + CACHE: CC12M Expert Embeddings (Resume-Safe)\n", "#\n", "# Every operation is atomic and resumable:\n", "# - Caption chunks: saved immediately per chunk, skipped if exists\n", "# - Expert .pt files: saved per expert per chunk, skipped if exists\n", "# - Upload: per file, skipped if exists in repo\n", "# - On restart: checks local → repo → only does what's missing\n", "#\n", "# Designed for multi-T4 coordination:\n", "# Set CHUNK_RANGE = (start, end) to assign different chunks to different GPUs\n", "# Each GPU only processes its assigned range\n", "# ============================================================================\n", "\n", "import gc\n", "import os\n", "import json\n", "import time\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "from tqdm import tqdm\n", "from huggingface_hub import HfApi\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "EXPERTS = [\n", " # (model_name, short_name, max_len, batch_size)\n", " (\"google-bert/bert-base-uncased\", \"bert\", 512, 2048),\n", " (\"answerdotai/ModernBERT-base\", \"modern\", 8192, 1024),\n", " (\"FacebookAI/roberta-base\", \"roberta\", 512, 2048),\n", " (\"albert/albert-base-v2\", \"albert\", 512, 2048),\n", " (\"distilbert/distilbert-base-uncased\", \"distil\", 512, 2048),\n", "]\n", "EXPERT_NAMES = [s for _, s, _, _ in EXPERTS]\n", "\n", "DATASET_REPO = \"AbstractPhil/conceptual-captions-12m-webdataset-berts\"\n", "CACHE_DIR = \"/home/claude/cc12m_expert_cache\"\n", "\n", "N_TOTAL = 36_000_000\n", "CHUNK_SIZE = 500_000 # 500K per chunk = 24 chunks\n", "\n", "# ── Multi-GPU assignment ──\n", "# 12M / 500K = 24 chunks\n", "# None = all chunks\n", "# GPU 1: CHUNK_RANGE = (0, 8)\n", "# GPU 2: CHUNK_RANGE = (8, 16)\n", "# GPU 3: CHUNK_RANGE = (16, 24)\n", "CHUNK_RANGE = (17, 100)\n", "\n", "api = HfApi()\n", "os.makedirs(CACHE_DIR, exist_ok=True)\n", "\n", "N_CHUNKS = (N_TOTAL + CHUNK_SIZE - 1) // CHUNK_SIZE\n", "if CHUNK_RANGE:\n", " my_chunks = list(range(CHUNK_RANGE[0], min(CHUNK_RANGE[1], N_CHUNKS)))\n", "else:\n", " my_chunks = list(range(N_CHUNKS))\n", "\n", "print(\"=\" * 65)\n", "print(\"EXTRACT + CACHE: CC12M Expert Embeddings (Resume-Safe)\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" Total: {N_TOTAL:,} captions, {N_CHUNKS} chunks × {CHUNK_SIZE:,}\")\n", "print(f\" My chunks: {my_chunks}\")\n", "print(f\" Experts: {len(EXPERTS)}\")\n", "print(f\" Repo: {DATASET_REPO}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# REPO STATE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "try:\n", " info = api.repo_info(DATASET_REPO, repo_type=\"dataset\")\n", "except Exception:\n", " api.create_repo(DATASET_REPO, repo_type=\"dataset\", private=False)\n", " info = api.repo_info(DATASET_REPO, repo_type=\"dataset\")\n", " print(f\" Created repo: {DATASET_REPO}\")\n", "\n", "# Single API call → full file table with sizes. Zero downloads.\n", "repo_table = {}\n", "if info.siblings:\n", " for s in info.siblings:\n", " repo_table[s.rfilename] = s.size or 0\n", "total_repo_mb = sum(repo_table.values()) / 1e6\n", "print(f\" Repo: {len(repo_table)} files, {total_repo_mb:.0f} MB total\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STATUS CHECK (local + repo table, no downloads)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def file_done(filename):\n", " \"\"\"Check if file exists locally (with valid size) OR in repo table.\"\"\"\n", " local = os.path.join(CACHE_DIR, filename)\n", " if os.path.exists(local):\n", " # Verify local file isn't truncated (>1KB for .pt, >10 bytes for .json)\n", " sz = os.path.getsize(local)\n", " if filename.endswith(\".pt\") and sz > 1000:\n", " return True\n", " if filename.endswith(\".json\") and sz > 10:\n", " return True\n", " # Check repo table (already fetched, no API call)\n", " return filename in repo_table\n", "\n", "def upload_if_needed(local_path, repo_name):\n", " \"\"\"Upload file if not already in repo table.\"\"\"\n", " if repo_name not in repo_table:\n", " api.upload_file(path_or_fileobj=local_path,\n", " path_in_repo=repo_name,\n", " repo_id=DATASET_REPO, repo_type=\"dataset\")\n", " repo_table[repo_name] = os.path.getsize(local_path)\n", " size_mb = os.path.getsize(local_path) / 1e6\n", " print(f\" ✓ Uploaded {repo_name} ({size_mb:.0f} MB)\")\n", " else:\n", " pass # silent skip — already in repo\n", "\n", "# Show status\n", "print(f\"\\n Status:\")\n", "for chunk_id in my_chunks:\n", " cap_done = file_done(f\"captions_{chunk_id:03d}.json\")\n", " expert_status = []\n", " for name in EXPERT_NAMES:\n", " done = file_done(f\"{name}_{chunk_id:03d}.pt\")\n", " expert_status.append(\"✓\" if done else \"·\")\n", " exp_str = \" \".join(expert_status)\n", " cap_str = \"✓\" if cap_done else \"·\"\n", " print(f\" Chunk {chunk_id:03d}: cap={cap_str} experts=[{exp_str}] \"\n", " f\"({'bert modern roberta albert distil'})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 1: DOWNLOAD CAPTION CHUNKS (no streaming, just fetch from repo)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 1: DOWNLOAD CAPTION CHUNKS\")\n", "print(f\"{'='*65}\")\n", "\n", "from huggingface_hub import hf_hub_download\n", "\n", "for chunk_id in my_chunks:\n", " fname = f\"captions_{chunk_id:03d}.json\"\n", " local = os.path.join(CACHE_DIR, fname)\n", "\n", " if os.path.exists(local) and os.path.getsize(local) > 10:\n", " continue # already have it\n", "\n", " if fname not in repo_table:\n", " print(f\" ⚠ {fname} not in repo! Run prep_captions.py first.\")\n", " continue\n", "\n", " print(f\" Downloading {fname}...\", end=\" \")\n", " hf_hub_download(repo_id=DATASET_REPO, filename=fname,\n", " repo_type=\"dataset\", local_dir=CACHE_DIR)\n", " print(f\"✓\")\n", "\n", "# Verify\n", "n_ready = sum(1 for c in my_chunks\n", " if os.path.exists(os.path.join(CACHE_DIR, f\"captions_{c:03d}.json\")))\n", "print(f\" Caption chunks ready: {n_ready}/{len(my_chunks)}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 2: EXPERT EXTRACTION (per chunk, per expert, resumable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 2: EXPERT EXTRACTION\")\n", "print(f\"{'='*65}\")\n", "\n", "for chunk_id in my_chunks:\n", " cap_path = os.path.join(CACHE_DIR, f\"captions_{chunk_id:03d}.json\")\n", " if not os.path.exists(cap_path):\n", " print(f\"\\n Chunk {chunk_id}: no captions, skipping\")\n", " continue\n", "\n", " with open(cap_path) as f:\n", " chunk_caps = json.load(f)\n", " chunk_n = len(chunk_caps)\n", "\n", " # Check which experts still need extraction for this chunk\n", " needed = []\n", " for model_name, short, max_len, batch_sz in EXPERTS:\n", " pt_name = f\"{short}_{chunk_id:03d}.pt\"\n", " if file_done(pt_name):\n", " continue\n", " needed.append((model_name, short, max_len, batch_sz))\n", "\n", " if not needed:\n", " print(f\"\\n Chunk {chunk_id}: all experts done ✓\")\n", " # Still upload any local files not yet in repo\n", " for _, short, _, _ in EXPERTS:\n", " pt_path = os.path.join(CACHE_DIR, f\"{short}_{chunk_id:03d}.pt\")\n", " if os.path.exists(pt_path):\n", " upload_if_needed(pt_path, f\"{short}_{chunk_id:03d}.pt\")\n", " continue\n", "\n", " print(f\"\\n ── Chunk {chunk_id} ({chunk_n:,} captions, \"\n", " f\"{len(needed)} experts remaining) ──\")\n", "\n", " for model_name, short, max_len, batch_sz in needed:\n", " pt_name = f\"{short}_{chunk_id:03d}.pt\"\n", " pt_local = os.path.join(CACHE_DIR, pt_name)\n", "\n", " print(f\" Extracting {short} (max_len={max_len}, batch={batch_sz})...\")\n", " from transformers import AutoModel, AutoTokenizer\n", "\n", " try:\n", " ext_model = AutoModel.from_pretrained(model_name).to(DEVICE).eval()\n", " ext_tok = AutoTokenizer.from_pretrained(model_name)\n", "\n", " all_emb = []\n", " t0 = time.time()\n", " with torch.no_grad():\n", " for i in tqdm(range(0, chunk_n, batch_sz),\n", " desc=f\" {short}\", leave=False):\n", " j = min(i + batch_sz, chunk_n)\n", " batch = chunk_caps[i:j]\n", " inputs = ext_tok(batch, max_length=max_len, padding=True,\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = ext_model(**inputs)\n", " m = inputs.attention_mask.unsqueeze(-1).float()\n", " pooled = (out.last_hidden_state * m).sum(1) / m.sum(1).clamp(min=1)\n", " all_emb.append(pooled.cpu())\n", " # Clear CUDA cache periodically\n", " if i % (batch_sz * 50) == 0 and i > 0:\n", " torch.cuda.empty_cache()\n", "\n", " emb = torch.cat(all_emb)\n", " if emb.shape[1] != 768:\n", " emb = emb[:, :768] if emb.shape[1] > 768 else F.pad(emb, (0, 768 - emb.shape[1]))\n", "\n", " elapsed = time.time() - t0\n", " torch.save(emb, pt_local)\n", " size_mb = os.path.getsize(pt_local) / 1e6\n", " print(f\" {short}: {emb.shape} in {elapsed:.0f}s ({size_mb:.0f} MB)\")\n", "\n", " # Upload immediately\n", " upload_if_needed(pt_local, pt_name)\n", "\n", " except torch.cuda.OutOfMemoryError:\n", " print(f\" ⚠ OOM on {short} chunk {chunk_id}! Clearing and continuing...\")\n", " torch.cuda.empty_cache()\n", " gc.collect()\n", " continue\n", "\n", " except Exception as e:\n", " print(f\" ⚠ Error on {short} chunk {chunk_id}: {e}\")\n", " continue\n", "\n", " finally:\n", " # Always free regardless of success/failure\n", " for var in ['ext_model', 'ext_tok', 'all_emb', 'emb']:\n", " if var in dir():\n", " try:\n", " exec(f\"del {var}\")\n", " except:\n", " pass\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# UPLOAD MANIFEST\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "manifest = {\n", " \"total_captions\": N_TOTAL,\n", " \"chunk_size\": CHUNK_SIZE,\n", " \"n_chunks\": N_CHUNKS,\n", " \"caption_fields\": [\"caption_llava\", \"caption_llava_short\", \"caption\"],\n", " \"experts\": EXPERT_NAMES,\n", " \"deduped_against\": \"consensus_500k\",\n", "}\n", "manifest_path = os.path.join(CACHE_DIR, \"captions_manifest.json\")\n", "with open(manifest_path, \"w\") as f:\n", " json.dump(manifest, f, indent=2)\n", "upload_if_needed(manifest_path, \"captions_manifest.json\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL STATUS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL STATUS\")\n", "print(f\"{'='*65}\")\n", "\n", "total_done = 0\n", "total_needed = 0\n", "for chunk_id in range(N_CHUNKS):\n", " for name in EXPERT_NAMES:\n", " total_needed += 1\n", " if file_done(f\"{name}_{chunk_id:03d}.pt\"):\n", " total_done += 1\n", "\n", "print(f\" Expert files: {total_done}/{total_needed} \"\n", " f\"({total_done/max(total_needed,1)*100:.0f}%)\")\n", "n_cap_files = sum(1 for c in range(N_CHUNKS) if file_done(f\"captions_{c:03d}.json\"))\n", "print(f\" Caption chunks: {n_cap_files}/{N_CHUNKS}\")\n", "\n", "# Local cache size\n", "total_size = sum(os.path.getsize(os.path.join(CACHE_DIR, f))\n", " for f in os.listdir(CACHE_DIR)\n", " if os.path.isfile(os.path.join(CACHE_DIR, f)))\n", "print(f\" Local cache: {total_size / 1e9:.1f} GB\")\n", "\n", "print(f\"\\n Repo: https://huggingface.co/datasets/{DATASET_REPO}\")\n", "print(f\"\\n Multi-GPU usage (24 chunks):\")\n", "print(f\" GPU 1: CHUNK_RANGE = (0, 8)\")\n", "print(f\" GPU 2: CHUNK_RANGE = (8, 16)\")\n", "print(f\" GPU 3: CHUNK_RANGE = (16, 24)\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "345cd29ad6f34ab9844b2b7eca55983d", "040874cead9d49089bc7f1d39ec2d6ec", "4c15925958724d12b02eb8ad4990524b", "fa44ba93e2eb4340917dec0324bf25ed", "bda0cb1dda5c43868ca1d9f2ec3bc40f", "1c6d7387c1454e43b6397cad7c238e69", "f4f73629cbe9425998f10d6c2a953b88", "14b9a314678546428685db0706d4126c", "69742faca967463480a1f915d59fbe6a", 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"1eb320c5d3ee49458a42a0cdc9ca51f4", "e0110a4e0af44e528d0721b1cd95da35", "e6a3228bfe47495e97b71b0e5ffd105c", "6b24b9fca87d41a8ab581e0c3bbcc637", "619b94bbd1bf485ca40d187a084a4646", "fc81dc9799b346b9a52efdac3a72c54e", "e6dcb7284f2a4981accdc5083b1ae799", "97863a2ac53640e49cf6ae920c54fc95", "74aaeca85dc64b6a893d841a7b28ddcb", "5e226ffe4fbf4c10a0eaaa2f80a7c1d8" ] }, "id": "dF-JJKo-mEX7", "outputId": "a5790797-3246-4527-b888-5a08dce1fcba" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "EXTRACT + CACHE: CC12M Expert Embeddings (Resume-Safe)\n", "=================================================================\n", " Device: cuda\n", " Total: 36,000,000 captions, 72 chunks × 500,000\n", " My chunks: [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]\n", " Experts: 5\n", " Repo: AbstractPhil/conceptual-captions-12m-webdataset-berts\n", " Repo: 144 files, 0 MB total\n", "\n", " Status:\n", " Chunk 017: cap=✓ experts=[✓ ✓ ✓ ✓ ✓] (bert modern roberta albert distil)\n", " Chunk 018: cap=✓ experts=[✓ ✓ ✓ ✓ ✓] (bert modern roberta albert distil)\n", " Chunk 019: cap=✓ experts=[✓ ✓ ✓ ✓ ✓] (bert modern roberta albert distil)\n", " Chunk 020: cap=✓ experts=[✓ ✓ ✓ ✓ ✓] (bert modern roberta albert distil)\n", " Chunk 021: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 022: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 023: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 024: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 025: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 026: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 027: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 028: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 029: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 030: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 031: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 032: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 033: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 034: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 035: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 036: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 037: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 038: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 039: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 040: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 041: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 042: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 043: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 044: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 045: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 046: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 047: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 048: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 049: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 050: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 051: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 052: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 053: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 054: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 055: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 056: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 057: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 058: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 059: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 060: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 061: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 062: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 063: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 064: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 065: cap=✓ experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 066: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 067: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 068: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 069: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 070: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", " Chunk 071: cap=· experts=[· · · · ·] (bert modern roberta albert distil)\n", "\n", "=================================================================\n", "PHASE 1: DOWNLOAD CAPTION CHUNKS\n", "=================================================================\n", " ⚠ captions_066.json not in repo! Run prep_captions.py first.\n", " ⚠ captions_067.json not in repo! Run prep_captions.py first.\n", " ⚠ captions_068.json not in repo! Run prep_captions.py first.\n", " ⚠ captions_069.json not in repo! Run prep_captions.py first.\n", " ⚠ captions_070.json not in repo! Run prep_captions.py first.\n", " ⚠ captions_071.json not in repo! Run prep_captions.py first.\n", " Caption chunks ready: 49/55\n", "\n", "=================================================================\n", "PHASE 2: EXPERT EXTRACTION\n", "=================================================================\n", "\n", " Chunk 17: all experts done ✓\n", "\n", " Chunk 18: all experts done ✓\n", "\n", " Chunk 19: all experts done ✓\n", "\n", " Chunk 20: all experts done ✓\n", "\n", " ── Chunk 21 (500,000 captions, 5 experts remaining) ──\n", " Extracting bert (max_len=512, batch=2048)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial_mag = (grad_f * emb_n).sum(dim=-1, keepdim=True)\n", " normal = radial_mag * emb_n\n", " tangential = grad_f - normal\n", " return tangential.to(grad.dtype), normal.to(grad.dtype)\n", "\n", "\n", "def cv_gate(cv_current, cv_target, tolerance=0.02):\n", " delta = cv_current - cv_target\n", " if abs(delta) <= tolerance: return 0.0\n", " if delta < 0:\n", " magnitude = min(abs(delta) / (cv_target + 1e-8), 1.0)\n", " return float(magnitude * 0.3)\n", " else:\n", " magnitude = min(delta / (cv_target + 1e-8), 1.0)\n", " return float(max(0.0, 0.1 * (1.0 - magnitude)))\n", "\n", "\n", "class TangentialGradientFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, gate_normal):\n", " ctx.save_for_backward(embedding)\n", " ctx.gate_normal = gate_normal\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, = ctx.saved_tensors\n", " tangential, normal = tangential_projection(grad_output, embedding)\n", " corrected = tangential + ctx.gate_normal * normal\n", " return corrected, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL: Simple conv2d → embedding → prototypes\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ShapeClassifier(nn.Module):\n", " \"\"\"\n", " Conv2d → flatten → project to hypersphere → prototype classification.\n", " The embedding lives on the unit hypersphere (L2 normalized).\n", " \"\"\"\n", " def __init__(self, d_embed=64, n_classes=3):\n", " super().__init__()\n", " self.d_embed = d_embed\n", "\n", " # Simple conv backbone\n", " self.conv = nn.Sequential(\n", " nn.Conv2d(1, 16, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2), # 16x16\n", " nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2), # 8x8\n", " nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),\n", " nn.AdaptiveAvgPool2d(1), # 64x1x1\n", " )\n", "\n", " # Project to embedding hypersphere\n", " self.proj = nn.Sequential(\n", " nn.Linear(64, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Prototype classifier (on hypersphere, like our NLI head)\n", " self.prototypes = nn.Parameter(\n", " F.normalize(torch.randn(n_classes, d_embed), dim=-1))\n", " self.temperature = nn.Parameter(torch.tensor(10.0))\n", "\n", " def forward(self, x):\n", " # Conv features\n", " feat = self.conv(x).flatten(1) # (B, 64)\n", "\n", " # Project and normalize to hypersphere\n", " emb = F.normalize(self.proj(feat), dim=-1) # (B, d_embed)\n", "\n", " # Cosine to prototypes\n", " protos_n = F.normalize(self.prototypes, dim=-1)\n", " logits = emb @ protos_n.T * self.temperature.abs()\n", "\n", " return logits, emb\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_model(use_autograd_gates=False, cv_target=0.15, epochs=30, tag=\"\"):\n", " \"\"\"Train shape classifier with or without geometric autograd gates.\"\"\"\n", "\n", " # Data\n", " train_imgs, train_labels = generate_dataset(n_per_class=2000)\n", " val_imgs, val_labels = generate_dataset(n_per_class=500)\n", " train_imgs = train_imgs.to(DEVICE)\n", " train_labels = train_labels.to(DEVICE)\n", " val_imgs = val_imgs.to(DEVICE)\n", " val_labels = val_labels.to(DEVICE)\n", "\n", " n_train = len(train_labels)\n", " n_val = len(val_labels)\n", "\n", " # Model\n", " model = ShapeClassifier(d_embed=64, n_classes=3).to(DEVICE)\n", " optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 128\n", "\n", " # Autograd state\n", " cv_current = cv_target\n", " gate_val = 0.0\n", "\n", " history = []\n", "\n", " for epoch in range(epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " logits, emb = model(train_imgs[idx])\n", "\n", " if use_autograd_gates:\n", " # Measure CV every few steps\n", " if n % 10 == 0:\n", " cv_current = pentachoron_cv(emb, n_samples=50)\n", " gate_val = cv_gate(cv_current, cv_target)\n", "\n", " # Apply tangential projection — intercept gradient\n", " emb_corrected = TangentialGradientFn.apply(\n", " emb, emb, gate_val)\n", "\n", " # Recompute logits through corrected embedding\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " logits = emb_corrected @ protos_n.T * model.temperature.abs()\n", "\n", " labels = train_labels[idx]\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " v_logits, v_emb = model(val_imgs)\n", " v_loss = F.cross_entropy(v_logits, val_labels).item()\n", " v_acc = (v_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(v_emb, n_samples=100)\n", "\n", " # Prototype separation\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " proto_sim = protos_n @ protos_n.T\n", " proto_off_diag = proto_sim[~torch.eye(3, dtype=bool, device=DEVICE)].mean().item()\n", "\n", " # Per-class embedding CV\n", " class_cvs = []\n", " for c in range(3):\n", " mask = val_labels == c\n", " if mask.sum() >= 5:\n", " class_cvs.append(pentachoron_cv(v_emb[mask], n_samples=50))\n", " else:\n", " class_cvs.append(0.0)\n", "\n", " history.append({\n", " \"epoch\": epoch + 1,\n", " \"train_acc\": train_acc,\n", " \"val_acc\": v_acc,\n", " \"val_cv\": v_cv,\n", " \"gate_cv\": gate_val,\n", " \"proto_sim\": proto_off_diag,\n", " \"class_cvs\": class_cvs,\n", " })\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"proto_sim={proto_off_diag:.3f} \"\n", " f\"class_cv=[{class_cvs[0]:.3f},{class_cvs[1]:.3f},{class_cvs[2]:.3f}]\")\n", "\n", " return history\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "torch.manual_seed(42)\n", "np.random.seed(42)\n", "\n", "# First: measure natural CV of the shape embeddings\n", "print(f\"\\n{'='*65}\")\n", "print(\"BASELINE: No geometric gates\")\n", "print(f\"{'='*65}\")\n", "h_baseline = train_model(use_autograd_gates=False, tag=\"baseline\")\n", "\n", "torch.manual_seed(42)\n", "np.random.seed(42)\n", "\n", "# Determine target CV from baseline final epoch\n", "target_cv = h_baseline[-1][\"val_cv\"]\n", "print(f\"\\n Measured natural CV: {target_cv:.4f}\")\n", "print(f\" Using as target for gated run\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(f\"GATED: Tangential projection + CV gate (target={target_cv:.4f})\")\n", "print(f\"{'='*65}\")\n", "h_gated = train_model(use_autograd_gates=True, cv_target=target_cv, tag=\"gated\")\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Metric':<25} {'Baseline':>10} {'Gated':>10}\")\n", "print(f\" {'-'*47}\")\n", "\n", "# Final epoch\n", "b = h_baseline[-1]\n", "g = h_gated[-1]\n", "\n", "metrics = [\n", " (\"Val accuracy\", f\"{b['val_acc']:.3f}\", f\"{g['val_acc']:.3f}\"),\n", " (\"Train accuracy\", f\"{b['train_acc']:.3f}\", f\"{g['train_acc']:.3f}\"),\n", " (\"Overfit gap\", f\"{b['train_acc']-b['val_acc']:.3f}\", f\"{g['train_acc']-g['val_acc']:.3f}\"),\n", " (\"Val CV\", f\"{b['val_cv']:.4f}\", f\"{g['val_cv']:.4f}\"),\n", " (\"Proto similarity\", f\"{b['proto_sim']:.3f}\", f\"{g['proto_sim']:.3f}\"),\n", " (\"CV tri\", f\"{b['class_cvs'][0]:.3f}\", f\"{g['class_cvs'][0]:.3f}\"),\n", " (\"CV circle\", f\"{b['class_cvs'][1]:.3f}\", f\"{g['class_cvs'][1]:.3f}\"),\n", " (\"CV pentagon\", f\"{b['class_cvs'][2]:.3f}\", f\"{g['class_cvs'][2]:.3f}\"),\n", "]\n", "\n", "for name, bv, gv in metrics:\n", " print(f\" {name:<25} {bv:>10} {gv:>10}\")\n", "\n", "# CV stability over training\n", "b_cvs = [h[\"val_cv\"] for h in h_baseline]\n", "g_cvs = [h[\"val_cv\"] for h in h_gated]\n", "print(f\"\\n CV trajectory (std over epochs):\")\n", "print(f\" Baseline: {np.std(b_cvs):.4f}\")\n", "print(f\" Gated: {np.std(g_cvs):.4f}\")\n", "print(f\" {'Gated more stable' if np.std(g_cvs) < np.std(b_cvs) else 'Baseline more stable'}\")\n", "\n", "# Gap trajectory\n", "b_gaps = [h[\"train_acc\"] - h[\"val_acc\"] for h in h_baseline]\n", "g_gaps = [h[\"train_acc\"] - h[\"val_acc\"] for h in h_gated]\n", "print(f\"\\n Overfit gap trajectory (mean ± std):\")\n", "print(f\" Baseline: {np.mean(b_gaps):.3f} ± {np.std(b_gaps):.3f}\")\n", "print(f\" Gated: {np.mean(g_gaps):.3f} ± {np.std(g_gaps):.3f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7GH8pIX_MiiC", "outputId": "2882f08b-912e-4e6a-87f4-975ae5cd0c5b" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOMETRIC AUTOGRAD VALIDATION\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "BASELINE: No geometric gates\n", "=================================================================\n", " E 1: t_acc=0.530 v_acc=0.753 cv=0.7109 gate=0.000 proto_sim=-0.076 class_cv=[0.603,0.075,0.465]\n", " E 5: t_acc=0.981 v_acc=0.987 cv=0.9707 gate=0.000 proto_sim=-0.150 class_cv=[1.066,0.712,1.114]\n", " E10: t_acc=0.989 v_acc=0.975 cv=0.9727 gate=0.000 proto_sim=-0.174 class_cv=[0.984,0.538,0.963]\n", " E15: t_acc=0.996 v_acc=0.994 cv=0.9118 gate=0.000 proto_sim=-0.197 class_cv=[0.703,0.817,0.996]\n", " E20: t_acc=0.997 v_acc=0.985 cv=0.8054 gate=0.000 proto_sim=-0.214 class_cv=[0.796,0.645,0.849]\n", " E25: t_acc=1.000 v_acc=0.999 cv=0.8446 gate=0.000 proto_sim=-0.231 class_cv=[0.871,0.606,0.864]\n", " E30: t_acc=1.000 v_acc=0.999 cv=0.7888 gate=0.000 proto_sim=-0.242 class_cv=[0.854,0.768,1.238]\n", "\n", " Measured natural CV: 0.7888\n", " Using as target for gated run\n", "\n", "=================================================================\n", "GATED: Tangential projection + CV gate (target=0.7888)\n", "=================================================================\n", " E 1: t_acc=0.530 v_acc=0.753 cv=0.7276 gate=0.128 proto_sim=-0.076 class_cv=[0.438,0.093,0.525]\n", " E 5: t_acc=0.973 v_acc=0.985 cv=1.0621 gate=0.068 proto_sim=-0.146 class_cv=[0.811,0.647,1.133]\n", " E10: t_acc=0.989 v_acc=0.992 cv=1.0036 gate=0.093 proto_sim=-0.170 class_cv=[0.655,0.642,1.016]\n", " E15: t_acc=0.997 v_acc=0.996 cv=0.7649 gate=0.052 proto_sim=-0.190 class_cv=[0.701,0.667,1.075]\n", " E20: t_acc=0.999 v_acc=0.995 cv=0.6960 gate=0.038 proto_sim=-0.206 class_cv=[0.670,0.635,0.942]\n", " E25: t_acc=0.997 v_acc=0.998 cv=0.7653 gate=0.088 proto_sim=-0.242 class_cv=[0.800,0.656,1.316]\n", " E30: t_acc=0.999 v_acc=0.998 cv=0.8755 gate=0.085 proto_sim=-0.253 class_cv=[0.721,0.669,1.009]\n", "\n", "=================================================================\n", "COMPARISON\n", "=================================================================\n", "\n", " Metric Baseline Gated\n", " -----------------------------------------------\n", " Val accuracy 0.999 0.998\n", " Train accuracy 1.000 0.999\n", " Overfit gap 0.001 0.001\n", " Val CV 0.7888 0.8755\n", " Proto similarity -0.242 -0.253\n", " CV tri 0.854 0.721\n", " CV circle 0.768 0.669\n", " CV pentagon 1.238 1.009\n", "\n", " CV trajectory (std over epochs):\n", " Baseline: 0.1125\n", " Gated: 0.1127\n", " Baseline more stable\n", "\n", " Overfit gap trajectory (mean ± std):\n", " Baseline: -0.008 ± 0.041\n", " Gated: -0.011 ± 0.043\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# GEOMETRIC AUTOGRAD VALIDATION\n", "#\n", "# Three synthetic shape classes with known geometric properties.\n", "# Simple conv2d classifier. Prove the tangential projection +\n", "# CV gating preserves manifold structure during training.\n", "#\n", "# Shapes (rendered as 32×32 grayscale):\n", "# Class 0: Perturbed triangles — rigid, low CV, sharp vertices\n", "# Class 1: Perturbed circles — smooth, high CV, uniform curvature\n", "# Class 2: Perturbed pentagons — intermediate, moderate CV\n", "#\n", "# All shapes live on a unit circle (2D hypersphere projection).\n", "# Perturbations are radial noise — push vertices off the circle.\n", "# The embedding space should preserve the geometric distinction:\n", "# triangles cluster separately from circles cluster separately from pentagons.\n", "#\n", "# Test: train classifier WITH and WITHOUT geometric autograd gates.\n", "# With gates: embedding CV stays near target, accuracy holds.\n", "# Without gates: embedding CV drifts, overfits faster.\n", "# ============================================================================\n", "\n", "import math\n", "import time\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from dataclasses import dataclass, field\n", "from typing import Optional\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOMETRIC AUTOGRAD VALIDATION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE GENERATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def render_polygon(n_vertices, img_size=32, perturbation=0.15, thickness=1):\n", " \"\"\"Render a perturbed regular polygon as grayscale image.\"\"\"\n", " img = np.zeros((img_size, img_size), dtype=np.float32)\n", " cx, cy = img_size / 2, img_size / 2\n", " radius = img_size * 0.35\n", "\n", " angles = np.linspace(0, 2 * np.pi, n_vertices, endpoint=False)\n", " angles += np.random.uniform(0, 2 * np.pi)\n", " radii = radius * (1.0 + np.random.normal(0, perturbation, n_vertices))\n", "\n", " points = []\n", " for a, r in zip(angles, radii):\n", " points.append((cx + r * np.cos(a), cy + r * np.sin(a)))\n", "\n", " for i in range(n_vertices):\n", " x0, y0 = points[i]\n", " x1, y1 = points[(i + 1) % n_vertices]\n", " n_steps = max(int(max(abs(x1 - x0), abs(y1 - y0)) * 2), 1)\n", " for t in np.linspace(0, 1, n_steps):\n", " px, py = int(x0 + t * (x1 - x0)), int(y0 + t * (y1 - y0))\n", " for dx in range(-thickness, thickness + 1):\n", " for dy in range(-thickness, thickness + 1):\n", " nx, ny = px + dx, py + dy\n", " if 0 <= nx < img_size and 0 <= ny < img_size:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "def render_star(n_points, img_size=32, perturbation=0.12, thickness=1):\n", " \"\"\"Render a perturbed star — alternating inner/outer radii.\"\"\"\n", " img = np.zeros((img_size, img_size), dtype=np.float32)\n", " cx, cy = img_size / 2, img_size / 2\n", " r_outer = img_size * 0.38\n", " r_inner = img_size * 0.15\n", "\n", " angles = np.linspace(0, 2 * np.pi, n_points * 2, endpoint=False)\n", " angles += np.random.uniform(0, 2 * np.pi)\n", "\n", " points = []\n", " for i, a in enumerate(angles):\n", " r = r_outer if i % 2 == 0 else r_inner\n", " r *= (1.0 + np.random.normal(0, perturbation))\n", " points.append((cx + r * np.cos(a), cy + r * np.sin(a)))\n", "\n", " for i in range(len(points)):\n", " x0, y0 = points[i]\n", " x1, y1 = points[(i + 1) % len(points)]\n", " n_steps = max(int(max(abs(x1 - x0), abs(y1 - y0)) * 2), 1)\n", " for t in np.linspace(0, 1, n_steps):\n", " px, py = int(x0 + t * (x1 - x0)), int(y0 + t * (y1 - y0))\n", " for dx in range(-thickness, thickness + 1):\n", " for dy in range(-thickness, thickness + 1):\n", " nx, ny = px + dx, py + dy\n", " if 0 <= nx < img_size and 0 <= ny < img_size:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "def render_cross(img_size=32, perturbation=0.15, thickness=2):\n", " \"\"\"Render a perturbed cross/plus shape.\"\"\"\n", " img = np.zeros((img_size, img_size), dtype=np.float32)\n", " cx, cy = img_size / 2, img_size / 2\n", " arm = img_size * 0.3\n", "\n", " for angle_base in [0, np.pi/2, np.pi, 3*np.pi/2]:\n", " a = angle_base + np.random.normal(0, perturbation * 0.3)\n", " r = arm * (1.0 + np.random.normal(0, perturbation))\n", " x1 = cx + r * np.cos(a)\n", " y1 = cy + r * np.sin(a)\n", " n_steps = max(int(r * 2), 1)\n", " for t in np.linspace(0, 1, n_steps):\n", " px, py = int(cx + t * (x1 - cx)), int(cy + t * (y1 - cy))\n", " for dx in range(-thickness, thickness + 1):\n", " for dy in range(-thickness, thickness + 1):\n", " nx, ny = px + dx, py + dy\n", " if 0 <= nx < img_size and 0 <= ny < img_size:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "def render_spiral(img_size=32, perturbation=0.1, thickness=1):\n", " \"\"\"Render a perturbed Archimedean spiral.\"\"\"\n", " img = np.zeros((img_size, img_size), dtype=np.float32)\n", " cx, cy = img_size / 2, img_size / 2\n", " n_turns = 2.5 + np.random.normal(0, 0.3)\n", "\n", " for t in np.linspace(0, n_turns * 2 * np.pi, 200):\n", " r = (img_size * 0.015) * t * (1.0 + np.random.normal(0, perturbation * 0.3))\n", " x = int(cx + r * np.cos(t))\n", " y = int(cy + r * np.sin(t))\n", " for dx in range(-thickness, thickness + 1):\n", " for dy in range(-thickness, thickness + 1):\n", " nx, ny = x + dx, y + dy\n", " if 0 <= nx < img_size and 0 <= ny < img_size:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "# Shape class definitions:\n", "# 0: Triangle — 3 vertices, rigid\n", "# 1: Circle — 32-gon, smooth\n", "# 2: Pentagon — 5 vertices, moderate\n", "# 3: Square — 4 vertices, orthogonal\n", "# 4: Hexagon — 6 vertices, honeycomb\n", "# 5: Star-5 — 5-point star, spiky\n", "# 6: Star-7 — 7-point star, more complex spikes\n", "# 7: Octagon — 8 vertices, near-circle\n", "# 8: Cross — 4 arms, different topology\n", "# 9: Spiral — continuous curve, no vertices\n", "\n", "SHAPE_NAMES = [\n", " \"triangle\", \"circle\", \"pentagon\", \"square\", \"hexagon\",\n", " \"star5\", \"star7\", \"octagon\", \"cross\", \"spiral\"\n", "]\n", "\n", "def generate_one(cls, img_size=32):\n", " if cls == 0: return render_polygon(3, img_size, perturbation=0.20)\n", " if cls == 1: return render_polygon(32, img_size, perturbation=0.05)\n", " if cls == 2: return render_polygon(5, img_size, perturbation=0.15)\n", " if cls == 3: return render_polygon(4, img_size, perturbation=0.12)\n", " if cls == 4: return render_polygon(6, img_size, perturbation=0.10)\n", " if cls == 5: return render_star(5, img_size, perturbation=0.12)\n", " if cls == 6: return render_star(7, img_size, perturbation=0.10)\n", " if cls == 7: return render_polygon(8, img_size, perturbation=0.08)\n", " if cls == 8: return render_cross( img_size, perturbation=0.15)\n", " if cls == 9: return render_spiral( img_size, perturbation=0.10)\n", "\n", "\n", "def generate_dataset(n_per_class=2000, img_size=32, n_classes=10):\n", " images, labels = [], []\n", " for _ in range(n_per_class):\n", " for c in range(n_classes):\n", " images.append(generate_one(c, img_size))\n", " labels.append(c)\n", " images = torch.tensor(np.array(images)).unsqueeze(1)\n", " labels = torch.tensor(labels, dtype=torch.long)\n", " perm = torch.randperm(len(labels))\n", " return images[perm], labels[perm]\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (from core.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cayley_menger_vol_sq(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B = pts.shape[0]\n", " cm = torch.zeros(B, 6, 6, device=pts.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1.0; cm[:, 1:, 0] = 1.0; cm[:, 1:, 1:] = d2\n", " return -torch.linalg.det(cm) / 9216.0\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol_sq(emb_f[idx].unsqueeze(0))[0]\n", " v = math.sqrt(max(v2.item(), 0.0))\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial_mag = (grad_f * emb_n).sum(dim=-1, keepdim=True)\n", " normal = radial_mag * emb_n\n", " tangential = grad_f - normal\n", " return tangential.to(grad.dtype), normal.to(grad.dtype)\n", "\n", "\n", "def cv_gate(cv_current, cv_target, tolerance=0.02):\n", " delta = cv_current - cv_target\n", " if abs(delta) <= tolerance: return 0.0\n", " if delta < 0:\n", " magnitude = min(abs(delta) / (cv_target + 1e-8), 1.0)\n", " return float(magnitude * 0.3)\n", " else:\n", " magnitude = min(delta / (cv_target + 1e-8), 1.0)\n", " return float(max(0.0, 0.1 * (1.0 - magnitude)))\n", "\n", "\n", "class TangentialGradientFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, gate_normal):\n", " ctx.save_for_backward(embedding)\n", " ctx.gate_normal = gate_normal\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, = ctx.saved_tensors\n", " tangential, normal = tangential_projection(grad_output, embedding)\n", " corrected = tangential + ctx.gate_normal * normal\n", " return corrected, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL: Simple conv2d → embedding → prototypes\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ShapeClassifier(nn.Module):\n", " \"\"\"\n", " Conv2d → flatten → project to hypersphere → prototype classification.\n", " The embedding lives on the unit hypersphere (L2 normalized).\n", " \"\"\"\n", " def __init__(self, d_embed=64, n_classes=10):\n", " super().__init__()\n", " self.d_embed = d_embed\n", "\n", " # Simple conv backbone\n", " self.conv = nn.Sequential(\n", " nn.Conv2d(1, 16, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2), # 16x16\n", " nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2), # 8x8\n", " nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),\n", " nn.AdaptiveAvgPool2d(1), # 64x1x1\n", " )\n", "\n", " # Project to embedding hypersphere\n", " self.proj = nn.Sequential(\n", " nn.Linear(64, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Prototype classifier (on hypersphere, like our NLI head)\n", " self.prototypes = nn.Parameter(\n", " F.normalize(torch.randn(n_classes, d_embed), dim=-1))\n", " self.temperature = nn.Parameter(torch.tensor(10.0))\n", "\n", " def forward(self, x):\n", " # Conv features\n", " feat = self.conv(x).flatten(1) # (B, 64)\n", "\n", " # Project and normalize to hypersphere\n", " emb = F.normalize(self.proj(feat), dim=-1) # (B, d_embed)\n", "\n", " # Cosine to prototypes\n", " protos_n = F.normalize(self.prototypes, dim=-1)\n", " logits = emb @ protos_n.T * self.temperature.abs()\n", "\n", " return logits, emb\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_model(use_autograd_gates=False, cv_target=0.2, epochs=30, tag=\"\"):\n", " \"\"\"Train shape classifier with or without geometric autograd gates.\"\"\"\n", "\n", " # Data\n", " train_imgs, train_labels = generate_dataset(n_per_class=2000)\n", " val_imgs, val_labels = generate_dataset(n_per_class=500)\n", " train_imgs = train_imgs.to(DEVICE)\n", " train_labels = train_labels.to(DEVICE)\n", " val_imgs = val_imgs.to(DEVICE)\n", " val_labels = val_labels.to(DEVICE)\n", "\n", " n_train = len(train_labels)\n", " n_val = len(val_labels)\n", "\n", " # Model\n", " model = ShapeClassifier(d_embed=64, n_classes=10).to(DEVICE)\n", " optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 128\n", "\n", " # Autograd state\n", " cv_current = cv_target\n", " gate_val = 0.0\n", "\n", " history = []\n", "\n", " for epoch in range(epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " logits, emb = model(train_imgs[idx])\n", "\n", " if use_autograd_gates:\n", " # Measure CV every few steps\n", " if n % 10 == 0:\n", " cv_current = pentachoron_cv(emb, n_samples=50)\n", " gate_val = cv_gate(cv_current, cv_target)\n", "\n", " # Apply tangential projection — intercept gradient\n", " emb_corrected = TangentialGradientFn.apply(\n", " emb, emb, gate_val)\n", "\n", " # Recompute logits through corrected embedding\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " logits = emb_corrected @ protos_n.T * model.temperature.abs()\n", "\n", " labels = train_labels[idx]\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " v_logits, v_emb = model(val_imgs)\n", " v_loss = F.cross_entropy(v_logits, val_labels).item()\n", " v_acc = (v_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(v_emb, n_samples=100)\n", "\n", " # Prototype separation\n", " n_cls = model.prototypes.shape[0]\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " proto_sim = protos_n @ protos_n.T\n", " proto_off_diag = proto_sim[~torch.eye(n_cls, dtype=bool, device=DEVICE)].mean().item()\n", "\n", " # Per-class embedding CV\n", " class_cvs = []\n", " for c in range(n_cls):\n", " mask = val_labels == c\n", " if mask.sum() >= 5:\n", " class_cvs.append(pentachoron_cv(v_emb[mask], n_samples=50))\n", " else:\n", " class_cvs.append(0.0)\n", "\n", " history.append({\n", " \"epoch\": epoch + 1,\n", " \"train_acc\": train_acc,\n", " \"val_acc\": v_acc,\n", " \"val_cv\": v_cv,\n", " \"gate_cv\": gate_val,\n", " \"proto_sim\": proto_off_diag,\n", " \"class_cvs\": class_cvs,\n", " })\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " cv_str = \",\".join(f\"{c:.2f}\" for c in class_cvs)\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"proto_sim={proto_off_diag:.3f} \"\n", " f\"class_cv=[{cv_str}]\")\n", "\n", " return history\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "torch.manual_seed(42)\n", "np.random.seed(42)\n", "\n", "# First: measure natural CV of the shape embeddings\n", "print(f\"\\n{'='*65}\")\n", "print(\"BASELINE: No geometric gates\")\n", "print(f\"{'='*65}\")\n", "h_baseline = train_model(use_autograd_gates=False, tag=\"baseline\")\n", "\n", "torch.manual_seed(42)\n", "np.random.seed(42)\n", "\n", "# Determine target CV from baseline final epoch\n", "target_cv = h_baseline[-1][\"val_cv\"]\n", "print(f\"\\n Measured natural CV: {target_cv:.4f}\")\n", "print(f\" Using as target for gated run\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(f\"GATED: Tangential projection + CV gate (target={target_cv:.4f})\")\n", "print(f\"{'='*65}\")\n", "h_gated = train_model(use_autograd_gates=True, cv_target=target_cv, tag=\"gated\")\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Metric':<25} {'Baseline':>10} {'Gated':>10}\")\n", "print(f\" {'-'*47}\")\n", "\n", "# Final epoch\n", "b = h_baseline[-1]\n", "g = h_gated[-1]\n", "\n", "metrics = [\n", " (\"Val accuracy\", f\"{b['val_acc']:.3f}\", f\"{g['val_acc']:.3f}\"),\n", " (\"Train accuracy\", f\"{b['train_acc']:.3f}\", f\"{g['train_acc']:.3f}\"),\n", " (\"Overfit gap\", f\"{b['train_acc']-b['val_acc']:.3f}\", f\"{g['train_acc']-g['val_acc']:.3f}\"),\n", " (\"Val CV\", f\"{b['val_cv']:.4f}\", f\"{g['val_cv']:.4f}\"),\n", " (\"Proto similarity\", f\"{b['proto_sim']:.3f}\", f\"{g['proto_sim']:.3f}\"),\n", "]\n", "for i, name in enumerate(SHAPE_NAMES):\n", " if i < len(b['class_cvs']) and i < len(g['class_cvs']):\n", " metrics.append((f\"CV {name}\", f\"{b['class_cvs'][i]:.3f}\", f\"{g['class_cvs'][i]:.3f}\"))\n", "\n", "for name, bv, gv in metrics:\n", " print(f\" {name:<25} {bv:>10} {gv:>10}\")\n", "\n", "# CV stability over training\n", "b_cvs = [h[\"val_cv\"] for h in h_baseline]\n", "g_cvs = [h[\"val_cv\"] for h in h_gated]\n", "print(f\"\\n CV trajectory (std over epochs):\")\n", "print(f\" Baseline: {np.std(b_cvs):.4f}\")\n", "print(f\" Gated: {np.std(g_cvs):.4f}\")\n", "print(f\" {'Gated more stable' if np.std(g_cvs) < np.std(b_cvs) else 'Baseline more stable'}\")\n", "\n", "# Gap trajectory\n", "b_gaps = [h[\"train_acc\"] - h[\"val_acc\"] for h in h_baseline]\n", "g_gaps = [h[\"train_acc\"] - h[\"val_acc\"] for h in h_gated]\n", "print(f\"\\n Overfit gap trajectory (mean ± std):\")\n", "print(f\" Baseline: {np.mean(b_gaps):.3f} ± {np.std(b_gaps):.3f}\")\n", "print(f\" Gated: {np.mean(g_gaps):.3f} ± {np.std(g_gaps):.3f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cX1egSL3PHh8", "outputId": "a296d8f4-5a4f-4689-def4-d7708a7fadb6" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOMETRIC AUTOGRAD VALIDATION\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "BASELINE: No geometric gates\n", "=================================================================\n", " E 1: t_acc=0.461 v_acc=0.753 cv=1.1264 gate=0.000 proto_sim=-0.013 class_cv=[1.19,0.73,0.81,0.57,0.72,0.64,0.46,1.24,0.60,1.19]\n", " E 5: t_acc=0.883 v_acc=0.897 cv=0.8867 gate=0.000 proto_sim=-0.054 class_cv=[0.66,0.59,0.74,0.60,0.58,0.69,0.69,0.60,0.69,1.25]\n", " E10: t_acc=0.943 v_acc=0.912 cv=0.7352 gate=0.000 proto_sim=-0.078 class_cv=[0.54,0.63,0.54,0.62,0.70,0.61,0.62,0.62,0.99,1.10]\n", " E15: t_acc=0.963 v_acc=0.976 cv=0.7560 gate=0.000 proto_sim=-0.086 class_cv=[0.54,0.51,0.53,0.48,0.58,0.53,0.52,0.63,0.51,1.26]\n", " E20: t_acc=0.985 v_acc=0.968 cv=0.6911 gate=0.000 proto_sim=-0.085 class_cv=[0.45,0.56,0.64,0.48,0.60,0.45,0.56,0.73,0.49,1.40]\n", " E25: t_acc=0.993 v_acc=0.987 cv=0.7185 gate=0.000 proto_sim=-0.083 class_cv=[0.44,0.72,0.53,0.53,0.63,0.49,0.69,1.08,0.53,1.00]\n", " E30: t_acc=0.993 v_acc=0.991 cv=0.7236 gate=0.000 proto_sim=-0.081 class_cv=[0.47,0.64,0.52,0.52,0.78,0.46,0.72,0.68,0.77,1.27]\n", "\n", " Measured natural CV: 0.7236\n", " Using as target for gated run\n", "\n", "=================================================================\n", "GATED: Tangential projection + CV gate (target=0.7236)\n", "=================================================================\n", " E 1: t_acc=0.458 v_acc=0.725 cv=1.2425 gate=0.036 proto_sim=-0.013 class_cv=[0.83,0.83,0.80,0.73,0.75,0.78,0.86,0.76,0.77,1.36]\n", " E 5: t_acc=0.877 v_acc=0.832 cv=0.9310 gate=0.094 proto_sim=-0.053 class_cv=[0.61,0.65,1.02,0.53,0.85,0.66,0.58,0.80,0.69,1.27]\n", " E10: t_acc=0.947 v_acc=0.922 cv=0.8779 gate=0.009 proto_sim=-0.080 class_cv=[0.52,0.76,0.69,0.63,0.58,0.60,0.52,0.72,0.50,0.94]\n", " E15: t_acc=0.974 v_acc=0.961 cv=0.7929 gate=0.090 proto_sim=-0.089 class_cv=[0.43,0.63,0.47,0.53,0.74,0.60,0.65,0.75,0.63,0.98]\n", " E20: t_acc=0.986 v_acc=0.975 cv=0.7265 gate=0.033 proto_sim=-0.089 class_cv=[0.47,0.46,0.45,0.50,0.59,0.56,0.50,0.77,0.57,1.22]\n", " E25: t_acc=0.992 v_acc=0.988 cv=0.6296 gate=0.059 proto_sim=-0.086 class_cv=[0.50,0.72,0.70,0.56,0.60,0.45,0.53,0.98,0.52,0.79]\n", " E30: t_acc=0.996 v_acc=0.989 cv=0.7476 gate=0.093 proto_sim=-0.085 class_cv=[0.44,0.59,0.56,0.51,0.62,0.52,0.64,0.50,0.83,0.91]\n", "\n", "=================================================================\n", "COMPARISON\n", "=================================================================\n", "\n", " Metric Baseline Gated\n", " -----------------------------------------------\n", " Val accuracy 0.991 0.989\n", " Train accuracy 0.993 0.996\n", " Overfit gap 0.002 0.007\n", " Val CV 0.7236 0.7476\n", " Proto similarity -0.081 -0.085\n", " CV triangle 0.474 0.436\n", " CV circle 0.644 0.592\n", " CV pentagon 0.522 0.559\n", " CV square 0.521 0.508\n", " CV hexagon 0.778 0.623\n", " CV star5 0.464 0.519\n", " CV star7 0.721 0.645\n", " CV octagon 0.683 0.500\n", " CV cross 0.771 0.829\n", " CV spiral 1.267 0.914\n", "\n", " CV trajectory (std over epochs):\n", " Baseline: 0.1144\n", " Gated: 0.1307\n", " Baseline more stable\n", "\n", " Overfit gap trajectory (mean ± std):\n", " Baseline: -0.007 ± 0.056\n", " Gated: 0.002 ± 0.053\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 1.3" ], "metadata": { "id": "d2WDxS0qSE7f" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# SHAPE ANCHOR EXTRACTION\n", "#\n", "# 30 shape classes. Extract 768-dim raw + 128-dim bank embeddings\n", "# from CaptionBERT using simple text prompts. Check cosine structure.\n", "# These become the patchwork embedding vectors.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "REPO_ID = \"AbstractPhil/geolip-captionbert-8192\"\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 30 SHAPE DEFINITIONS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "SHAPES = [\n", " # Polygons (vertex count progression)\n", " {\"name\": \"triangle\", \"prompt\": \"a triangle shape with three vertices\", \"type\": \"polygon\", \"vertices\": 3},\n", " {\"name\": \"square\", \"prompt\": \"a square shape with four equal sides\", \"type\": \"polygon\", \"vertices\": 4},\n", " {\"name\": \"pentagon\", \"prompt\": \"a pentagon shape with five sides\", \"type\": \"polygon\", \"vertices\": 5},\n", " {\"name\": \"hexagon\", \"prompt\": \"a hexagon shape with six sides\", \"type\": \"polygon\", \"vertices\": 6},\n", " {\"name\": \"heptagon\", \"prompt\": \"a heptagon shape with seven sides\", \"type\": \"polygon\", \"vertices\": 7},\n", " {\"name\": \"octagon\", \"prompt\": \"an octagon shape with eight sides\", \"type\": \"polygon\", \"vertices\": 8},\n", " {\"name\": \"nonagon\", \"prompt\": \"a nonagon shape with nine sides\", \"type\": \"polygon\", \"vertices\": 9},\n", " {\"name\": \"decagon\", \"prompt\": \"a decagon shape with ten sides\", \"type\": \"polygon\", \"vertices\": 10},\n", " {\"name\": \"dodecagon\", \"prompt\": \"a dodecagon shape with twelve sides\", \"type\": \"polygon\", \"vertices\": 12},\n", "\n", " # Smooth / curves\n", " {\"name\": \"circle\", \"prompt\": \"a circle shape perfectly round\", \"type\": \"curve\", \"vertices\": 0},\n", " {\"name\": \"ellipse\", \"prompt\": \"an ellipse shape elongated oval\", \"type\": \"curve\", \"vertices\": 0},\n", " {\"name\": \"spiral\", \"prompt\": \"a spiral shape curving inward\", \"type\": \"curve\", \"vertices\": 0},\n", " {\"name\": \"wave\", \"prompt\": \"a wave shape with smooth oscillations\", \"type\": \"curve\", \"vertices\": 0},\n", " {\"name\": \"crescent\", \"prompt\": \"a crescent moon shape curved arc\", \"type\": \"curve\", \"vertices\": 0},\n", "\n", " # Stars (spike count progression)\n", " {\"name\": \"star3\", \"prompt\": \"a three pointed star shape\", \"type\": \"star\", \"vertices\": 6},\n", " {\"name\": \"star4\", \"prompt\": \"a four pointed star shape\", \"type\": \"star\", \"vertices\": 8},\n", " {\"name\": \"star5\", \"prompt\": \"a five pointed star shape\", \"type\": \"star\", \"vertices\": 10},\n", " {\"name\": \"star6\", \"prompt\": \"a six pointed star of david shape\", \"type\": \"star\", \"vertices\": 12},\n", " {\"name\": \"star7\", \"prompt\": \"a seven pointed star shape\", \"type\": \"star\", \"vertices\": 14},\n", " {\"name\": \"star8\", \"prompt\": \"an eight pointed star shape\", \"type\": \"star\", \"vertices\": 16},\n", "\n", " # Structural / topological\n", " {\"name\": \"cross\", \"prompt\": \"a cross shape with four arms\", \"type\": \"structure\", \"vertices\": 12},\n", " {\"name\": \"diamond\", \"prompt\": \"a diamond shape rotated square\", \"type\": \"structure\", \"vertices\": 4},\n", " {\"name\": \"arrow\", \"prompt\": \"an arrow shape pointing right\", \"type\": \"structure\", \"vertices\": 7},\n", " {\"name\": \"heart\", \"prompt\": \"a heart shape with curved top\", \"type\": \"structure\", \"vertices\": 0},\n", " {\"name\": \"ring\", \"prompt\": \"a ring shape circle with hole\", \"type\": \"structure\", \"vertices\": 0},\n", " {\"name\": \"semicircle\", \"prompt\": \"a semicircle shape half circle\", \"type\": \"structure\", \"vertices\": 0},\n", " {\"name\": \"trapezoid\", \"prompt\": \"a trapezoid shape with parallel sides\", \"type\": \"structure\", \"vertices\": 4},\n", " {\"name\": \"parallelogram\", \"prompt\": \"a parallelogram shape slanted rectangle\", \"type\": \"structure\", \"vertices\": 4},\n", " {\"name\": \"rhombus\", \"prompt\": \"a rhombus shape with equal sides tilted\", \"type\": \"structure\", \"vertices\": 4},\n", " {\"name\": \"chevron\", \"prompt\": \"a chevron shape like a v pointing up\", \"type\": \"structure\", \"vertices\": 6},\n", "]\n", "\n", "SHAPE_NAMES = [s[\"name\"] for s in SHAPES]\n", "PROMPTS = [s[\"prompt\"] for s in SHAPES]\n", "N_SHAPES = len(SHAPES)\n", "\n", "print(\"=\" * 65)\n", "print(f\"SHAPE ANCHOR EXTRACTION: {N_SHAPES} shapes\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading CaptionBERT...\")\n", "from transformers import AutoModel, AutoTokenizer\n", "\n", "model = AutoModel.from_pretrained(REPO_ID, trust_remote_code=True).to(DEVICE).eval()\n", "tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)\n", "\n", "has_bank = model.bank is not None\n", "print(f\" Bank: {'present' if has_bank else 'absent'}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXTRACT EMBEDDINGS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Extracting embeddings for {N_SHAPES} prompts...\")\n", "\n", "with torch.no_grad():\n", " inputs = tokenizer(PROMPTS, max_length=64, padding=\"max_length\",\n", " truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out = model(**inputs)\n", "\n", " raw_emb = out.last_hidden_state # (30, 768) L2-normalized\n", " enriched = out.enriched # (30, 896) if bank present\n", " geo_ctx = out.geometric_context # dict\n", "\n", " if enriched is not None:\n", " bank_emb = enriched[:, 768:] # (30, 128) bank context\n", " else:\n", " bank_emb = None\n", "\n", "print(f\" Raw: {raw_emb.shape}\")\n", "if bank_emb is not None:\n", " print(f\" Bank 128-dim: {bank_emb.shape}\")\n", "print(f\" Enriched: {enriched.shape if enriched is not None else 'None'}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RAW 768-DIM SIMILARITY STRUCTURE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"RAW 768-DIM COSINE SIMILARITY\")\n", "print(f\"{'='*65}\")\n", "\n", "raw_sim = raw_emb @ raw_emb.T # already L2-normalized\n", "\n", "# Overall stats\n", "mask = ~torch.eye(N_SHAPES, dtype=bool, device=DEVICE)\n", "off_diag = raw_sim[mask]\n", "print(f\"\\n Off-diagonal stats:\")\n", "print(f\" Mean: {off_diag.mean():.3f}\")\n", "print(f\" Std: {off_diag.std():.3f}\")\n", "print(f\" Min: {off_diag.min():.3f}\")\n", "print(f\" Max: {off_diag.max():.3f}\")\n", "\n", "# Top-10 most similar pairs\n", "pairs = []\n", "for i in range(N_SHAPES):\n", " for j in range(i + 1, N_SHAPES):\n", " pairs.append((raw_sim[i, j].item(), SHAPE_NAMES[i], SHAPE_NAMES[j]))\n", "pairs.sort(reverse=True)\n", "\n", "print(f\"\\n Top-10 most similar (RAW 768):\")\n", "for sim, a, b in pairs[:10]:\n", " print(f\" {sim:.3f} {a} ↔ {b}\")\n", "\n", "print(f\"\\n Top-10 most dissimilar (RAW 768):\")\n", "for sim, a, b in pairs[-10:]:\n", " print(f\" {sim:.3f} {a} ↔ {b}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BANK 128-DIM SIMILARITY STRUCTURE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if bank_emb is not None:\n", " print(f\"\\n{'='*65}\")\n", " print(\"BANK 128-DIM COSINE SIMILARITY\")\n", " print(f\"{'='*65}\")\n", "\n", " bank_n = F.normalize(bank_emb, dim=-1)\n", " bank_sim = bank_n @ bank_n.T\n", "\n", " off_diag_bank = bank_sim[mask]\n", " print(f\"\\n Off-diagonal stats:\")\n", " print(f\" Mean: {off_diag_bank.mean():.3f}\")\n", " print(f\" Std: {off_diag_bank.std():.3f}\")\n", " print(f\" Min: {off_diag_bank.min():.3f}\")\n", " print(f\" Max: {off_diag_bank.max():.3f}\")\n", "\n", " bank_pairs = []\n", " for i in range(N_SHAPES):\n", " for j in range(i + 1, N_SHAPES):\n", " bank_pairs.append((bank_sim[i, j].item(), SHAPE_NAMES[i], SHAPE_NAMES[j]))\n", " bank_pairs.sort(reverse=True)\n", "\n", " print(f\"\\n Top-10 most similar (BANK 128):\")\n", " for sim, a, b in bank_pairs[:10]:\n", " print(f\" {sim:.3f} {a} ↔ {b}\")\n", "\n", " print(f\"\\n Top-10 most dissimilar (BANK 128):\")\n", " for sim, a, b in bank_pairs[-10:]:\n", " print(f\" {sim:.3f} {a} ↔ {b}\")\n", "\n", " # Compare raw vs bank: where do they disagree?\n", " print(f\"\\n Biggest raw↔bank disagreements:\")\n", " disagreements = []\n", " for i in range(N_SHAPES):\n", " for j in range(i + 1, N_SHAPES):\n", " r = raw_sim[i, j].item()\n", " b_val = bank_sim[i, j].item()\n", " disagreements.append((abs(r - b_val), r, b_val, SHAPE_NAMES[i], SHAPE_NAMES[j]))\n", " disagreements.sort(reverse=True)\n", " for delta, r, b_val, a, b in disagreements[:10]:\n", " direction = \"bank SEPARATES\" if b_val < r else \"bank CONNECTS\"\n", " print(f\" Δ={delta:+.3f} raw={r:.3f} bank={b_val:.3f} {a}↔{b} ({direction})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ENRICHED 896-DIM SIMILARITY (full patchwork space)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if enriched is not None:\n", " print(f\"\\n{'='*65}\")\n", " print(\"ENRICHED 896-DIM COSINE SIMILARITY\")\n", " print(f\"{'='*65}\")\n", "\n", " enr_n = F.normalize(enriched, dim=-1)\n", " enr_sim = enr_n @ enr_n.T\n", "\n", " off_diag_enr = enr_sim[mask]\n", " print(f\"\\n Off-diagonal stats:\")\n", " print(f\" Mean: {off_diag_enr.mean():.3f}\")\n", " print(f\" Std: {off_diag_enr.std():.3f}\")\n", " print(f\" Min: {off_diag_enr.min():.3f}\")\n", " print(f\" Max: {off_diag_enr.max():.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TYPE CLUSTER ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TYPE CLUSTER ANALYSIS\")\n", "print(f\"{'='*65}\")\n", "\n", "types = list(set(s[\"type\"] for s in SHAPES))\n", "types.sort()\n", "\n", "for space_name, embs in [(\"raw_768\", raw_emb), (\"bank_128\", bank_n if bank_emb is not None else None)]:\n", " if embs is None:\n", " continue\n", " print(f\"\\n {space_name}:\")\n", " sim_mat = F.normalize(embs, dim=-1) @ F.normalize(embs, dim=-1).T\n", "\n", " for t in types:\n", " indices = [i for i, s in enumerate(SHAPES) if s[\"type\"] == t]\n", " if len(indices) < 2:\n", " continue\n", " # Intra-type similarity\n", " intra = []\n", " for i in indices:\n", " for j in indices:\n", " if i != j:\n", " intra.append(sim_mat[i, j].item())\n", " # Inter-type similarity\n", " inter = []\n", " for i in indices:\n", " for j in range(N_SHAPES):\n", " if j not in indices:\n", " inter.append(sim_mat[i, j].item())\n", "\n", " intra_mean = np.mean(intra)\n", " inter_mean = np.mean(inter)\n", " sep = intra_mean - inter_mean\n", " print(f\" {t:12s}: intra={intra_mean:.3f} inter={inter_mean:.3f} \"\n", " f\"separation={sep:+.3f} n={len(indices)}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXPORT ANCHORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"ANCHOR EXPORT\")\n", "print(f\"{'='*65}\")\n", "\n", "anchors = {\n", " \"shape_names\": SHAPE_NAMES,\n", " \"shapes\": SHAPES,\n", " \"raw_768\": raw_emb.cpu(),\n", " \"enriched_896\": enriched.cpu() if enriched is not None else None,\n", " \"bank_128\": bank_emb.cpu() if bank_emb is not None else None,\n", " \"raw_sim\": raw_sim.cpu(),\n", " \"bank_sim\": bank_sim.cpu() if bank_emb is not None else None,\n", "}\n", "\n", "torch.save(anchors, \"shape_anchors_30.pt\")\n", "print(f\" Saved: shape_anchors_30.pt\")\n", "print(f\" raw_768: {anchors['raw_768'].shape}\")\n", "if anchors['bank_128'] is not None:\n", " print(f\" bank_128: {anchors['bank_128'].shape}\")\n", "if anchors['enriched_896'] is not None:\n", " print(f\" enriched_896: {anchors['enriched_896'].shape}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "a922ca2e61a14c5bb47f2941013c18d1", "ee8369f90dd6490282d4f797249aff4b", "f033eaaf26024f42a1271618877204ea", "4b5f402ecc974cc9b4b4c635f9104c26", "e37aa76c8e5a4f40b916e5cd88185092", "aaf54a147aae490ca04081e08e8f9cf8", "84f078f90909404297a7a20ff71381ce", "7adf85639b0c4ea5a3ee87ca52d3c173", "7d93bae4fe184e2399d63d1011334f6d", "8a96eaae833f46e4979b062a44fa5ace", "8cc267cde7f44de4806f71d9801ee5f5", "d943da1d02854e458e4b0b6033434c91", "910847f0c85a468aa9553aca6dbc6272", 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"89d6a8adc8ff49babe92f4e1545ffee3", "1e933d5341b3444a8fe78c2a431910b2", "778b6f5fc70d4a6884f8e2777a5be712", "3b187d2a031540a98e78f30d5ed48f97", "e0bf429b162c4a0e8f6570bc0899ff44", "9960066074cd40109e02a4b09251bcdd", "d64348bc4af34ee398ffa2f1ba341f70", "324488d8bf9444349e261897fd3c5fcc", "b3c4ee83596b477ead16febca4209959", "d92188aa69be4396b028df10686a544e", "ce6d0b8c93f2420eba759c64ff656c97", "6e1a1e6c5d444fb39a3de96c2027e7df", "e55b9b578fa84552a104eb94cbeb680b", "4f5aedee1e3e4838803ead3506ccb097", "093c6e4e6feb4c4cb59a029ac7561996", "3c169585d70945279a5c55d1bbf607e2", "b3372036ac1e4f1195432b05bb28b5ac", "db8ff02eb29f42e5bc9a49b572a8475a", "3c8136cd08a44feb872b5c986a552eb7", "84dce0b2fc9341ac81c85857aec23258", "e030aae68fdc455d8702276093c745e2", "c26b3538808b4b5f907dc6b34501177e", "88d26ed30c96464d80d675fdf13206c7", "fbc7e439325d4628b3e3312790e1e7ed", "cb3a2497fb454cc98b1b7ed11b59d77a", "f069f6e741c848fcb8373afcea2468f3" ] }, "id": "E7RRMLKaSGZS", "outputId": "0d5c213d-9732-4350-e57b-0898d3440be5" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "SHAPE ANCHOR EXTRACTION: 30 shapes\n", "=================================================================\n", " Device: cuda\n", "\n", " Loading CaptionBERT...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a922ca2e61a14c5bb47f2941013c18d1" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "modeling_caption_bert.py: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d943da1d02854e458e4b0b6033434c91" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "A new version of the following files was downloaded from https://huggingface.co/AbstractPhil/geolip-captionbert-8192:\n", "- modeling_caption_bert.py\n", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/130M [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial_mag = (grad_f * emb_n).sum(dim=-1, keepdim=True)\n", " normal = radial_mag * emb_n\n", " tangential = grad_f - normal\n", " return tangential.to(grad.dtype), normal.to(grad.dtype)\n", "\n", "\n", "def cv_gate(cv_current, cv_target, tolerance=0.02):\n", " delta = cv_current - cv_target\n", " if abs(delta) <= tolerance: return 0.0\n", " if delta < 0:\n", " return float(min(abs(delta) / (cv_target + 1e-8), 1.0) * 0.3)\n", " else:\n", " return float(max(0.0, 0.1 * (1.0 - min(delta / (cv_target + 1e-8), 1.0))))\n", "\n", "\n", "class TangentialGradientFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, gate_normal):\n", " ctx.save_for_backward(embedding)\n", " ctx.gate_normal = gate_normal\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, = ctx.saved_tensors\n", " tangential, normal = tangential_projection(grad_output, embedding)\n", " return tangential + ctx.gate_normal * normal, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# 30 SHAPE RENDERERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _draw_line(img, x0, y0, x1, y1, thickness=1):\n", " n = max(int(max(abs(x1 - x0), abs(y1 - y0)) * 2), 1)\n", " sz = img.shape[0]\n", " for t in np.linspace(0, 1, n):\n", " px, py = int(x0 + t * (x1 - x0)), int(y0 + t * (y1 - y0))\n", " for dx in range(-thickness, thickness + 1):\n", " for dy in range(-thickness, thickness + 1):\n", " nx, ny = px + dx, py + dy\n", " if 0 <= nx < sz and 0 <= ny < sz:\n", " img[ny, nx] = 1.0\n", "\n", "\n", "def render_polygon(n_verts, sz=32, perturb=0.15, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy, r = sz/2, sz/2, sz*0.35\n", " angles = np.linspace(0, 2*np.pi, n_verts, endpoint=False)\n", " angles += np.random.uniform(0, 2*np.pi)\n", " radii = r * (1.0 + np.random.normal(0, perturb, n_verts))\n", " pts = [(cx + ri*np.cos(a), cy + ri*np.sin(a)) for a, ri in zip(angles, radii)]\n", " for i in range(n_verts):\n", " _draw_line(img, *pts[i], *pts[(i+1) % n_verts], thick)\n", " return img\n", "\n", "\n", "def render_star(n_pts, sz=32, perturb=0.12, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " r_out, r_in = sz*0.38, sz*0.15\n", " angles = np.linspace(0, 2*np.pi, n_pts*2, endpoint=False)\n", " angles += np.random.uniform(0, 2*np.pi)\n", " pts = []\n", " for i, a in enumerate(angles):\n", " r = (r_out if i % 2 == 0 else r_in) * (1.0 + np.random.normal(0, perturb))\n", " pts.append((cx + r*np.cos(a), cy + r*np.sin(a)))\n", " for i in range(len(pts)):\n", " _draw_line(img, *pts[i], *pts[(i+1) % len(pts)], thick)\n", " return img\n", "\n", "\n", "def render_cross(sz=32, perturb=0.15, thick=2):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy, arm = sz/2, sz/2, sz*0.3\n", " for ab in [0, np.pi/2, np.pi, 3*np.pi/2]:\n", " a = ab + np.random.normal(0, perturb*0.3)\n", " r = arm * (1.0 + np.random.normal(0, perturb))\n", " _draw_line(img, cx, cy, cx + r*np.cos(a), cy + r*np.sin(a), thick)\n", " return img\n", "\n", "\n", "def render_spiral(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " turns = 2.5 + np.random.normal(0, 0.3)\n", " for t in np.linspace(0, turns*2*np.pi, 200):\n", " r = sz*0.015 * t * (1.0 + np.random.normal(0, perturb*0.3))\n", " x, y = int(cx + r*np.cos(t)), int(cy + r*np.sin(t))\n", " if 0 <= x < sz and 0 <= y < sz:\n", " img[y, x] = 1.0\n", " return img\n", "\n", "\n", "def render_wave(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " freq = 2 + np.random.normal(0, 0.3)\n", " amp = sz * 0.15 * (1.0 + np.random.normal(0, perturb))\n", " for x in range(sz):\n", " y = int(sz/2 + amp * np.sin(2*np.pi*freq*x/sz + np.random.normal(0, perturb*0.5)))\n", " for dy in range(-thick, thick+1):\n", " ny = y + dy\n", " if 0 <= ny < sz:\n", " img[ny, x] = 1.0\n", " return img\n", "\n", "\n", "def render_crescent(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy, r = sz/2, sz/2, sz*0.35\n", " r2 = r * (0.7 + np.random.normal(0, perturb*0.3))\n", " offset = r * (0.3 + np.random.normal(0, perturb*0.2))\n", " for a in np.linspace(0, 2*np.pi, 300):\n", " x1 = cx + r*(1+np.random.normal(0, perturb*0.1))*np.cos(a)\n", " y1 = cy + r*(1+np.random.normal(0, perturb*0.1))*np.sin(a)\n", " x2 = cx + offset + r2*np.cos(a)\n", " y2 = cy + r2*np.sin(a)\n", " d1 = math.sqrt((x1-cx)**2 + (y1-cy)**2)\n", " d2 = math.sqrt((x2-(cx+offset))**2 + (y2-cy)**2)\n", " if d1 <= r*1.1 and d2 >= r2*0.9:\n", " ix, iy = int(x1), int(y1)\n", " if 0 <= ix < sz and 0 <= iy < sz:\n", " img[iy, ix] = 1.0\n", " return img\n", "\n", "\n", "def render_ellipse(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " a = sz*0.38*(1+np.random.normal(0, perturb))\n", " b = sz*0.22*(1+np.random.normal(0, perturb))\n", " rot = np.random.uniform(0, np.pi)\n", " for t in np.linspace(0, 2*np.pi, 200):\n", " x = a*np.cos(t); y = b*np.sin(t)\n", " xr = cx + x*np.cos(rot) - y*np.sin(rot)\n", " yr = cy + x*np.sin(rot) + y*np.cos(rot)\n", " ix, iy = int(xr), int(yr)\n", " for dx in range(-thick, thick+1):\n", " for dy in range(-thick, thick+1):\n", " nx, ny = ix+dx, iy+dy\n", " if 0 <= nx < sz and 0 <= ny < sz:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "def render_heart(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz*0.45\n", " scale = sz*0.017*(1+np.random.normal(0, perturb))\n", " for t in np.linspace(0, 2*np.pi, 300):\n", " x = 16*np.sin(t)**3\n", " y = -(13*np.cos(t) - 5*np.cos(2*t) - 2*np.cos(3*t) - np.cos(4*t))\n", " ix, iy = int(cx + x*scale), int(cy + y*scale)\n", " for dx in range(-thick, thick+1):\n", " for dy in range(-thick, thick+1):\n", " nx, ny = ix+dx, iy+dy\n", " if 0 <= nx < sz and 0 <= ny < sz:\n", " img[ny, nx] = 1.0\n", " return img\n", "\n", "\n", "def render_ring(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " r1 = sz*0.35*(1+np.random.normal(0, perturb))\n", " r2 = sz*0.22*(1+np.random.normal(0, perturb))\n", " for a in np.linspace(0, 2*np.pi, 300):\n", " for r in [r1, r2]:\n", " x, y = int(cx+r*np.cos(a)), int(cy+r*np.sin(a))\n", " if 0 <= x < sz and 0 <= y < sz:\n", " img[y, x] = 1.0\n", " return img\n", "\n", "\n", "def render_semicircle(sz=32, perturb=0.1, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy, r = sz/2, sz*0.6, sz*0.35*(1+np.random.normal(0, perturb))\n", " for a in np.linspace(np.pi, 2*np.pi, 150):\n", " x, y = int(cx+r*np.cos(a)), int(cy+r*np.sin(a))\n", " for dx in range(-thick, thick+1):\n", " for dy in range(-thick, thick+1):\n", " if 0 <= x+dx < sz and 0 <= y+dy < sz:\n", " img[y+dy, x+dx] = 1.0\n", " _draw_line(img, cx-r, cy, cx+r, cy, thick)\n", " return img\n", "\n", "\n", "def render_arrow(sz=32, perturb=0.12, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " length = sz*0.35*(1+np.random.normal(0, perturb))\n", " head = length*0.35\n", " a = np.random.uniform(0, 2*np.pi)\n", " x1, y1 = cx - length*np.cos(a), cy - length*np.sin(a)\n", " x2, y2 = cx + length*np.cos(a), cy + length*np.sin(a)\n", " _draw_line(img, x1, y1, x2, y2, thick)\n", " for da in [0.7, -0.7]:\n", " hx = x2 - head*np.cos(a+da)\n", " hy = y2 - head*np.sin(a+da)\n", " _draw_line(img, x2, y2, hx, hy, thick)\n", " return img\n", "\n", "\n", "def render_chevron(sz=32, perturb=0.12, thick=1):\n", " img = np.zeros((sz, sz), dtype=np.float32)\n", " cx, cy = sz/2, sz/2\n", " w = sz*0.3*(1+np.random.normal(0, perturb))\n", " h = sz*0.25*(1+np.random.normal(0, perturb))\n", " _draw_line(img, cx-w, cy+h, cx, cy-h, thick)\n", " _draw_line(img, cx, cy-h, cx+w, cy+h, thick)\n", " return img\n", "\n", "\n", "# Class→renderer mapping (30 classes)\n", "SHAPE_NAMES = [\n", " \"triangle\", \"square\", \"pentagon\", \"hexagon\", \"heptagon\",\n", " \"octagon\", \"nonagon\", \"decagon\", \"dodecagon\",\n", " \"circle\", \"ellipse\", \"spiral\", \"wave\", \"crescent\",\n", " \"star3\", \"star4\", \"star5\", \"star6\", \"star7\", \"star8\",\n", " \"cross\", \"diamond\", \"arrow\", \"heart\", \"ring\",\n", " \"semicircle\", \"trapezoid\", \"parallelogram\", \"rhombus\", \"chevron\",\n", "]\n", "\n", "def generate_one(cls, sz=32):\n", " if cls == 0: return render_polygon(3, sz, 0.20)\n", " if cls == 1: return render_polygon(4, sz, 0.12)\n", " if cls == 2: return render_polygon(5, sz, 0.15)\n", " if cls == 3: return render_polygon(6, sz, 0.10)\n", " if cls == 4: return render_polygon(7, sz, 0.10)\n", " if cls == 5: return render_polygon(8, sz, 0.08)\n", " if cls == 6: return render_polygon(9, sz, 0.08)\n", " if cls == 7: return render_polygon(10, sz, 0.07)\n", " if cls == 8: return render_polygon(12, sz, 0.06)\n", " if cls == 9: return render_polygon(32, sz, 0.03) # circle\n", " if cls == 10: return render_ellipse(sz, 0.10)\n", " if cls == 11: return render_spiral(sz, 0.10)\n", " if cls == 12: return render_wave(sz, 0.10)\n", " if cls == 13: return render_crescent(sz, 0.10)\n", " if cls == 14: return render_star(3, sz, 0.12)\n", " if cls == 15: return render_star(4, sz, 0.12)\n", " if cls == 16: return render_star(5, sz, 0.12)\n", " if cls == 17: return render_star(6, sz, 0.10)\n", " if cls == 18: return render_star(7, sz, 0.10)\n", " if cls == 19: return render_star(8, sz, 0.08)\n", " if cls == 20: return render_cross(sz, 0.15)\n", " if cls == 21: return render_polygon(4, sz, 0.10) # diamond (rotated square handled by random rotation)\n", " if cls == 22: return render_arrow(sz, 0.12)\n", " if cls == 23: return render_heart(sz, 0.10)\n", " if cls == 24: return render_ring(sz, 0.10)\n", " if cls == 25: return render_semicircle(sz, 0.10)\n", " if cls == 26: return render_polygon(4, sz, 0.15) # trapezoid-ish\n", " if cls == 27: return render_polygon(4, sz, 0.18) # parallelogram-ish\n", " if cls == 28: return render_polygon(4, sz, 0.10) # rhombus-ish\n", " if cls == 29: return render_chevron(sz, 0.12)\n", " return render_polygon(3, sz, 0.15)\n", "\n", "\n", "N_CLASSES = len(SHAPE_NAMES)\n", "\n", "\n", "def generate_dataset(n_per_class=1000, sz=32):\n", " imgs, labels = [], []\n", " for _ in range(n_per_class):\n", " for c in range(N_CLASSES):\n", " imgs.append(generate_one(c, sz))\n", " labels.append(c)\n", " imgs = torch.tensor(np.array(imgs)).unsqueeze(1)\n", " labels = torch.tensor(labels, dtype=torch.long)\n", " perm = torch.randperm(len(labels))\n", " return imgs[perm], labels[perm]\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ShapeNet(nn.Module):\n", " def __init__(self, d_embed=768, n_classes=30):\n", " super().__init__()\n", " self.conv = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(),\n", " nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.proj = nn.Sequential(\n", " nn.Linear(128, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", " self.prototypes = nn.Parameter(\n", " F.normalize(torch.randn(n_classes, d_embed), dim=-1))\n", " self.temperature = nn.Parameter(torch.tensor(10.0))\n", "\n", " def forward(self, x):\n", " feat = self.conv(x).flatten(1)\n", " emb = F.normalize(self.proj(feat), dim=-1)\n", " protos_n = F.normalize(self.prototypes, dim=-1)\n", " logits = emb @ protos_n.T * self.temperature.abs()\n", " return logits, emb\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD CAPTIONBERT ANCHORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading CaptionBERT shape anchors...\")\n", "import os\n", "\n", "anchors_path = \"shape_anchors_30.pt\"\n", "if not os.path.exists(anchors_path):\n", " print(f\" ⚠ {anchors_path} not found — run shape_anchor_extraction.py first\")\n", " exit()\n", "\n", "anchors = torch.load(anchors_path, weights_only=False)\n", "anchor_768 = F.normalize(anchors[\"raw_768\"].to(DEVICE), dim=-1) # (30, 768)\n", "anchor_128 = anchors.get(\"bank_128\")\n", "if anchor_128 is not None:\n", " anchor_128 = F.normalize(anchor_128.to(DEVICE), dim=-1) # (30, 128)\n", "print(f\" Anchors: {anchor_768.shape} (768-dim from CaptionBERT)\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train(use_gates=False, epochs=30, tag=\"\"):\n", " torch.manual_seed(42); np.random.seed(42)\n", "\n", " print(f\"\\n Generating data...\")\n", " train_imgs, train_labels = generate_dataset(n_per_class=500)\n", " val_imgs, val_labels = generate_dataset(n_per_class=100)\n", " train_imgs, train_labels = train_imgs.to(DEVICE), train_labels.to(DEVICE)\n", " val_imgs, val_labels = val_imgs.to(DEVICE), val_labels.to(DEVICE)\n", " n_train, n_val = len(train_labels), len(val_labels)\n", " print(f\" Train: {n_train:,} Val: {n_val:,} Classes: {N_CLASSES}\")\n", "\n", " model = ShapeNet(d_embed=768, n_classes=N_CLASSES).to(DEVICE)\n", "\n", " # Initialize prototypes from CaptionBERT anchors\n", " with torch.no_grad():\n", " model.prototypes.copy_(anchor_768)\n", " print(f\" Prototypes initialized from CaptionBERT anchors\")\n", "\n", " optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256\n", "\n", " cv_current = 0.5\n", " cv_target = None # measured from first epoch\n", " history = []\n", "\n", " for epoch in range(epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, total_anchor_loss = 0, 0, 0.0\n", " n, gate_val = 0, 0.0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " logits, emb = model(train_imgs[idx])\n", " labels = train_labels[idx]\n", "\n", " # Classification loss\n", " l_cls = F.cross_entropy(logits, labels)\n", "\n", " # Anchor alignment: pull each embedding toward its class anchor\n", " target_anchors = anchor_768[labels] # (B, 768)\n", " l_anchor = 1.0 - F.cosine_similarity(emb, target_anchors, dim=-1).mean()\n", "\n", " if use_gates:\n", " if n % 10 == 0:\n", " cv_current = pentachoron_cv(emb, n_samples=50)\n", " if cv_target is None and epoch == 0 and n == 0:\n", " cv_target = cv_current if cv_current > 0 else 0.5\n", " if cv_target is not None:\n", " gate_val = cv_gate(cv_current, cv_target)\n", "\n", " emb_gated = TangentialGradientFn.apply(emb, emb, gate_val)\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " logits_gated = emb_gated @ protos_n.T * model.temperature.abs()\n", " l_cls = F.cross_entropy(logits_gated, labels)\n", "\n", " target_anchors_gated = anchor_768[labels]\n", " l_anchor = 1.0 - F.cosine_similarity(emb_gated, target_anchors_gated, dim=-1).mean()\n", "\n", " loss = l_cls + 0.5 * l_anchor\n", "\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += l_cls.item()\n", " total_anchor_loss += l_anchor.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", " d = max(n, 1)\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " val_logits, val_emb = model(val_imgs)\n", " v_acc = (val_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_loss = F.cross_entropy(val_logits, val_labels).item()\n", " v_cv = pentachoron_cv(val_emb, n_samples=100)\n", "\n", " # Anchor alignment on val\n", " val_targets = anchor_768[val_labels]\n", " v_anchor_cos = F.cosine_similarity(val_emb, val_targets, dim=-1).mean().item()\n", "\n", " # Prototype drift from original anchors\n", " protos_n = F.normalize(model.prototypes, dim=-1)\n", " proto_anchor_cos = F.cosine_similarity(protos_n, anchor_768, dim=-1).mean().item()\n", "\n", " # Per-type accuracy\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9, 14)),\n", " \"star\": list(range(14, 20)), \"structure\": list(range(20, 30))}\n", " type_accs = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids:\n", " tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " type_accs[tname] = (val_logits.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if cv_target is None:\n", " cv_target = v_cv\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"gate\": gate_val, \"anchor_cos\": v_anchor_cos,\n", " \"proto_drift\": proto_anchor_cos, \"type_accs\": type_accs,\n", " })\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " ta = \" \".join(f\"{t}={a:.2f}\" for t, a in type_accs.items())\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"anc_cos={v_anchor_cos:.3f} proto_drift={proto_anchor_cos:.3f} \"\n", " f\"[{ta}]\")\n", "\n", " return history\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BASELINE: Adam only\")\n", "print(f\"{'='*65}\")\n", "h_base = train(use_gates=False, epochs=30)\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATED: Adam + Geometric Optimizer\")\n", "print(f\"{'='*65}\")\n", "h_gated = train(use_gates=True, epochs=30)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "b, g = h_base[-1], h_gated[-1]\n", "\n", "print(f\"\\n {'Metric':<25} {'Baseline':>10} {'Gated':>10}\")\n", "print(f\" {'-'*47}\")\n", "for name, bv, gv in [\n", " (\"Val accuracy\", f\"{b['val_acc']:.3f}\", f\"{g['val_acc']:.3f}\"),\n", " (\"Train accuracy\", f\"{b['train_acc']:.3f}\", f\"{g['train_acc']:.3f}\"),\n", " (\"Overfit gap\", f\"{b['train_acc']-b['val_acc']:.3f}\", f\"{g['train_acc']-g['val_acc']:.3f}\"),\n", " (\"Val CV\", f\"{b['val_cv']:.4f}\", f\"{g['val_cv']:.4f}\"),\n", " (\"Anchor cos\", f\"{b['anchor_cos']:.3f}\", f\"{g['anchor_cos']:.3f}\"),\n", " (\"Proto drift\", f\"{b['proto_drift']:.3f}\", f\"{g['proto_drift']:.3f}\"),\n", "]:\n", " print(f\" {name:<25} {bv:>10} {gv:>10}\")\n", "\n", "for tname in [\"polygon\", \"curve\", \"star\", \"structure\"]:\n", " bv = b[\"type_accs\"].get(tname, 0)\n", " gv = g[\"type_accs\"].get(tname, 0)\n", " print(f\" {'Acc ' + tname:<25} {bv:>10.3f} {gv:>10.3f}\")\n", "\n", "# CV stability\n", "b_cvs = [h[\"val_cv\"] for h in h_base]\n", "g_cvs = [h[\"val_cv\"] for h in h_gated]\n", "print(f\"\\n CV stability (std): baseline={np.std(b_cvs):.4f} gated={np.std(g_cvs):.4f}\")\n", "\n", "# Anchor alignment trajectory\n", "b_anc = [h[\"anchor_cos\"] for h in h_base]\n", "g_anc = [h[\"anchor_cos\"] for h in h_gated]\n", "print(f\" Anchor cos (final): baseline={b_anc[-1]:.3f} gated={g_anc[-1]:.3f}\")\n", "\n", "# Proto drift trajectory\n", "b_drift = [h[\"proto_drift\"] for h in h_base]\n", "g_drift = [h[\"proto_drift\"] for h in h_gated]\n", "print(f\" Proto drift (final): baseline={b_drift[-1]:.3f} gated={g_drift[-1]:.3f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7rdV9krCUL75", "outputId": "b336e863-93fb-4ce7-a0cd-86bdbba41a2f" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOMETRIC OPTIMIZER: 30-Shape Trainer\n", "=================================================================\n", " Device: cuda\n", "\n", " Loading CaptionBERT shape anchors...\n", " Anchors: torch.Size([30, 768]) (768-dim from CaptionBERT)\n", "\n", "=================================================================\n", "BASELINE: Adam only\n", "=================================================================\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000 Classes: 30\n", " Prototypes initialized from CaptionBERT anchors\n", " E 1: t_acc=0.124 v_acc=0.279 cv=0.9626 gate=0.000 anc_cos=0.736 proto_drift=0.886 [polygon=0.28 curve=0.20 star=0.46 structure=0.21]\n", " E 5: t_acc=0.629 v_acc=0.652 cv=0.9587 gate=0.000 anc_cos=0.687 proto_drift=0.443 [polygon=0.31 curve=0.98 star=0.81 structure=0.70]\n", " E10: t_acc=0.693 v_acc=0.684 cv=0.6274 gate=0.000 anc_cos=0.738 proto_drift=0.305 [polygon=0.38 curve=0.98 star=0.81 structure=0.74]\n", " E15: t_acc=0.730 v_acc=0.721 cv=0.6016 gate=0.000 anc_cos=0.746 proto_drift=0.252 [polygon=0.46 curve=0.94 star=0.93 structure=0.72]\n", " E20: t_acc=0.760 v_acc=0.755 cv=0.6711 gate=0.000 anc_cos=0.767 proto_drift=0.229 [polygon=0.55 curve=0.99 star=0.97 structure=0.69]\n", " E25: t_acc=0.776 v_acc=0.757 cv=0.5893 gate=0.000 anc_cos=0.791 proto_drift=0.216 [polygon=0.51 curve=0.98 star=0.99 structure=0.73]\n", " E30: t_acc=0.780 v_acc=0.769 cv=0.6102 gate=0.000 anc_cos=0.775 proto_drift=0.210 [polygon=0.60 curve=1.00 star=0.98 structure=0.68]\n", "\n", "=================================================================\n", "GATED: Adam + Geometric Optimizer\n", "=================================================================\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000 Classes: 30\n", " Prototypes initialized from CaptionBERT anchors\n", " E 1: t_acc=0.124 v_acc=0.279 cv=1.3543 gate=0.041 anc_cos=0.736 proto_drift=0.886 [polygon=0.28 curve=0.20 star=0.46 structure=0.22]\n", " E 5: t_acc=0.641 v_acc=0.594 cv=0.8629 gate=0.094 anc_cos=0.676 proto_drift=0.440 [polygon=0.19 curve=0.85 star=0.81 structure=0.70]\n", " E10: t_acc=0.693 v_acc=0.708 cv=0.6606 gate=0.000 anc_cos=0.715 proto_drift=0.303 [polygon=0.49 curve=0.99 star=0.86 structure=0.67]\n", " E15: t_acc=0.729 v_acc=0.734 cv=0.5532 gate=0.080 anc_cos=0.752 proto_drift=0.248 [polygon=0.48 curve=0.96 star=0.93 structure=0.73]\n", " E20: t_acc=0.762 v_acc=0.754 cv=0.5324 gate=0.085 anc_cos=0.768 proto_drift=0.221 [polygon=0.61 curve=0.99 star=0.93 structure=0.66]\n", " E25: t_acc=0.777 v_acc=0.739 cv=0.5853 gate=0.076 anc_cos=0.771 proto_drift=0.210 [polygon=0.47 curve=0.96 star=0.98 structure=0.73]\n", " E30: t_acc=0.792 v_acc=0.759 cv=0.5771 gate=0.110 anc_cos=0.775 proto_drift=0.206 [polygon=0.53 curve=1.00 star=0.93 structure=0.74]\n", "\n", "=================================================================\n", "COMPARISON\n", "=================================================================\n", "\n", " Metric Baseline Gated\n", " -----------------------------------------------\n", " Val accuracy 0.769 0.759\n", " Train accuracy 0.780 0.792\n", " Overfit gap 0.011 0.033\n", " Val CV 0.6102 0.5771\n", " Anchor cos 0.775 0.775\n", " Proto drift 0.210 0.206\n", " Acc polygon 0.599 0.534\n", " Acc curve 0.996 0.996\n", " Acc star 0.980 0.932\n", " Acc structure 0.682 0.740\n", "\n", " CV stability (std): baseline=0.1453 gated=0.1824\n", " Anchor cos (final): baseline=0.775 gated=0.775\n", " Proto drift (final): baseline=0.210 gated=0.206\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# HYPERSPHERE BIAS MASK\n", "#\n", "# 1. Compute all 30×30 pairwise cosine gaps between CaptionBERT anchors\n", "# 2. Build a differentiation matrix: where gaps are too small = confusion zones\n", "# 3. Project the differentiation onto a bias field in embedding space\n", "# 4. Mask: only activate where the raw embedding is ambiguous\n", "#\n", "# The bias compensates for missing discriminative signal.\n", "# The mask prevents it from corrupting already-separated regions.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "class HypersphereBiasMask(nn.Module):\n", " \"\"\"\n", " Learnable bias field on the hypersphere, gated by a confusion mask.\n", "\n", " From N class anchors on the unit sphere:\n", " 1. Pairwise gap matrix G[i,j] = 1 - cos(anchor_i, anchor_j)\n", " 2. Confusion zones: where G[i,j] < threshold (classes too similar)\n", " 3. Bias vectors: one per class, learned, tangential to sphere at anchor\n", " 4. Mask: sigmoid gate that opens where embedding falls in confusion zone\n", "\n", " The bias ONLY activates when the embedding is in ambiguous territory.\n", " Clean separations pass through untouched.\n", " \"\"\"\n", "\n", " def __init__(self, anchors: torch.Tensor, d_embed: int = 768,\n", " confusion_threshold: float = 0.3):\n", " \"\"\"\n", " Args:\n", " anchors: (N, D) L2-normalized class anchor embeddings\n", " d_embed: embedding dimension\n", " confusion_threshold: gap below this = confusion zone\n", " \"\"\"\n", " super().__init__()\n", " N = anchors.shape[0]\n", " self.n_classes = N\n", " self.d_embed = d_embed\n", "\n", " # Store anchors (frozen reference points on hypersphere)\n", " self.register_buffer(\"anchors\", F.normalize(anchors.float(), dim=-1))\n", "\n", " # ── Pairwise gap analysis ──\n", " cos_sim = self.anchors @ self.anchors.T # (N, N)\n", " gap_matrix = 1.0 - cos_sim # (N, N) — 0 = identical, 2 = opposite\n", "\n", " # Confusion mask: 1 where classes are too similar, 0 where well-separated\n", " confusion = (gap_matrix < confusion_threshold).float()\n", " confusion.fill_diagonal_(0) # don't confuse with self\n", " self.register_buffer(\"confusion_matrix\", confusion)\n", " self.register_buffer(\"gap_matrix\", gap_matrix)\n", "\n", " # Per-class confusion degree (how many neighbors are too close)\n", " confusion_degree = confusion.sum(dim=1) # (N,)\n", " self.register_buffer(\"confusion_degree\", confusion_degree)\n", "\n", " # ── Learnable bias vectors ──\n", " # One bias per class, initialized tangential to sphere at anchor point\n", " # Magnitude proportional to confusion degree\n", " bias_init = torch.randn(N, d_embed) * 0.01\n", " # Project to tangential: remove radial component\n", " radial = (bias_init * self.anchors.cpu()).sum(-1, keepdim=True) * self.anchors.cpu()\n", " bias_init = bias_init - radial\n", " self.bias_vectors = nn.Parameter(bias_init)\n", "\n", " # ── Learnable confusion gate ──\n", " # Projects embedding into confusion-aware space\n", " # Low-rank: only needs to detect WHICH confusion zone we're in\n", " rank = min(N, 32)\n", " self.gate_proj = nn.Linear(d_embed, rank, bias=False)\n", " self.gate_out = nn.Linear(rank, N, bias=True)\n", " nn.init.zeros_(self.gate_out.bias)\n", "\n", " # Gate temperature (sharpness of confusion detection)\n", " self.gate_temp = nn.Parameter(torch.tensor(5.0))\n", "\n", " # Report\n", " n_confused = int(confusion.sum().item()) // 2 # symmetric, halve\n", " n_total = N * (N - 1) // 2\n", " print(f\" HypersphereBiasMask: {N} classes, {d_embed}-dim\")\n", " print(f\" Confusion zones: {n_confused}/{n_total} pairs \"\n", " f\"(threshold={confusion_threshold:.2f})\")\n", " print(f\" Most confused: \", end=\"\")\n", " top_confused = confusion_degree.topk(min(5, N))\n", " for idx, deg in zip(top_confused.indices, top_confused.values):\n", " if deg > 0:\n", " print(f\"{idx.item()}({deg.item():.0f}) \", end=\"\")\n", " print()\n", "\n", " def forward(self, emb: torch.Tensor, labels: torch.Tensor = None):\n", " \"\"\"\n", " Apply bias correction to embedding.\n", "\n", " Args:\n", " emb: (B, D) L2-normalized embedding on hypersphere\n", " labels: (B,) optional — if provided, uses ground truth for bias lookup\n", " if None, uses nearest anchor (inference mode)\n", "\n", " Returns:\n", " corrected: (B, D) L2-normalized corrected embedding\n", " diagnostics: dict with gate activations, bias magnitudes\n", " \"\"\"\n", " B = emb.shape[0]\n", " emb_f = emb.float()\n", "\n", " # ── Determine which confusion zone each sample is in ──\n", " # Cosine to all anchors\n", " anchor_cos = emb_f @ self.anchors.T # (B, N)\n", "\n", " if labels is not None:\n", " # Training: use ground truth class\n", " primary_class = labels\n", " else:\n", " # Inference: nearest anchor\n", " primary_class = anchor_cos.argmax(dim=-1)\n", "\n", " # ── Confusion gate ──\n", " # How much is this embedding in a confusion zone?\n", " gate_hidden = self.gate_proj(emb_f) # (B, rank)\n", " gate_logits = self.gate_out(F.gelu(gate_hidden)) # (B, N)\n", " gate = torch.sigmoid(gate_logits * self.gate_temp.abs()) # (B, N) in [0,1]\n", "\n", " # Mask: only activate gates for classes that ARE confused with primary\n", " # For each sample, look up which classes confuse with its primary class\n", " confusion_mask = self.confusion_matrix[primary_class] # (B, N)\n", " gated = gate * confusion_mask # (B, N) — only confused classes active\n", "\n", " # ── Bias application ──\n", " # Weighted sum of bias vectors, weighted by confusion gate\n", " bias_tangential = F.normalize(\n", " self.bias_vectors - (self.bias_vectors * self.anchors).sum(-1, keepdim=True) * self.anchors,\n", " dim=-1) # (N, D) — tangential bias vectors\n", "\n", " # Aggregate bias: sum of gated tangential corrections\n", " applied_bias = gated @ bias_tangential # (B, D)\n", "\n", " # Scale bias by confusion magnitude (more confused = more correction)\n", " confusion_mag = gated.sum(dim=-1, keepdim=True) # (B, 1)\n", " scale = torch.sigmoid(confusion_mag - 1.0) # soft threshold: >1 confused neighbor = activate\n", " applied_bias = applied_bias * scale\n", "\n", " # ── Apply and re-normalize ──\n", " corrected = F.normalize(emb_f + applied_bias, dim=-1)\n", "\n", " # ── Diagnostics ──\n", " diagnostics = {\n", " \"gate_mean\": gate.mean().item(),\n", " \"gate_max\": gate.max().item(),\n", " \"confusion_active\": (gated > 0.1).float().sum(dim=-1).mean().item(),\n", " \"bias_magnitude\": applied_bias.norm(dim=-1).mean().item(),\n", " \"scale_mean\": scale.mean().item(),\n", " }\n", "\n", " return corrected.to(emb.dtype), diagnostics\n", "\n", " def get_confusion_report(self, shape_names=None):\n", " \"\"\"Print which classes are confused with which.\"\"\"\n", " lines = []\n", " for i in range(self.n_classes):\n", " confused_with = (self.confusion_matrix[i] > 0).nonzero(as_tuple=True)[0]\n", " if len(confused_with) > 0:\n", " name_i = shape_names[i] if shape_names else str(i)\n", " neighbors = []\n", " for j in confused_with:\n", " j = j.item()\n", " name_j = shape_names[j] if shape_names else str(j)\n", " gap = self.gap_matrix[i, j].item()\n", " neighbors.append(f\"{name_j}({gap:.3f})\")\n", " lines.append(f\" {name_i}: {', '.join(neighbors)}\")\n", " return \"\\n\".join(lines)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# INTEGRATION TEST\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if __name__ == \"__main__\":\n", " import os\n", "\n", " print(\"=\" * 65)\n", " print(\"HYPERSPHERE BIAS MASK: Integration Test\")\n", " print(\"=\" * 65)\n", "\n", " # Load anchors\n", " anchors_path = \"shape_anchors_30.pt\"\n", " if not os.path.exists(anchors_path):\n", " print(f\" ⚠ Run shape_anchor_extraction.py first\")\n", " exit()\n", "\n", " data = torch.load(anchors_path, weights_only=False)\n", " anchor_768 = F.normalize(data[\"raw_768\"].to(DEVICE), dim=-1)\n", " shape_names = data[\"shape_names\"]\n", "\n", " print(f\"\\n Anchors: {anchor_768.shape}\")\n", "\n", " # Build bias mask\n", " mask = HypersphereBiasMask(\n", " anchor_768, d_embed=768, confusion_threshold=0.3\n", " ).to(DEVICE)\n", "\n", " n_params = sum(p.numel() for p in mask.parameters())\n", " print(f\" Mask params: {n_params:,}\")\n", "\n", " # Confusion report\n", " print(f\"\\n Confusion zones (gap < 0.3):\")\n", " print(mask.get_confusion_report(shape_names))\n", "\n", " # Test forward\n", " print(f\"\\n Forward test:\")\n", " fake_emb = F.normalize(torch.randn(16, 768, device=DEVICE), dim=-1)\n", " fake_labels = torch.randint(0, 30, (16,), device=DEVICE)\n", "\n", " corrected, diag = mask(fake_emb, fake_labels)\n", " print(f\" Input: {fake_emb.shape}, norms={fake_emb.norm(dim=-1).mean():.4f}\")\n", " print(f\" Output: {corrected.shape}, norms={corrected.norm(dim=-1).mean():.4f}\")\n", " print(f\" Diagnostics: {diag}\")\n", "\n", " # Verify gradients flow\n", " fake_emb.requires_grad_(True)\n", " corrected, _ = mask(fake_emb, fake_labels)\n", " loss = corrected.sum()\n", " loss.backward()\n", " print(f\" Grad flows: {fake_emb.grad is not None and fake_emb.grad.abs().sum() > 0}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "89Wf0v_LW86t", "outputId": "604247ac-90c0-412f-b3c8-ebad3de72016" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "HYPERSPHERE BIAS MASK: Integration Test\n", "=================================================================\n", "\n", " Anchors: torch.Size([30, 768])\n", " HypersphereBiasMask: 30 classes, 768-dim\n", " Confusion zones: 431/435 pairs (threshold=0.30)\n", " Most confused: 3(29) 0(29) 4(29) 7(29) 5(29) \n", " Mask params: 47,011\n", "\n", " Confusion zones (gap < 0.3):\n", " triangle: square(0.060), pentagon(0.059), hexagon(0.047), heptagon(0.063), octagon(0.066), nonagon(0.075), decagon(0.090), dodecagon(0.110), circle(0.091), ellipse(0.193), spiral(0.141), wave(0.167), crescent(0.186), star3(0.076), star4(0.056), star5(0.077), star6(0.129), star7(0.102), star8(0.082), cross(0.055), diamond(0.129), arrow(0.177), heart(0.097), ring(0.109), semicircle(0.131), trapezoid(0.090), parallelogram(0.154), rhombus(0.086), chevron(0.204)\n", " square: triangle(0.060), pentagon(0.031), hexagon(0.032), heptagon(0.081), octagon(0.066), nonagon(0.056), decagon(0.115), dodecagon(0.100), circle(0.178), spiral(0.247), wave(0.260), star3(0.180), star4(0.141), star5(0.174), star6(0.226), star7(0.218), star8(0.175), cross(0.059), diamond(0.217), arrow(0.236), heart(0.155), ring(0.182), semicircle(0.193), trapezoid(0.090), parallelogram(0.211), rhombus(0.106), chevron(0.281)\n", " pentagon: triangle(0.059), square(0.031), hexagon(0.036), heptagon(0.055), octagon(0.038), nonagon(0.038), decagon(0.083), dodecagon(0.067), circle(0.165), ellipse(0.249), spiral(0.208), wave(0.205), star3(0.168), star4(0.132), star5(0.151), star6(0.212), star7(0.191), star8(0.161), cross(0.050), diamond(0.207), arrow(0.223), heart(0.108), ring(0.143), semicircle(0.167), trapezoid(0.060), parallelogram(0.204), rhombus(0.085), chevron(0.250)\n", " hexagon: triangle(0.047), square(0.032), pentagon(0.036), heptagon(0.028), octagon(0.053), nonagon(0.060), decagon(0.090), dodecagon(0.078), circle(0.128), ellipse(0.231), spiral(0.184), wave(0.194), crescent(0.257), star3(0.125), star4(0.097), star5(0.119), star6(0.157), star7(0.150), star8(0.118), cross(0.058), diamond(0.169), arrow(0.171), heart(0.131), ring(0.133), semicircle(0.140), trapezoid(0.069), parallelogram(0.168), rhombus(0.081), chevron(0.201)\n", " heptagon: triangle(0.063), square(0.081), pentagon(0.055), hexagon(0.028), octagon(0.049), nonagon(0.085), decagon(0.075), dodecagon(0.075), circle(0.131), ellipse(0.183), spiral(0.152), wave(0.164), crescent(0.233), star3(0.113), star4(0.088), star5(0.098), star6(0.112), star7(0.108), star8(0.093), cross(0.066), diamond(0.161), arrow(0.176), heart(0.140), ring(0.113), semicircle(0.140), trapezoid(0.073), parallelogram(0.190), rhombus(0.089), chevron(0.206)\n", " octagon: triangle(0.066), square(0.066), pentagon(0.038), hexagon(0.053), heptagon(0.049), nonagon(0.039), decagon(0.055), dodecagon(0.037), circle(0.119), ellipse(0.158), spiral(0.153), wave(0.139), crescent(0.246), star3(0.140), star4(0.115), star5(0.125), star6(0.160), star7(0.146), star8(0.115), cross(0.088), diamond(0.144), arrow(0.190), heart(0.146), ring(0.132), semicircle(0.111), trapezoid(0.029), parallelogram(0.143), rhombus(0.067), chevron(0.204)\n", " nonagon: triangle(0.075), square(0.056), pentagon(0.038), hexagon(0.060), heptagon(0.085), octagon(0.039), decagon(0.070), dodecagon(0.057), circle(0.138), ellipse(0.220), spiral(0.195), wave(0.208), star3(0.177), star4(0.146), star5(0.169), star6(0.222), star7(0.203), star8(0.179), cross(0.092), diamond(0.180), arrow(0.217), heart(0.119), ring(0.151), semicircle(0.130), trapezoid(0.065), parallelogram(0.143), rhombus(0.069), chevron(0.229)\n", " decagon: triangle(0.090), square(0.115), pentagon(0.083), hexagon(0.090), heptagon(0.075), octagon(0.055), nonagon(0.070), dodecagon(0.037), circle(0.137), ellipse(0.199), spiral(0.198), wave(0.211), crescent(0.261), star3(0.165), star4(0.136), star5(0.146), star6(0.188), star7(0.156), star8(0.157), cross(0.101), diamond(0.129), arrow(0.206), heart(0.143), ring(0.105), semicircle(0.155), trapezoid(0.078), parallelogram(0.177), rhombus(0.109), chevron(0.233)\n", " dodecagon: triangle(0.110), square(0.100), pentagon(0.067), hexagon(0.078), heptagon(0.075), octagon(0.037), nonagon(0.057), decagon(0.037), circle(0.132), ellipse(0.192), spiral(0.185), wave(0.181), crescent(0.280), star3(0.181), star4(0.155), star5(0.166), star6(0.214), star7(0.183), star8(0.167), cross(0.125), diamond(0.139), arrow(0.216), heart(0.155), ring(0.102), semicircle(0.140), trapezoid(0.047), parallelogram(0.176), rhombus(0.110), chevron(0.234)\n", " circle: triangle(0.091), square(0.178), pentagon(0.165), hexagon(0.128), heptagon(0.131), octagon(0.119), nonagon(0.138), decagon(0.137), dodecagon(0.132), ellipse(0.096), spiral(0.057), wave(0.118), crescent(0.106), star3(0.064), star4(0.060), star5(0.062), star6(0.110), star7(0.074), star8(0.066), cross(0.165), diamond(0.046), arrow(0.144), heart(0.139), ring(0.101), semicircle(0.047), trapezoid(0.106), parallelogram(0.066), rhombus(0.125), chevron(0.138)\n", " ellipse: triangle(0.193), pentagon(0.249), hexagon(0.231), heptagon(0.183), octagon(0.158), nonagon(0.220), decagon(0.199), dodecagon(0.192), circle(0.096), spiral(0.064), wave(0.107), crescent(0.142), star3(0.112), star4(0.119), star5(0.097), star6(0.121), star7(0.090), star8(0.086), cross(0.253), diamond(0.125), arrow(0.146), heart(0.245), ring(0.207), semicircle(0.078), trapezoid(0.154), parallelogram(0.110), rhombus(0.160), chevron(0.154)\n", " spiral: triangle(0.141), square(0.247), pentagon(0.208), hexagon(0.184), heptagon(0.152), octagon(0.153), nonagon(0.195), decagon(0.198), dodecagon(0.185), circle(0.057), ellipse(0.064), wave(0.088), crescent(0.127), star3(0.082), star4(0.085), star5(0.081), star6(0.099), star7(0.074), star8(0.073), cross(0.186), diamond(0.122), arrow(0.151), heart(0.178), ring(0.144), semicircle(0.075), trapezoid(0.132), parallelogram(0.109), rhombus(0.146), chevron(0.148)\n", " wave: triangle(0.167), square(0.260), pentagon(0.205), hexagon(0.194), heptagon(0.164), octagon(0.139), nonagon(0.208), decagon(0.211), dodecagon(0.181), circle(0.118), ellipse(0.107), spiral(0.088), crescent(0.173), star3(0.142), star4(0.144), star5(0.133), star6(0.148), star7(0.132), star8(0.112), cross(0.226), diamond(0.169), arrow(0.200), heart(0.212), ring(0.185), semicircle(0.110), trapezoid(0.116), parallelogram(0.148), rhombus(0.165), chevron(0.166)\n", " crescent: triangle(0.186), hexagon(0.257), heptagon(0.233), octagon(0.246), decagon(0.261), dodecagon(0.280), circle(0.106), ellipse(0.142), spiral(0.127), wave(0.173), star3(0.063), star4(0.088), star5(0.079), star6(0.107), star7(0.073), star8(0.085), cross(0.260), diamond(0.138), arrow(0.205), heart(0.211), ring(0.209), semicircle(0.146), trapezoid(0.243), parallelogram(0.176), rhombus(0.209), chevron(0.192)\n", " star3: triangle(0.076), square(0.180), pentagon(0.168), hexagon(0.125), heptagon(0.113), octagon(0.140), nonagon(0.177), decagon(0.165), dodecagon(0.181), circle(0.064), ellipse(0.112), spiral(0.082), wave(0.142), crescent(0.063), star4(0.007), star5(0.009), star6(0.043), star7(0.015), star8(0.017), cross(0.130), diamond(0.097), arrow(0.116), heart(0.133), ring(0.126), semicircle(0.098), trapezoid(0.156), parallelogram(0.133), rhombus(0.134), chevron(0.128)\n", " star4: triangle(0.056), square(0.141), pentagon(0.132), hexagon(0.097), heptagon(0.088), octagon(0.115), nonagon(0.146), decagon(0.136), dodecagon(0.155), circle(0.060), ellipse(0.119), spiral(0.085), wave(0.144), crescent(0.088), star3(0.007), star5(0.007), star6(0.045), star7(0.018), star8(0.017), cross(0.098), diamond(0.090), arrow(0.113), heart(0.124), ring(0.117), semicircle(0.095), trapezoid(0.132), parallelogram(0.129), rhombus(0.120), chevron(0.128)\n", " star5: triangle(0.077), square(0.174), pentagon(0.151), hexagon(0.119), heptagon(0.098), octagon(0.125), nonagon(0.169), decagon(0.146), dodecagon(0.166), circle(0.062), ellipse(0.097), spiral(0.081), wave(0.133), crescent(0.079), star3(0.009), star4(0.007), star6(0.039), star7(0.009), star8(0.012), cross(0.118), diamond(0.090), arrow(0.116), heart(0.134), ring(0.122), semicircle(0.094), trapezoid(0.139), parallelogram(0.133), rhombus(0.132), chevron(0.128)\n", " star6: triangle(0.129), square(0.226), pentagon(0.212), hexagon(0.157), heptagon(0.112), octagon(0.160), nonagon(0.222), decagon(0.188), dodecagon(0.214), circle(0.110), ellipse(0.121), spiral(0.099), wave(0.148), crescent(0.107), star3(0.043), star4(0.045), star5(0.039), star7(0.029), star8(0.037), cross(0.164), diamond(0.133), arrow(0.155), heart(0.223), ring(0.173), semicircle(0.127), trapezoid(0.174), parallelogram(0.168), rhombus(0.167), chevron(0.162)\n", " star7: triangle(0.102), square(0.218), pentagon(0.191), hexagon(0.150), heptagon(0.108), octagon(0.146), nonagon(0.203), decagon(0.156), dodecagon(0.183), circle(0.074), ellipse(0.090), spiral(0.074), wave(0.132), crescent(0.073), star3(0.015), star4(0.018), star5(0.009), star6(0.029), star8(0.015), cross(0.146), diamond(0.094), arrow(0.124), heart(0.160), ring(0.123), semicircle(0.106), trapezoid(0.161), parallelogram(0.151), rhombus(0.155), chevron(0.136)\n", " star8: triangle(0.082), square(0.175), pentagon(0.161), hexagon(0.118), heptagon(0.093), octagon(0.115), nonagon(0.179), decagon(0.157), dodecagon(0.167), circle(0.066), ellipse(0.086), spiral(0.073), wave(0.112), crescent(0.085), star3(0.017), star4(0.017), star5(0.012), star6(0.037), star7(0.015), cross(0.127), diamond(0.098), arrow(0.103), heart(0.166), ring(0.128), semicircle(0.086), trapezoid(0.133), parallelogram(0.137), rhombus(0.141), chevron(0.139)\n", " cross: triangle(0.055), square(0.059), pentagon(0.050), hexagon(0.058), heptagon(0.066), octagon(0.088), nonagon(0.092), decagon(0.101), dodecagon(0.125), circle(0.165), ellipse(0.253), spiral(0.186), wave(0.226), crescent(0.260), star3(0.130), star4(0.098), star5(0.118), star6(0.164), star7(0.146), star8(0.127), diamond(0.220), arrow(0.198), heart(0.117), ring(0.141), semicircle(0.181), trapezoid(0.104), parallelogram(0.219), rhombus(0.105), chevron(0.240)\n", " diamond: triangle(0.129), square(0.217), pentagon(0.207), hexagon(0.169), heptagon(0.161), octagon(0.144), nonagon(0.180), decagon(0.129), dodecagon(0.139), circle(0.046), ellipse(0.125), spiral(0.122), wave(0.169), crescent(0.138), star3(0.097), star4(0.090), star5(0.090), star6(0.133), star7(0.094), star8(0.098), cross(0.220), arrow(0.174), heart(0.201), ring(0.107), semicircle(0.095), trapezoid(0.139), parallelogram(0.116), rhombus(0.177), chevron(0.184)\n", " arrow: triangle(0.177), square(0.236), pentagon(0.223), hexagon(0.171), heptagon(0.176), octagon(0.190), nonagon(0.217), decagon(0.206), dodecagon(0.216), circle(0.144), ellipse(0.146), spiral(0.151), wave(0.200), crescent(0.205), star3(0.116), star4(0.113), star5(0.116), star6(0.155), star7(0.124), star8(0.103), cross(0.198), diamond(0.174), heart(0.233), ring(0.222), semicircle(0.117), trapezoid(0.190), parallelogram(0.138), rhombus(0.150), chevron(0.105)\n", " heart: triangle(0.097), square(0.155), pentagon(0.108), hexagon(0.131), heptagon(0.140), octagon(0.146), nonagon(0.119), decagon(0.143), dodecagon(0.155), circle(0.139), ellipse(0.245), spiral(0.178), wave(0.212), crescent(0.211), star3(0.133), star4(0.124), star5(0.134), star6(0.223), star7(0.160), star8(0.166), cross(0.117), diamond(0.201), arrow(0.233), ring(0.120), semicircle(0.186), trapezoid(0.160), parallelogram(0.211), rhombus(0.122), chevron(0.226)\n", " ring: triangle(0.109), square(0.182), pentagon(0.143), hexagon(0.133), heptagon(0.113), octagon(0.132), nonagon(0.151), decagon(0.105), dodecagon(0.102), circle(0.101), ellipse(0.207), spiral(0.144), wave(0.185), crescent(0.209), star3(0.126), star4(0.117), star5(0.122), star6(0.173), star7(0.123), star8(0.128), cross(0.141), diamond(0.107), arrow(0.222), heart(0.120), semicircle(0.132), trapezoid(0.126), parallelogram(0.226), rhombus(0.196), chevron(0.230)\n", " semicircle: triangle(0.131), square(0.193), pentagon(0.167), hexagon(0.140), heptagon(0.140), octagon(0.111), nonagon(0.130), decagon(0.155), dodecagon(0.140), circle(0.047), ellipse(0.078), spiral(0.075), wave(0.110), crescent(0.146), star3(0.098), star4(0.095), star5(0.094), star6(0.127), star7(0.106), star8(0.086), cross(0.181), diamond(0.095), arrow(0.117), heart(0.186), ring(0.132), trapezoid(0.084), parallelogram(0.052), rhombus(0.107), chevron(0.104)\n", " trapezoid: triangle(0.090), square(0.090), pentagon(0.060), hexagon(0.069), heptagon(0.073), octagon(0.029), nonagon(0.065), decagon(0.078), dodecagon(0.047), circle(0.106), ellipse(0.154), spiral(0.132), wave(0.116), crescent(0.243), star3(0.156), star4(0.132), star5(0.139), star6(0.174), star7(0.161), star8(0.133), cross(0.104), diamond(0.139), arrow(0.190), heart(0.160), ring(0.126), semicircle(0.084), parallelogram(0.114), rhombus(0.069), chevron(0.178)\n", " parallelogram: triangle(0.154), square(0.211), pentagon(0.204), hexagon(0.168), heptagon(0.190), octagon(0.143), nonagon(0.143), decagon(0.177), dodecagon(0.176), circle(0.066), ellipse(0.110), spiral(0.109), wave(0.148), crescent(0.176), star3(0.133), star4(0.129), star5(0.133), star6(0.168), star7(0.151), star8(0.137), cross(0.219), diamond(0.116), arrow(0.138), heart(0.211), ring(0.226), semicircle(0.052), trapezoid(0.114), rhombus(0.093), chevron(0.113)\n", " rhombus: triangle(0.086), square(0.106), pentagon(0.085), hexagon(0.081), heptagon(0.089), octagon(0.067), nonagon(0.069), decagon(0.109), dodecagon(0.110), circle(0.125), ellipse(0.160), spiral(0.146), wave(0.165), crescent(0.209), star3(0.134), star4(0.120), star5(0.132), star6(0.167), star7(0.155), star8(0.141), cross(0.105), diamond(0.177), arrow(0.150), heart(0.122), ring(0.196), semicircle(0.107), trapezoid(0.069), parallelogram(0.093), chevron(0.157)\n", " chevron: triangle(0.204), square(0.281), pentagon(0.250), hexagon(0.201), heptagon(0.206), octagon(0.204), nonagon(0.229), decagon(0.233), dodecagon(0.234), circle(0.138), ellipse(0.154), spiral(0.148), wave(0.166), crescent(0.192), star3(0.128), star4(0.128), star5(0.128), star6(0.162), star7(0.136), star8(0.139), cross(0.240), diamond(0.184), arrow(0.105), heart(0.226), ring(0.230), semicircle(0.104), trapezoid(0.178), parallelogram(0.113), rhombus(0.157)\n", "\n", " Forward test:\n", " Input: torch.Size([16, 768]), norms=1.0000\n", " Output: torch.Size([16, 768]), norms=1.0000\n", " Diagnostics: {'gate_mean': 0.5001546740531921, 'gate_max': 0.5242846012115479, 'confusion_active': 28.8125, 'bias_magnitude': 2.6136488914489746, 'scale_mean': 0.9999983906745911}\n", " Grad flows: True\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# GEOMETRIC CONSTELLATION\n", "#\n", "# Pure abstract coordinate system on the unit hypersphere.\n", "# No BERT. No semantics. No labels until assigned.\n", "#\n", "# Each anchor is a 4D local sphere (tangent frame at that point).\n", "# The full constellation has 5D pentachoral structure.\n", "# Conv4d ingests raw data → triangulation position → rigidity accumulation.\n", "#\n", "# The optimizer protects the constellation.\n", "# The patchwork learns to navigate it.\n", "# Rigidity crystallizes from the data itself.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from dataclasses import dataclass\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "torch.set_float32_matmul_precision('high')\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cayley_menger_vol_sq(pts):\n", " \"\"\"Pentachoron volume² from 5 points. (B, 5, D) → (B,)\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B = pts.shape[0]\n", " cm = torch.zeros(B, 6, 6, device=pts.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1.0; cm[:, 1:, 0] = 1.0; cm[:, 1:, 1:] = d2\n", " return -torch.linalg.det(cm) / 9216.0\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n_samples=100):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol_sq(emb_f[idx].unsqueeze(0))[0]\n", " v = math.sqrt(max(v2.item(), 0.0))\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "class TangentialGradFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, emb, gate):\n", " ctx.save_for_backward(emb)\n", " ctx.gate = gate\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad):\n", " emb, = ctx.saved_tensors\n", " tang, norm = tangential_projection(grad, emb)\n", " return tang + ctx.gate * norm, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION: 30 abstract anchors, each a 4D local sphere\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " \"\"\"\n", " N anchor points on the D-dim unit hypersphere.\n", " Each anchor carries a 4D local tangent frame (orthonormal basis\n", " of its tangent space). The frame defines a local 4-sphere at\n", " that point — a submanifold where nearby structure is measured.\n", "\n", " The full constellation has 5D pentachoral regularity:\n", " any 5 anchors form a pentachoron whose volume is monitored.\n", "\n", " Xavier initialization guarantees near-orthogonality in high-D.\n", " Expected pairwise cosine ≈ 0 ± 1/√D.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_embed=768, d_local=4):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.d_embed = d_embed\n", " self.d_local = d_local\n", "\n", " # Anchor positions on hypersphere (Xavier → normalize)\n", " anchor_init = torch.randn(n_anchors, d_embed)\n", " anchor_init = F.normalize(anchor_init, dim=-1)\n", " self.anchors = nn.Parameter(anchor_init)\n", "\n", " # Local 4D tangent frames at each anchor\n", " # Each is (d_local, d_embed) — 4 orthonormal basis vectors\n", " # tangent to the sphere at the anchor point\n", " frames = torch.randn(n_anchors, d_local, d_embed)\n", " self.local_frames = nn.Parameter(frames)\n", "\n", " # Rigidity accumulator: running stats per anchor\n", " # How rigid/crystalline the local geometry has become\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", " self.register_buffer(\"local_cv\", torch.zeros(n_anchors))\n", "\n", " def orthogonalize_frames(self):\n", " \"\"\"Project frames tangential to sphere and orthonormalize.\"\"\"\n", " with torch.no_grad():\n", " anchors_n = F.normalize(self.anchors.data, dim=-1)\n", " for i in range(self.n_anchors):\n", " frame = self.local_frames.data[i] # (4, D)\n", " a = anchors_n[i] # (D,)\n", "\n", " # Remove radial component (make tangential)\n", " radial = (frame @ a).unsqueeze(-1) * a.unsqueeze(0) # (4, D)\n", " frame = frame - radial\n", "\n", " # Gram-Schmidt orthonormalize the 4 tangent vectors\n", " ortho = []\n", " for j in range(self.d_local):\n", " v = frame[j]\n", " for u in ortho:\n", " v = v - (v @ u) * u\n", " v = F.normalize(v, dim=-1)\n", " ortho.append(v)\n", " self.local_frames.data[i] = torch.stack(ortho)\n", "\n", " def triangulate(self, emb):\n", " \"\"\"\n", " Compute triangulation coordinates: angular distances to all anchors.\n", "\n", " Args:\n", " emb: (B, D) L2-normalized embeddings\n", "\n", " Returns:\n", " tri_coords: (B, N) cosine distances to each anchor\n", " local_coords: (B, N, 4) projection onto each anchor's local frame\n", " nearest: (B,) index of nearest anchor\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1) # (N, D)\n", "\n", " # Global triangulation: cosine to each anchor\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_coords = 1.0 - cos_sim # (B, N) distances\n", "\n", " # Local coordinates: project onto each anchor's 4D tangent frame\n", " # For each anchor, project the residual (emb - anchor projection) into local frame\n", " # emb_centered = emb - cos_sim * anchor gives the tangential displacement\n", " B = emb.shape[0]\n", " local_coords = torch.zeros(B, self.n_anchors, self.d_local,\n", " device=emb.device, dtype=emb.dtype)\n", "\n", " for i in range(self.n_anchors):\n", " # Tangential displacement from anchor i\n", " displacement = emb - cos_sim[:, i:i+1] * anchors_n[i:i+1] # (B, D)\n", " # Project into local 4D frame\n", " frame = self.local_frames[i] # (4, D)\n", " local_coords[:, i] = displacement @ frame.T # (B, 4)\n", "\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", "\n", " return tri_coords, local_coords, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, emb, labels):\n", " \"\"\"\n", " Accumulate rigidity from training data.\n", " Rigidity = how consistent the local geometry is around each anchor.\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5:\n", " continue\n", "\n", " cluster = emb[mask]\n", " self.visit_count[i] += mask.sum().float()\n", "\n", " # Local CV: pentachoron regularity within this cluster\n", " cv = pentachoron_cv(cluster, n_samples=50)\n", " # Exponential moving average\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.local_cv[i] = (1 - alpha) * self.local_cv[i] + alpha * cv\n", "\n", " # Rigidity: inverse of CV (more regular = more rigid)\n", " self.rigidity[i] = 1.0 / (self.local_cv[i] + 0.01)\n", "\n", " def constellation_health(self):\n", " \"\"\"Global pentachoral regularity of the anchor constellation.\"\"\"\n", " anchors_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = anchors_n @ anchors_n.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=anchors_n.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_cos\": cos[mask].min().item(),\n", " \"max_cos\": cos[mask].max().item(),\n", " \"cv\": pentachoron_cv(anchors_n, n_samples=200),\n", " \"mean_rigidity\": self.rigidity.mean().item(),\n", " \"max_rigidity\": self.rigidity.max().item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONV4D INGESTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Conv4dBlock(nn.Module):\n", " \"\"\"\n", " Process triangulation coordinates as a 4D structure.\n", "\n", " Input: (B, N, 4) local coordinates at N anchors\n", " Reshape to (B, 1, N, 4) — treat as 1-channel 2D image where\n", " height=N (anchors), width=4 (local frame dims).\n", "\n", " Conv2d operates on this — spatial correlations across anchors\n", " and across local frame dimensions ARE the geometric signal.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_local=4, d_out=256):\n", " super().__init__()\n", " self.conv = nn.Sequential(\n", " nn.Conv2d(1, 32, (3, 3), padding=(1, 1)), nn.GELU(),\n", " nn.Conv2d(32, 64, (3, 3), padding=(1, 1)), nn.GELU(),\n", " nn.Conv2d(64, 128, (3, 1), padding=(1, 0)), nn.GELU(),\n", " nn.AdaptiveAvgPool2d((1, 1)),\n", " )\n", " self.proj = nn.Linear(128, d_out)\n", "\n", " def forward(self, local_coords):\n", " \"\"\"\n", " Args:\n", " local_coords: (B, N, 4) — local frame projections at each anchor\n", "\n", " Returns:\n", " features: (B, d_out) — geometric features from 4D structure\n", " \"\"\"\n", " x = local_coords.unsqueeze(1) # (B, 1, N, 4)\n", " x = self.conv(x).flatten(1) # (B, 128)\n", " return self.proj(x) # (B, d_out)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ConstellationClassifier(nn.Module):\n", " \"\"\"\n", " Image → conv backbone → hypersphere embedding → triangulation\n", " → conv4d on local coords → prototype logits\n", "\n", " The constellation is the coordinate system.\n", " The conv4d reads the geometric structure.\n", " The prototypes are points in the triangulation space.\n", " \"\"\"\n", "\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " d_local=4, d_hidden=256):\n", " super().__init__()\n", " self.n_classes = n_classes\n", "\n", " # Image backbone (simple, 1-channel input)\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(),\n", " nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation (abstract coordinate system)\n", " self.constellation = Constellation(n_anchors, d_embed, d_local)\n", "\n", " # Conv4d: read geometric structure from local coordinates\n", " self.conv4d = Conv4dBlock(n_anchors, d_local, d_hidden)\n", "\n", " # Global triangulation path\n", " self.tri_proj = nn.Sequential(\n", " nn.Linear(n_anchors, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " )\n", "\n", " # Combine local (conv4d) + global (triangulation) → classify\n", " self.classifier = nn.Sequential(\n", " nn.Linear(d_hidden * 2, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " \"\"\"\n", " Args:\n", " x: (B, 1, 32, 32) grayscale images\n", "\n", " Returns:\n", " logits: (B, n_classes)\n", " emb: (B, d_embed) L2-normalized hypersphere embedding\n", " tri_coords: (B, n_anchors) triangulation distances\n", " local_coords: (B, n_anchors, 4) local frame projections\n", " nearest: (B,) nearest anchor index\n", " \"\"\"\n", " # Backbone → hypersphere\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", "\n", " # Triangulate against constellation\n", " tri_coords, local_coords, nearest = self.constellation.triangulate(emb)\n", "\n", " # Conv4d on local structure\n", " local_feat = self.conv4d(local_coords) # (B, d_hidden)\n", "\n", " # Global triangulation features\n", " global_feat = self.tri_proj(tri_coords) # (B, d_hidden)\n", "\n", " # Combine and classify\n", " combined = torch.cat([local_feat, global_feat], dim=-1) # (B, 2*d_hidden)\n", " logits = self.classifier(combined)\n", "\n", " return logits, emb, tri_coords, local_coords, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (reuse from trainer)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _draw(img, x0, y0, x1, y1, t=1):\n", " n = max(int(max(abs(x1-x0), abs(y1-y0))*2), 1); sz = img.shape[0]\n", " for s in np.linspace(0, 1, n):\n", " px, py = int(x0+s*(x1-x0)), int(y0+s*(y1-y0))\n", " for dx in range(-t, t+1):\n", " for dy in range(-t, t+1):\n", " nx, ny = px+dx, py+dy\n", " if 0 <= nx < sz and 0 <= ny < sz: img[ny, nx] = 1.0\n", "\n", "def render_poly(nv, sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,r = sz/2,sz/2,sz*0.35\n", " a = np.linspace(0,2*np.pi,nv,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " ri = r*(1+np.random.normal(0,p,nv))\n", " pts = [(cx+ri[i]*np.cos(a[i]),cy+ri[i]*np.sin(a[i])) for i in range(nv)]\n", " for i in range(nv): _draw(img,*pts[i],*pts[(i+1)%nv])\n", " return img\n", "\n", "def render_star(np_, sz=32, p=0.12):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " ro,ri_ = sz*0.38,sz*0.15\n", " a = np.linspace(0,2*np.pi,np_*2,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " pts = [(cx+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.cos(a[i]),\n", " cy+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.sin(a[i])) for i in range(len(a))]\n", " for i in range(len(pts)): _draw(img,*pts[i],*pts[(i+1)%len(pts)])\n", " return img\n", "\n", "def render_cross(sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,arm = sz/2,sz/2,sz*0.3\n", " for ab in [0,np.pi/2,np.pi,3*np.pi/2]:\n", " a = ab+np.random.normal(0,p*0.3); r = arm*(1+np.random.normal(0,p))\n", " _draw(img,cx,cy,cx+r*np.cos(a),cy+r*np.sin(a),2)\n", " return img\n", "\n", "def render_spiral(sz=32, p=0.1):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " for t in np.linspace(0,5*np.pi,200):\n", " r = sz*0.015*t*(1+np.random.normal(0,p*0.3))\n", " x,y = int(cx+r*np.cos(t)),int(cy+r*np.sin(t))\n", " if 0<=x= r2*0.9:\n", " ix,iy = int(x1),int(y1)\n", " if 0<=ix 0:\n", " cv_target = cv_now\n", " if cv_target:\n", " delta = cv_now - cv_target\n", " if abs(delta) <= 0.02: gate_val = 0.0\n", " elif delta < 0: gate_val = min(abs(delta)/(cv_target+1e-8), 1.0) * 0.3\n", " else: gate_val = max(0.0, 0.1*(1-min(delta/(cv_target+1e-8),1.0)))\n", "\n", " # Apply tangential gate\n", " emb_gated = TangentialGradFn.apply(emb, emb, gate_val)\n", "\n", " # Recompute logits through gated embedding\n", " tri_g, local_g, _ = model.constellation.triangulate(emb_gated)\n", " local_feat = model.conv4d(local_g)\n", " global_feat = model.tri_proj(tri_g)\n", " logits = model.classifier(torch.cat([local_feat, global_feat], dim=-1))\n", "\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " # Update rigidity\n", " model.constellation.update_rigidity(emb.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " v_logits, v_emb, v_tri, v_local, v_nearest = model(val_imgs)\n", " v_acc = (v_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(v_emb, n_samples=100)\n", "\n", " health = model.constellation.constellation_health()\n", "\n", " # Per-type accuracy\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (v_logits.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"cos={health['mean_cos']:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} \"\n", " f\"[{ta_str}]\")\n", "\n", " # Final constellation state\n", " health = model.constellation.constellation_health()\n", " print(f\"\\n Final constellation:\")\n", " print(f\" Mean cos: {health['mean_cos']:.4f}\")\n", " print(f\" CV: {health['cv']:.4f}\")\n", " print(f\" Rigidity: mean={health['mean_rigidity']:.1f} max={health['max_rigidity']:.1f}\")\n", "\n", " # Rigidity per class\n", " print(f\"\\n Per-anchor rigidity:\")\n", " rig = model.constellation.rigidity.cpu()\n", " for i in range(30):\n", " bar = \"█\" * int(rig[i].item())\n", " print(f\" {SHAPE_NAMES[i]:15s}: {rig[i]:.1f} {bar}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8sIn2orOhNxO", "outputId": "778b2e29-be4d-4bf3-d87e-4ccd8c213487" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "CONSTELLATION CLASSIFIER: Pure Geometric Coordinates\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000 Classes: 30\n", " Total params: 533,022\n", " Constellation params: 115,200\n", "\n", " Initial constellation:\n", " Mean cos: -0.0007 (want ≈0)\n", " Std cos: 0.0369\n", " Min cos: -0.1060\n", " Max cos: 0.0980\n", " CV: 0.0236\n", " E 1: t_acc=0.095 v_acc=0.196 cv=1.1643 gate=0.000 cos=-0.0071 rig=28.5/100.0 [polygon=0.13 curve=0.20 star=0.24 structure=0.22]\n", " E 5: t_acc=0.594 v_acc=0.608 cv=1.3266 gate=0.000 cos=-0.0110 rig=14.1/100.0 [polygon=0.22 curve=1.00 star=0.75 structure=0.68]\n", " E10: t_acc=0.663 v_acc=0.664 cv=1.5386 gate=0.000 cos=-0.0104 rig=11.4/100.0 [polygon=0.36 curve=0.94 star=0.80 structure=0.72]\n", " E15: t_acc=0.664 v_acc=0.667 cv=1.1608 gate=0.000 cos=-0.0077 rig=10.7/100.0 [polygon=0.37 curve=0.99 star=0.84 structure=0.68]\n", " E20: t_acc=0.699 v_acc=0.661 cv=1.1939 gate=0.000 cos=-0.0058 rig=10.1/100.0 [polygon=0.41 curve=0.98 star=0.79 structure=0.65]\n", " E25: t_acc=0.713 v_acc=0.713 cv=1.3698 gate=0.000 cos=-0.0039 rig=9.9/100.0 [polygon=0.43 curve=1.00 star=0.92 structure=0.70]\n", " E30: t_acc=0.724 v_acc=0.718 cv=1.0562 gate=0.000 cos=-0.0018 rig=9.6/100.0 [polygon=0.50 curve=0.99 star=0.93 structure=0.65]\n", " E35: t_acc=0.730 v_acc=0.711 cv=1.2700 gate=0.000 cos=0.0005 rig=9.5/100.0 [polygon=0.39 curve=0.99 star=0.92 structure=0.73]\n", " E40: t_acc=0.728 v_acc=0.731 cv=1.1202 gate=0.000 cos=0.0025 rig=9.4/100.0 [polygon=0.43 curve=1.00 star=0.95 structure=0.74]\n", "\n", " Final constellation:\n", " Mean cos: 0.0025\n", " CV: 0.3251\n", " Rigidity: mean=9.4 max=100.0\n", "\n", " Per-anchor rigidity:\n", " triangle : 1.9 █\n", " square : 1.9 █\n", " pentagon : 1.8 █\n", " hexagon : 2.0 █\n", " heptagon : 2.0 ██\n", " octagon : 2.0 ██\n", " nonagon : 2.1 ██\n", " decagon : 2.0 █\n", " dodecagon : 2.1 ██\n", " circle : 2.3 ██\n", " ellipse : 1.9 █\n", " spiral : 8.2 ████████\n", " wave : 20.9 ████████████████████\n", " crescent : 100.0 ████████████████████████████████████████████████████████████████████████████████████████████████████\n", " star3 : 2.3 ██\n", " star4 : 1.8 █\n", " star5 : 2.1 ██\n", " star6 : 2.4 ██\n", " star7 : 2.7 ██\n", " star8 : 3.2 ███\n", " cross : 2.6 ██\n", " diamond : 1.9 █\n", " arrow : 1.8 █\n", " heart : 1.3 █\n", " ring : 1.3 █\n", " semicircle : 100.0 ████████████████████████████████████████████████████████████████████████████████████████████████████\n", " trapezoid : 1.8 █\n", " parallelogram : 1.8 █\n", " rhombus : 1.9 █\n", " chevron : 1.6 █\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# GEOMETRIC CONSTELLATION\n", "#\n", "# Pure abstract coordinate system on the unit hypersphere.\n", "# No BERT. No semantics. No labels until assigned.\n", "#\n", "# Each anchor is a 4D local sphere (tangent frame at that point).\n", "# The full constellation has 5D pentachoral structure.\n", "# Conv4d ingests raw data → triangulation position → rigidity accumulation.\n", "#\n", "# The optimizer protects the constellation.\n", "# The patchwork learns to navigate it.\n", "# Rigidity crystallizes from the data itself.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from dataclasses import dataclass\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cayley_menger_vol_sq(pts):\n", " \"\"\"Pentachoron volume² from 5 points. (B, 5, D) → (B,)\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B = pts.shape[0]\n", " cm = torch.zeros(B, 6, 6, device=pts.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1.0; cm[:, 1:, 0] = 1.0; cm[:, 1:, 1:] = d2\n", " return -torch.linalg.det(cm) / 9216.0\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n_samples=100):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol_sq(emb_f[idx].unsqueeze(0))[0]\n", " v = math.sqrt(max(v2.item(), 0.0))\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "class TangentialGradFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, emb, gate):\n", " ctx.save_for_backward(emb)\n", " ctx.gate = gate\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad):\n", " emb, = ctx.saved_tensors\n", " tang, norm = tangential_projection(grad, emb)\n", " return tang + ctx.gate * norm, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION: 30 abstract anchors, each a 4D local sphere\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " \"\"\"\n", " N anchor points on the D-dim unit hypersphere.\n", " Each anchor carries a 4D local tangent frame (orthonormal basis\n", " of its tangent space). The frame defines a local 4-sphere at\n", " that point — a submanifold where nearby structure is measured.\n", "\n", " The full constellation has 5D pentachoral regularity:\n", " any 5 anchors form a pentachoron whose volume is monitored.\n", "\n", " Xavier initialization guarantees near-orthogonality in high-D.\n", " Expected pairwise cosine ≈ 0 ± 1/√D.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_embed=768, d_local=4):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.d_embed = d_embed\n", " self.d_local = d_local\n", "\n", " # Anchor positions on hypersphere (Xavier → normalize)\n", " anchor_init = torch.randn(n_anchors, d_embed)\n", " anchor_init = F.normalize(anchor_init, dim=-1)\n", " self.anchors = nn.Parameter(anchor_init)\n", "\n", " # Local 4D tangent frames at each anchor\n", " # Each is (d_local, d_embed) — 4 orthonormal basis vectors\n", " # tangent to the sphere at the anchor point\n", " frames = torch.randn(n_anchors, d_local, d_embed)\n", " self.local_frames = nn.Parameter(frames)\n", "\n", " # Rigidity accumulator: running stats per anchor\n", " # How rigid/crystalline the local geometry has become\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", " self.register_buffer(\"local_cv\", torch.zeros(n_anchors))\n", "\n", " def orthogonalize_frames(self):\n", " \"\"\"Project frames tangential to sphere and orthonormalize.\"\"\"\n", " with torch.no_grad():\n", " anchors_n = F.normalize(self.anchors.data, dim=-1)\n", " for i in range(self.n_anchors):\n", " frame = self.local_frames.data[i] # (4, D)\n", " a = anchors_n[i] # (D,)\n", "\n", " # Remove radial component (make tangential)\n", " radial = (frame @ a).unsqueeze(-1) * a.unsqueeze(0) # (4, D)\n", " frame = frame - radial\n", "\n", " # Gram-Schmidt orthonormalize the 4 tangent vectors\n", " ortho = []\n", " for j in range(self.d_local):\n", " v = frame[j]\n", " for u in ortho:\n", " v = v - (v @ u) * u\n", " v = F.normalize(v, dim=-1)\n", " ortho.append(v)\n", " self.local_frames.data[i] = torch.stack(ortho)\n", "\n", " def triangulate(self, emb):\n", " \"\"\"\n", " Compute triangulation coordinates: angular distances to all anchors.\n", "\n", " Args:\n", " emb: (B, D) L2-normalized embeddings\n", "\n", " Returns:\n", " tri_coords: (B, N) cosine distances to each anchor\n", " local_coords: (B, N, 4) projection onto each anchor's local frame\n", " nearest: (B,) index of nearest anchor\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1) # (N, D)\n", "\n", " # Global triangulation: cosine to each anchor\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_coords = 1.0 - cos_sim # (B, N) distances\n", "\n", " # Local coordinates: project onto each anchor's 4D tangent frame\n", " # For each anchor, project the residual (emb - anchor projection) into local frame\n", " # emb_centered = emb - cos_sim * anchor gives the tangential displacement\n", " B = emb.shape[0]\n", " local_coords = torch.zeros(B, self.n_anchors, self.d_local,\n", " device=emb.device, dtype=emb.dtype)\n", "\n", " for i in range(self.n_anchors):\n", " # Tangential displacement from anchor i\n", " displacement = emb - cos_sim[:, i:i+1] * anchors_n[i:i+1] # (B, D)\n", " # Project into local 4D frame\n", " frame = self.local_frames[i] # (4, D)\n", " local_coords[:, i] = displacement @ frame.T # (B, 4)\n", "\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", "\n", " return tri_coords, local_coords, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, emb, labels):\n", " \"\"\"\n", " Accumulate rigidity from training data.\n", " Rigidity = how consistent the local geometry is around each anchor.\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5:\n", " continue\n", "\n", " cluster = emb[mask]\n", " self.visit_count[i] += mask.sum().float()\n", "\n", " # Local CV: pentachoron regularity within this cluster\n", " cv = pentachoron_cv(cluster, n_samples=50)\n", " # Exponential moving average\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.local_cv[i] = (1 - alpha) * self.local_cv[i] + alpha * cv\n", "\n", " # Rigidity: inverse of CV (more regular = more rigid)\n", " self.rigidity[i] = 1.0 / (self.local_cv[i] + 0.01)\n", "\n", " def constellation_health(self):\n", " \"\"\"Global pentachoral regularity of the anchor constellation.\"\"\"\n", " anchors_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = anchors_n @ anchors_n.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=anchors_n.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_cos\": cos[mask].min().item(),\n", " \"max_cos\": cos[mask].max().item(),\n", " \"cv\": pentachoron_cv(anchors_n, n_samples=200),\n", " \"mean_rigidity\": self.rigidity.mean().item(),\n", " \"max_rigidity\": self.rigidity.max().item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONV4D INGESTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n):\n", " \"\"\"All ordered compositions of integer n. |compositions| = 2^(n-1).\"\"\"\n", " if n == 0: yield (); return\n", " if n == 1: yield (1,); return\n", " for i in range(1, n + 1):\n", " for rest in integer_compositions(n - i):\n", " yield (i,) + rest\n", "\n", "\n", "class Conv4dBlock(nn.Module):\n", " \"\"\"\n", " True compositional 4D convolution via integer partition paths.\n", "\n", " 4 components (one per local tangent frame dimension) processed\n", " through all 2^3 = 8 ordered compositions of 4.\n", "\n", " Each composition defines a grouping: (1,3) means \"dim 0 alone,\n", " then dims 1-2-3 fused.\" (2,2) means \"dims 0-1 fused, then 2-3 fused.\"\n", " (4,) means \"all 4 dims fused at once.\"\n", "\n", " Same structure as conv5d NLI head — proven architecture.\n", " Per-anchor projections: each anchor's 4D local coords become\n", " 4 projected vectors that feed the composition paths.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_local=4, d_path=128, d_out=256):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.d_local = d_local\n", " self.d_path = d_path\n", "\n", " # Project each local dim (across all anchors) to shared path dimension\n", " # local_coords[:, :, d] is (B, N) — all anchors' d-th frame coordinate\n", " self.dim_projs = nn.ModuleList([\n", " nn.Sequential(nn.Linear(n_anchors, d_path), nn.LayerNorm(d_path))\n", " for _ in range(d_local)\n", " ])\n", "\n", " # Enumerate all compositions of 4\n", " self.compositions = list(integer_compositions(d_local))\n", " n_paths = len(self.compositions)\n", "\n", " # Per-group fusion layers\n", " self.group_fusions = nn.ModuleDict()\n", " for k in range(1, d_local + 1):\n", " self.group_fusions[str(k)] = nn.Sequential(\n", " nn.Linear(k * d_path, d_path),\n", " nn.GELU(),\n", " nn.LayerNorm(d_path),\n", " )\n", "\n", " # Learned path weights\n", " self.path_weights = nn.Parameter(torch.ones(n_paths) / n_paths)\n", "\n", " # Output projection\n", " self.out_proj = nn.Linear(d_path, d_out)\n", "\n", " print(f\" Conv4dBlock: {d_local} dims → {n_paths} composition paths, d_path={d_path}\")\n", "\n", " def forward(self, local_coords, tri_coords):\n", " \"\"\"\n", " Args:\n", " local_coords: (B, N, 4) — projections onto each anchor's 4D frame\n", " tri_coords: (B, N) — triangulation distances (activation weights)\n", "\n", " Returns:\n", " features: (B, d_out)\n", " \"\"\"\n", " # Each local dim across all anchors becomes a component\n", " # local_coords[:, :, d] = (B, N) — anchor activations for dim d\n", " # Project each dim: (B, N) → (B, d_path)\n", " components = []\n", " for d in range(self.d_local):\n", " components.append(self.dim_projs[d](local_coords[:, :, d])) # (B, d_path)\n", "\n", " # Process each composition path\n", " weights = F.softmax(self.path_weights, dim=0)\n", " path_outputs = []\n", "\n", " for composition in self.compositions:\n", " idx = 0\n", " group_outputs = []\n", " for group_size in composition:\n", " group = torch.cat(components[idx:idx + group_size], dim=-1) # (B, k*d_path)\n", " fused = self.group_fusions[str(group_size)](group) # (B, d_path)\n", " group_outputs.append(fused)\n", " idx += group_size\n", "\n", " # Sequential composition: feed each group's output forward\n", " path_out = group_outputs[0]\n", " for g in group_outputs[1:]:\n", " path_out = path_out + g # residual accumulation\n", " path_outputs.append(path_out)\n", "\n", " # Weighted combination of all paths\n", " stacked = torch.stack(path_outputs, dim=1) # (B, n_paths, d_path)\n", " combined = (stacked * weights.unsqueeze(0).unsqueeze(-1)).sum(dim=1) # (B, d_path)\n", "\n", " return self.out_proj(combined) # (B, d_out)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ConstellationClassifier(nn.Module):\n", " \"\"\"\n", " Image → conv backbone → hypersphere embedding → triangulation\n", " → conv4d on local coords → prototype logits\n", "\n", " The constellation is the coordinate system.\n", " The conv4d reads the geometric structure.\n", " The prototypes are points in the triangulation space.\n", " \"\"\"\n", "\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " d_local=4, d_hidden=256):\n", " super().__init__()\n", " self.n_classes = n_classes\n", "\n", " # Image backbone (simple, 1-channel input)\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(),\n", " nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation (abstract coordinate system)\n", " self.constellation = Constellation(n_anchors, d_embed, d_local)\n", "\n", " # Conv4d: read geometric structure from local coordinates\n", " self.conv4d = Conv4dBlock(n_anchors=n_anchors, d_local=d_local,\n", " d_path=128, d_out=d_hidden)\n", "\n", " # Global triangulation path\n", " self.tri_proj = nn.Sequential(\n", " nn.Linear(n_anchors, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " )\n", "\n", " # Combine local (conv4d) + global (triangulation) → classify\n", " self.classifier = nn.Sequential(\n", " nn.Linear(d_hidden * 2, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " \"\"\"\n", " Args:\n", " x: (B, 1, 32, 32) grayscale images\n", "\n", " Returns:\n", " logits: (B, n_classes)\n", " emb: (B, d_embed) L2-normalized hypersphere embedding\n", " tri_coords: (B, n_anchors) triangulation distances\n", " local_coords: (B, n_anchors, 4) local frame projections\n", " nearest: (B,) nearest anchor index\n", " \"\"\"\n", " # Backbone → hypersphere\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", "\n", " # Triangulate against constellation\n", " tri_coords, local_coords, nearest = self.constellation.triangulate(emb)\n", "\n", " # Conv4d on local structure\n", " local_feat = self.conv4d(local_coords, tri_coords) # (B, d_hidden)\n", "\n", " # Global triangulation features\n", " global_feat = self.tri_proj(tri_coords) # (B, d_hidden)\n", "\n", " # Combine and classify\n", " combined = torch.cat([local_feat, global_feat], dim=-1) # (B, 2*d_hidden)\n", " logits = self.classifier(combined)\n", "\n", " return logits, emb, tri_coords, local_coords, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (reuse from trainer)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _draw(img, x0, y0, x1, y1, t=1):\n", " n = max(int(max(abs(x1-x0), abs(y1-y0))*2), 1); sz = img.shape[0]\n", " for s in np.linspace(0, 1, n):\n", " px, py = int(x0+s*(x1-x0)), int(y0+s*(y1-y0))\n", " for dx in range(-t, t+1):\n", " for dy in range(-t, t+1):\n", " nx, ny = px+dx, py+dy\n", " if 0 <= nx < sz and 0 <= ny < sz: img[ny, nx] = 1.0\n", "\n", "def render_poly(nv, sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,r = sz/2,sz/2,sz*0.35\n", " a = np.linspace(0,2*np.pi,nv,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " ri = r*(1+np.random.normal(0,p,nv))\n", " pts = [(cx+ri[i]*np.cos(a[i]),cy+ri[i]*np.sin(a[i])) for i in range(nv)]\n", " for i in range(nv): _draw(img,*pts[i],*pts[(i+1)%nv])\n", " return img\n", "\n", "def render_star(np_, sz=32, p=0.12):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " ro,ri_ = sz*0.38,sz*0.15\n", " a = np.linspace(0,2*np.pi,np_*2,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " pts = [(cx+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.cos(a[i]),\n", " cy+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.sin(a[i])) for i in range(len(a))]\n", " for i in range(len(pts)): _draw(img,*pts[i],*pts[(i+1)%len(pts)])\n", " return img\n", "\n", "def render_cross(sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,arm = sz/2,sz/2,sz*0.3\n", " for ab in [0,np.pi/2,np.pi,3*np.pi/2]:\n", " a = ab+np.random.normal(0,p*0.3); r = arm*(1+np.random.normal(0,p))\n", " _draw(img,cx,cy,cx+r*np.cos(a),cy+r*np.sin(a),2)\n", " return img\n", "\n", "def render_spiral(sz=32, p=0.1):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " for t in np.linspace(0,5*np.pi,200):\n", " r = sz*0.015*t*(1+np.random.normal(0,p*0.3))\n", " x,y = int(cx+r*np.cos(t)),int(cy+r*np.sin(t))\n", " if 0<=x= r2*0.9:\n", " ix,iy = int(x1),int(y1)\n", " if 0<=ix 0:\n", " cv_target = cv_now\n", " if cv_target:\n", " delta = cv_now - cv_target\n", " if abs(delta) <= 0.02: gate_val = 0.0\n", " elif delta < 0: gate_val = min(abs(delta)/(cv_target+1e-8), 1.0) * 0.3\n", " else: gate_val = max(0.0, 0.1*(1-min(delta/(cv_target+1e-8),1.0)))\n", "\n", " # Apply tangential gate\n", " emb_gated = TangentialGradFn.apply(emb, emb, gate_val)\n", "\n", " # Recompute logits through gated embedding\n", " tri_g, local_g, _ = model.constellation.triangulate(emb_gated)\n", " local_feat = model.conv4d(local_g, tri_g)\n", " global_feat = model.tri_proj(tri_g)\n", " logits = model.classifier(torch.cat([local_feat, global_feat], dim=-1))\n", "\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " # Update rigidity\n", " model.constellation.update_rigidity(emb.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " v_logits, v_emb, v_tri, v_local, v_nearest = model(val_imgs)\n", " v_acc = (v_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(v_emb, n_samples=100)\n", "\n", " health = model.constellation.constellation_health()\n", "\n", " # Per-type accuracy\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (v_logits.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"cos={health['mean_cos']:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} \"\n", " f\"[{ta_str}]\")\n", "\n", " # Final constellation state\n", " health = model.constellation.constellation_health()\n", " print(f\"\\n Final constellation:\")\n", " print(f\" Mean cos: {health['mean_cos']:.4f}\")\n", " print(f\" CV: {health['cv']:.4f}\")\n", " print(f\" Rigidity: mean={health['mean_rigidity']:.1f} max={health['max_rigidity']:.1f}\")\n", "\n", " # Rigidity per class\n", " print(f\"\\n Per-anchor rigidity:\")\n", " rig = model.constellation.rigidity.cpu()\n", " for i in range(30):\n", " bar = \"█\" * int(rig[i].item())\n", " print(f\" {SHAPE_NAMES[i]:15s}: {rig[i]:.1f} {bar}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "N_W_jw7Nkhki", "outputId": "2b87ab26-c97e-4c0d-c60b-8998f6c2818a" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "CONSTELLATION CLASSIFIER: Pure Geometric Coordinates\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000 Classes: 30\n", " Conv4dBlock: 4 dims → 8 composition paths, d_path=128\n", " Total params: 671,782\n", " Constellation params: 115,200\n", "\n", " Initial constellation:\n", " Mean cos: -0.0007 (want ≈0)\n", " Std cos: 0.0369\n", " Min cos: -0.1060\n", " Max cos: 0.0980\n", " CV: 0.0236\n", " E 1: t_acc=0.112 v_acc=0.227 cv=1.1740 gate=0.000 cos=-0.0030 rig=28.4/100.0 [polygon=0.01 curve=0.40 star=0.32 structure=0.28]\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_67512/2215773360.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/tmp/ipykernel_67512/2215773360.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;31m# Update rigidity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstellation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_rigidity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0mtotal_correct\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m 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No semantics. No labels until assigned.\n", "#\n", "# Each anchor is a 4D local sphere (tangent frame at that point).\n", "# The full constellation has 5D pentachoral structure.\n", "# Conv4d ingests raw data → triangulation position → rigidity accumulation.\n", "#\n", "# The optimizer protects the constellation.\n", "# The patchwork learns to navigate it.\n", "# Rigidity crystallizes from the data itself.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from dataclasses import dataclass\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cayley_menger_vol_sq(pts):\n", " \"\"\"Pentachoron volume² from 5 points. (B, 5, D) → (B,)\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B = pts.shape[0]\n", " cm = torch.zeros(B, 6, 6, device=pts.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1.0; cm[:, 1:, 0] = 1.0; cm[:, 1:, 1:] = d2\n", " return -torch.linalg.det(cm) / 9216.0\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n_samples=100):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol_sq(emb_f[idx].unsqueeze(0))[0]\n", " v = math.sqrt(max(v2.item(), 0.0))\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "class TangentialGradFn(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, emb, gate):\n", " ctx.save_for_backward(emb)\n", " ctx.gate = gate\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad):\n", " emb, = ctx.saved_tensors\n", " tang, norm = tangential_projection(grad, emb)\n", " return tang + ctx.gate * norm, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION: 30 abstract anchors, each a 4D local sphere\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " \"\"\"\n", " N anchor points on the D-dim unit hypersphere.\n", " Each anchor carries a 4D local tangent frame (orthonormal basis\n", " of its tangent space). The frame defines a local 4-sphere at\n", " that point — a submanifold where nearby structure is measured.\n", "\n", " The full constellation has 5D pentachoral regularity:\n", " any 5 anchors form a pentachoron whose volume is monitored.\n", "\n", " Xavier initialization guarantees near-orthogonality in high-D.\n", " Expected pairwise cosine ≈ 0 ± 1/√D.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_embed=768, d_local=4):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.d_embed = d_embed\n", " self.d_local = d_local\n", "\n", " # Anchor positions on hypersphere (Xavier → normalize)\n", " anchor_init = torch.randn(n_anchors, d_embed)\n", " anchor_init = F.normalize(anchor_init, dim=-1)\n", " self.anchors = nn.Parameter(anchor_init)\n", "\n", " # Local 4D tangent frames at each anchor\n", " # Each is (d_local, d_embed) — 4 orthonormal basis vectors\n", " # tangent to the sphere at the anchor point\n", " frames = torch.randn(n_anchors, d_local, d_embed)\n", " self.local_frames = nn.Parameter(frames)\n", "\n", " # Rigidity accumulator: running stats per anchor\n", " # How rigid/crystalline the local geometry has become\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", " self.register_buffer(\"local_cv\", torch.zeros(n_anchors))\n", "\n", " def orthogonalize_frames(self):\n", " \"\"\"Project frames tangential to sphere and orthonormalize.\"\"\"\n", " with torch.no_grad():\n", " anchors_n = F.normalize(self.anchors.data, dim=-1)\n", " for i in range(self.n_anchors):\n", " frame = self.local_frames.data[i] # (4, D)\n", " a = anchors_n[i] # (D,)\n", "\n", " # Remove radial component (make tangential)\n", " radial = (frame @ a).unsqueeze(-1) * a.unsqueeze(0) # (4, D)\n", " frame = frame - radial\n", "\n", " # Gram-Schmidt orthonormalize the 4 tangent vectors\n", " ortho = []\n", " for j in range(self.d_local):\n", " v = frame[j]\n", " for u in ortho:\n", " v = v - (v @ u) * u\n", " v = F.normalize(v, dim=-1)\n", " ortho.append(v)\n", " self.local_frames.data[i] = torch.stack(ortho)\n", "\n", " def triangulate(self, emb):\n", " \"\"\"\n", " Compute triangulation coordinates: angular distances to all anchors.\n", "\n", " Args:\n", " emb: (B, D) L2-normalized embeddings\n", "\n", " Returns:\n", " tri_coords: (B, N) cosine distances to each anchor\n", " local_coords: (B, N, 4) projection onto each anchor's local frame\n", " nearest: (B,) index of nearest anchor\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1) # (N, D)\n", "\n", " # Global triangulation: cosine to each anchor\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_coords = 1.0 - cos_sim # (B, N) distances\n", "\n", " # Local coordinates: project onto each anchor's 4D tangent frame\n", " # For each anchor, project the residual (emb - anchor projection) into local frame\n", " # emb_centered = emb - cos_sim * anchor gives the tangential displacement\n", " B = emb.shape[0]\n", " local_coords = torch.zeros(B, self.n_anchors, self.d_local,\n", " device=emb.device, dtype=emb.dtype)\n", "\n", " for i in range(self.n_anchors):\n", " # Tangential displacement from anchor i\n", " displacement = emb - cos_sim[:, i:i+1] * anchors_n[i:i+1] # (B, D)\n", " # Project into local 4D frame\n", " frame = self.local_frames[i] # (4, D)\n", " local_coords[:, i] = displacement @ frame.T # (B, 4)\n", "\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", "\n", " return tri_coords, local_coords, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, emb, labels):\n", " \"\"\"\n", " Accumulate rigidity from training data.\n", " Rigidity = how consistent the local geometry is around each anchor.\n", " \"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5:\n", " continue\n", "\n", " cluster = emb[mask]\n", " self.visit_count[i] += mask.sum().float()\n", "\n", " # Local CV: pentachoron regularity within this cluster\n", " cv = pentachoron_cv(cluster, n_samples=50)\n", " # Exponential moving average\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.local_cv[i] = (1 - alpha) * self.local_cv[i] + alpha * cv\n", "\n", " # Rigidity: inverse of CV (more regular = more rigid)\n", " self.rigidity[i] = 1.0 / (self.local_cv[i] + 0.01)\n", "\n", " def constellation_health(self):\n", " \"\"\"Global pentachoral regularity of the anchor constellation.\"\"\"\n", " anchors_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = anchors_n @ anchors_n.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=anchors_n.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_cos\": cos[mask].min().item(),\n", " \"max_cos\": cos[mask].max().item(),\n", " \"cv\": pentachoron_cv(anchors_n, n_samples=200),\n", " \"mean_rigidity\": self.rigidity.mean().item(),\n", " \"max_rigidity\": self.rigidity.max().item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONV4D INGESTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def integer_compositions(n):\n", " \"\"\"All ordered compositions of integer n. |compositions| = 2^(n-1).\"\"\"\n", " if n == 0: yield (); return\n", " if n == 1: yield (1,); return\n", " for i in range(1, n + 1):\n", " for rest in integer_compositions(n - i):\n", " yield (i,) + rest\n", "\n", "\n", "class Conv4dBlock(nn.Module):\n", " \"\"\"\n", " True compositional 4D convolution via integer partition paths.\n", "\n", " 4 components (one per local tangent frame dimension) processed\n", " through all 2^3 = 8 ordered compositions of 4.\n", "\n", " Each composition defines a grouping: (1,3) means \"dim 0 alone,\n", " then dims 1-2-3 fused.\" (2,2) means \"dims 0-1 fused, then 2-3 fused.\"\n", " (4,) means \"all 4 dims fused at once.\"\n", "\n", " Same structure as conv5d NLI head — proven architecture.\n", " Per-anchor projections: each anchor's 4D local coords become\n", " 4 projected vectors that feed the composition paths.\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, d_local=4, d_path=128, d_out=256):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.d_local = d_local\n", " self.d_path = d_path\n", "\n", " # Project each local dim (across all anchors) to shared path dimension\n", " # local_coords[:, :, d] is (B, N) — all anchors' d-th frame coordinate\n", " self.dim_projs = nn.ModuleList([\n", " nn.Sequential(nn.Linear(n_anchors, d_path), nn.LayerNorm(d_path))\n", " for _ in range(d_local)\n", " ])\n", "\n", " # Enumerate all compositions of 4\n", " self.compositions = list(integer_compositions(d_local))\n", " n_paths = len(self.compositions)\n", "\n", " # Per-group fusion layers\n", " self.group_fusions = nn.ModuleDict()\n", " for k in range(1, d_local + 1):\n", " self.group_fusions[str(k)] = nn.Sequential(\n", " nn.Linear(k * d_path, d_path),\n", " nn.GELU(),\n", " nn.LayerNorm(d_path),\n", " )\n", "\n", " # Learned path weights\n", " self.path_weights = nn.Parameter(torch.ones(n_paths) / n_paths)\n", "\n", " # Output projection\n", " self.out_proj = nn.Linear(d_path, d_out)\n", "\n", " print(f\" Conv4dBlock: {d_local} dims → {n_paths} composition paths, d_path={d_path}\")\n", "\n", " def forward(self, local_coords, tri_coords):\n", " \"\"\"\n", " Args:\n", " local_coords: (B, N, 4) — projections onto each anchor's 4D frame\n", " tri_coords: (B, N) — triangulation distances (activation weights)\n", "\n", " Returns:\n", " features: (B, d_out)\n", " \"\"\"\n", " # Each local dim across all anchors becomes a component\n", " # local_coords[:, :, d] = (B, N) — anchor activations for dim d\n", " # Project each dim: (B, N) → (B, d_path)\n", " components = []\n", " for d in range(self.d_local):\n", " components.append(self.dim_projs[d](local_coords[:, :, d])) # (B, d_path)\n", "\n", " # Process each composition path\n", " weights = F.softmax(self.path_weights, dim=0)\n", " path_outputs = []\n", "\n", " for composition in self.compositions:\n", " idx = 0\n", " group_outputs = []\n", " for group_size in composition:\n", " group = torch.cat(components[idx:idx + group_size], dim=-1) # (B, k*d_path)\n", " fused = self.group_fusions[str(group_size)](group) # (B, d_path)\n", " group_outputs.append(fused)\n", " idx += group_size\n", "\n", " # Sequential composition: feed each group's output forward\n", " path_out = group_outputs[0]\n", " for g in group_outputs[1:]:\n", " path_out = path_out + g # residual accumulation\n", " path_outputs.append(path_out)\n", "\n", " # Weighted combination of all paths\n", " stacked = torch.stack(path_outputs, dim=1) # (B, n_paths, d_path)\n", " combined = (stacked * weights.unsqueeze(0).unsqueeze(-1)).sum(dim=1) # (B, d_path)\n", "\n", " return self.out_proj(combined) # (B, d_out)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ConstellationClassifier(nn.Module):\n", " \"\"\"\n", " Image → conv backbone → hypersphere embedding → triangulation\n", " → conv4d on local coords → prototype logits\n", "\n", " The constellation is the coordinate system.\n", " The conv4d reads the geometric structure.\n", " The prototypes are points in the triangulation space.\n", " \"\"\"\n", "\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " d_local=4, d_hidden=256):\n", " super().__init__()\n", " self.n_classes = n_classes\n", "\n", " # Image backbone (simple, 1-channel input)\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(),\n", " nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed),\n", " nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation (abstract coordinate system)\n", " self.constellation = Constellation(n_anchors, d_embed, d_local)\n", "\n", " # Conv4d: read geometric structure from local coordinates\n", " self.conv4d = Conv4dBlock(n_anchors=n_anchors, d_local=d_local,\n", " d_path=128, d_out=d_hidden)\n", "\n", " # Global triangulation path\n", " self.tri_proj = nn.Sequential(\n", " nn.Linear(n_anchors, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " )\n", "\n", " # Combine local (conv4d) + global (triangulation) → classify\n", " self.classifier = nn.Sequential(\n", " nn.Linear(d_hidden * 2, d_hidden),\n", " nn.GELU(),\n", " nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " \"\"\"\n", " Args:\n", " x: (B, 1, 32, 32) grayscale images\n", "\n", " Returns:\n", " logits: (B, n_classes)\n", " emb: (B, d_embed) L2-normalized hypersphere embedding\n", " tri_coords: (B, n_anchors) triangulation distances\n", " local_coords: (B, n_anchors, 4) local frame projections\n", " nearest: (B,) nearest anchor index\n", " \"\"\"\n", " # Backbone → hypersphere\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", "\n", " # Triangulate against constellation\n", " tri_coords, local_coords, nearest = self.constellation.triangulate(emb)\n", "\n", " # Conv4d on local structure\n", " local_feat = self.conv4d(local_coords, tri_coords) # (B, d_hidden)\n", "\n", " # Global triangulation features\n", " global_feat = self.tri_proj(tri_coords) # (B, d_hidden)\n", "\n", " # Combine and classify\n", " combined = torch.cat([local_feat, global_feat], dim=-1) # (B, 2*d_hidden)\n", " logits = self.classifier(combined)\n", "\n", " return logits, emb, tri_coords, local_coords, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (reuse from trainer)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _draw(img, x0, y0, x1, y1, t=1):\n", " n = max(int(max(abs(x1-x0), abs(y1-y0))*2), 1); sz = img.shape[0]\n", " for s in np.linspace(0, 1, n):\n", " px, py = int(x0+s*(x1-x0)), int(y0+s*(y1-y0))\n", " for dx in range(-t, t+1):\n", " for dy in range(-t, t+1):\n", " nx, ny = px+dx, py+dy\n", " if 0 <= nx < sz and 0 <= ny < sz: img[ny, nx] = 1.0\n", "\n", "def render_poly(nv, sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,r = sz/2,sz/2,sz*0.35\n", " a = np.linspace(0,2*np.pi,nv,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " ri = r*(1+np.random.normal(0,p,nv))\n", " pts = [(cx+ri[i]*np.cos(a[i]),cy+ri[i]*np.sin(a[i])) for i in range(nv)]\n", " for i in range(nv): _draw(img,*pts[i],*pts[(i+1)%nv])\n", " return img\n", "\n", "def render_star(np_, sz=32, p=0.12):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " ro,ri_ = sz*0.38,sz*0.15\n", " a = np.linspace(0,2*np.pi,np_*2,endpoint=False)+np.random.uniform(0,2*np.pi)\n", " pts = [(cx+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.cos(a[i]),\n", " cy+(ro if i%2==0 else ri_)*(1+np.random.normal(0,p))*np.sin(a[i])) for i in range(len(a))]\n", " for i in range(len(pts)): _draw(img,*pts[i],*pts[(i+1)%len(pts)])\n", " return img\n", "\n", "def render_cross(sz=32, p=0.15):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy,arm = sz/2,sz/2,sz*0.3\n", " for ab in [0,np.pi/2,np.pi,3*np.pi/2]:\n", " a = ab+np.random.normal(0,p*0.3); r = arm*(1+np.random.normal(0,p))\n", " _draw(img,cx,cy,cx+r*np.cos(a),cy+r*np.sin(a),2)\n", " return img\n", "\n", "def render_spiral(sz=32, p=0.1):\n", " img = np.zeros((sz,sz), dtype=np.float32); cx,cy = sz/2,sz/2\n", " for t in np.linspace(0,5*np.pi,200):\n", " r = sz*0.015*t*(1+np.random.normal(0,p*0.3))\n", " x,y = int(cx+r*np.cos(t)),int(cy+r*np.sin(t))\n", " if 0<=x= r2*0.9:\n", " ix,iy = int(x1),int(y1)\n", " if 0<=ix 0:\n", " cv_target = cv_now\n", " if cv_target:\n", " delta = cv_now - cv_target\n", " if abs(delta) <= 0.02: gate_val = 0.0\n", " elif delta < 0: gate_val = min(abs(delta)/(cv_target+1e-8), 1.0) * 0.3\n", " else: gate_val = max(0.0, 0.1*(1-min(delta/(cv_target+1e-8),1.0)))\n", "\n", " # Apply tangential gate\n", " emb_gated = TangentialGradFn.apply(emb, emb, gate_val)\n", "\n", " # Recompute logits through gated embedding\n", " tri_g, local_g, _ = model.constellation.triangulate(emb_gated)\n", " local_feat = model.conv4d(local_g, tri_g)\n", " global_feat = model.tri_proj(tri_g)\n", " logits = model.classifier(torch.cat([local_feat, global_feat], dim=-1))\n", "\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " # Update rigidity\n", " model.constellation.update_rigidity(emb.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " v_logits, v_emb, v_tri, v_local, v_nearest = model(val_imgs)\n", " v_acc = (v_logits.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(v_emb, n_samples=100)\n", "\n", " health = model.constellation.constellation_health()\n", "\n", " # Per-type accuracy\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (v_logits.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} gate={gate_val:.3f} \"\n", " f\"cos={health['mean_cos']:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} \"\n", " f\"[{ta_str}]\")\n", "\n", " # Final constellation state\n", " health = model.constellation.constellation_health()\n", " print(f\"\\n Final constellation:\")\n", " print(f\" Mean cos: {health['mean_cos']:.4f}\")\n", " print(f\" CV: {health['cv']:.4f}\")\n", " print(f\" Rigidity: mean={health['mean_rigidity']:.1f} max={health['max_rigidity']:.1f}\")\n", "\n", " # Rigidity per class\n", " print(f\"\\n Per-anchor rigidity:\")\n", " rig = model.constellation.rigidity.cpu()\n", " for i in range(30):\n", " bar = \"█\" * int(rig[i].item())\n", " print(f\" {SHAPE_NAMES[i]:15s}: {rig[i]:.1f} {bar}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train()" ], "metadata": { "id": "_-BLrco2llQ0" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# attempt 5 - returning to basics with a sweep" ], "metadata": { "id": "cp9qrl1ApVHr" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cayley_menger_vol_sq(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B = pts.shape[0]\n", " cm = torch.zeros(B, 6, 6, device=pts.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1.0; cm[:, 1:, 0] = 1.0; cm[:, 1:, 1:] = d2\n", " return -torch.linalg.det(cm) / 9216.0\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n_samples=100):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol_sq(emb_f[idx].unsqueeze(0))[0]\n", " v = math.sqrt(max(v2.item(), 0.0))\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Active geometric gradient surgery with CV-aware modulation.\n", "\n", " CV target: 0.2 ± 0.13 (empirically validated universal band)\n", " When CV is within band: gentle corrections\n", " When CV drifts outside: stronger corrections to pull back\n", "\n", " Three correction axes:\n", " 1. Tangential projection (prevent leaving hypersphere)\n", " 2. Equidistance correction (push toward uniform anchor distances)\n", " 3. Separation preservation (prevent anchor collapse)\n", "\n", " All three are modulated by cv_deviation — how far from 0.2 we are.\n", " \"\"\"\n", "\n", " CV_TARGET = 0.2\n", " CV_TOLERANCE = 0.13\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, equi_strength, sep_strength, cv_current):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.equi_strength = equi_strength\n", " ctx.sep_strength = sep_strength\n", " ctx.cv_current = cv_current\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " equi_strength = ctx.equi_strength\n", " sep_strength = ctx.sep_strength\n", " cv_current = ctx.cv_current\n", "\n", " B, D = embedding.shape\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # ── CV-aware modulation ──\n", " # How far are we from the 0.2 target?\n", " cv_delta = cv_current - GeometricAutograd.CV_TARGET\n", " cv_abs = abs(cv_delta)\n", "\n", " if cv_abs <= GeometricAutograd.CV_TOLERANCE:\n", " # Inside band — gentle, proportional modulation\n", " cv_mod = cv_abs / (GeometricAutograd.CV_TOLERANCE + 1e-8) # 0 at center, 1 at edge\n", " else:\n", " # Outside band — full strength, clamped\n", " cv_mod = 1.0\n", "\n", " # Direction matters:\n", " # cv_delta < 0 (too smooth, below 0.2): allow more normal gradient to BUILD texture\n", " # cv_delta > 0 (too rigid, above 0.2): strengthen tangential to PROTECT smoothness\n", " if cv_delta < 0:\n", " # Too smooth — relax tangential constraint, let structure build\n", " effective_tang = tang_only * (1.0 - 0.3 * cv_mod)\n", " effective_equi = equi_strength * (1.0 + 0.5 * cv_mod)\n", " effective_sep = sep_strength\n", " else:\n", " # Too rigid — strengthen tangential, reduce equidistance push\n", " effective_tang = min(tang_only * (1.0 + 0.5 * cv_mod), 1.0)\n", " effective_equi = equi_strength * (1.0 - 0.3 * cv_mod)\n", " effective_sep = sep_strength * (1.0 + 0.3 * cv_mod)\n", "\n", " # ── 1. Tangential projection ──\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - effective_tang) * norm\n", "\n", " # ── 2. Equidistance correction ──\n", " # Push each embedding toward equal distance from all anchors\n", " if equi_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T # (B, N)\n", " mean_cos = cos_to_anchors.mean(dim=-1, keepdim=True) # (B, 1)\n", " deviation = cos_to_anchors - mean_cos # (B, N) — how far from uniform\n", "\n", " # Gradient correction: for each anchor where deviation is positive\n", " # (too close), push away. Where negative (too far), pull closer.\n", " # This is the gradient of equidistance loss = ||cos - mean_cos||²\n", " equi_grad = (deviation @ anchors_n) / anchors_n.shape[0] # (B, D)\n", "\n", " # Project equi_grad tangential (keep on sphere)\n", " equi_tang, _ = tangential_projection(equi_grad, emb_n)\n", " corrected = corrected - equi_strength * equi_tang\n", "\n", " # ── 3. Separation preservation ──\n", " # Penalize gradients that would make the embedding collapse\n", " # toward its nearest anchor (reduce diversity)\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T # (B, N)\n", " nearest_idx = cos_to_anchors.argmax(dim=-1) # (B,)\n", " nearest_anchor = anchors_n[nearest_idx] # (B, D)\n", "\n", " # Component of gradient pointing toward nearest anchor\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor # (B, D)\n", "\n", " # If toward_nearest > 0, we're moving TOWARD the anchor → attenuate\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0:\n", " cv_current = cv_measured\n", "\n", " # Apply active geometric autograd\n", " anchors = model.constellation.anchors\n", " emb_corrected = GeometricAutograd.apply(\n", " emb, emb, anchors,\n", " tang_only, equi_strength, sep_strength, cv_current)\n", "\n", " # Recompute through corrected embedding\n", " tri_g, _ = model.constellation.triangulate(emb_corrected)\n", " pw_feat = model.patchwork(tri_g)\n", " logits = model.mlp(pw_feat)\n", "\n", " loss = F.cross_entropy(logits, labels)\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = pentachoron_cv(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(30, dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang_only, equi_strength, sep_strength)\n", " (\"baseline\", 0.0, 0.0, 0.0), # no autograd — full gradient\n", " (\"tang_50\", 0.5, 0.0, 0.0), # 50% tangential, no correction\n", " (\"tang_100\", 1.0, 0.0, 0.0), # pure tangential, no normal at all\n", " (\"equi_low\", 0.5, 0.1, 0.0), # mild equidistance push\n", " (\"equi_med\", 0.5, 0.5, 0.0), # moderate equidistance\n", " (\"equi_high\", 0.5, 1.0, 0.0), # strong equidistance\n", " (\"sep_low\", 0.5, 0.0, 0.3), # mild separation preservation\n", " (\"sep_high\", 0.5, 0.0, 0.8), # strong separation preservation\n", " (\"equi+sep\", 0.5, 0.5, 0.3), # equidistance + separation\n", " (\"full_gentle\", 0.3, 0.3, 0.2), # all three, gentle\n", " (\"full_strong\", 0.7, 0.8, 0.5), # all three, strong\n", " (\"max\", 1.0, 1.0, 1.0), # maximum everything\n", "]\n", "\n", "results = {}\n", "for name, to, eq, sp in configs:\n", " print(f\"\\n ── {name} (tang={to}, equi={eq}, sep={sp}) ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, equi_strength=eq, sep_strength=sp,\n", " epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NPGcUZZQpULq", "outputId": "3d0f03c3-3294-485f-aba1-f05698056973" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── baseline (tang=0.0, equi=0.0, sep=0.0) ──\n", " E 1: t=0.060 v=0.122 cv=1.5204(Δ+1.320) equi=0.3137 rig=64.1/95.4 [polygon=0.09 curve=0.00 star=0.00 structure=0.28]\n", " E10: t=0.633 v=0.652 cv=1.5847(Δ+1.385) equi=0.4243 rig=38.5/99.7 [polygon=0.37 curve=0.96 star=0.83 structure=0.64]\n", " E20: t=0.690 v=0.679 cv=1.7496(Δ+1.550) equi=0.4353 rig=34.2/99.8 [polygon=0.36 curve=1.00 star=0.84 structure=0.71]\n", " E30: t=0.714 v=0.691 cv=1.0222(Δ+0.822) equi=0.4366 rig=32.2/99.9 [polygon=0.41 curve=0.98 star=0.82 structure=0.72]\n", "\n", " ── tang_50 (tang=0.5, equi=0.0, sep=0.0) ──\n", " E 1: t=0.059 v=0.104 cv=1.5492(Δ+1.349) equi=0.3091 rig=64.7/95.4 [polygon=0.04 curve=0.00 star=0.00 structure=0.28]\n", " E10: t=0.655 v=0.633 cv=1.6212(Δ+1.421) equi=0.4178 rig=39.2/99.7 [polygon=0.38 curve=0.99 star=0.78 structure=0.59]\n", " E20: t=0.690 v=0.691 cv=1.7714(Δ+1.571) equi=0.4288 rig=34.7/99.8 [polygon=0.37 curve=0.99 star=0.88 structure=0.72]\n", " E30: t=0.713 v=0.692 cv=1.1747(Δ+0.975) equi=0.4328 rig=32.6/99.9 [polygon=0.41 curve=0.99 star=0.83 structure=0.72]\n", "\n", " ── tang_100 (tang=1.0, equi=0.0, sep=0.0) ──\n", " E 1: t=0.061 v=0.148 cv=1.4965(Δ+1.296) equi=0.3163 rig=63.2/95.4 [polygon=0.14 curve=0.00 star=0.02 structure=0.31]\n", " E10: t=0.649 v=0.661 cv=1.6233(Δ+1.423) equi=0.4293 rig=38.0/99.7 [polygon=0.44 curve=0.99 star=0.80 structure=0.62]\n", " E20: t=0.691 v=0.672 cv=1.6681(Δ+1.468) equi=0.4473 rig=34.0/99.8 [polygon=0.36 curve=0.99 star=0.83 structure=0.70]\n", " E30: t=0.717 v=0.705 cv=1.0296(Δ+0.830) equi=0.4534 rig=32.1/99.9 [polygon=0.42 curve=0.98 star=0.85 structure=0.73]\n", "\n", " ── equi_low (tang=0.5, equi=0.1, sep=0.0) ──\n", " E 1: t=0.035 v=0.033 cv=0.0042(Δ-0.196) equi=0.0436 rig=92.2/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.034 v=0.033 cv=0.0001(Δ-0.200) equi=0.0438 rig=98.4/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.034 v=0.033 cv=0.0000(Δ-0.200) equi=0.0412 rig=98.6/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.034 v=0.033 cv=0.0000(Δ-0.200) equi=0.0427 rig=98.9/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── equi_med (tang=0.5, equi=0.5, sep=0.0) ──\n", " E 1: t=0.035 v=0.033 cv=0.0045(Δ-0.195) equi=0.0435 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.032 v=0.033 cv=0.0001(Δ-0.200) equi=0.0439 rig=98.3/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0419 rig=98.7/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0424 rig=98.8/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── equi_high (tang=0.5, equi=1.0, sep=0.0) ──\n", " E 1: t=0.035 v=0.033 cv=0.0046(Δ-0.195) equi=0.0435 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.033 v=0.033 cv=0.0001(Δ-0.200) equi=0.0438 rig=98.3/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0001(Δ-0.200) equi=0.0416 rig=98.7/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.034 v=0.033 cv=0.0000(Δ-0.200) equi=0.0422 rig=98.8/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── sep_low (tang=0.5, equi=0.0, sep=0.3) ──\n", " E 1: t=0.057 v=0.122 cv=1.8120(Δ+1.612) equi=0.3030 rig=66.1/95.4 [polygon=0.11 curve=0.00 star=0.00 structure=0.26]\n", " E10: t=0.644 v=0.665 cv=1.6784(Δ+1.478) equi=0.4178 rig=39.8/99.7 [polygon=0.45 curve=0.98 star=0.80 structure=0.62]\n", " E20: t=0.695 v=0.660 cv=1.7735(Δ+1.573) equi=0.4334 rig=36.9/99.8 [polygon=0.34 curve=1.00 star=0.79 structure=0.70]\n", " E30: t=0.718 v=0.657 cv=1.2410(Δ+1.041) equi=0.4440 rig=35.7/99.9 [polygon=0.42 curve=0.95 star=0.69 structure=0.71]\n", "\n", " ── sep_high (tang=0.5, equi=0.0, sep=0.8) ──\n", " E 1: t=0.047 v=0.075 cv=1.3342(Δ+1.134) equi=0.2451 rig=75.1/95.4 [polygon=0.07 curve=0.00 star=0.00 structure=0.16]\n", " E10: t=0.597 v=0.624 cv=1.6212(Δ+1.421) equi=0.4413 rig=47.1/99.7 [polygon=0.34 curve=1.00 star=0.78 structure=0.60]\n", " E20: t=0.698 v=0.693 cv=1.7664(Δ+1.566) equi=0.4270 rig=41.0/99.8 [polygon=0.38 curve=0.99 star=0.84 structure=0.73]\n", " E30: t=0.709 v=0.712 cv=1.7948(Δ+1.595) equi=0.4317 rig=38.1/99.9 [polygon=0.45 curve=0.96 star=0.91 structure=0.71]\n", "\n", " ── equi+sep (tang=0.5, equi=0.5, sep=0.3) ──\n", " E 1: t=0.035 v=0.033 cv=0.0045(Δ-0.195) equi=0.0435 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.032 v=0.033 cv=0.0001(Δ-0.200) equi=0.0435 rig=98.3/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0419 rig=98.6/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.034 v=0.033 cv=0.0000(Δ-0.200) equi=0.0429 rig=98.9/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── full_gentle (tang=0.3, equi=0.3, sep=0.2) ──\n", " E 1: t=0.035 v=0.033 cv=0.0044(Δ-0.196) equi=0.0435 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.033 v=0.033 cv=0.0001(Δ-0.200) equi=0.0439 rig=98.4/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0419 rig=98.7/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.035 v=0.033 cv=0.0000(Δ-0.200) equi=0.0421 rig=98.9/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── full_strong (tang=0.7, equi=0.8, sep=0.5) ──\n", " E 1: t=0.035 v=0.033 cv=0.0046(Δ-0.195) equi=0.0435 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.032 v=0.033 cv=0.0001(Δ-0.200) equi=0.0440 rig=98.3/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0417 rig=98.7/99.8 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E30: t=0.034 v=0.033 cv=0.0000(Δ-0.200) equi=0.0421 rig=98.8/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", " ── max (tang=1.0, equi=1.0, sep=1.0) ──\n", " E 1: t=0.035 v=0.033 cv=0.0046(Δ-0.195) equi=0.0436 rig=92.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.032 v=0.033 cv=0.0001(Δ-0.200) equi=0.0438 rig=98.3/99.7 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E20: t=0.033 v=0.033 cv=0.0000(Δ-0.200) equi=0.0418 rig=98.7/99.8 [polygon=0.11 curve=0.00 star=0.00 structure=0.00]\n", " E30: t=0.035 v=0.033 cv=0.0000(Δ-0.200) equi=0.0424 rig=98.8/99.9 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", "\n", "\n", "=================================================================\n", "SWEEP RESULTS\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv Δcv eq_std poly curve star struct\n", " ------------------------------------------------------------------------------------------\n", " baseline 0.691 0.714 +0.022 1.0222 +0.8222 0.4366 0.41 0.98 0.82 0.72\n", " tang_50 0.692 0.713 +0.021 1.1747 +0.9747 0.4328 0.41 0.99 0.83 0.72\n", " tang_100 0.705 0.717 +0.012 1.0296 +0.8296 0.4534 0.42 0.98 0.85 0.73\n", " equi_low 0.033 0.034 +0.001 0.0000 -0.2000 0.0427 0.00 0.00 0.00 0.10\n", " equi_med 0.033 0.033 -0.000 0.0000 -0.2000 0.0424 0.00 0.00 0.00 0.10\n", " equi_high 0.033 0.034 +0.001 0.0000 -0.2000 0.0422 0.00 0.00 0.00 0.10\n", " sep_low 0.657 0.718 +0.060 1.2410 +1.0410 0.4440 0.42 0.95 0.69 0.71\n", " sep_high 0.712 0.709 -0.003 1.7948 +1.5948 0.4317 0.45 0.96 0.91 0.71\n", " equi+sep 0.033 0.034 +0.001 0.0000 -0.2000 0.0429 0.00 0.00 0.00 0.10\n", " full_gentle 0.033 0.035 +0.001 0.0000 -0.2000 0.0421 0.00 0.00 0.00 0.10\n", " full_strong 0.033 0.034 +0.001 0.0000 -0.2000 0.0421 0.00 0.00 0.00 0.10\n", " max 0.033 0.035 +0.001 0.0000 -0.2000 0.0424 0.00 0.00 0.00 0.10\n", "\n", " Best accuracy: sep_high (val_acc=0.712)\n", " Best structure: tang_100 (struct=0.732)\n", " Closest to CV=0.2: full_strong (cv=0.0000, Δ=-0.2000)\n", " Most equidistant: full_gentle (equi_std=0.0421)\n", " Most stable CV: full_gentle (cv_std=0.0008)\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# attempt 6" ], "metadata": { "id": "qhTdgHbNvoKy" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (production versions, differentiable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "# ── Production Cayley-Menger (generic, differentiable) ──\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"\n", " Differentiable CV loss. Proper loss term, not gradient surgery.\n", " Flows gradient through torch.stack → torch.sqrt → torch.std/mean.\n", " \"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV measurement for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "# ── Autograd: tangential projection + separation only ──\n", "# NO gradient injection. CV is a loss term, not gradient surgery.\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering only. Two operations:\n", " 1. Tangential projection (keep gradients on hypersphere surface)\n", " 2. Separation preservation (attenuate collapse toward nearest anchor)\n", "\n", " CV regulation is handled by cv_loss in the training loop.\n", " Not here. Loss terms flow gradient naturally. Surgery doesn't.\n", " \"\"\"\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " sep_strength = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - tang_only) * norm\n", "\n", " # 2. Separation preservation\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_idx = cos_to_anchors.argmax(dim=-1)\n", " nearest_anchor = anchors_n[nearest_idx]\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 else 0.0\n", "\n", " loss = l_cls + cv_weight * l_cv\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(30, dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang_only, cv_weight, sep_strength)\n", " (\"baseline\", 0.0, 0.0, 0.0), # no autograd, no CV loss\n", " (\"cv_only_01\", 0.0, 0.1, 0.0), # just CV loss, gentle\n", " (\"cv_only_05\", 0.0, 0.5, 0.0), # just CV loss, moderate\n", " (\"cv_only_10\", 0.0, 1.0, 0.0), # just CV loss, strong\n", " (\"tang_50\", 0.5, 0.0, 0.0), # tangential only\n", " (\"tang_100\", 1.0, 0.0, 0.0), # pure tangential\n", " (\"tang+cv\", 0.5, 0.1, 0.0), # tangential + CV loss\n", " (\"sep_low\", 0.5, 0.0, 0.3), # tangential + separation\n", " (\"sep_high\", 0.5, 0.0, 0.8), # tangential + strong separation\n", " (\"tang+cv+sep\", 0.5, 0.1, 0.3), # all three, gentle\n", " (\"full_med\", 0.5, 0.5, 0.3), # all three, moderate\n", " (\"full_strong\", 0.7, 1.0, 0.5), # all three, strong\n", "]\n", "\n", "results = {}\n", "for name, to, cw, sp in configs:\n", " print(f\"\\n ── {name} (tang={to}, cv_w={cw}, sep={sp}) ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, cv_weight=cw, sep_strength=sp,\n", " epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R986YIXXvpM4", "outputId": "1bcb0fa4-3769-4dc4-d2a2-851af4ed2543" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── baseline (tang=0.0, cv_w=0.0, sep=0.0) ──\n", " E 1: t=0.059 v=0.107 cv=1.3852(Δ+1.185) equi=0.3076 rig=64.7/95.4 [polygon=0.04 curve=0.00 star=0.00 structure=0.28]\n", " E10: t=0.627 v=0.572 cv=1.2892(Δ+1.089) equi=0.4287 rig=39.3/99.6 [polygon=0.35 curve=0.64 star=0.64 structure=0.70]\n", " E20: t=0.695 v=0.692 cv=1.6082(Δ+1.408) equi=0.4377 rig=34.9/99.8 [polygon=0.47 curve=0.99 star=0.84 structure=0.65]\n", " E30: t=0.710 v=0.719 cv=1.5066(Δ+1.307) equi=0.4413 rig=32.8/99.9 [polygon=0.53 curve=0.98 star=0.92 structure=0.64]\n", "\n", " ── cv_only_01 (tang=0.0, cv_w=0.1, sep=0.0) ──\n", " E 1: t=0.037 v=0.067 cv=0.1085(Δ-0.091) equi=0.1288 rig=77.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.412 v=0.443 cv=0.2349(Δ+0.035) equi=0.6101 rig=43.7/99.7 [polygon=0.21 curve=0.68 star=0.50 structure=0.50]\n", " E20: t=0.484 v=0.464 cv=0.3097(Δ+0.110) equi=0.6463 rig=39.9/99.8 [polygon=0.14 curve=0.77 star=0.61 structure=0.51]\n", " E30: t=0.480 v=0.548 cv=0.2297(Δ+0.030) equi=0.6814 rig=39.3/99.9 [polygon=0.12 curve=0.93 star=0.76 structure=0.61]\n", "\n", " ── cv_only_05 (tang=0.0, cv_w=0.5, sep=0.0) ──\n", " E 1: t=0.036 v=0.033 cv=0.0033(Δ-0.197) equi=0.0534 rig=86.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.325 v=0.336 cv=0.2438(Δ+0.044) equi=0.5525 rig=52.2/99.7 [polygon=0.19 curve=0.46 star=0.39 structure=0.38]\n", " E20: t=0.421 v=0.387 cv=0.3111(Δ+0.111) equi=0.6215 rig=46.5/99.8 [polygon=0.13 curve=0.85 star=0.41 structure=0.38]\n", " E30: t=0.472 v=0.478 cv=0.1963(Δ-0.004) equi=0.6698 rig=45.4/99.9 [polygon=0.13 curve=0.94 star=0.50 structure=0.55]\n", "\n", " ── cv_only_10 (tang=0.0, cv_w=1.0, sep=0.0) ──\n", " E 1: t=0.037 v=0.033 cv=0.1595(Δ-0.041) equi=0.0893 rig=80.2/95.4 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E10: t=0.340 v=0.323 cv=0.3022(Δ+0.102) equi=0.5654 rig=53.6/99.7 [polygon=0.17 curve=0.51 star=0.36 structure=0.35]\n", " E20: t=0.411 v=0.409 cv=0.0839(Δ-0.116) equi=0.6098 rig=50.5/99.8 [polygon=0.20 curve=0.66 star=0.46 structure=0.44]\n", " E30: t=0.401 v=0.428 cv=0.2638(Δ+0.064) equi=0.6322 rig=49.8/99.9 [polygon=0.11 curve=0.94 star=0.44 structure=0.45]\n", "\n", " ── tang_50 (tang=0.5, cv_w=0.0, sep=0.0) ──\n", " E 1: t=0.062 v=0.144 cv=1.3406(Δ+1.141) equi=0.3156 rig=64.0/95.4 [polygon=0.16 curve=0.00 star=0.01 structure=0.28]\n", " E10: t=0.613 v=0.627 cv=1.2176(Δ+1.018) equi=0.4340 rig=38.7/99.6 [polygon=0.27 curve=0.99 star=0.73 structure=0.71]\n", " E20: t=0.693 v=0.691 cv=1.5596(Δ+1.360) equi=0.4483 rig=34.4/99.8 [polygon=0.43 curve=0.99 star=0.85 structure=0.68]\n", " E30: t=0.706 v=0.711 cv=1.5108(Δ+1.311) equi=0.4530 rig=32.4/99.9 [polygon=0.52 curve=0.96 star=0.91 structure=0.64]\n", "\n", " ── tang_100 (tang=1.0, cv_w=0.0, sep=0.0) ──\n", " E 1: t=0.059 v=0.103 cv=1.3765(Δ+1.177) equi=0.3087 rig=64.7/95.4 [polygon=0.04 curve=0.00 star=0.00 structure=0.27]\n", " E10: t=0.603 v=0.622 cv=1.2778(Δ+1.078) equi=0.4286 rig=39.3/99.6 [polygon=0.30 curve=0.88 star=0.76 structure=0.70]\n", " E20: t=0.682 v=0.704 cv=1.6201(Δ+1.420) equi=0.4383 rig=34.8/99.8 [polygon=0.49 curve=0.99 star=0.88 structure=0.65]\n", " E30: t=0.690 v=0.720 cv=1.5335(Δ+1.334) equi=0.4431 rig=32.6/99.9 [polygon=0.52 curve=0.98 star=0.92 structure=0.65]\n", "\n", " ── tang+cv (tang=0.5, cv_w=0.1, sep=0.0) ──\n", " E 1: t=0.037 v=0.067 cv=0.0958(Δ-0.104) equi=0.1289 rig=77.3/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.384 v=0.408 cv=0.2791(Δ+0.079) equi=0.5997 rig=40.5/99.7 [polygon=0.19 curve=0.63 star=0.48 structure=0.45]\n", " E20: t=0.484 v=0.476 cv=0.4124(Δ+0.212) equi=0.6362 rig=37.3/99.8 [polygon=0.17 curve=0.85 star=0.57 structure=0.51]\n", " E30: t=0.542 v=0.572 cv=0.4158(Δ+0.216) equi=0.6602 rig=37.9/99.9 [polygon=0.22 curve=0.84 star=0.78 structure=0.63]\n", "\n", " ── sep_low (tang=0.5, cv_w=0.0, sep=0.3) ──\n", " E 1: t=0.057 v=0.082 cv=2.4773(Δ+2.277) equi=0.3104 rig=66.2/95.4 [polygon=0.10 curve=0.00 star=0.00 structure=0.15]\n", " E10: t=0.636 v=0.622 cv=1.2112(Δ+1.011) equi=0.4287 rig=39.3/99.6 [polygon=0.33 curve=0.91 star=0.69 structure=0.70]\n", " E20: t=0.698 v=0.692 cv=1.6965(Δ+1.496) equi=0.4374 rig=35.2/99.8 [polygon=0.45 curve=0.99 star=0.88 structure=0.65]\n", " E30: t=0.723 v=0.709 cv=1.6462(Δ+1.446) equi=0.4423 rig=33.5/99.9 [polygon=0.53 curve=0.93 star=0.90 structure=0.65]\n", "\n", " ── sep_high (tang=0.5, cv_w=0.0, sep=0.8) ──\n", " E 1: t=0.046 v=0.067 cv=2.0157(Δ+1.816) equi=0.2379 rig=75.6/95.4 [polygon=0.11 curve=0.00 star=0.00 structure=0.10]\n", " E10: t=0.607 v=0.627 cv=1.2423(Δ+1.042) equi=0.4197 rig=42.6/99.6 [polygon=0.30 curve=0.93 star=0.75 structure=0.69]\n", " E20: t=0.695 v=0.661 cv=1.5614(Δ+1.361) equi=0.4394 rig=38.7/99.8 [polygon=0.42 curve=1.00 star=0.75 structure=0.66]\n", " E30: t=0.716 v=0.730 cv=1.6925(Δ+1.492) equi=0.4530 rig=37.4/99.9 [polygon=0.56 curve=0.96 star=0.94 structure=0.65]\n", "\n", " ── tang+cv+sep (tang=0.5, cv_w=0.1, sep=0.3) ──\n", " E 1: t=0.036 v=0.035 cv=0.3356(Δ+0.136) equi=0.0667 rig=82.2/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.405 v=0.368 cv=0.3274(Δ+0.127) equi=0.5394 rig=40.9/99.7 [polygon=0.24 curve=0.43 star=0.43 structure=0.42]\n", " E20: t=0.483 v=0.386 cv=0.6309(Δ+0.431) equi=0.5652 rig=37.9/99.8 [polygon=0.14 curve=0.56 star=0.62 structure=0.38]\n", " E30: t=0.507 v=0.552 cv=0.5011(Δ+0.301) equi=0.5835 rig=39.2/99.9 [polygon=0.15 curve=0.95 star=0.77 structure=0.58]\n", "\n", " ── full_med (tang=0.5, cv_w=0.5, sep=0.3) ──\n", " E 1: t=0.036 v=0.033 cv=0.0000(Δ-0.200) equi=0.0466 rig=88.9/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.329 v=0.360 cv=0.0764(Δ-0.124) equi=0.6438 rig=59.3/99.7 [polygon=0.16 curve=0.66 star=0.45 structure=0.33]\n", " E20: t=0.415 v=0.461 cv=0.1753(Δ-0.025) equi=0.6993 rig=54.9/99.8 [polygon=0.17 curve=0.76 star=0.59 structure=0.50]\n", " E30: t=0.540 v=0.575 cv=0.3207(Δ+0.121) equi=0.7342 rig=56.0/99.9 [polygon=0.19 curve=0.96 star=0.79 structure=0.60]\n", "\n", " ── full_strong (tang=0.7, cv_w=1.0, sep=0.5) ──\n", " E 1: t=0.036 v=0.033 cv=0.5715(Δ+0.371) equi=0.0628 rig=82.1/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.317 v=0.272 cv=0.3366(Δ+0.137) equi=0.5780 rig=58.3/99.7 [polygon=0.14 curve=0.53 star=0.23 structure=0.29]\n", " E20: t=0.371 v=0.391 cv=0.0899(Δ-0.110) equi=0.6330 rig=54.3/99.8 [polygon=0.19 curve=0.71 star=0.45 structure=0.38]\n", " E30: t=0.410 v=0.476 cv=0.2337(Δ+0.034) equi=0.6569 rig=54.6/99.9 [polygon=0.16 curve=0.91 star=0.50 structure=0.53]\n", "\n", "\n", "=================================================================\n", "SWEEP RESULTS\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv Δcv eq_std poly curve star struct\n", " ------------------------------------------------------------------------------------------\n", " baseline 0.719 0.710 -0.009 1.5066 +1.3066 0.4413 0.53 0.98 0.92 0.64\n", " cv_only_01 0.548 0.480 -0.068 0.2297 +0.0297 0.6814 0.12 0.93 0.76 0.61\n", " cv_only_05 0.478 0.472 -0.006 0.1963 -0.0037 0.6698 0.13 0.94 0.50 0.55\n", " cv_only_10 0.428 0.401 -0.027 0.2638 +0.0638 0.6322 0.11 0.94 0.44 0.45\n", " tang_50 0.711 0.706 -0.004 1.5108 +1.3108 0.4530 0.52 0.96 0.91 0.64\n", " tang_100 0.720 0.690 -0.030 1.5335 +1.3335 0.4431 0.52 0.98 0.92 0.65\n", " tang+cv 0.572 0.542 -0.030 0.4158 +0.2158 0.6602 0.22 0.84 0.78 0.63\n", " sep_low 0.709 0.723 +0.014 1.6462 +1.4462 0.4423 0.53 0.93 0.90 0.65\n", " sep_high 0.730 0.716 -0.014 1.6925 +1.4925 0.4530 0.56 0.96 0.94 0.65\n", " tang+cv+sep 0.552 0.507 -0.045 0.5011 +0.3011 0.5835 0.15 0.95 0.77 0.58\n", " full_med 0.575 0.540 -0.035 0.3207 +0.1207 0.7342 0.19 0.96 0.79 0.60\n", " full_strong 0.476 0.410 -0.066 0.2337 +0.0337 0.6569 0.16 0.91 0.50 0.53\n", "\n", " Best accuracy: sep_high (val_acc=0.730)\n", " Best structure: tang_100 (struct=0.649)\n", " Closest to CV=0.2: cv_only_05 (cv=0.1963, Δ=-0.0037)\n", " Most equidistant: baseline (equi_std=0.4413)\n", " Most stable CV: cv_only_01 (cv_std=0.0700)\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 8" ], "metadata": { "id": "rO_wFKAXzV_b" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (production versions, differentiable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "# ── Production Cayley-Menger (generic, differentiable) ──\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"\n", " Differentiable CV loss. Proper loss term, not gradient surgery.\n", " Flows gradient through torch.stack → torch.sqrt → torch.std/mean.\n", " \"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV measurement for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "# ── Autograd: tangential projection + separation only ──\n", "# NO gradient injection. CV is a loss term, not gradient surgery.\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering only. Two operations:\n", " 1. Tangential projection (keep gradients on hypersphere surface)\n", " 2. Separation preservation (attenuate collapse toward nearest anchor)\n", "\n", " CV regulation is handled by cv_loss in the training loop.\n", " Not here. Loss terms flow gradient naturally. Surgery doesn't.\n", " \"\"\"\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " sep_strength = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - tang_only) * norm\n", "\n", " # 2. Separation preservation\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_idx = cos_to_anchors.argmax(dim=-1)\n", " nearest_anchor = anchors_n[nearest_idx]\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 else 0.0\n", "\n", " loss = l_cls + cv_weight * l_cv\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(30, dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang_only, cv_weight, sep_strength)\n", " # Base: sep_high was best at 0.730 (tang=0.5, sep=0.8)\n", " (\"no_cv\", 0.5, 0.0, 0.8), # best config, no CV loss\n", " (\"cv_0.001\", 0.5, 0.001, 0.8), # whisper\n", " (\"cv_0.005\", 0.5, 0.005, 0.8), # nudge\n", " (\"cv_0.01\", 0.5, 0.01, 0.8), # gentle\n", " (\"cv_0.03\", 0.5, 0.03, 0.8), # moderate\n", " (\"cv_0.06\", 0.5, 0.06, 0.8), # firm\n", "]\n", "\n", "results = {}\n", "for name, to, cw, sp in configs:\n", " print(f\"\\n ── {name} (tang={to}, cv_w={cw}, sep={sp}) ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, cv_weight=cw, sep_strength=sp,\n", " epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TgQeGAJrzZ0B", "outputId": "40c6534f-5849-4aba-c949-a94d7c56bd65" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── no_cv (tang=0.5, cv_w=0.0, sep=0.8) ──\n", " E 1: t=0.046 v=0.067 cv=2.0157(Δ+1.816) equi=0.2379 rig=75.6/95.4 [polygon=0.11 curve=0.00 star=0.00 structure=0.10]\n", " E10: t=0.656 v=0.664 cv=1.5363(Δ+1.336) equi=0.4207 rig=43.9/99.6 [polygon=0.35 curve=0.98 star=0.83 structure=0.69]\n", " E20: t=0.707 v=0.688 cv=1.5388(Δ+1.339) equi=0.4321 rig=38.9/99.8 [polygon=0.45 curve=1.00 star=0.84 structure=0.66]\n", " E30: t=0.730 v=0.721 cv=1.7346(Δ+1.535) equi=0.4400 rig=37.3/99.9 [polygon=0.54 curve=0.99 star=0.89 structure=0.65]\n", "\n", " ── cv_0.001 (tang=0.5, cv_w=0.001, sep=0.8) ──\n", " E 1: t=0.047 v=0.069 cv=2.7001(Δ+2.500) equi=0.2385 rig=74.5/95.4 [polygon=0.11 curve=0.00 star=0.00 structure=0.11]\n", " E10: t=0.593 v=0.620 cv=2.0415(Δ+1.841) equi=0.4249 rig=44.9/99.7 [polygon=0.39 curve=0.92 star=0.72 structure=0.62]\n", " E20: t=0.703 v=0.691 cv=1.6790(Δ+1.479) equi=0.4271 rig=39.4/99.8 [polygon=0.39 curve=0.99 star=0.85 structure=0.71]\n", " E30: t=0.706 v=0.712 cv=1.5553(Δ+1.355) equi=0.4291 rig=37.2/99.9 [polygon=0.40 curve=0.99 star=0.91 structure=0.74]\n", "\n", " ── cv_0.005 (tang=0.5, cv_w=0.005, sep=0.8) ──\n", " E 1: t=0.045 v=0.067 cv=2.0915(Δ+1.892) equi=0.2243 rig=72.6/95.4 [polygon=0.11 curve=0.00 star=0.00 structure=0.10]\n", " E10: t=0.500 v=0.482 cv=1.5678(Δ+1.368) equi=0.5290 rig=47.9/99.7 [polygon=0.26 curve=0.75 star=0.51 structure=0.53]\n", " E20: t=0.652 v=0.667 cv=1.7886(Δ+1.589) equi=0.4829 rig=45.5/99.8 [polygon=0.34 curve=0.97 star=0.82 structure=0.72]\n", " E30: t=0.690 v=0.697 cv=1.5407(Δ+1.341) equi=0.4622 rig=42.1/99.9 [polygon=0.38 curve=0.97 star=0.88 structure=0.73]\n", "\n", " ── cv_0.01 (tang=0.5, cv_w=0.01, sep=0.8) ──\n", " E 1: t=0.045 v=0.067 cv=1.5259(Δ+1.326) equi=0.2111 rig=71.8/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.507 v=0.352 cv=0.4560(Δ+0.256) equi=0.5352 rig=44.7/99.7 [polygon=0.21 curve=0.17 star=0.46 structure=0.51]\n", " E20: t=0.601 v=0.544 cv=0.4234(Δ+0.223) equi=0.5848 rig=46.1/99.8 [polygon=0.20 curve=0.86 star=0.75 structure=0.58]\n", " E30: t=0.649 v=0.640 cv=0.3353(Δ+0.135) equi=0.6070 rig=48.5/99.9 [polygon=0.29 curve=0.97 star=0.85 structure=0.67]\n", "\n", " ── cv_0.03 (tang=0.5, cv_w=0.03, sep=0.8) ──\n", " E 1: t=0.040 v=0.067 cv=0.5728(Δ+0.373) equi=0.1717 rig=75.2/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.478 v=0.404 cv=0.2299(Δ+0.030) equi=0.5294 rig=47.0/99.7 [polygon=0.22 curve=0.60 star=0.34 structure=0.52]\n", " E20: t=0.606 v=0.595 cv=0.2187(Δ+0.019) equi=0.5691 rig=48.3/99.8 [polygon=0.26 curve=0.97 star=0.73 structure=0.63]\n", " E30: t=0.632 v=0.648 cv=0.2985(Δ+0.099) equi=0.5909 rig=50.2/99.9 [polygon=0.30 curve=0.98 star=0.85 structure=0.67]\n", "\n", " ── cv_0.06 (tang=0.5, cv_w=0.06, sep=0.8) ──\n", " E 1: t=0.036 v=0.033 cv=0.0021(Δ-0.198) equi=0.0777 rig=82.9/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.00]\n", " E10: t=0.421 v=0.374 cv=0.2990(Δ+0.099) equi=0.5183 rig=41.8/99.7 [polygon=0.20 curve=0.54 star=0.49 structure=0.38]\n", " E20: t=0.527 v=0.526 cv=0.2273(Δ+0.027) equi=0.5708 rig=41.9/99.8 [polygon=0.19 curve=0.90 star=0.68 structure=0.55]\n", " E30: t=0.568 v=0.586 cv=0.2331(Δ+0.033) equi=0.5957 rig=43.3/99.9 [polygon=0.24 curve=0.96 star=0.76 structure=0.61]\n", "\n", "\n", "=================================================================\n", "SWEEP RESULTS\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv Δcv eq_std poly curve star struct\n", " ------------------------------------------------------------------------------------------\n", " no_cv 0.721 0.730 +0.009 1.7346 +1.5346 0.4400 0.54 0.99 0.89 0.65\n", " cv_0.001 0.712 0.706 -0.005 1.5553 +1.3553 0.4291 0.40 0.99 0.91 0.74\n", " cv_0.005 0.697 0.690 -0.006 1.5407 +1.3407 0.4622 0.38 0.97 0.88 0.73\n", " cv_0.01 0.640 0.649 +0.010 0.3353 +0.1353 0.6070 0.29 0.97 0.85 0.67\n", " cv_0.03 0.648 0.632 -0.016 0.2985 +0.0985 0.5909 0.30 0.98 0.85 0.67\n", " cv_0.06 0.586 0.568 -0.018 0.2331 +0.0331 0.5957 0.24 0.96 0.76 0.61\n", "\n", " Best accuracy: no_cv (val_acc=0.721)\n", " Best structure: cv_0.001 (struct=0.735)\n", " Closest to CV=0.2: cv_0.06 (cv=0.2331, Δ=+0.0331)\n", " Most equidistant: cv_0.001 (equi_std=0.4291)\n", " Most stable CV: cv_0.06 (cv_std=0.1087)\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 9 super parent" ], "metadata": { "id": "wQv6VqTR1d6F" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (production versions, differentiable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "# ── Production Cayley-Menger (generic, differentiable) ──\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"\n", " Differentiable CV loss. Proper loss term, not gradient surgery.\n", " Flows gradient through torch.stack → torch.sqrt → torch.std/mean.\n", " \"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV measurement for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "# ── Autograd: tangential projection + separation only ──\n", "# NO gradient injection. CV is a loss term, not gradient surgery.\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering only. Two operations:\n", " 1. Tangential projection (keep gradients on hypersphere surface)\n", " 2. Separation preservation (attenuate collapse toward nearest anchor)\n", "\n", " CV regulation is handled by cv_loss in the training loop.\n", " Not here. Loss terms flow gradient naturally. Surgery doesn't.\n", " \"\"\"\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " sep_strength = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - tang_only) * norm\n", "\n", " # 2. Separation preservation\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_idx = cos_to_anchors.argmax(dim=-1)\n", " nearest_anchor = anchors_n[nearest_idx]\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 else 0.0\n", "\n", " loss = l_cls + cv_weight * l_cv\n", " loss.backward()\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(30, dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang_only, cv_weight, sep_strength)\n", " # Base: tang=0.01 (near-zero filtering), sep=1.0 (strong separation)\n", " (\"no_cv\", 0.01, 0.0, 1.0),\n", " (\"cv_0.001\", 0.01, 0.001, 1.0),\n", " (\"cv_0.0001\", 0.01, 0.0001, 1.0),\n", " (\"cv_0.002\", 0.01, 0.002, 1.0),\n", " (\"cv_0.0002\", 0.01, 0.0002, 1.0),\n", " (\"cv_0.00005\", 0.01, 0.00005, 1.0),\n", "]\n", "\n", "results = {}\n", "for name, to, cw, sp in configs:\n", " print(f\"\\n ── {name} (tang={to}, cv_w={cw}, sep={sp}) ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, cv_weight=cw, sep_strength=sp,\n", " epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fYM3y9Eb1cm6", "outputId": "e65f976d-06e1-4cf3-f85b-46820bfe0bdc" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── no_cv (tang=0.01, cv_w=0.0, sep=1.0) ──\n", " E 1: t=0.038 v=0.067 cv=2.2276(Δ+2.028) equi=0.1215 rig=78.5/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.610 v=0.598 cv=1.6607(Δ+1.461) equi=0.4355 rig=47.3/99.6 [polygon=0.21 curve=0.82 star=0.79 structure=0.72]\n", " E20: t=0.697 v=0.672 cv=2.4070(Δ+2.207) equi=0.4336 rig=41.0/99.8 [polygon=0.43 curve=1.00 star=0.77 structure=0.67]\n", " E30: t=0.713 v=0.707 cv=2.0122(Δ+1.812) equi=0.4358 rig=38.8/99.9 [polygon=0.52 curve=0.99 star=0.89 structure=0.63]\n", "\n", " ── cv_0.001 (tang=0.01, cv_w=0.001, sep=1.0) ──\n", " E 1: t=0.039 v=0.067 cv=2.3196(Δ+2.120) equi=0.1203 rig=77.9/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.569 v=0.473 cv=2.3217(Δ+2.122) equi=0.4763 rig=47.6/99.7 [polygon=0.38 curve=0.31 star=0.61 structure=0.56]\n", " E20: t=0.698 v=0.695 cv=1.8136(Δ+1.614) equi=0.4551 rig=41.8/99.8 [polygon=0.38 curve=0.99 star=0.88 structure=0.72]\n", " E30: t=0.705 v=0.719 cv=1.9659(Δ+1.766) equi=0.4504 rig=39.4/99.9 [polygon=0.47 curve=0.99 star=0.86 structure=0.72]\n", "\n", " ── cv_0.0001 (tang=0.01, cv_w=0.0001, sep=1.0) ──\n", " E 1: t=0.038 v=0.067 cv=2.7230(Δ+2.523) equi=0.1214 rig=78.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.582 v=0.599 cv=2.2493(Δ+2.049) equi=0.4720 rig=47.6/99.7 [polygon=0.35 curve=0.79 star=0.82 structure=0.60]\n", " E20: t=0.686 v=0.674 cv=2.0494(Δ+1.849) equi=0.4873 rig=43.3/99.8 [polygon=0.39 curve=0.96 star=0.88 structure=0.66]\n", " E30: t=0.706 v=0.685 cv=1.7401(Δ+1.540) equi=0.4839 rig=41.4/99.9 [polygon=0.39 curve=1.00 star=0.83 structure=0.71]\n", "\n", " ── cv_0.002 (tang=0.01, cv_w=0.002, sep=1.0) ──\n", " E 1: t=0.038 v=0.067 cv=2.4644(Δ+2.264) equi=0.1173 rig=77.3/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.547 v=0.483 cv=2.0481(Δ+1.848) equi=0.4973 rig=49.3/99.7 [polygon=0.30 curve=0.28 star=0.82 structure=0.55]\n", " E20: t=0.676 v=0.666 cv=2.2396(Δ+2.040) equi=0.4995 rig=45.8/99.8 [polygon=0.37 curve=0.95 star=0.82 structure=0.70]\n", " E30: t=0.699 v=0.693 cv=2.0057(Δ+1.806) equi=0.4989 rig=43.8/99.9 [polygon=0.42 curve=0.97 star=0.83 structure=0.72]\n", "\n", " ── cv_0.0002 (tang=0.01, cv_w=0.0002, sep=1.0) ──\n", " E 1: t=0.038 v=0.067 cv=2.6926(Δ+2.493) equi=0.1215 rig=78.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.619 v=0.588 cv=1.6465(Δ+1.447) equi=0.4608 rig=44.5/99.7 [polygon=0.35 curve=0.78 star=0.75 structure=0.61]\n", " E20: t=0.683 v=0.685 cv=1.7645(Δ+1.565) equi=0.4814 rig=40.0/99.8 [polygon=0.36 curve=0.97 star=0.90 structure=0.70]\n", " E30: t=0.702 v=0.673 cv=1.8261(Δ+1.626) equi=0.4935 rig=38.1/99.9 [polygon=0.41 curve=0.83 star=0.89 structure=0.70]\n", "\n", " ── cv_0.00005 (tang=0.01, cv_w=5e-05, sep=1.0) ──\n", " E 1: t=0.038 v=0.067 cv=2.7240(Δ+2.524) equi=0.1215 rig=78.4/95.4 [polygon=0.00 curve=0.00 star=0.17 structure=0.10]\n", " E10: t=0.599 v=0.593 cv=1.8421(Δ+1.642) equi=0.4867 rig=45.7/99.7 [polygon=0.40 curve=0.76 star=0.76 structure=0.59]\n", " E20: t=0.700 v=0.686 cv=1.6767(Δ+1.477) equi=0.4966 rig=41.3/99.8 [polygon=0.40 curve=0.97 star=0.83 structure=0.71]\n", " E30: t=0.715 v=0.722 cv=1.7610(Δ+1.561) equi=0.5068 rig=39.7/99.9 [polygon=0.46 curve=0.99 star=0.88 structure=0.73]\n", "\n", "\n", "=================================================================\n", "SWEEP RESULTS\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv Δcv eq_std poly curve star struct\n", " ------------------------------------------------------------------------------------------\n", " no_cv 0.707 0.713 +0.006 2.0122 +1.8122 0.4358 0.52 0.99 0.89 0.63\n", " cv_0.001 0.719 0.705 -0.014 1.9659 +1.7659 0.4504 0.47 0.99 0.86 0.72\n", " cv_0.0001 0.685 0.706 +0.021 1.7401 +1.5401 0.4839 0.39 1.00 0.83 0.71\n", " cv_0.002 0.693 0.699 +0.007 2.0057 +1.8057 0.4989 0.42 0.97 0.83 0.72\n", " cv_0.0002 0.673 0.702 +0.029 1.8261 +1.6261 0.4935 0.41 0.83 0.89 0.70\n", " cv_0.00005 0.722 0.715 -0.007 1.7610 +1.5610 0.5068 0.46 0.99 0.88 0.73\n", "\n", " Best accuracy: cv_0.00005 (val_acc=0.722)\n", " Best structure: cv_0.00005 (struct=0.729)\n", " Closest to CV=0.2: cv_0.0001 (cv=1.7401, Δ=+1.5401)\n", " Most equidistant: no_cv (equi_std=0.4358)\n", " Most stable CV: cv_0.0001 (cv_std=0.3156)\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (production versions, differentiable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "# ── Production Cayley-Menger (generic, differentiable) ──\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"\n", " Differentiable CV loss. Proper loss term, not gradient surgery.\n", " Flows gradient through torch.stack → torch.sqrt → torch.std/mean.\n", " \"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV measurement for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "# ── Autograd: tangential projection + separation only ──\n", "# NO gradient injection. CV is a loss term, not gradient surgery.\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering only. Two operations:\n", " 1. Tangential projection (keep gradients on hypersphere surface)\n", " 2. Separation preservation (attenuate collapse toward nearest anchor)\n", "\n", " CV regulation is handled by cv_loss in the training loop.\n", " Not here. Loss terms flow gradient naturally. Surgery doesn't.\n", " \"\"\"\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " sep_strength = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - tang_only) * norm\n", "\n", " # 2. Separation preservation\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_idx = cos_to_anchors.argmax(dim=-1)\n", " nearest_anchor = anchors_n[nearest_idx]\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " mask = labels == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 or sep_strength > 0):\n", " # Apply geometric autograd\n", " anchors = model.constellation.anchors\n", " emb_corrected = GeometricAutograd.apply(\n", " emb, emb, anchors, tang_only, sep_strength)\n", "\n", " # Recompute through corrected embedding\n", " tri_g, _ = model.constellation.triangulate(emb_corrected)\n", " pw_feat = model.patchwork(tri_g)\n", " logits = model.mlp(pw_feat)\n", "\n", " # Losses\n", " l_cls = F.cross_entropy(logits, labels)\n", " l_cv = cv_loss(emb, target=0.2, n_samples=16) if cv_weight > 0 else 0.0\n", "\n", " loss = l_cls + cv_weight * l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach(), labels)\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(30, dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang_only, cv_weight, sep_strength, use_autograd)\n", " (\"raw_adamw\", 0.0, 0.0, 0.0, False), # PURE AdamW, zero surgery\n", " (\"cv_only_1e-4\", 0.0, 0.0001, 0.0, False), # just CV loss, no surgery\n", " (\"cv_only_5e-4\", 0.0, 0.0005, 0.0, False), # just CV loss, no surgery\n", " (\"cv_only_1e-3\", 0.0, 0.001, 0.0, False), # just CV loss, no surgery\n", " (\"sep_only\", 0.01, 0.0, 1.0, True), # just separation gate\n", " (\"tang_only\", 0.5, 0.0, 0.0, True), # just tangential gate\n", " (\"sep+cv_1e-4\", 0.01, 0.0001, 1.0, True), # sep + micro CV\n", " (\"sep+cv_5e-4\", 0.01, 0.0005, 1.0, True), # sep + small CV\n", " (\"sep+cv_1e-3\", 0.01, 0.001, 1.0, True), # sep + CV (prev best)\n", " (\"full_micro\", 0.01, 0.0001, 1.0, True), # everything, lightest touch\n", "]\n", "\n", "results = {}\n", "for name, to, cw, sp, ua in configs:\n", " ag_str = \"ON\" if ua and (to > 0 or sp > 0) else \"OFF\"\n", " print(f\"\\n ── {name} (tang={to}, cv_w={cw}, sep={sp}, autograd={ag_str}) ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, cv_weight=cw, sep_strength=sp,\n", " use_autograd=ua, epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "sYpvkYcY5Kfw", "outputId": "2674eb01-d773-4ee9-a219-f100b523df2d" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── raw_adamw (tang=0.0, cv_w=0.0, sep=0.0, autograd=OFF) ──\n" ] }, { "output_type": "error", "ename": "IndexError", "evalue": "The shape of the mask [30, 30] at index 0 does not match the shape of the indexed tensor [512, 512] at index 0", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_67512/3465209737.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0mag_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ON\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mua\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mto\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msp\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"OFF\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\n ── {name} (tang={to}, cv_w={cw}, sep={sp}, autograd={ag_str}) ──\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 570\u001b[0;31m hist, health, _ = train_once(\n\u001b[0m\u001b[1;32m 571\u001b[0m \u001b[0mtang_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep_strength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 572\u001b[0m use_autograd=ua, epochs=30, verbose=True)\n", "\u001b[0;32m/tmp/ipykernel_67512/3465209737.py\u001b[0m in \u001b[0;36mtrain_once\u001b[0;34m(tang_only, cv_weight, sep_strength, use_autograd, epochs, seed, verbose)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0mcos_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_n\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0ma_n\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m \u001b[0mequi_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcos_mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 516\u001b[0m types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", "\u001b[0;31mIndexError\u001b[0m: The shape of the mask [30, 30] at index 0 does not match the shape of the indexed tensor [512, 512] at index 0" ] } ] }, { "cell_type": "markdown", "source": [ "# autograd upgrade test" ], "metadata": { "id": "PEr9AUgaE2Gi" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# GEOMETRIC AUTOGRAD — COMPLETE\n", "#\n", "# Every validated loss from the research, properly placed:\n", "#\n", "# FORWARD (differentiable loss terms, flow gradient naturally):\n", "# 1. CV loss — manifold regularity, target 0.2, micro weight\n", "# 2. Anchor spread — prevent anchor collapse (bank analog)\n", "# 3. Anchor entropy — sharpness of anchor assignment\n", "# 4. Anchor ortho — constellation stays near-orthogonal\n", "# 5. Cluster variance — cross-anchor differentiation preservation\n", "#\n", "# BACKWARD (gradient filtering, no injection):\n", "# 1. Tangential projection — keep gradients on hypersphere\n", "# 2. Separation preservation — anti-collapse toward nearest anchor\n", "# 3. Orthogonal drift guard — anchor gradients stay tangential\n", "#\n", "# Adam (NOT AdamW). The geometry IS the regularization.\n", "# ============================================================================\n", "\n", "import math\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CAYLEY-MENGER (production, differentiable, generic)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FORWARD LOSSES (differentiable, return scalar tensors)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"Manifold regularity. |CV(pentachoron volumes) - target|.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5:\n", " return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "def anchor_spread_loss(anchors):\n", " \"\"\"\n", " Prevent anchor collapse. Penalize high cosine between any anchor pair.\n", " Analog of bank's anchor spread from production trainer.\n", " \"\"\"\n", " anchors_n = F.normalize(anchors, dim=-1)\n", " sim = anchors_n @ anchors_n.T\n", " # Zero diagonal\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " \"\"\"\n", " Anchor assignment sharpness. Embeddings should have clear\n", " nearest anchors, not smeared across the constellation.\n", " Higher entropy = more diffuse = less useful triangulation.\n", " \"\"\"\n", " anchors_n = F.normalize(anchors, dim=-1)\n", " cos = emb @ anchors_n.T # (B, N)\n", " probs = F.softmax(cos * sharpness, dim=-1) # (B, N)\n", " entropy = -(probs * (probs + 1e-12).log()).sum(-1).mean() # scalar\n", " return entropy\n", "\n", "\n", "def anchor_ortho_loss(anchors):\n", " \"\"\"\n", " Constellation orthogonality. Xavier gives near-orthogonal init.\n", " This loss prevents training from collapsing that property.\n", " Analog of rotation orthogonality from bank trainer.\n", " \"\"\"\n", " anchors_n = F.normalize(anchors, dim=-1)\n", " gram = anchors_n @ anchors_n.T # (N, N)\n", " N = anchors.shape[0]\n", " target = torch.eye(N, device=anchors.device)\n", " # Only penalize off-diagonal — diagonal should be 1\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "\n", "def cluster_variance_loss(emb, anchors):\n", " \"\"\"\n", " Cross-anchor differentiation. Each anchor's local neighborhood\n", " should have distinct geometry from other anchors' neighborhoods.\n", " Analog of cross-expert variance from bank trainer.\n", "\n", " Measures: variance of per-anchor mean distances. Low variance\n", " means all anchors see the same thing — no differentiation.\n", " We MAXIMIZE variance (minimize negative variance).\n", " \"\"\"\n", " anchors_n = F.normalize(anchors, dim=-1)\n", " cos = emb @ anchors_n.T # (B, N)\n", " # Per-anchor mean cosine from batch\n", " per_anchor_mean = cos.mean(dim=0) # (N,)\n", " # We want high variance — anchors should differ\n", " return -per_anchor_mean.var()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BACKWARD FILTERING (gradient surgery, no injection)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " \"\"\"Decompose gradient into tangential and normal (radial) components.\"\"\"\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering for embedding tensors.\n", " 1. Tangential projection — gradients stay on hypersphere surface\n", " 2. Separation preservation — attenuate collapse toward nearest anchor\n", " \"\"\"\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_strength, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_strength = tang_strength\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang = ctx.tang_strength\n", " sep = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - tang) * norm_grad\n", "\n", " # 2. Separation preservation\n", " if sep > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_anchor = anchors_n[cos_to_anchors.argmax(dim=-1)]\n", " toward = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest_anchor\n", " is_collapsing = (toward > 0).float()\n", " corrected = corrected - sep * is_collapsing * collapse\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "class AnchorAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering for anchor parameters.\n", " Anchors drift tangentially on the hypersphere — never radially.\n", " Each anchor's gradient is projected tangential to the sphere\n", " at that anchor's position.\n", " \"\"\"\n", " @staticmethod\n", " def forward(ctx, anchors, drift_strength):\n", " ctx.save_for_backward(anchors)\n", " ctx.drift_strength = drift_strength\n", " return anchors\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " anchors, = ctx.saved_tensors\n", " drift = ctx.drift_strength\n", "\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " N, D = anchors_n.shape\n", "\n", " # Per-anchor tangential projection\n", " corrected = torch.zeros_like(grad_f)\n", " for i in range(N):\n", " g = grad_f[i]\n", " a = anchors_n[i]\n", " radial = (g * a).sum() * a\n", " corrected[i] = (g - radial) * drift\n", "\n", " return corrected.to(grad_output.dtype), None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMBINED INTERFACE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeometricOptimizer(nn.Module):\n", " \"\"\"\n", " Complete geometric autograd system.\n", "\n", " Forward: computes all geometric losses, returns combined scalar\n", " Backward: applies gradient filtering at embedding + anchor boundaries\n", "\n", " Usage in training loop:\n", " emb_filtered = geo_opt.filter_embedding(emb, anchors)\n", " anchors_filtered = geo_opt.filter_anchors(anchors)\n", " # ... compute task logits from filtered tensors ...\n", " l_task = F.cross_entropy(logits, labels)\n", " l_geo = geo_opt.geometric_loss(emb, anchors)\n", " loss = l_task + l_geo\n", " loss.backward()\n", " # Gradients are already filtered by the autograd functions\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " # Backward: gradient filtering strengths\n", " tang_strength: float = 0.01,\n", " sep_strength: float = 1.0,\n", " anchor_drift: float = 0.5,\n", " # Forward: loss weights\n", " w_cv: float = 1e-4,\n", " w_spread: float = 1e-3,\n", " w_entropy: float = 1e-4,\n", " w_ortho: float = 1e-3,\n", " w_cluster: float = 1e-4,\n", " # CV target\n", " cv_target: float = 0.2,\n", " ):\n", " super().__init__()\n", " # Backward params\n", " self.tang_strength = tang_strength\n", " self.sep_strength = sep_strength\n", " self.anchor_drift = anchor_drift\n", " # Forward weights\n", " self.w_cv = w_cv\n", " self.w_spread = w_spread\n", " self.w_entropy = w_entropy\n", " self.w_ortho = w_ortho\n", " self.w_cluster = w_cluster\n", " self.cv_target = cv_target\n", "\n", " def filter_embedding(self, emb, anchors):\n", " \"\"\"Apply backward filtering to embedding tensor.\"\"\"\n", " return EmbeddingAutograd.apply(\n", " emb, emb, anchors,\n", " self.tang_strength, self.sep_strength)\n", "\n", " def filter_anchors(self, anchors):\n", " \"\"\"Apply backward filtering to anchor parameters.\"\"\"\n", " return AnchorAutograd.apply(anchors, self.anchor_drift)\n", "\n", " def geometric_loss(self, emb, anchors):\n", " \"\"\"\n", " Compute all forward geometric losses.\n", " Returns scalar tensor + diagnostics dict.\n", " \"\"\"\n", " losses = {}\n", " total = torch.tensor(0.0, device=emb.device)\n", "\n", " if self.w_cv > 0:\n", " l = cv_loss(emb, target=self.cv_target)\n", " losses[\"cv\"] = l.item()\n", " total = total + self.w_cv * l\n", "\n", " if self.w_spread > 0:\n", " l = anchor_spread_loss(anchors)\n", " losses[\"spread\"] = l.item()\n", " total = total + self.w_spread * l\n", "\n", " if self.w_entropy > 0:\n", " l = anchor_entropy_loss(emb, anchors)\n", " losses[\"entropy\"] = l.item()\n", " total = total + self.w_entropy * l\n", "\n", " if self.w_ortho > 0:\n", " l = anchor_ortho_loss(anchors)\n", " losses[\"ortho\"] = l.item()\n", " total = total + self.w_ortho * l\n", "\n", " if self.w_cluster > 0:\n", " l = cluster_variance_loss(emb, anchors)\n", " losses[\"cluster_var\"] = l.item()\n", " total = total + self.w_cluster * l\n", "\n", " losses[\"total\"] = total.item()\n", " return total, losses\n", "\n", " def state_dict_str(self):\n", " return (f\"tang={self.tang_strength} sep={self.sep_strength} \"\n", " f\"drift={self.anchor_drift} | \"\n", " f\"cv={self.w_cv} spread={self.w_spread} \"\n", " f\"entropy={self.w_entropy} ortho={self.w_ortho} \"\n", " f\"cluster={self.w_cluster}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DIAGNOSTIC (non-differentiable measurement)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "\n", "@torch.no_grad()\n", "def full_diagnostics(emb, anchors):\n", " \"\"\"Complete geometric health report.\"\"\"\n", " anchors_n = F.normalize(anchors, dim=-1)\n", " emb_n = F.normalize(emb, dim=-1)\n", "\n", " # Anchor pairwise\n", " cos = anchors_n @ anchors_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", "\n", " # Embedding→anchor distances\n", " tri = emb_n @ anchors_n.T\n", " nearest = tri.argmax(dim=-1)\n", "\n", " # Anchor assignment distribution\n", " counts = torch.zeros(N, device=anchors.device)\n", " for i in range(N):\n", " counts[i] = (nearest == i).sum().float()\n", "\n", " # Entropy of assignment\n", " probs = F.softmax(tri * 10, dim=-1)\n", " entropy = -(probs * (probs + 1e-12).log()).sum(-1).mean().item()\n", "\n", " return {\n", " \"cv\": cv_metric(emb),\n", " \"anchor_cos_mean\": cos[mask].mean().item(),\n", " \"anchor_cos_std\": cos[mask].std().item(),\n", " \"anchor_cos_max\": cos[mask].max().item(),\n", " \"anchor_spread\": cos[mask].pow(2).mean().item(),\n", " \"entropy\": entropy,\n", " \"assignment_std\": counts.std().item(),\n", " \"assignment_max\": counts.max().item(),\n", " \"assignment_min\": counts.min().item(),\n", " \"tri_mean\": tri.mean().item(),\n", " \"tri_std\": tri.std().item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# INTEGRATION TEST\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if __name__ == \"__main__\":\n", " DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", " print(\"=\" * 65)\n", " print(\"GEOMETRIC AUTOGRAD — COMPLETE\")\n", " print(\"=\" * 65)\n", "\n", " # Create test tensors\n", " anchors = nn.Parameter(F.normalize(torch.randn(30, 768, device=DEVICE), dim=-1))\n", " emb = F.normalize(torch.randn(64, 768, device=DEVICE), dim=-1)\n", " emb = emb.requires_grad_(True)\n", "\n", " geo = GeometricOptimizer(\n", " tang_strength=0.01, sep_strength=1.0, anchor_drift=0.5,\n", " w_cv=1e-4, w_spread=1e-3, w_entropy=1e-4,\n", " w_ortho=1e-3, w_cluster=1e-4, cv_target=0.2,\n", " )\n", " print(f\"\\n Config: {geo.state_dict_str()}\")\n", "\n", " # Forward losses\n", " emb_f = geo.filter_embedding(emb, anchors)\n", " anchors_f = geo.filter_anchors(anchors)\n", " l_geo, diag = geo.geometric_loss(emb, anchors)\n", " print(f\"\\n Forward losses:\")\n", " for k, v in diag.items():\n", " print(f\" {k}: {v:.6f}\")\n", "\n", " # Backward test\n", " fake_logits = emb_f @ anchors_f.T\n", " fake_labels = torch.randint(0, 30, (64,), device=DEVICE)\n", " l_task = F.cross_entropy(fake_logits, fake_labels)\n", " loss = l_task + l_geo\n", " loss.backward()\n", "\n", " print(f\"\\n Gradient check:\")\n", " print(f\" emb grad: {emb.grad is not None and emb.grad.abs().sum() > 0}\")\n", " print(f\" anchor grad: {anchors.grad is not None and anchors.grad.abs().sum() > 0}\")\n", "\n", " # Verify anchor grads are tangential\n", " if anchors.grad is not None:\n", " a_n = F.normalize(anchors.detach(), dim=-1)\n", " radial_component = (anchors.grad * a_n).sum(dim=-1)\n", " print(f\" anchor grad radial component: {radial_component.abs().mean():.8f} (should be ~0)\")\n", "\n", " # Full diagnostics\n", " diag = full_diagnostics(emb.detach(), anchors.detach())\n", " print(f\"\\n Diagnostics:\")\n", " for k, v in diag.items():\n", " print(f\" {k}: {v:.4f}\")\n", "\n", " print(f\"\\n{'='*65}\")\n", " print(\"DONE\")\n", " print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ubEAc0RLE3O6", "outputId": "d9d10256-dd6d-41d0-9087-518fa5c0c6ca" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOMETRIC AUTOGRAD — COMPLETE\n", "=================================================================\n", "\n", " Config: tang=0.01 sep=1.0 drift=0.5 | cv=0.0001 spread=0.001 entropy=0.0001 ortho=0.001 cluster=0.0001\n", "\n", " Forward losses:\n", " cv: 0.182105\n", " spread: 0.001196\n", " entropy: 3.338384\n", " ortho: 0.001237\n", " cluster_var: -0.000025\n", " total: 0.000354\n", "\n", " Gradient check:\n", " emb grad: True\n", " anchor grad: True\n", " anchor grad radial component: 0.00000000 (should be ~0)\n", "\n", " Diagnostics:\n", " cv: 0.0220\n", " anchor_cos_mean: 0.0004\n", " anchor_cos_std: 0.0352\n", " anchor_cos_max: 0.1029\n", " anchor_spread: 0.0012\n", " entropy: 3.3384\n", " assignment_std: 1.6132\n", " assignment_max: 7.0000\n", " assignment_min: 0.0000\n", " tri_mean: 0.0008\n", " tri_std: 0.0363\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# PATCHWORK + GEOMETRIC AUTOGRAD (COMPLETE)\n", "#\n", "# Adam (not AdamW). Full forward loss suite. Full backward filtering.\n", "# Clean comparison: raw Adam vs geometric optimizer.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC AUTOGRAD (inline — complete system)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def cluster_variance_loss(emb, anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " cos = emb @ a_n.T\n", " return -cos.mean(dim=0).var()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_g, norm_g = tangential_projection(grad_f, emb_n)\n", " corrected = tang_g + (1.0 - ctx.tang) * norm_g\n", " if ctx.sep > 0:\n", " cos = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "class AnchorAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, anchors, drift):\n", " ctx.save_for_backward(anchors)\n", " ctx.drift = drift\n", " return anchors\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " anchors, = ctx.saved_tensors\n", " a_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " N = a_n.shape[0]\n", " corrected = torch.zeros_like(grad_f)\n", " for i in range(N):\n", " g, a = grad_f[i], a_n[i]\n", " corrected[i] = (g - (g * a).sum() * a) * ctx.drift\n", " return corrected.to(grad_output.dtype), None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " a_n = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a_n.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist, labels):\n", " for i in range(self.n_anchors):\n", " m = labels == i\n", " if m.sum() < 5: continue\n", " self.visit_count[i] += m.sum().float()\n", " spread = tri_dist[m].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(spread+0.01)\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " m = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\"mean_cos\": cos[m].mean().item(), \"std_cos\": cos[m].std().item()}\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=30, n_comp=6, d_comp=64):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " assignments = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"assignments\", assignments)\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_comp):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp*2), nn.GELU(),\n", " nn.Linear(d_comp*2, d_comp), nn.LayerNorm(d_comp)))\n", "\n", " def forward(self, tri_dist):\n", " parts = []\n", " for k in range(self.n_comp):\n", " mask = self.assignments == k\n", " parts.append(self.compartments[k](tri_dist[:, mask]))\n", " return torch.cat(parts, dim=-1)\n", "\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_comp=6, d_comp=64, d_hidden=128):\n", " super().__init__()\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1,32,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32,64,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64,128,3,padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.embed_proj = nn.Sequential(nn.Linear(128, d_embed), nn.LayerNorm(d_embed))\n", " self.constellation = Constellation(n_anchors, d_embed)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", " pw_dim = n_comp * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes))\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri, nearest = self.constellation.triangulate(emb)\n", " logits = self.mlp(self.patchwork(tri))\n", " return logits, emb, tri, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch+1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " \"geo_loss\": total_geo/d if use_geo else 0,\n", " })\n", "\n", " if (epoch+1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t,a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_d = v_cv - 0.2\n", " geo_str = f\"geo={total_geo/d:.5f} \" if use_geo else \"\"\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_d:+.3f}) {geo_str}\"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " return history\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"=\" * 65)\n", "print(\"PATCHWORK + COMPLETE GEOMETRIC AUTOGRAD\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "print(f\" Optimizer: Adam (lr=1e-3, NO weight decay)\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"RAW ADAM (no geometric autograd)\")\n", "print(f\"{'='*65}\")\n", "h_raw = train(use_geo=False, epochs=30)\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEOMETRIC OPTIMIZER (full loss suite + backward filtering)\")\n", "print(f\"{'='*65}\")\n", "h_geo = train(use_geo=True, epochs=30)\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "r, g = h_raw[-1], h_geo[-1]\n", "print(f\"\\n {'Metric':<25} {'Raw Adam':>10} {'Geo Opt':>10} {'Δ':>8}\")\n", "print(f\" {'-'*55}\")\n", "\n", "for name, rv, gv in [\n", " (\"Val accuracy\", r['val_acc'], g['val_acc']),\n", " (\"Train accuracy\", r['train_acc'],g['train_acc']),\n", " (\"Overfit gap\", r['train_acc']-r['val_acc'], g['train_acc']-g['val_acc']),\n", " (\"Val CV\", r['val_cv'], g['val_cv']),\n", " (\"Equi std\", r['equi_std'], g['equi_std']),\n", "]:\n", " delta = gv - rv\n", " print(f\" {name:<25} {rv:>10.4f} {gv:>10.4f} {delta:>+8.4f}\")\n", "\n", "for tname in [\"polygon\",\"curve\",\"star\",\"structure\"]:\n", " rv = r[\"type_accs\"].get(tname, 0)\n", " gv = g[\"type_accs\"].get(tname, 0)\n", " print(f\" {'Acc '+tname:<25} {rv:>10.3f} {gv:>10.3f} {gv-rv:>+8.3f}\")\n", "\n", "# Trajectory\n", "r_cvs = [h[\"val_cv\"] for h in h_raw]\n", "g_cvs = [h[\"val_cv\"] for h in h_geo]\n", "print(f\"\\n CV stability (std): raw={np.std(r_cvs):.4f} geo={np.std(g_cvs):.4f}\")\n", "\n", "r_gaps = [h[\"train_acc\"]-h[\"val_acc\"] for h in h_raw]\n", "g_gaps = [h[\"train_acc\"]-h[\"val_acc\"] for h in h_geo]\n", "print(f\" Gap trajectory: raw={np.mean(r_gaps):+.3f}±{np.std(r_gaps):.3f} \"\n", " f\"geo={np.mean(g_gaps):+.3f}±{np.std(g_gaps):.3f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oZiBlEk9GO-G", "outputId": "83b954e8-5987-45e1-9958-122bbe3324eb" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "PATCHWORK + COMPLETE GEOMETRIC AUTOGRAD\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", " Optimizer: Adam (lr=1e-3, NO weight decay)\n", "\n", "=================================================================\n", "RAW ADAM (no geometric autograd)\n", "=================================================================\n", " E 1: t=0.082 v=0.156 cv=1.0648(Δ+0.865) rig=58.7/95.4 [polygon=0.04 curve=0.00 star=0.28 structure=0.26]\n", " E 5: t=0.386 v=0.449 cv=1.4223(Δ+1.222) rig=47.9/99.2 [polygon=0.21 curve=0.28 star=0.69 structure=0.60]\n", " E10: t=0.612 v=0.554 cv=1.3072(Δ+1.107) rig=43.6/99.6 [polygon=0.32 curve=0.88 star=0.44 structure=0.67]\n", " E15: t=0.623 v=0.628 cv=1.7028(Δ+1.503) rig=40.3/99.8 [polygon=0.30 curve=0.94 star=0.82 structure=0.65]\n", " E20: t=0.668 v=0.657 cv=1.5804(Δ+1.380) rig=38.2/99.8 [polygon=0.30 curve=0.99 star=0.84 structure=0.70]\n", " E25: t=0.679 v=0.678 cv=1.2974(Δ+1.097) rig=36.8/99.9 [polygon=0.41 curve=0.99 star=0.74 structure=0.73]\n", " E30: t=0.700 v=0.700 cv=1.5202(Δ+1.320) rig=35.8/99.9 [polygon=0.43 curve=0.99 star=0.85 structure=0.71]\n", "\n", "=================================================================\n", "GEOMETRIC OPTIMIZER (full loss suite + backward filtering)\n", "=================================================================\n", " E 1: t=0.034 v=0.033 cv=0.2897(Δ+0.090) geo=0.57607 rig=77.7/95.4 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " E 5: t=0.179 v=0.073 cv=0.7319(Δ+0.532) geo=0.37683 rig=73.8/99.3 [polygon=0.00 curve=0.25 star=0.09 structure=0.04]\n", " E10: t=0.246 v=0.225 cv=0.4548(Δ+0.255) geo=0.26154 rig=69.9/99.7 [polygon=0.10 curve=0.40 star=0.28 structure=0.22]\n", " E15: t=0.216 v=0.260 cv=0.1401(Δ-0.060) geo=0.22908 rig=69.4/99.8 [polygon=0.07 curve=0.50 star=0.25 structure=0.32]\n", " E20: t=0.272 v=0.326 cv=0.5327(Δ+0.333) geo=0.28561 rig=69.3/99.8 [polygon=0.15 curve=0.50 star=0.42 structure=0.34]\n", " E25: t=0.310 v=0.259 cv=0.3330(Δ+0.133) geo=0.34407 rig=69.3/99.9 [polygon=0.03 curve=0.59 star=0.18 structure=0.34]\n", " E30: t=0.275 v=0.211 cv=0.2353(Δ+0.035) geo=0.25819 rig=69.4/99.9 [polygon=0.00 curve=0.41 star=0.30 structure=0.25]\n", "\n", "\n", "=================================================================\n", "COMPARISON\n", "=================================================================\n", "\n", " Metric Raw Adam Geo Opt Δ\n", " -------------------------------------------------------\n", " Val accuracy 0.7000 0.2110 -0.4890\n", " Train accuracy 0.7003 0.2746 -0.4257\n", " Overfit gap 0.0003 0.0636 +0.0633\n", " Val CV 1.5202 0.2353 -1.2849\n", " Equi std 0.2482 0.3204 +0.0722\n", " Acc polygon 0.434 0.003 -0.431\n", " Acc curve 0.988 0.410 -0.578\n", " Acc star 0.848 0.297 -0.552\n", " Acc structure 0.706 0.247 -0.459\n", "\n", " CV stability (std): raw=0.2049 geo=0.1571\n", " Gap trajectory: raw=-0.012±0.035 geo=-0.003±0.056\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# RIGID PATCHWORK CLASSIFIER + GATE SWEEP\n", "#\n", "# No conv4d. No composition paths. No splatting.\n", "#\n", "# Patchwork: partition 30 anchors into K compartments.\n", "# Each compartment gets its own MLP that processes the triangulation\n", "# distances for its assigned anchors. Compartment outputs concatenate.\n", "# Final MLP → classifier.\n", "#\n", "# Gate sweep: vary the CV gate tolerance and normal passthrough\n", "# to find the behavior regime.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (production versions, differentiable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "\n", "# ── Production Cayley-Menger (generic, differentiable) ──\n", "\n", "def cayley_menger_vol2(pts):\n", " \"\"\"Differentiable pentachoron volume². Generic for any V vertices.\"\"\"\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " \"\"\"\n", " Differentiable CV loss. Proper loss term, not gradient surgery.\n", " Flows gradient through torch.stack → torch.sqrt → torch.std/mean.\n", " \"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " \"\"\"Non-differentiable CV measurement for logging.\"\"\"\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " vols_t = torch.tensor(vols)\n", " return float(vols_t.std() / (vols_t.mean() + 1e-8))\n", "\n", "\n", "# ── Autograd: tangential projection + separation only ──\n", "# NO gradient injection. CV is a loss term, not gradient surgery.\n", "\n", "class GeometricAutograd(torch.autograd.Function):\n", " \"\"\"\n", " Gradient filtering only. Two operations:\n", " 1. Tangential projection (keep gradients on hypersphere surface)\n", " 2. Separation preservation (attenuate collapse toward nearest anchor)\n", "\n", " CV regulation is handled by cv_loss in the training loop.\n", " Not here. Loss terms flow gradient naturally. Surgery doesn't.\n", " \"\"\"\n", "\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang_only, sep_strength):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang_only = tang_only\n", " ctx.sep_strength = sep_strength\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " tang_only = ctx.tang_only\n", " sep_strength = ctx.sep_strength\n", "\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", "\n", " # 1. Tangential projection\n", " tang, norm = tangential_projection(grad_f, emb_n)\n", " corrected = tang + (1.0 - tang_only) * norm\n", "\n", " # 2. Separation preservation\n", " if sep_strength > 0:\n", " cos_to_anchors = emb_n @ anchors_n.T\n", " nearest_idx = cos_to_anchors.argmax(dim=-1)\n", " nearest_anchor = anchors_n[nearest_idx]\n", " toward_nearest = (corrected * nearest_anchor).sum(dim=-1, keepdim=True)\n", " collapse_component = toward_nearest * nearest_anchor\n", " is_collapsing = (toward_nearest > 0).float()\n", " corrected = corrected - sep_strength * is_collapsing * collapse_component\n", "\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ── Anchor gradient filtering ──\n", "\n", "class AnchorAutograd(torch.autograd.Function):\n", " \"\"\"Anchor gradients projected tangential per-anchor. No radial drift.\"\"\"\n", " @staticmethod\n", " def forward(ctx, anchors, drift):\n", " ctx.save_for_backward(anchors)\n", " ctx.drift = drift\n", " return anchors\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " anchors, = ctx.saved_tensors\n", " a_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " N = a_n.shape[0]\n", " corrected = torch.zeros_like(grad_f)\n", " for i in range(N):\n", " g = grad_f[i]; a = a_n[i]\n", " corrected[i] = (g - (g * a).sum() * a) * ctx.drift\n", " return corrected.to(grad_output.dtype), None\n", "\n", "\n", "# ── Additional forward losses (from bank research) ──\n", "\n", "def anchor_spread_loss(anchors):\n", " \"\"\"Prevent anchor collapse. Off-diagonal cosine² → 0.\"\"\"\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " \"\"\"Anchor assignment sharpness. Lower entropy = crisper triangulation.\"\"\"\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "\n", "def anchor_ortho_loss(anchors):\n", " \"\"\"Constellation orthogonality. Off-diagonal gram → 0.\"\"\"\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "\n", "def cluster_variance_loss(emb, anchors):\n", " \"\"\"Maximize cross-anchor differentiation. -var(per-anchor mean cos).\"\"\"\n", " a_n = F.normalize(anchors, dim=-1)\n", " per_anchor_mean = (emb @ a_n.T).mean(dim=0)\n", " return -per_anchor_mean.var()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION (pure Xavier, no semantics)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " anchors = F.normalize(torch.randn(n_anchors, d_embed), dim=-1)\n", " self.anchors = nn.Parameter(anchors)\n", "\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T # (B, N)\n", " tri_dist = 1.0 - cos_sim # (B, N)\n", " nearest = cos_sim.argmax(dim=-1) # (B,)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist):\n", " \"\"\"\n", " Rigidity by nearest-anchor assignment, NOT by class label.\n", " Anchors are geometric reference points, not class proxies.\n", " \"\"\"\n", " nearest = tri_dist.argmin(dim=-1) # (B,) — nearest anchor per sample\n", " for i in range(self.n_anchors):\n", " mask = nearest == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " cluster_dists = tri_dist[mask]\n", " spread = cluster_dists.std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " old = self.rigidity[i]\n", " self.rigidity[i] = (1 - alpha) * old + alpha * (1.0 / (spread + 0.01))\n", "\n", " def health(self):\n", " a = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a @ a.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a.device)\n", " return {\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"min_gap\": (1 - cos[mask].max()).item(),\n", " \"max_gap\": (1 - cos[mask].min()).item(),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK: compartmentalized anchor groups → MLPs → concat\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Patchwork(nn.Module):\n", " \"\"\"\n", " Partition N anchors into K compartments.\n", " Each compartment has its own MLP processing the triangulation\n", " distances for its anchors.\n", "\n", " Compartment assignments are fixed at init (evenly split).\n", " Each compartment MLP: (B, anchors_per_compartment) → (B, d_comp)\n", " All compartments concatenate → (B, K * d_comp)\n", " \"\"\"\n", "\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", "\n", " # Assign anchors to compartments (evenly)\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", "\n", " # Per-compartment MLP\n", " anchors_per = n_anchors // n_compartments\n", " remainder = n_anchors % n_compartments\n", "\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2),\n", " nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp),\n", " nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " \"\"\"\n", " Args:\n", " tri_dist: (B, N) triangulation distances to all anchors\n", "\n", " Returns:\n", " features: (B, K * d_comp)\n", " \"\"\"\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " comp_input = tri_dist[:, mask] # (B, n_k)\n", " parts.append(self.compartments[k](comp_input)) # (B, d_comp)\n", " return torch.cat(parts, dim=-1) # (B, K * d_comp)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256):\n", " super().__init__()\n", "\n", " # Image backbone\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(\n", " nn.Linear(128, d_embed), nn.LayerNorm(d_embed),\n", " )\n", "\n", " # Constellation\n", " self.constellation = Constellation(n_anchors, d_embed)\n", "\n", " # Patchwork\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", "\n", " # Funnel MLP\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 or sep_strength > 0):\n", " emb_corrected = GeometricAutograd.apply(\n", " emb, emb, anchors, tang_only, sep_strength)\n", " tri_g, _ = model.constellation.triangulate(emb_corrected)\n", " pw_feat = model.patchwork(tri_g)\n", " logits = model.mlp(pw_feat)\n", "\n", " if use_autograd and anchor_drift > 0:\n", " _ = AnchorAutograd.apply(anchors, anchor_drift)\n", "\n", " # Task loss\n", " l_cls = F.cross_entropy(logits, labels)\n", "\n", " # Geometric losses (all differentiable, proven micro weights)\n", " l_geo = torch.tensor(0.0, device=DEVICE)\n", " if cv_weight > 0:\n", " l_geo = l_geo + cv_weight * cv_loss(emb, target=0.2, n_samples=16)\n", " if w_spread > 0:\n", " l_geo = l_geo + w_spread * anchor_spread_loss(anchors)\n", " if w_entropy > 0:\n", " l_geo = l_geo + w_entropy * anchor_entropy_loss(emb, anchors)\n", " if w_ortho > 0:\n", " l_geo = l_geo + w_ortho * anchor_ortho_loss(anchors)\n", " if w_cluster > 0:\n", " l_geo = l_geo + w_cluster * cluster_variance_loss(emb, anchors)\n", "\n", " loss = l_cls + l_geo\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach())\n", "\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Val\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " # Anchor health\n", " health = model.constellation.health()\n", "\n", " # Measure equidistance quality\n", " a_n = F.normalize(model.constellation.anchors, dim=-1)\n", " cos_mat = a_n @ a_n.T\n", " mask = ~torch.eye(a_n.shape[0], dtype=bool, device=DEVICE)\n", " equi_std = cos_mat[mask].std().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\n", " \"epoch\": epoch + 1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"equi_std\": equi_std, \"type_accs\": ta,\n", " })\n", "\n", " if verbose and ((epoch + 1) % 10 == 0 or epoch == 0):\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model.constellation.rigidity\n", " cv_delta = v_cv - 0.2\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f}(Δ{cv_delta:+.3f}) equi={equi_std:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " health = model.constellation.health()\n", " return history, health, model\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GATE SWEEP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GATE SWEEP: Varying gate parameters\")\n", "print(f\"{'='*65}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" 30 classes, 15K train, 3K val\")\n", "\n", "configs = [\n", " # (name, tang, cv_w, sep, drift, spread, entropy, ortho, cluster, use_ag)\n", " # Proven base\n", " (\"raw_adam\", 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, False),\n", " (\"proven\", 0.01, 0.001, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, True),\n", " # + one new loss each\n", " (\"+spread\", 0.01, 0.001, 1.0, 0.0, 1e-3, 0.0, 0.0, 0.0, True),\n", " (\"+entropy\", 0.01, 0.001, 1.0, 0.0, 0.0, 1e-4, 0.0, 0.0, True),\n", " (\"+ortho\", 0.01, 0.001, 1.0, 0.0, 0.0, 0.0, 1e-3, 0.0, True),\n", " (\"+cluster\", 0.01, 0.001, 1.0, 0.0, 0.0, 0.0, 0.0, 1e-4, True),\n", " (\"+drift\", 0.01, 0.001, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0, True),\n", " # best combos\n", " (\"+spr+ort\", 0.01, 0.001, 1.0, 0.0, 1e-3, 0.0, 1e-3, 0.0, True),\n", " (\"+all_micro\", 0.01, 0.001, 1.0, 0.5, 1e-3, 1e-4, 1e-3, 1e-4, True),\n", "]\n", "\n", "results = {}\n", "for name, to, cw, sp, dr, ws, we, wo, wc, ua in configs:\n", " print(f\"\\n ── {name} ──\")\n", " hist, health, _ = train_once(\n", " tang_only=to, cv_weight=cw, sep_strength=sp,\n", " anchor_drift=dr, w_spread=ws, w_entropy=we,\n", " w_ortho=wo, w_cluster=wc,\n", " use_autograd=ua, epochs=30, verbose=True)\n", " final = hist[-1]\n", " results[name] = {\n", " \"val_acc\": final[\"val_acc\"],\n", " \"train_acc\": final[\"train_acc\"],\n", " \"gap\": final[\"train_acc\"] - final[\"val_acc\"],\n", " \"val_cv\": final[\"val_cv\"],\n", " \"equi_std\": final[\"equi_std\"],\n", " \"health\": health,\n", " \"type_accs\": final[\"type_accs\"],\n", " \"cv_std\": np.std([h[\"val_cv\"] for h in hist]),\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"SWEEP RESULTS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Config':<15} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'Δcv':>7} {'eq_std':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*90}\")\n", "\n", "for name in [c[0] for c in configs]:\n", " r = results[name]\n", " ta = r[\"type_accs\"]\n", " cv_delta = r[\"val_cv\"] - 0.2\n", " print(f\" {name:<15} {r['val_acc']:>6.3f} {r['train_acc']:>6.3f} {r['gap']:>+6.3f} \"\n", " f\"{r['val_cv']:>7.4f} {cv_delta:>+7.4f} {r['equi_std']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Find best overall\n", "best = max(results.items(), key=lambda x: x[1][\"val_acc\"])\n", "print(f\"\\n Best accuracy: {best[0]} (val_acc={best[1]['val_acc']:.3f})\")\n", "\n", "# Find best structure accuracy (hardest category)\n", "best_struct = max(results.items(), key=lambda x: x[1][\"type_accs\"].get(\"structure\", 0))\n", "print(f\" Best structure: {best_struct[0]} (struct={best_struct[1]['type_accs'].get('structure',0):.3f})\")\n", "\n", "# Find closest to CV target 0.2\n", "closest_cv = min(results.items(), key=lambda x: abs(x[1][\"val_cv\"] - 0.2))\n", "print(f\" Closest to CV=0.2: {closest_cv[0]} (cv={closest_cv[1]['val_cv']:.4f}, Δ={closest_cv[1]['val_cv']-0.2:+.4f})\")\n", "\n", "# Find most equidistant constellation\n", "best_equi = min(results.items(), key=lambda x: x[1][\"equi_std\"])\n", "print(f\" Most equidistant: {best_equi[0]} (equi_std={best_equi[1]['equi_std']:.4f})\")\n", "\n", "# Find most stable CV trajectory\n", "best_cv = min(results.items(), key=lambda x: x[1][\"cv_std\"])\n", "print(f\" Most stable CV: {best_cv[0]} (cv_std={best_cv[1]['cv_std']:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jQ1qlgMJKs6u", "outputId": "94b9b1e0-dadf-45a7-ce58-e8583c78c4cf" }, "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "GATE SWEEP: Varying gate parameters\n", "=================================================================\n", " Device: cuda\n", " 30 classes, 15K train, 3K val\n", "\n", " ── raw_adam ──\n", " E 1: t=0.059 v=0.115 cv=1.5252(Δ+1.325) equi=0.2479 rig=1.9/20.4 [polygon=0.02 curve=0.00 star=0.17 structure=0.22]\n", " E10: t=0.621 v=0.576 cv=1.2911(Δ+1.091) equi=0.3654 rig=8.3/47.1 [polygon=0.32 curve=0.54 star=0.79 structure=0.70]\n", " E20: t=0.676 v=0.644 cv=1.5926(Δ+1.393) equi=0.3922 rig=10.3/47.1 [polygon=0.39 curve=1.00 star=0.68 structure=0.68]\n", " E30: t=0.681 v=0.617 cv=1.3917(Δ+1.192) equi=0.4075 rig=12.9/51.8 [polygon=0.39 curve=0.75 star=0.86 structure=0.61]\n", "\n", " ── proven ──\n", " E 1: t=0.081 v=0.163 cv=0.8799(Δ+0.680) equi=0.2809 rig=2.3/49.7 [polygon=0.06 curve=0.00 star=0.19 structure=0.33]\n", " E10: t=0.557 v=0.515 cv=1.9705(Δ+1.770) equi=0.4242 rig=3.8/55.2 [polygon=0.20 curve=0.58 star=0.69 structure=0.66]\n", " E20: t=0.652 v=0.612 cv=1.5741(Δ+1.374) equi=0.4145 rig=4.4/55.2 [polygon=0.43 curve=0.97 star=0.54 structure=0.64]\n", " E30: t=0.706 v=0.722 cv=1.3629(Δ+1.163) equi=0.4157 rig=4.5/55.2 [polygon=0.45 curve=0.99 star=0.93 structure=0.71]\n", "\n", " ── +spread ──\n", " E 1: t=0.076 v=0.077 cv=0.8828(Δ+0.683) equi=0.2790 rig=2.4/50.0 [polygon=0.02 curve=0.00 star=0.00 structure=0.22]\n", " E10: t=0.575 v=0.574 cv=2.0663(Δ+1.866) equi=0.4199 rig=5.1/74.1 [polygon=0.28 curve=0.83 star=0.78 structure=0.59]\n", " E20: t=0.670 v=0.662 cv=1.7225(Δ+1.523) equi=0.4080 rig=5.4/74.2 [polygon=0.35 curve=0.97 star=0.81 structure=0.70]\n", " E30: t=0.686 v=0.669 cv=1.4491(Δ+1.249) equi=0.4212 rig=6.1/74.2 [polygon=0.41 curve=0.98 star=0.71 structure=0.72]\n", "\n", " ── +entropy ──\n", " E 1: t=0.065 v=0.175 cv=1.0849(Δ+0.885) equi=0.2723 rig=2.2/49.3 [polygon=0.09 curve=0.00 star=0.23 structure=0.31]\n", " E10: t=0.594 v=0.570 cv=1.7920(Δ+1.592) equi=0.3969 rig=4.6/76.2 [polygon=0.30 curve=0.76 star=0.78 structure=0.60]\n", " E20: t=0.670 v=0.647 cv=1.5763(Δ+1.376) equi=0.4043 rig=5.6/76.2 [polygon=0.48 curve=0.89 star=0.70 structure=0.64]\n", " E30: t=0.711 v=0.674 cv=1.4945(Δ+1.294) equi=0.4237 rig=5.5/76.2 [polygon=0.42 curve=0.97 star=0.70 structure=0.74]\n", "\n", " ── +ortho ──\n", " E 1: t=0.081 v=0.160 cv=0.8828(Δ+0.683) equi=0.2801 rig=2.3/48.9 [polygon=0.05 curve=0.00 star=0.18 structure=0.32]\n", " E10: t=0.544 v=0.598 cv=1.7545(Δ+1.555) equi=0.4085 rig=5.0/67.7 [polygon=0.36 curve=0.94 star=0.68 structure=0.59]\n", " E20: t=0.652 v=0.694 cv=1.4813(Δ+1.281) equi=0.4107 rig=5.3/67.7 [polygon=0.39 curve=0.98 star=0.88 structure=0.72]\n", " E30: t=0.690 v=0.695 cv=1.3454(Δ+1.145) equi=0.4171 rig=7.5/86.6 [polygon=0.40 curve=0.99 star=0.85 structure=0.72]\n", "\n", " ── +cluster ──\n", " E 1: t=0.080 v=0.166 cv=0.8774(Δ+0.677) equi=0.2821 rig=2.3/48.3 [polygon=0.07 curve=0.00 star=0.19 structure=0.32]\n", " E10: t=0.532 v=0.531 cv=1.8229(Δ+1.623) equi=0.4151 rig=3.9/61.7 [polygon=0.34 curve=0.55 star=0.76 structure=0.56]\n", " E20: t=0.677 v=0.677 cv=1.4423(Δ+1.242) equi=0.4043 rig=4.4/61.7 [polygon=0.37 curve=0.93 star=0.91 structure=0.69]\n", " E30: t=0.717 v=0.701 cv=1.3034(Δ+1.103) equi=0.4131 rig=4.6/61.7 [polygon=0.44 curve=0.93 star=0.91 structure=0.70]\n", "\n", " ── +drift ──\n", " E 1: t=0.080 v=0.171 cv=0.8755(Δ+0.676) equi=0.2823 rig=2.3/48.5 [polygon=0.08 curve=0.00 star=0.20 structure=0.32]\n", " E10: t=0.552 v=0.567 cv=1.8316(Δ+1.632) equi=0.4106 rig=3.5/50.8 [polygon=0.30 curve=0.81 star=0.77 structure=0.57]\n", " E20: t=0.645 v=0.637 cv=1.4283(Δ+1.228) equi=0.4062 rig=4.3/50.8 [polygon=0.40 curve=0.99 star=0.58 structure=0.71]\n", " E30: t=0.698 v=0.709 cv=1.3480(Δ+1.148) equi=0.4134 rig=4.7/50.8 [polygon=0.41 curve=1.00 star=0.91 structure=0.72]\n", "\n", " ── +spr+ort ──\n", " E 1: t=0.081 v=0.165 cv=0.8708(Δ+0.671) equi=0.2823 rig=2.3/50.7 [polygon=0.02 curve=0.00 star=0.23 structure=0.34]\n", " E10: t=0.553 v=0.542 cv=1.7529(Δ+1.553) equi=0.4089 rig=3.9/50.7 [polygon=0.38 curve=0.59 star=0.75 structure=0.54]\n", " E20: t=0.674 v=0.671 cv=1.3366(Δ+1.137) equi=0.4105 rig=5.5/67.1 [polygon=0.39 curve=0.96 star=0.86 structure=0.66]\n", " E30: t=0.698 v=0.723 cv=1.3881(Δ+1.188) equi=0.4224 rig=7.6/84.3 [polygon=0.46 curve=0.97 star=0.94 structure=0.71]\n", "\n", " ── +all_micro ──\n", " E 1: t=0.077 v=0.186 cv=0.9858(Δ+0.786) equi=0.2783 rig=2.2/47.2 [polygon=0.06 curve=0.00 star=0.27 structure=0.34]\n", " E10: t=0.567 v=0.555 cv=1.9116(Δ+1.712) equi=0.4007 rig=3.3/48.2 [polygon=0.38 curve=0.54 star=0.80 structure=0.57]\n", " E20: t=0.666 v=0.645 cv=1.4778(Δ+1.278) equi=0.3976 rig=4.6/48.2 [polygon=0.28 curve=0.98 star=0.83 structure=0.69]\n", " E30: t=0.700 v=0.694 cv=1.5181(Δ+1.318) equi=0.4077 rig=4.6/48.2 [polygon=0.40 curve=0.97 star=0.85 structure=0.72]\n", "\n", "\n", "=================================================================\n", "SWEEP RESULTS\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv Δcv eq_std poly curve star struct\n", " ------------------------------------------------------------------------------------------\n", " raw_adam 0.617 0.681 +0.064 1.3917 +1.1917 0.4075 0.39 0.75 0.86 0.61\n", " proven 0.722 0.706 -0.016 1.3629 +1.1629 0.4157 0.45 0.99 0.93 0.71\n", " +spread 0.669 0.686 +0.017 1.4491 +1.2491 0.4212 0.41 0.98 0.71 0.72\n", " +entropy 0.674 0.711 +0.037 1.4945 +1.2945 0.4237 0.42 0.97 0.70 0.74\n", " +ortho 0.695 0.690 -0.005 1.3454 +1.1454 0.4171 0.40 0.99 0.85 0.72\n", " +cluster 0.701 0.717 +0.016 1.3034 +1.1034 0.4131 0.44 0.93 0.91 0.70\n", " +drift 0.709 0.698 -0.012 1.3480 +1.1480 0.4134 0.41 1.00 0.91 0.72\n", " +spr+ort 0.723 0.698 -0.025 1.3881 +1.1881 0.4224 0.46 0.97 0.94 0.71\n", " +all_micro 0.694 0.700 +0.007 1.5181 +1.3181 0.4077 0.40 0.97 0.85 0.72\n", "\n", " Best accuracy: +spr+ort (val_acc=0.723)\n", " Best structure: +entropy (struct=0.737)\n", " Closest to CV=0.2: +cluster (cv=1.3034, Δ=+1.1034)\n", " Most equidistant: raw_adam (equi_std=0.4075)\n", " Most stable CV: raw_adam (cv_std=0.1734)\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 2 experiment 2" ], "metadata": { "id": "lkxcvlxpO6Uh" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# TWO-STAGE GEOMETRIC BOOTSTRAP\n", "#\n", "# Stage 1: TEACHER — +spr+ort config (best accuracy 0.723)\n", "# tang=0.01, cv=0.001, sep=1.0, spread=1e-3, ortho=1e-3\n", "# Trains to ceiling. Anchors crystallize. Manifold forms.\n", "#\n", "# Stage 2: STUDENT — +entropy config (best structure 0.737)\n", "# tang=0.01, cv=0.001, sep=1.0, entropy=1e-4\n", "# Anchors initialized FROM teacher's learned geometry.\n", "# Coordinate system pre-solved. Student focuses on encoding.\n", "#\n", "# Stage 3: COMPARISON — raw Adam, proven, teacher, student side by side\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"TWO-STAGE GEOMETRIC BOOTSTRAP\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def cluster_variance_loss(emb, anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " per_anchor_mean = (emb @ a_n.T).mean(dim=0)\n", " return -per_anchor_mean.var()\n", "\n", "\n", "# ── Backward filtering ──\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONSTELLATION + PATCHWORK + CLASSIFIER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T\n", " tri_dist = 1.0 - cos_sim\n", " nearest = cos_sim.argmax(dim=-1)\n", " return tri_dist, nearest\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist):\n", " nearest = tri_dist.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " mask = nearest == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " spread = tri_dist[mask].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1 - alpha) * self.rigidity[i] + alpha * (1.0 / (spread + 0.01))\n", "\n", " @torch.no_grad()\n", " def geometry_snapshot(self):\n", " \"\"\"Extract learned geometry for student initialization.\"\"\"\n", " a_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a_n @ a_n.T\n", " N = self.n_anchors\n", " mask = ~torch.eye(N, dtype=bool, device=a_n.device)\n", " return {\n", " \"anchors\": a_n.clone(),\n", " \"rigidity\": self.rigidity.clone(),\n", " \"visit_count\": self.visit_count.clone(),\n", " \"pairwise_cos\": cos.clone(),\n", " \"mean_cos\": cos[mask].mean().item(),\n", " \"std_cos\": cos[mask].std().item(),\n", " \"cv\": cv_metric(a_n, n_samples=200),\n", " }\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_compartments = n_compartments\n", " self.d_comp = d_comp\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp),\n", " ))\n", "\n", " def forward(self, tri_dist):\n", " parts = []\n", " for k in range(self.n_compartments):\n", " mask = self.assignments == k\n", " parts.append(self.compartments[k](tri_dist[:, mask]))\n", " return torch.cat(parts, dim=-1)\n", "\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256, init_anchors=None):\n", " super().__init__()\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1),\n", " )\n", " self.embed_proj = nn.Sequential(nn.Linear(128, d_embed), nn.LayerNorm(d_embed))\n", " self.constellation = Constellation(n_anchors, d_embed, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes),\n", " )\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri_dist, nearest = self.constellation.triangulate(emb)\n", " pw_feat = self.patchwork(tri_dist)\n", " logits = self.mlp(pw_feat)\n", " return logits, emb, tri_dist, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img, x0, y0, x1, y1, t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1); sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 or sep > 0):\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, tang, sep)\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " pw = model.patchwork(tri_g)\n", " logits = model.mlp(pw)\n", "\n", " l_cls = F.cross_entropy(logits, labels)\n", " l_geo = torch.tensor(0.0, device=DEVICE)\n", " if cv_w > 0: l_geo = l_geo + cv_w * cv_loss(emb, target=0.2)\n", " if w_spread > 0: l_geo = l_geo + w_spread * anchor_spread_loss(anchors)\n", " if w_entropy > 0: l_geo = l_geo + w_entropy * anchor_entropy_loss(emb, anchors)\n", " if w_ortho > 0: l_geo = l_geo + w_ortho * anchor_ortho_loss(anchors)\n", " if w_cluster > 0: l_geo = l_geo + w_cluster * cluster_variance_loss(emb, anchors)\n", "\n", " loss = l_cls + l_geo\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach())\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, vt, vn = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve, n_samples=100)\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " history.append({\"epoch\": epoch+1, \"train_acc\": train_acc, \"val_acc\": v_acc,\n", " \"val_cv\": v_cv, \"type_accs\": ta})\n", "\n", " if (epoch+1) % 5 == 0 or epoch == 0:\n", " rig = model.constellation.rigidity\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " print(f\" {tag}E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cv={v_cv:.4f} rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", " return history\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATE DATA (shared across all stages)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Generating shared data...\")\n", "torch.manual_seed(42); np.random.seed(42)\n", "train_imgs, train_labels = gen_data(n_per=500)\n", "val_imgs, val_labels = gen_data(n_per=100)\n", "train_imgs, train_labels = train_imgs.to(DEVICE), train_labels.to(DEVICE)\n", "val_imgs, val_labels = val_imgs.to(DEVICE), val_labels.to(DEVICE)\n", "print(f\" Train: {len(train_labels):,} Val: {len(val_labels):,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 0: RAW ADAM BASELINE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 0: RAW ADAM (no geometric autograd)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "model_raw = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "h_raw = train(model_raw, train_imgs, train_labels, val_imgs, val_labels,\n", " use_autograd=False, tag=\"[RAW] \")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 1: TEACHER (+spr+ort, best accuracy config)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 1: TEACHER (+spr+ort)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "model_teacher = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "h_teacher = train(model_teacher, train_imgs, train_labels, val_imgs, val_labels,\n", " tang=0.01, sep=1.0, cv_w=0.001,\n", " w_spread=1e-3, w_ortho=1e-3,\n", " tag=\"[TEACH] \")\n", "\n", "# Extract teacher geometry\n", "teacher_geo = model_teacher.constellation.geometry_snapshot()\n", "print(f\"\\n Teacher geometry:\")\n", "print(f\" Anchor mean_cos: {teacher_geo['mean_cos']:.4f}\")\n", "print(f\" Anchor std_cos: {teacher_geo['std_cos']:.4f}\")\n", "print(f\" Anchor CV: {teacher_geo['cv']:.4f}\")\n", "print(f\" Rigidity: mean={teacher_geo['rigidity'].mean():.1f} \"\n", " f\"max={teacher_geo['rigidity'].max():.1f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 2: STUDENT (initialized from teacher anchors, +entropy config)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 2: STUDENT (teacher anchors + entropy config)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "model_student = PatchworkClassifier(\n", " n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=teacher_geo[\"anchors\"],\n", ").to(DEVICE)\n", "\n", "# Verify student starts with teacher geometry\n", "student_init_geo = model_student.constellation.geometry_snapshot()\n", "print(f\" Student init anchors match teacher: \"\n", " f\"{torch.allclose(teacher_geo['anchors'], student_init_geo['anchors'])}\")\n", "\n", "h_student = train(model_student, train_imgs, train_labels, val_imgs, val_labels,\n", " tang=0.01, sep=1.0, cv_w=0.001,\n", " w_entropy=1e-4,\n", " tag=\"[STUD] \")\n", "\n", "student_geo = model_student.constellation.geometry_snapshot()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 2B: STUDENT FROM TEACHER, SAME CONFIG AS TEACHER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 2B: STUDENT (teacher anchors + SAME teacher config)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "model_student2 = PatchworkClassifier(\n", " n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=teacher_geo[\"anchors\"],\n", ").to(DEVICE)\n", "\n", "h_student2 = train(model_student2, train_imgs, train_labels, val_imgs, val_labels,\n", " tang=0.01, sep=1.0, cv_w=0.001,\n", " w_spread=1e-3, w_ortho=1e-3,\n", " tag=\"[STU2] \")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "runs = [\n", " (\"Raw Adam\", h_raw),\n", " (\"Teacher\", h_teacher),\n", " (\"Student+entropy\", h_student),\n", " (\"Student+same\", h_student2),\n", "]\n", "\n", "print(f\"\\n {'Config':<20} {'v_acc':>6} {'t_acc':>6} {'gap':>6} \"\n", " f\"{'cv':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*75}\")\n", "\n", "for name, hist in runs:\n", " f = hist[-1]\n", " ta = f[\"type_accs\"]\n", " gap = f[\"train_acc\"] - f[\"val_acc\"]\n", " print(f\" {name:<20} {f['val_acc']:>6.3f} {f['train_acc']:>6.3f} {gap:>+6.3f} \"\n", " f\"{f['val_cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Trajectory comparison\n", "print(f\"\\n Val accuracy trajectory:\")\n", "print(f\" {'Epoch':<8}\", end=\"\")\n", "for name, _ in runs:\n", " print(f\" {name:<15}\", end=\"\")\n", "print()\n", "for e in [0, 4, 9, 14, 19, 24, 29]:\n", " print(f\" E{e+1:<6}\", end=\"\")\n", " for _, hist in runs:\n", " if e < len(hist):\n", " print(f\" {hist[e]['val_acc']:<15.3f}\", end=\"\")\n", " print()\n", "\n", "print(f\"\\n Teacher→Student anchor drift:\")\n", "t_a = teacher_geo[\"anchors\"]\n", "s_a = F.normalize(model_student.constellation.anchors.detach(), dim=-1)\n", "drift = 1.0 - F.cosine_similarity(t_a, s_a, dim=-1)\n", "print(f\" Mean drift: {drift.mean():.4f}\")\n", "print(f\" Max drift: {drift.max():.4f}\")\n", "print(f\" Min drift: {drift.min():.4f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ShIlHKtGO8Il", "outputId": "3121f3cd-4b0c-435a-87a3-284174bce124" }, "execution_count": 29, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "TWO-STAGE GEOMETRIC BOOTSTRAP\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating shared data...\n", " Train: 15,000 Val: 3,000\n", "\n", "=================================================================\n", "STAGE 0: RAW ADAM (no geometric autograd)\n", "=================================================================\n", " [RAW] E 1: t=0.073 v=0.198 cv=1.1719 rig=3.7/26.1 [polygon=0.18 curve=0.00 star=0.14 structure=0.34]\n", " [RAW] E 5: t=0.536 v=0.592 cv=1.5359 rig=5.8/31.6 [polygon=0.22 curve=0.97 star=0.70 structure=0.67]\n", " [RAW] E10: t=0.618 v=0.623 cv=1.5509 rig=10.0/95.7 [polygon=0.32 curve=0.99 star=0.66 structure=0.69]\n", " [RAW] E15: t=0.622 v=0.633 cv=1.3648 rig=9.1/53.5 [polygon=0.32 curve=0.99 star=0.73 structure=0.67]\n", " [RAW] E20: t=0.640 v=0.658 cv=1.3636 rig=9.7/35.7 [polygon=0.42 curve=0.99 star=0.79 structure=0.62]\n", " [RAW] E25: t=0.657 v=0.685 cv=1.3346 rig=10.3/29.6 [polygon=0.45 curve=1.00 star=0.81 structure=0.67]\n", " [RAW] E30: t=0.679 v=0.626 cv=1.3669 rig=10.9/32.5 [polygon=0.30 curve=1.00 star=0.77 structure=0.65]\n", "\n", "=================================================================\n", "STAGE 1: TEACHER (+spr+ort)\n", "=================================================================\n", " [TEACH] E 1: t=0.072 v=0.167 cv=1.3780 rig=1.8/23.1 [polygon=0.12 curve=0.00 star=0.16 structure=0.30]\n", " [TEACH] E 5: t=0.435 v=0.426 cv=1.5857 rig=2.6/20.5 [polygon=0.22 curve=0.77 star=0.25 structure=0.54]\n", " [TEACH] E10: t=0.576 v=0.576 cv=1.5161 rig=4.3/30.6 [polygon=0.22 curve=0.99 star=0.66 structure=0.64]\n", " [TEACH] E15: t=0.622 v=0.647 cv=1.2345 rig=5.7/30.8 [polygon=0.35 curve=1.00 star=0.72 structure=0.70]\n", " [TEACH] E20: t=0.637 v=0.638 cv=1.3775 rig=9.5/85.6 [polygon=0.35 curve=0.97 star=0.75 structure=0.67]\n", " [TEACH] E25: t=0.628 v=0.662 cv=1.4470 rig=9.6/97.5 [polygon=0.34 curve=0.99 star=0.79 structure=0.71]\n", " [TEACH] E30: t=0.629 v=0.645 cv=1.5645 rig=9.4/98.5 [polygon=0.38 curve=0.91 star=0.72 structure=0.71]\n", "\n", " Teacher geometry:\n", " Anchor mean_cos: -0.0156\n", " Anchor std_cos: 0.4305\n", " Anchor CV: 0.2886\n", " Rigidity: mean=9.4 max=98.5\n", "\n", "=================================================================\n", "STAGE 2: STUDENT (teacher anchors + entropy config)\n", "=================================================================\n", " Student init anchors match teacher: True\n", " [STUD] E 1: t=0.061 v=0.164 cv=1.6991 rig=2.9/20.8 [polygon=0.02 curve=0.20 star=0.17 structure=0.27]\n", " [STUD] E 5: t=0.455 v=0.509 cv=2.1090 rig=4.1/25.4 [polygon=0.23 curve=0.76 star=0.66 structure=0.54]\n", " [STUD] E10: t=0.581 v=0.614 cv=1.4839 rig=4.1/22.5 [polygon=0.34 curve=0.93 star=0.78 structure=0.60]\n", " [STUD] E15: t=0.598 v=0.670 cv=2.2675 rig=4.7/18.7 [polygon=0.32 curve=0.99 star=0.83 structure=0.73]\n", " [STUD] E20: t=0.650 v=0.630 cv=2.0921 rig=5.4/20.0 [polygon=0.29 curve=0.98 star=0.75 structure=0.69]\n", " [STUD] E25: t=0.643 v=0.665 cv=1.9514 rig=5.3/18.7 [polygon=0.38 curve=0.98 star=0.76 structure=0.71]\n", " [STUD] E30: t=0.677 v=0.698 cv=2.1624 rig=7.6/68.6 [polygon=0.39 curve=0.98 star=0.88 structure=0.73]\n", "\n", "=================================================================\n", "STAGE 2B: STUDENT (teacher anchors + SAME teacher config)\n", "=================================================================\n", " [STU2] E 1: t=0.075 v=0.189 cv=1.3650 rig=6.4/40.1 [polygon=0.00 curve=0.20 star=0.25 structure=0.32]\n", " [STU2] E 5: t=0.536 v=0.526 cv=1.7598 rig=11.4/57.5 [polygon=0.33 curve=0.77 star=0.57 structure=0.55]\n", " [STU2] E10: t=0.604 v=0.652 cv=1.7082 rig=11.5/57.2 [polygon=0.40 curve=1.00 star=0.82 structure=0.60]\n", " [STU2] E15: t=0.647 v=0.618 cv=1.3833 rig=11.9/57.2 [polygon=0.26 curve=1.00 star=0.77 structure=0.65]\n", " [STU2] E20: t=0.655 v=0.603 cv=1.6166 rig=15.2/85.7 [polygon=0.37 curve=0.94 star=0.46 structure=0.73]\n", " [STU2] E25: t=0.690 v=0.700 cv=1.3475 rig=15.6/99.1 [polygon=0.39 curve=0.99 star=0.86 structure=0.75]\n", " [STU2] E30: t=0.681 v=0.672 cv=1.5182 rig=15.3/97.8 [polygon=0.39 curve=1.00 star=0.75 structure=0.72]\n", "\n", "=================================================================\n", "COMPARISON\n", "=================================================================\n", "\n", " Config v_acc t_acc gap cv poly curve star struct\n", " ---------------------------------------------------------------------------\n", " Raw Adam 0.626 0.679 +0.053 1.3669 0.30 1.00 0.77 0.65\n", " Teacher 0.645 0.629 -0.017 1.5645 0.38 0.91 0.72 0.71\n", " Student+entropy 0.698 0.677 -0.021 2.1624 0.39 0.98 0.88 0.73\n", " Student+same 0.672 0.681 +0.010 1.5182 0.39 1.00 0.75 0.72\n", "\n", " Val accuracy trajectory:\n", " Epoch Raw Adam Teacher Student+entropy Student+same \n", " E1 0.198 0.167 0.164 0.189 \n", " E5 0.592 0.426 0.509 0.526 \n", " E10 0.623 0.576 0.614 0.652 \n", " E15 0.633 0.647 0.670 0.618 \n", " E20 0.658 0.638 0.630 0.603 \n", " E25 0.685 0.662 0.665 0.700 \n", " E30 0.626 0.645 0.698 0.672 \n", "\n", " Teacher→Student anchor drift:\n", " Mean drift: 0.3036\n", " Max drift: 0.4506\n", " Min drift: 0.1311\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 2 dual teacher" ], "metadata": { "id": "dF5q0uoqRBqG" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\n", "#\n", "# Teacher A: Raw Adam (0.626) — learned without geometric guidance\n", "# Teacher B: Geometric (+spr+ort, 0.645) — learned with manifold control\n", "#\n", "# Both teachers encode the full training set.\n", "# Procrustes alignment finds the shared geometric center.\n", "# Student distills the consensus + trains with entropy config.\n", "#\n", "# Same pipeline as CaptionBERT 5-expert consensus, simplified to 2.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ── Backward filtering ──\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES ALIGNMENT (production, from cotrain_bank.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; Ns = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(Ns - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(Ns - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten, \"cos_after\": cos_after}\n", "\n", "def apply_align(emb, info):\n", " x = emb.float() - info[\"source_mean\"]\n", " x = x @ info[\"source_whitener\"]\n", " x = x @ info[\"rotation\"].T\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T\n", " return 1.0 - cos_sim, cos_sim.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist):\n", " nearest = tri_dist.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " mask = nearest == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " spread = tri_dist[mask].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1 - alpha) * self.rigidity[i] + alpha / (spread + 0.01)\n", "\n", " @torch.no_grad()\n", " def geometry_snapshot(self):\n", " a_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a_n @ a_n.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a_n.device)\n", " return {\"anchors\": a_n.clone(), \"rigidity\": self.rigidity.clone(),\n", " \"mean_cos\": cos[mask].mean().item(), \"std_cos\": cos[mask].std().item()}\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_compartments = n_compartments\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp)))\n", "\n", " def forward(self, tri_dist):\n", " parts = []\n", " for k in range(self.n_compartments):\n", " parts.append(self.compartments[k](tri_dist[:, self.assignments == k]))\n", " return torch.cat(parts, dim=-1)\n", "\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256, init_anchors=None):\n", " super().__init__()\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.embed_proj = nn.Sequential(nn.Linear(128, d_embed), nn.LayerNorm(d_embed))\n", " self.constellation = Constellation(n_anchors, d_embed, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes))\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri, nearest = self.constellation.triangulate(emb)\n", " return self.mlp(self.patchwork(tri)), emb, tri, nearest\n", "\n", " def encode(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " return F.normalize(self.embed_proj(feat), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 or sep > 0):\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, tang, sep)\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " logits = model.mlp(model.patchwork(tri_g))\n", " l_cls = F.cross_entropy(logits, labels)\n", " l_geo = torch.tensor(0.0, device=DEVICE)\n", " if cv_w > 0: l_geo = l_geo + cv_w * cv_loss(emb)\n", " if w_spread > 0: l_geo = l_geo + w_spread * anchor_spread_loss(anchors)\n", " if w_ortho > 0: l_geo = l_geo + w_ortho * anchor_ortho_loss(anchors)\n", " if w_entropy > 0: l_geo = l_geo + w_entropy * anchor_entropy_loss(emb, anchors)\n", " (l_cls + l_geo).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " n += 1\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve)\n", " if (epoch+1) % 10 == 0 or epoch == 0:\n", " print(f\" {tag} E{epoch+1:2d}: t={total_correct/n_train:.3f} \"\n", " f\"v={v_acc:.3f} cv={v_cv:.4f}\")\n", " return v_acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 1: TRAIN BOTH TEACHERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 1A: TEACHER A — Raw Adam\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "teacher_a = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "va_a = train_teacher(teacher_a, \"[A]\", use_autograd=False, epochs=30)\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 1B: TEACHER B — Geometric (+spr+ort)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "teacher_b = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "va_b = train_teacher(teacher_b, \"[B]\", use_autograd=True,\n", " tang=0.01, sep=1.0, cv_w=0.001,\n", " w_spread=1e-3, w_ortho=1e-3, epochs=30)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 2: EXTRACT EMBEDDINGS + GPA ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 2: EXTRACT + PROCRUSTES ALIGN\")\n", "print(f\"{'='*65}\")\n", "\n", "teacher_a.eval(); teacher_b.eval()\n", "with torch.no_grad():\n", " emb_a = teacher_a.encode(train_imgs) # (N, 768)\n", " emb_b = teacher_b.encode(train_imgs) # (N, 768)\n", " val_emb_a = teacher_a.encode(val_imgs)\n", " val_emb_b = teacher_b.encode(val_imgs)\n", "\n", "print(f\" Teacher A embeddings: {emb_a.shape}\")\n", "print(f\" Teacher B embeddings: {emb_b.shape}\")\n", "print(f\" Raw cos(A, B): {F.cosine_similarity(emb_a[:1000], emb_b[:1000], dim=-1).mean():.4f}\")\n", "\n", "# GPA: iterative Procrustes to find geometric center\n", "current = {\"a\": emb_a.float(), \"b\": emb_b.float()}\n", "for gpa_iter in range(10):\n", " mean_shape = (current[\"a\"] + current[\"b\"]) / 2\n", " total_delta = 0.0\n", " new_current = {}\n", " for name in [\"a\", \"b\"]:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " total_delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if (gpa_iter + 1) % 5 == 0 or gpa_iter == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", "# Build consensus\n", "mean_shape = (current[\"a\"] + current[\"b\"]) / 2\n", "consensus = F.normalize(mean_shape, dim=-1)\n", "\n", "# Align val embeddings too\n", "val_aligned = {}\n", "for name, emb_full in [(\"a\", emb_a), (\"b\", emb_b)]:\n", " info = procrustes_align(emb_full, mean_shape)\n", " val_aligned[name] = apply_align(\n", " val_emb_a if name == \"a\" else val_emb_b, info)\n", "\n", "val_consensus = F.normalize((val_aligned[\"a\"] + val_aligned[\"b\"]) / 2, dim=-1)\n", "\n", "# Check alignment quality\n", "for name in [\"a\", \"b\"]:\n", " aligned = current[name] if name == \"a\" else current[\"b\"]\n", " cos = F.cosine_similarity(consensus[:1000], F.normalize(aligned[:1000], dim=-1), dim=-1).mean()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", "consensus_cv = cv_metric(consensus[:2000])\n", "print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", "# Extract consensus anchors: cluster consensus embeddings\n", "# Use the per-class centroids as initial anchor positions\n", "anchor_centroids = []\n", "for c in range(30):\n", " mask = train_labels == c\n", " if mask.sum() > 0:\n", " anchor_centroids.append(consensus[mask].mean(dim=0))\n", " else:\n", " anchor_centroids.append(torch.randn(768, device=DEVICE))\n", "consensus_anchors = F.normalize(torch.stack(anchor_centroids), dim=-1)\n", "print(f\" Consensus anchors: {consensus_anchors.shape}\")\n", "\n", "# Also keep the pure geometric center from teacher anchors\n", "geo_a = teacher_a.constellation.geometry_snapshot()\n", "geo_b = teacher_b.constellation.geometry_snapshot()\n", "print(f\" Teacher A anchors cos: {geo_a['mean_cos']:.4f}\")\n", "print(f\" Teacher B anchors cos: {geo_b['mean_cos']:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 3: STUDENT DISTILLATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 3: STUDENT — Consensus distillation + classification\")\n", "print(f\"{'='*65}\")\n", "\n", "torch.manual_seed(42)\n", "model_student = PatchworkClassifier(\n", " n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=consensus_anchors,\n", ").to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model_student.parameters(), lr=1e-3)\n", "BATCH = 256\n", "EPOCHS = 30\n", "\n", "# Move consensus targets to device\n", "train_targets = consensus.to(DEVICE) # (N, 768) L2-normalized\n", "val_targets = val_consensus.to(DEVICE)\n", "\n", "for epoch in range(EPOCHS):\n", " model_student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " logits, emb, tri, nearest = model_student(train_imgs[idx])\n", " labels = train_labels[idx]\n", " tgt = train_targets[idx]\n", " anchors = model_student.constellation.anchors\n", "\n", " # Apply geometric autograd\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, 0.01, 1.0)\n", " tri_g, _ = model_student.constellation.triangulate(emb_g)\n", " logits = model_student.mlp(model_student.patchwork(tri_g))\n", "\n", " # Three losses:\n", " # 1. Classification (task)\n", " l_cls = F.cross_entropy(logits, labels)\n", "\n", " # 2. Consensus distillation (InfoNCE + MSE)\n", " l_nce, nce_acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", "\n", " # 3. Geometric (micro CV + entropy)\n", " l_cv = cv_loss(emb, target=0.2)\n", " l_ent = anchor_entropy_loss(emb, anchors)\n", "\n", " loss = l_cls + 0.5 * l_nce + 0.5 * l_mse + 0.001 * l_cv + 1e-4 * l_ent\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model_student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model_student.constellation.update_rigidity(tri.detach())\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model_student.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model_student(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve)\n", " v_cos = F.cosine_similarity(ve, val_targets, dim=-1).mean().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if (epoch+1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model_student.constellation.rigidity\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cos={v_cos:.3f} cv={v_cv:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "# Get final metrics for all models\n", "model_student.eval(); teacher_a.eval(); teacher_b.eval()\n", "with torch.no_grad():\n", " results = {}\n", " for name, mdl in [(\"Teacher_A\", teacher_a), (\"Teacher_B\", teacher_b),\n", " (\"Student\", model_student)]:\n", " vl, ve, _, _ = mdl(val_imgs)\n", " acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", " results[name] = {\"acc\": acc, \"cv\": cv_metric(ve), \"types\": ta}\n", "\n", "print(f\"\\n {'Model':<15} {'v_acc':>6} {'cv':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*55}\")\n", "for name, r in results.items():\n", " ta = r[\"types\"]\n", " print(f\" {name:<15} {r['acc']:>6.3f} {r['cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Anchor drift from consensus\n", "s_anchors = F.normalize(model_student.constellation.anchors.detach(), dim=-1)\n", "drift = 1.0 - F.cosine_similarity(consensus_anchors, s_anchors, dim=-1)\n", "print(f\"\\n Student anchor drift from consensus: mean={drift.mean():.4f} max={drift.max():.4f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7H9kBdSiRDlb", "outputId": "967d2137-ed4d-44f3-c49a-dc382a9cfe95" }, "execution_count": 30, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000\n", "\n", "=================================================================\n", "STAGE 1A: TEACHER A — Raw Adam\n", "=================================================================\n", " [A] E 1: t=0.073 v=0.200 cv=1.3069\n", " [A] E10: t=0.612 v=0.613 cv=1.4364\n", " [A] E20: t=0.655 v=0.590 cv=1.4770\n", " [A] E30: t=0.690 v=0.699 cv=1.3797\n", "\n", "=================================================================\n", "STAGE 1B: TEACHER B — Geometric (+spr+ort)\n", "=================================================================\n", " [B] E 1: t=0.072 v=0.184 cv=1.4589\n", " [B] E10: t=0.578 v=0.606 cv=1.5603\n", " [B] E20: t=0.614 v=0.667 cv=1.5950\n", " [B] E30: t=0.658 v=0.649 cv=1.8004\n", "\n", "=================================================================\n", "STAGE 2: EXTRACT + PROCRUSTES ALIGN\n", "=================================================================\n", " Teacher A embeddings: torch.Size([15000, 768])\n", " Teacher B embeddings: torch.Size([15000, 768])\n", " Raw cos(A, B): 0.4360\n", " GPA iter 1: delta=0.12673541\n", " GPA iter 5: delta=0.01321763\n", " GPA iter 10: delta=0.00224325\n", " cos(consensus, a): 0.8251\n", " cos(consensus, b): 0.8226\n", " Consensus CV: 0.1774\n", " Consensus anchors: torch.Size([30, 768])\n", " Teacher A anchors cos: 0.0008\n", " Teacher B anchors cos: -0.0160\n", "\n", "=================================================================\n", "STAGE 3: STUDENT — Consensus distillation + classification\n", "=================================================================\n", " E 1: t=0.081 v=0.203 cos=0.230 cv=1.1871 rig=4.8/34.4 [polygon=0.04 curve=0.00 star=0.36 structure=0.35]\n", " E 5: t=0.610 v=0.618 cos=0.451 cv=0.6686 rig=12.9/98.8 [polygon=0.38 curve=0.83 star=0.67 structure=0.70]\n", " E10: t=0.660 v=0.659 cos=0.550 cv=0.5453 rig=15.5/99.6 [polygon=0.41 curve=0.94 star=0.71 structure=0.72]\n", " E15: t=0.711 v=0.702 cos=0.625 cv=0.4492 rig=18.7/97.8 [polygon=0.39 curve=0.88 star=0.93 structure=0.76]\n", " E20: t=0.735 v=0.703 cos=0.671 cv=0.4598 rig=18.8/96.4 [polygon=0.45 curve=1.00 star=0.84 structure=0.70]\n", " E25: t=0.745 v=0.736 cos=0.693 cv=0.4261 rig=18.3/92.9 [polygon=0.48 curve=1.00 star=0.92 structure=0.73]\n", " E30: t=0.763 v=0.761 cos=0.704 cv=0.3359 rig=17.9/90.4 [polygon=0.50 curve=0.98 star=0.97 structure=0.76]\n", "\n", "=================================================================\n", "FINAL COMPARISON\n", "=================================================================\n", "\n", " Model v_acc cv poly curve star struct\n", " -------------------------------------------------------\n", " Teacher_A 0.699 1.4312 0.42 0.99 0.83 0.72\n", " Teacher_B 0.649 1.5969 0.38 0.95 0.79 0.66\n", " Student 0.761 0.3329 0.50 0.98 0.97 0.76\n", "\n", " Student anchor drift from consensus: mean=0.4458 max=0.6453\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# experiment 4 - lets make monster babies" ], "metadata": { "id": "ZDQ-MLLiTQLf" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\n", "#\n", "# Teacher A: Raw Adam (0.626) — learned without geometric guidance\n", "# Teacher B: Geometric (+spr+ort, 0.645) — learned with manifold control\n", "#\n", "# Both teachers encode the full training set.\n", "# Procrustes alignment finds the shared geometric center.\n", "# Student distills the consensus + trains with entropy config.\n", "#\n", "# Same pipeline as CaptionBERT 5-expert consensus, simplified to 2.\n", "# ============================================================================\n", "\n", "import math\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1)\n", " b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ── Backward filtering ──\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES ALIGNMENT (production, from cotrain_bank.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; Ns = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(Ns - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(Ns - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten, \"cos_after\": cos_after}\n", "\n", "def apply_align(emb, info):\n", " x = emb.float() - info[\"source_mean\"]\n", " x = x @ info[\"source_whitener\"]\n", " x = x @ info[\"rotation\"].T\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " cos_sim = emb @ anchors_n.T\n", " return 1.0 - cos_sim, cos_sim.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist):\n", " nearest = tri_dist.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " mask = nearest == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " spread = tri_dist[mask].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1 - alpha) * self.rigidity[i] + alpha / (spread + 0.01)\n", "\n", " @torch.no_grad()\n", " def geometry_snapshot(self):\n", " a_n = F.normalize(self.anchors.detach(), dim=-1)\n", " cos = a_n @ a_n.T\n", " mask = ~torch.eye(self.n_anchors, dtype=bool, device=a_n.device)\n", " return {\"anchors\": a_n.clone(), \"rigidity\": self.rigidity.clone(),\n", " \"mean_cos\": cos[mask].mean().item(), \"std_cos\": cos[mask].std().item()}\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_compartments = n_compartments\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp)))\n", "\n", " def forward(self, tri_dist):\n", " parts = []\n", " for k in range(self.n_compartments):\n", " parts.append(self.compartments[k](tri_dist[:, self.assignments == k]))\n", " return torch.cat(parts, dim=-1)\n", "\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_compartments=6, d_comp=64, d_hidden=256, init_anchors=None):\n", " super().__init__()\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.embed_proj = nn.Sequential(nn.Linear(128, d_embed), nn.LayerNorm(d_embed))\n", " self.constellation = Constellation(n_anchors, d_embed, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_compartments, d_comp)\n", " pw_dim = n_compartments * d_comp\n", " self.mlp = nn.Sequential(\n", " nn.Linear(pw_dim, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, d_hidden), nn.GELU(), nn.LayerNorm(d_hidden),\n", " nn.Linear(d_hidden, n_classes))\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri, nearest = self.constellation.triangulate(emb)\n", " return self.mlp(self.patchwork(tri)), emb, tri, nearest\n", "\n", " def encode(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " return F.normalize(self.embed_proj(feat), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " ix,iy=int(x1),int(y1)\n", " if 0<=ix 0 or sep > 0):\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, tang, sep)\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " logits = model.mlp(model.patchwork(tri_g))\n", " l_cls = F.cross_entropy(logits, labels)\n", " l_geo = torch.tensor(0.0, device=DEVICE)\n", " if cv_w > 0: l_geo = l_geo + cv_w * cv_loss(emb)\n", " if w_spread > 0: l_geo = l_geo + w_spread * anchor_spread_loss(anchors)\n", " if w_ortho > 0: l_geo = l_geo + w_ortho * anchor_ortho_loss(anchors)\n", " if w_entropy > 0: l_geo = l_geo + w_entropy * anchor_entropy_loss(emb, anchors)\n", " (l_cls + l_geo).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " n += 1\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve)\n", " if (epoch+1) % 10 == 0 or epoch == 0:\n", " print(f\" {tag} E{epoch+1:2d}: t={total_correct/n_train:.3f} \"\n", " f\"v={v_acc:.3f} cv={v_cv:.4f}\")\n", " return v_acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 1: TRAIN BOTH TEACHERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 1A: TEACHER A — Raw Adam\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "teacher_a = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "va_a = train_teacher(teacher_a, \"[A]\", use_autograd=False, epochs=30)\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 1B: TEACHER B — Geometric (+spr+ort)\")\n", "print(f\"{'='*65}\")\n", "torch.manual_seed(42)\n", "teacher_b = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "va_b = train_teacher(teacher_b, \"[B]\", use_autograd=True,\n", " tang=0.01, sep=1.0, cv_w=0.001,\n", " w_spread=1e-3, w_ortho=1e-3, epochs=30)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 2: EXTRACT EMBEDDINGS + GPA ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 2: EXTRACT + PROCRUSTES ALIGN\")\n", "print(f\"{'='*65}\")\n", "\n", "teacher_a.eval(); teacher_b.eval()\n", "with torch.no_grad():\n", " emb_a = teacher_a.encode(train_imgs) # (N, 768)\n", " emb_b = teacher_b.encode(train_imgs) # (N, 768)\n", " val_emb_a = teacher_a.encode(val_imgs)\n", " val_emb_b = teacher_b.encode(val_imgs)\n", "\n", "print(f\" Teacher A embeddings: {emb_a.shape}\")\n", "print(f\" Teacher B embeddings: {emb_b.shape}\")\n", "print(f\" Raw cos(A, B): {F.cosine_similarity(emb_a[:1000], emb_b[:1000], dim=-1).mean():.4f}\")\n", "\n", "# GPA: iterative Procrustes to find geometric center\n", "current = {\"a\": emb_a.float(), \"b\": emb_b.float()}\n", "for gpa_iter in range(10):\n", " mean_shape = (current[\"a\"] + current[\"b\"]) / 2\n", " total_delta = 0.0\n", " new_current = {}\n", " for name in [\"a\", \"b\"]:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " total_delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if (gpa_iter + 1) % 5 == 0 or gpa_iter == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\")\n", " break\n", "\n", "# Build consensus\n", "mean_shape = (current[\"a\"] + current[\"b\"]) / 2\n", "consensus = F.normalize(mean_shape, dim=-1)\n", "\n", "# Align val embeddings too\n", "val_aligned = {}\n", "for name, emb_full in [(\"a\", emb_a), (\"b\", emb_b)]:\n", " info = procrustes_align(emb_full, mean_shape)\n", " val_aligned[name] = apply_align(\n", " val_emb_a if name == \"a\" else val_emb_b, info)\n", "\n", "val_consensus = F.normalize((val_aligned[\"a\"] + val_aligned[\"b\"]) / 2, dim=-1)\n", "\n", "# Check alignment quality\n", "for name in [\"a\", \"b\"]:\n", " aligned = current[name] if name == \"a\" else current[\"b\"]\n", " cos = F.cosine_similarity(consensus[:1000], F.normalize(aligned[:1000], dim=-1), dim=-1).mean()\n", " print(f\" cos(consensus, {name}): {cos:.4f}\")\n", "\n", "consensus_cv = cv_metric(consensus[:2000])\n", "print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", "# Extract consensus anchors: cluster consensus embeddings\n", "# Use the per-class centroids as initial anchor positions\n", "anchor_centroids = []\n", "for c in range(30):\n", " mask = train_labels == c\n", " if mask.sum() > 0:\n", " anchor_centroids.append(consensus[mask].mean(dim=0))\n", " else:\n", " anchor_centroids.append(torch.randn(768, device=DEVICE))\n", "consensus_anchors = F.normalize(torch.stack(anchor_centroids), dim=-1)\n", "print(f\" Consensus anchors: {consensus_anchors.shape}\")\n", "\n", "# Also keep the pure geometric center from teacher anchors\n", "geo_a = teacher_a.constellation.geometry_snapshot()\n", "geo_b = teacher_b.constellation.geometry_snapshot()\n", "print(f\" Teacher A anchors cos: {geo_a['mean_cos']:.4f}\")\n", "print(f\" Teacher B anchors cos: {geo_b['mean_cos']:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 3: STUDENT DISTILLATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"STAGE 3: STUDENT — Consensus distillation + classification\")\n", "print(f\"{'='*65}\")\n", "\n", "torch.manual_seed(42)\n", "model_student = PatchworkClassifier(\n", " n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=consensus_anchors,\n", ").to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model_student.parameters(), lr=1e-3)\n", "BATCH = 256\n", "EPOCHS = 30\n", "\n", "# Move consensus targets to device\n", "train_targets = consensus.to(DEVICE) # (N, 768) L2-normalized\n", "val_targets = val_consensus.to(DEVICE)\n", "\n", "for epoch in range(EPOCHS):\n", " model_student.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " logits, emb, tri, nearest = model_student(train_imgs[idx])\n", " labels = train_labels[idx]\n", " tgt = train_targets[idx]\n", " anchors = model_student.constellation.anchors\n", "\n", " # Apply geometric autograd\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, 0.01, 1.0)\n", " tri_g, _ = model_student.constellation.triangulate(emb_g)\n", " logits = model_student.mlp(model_student.patchwork(tri_g))\n", "\n", " # Three losses:\n", " # 1. Classification (task)\n", " l_cls = F.cross_entropy(logits, labels)\n", "\n", " # 2. Consensus distillation (InfoNCE + MSE)\n", " l_nce, nce_acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", "\n", " # 3. Geometric (micro CV + entropy)\n", " l_cv = cv_loss(emb, target=0.2)\n", " l_ent = anchor_entropy_loss(emb, anchors)\n", "\n", " loss = l_cls + 0.5 * l_nce + 0.5 * l_mse + 0.001 * l_cv + 1e-4 * l_ent\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model_student.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model_student.constellation.update_rigidity(tri.detach())\n", " total_correct += (logits.argmax(-1) == labels).sum().item()\n", " total_loss += loss.item()\n", " n += 1\n", "\n", " train_acc = total_correct / n_train\n", "\n", " # Validation\n", " model_student.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model_student(val_imgs)\n", " v_acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " v_cv = cv_metric(ve)\n", " v_cos = F.cosine_similarity(ve, val_targets, dim=-1).mean().item()\n", "\n", " types = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", "\n", " if (epoch+1) % 5 == 0 or epoch == 0:\n", " ta_str = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " rig = model_student.constellation.rigidity\n", " print(f\" E{epoch+1:2d}: t={train_acc:.3f} v={v_acc:.3f} \"\n", " f\"cos={v_cos:.3f} cv={v_cv:.4f} \"\n", " f\"rig={rig.mean():.1f}/{rig.max():.1f} [{ta_str}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL COMPARISON\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "# Get final metrics for all models\n", "model_student.eval(); teacher_a.eval(); teacher_b.eval()\n", "with torch.no_grad():\n", " results = {}\n", " for name, mdl in [(\"Teacher_A\", teacher_a), (\"Teacher_B\", teacher_b),\n", " (\"Student\", model_student)]:\n", " vl, ve, _, _ = mdl(val_imgs)\n", " acc = (vl.argmax(-1) == val_labels).float().mean().item()\n", " ta = {}\n", " for tname, tids in types.items():\n", " tmask = torch.zeros(n_val, dtype=bool, device=DEVICE)\n", " for tid in tids: tmask |= (val_labels == tid)\n", " if tmask.sum() > 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == val_labels[tmask]).float().mean().item()\n", " results[name] = {\"acc\": acc, \"cv\": cv_metric(ve), \"types\": ta}\n", "\n", "print(f\"\\n {'Model':<15} {'v_acc':>6} {'cv':>7} {'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*55}\")\n", "for name, r in results.items():\n", " ta = r[\"types\"]\n", " print(f\" {name:<15} {r['acc']:>6.3f} {r['cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Anchor drift from consensus\n", "s_anchors = F.normalize(model_student.constellation.anchors.detach(), dim=-1)\n", "drift = 1.0 - F.cosine_similarity(consensus_anchors, s_anchors, dim=-1)\n", "print(f\"\\n Student anchor drift from consensus: mean={drift.mean():.4f} max={drift.max():.4f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OXPxV2NOTSng", "outputId": "7982d18c-01be-44cd-e0d4-c2f4620a8960" }, "execution_count": 31, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DUAL-TEACHER PROCRUSTES CONSENSUS DISTILLATION\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000\n", "\n", "=================================================================\n", "STAGE 1A: TEACHER A — Raw Adam\n", "=================================================================\n", " [A] E 1: t=0.073 v=0.196 cv=1.3041\n", " [A] E10: t=0.619 v=0.659 cv=1.4380\n", " [A] E20: t=0.655 v=0.627 cv=1.4257\n", " [A] E30: t=0.675 v=0.667 cv=1.3820\n", "\n", "=================================================================\n", "STAGE 1B: TEACHER B — Geometric (+spr+ort)\n", "=================================================================\n", " [B] E 1: t=0.071 v=0.172 cv=1.4191\n", " [B] E10: t=0.597 v=0.630 cv=1.5348\n", " [B] E20: t=0.630 v=0.649 cv=1.5054\n", " [B] E30: t=0.679 v=0.671 cv=1.7554\n", "\n", "=================================================================\n", "STAGE 2: EXTRACT + PROCRUSTES ALIGN\n", "=================================================================\n", " Teacher A embeddings: torch.Size([15000, 768])\n", " Teacher B embeddings: torch.Size([15000, 768])\n", " Raw cos(A, B): 0.4257\n", " GPA iter 1: delta=0.13052756\n", " GPA iter 5: delta=0.01328304\n", " GPA iter 10: delta=0.00237650\n", " cos(consensus, a): 0.8274\n", " cos(consensus, b): 0.8206\n", " Consensus CV: 0.1775\n", " Consensus anchors: torch.Size([30, 768])\n", " Teacher A anchors cos: 0.0007\n", " Teacher B anchors cos: -0.0169\n", "\n", "=================================================================\n", "STAGE 3: STUDENT — Consensus distillation + classification\n", "=================================================================\n", " E 1: t=0.081 v=0.202 cos=0.215 cv=1.3131 rig=7.9/65.1 [polygon=0.08 curve=0.00 star=0.27 structure=0.37]\n", " E 5: t=0.588 v=0.643 cos=0.442 cv=0.6927 rig=14.2/98.8 [polygon=0.33 curve=0.99 star=0.72 structure=0.70]\n", " E10: t=0.671 v=0.657 cos=0.502 cv=0.6738 rig=18.9/99.5 [polygon=0.36 curve=0.86 star=0.84 structure=0.72]\n", " E15: t=0.706 v=0.687 cos=0.612 cv=0.4608 rig=19.1/99.2 [polygon=0.39 curve=0.91 star=0.92 structure=0.70]\n", " E20: t=0.718 v=0.700 cos=0.622 cv=0.5890 rig=18.9/98.5 [polygon=0.40 curve=0.99 star=0.83 structure=0.75]\n", " E25: t=0.733 v=0.739 cos=0.632 cv=0.6020 rig=18.7/98.4 [polygon=0.46 curve=0.99 star=0.96 structure=0.73]\n", " E30: t=0.741 v=0.738 cos=0.616 cv=0.6000 rig=18.4/98.3 [polygon=0.47 curve=0.98 star=0.92 structure=0.75]\n", "\n", "=================================================================\n", "FINAL COMPARISON\n", "=================================================================\n", "\n", " Model v_acc cv poly curve star struct\n", " -------------------------------------------------------\n", " Teacher_A 0.667 1.4196 0.34 0.96 0.83 0.72\n", " Teacher_B 0.671 1.4580 0.36 0.94 0.83 0.72\n", " Student 0.738 0.5676 0.47 0.98 0.92 0.75\n", "\n", " Student anchor drift from consensus: mean=0.4724 max=0.8531\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# MULTI-GENERATIONAL GEOMETRIC EVOLUTION\n", "#\n", "# Generation 0: 2 founders (Raw Adam + Geometric)\n", "# → GPA consensus → 3 offspring\n", "#\n", "# Generation 1: 3 offspring + 1 new founder = 4 ancestors\n", "# → GPA consensus → 4 offspring\n", "#\n", "# Generation 2: 4 offspring + 1 new founder = 5 ancestors\n", "# → GPA consensus → final descendant\n", "#\n", "# Each generation inherits consensus geometry from all ancestors.\n", "# Procrustes alignment finds the shared geometric center across\n", "# increasingly diverse lineages. The final descendant carries\n", "# the distilled agreement of all 5 independent training runs.\n", "# ============================================================================\n", "\n", "import math\n", "import gc\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"MULTI-GENERATIONAL GEOMETRIC EVOLUTION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES (GPA for N models)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " evals = torch.clamp(evals, min=eps)\n", " return evecs @ torch.diag(evals.rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; Ns = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(Ns - 1, 1)\n", " t_cov = (Tc.T @ Tc) / max(Ns - 1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0), \"source_whitener\": s_whiten}\n", "\n", "def apply_align(emb, info):\n", " x = emb.float() - info[\"source_mean\"]\n", " return x @ info[\"source_whitener\"] @ info[\"rotation\"].T\n", "\n", "def gpa_consensus(embeddings_list, n_iters=15):\n", " \"\"\"Generalized Procrustes Analysis for N embedding sets.\"\"\"\n", " N_models = len(embeddings_list)\n", " current = {i: e.float() for i, e in enumerate(embeddings_list)}\n", "\n", " for gpa_iter in range(n_iters):\n", " mean_shape = sum(current[i] for i in range(N_models)) / N_models\n", " total_delta = 0.0\n", " new_current = {}\n", " for i in range(N_models):\n", " info = procrustes_align(current[i], mean_shape)\n", " new_current[i] = apply_align(current[i], info)\n", " total_delta += (new_current[i] - current[i]).pow(2).mean().item()\n", " current = new_current\n", " if total_delta < 1e-8:\n", " break\n", "\n", " mean_shape = sum(current[i] for i in range(N_models)) / N_models\n", " consensus = F.normalize(mean_shape, dim=-1)\n", "\n", " # Per-model alignment quality\n", " cos_scores = []\n", " for i in range(N_models):\n", " c = F.cosine_similarity(consensus[:2000],\n", " F.normalize(current[i][:2000], dim=-1), dim=-1).mean().item()\n", " cos_scores.append(c)\n", "\n", " return consensus, cos_scores\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=30, d_embed=768, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " a_n = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a_n.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri_dist):\n", " nearest = tri_dist.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " mask = nearest == i\n", " if mask.sum() < 5: continue\n", " self.visit_count[i] += mask.sum().float()\n", " spread = tri_dist[mask].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(spread+0.01)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=30, n_compartments=6, d_comp=64):\n", " super().__init__()\n", " self.n_compartments = n_compartments\n", " assignments = torch.arange(n_anchors) % n_compartments\n", " self.register_buffer(\"assignments\", assignments)\n", " self.compartments = nn.ModuleList()\n", " for k in range(n_compartments):\n", " n_k = (assignments == k).sum().item()\n", " self.compartments.append(nn.Sequential(\n", " nn.Linear(n_k, d_comp*2), nn.GELU(),\n", " nn.Linear(d_comp*2, d_comp), nn.LayerNorm(d_comp)))\n", " def forward(self, tri):\n", " return torch.cat([self.compartments[k](tri[:, self.assignments==k])\n", " for k in range(self.n_compartments)], dim=-1)\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, n_classes=30, n_anchors=30, d_embed=768,\n", " n_comp=6, d_comp=64, d_hid=256, init_anchors=None):\n", " super().__init__()\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1,32,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32,64,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64,128,3,padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.embed_proj = nn.Sequential(nn.Linear(128, d_embed), nn.LayerNorm(d_embed))\n", " self.constellation = Constellation(n_anchors, d_embed, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", " self.mlp = nn.Sequential(\n", " nn.Linear(n_comp*d_comp, d_hid), nn.GELU(), nn.LayerNorm(d_hid),\n", " nn.Linear(d_hid, d_hid), nn.GELU(), nn.LayerNorm(d_hid),\n", " nn.Linear(d_hid, n_classes))\n", "\n", " def forward(self, x):\n", " feat = self.backbone(x).flatten(1)\n", " emb = F.normalize(self.embed_proj(feat), dim=-1)\n", " tri, nearest = self.constellation.triangulate(emb)\n", " return self.mlp(self.patchwork(tri)), emb, tri, nearest\n", "\n", " def encode(self, x):\n", " return F.normalize(self.embed_proj(self.backbone(x).flatten(1)), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS (compact)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " if 0<=int(x1) 0:\n", " ta[tname] = (vl.argmax(-1)[tmask] == labels[tmask]).float().mean().item()\n", " return acc, cv, ta\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING CONFIGS (variation = different geometric losses)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "CONFIGS = {\n", " \"raw\": {\"use_ag\": False, \"tang\": 0, \"sep\": 0, \"cv_w\": 0, \"spr\": 0, \"ort\": 0, \"ent\": 0},\n", " \"geo_a\": {\"use_ag\": True, \"tang\": 0.01, \"sep\": 1.0, \"cv_w\": 0.001, \"spr\": 1e-3, \"ort\": 1e-3, \"ent\": 0},\n", " \"geo_b\": {\"use_ag\": True, \"tang\": 0.01, \"sep\": 1.0, \"cv_w\": 0.001, \"spr\": 0, \"ort\": 0, \"ent\": 1e-4},\n", " \"geo_c\": {\"use_ag\": True, \"tang\": 0.01, \"sep\": 0.5, \"cv_w\": 0.001, \"spr\": 5e-4, \"ort\": 5e-4, \"ent\": 5e-5},\n", " \"new_raw\": {\"use_ag\": False, \"tang\": 0, \"sep\": 0, \"cv_w\": 0, \"spr\": 0, \"ort\": 0, \"ent\": 0},\n", " \"new_geo\": {\"use_ag\": True, \"tang\": 0.01, \"sep\": 0.8, \"cv_w\": 0.001, \"spr\": 1e-3, \"ort\": 0, \"ent\": 1e-4},\n", "}\n", "\n", "\n", "def train_model(model, train_imgs, train_labels, cfg, epochs=30, tag=\"\"):\n", " \"\"\"Train with specified config.\"\"\"\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; n_train = len(train_labels)\n", " for epoch in range(epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " tc, n = 0, 0\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " logits, emb, tri, nearest = model(train_imgs[idx])\n", " labels = train_labels[idx]\n", " anchors = model.constellation.anchors\n", " if cfg[\"use_ag\"] and (cfg[\"tang\"] > 0 or cfg[\"sep\"] > 0):\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, cfg[\"tang\"], cfg[\"sep\"])\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " logits = model.mlp(model.patchwork(tri_g))\n", " l = F.cross_entropy(logits, labels)\n", " lg = torch.tensor(0.0, device=DEVICE)\n", " if cfg[\"cv_w\"] > 0: lg = lg + cfg[\"cv_w\"] * cv_loss(emb)\n", " if cfg[\"spr\"] > 0: lg = lg + cfg[\"spr\"] * anchor_spread_loss(anchors)\n", " if cfg[\"ort\"] > 0: lg = lg + cfg[\"ort\"] * anchor_ortho_loss(anchors)\n", " if cfg[\"ent\"] > 0: lg = lg + cfg[\"ent\"] * anchor_entropy_loss(emb, anchors)\n", " (l + lg).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (logits.argmax(-1) == labels).sum().item(); n += 1\n", " if (epoch+1) % 10 == 0 or epoch == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " ta_s = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " print(f\" {tag}E{epoch+1:2d}: t={tc/n_train:.3f} v={acc:.3f} cv={cv:.4f} [{ta_s}]\")\n", "\n", "\n", "def train_distilled(model, train_imgs, train_labels, consensus, cfg, epochs=30, tag=\"\"):\n", " \"\"\"Train with consensus distillation + task loss.\"\"\"\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; n_train = len(train_labels)\n", " for epoch in range(epochs):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " tc, n = 0, 0\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " logits, emb, tri, nearest = model(train_imgs[idx])\n", " labels = train_labels[idx]; tgt = consensus[idx]\n", " anchors = model.constellation.anchors\n", " if cfg[\"use_ag\"] and (cfg[\"tang\"] > 0 or cfg[\"sep\"] > 0):\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, cfg[\"tang\"], cfg[\"sep\"])\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " logits = model.mlp(model.patchwork(tri_g))\n", " l_cls = F.cross_entropy(logits, labels)\n", " l_nce, _ = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " lg = torch.tensor(0.0, device=DEVICE)\n", " if cfg[\"cv_w\"] > 0: lg = lg + cfg[\"cv_w\"] * cv_loss(emb)\n", " if cfg[\"ent\"] > 0: lg = lg + cfg[\"ent\"] * anchor_entropy_loss(emb, anchors)\n", " loss = l_cls + 0.5 * l_nce + 0.5 * l_mse + lg\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (logits.argmax(-1) == labels).sum().item(); n += 1\n", " if (epoch+1) % 10 == 0 or epoch == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " cos = F.cosine_similarity(\n", " model.encode(val_imgs[:1000]), consensus[:1000].to(DEVICE), dim=-1).mean().item()\n", " ta_s = \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", " print(f\" {tag}E{epoch+1:2d}: t={tc/n_train:.3f} v={acc:.3f} cos={cos:.3f} cv={cv:.4f} [{ta_s}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATE DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Generating data...\")\n", "torch.manual_seed(42); np.random.seed(42)\n", "train_imgs, train_labels = gen_data(n_per=500)\n", "val_imgs, val_labels = gen_data(n_per=100)\n", "train_imgs, train_labels = train_imgs.to(DEVICE), train_labels.to(DEVICE)\n", "val_imgs, val_labels = val_imgs.to(DEVICE), val_labels.to(DEVICE)\n", "n_train, n_val = len(train_labels), len(val_labels)\n", "print(f\" Train: {n_train:,} Val: {n_val:,}\")\n", "\n", "all_results = {}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 0: FOUNDERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GENERATION 0: FOUNDERS (2 independent models)\")\n", "print(f\"{'='*65}\")\n", "\n", "founders = {}\n", "for name, cfg_key in [(\"F0_raw\", \"raw\"), (\"F0_geo\", \"geo_a\")]:\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(hash(name) % 2**32)\n", " m = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", " train_model(m, train_imgs, train_labels, CONFIGS[cfg_key], epochs=30, tag=f\"[{name}] \")\n", " founders[name] = m\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"types\": ta, \"gen\": 0}\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 1: 3 OFFSPRING FROM 2 FOUNDERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GENERATION 1: 3 OFFSPRING from 2 founders\")\n", "print(f\"{'='*65}\")\n", "\n", "# Extract + GPA\n", "print(f\"\\n Extracting founder embeddings...\")\n", "founder_embs = {}\n", "for name, m in founders.items():\n", " m.eval()\n", " with torch.no_grad():\n", " founder_embs[name] = m.encode(train_imgs)\n", "\n", "consensus_g1, cos_g1 = gpa_consensus(list(founder_embs.values()))\n", "consensus_cv_g1 = cv_metric(consensus_g1[:2000])\n", "print(f\" GPA consensus: CV={consensus_cv_g1:.4f}, cos={cos_g1}\")\n", "\n", "# Per-class centroids as anchors\n", "anchors_g1 = F.normalize(torch.stack([\n", " consensus_g1[train_labels == c].mean(0) for c in range(30)]), dim=-1)\n", "\n", "gen1 = {}\n", "for i, cfg_key in enumerate([\"geo_a\", \"geo_b\", \"geo_c\"]):\n", " name = f\"G1_{i}\"\n", " print(f\"\\n ── {name} ({cfg_key}) ──\")\n", " torch.manual_seed(hash(name) % 2**32)\n", " m = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=anchors_g1).to(DEVICE)\n", " train_distilled(m, train_imgs, train_labels, consensus_g1, CONFIGS[cfg_key],\n", " epochs=30, tag=f\"[{name}] \")\n", " gen1[name] = m\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"types\": ta, \"gen\": 1}\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "# Free founders\n", "del founders; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 2: 4 OFFSPRING FROM 3 G1 + 1 NEW FOUNDER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GENERATION 2: 4 OFFSPRING from 3 G1 + 1 new founder\")\n", "print(f\"{'='*65}\")\n", "\n", "# Train new founder\n", "print(f\"\\n ── New founder ──\")\n", "torch.manual_seed(hash(\"new_raw_g2\") % 2**32)\n", "new_founder = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "train_model(new_founder, train_imgs, train_labels, CONFIGS[\"new_raw\"], epochs=30,\n", " tag=\"[NEW] \")\n", "acc_nf, _, _ = eval_model(new_founder, val_imgs, val_labels)\n", "all_results[\"F1_new\"] = {\"acc\": acc_nf, \"cv\": 0, \"types\": {}, \"gen\": 1}\n", "\n", "# Extract all G1 + new founder\n", "print(f\"\\n Extracting G1 + new founder...\")\n", "g2_embs = {}\n", "for name, m in gen1.items():\n", " m.eval()\n", " with torch.no_grad():\n", " g2_embs[name] = m.encode(train_imgs)\n", "new_founder.eval()\n", "with torch.no_grad():\n", " g2_embs[\"new\"] = new_founder.encode(train_imgs)\n", "\n", "consensus_g2, cos_g2 = gpa_consensus(list(g2_embs.values()))\n", "consensus_cv_g2 = cv_metric(consensus_g2[:2000])\n", "print(f\" GPA consensus (4 models): CV={consensus_cv_g2:.4f}, cos={cos_g2}\")\n", "\n", "anchors_g2 = F.normalize(torch.stack([\n", " consensus_g2[train_labels == c].mean(0) for c in range(30)]), dim=-1)\n", "\n", "gen2 = {}\n", "for i, cfg_key in enumerate([\"geo_a\", \"geo_b\", \"geo_c\", \"new_geo\"]):\n", " name = f\"G2_{i}\"\n", " print(f\"\\n ── {name} ({cfg_key}) ──\")\n", " torch.manual_seed(hash(name) % 2**32)\n", " m = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=anchors_g2).to(DEVICE)\n", " train_distilled(m, train_imgs, train_labels, consensus_g2, CONFIGS[cfg_key],\n", " epochs=30, tag=f\"[{name}] \")\n", " gen2[name] = m\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"types\": ta, \"gen\": 2}\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "del gen1, new_founder; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 3: FINAL DESCENDANT FROM 4 G2 + 1 NEW FOUNDER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GENERATION 3: FINAL DESCENDANT from 4 G2 + 1 new founder\")\n", "print(f\"{'='*65}\")\n", "\n", "# New founder\n", "print(f\"\\n ── New founder ──\")\n", "torch.manual_seed(hash(\"new_geo_g3\") % 2**32)\n", "new_founder2 = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768).to(DEVICE)\n", "train_model(new_founder2, train_imgs, train_labels, CONFIGS[\"new_geo\"], epochs=30,\n", " tag=\"[NEW2] \")\n", "acc_nf2, _, _ = eval_model(new_founder2, val_imgs, val_labels)\n", "all_results[\"F2_new\"] = {\"acc\": acc_nf2, \"cv\": 0, \"types\": {}, \"gen\": 2}\n", "\n", "# Extract all G2 + new founder\n", "print(f\"\\n Extracting G2 + new founder...\")\n", "g3_embs = {}\n", "for name, m in gen2.items():\n", " m.eval()\n", " with torch.no_grad():\n", " g3_embs[name] = m.encode(train_imgs)\n", "new_founder2.eval()\n", "with torch.no_grad():\n", " g3_embs[\"new2\"] = new_founder2.encode(train_imgs)\n", "\n", "consensus_g3, cos_g3 = gpa_consensus(list(g3_embs.values()))\n", "consensus_cv_g3 = cv_metric(consensus_g3[:2000])\n", "print(f\" GPA consensus (5 models): CV={consensus_cv_g3:.4f}, cos={cos_g3}\")\n", "\n", "anchors_g3 = F.normalize(torch.stack([\n", " consensus_g3[train_labels == c].mean(0) for c in range(30)]), dim=-1)\n", "\n", "# Final descendant — best config\n", "print(f\"\\n ── FINAL DESCENDANT ──\")\n", "torch.manual_seed(42)\n", "final = PatchworkClassifier(n_classes=30, n_anchors=30, d_embed=768,\n", " init_anchors=anchors_g3).to(DEVICE)\n", "train_distilled(final, train_imgs, train_labels, consensus_g3, CONFIGS[\"geo_b\"],\n", " epochs=30, tag=\"[FINAL] \")\n", "acc_f, cv_f, ta_f = eval_model(final, val_imgs, val_labels)\n", "all_results[\"FINAL\"] = {\"acc\": acc_f, \"cv\": cv_f, \"types\": ta_f, \"gen\": 3}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVOLUTION SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"EVOLUTION SUMMARY\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Model':<12} {'Gen':>3} {'v_acc':>6} {'cv':>7} \"\n", " f\"{'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*55}\")\n", "\n", "for name in sorted(all_results.keys(), key=lambda x: (all_results[x][\"gen\"], x)):\n", " r = all_results[name]\n", " ta = r.get(\"types\", {})\n", " print(f\" {name:<12} {r['gen']:>3} {r['acc']:>6.3f} {r['cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "# Generation averages\n", "print(f\"\\n Per-generation averages:\")\n", "for gen in range(4):\n", " gen_accs = [r[\"acc\"] for r in all_results.values() if r[\"gen\"] == gen]\n", " if gen_accs:\n", " print(f\" Gen {gen}: mean_acc={np.mean(gen_accs):.3f} \"\n", " f\"best={max(gen_accs):.3f} n={len(gen_accs)}\")\n", "\n", "print(f\"\\n Consensus CV progression: \"\n", " f\"G1={consensus_cv_g1:.4f} → G2={consensus_cv_g2:.4f} → G3={consensus_cv_g3:.4f}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lJLxVOkAUuNW", "outputId": "ed3d0011-431e-47e2-ed69-eb6df0f217b0" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "MULTI-GENERATIONAL GEOMETRIC EVOLUTION\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating data...\n", " Train: 15,000 Val: 3,000\n", "\n", "=================================================================\n", "GENERATION 0: FOUNDERS (2 independent models)\n", "=================================================================\n", "\n", " ── F0_raw ──\n", " [F0_raw] E 1: t=0.078 v=0.115 cv=1.2050 [polygon=0.00 curve=0.20 star=0.18 structure=0.14]\n", " [F0_raw] E10: t=0.612 v=0.614 cv=1.4293 [polygon=0.37 curve=0.99 star=0.55 structure=0.69]\n", " [F0_raw] E20: t=0.685 v=0.666 cv=1.5669 [polygon=0.39 curve=0.98 star=0.73 structure=0.72]\n", " [F0_raw] E30: t=0.689 v=0.586 cv=1.2182 [polygon=0.48 curve=0.29 star=0.91 structure=0.64]\n", " → F0_raw: val=0.586\n", "\n", " ── F0_geo ──\n", " [F0_geo] E 1: t=0.068 v=0.138 cv=1.5701 [polygon=0.02 curve=0.20 star=0.18 structure=0.19]\n", " [F0_geo] E10: t=0.559 v=0.618 cv=1.6912 [polygon=0.32 curve=0.93 star=0.71 structure=0.68]\n", " [F0_geo] E20: t=0.639 v=0.603 cv=1.4958 [polygon=0.32 curve=0.99 star=0.54 structure=0.71]\n", " [F0_geo] E30: t=0.688 v=0.663 cv=1.5600 [polygon=0.36 curve=1.00 star=0.75 structure=0.72]\n", " → F0_geo: val=0.663\n", "\n", "=================================================================\n", "GENERATION 1: 3 OFFSPRING from 2 founders\n", "=================================================================\n", "\n", " Extracting founder embeddings...\n", " GPA consensus: CV=0.1258, cos=[0.8334668278694153, 0.813448965549469]\n", "\n", " ── G1_0 (geo_a) ──\n", " [G1_0] E 1: t=0.079 v=0.156 cos=-0.004 cv=1.1823 [polygon=0.11 curve=0.00 star=0.22 structure=0.24]\n", " [G1_0] E10: t=0.636 v=0.622 cos=-0.000 cv=0.7919 [polygon=0.31 curve=1.00 star=0.75 structure=0.64]\n", " [G1_0] E20: t=0.725 v=0.723 cos=0.002 cv=0.6384 [polygon=0.44 curve=0.98 star=0.91 structure=0.74]\n", " [G1_0] E30: t=0.752 v=0.746 cos=0.002 cv=0.5987 [polygon=0.50 curve=0.99 star=0.93 structure=0.74]\n", " → G1_0: val=0.746\n", "\n", " ── G1_1 (geo_b) ──\n", " [G1_1] E 1: t=0.086 v=0.277 cos=-0.005 cv=1.4235 [polygon=0.14 curve=0.38 star=0.23 structure=0.38]\n", " [G1_1] E10: t=0.623 v=0.575 cos=-0.000 cv=0.8024 [polygon=0.22 curve=0.79 star=0.75 structure=0.68]\n", " [G1_1] E20: t=0.703 v=0.668 cos=0.001 cv=0.6334 [polygon=0.41 curve=0.87 star=0.80 structure=0.72]\n", " [G1_1] E30: t=0.746 v=0.749 cos=0.001 cv=0.4028 [polygon=0.49 curve=1.00 star=0.95 structure=0.74]\n", " → G1_1: val=0.749\n", "\n", " ── G1_2 (geo_c) ──\n", " [G1_2] E 1: t=0.098 v=0.243 cos=-0.006 cv=1.1072 [polygon=0.15 curve=0.03 star=0.29 structure=0.40]\n", " [G1_2] E10: t=0.657 v=0.617 cos=-0.000 cv=0.5284 [polygon=0.37 curve=0.76 star=0.66 structure=0.74]\n", " [G1_2] E20: t=0.737 v=0.731 cos=0.001 cv=0.3207 [polygon=0.48 curve=1.00 star=0.94 structure=0.70]\n", " [G1_2] E30: t=0.770 v=0.761 cos=0.001 cv=0.3522 [polygon=0.49 curve=1.00 star=0.99 structure=0.75]\n", " → G1_2: val=0.761\n", "\n", "=================================================================\n", "GENERATION 2: 4 OFFSPRING from 3 G1 + 1 new founder\n", "=================================================================\n", "\n", " ── New founder ──\n", " [NEW] E 1: t=0.075 v=0.177 cv=1.4604 [polygon=0.14 curve=0.20 star=0.12 structure=0.23]\n", " [NEW] E10: t=0.568 v=0.618 cv=1.4712 [polygon=0.30 curve=0.99 star=0.72 structure=0.66]\n", " [NEW] E20: t=0.655 v=0.639 cv=1.2712 [polygon=0.34 curve=0.99 star=0.75 structure=0.68]\n", " [NEW] E30: t=0.683 v=0.690 cv=1.3805 [polygon=0.44 curve=0.96 star=0.83 structure=0.70]\n", "\n", " Extracting G1 + new founder...\n", " GPA consensus (4 models): CV=0.1031, cos=[0.6878579258918762, 0.6954009532928467, 0.6990697383880615, 0.6920183897018433]\n", "\n", " ── G2_0 (geo_a) ──\n", " [G2_0] E 1: t=0.077 v=0.208 cos=-0.009 cv=1.3452 [polygon=0.13 curve=0.20 star=0.17 structure=0.31]\n", " [G2_0] E10: t=0.632 v=0.665 cos=-0.001 cv=0.5819 [polygon=0.44 curve=0.97 star=0.81 structure=0.63]\n", " [G2_0] E20: t=0.719 v=0.749 cos=0.001 cv=0.4937 [polygon=0.48 curve=0.99 star=0.94 structure=0.76]\n", " [G2_0] E30: t=0.757 v=0.750 cos=0.000 cv=0.3473 [polygon=0.49 curve=0.99 star=0.94 structure=0.75]\n", " → G2_0: val=0.750\n", "\n", " ── G2_1 (geo_b) ──\n", " [G2_1] E 1: t=0.088 v=0.250 cos=-0.005 cv=1.4912 [polygon=0.09 curve=0.20 star=0.27 structure=0.41]\n", " [G2_1] E10: t=0.637 v=0.673 cos=0.001 cv=0.5785 [polygon=0.35 curve=1.00 star=0.78 structure=0.74]\n", " [G2_1] E20: t=0.731 v=0.722 cos=0.001 cv=0.4775 [polygon=0.45 curve=0.98 star=0.96 structure=0.70]\n", " [G2_1] E30: t=0.758 v=0.751 cos=0.001 cv=0.4243 [polygon=0.49 curve=0.95 star=0.97 structure=0.76]\n", " → G2_1: val=0.751\n", "\n", " ── G2_2 (geo_c) ──\n", " [G2_2] E 1: t=0.086 v=0.225 cos=-0.003 cv=1.2452 [polygon=0.12 curve=0.20 star=0.28 structure=0.30]\n", " [G2_2] E10: t=0.662 v=0.690 cos=0.000 cv=0.5007 [polygon=0.38 curve=0.99 star=0.86 structure=0.72]\n", " [G2_2] E20: t=0.726 v=0.731 cos=0.002 cv=0.3399 [polygon=0.45 curve=1.00 star=0.95 structure=0.72]\n", " [G2_2] E30: t=0.766 v=0.766 cos=0.002 cv=0.3048 [polygon=0.54 curve=0.98 star=0.96 structure=0.75]\n", " → G2_2: val=0.766\n", "\n", " ── G2_3 (new_geo) ──\n", " [G2_3] E 1: t=0.094 v=0.251 cos=-0.010 cv=1.2798 [polygon=0.00 curve=0.40 star=0.21 structure=0.43]\n", " [G2_3] E10: t=0.659 v=0.625 cos=-0.000 cv=0.5776 [polygon=0.31 curve=0.80 star=0.84 structure=0.70]\n", " [G2_3] E20: t=0.732 v=0.753 cos=0.000 cv=0.3896 [polygon=0.52 curve=0.99 star=0.96 structure=0.72]\n", " [G2_3] E30: t=0.768 v=0.764 cos=0.001 cv=0.3200 [polygon=0.55 curve=0.98 star=0.97 structure=0.73]\n", " → G2_3: val=0.764\n", "\n", "=================================================================\n", "GENERATION 3: FINAL DESCENDANT from 4 G2 + 1 new founder\n", "=================================================================\n", "\n", " ── New founder ──\n", " [NEW2] E 1: t=0.054 v=0.081 cv=1.2044 [polygon=0.08 curve=0.20 star=0.00 structure=0.07]\n", " [NEW2] E10: t=0.604 v=0.638 cv=1.4442 [polygon=0.29 curve=0.98 star=0.79 structure=0.69]\n", " [NEW2] E20: t=0.668 v=0.586 cv=1.7902 [polygon=0.39 curve=0.92 star=0.45 structure=0.68]\n", " [NEW2] E30: t=0.690 v=0.699 cv=1.5974 [polygon=0.48 curve=0.98 star=0.89 structure=0.64]\n", "\n", " Extracting G2 + new founder...\n", " GPA consensus (5 models): CV=0.1456, cos=[0.6665045022964478, 0.6673623323440552, 0.6707176566123962, 0.6691686511039734, 0.6510789394378662]\n", "\n", " ── FINAL DESCENDANT ──\n", " [FINAL] E 1: t=0.088 v=0.246 cos=-0.006 cv=1.2441 [polygon=0.11 curve=0.00 star=0.23 structure=0.50]\n", " [FINAL] E10: t=0.654 v=0.672 cos=0.001 cv=0.5332 [polygon=0.42 curve=0.99 star=0.83 structure=0.65]\n", " [FINAL] E20: t=0.714 v=0.690 cos=0.002 cv=0.3923 [polygon=0.46 curve=0.97 star=0.73 structure=0.73]\n", " [FINAL] E30: t=0.761 v=0.764 cos=0.002 cv=0.3244 [polygon=0.55 curve=1.00 star=0.96 structure=0.73]\n", "\n", "\n", "=================================================================\n", "EVOLUTION SUMMARY\n", "=================================================================\n", "\n", " Model Gen v_acc cv poly curve star struct\n", " -------------------------------------------------------\n", " F0_geo 0 0.663 1.8428 0.36 1.00 0.75 0.72\n", " F0_raw 0 0.586 1.2137 0.48 0.29 0.91 0.64\n", " F1_new 1 0.690 0.0000 0.00 0.00 0.00 0.00\n", " G1_0 1 0.746 0.5866 0.50 0.99 0.93 0.74\n", " G1_1 1 0.749 0.4433 0.49 1.00 0.95 0.74\n", " G1_2 1 0.761 0.2822 0.49 1.00 0.99 0.75\n", " F2_new 2 0.699 0.0000 0.00 0.00 0.00 0.00\n", " G2_0 2 0.750 0.4110 0.49 0.99 0.94 0.75\n", " G2_1 2 0.751 0.4170 0.49 0.95 0.97 0.76\n", " G2_2 2 0.766 0.3079 0.54 0.98 0.96 0.75\n", " G2_3 2 0.764 0.3613 0.55 0.98 0.97 0.73\n", " FINAL 3 0.764 0.2954 0.55 1.00 0.96 0.73\n", "\n", " Per-generation averages:\n", " Gen 0: mean_acc=0.625 best=0.663 n=2\n", " Gen 1: mean_acc=0.737 best=0.761 n=4\n", " Gen 2: mean_acc=0.746 best=0.766 n=5\n", " Gen 3: mean_acc=0.764 best=0.764 n=1\n", "\n", " Consensus CV progression: G1=0.1258 → G2=0.1031 → G3=0.1456\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# super generational" ], "metadata": { "id": "b8t-YMfylI1R" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DATA-DIVERSE GEOMETRIC EVOLUTION\n", "#\n", "# Each generation trains on differently-perturbed data.\n", "# Consensus captures what's INVARIANT across perturbations.\n", "#\n", "# Gen 0: 2 founders, Dataset A (standard)\n", "# → GPA → consensus anchors\n", "#\n", "# Gen 1: 2 students distilled from Gen 0 consensus\n", "# Student S1: Dataset B (high noise, thick strokes)\n", "# Student S2: Dataset C (thin strokes, shifted centers)\n", "# → GPA consensus of S1 + S2\n", "#\n", "# Gen 2: 3 offspring from Gen 1 consensus + 1 new founder on Dataset D\n", "# → GPA consensus of 4\n", "#\n", "# Gen 3: 5 models, each on Dataset E (identical perturbation style,\n", "# different random samples)\n", "# → GPA consensus of 5\n", "#\n", "# Gen 4 (FINAL): 3 triplets, each selecting different 5 parents\n", "# from the ENTIRE lineage pool\n", "# ============================================================================\n", "\n", "import math\n", "import gc\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"DATA-DIVERSE GEOMETRIC EVOLUTION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " return (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); Sc = S - s_mean; Ns = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(Ns-1, 1)\n", " t_mean = T.mean(0, keepdim=True); Tc = T - t_mean\n", " t_cov = (Tc.T @ Tc) / max(Ns-1, 1)\n", " s_w = symmetric_inv_sqrt(s_cov); t_w = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_w, dim=-1); Tc_w = F.normalize(Tc @ t_w, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " return {\"rotation\": U @ Vt, \"source_mean\": s_mean.squeeze(0), \"source_whitener\": s_w}\n", "\n", "def apply_align(emb, info):\n", " return (emb.float() - info[\"source_mean\"]) @ info[\"source_whitener\"] @ info[\"rotation\"].T\n", "\n", "def gpa_consensus(embeddings_list, n_iters=15):\n", " N = len(embeddings_list)\n", " cur = {i: e.float() for i, e in enumerate(embeddings_list)}\n", " for it in range(n_iters):\n", " mean = sum(cur[i] for i in range(N)) / N\n", " delta = 0.0\n", " new_cur = {}\n", " for i in range(N):\n", " info = procrustes_align(cur[i], mean)\n", " new_cur[i] = apply_align(cur[i], info)\n", " delta += (new_cur[i] - cur[i]).pow(2).mean().item()\n", " cur = new_cur\n", " if delta < 1e-8: break\n", " mean = sum(cur[i] for i in range(N)) / N\n", " return F.normalize(mean, dim=-1)\n", "\n", "def consensus_anchors(consensus, n_anchors=1024):\n", " \"\"\"\n", " K-means on consensus embeddings. Anchors discover their own\n", " regions of the manifold independent of class boundaries.\n", " \"\"\"\n", " emb = consensus.detach().float()\n", " N, D = emb.shape\n", "\n", " # Init: random subset\n", " idx = torch.randperm(N)[:n_anchors]\n", " centers = emb[idx].clone()\n", "\n", " for _ in range(30):\n", " # Assign\n", " cos = emb @ F.normalize(centers, dim=-1).T\n", " assignments = cos.argmax(dim=-1)\n", " # Update\n", " new_centers = torch.zeros_like(centers)\n", " for k in range(n_anchors):\n", " mask = assignments == k\n", " if mask.sum() > 0:\n", " new_centers[k] = emb[mask].mean(0)\n", " else:\n", " new_centers[k] = emb[torch.randint(N, (1,))].squeeze(0)\n", " delta = (F.normalize(new_centers, dim=-1) - F.normalize(centers, dim=-1)).pow(2).sum()\n", " centers = new_centers\n", " if delta < 1e-6: break\n", "\n", " return F.normalize(centers, dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=1024, d_embed=64, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", " @torch.no_grad()\n", " def update_rigidity(self, tri):\n", " nearest = tri.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " m = nearest == i\n", " if m.sum() < 5: continue\n", " self.visit_count[i] += m.sum().float()\n", " sp = tri[m].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(sp+0.01)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=1024, n_comp=6, d_comp=64):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn==k).sum().item(), d_comp*2), nn.GELU(),\n", " nn.Linear(d_comp*2, d_comp), nn.LayerNorm(d_comp)) for k in range(n_comp)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn==k]) for k in range(self.n_comp)], -1)\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, nc=30, na=1024, de=256, ncomp=6, dc=64, dh=256, init_a=None):\n", " super().__init__()\n", " if init_a is not None:\n", " na = init_a.shape[0] # infer from provided anchors\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1,32,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32,64,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64,128,3,padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.proj = nn.Sequential(nn.Linear(128, de), nn.LayerNorm(de))\n", " self.constellation = Constellation(na, de, init_a)\n", " self.patchwork = Patchwork(na, ncomp, dc)\n", " self.mlp = nn.Sequential(\n", " nn.Linear(ncomp*dc, dh), nn.GELU(), nn.LayerNorm(dh),\n", " nn.Linear(dh, dh), nn.GELU(), nn.LayerNorm(dh),\n", " nn.Linear(dh, nc))\n", " def forward(self, x):\n", " emb = F.normalize(self.proj(self.backbone(x).flatten(1)), dim=-1)\n", " tri, near = self.constellation.triangulate(emb)\n", " return self.mlp(self.patchwork(tri)), emb, tri, near\n", " def encode(self, x):\n", " return F.normalize(self.proj(self.backbone(x).flatten(1)), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS WITH PERTURBATION PROFILES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " if 0<=int(x1) 0 else 0\n", " cy_off = np.random.randint(-sh, sh+1) if sh > 0 else 0\n", " base_p = [0.20,0.12,0.15,0.10,0.10,0.08,0.08,0.07,0.06,0.03,\n", " 0.10,0.10,0.10,0.10,0.12,0.12,0.12,0.12,0.12,0.12,\n", " 0.15,0.10,0.12,0.10,0.10,0.10,0.15,0.18,0.10,0.12]\n", " p = base_p[c] * ps\n", " kw = {\"sz\": sz, \"cx_off\": cx_off, \"cy_off\": cy_off}\n", " R = [lambda: rpoly(3,p=p,t=t,**kw), lambda: rpoly(4,p=p,t=t,**kw),\n", " lambda: rpoly(5,p=p,t=t,**kw), lambda: rpoly(6,p=p,t=t,**kw),\n", " lambda: rpoly(7,p=p,t=t,**kw), lambda: rpoly(8,p=p,t=t,**kw),\n", " lambda: rpoly(9,p=p,t=t,**kw), lambda: rpoly(10,p=p,t=t,**kw),\n", " lambda: rpoly(12,p=p,t=t,**kw), lambda: rpoly(32,p=p*0.3,t=t,**kw),\n", " lambda: rellipse(p=p,**kw), lambda: rspiral(p=p,**kw),\n", " lambda: rwave(p=p,**kw), lambda: rcrescent(p=p,**kw),\n", " lambda: rstar(3,p=p,t=t,**kw), lambda: rstar(4,p=p,t=t,**kw),\n", " lambda: rstar(5,p=p,t=t,**kw), lambda: rstar(6,p=p,t=t,**kw),\n", " lambda: rstar(7,p=p,t=t,**kw), lambda: rstar(8,p=p,t=t,**kw),\n", " lambda: rcross(p=p,t=t,**kw), lambda: rpoly(4,p=p,t=t,**kw),\n", " lambda: rarrow(p=p,t=t,**kw), lambda: rheart(p=p,**kw),\n", " lambda: rring(p=p,**kw), lambda: rsemicirc(p=p,t=t,**kw),\n", " lambda: rpoly(4,p=p*1.2,t=t,**kw), lambda: rpoly(4,p=p*1.5,t=t,**kw),\n", " lambda: rpoly(4,p=p,t=t,**kw), lambda: rchevron(p=p,t=t,**kw)]\n", " img = R[c]()\n", " if pr[\"noise\"] > 0:\n", " img = img + np.random.normal(0, pr[\"noise\"], img.shape).astype(np.float32)\n", " img = np.clip(img, 0, 1)\n", " return img\n", "\n", "def gen_data(n_per=500, sz=32, profile=\"A\", seed=None):\n", " if seed is not None: np.random.seed(seed)\n", " imgs, labels = [], []\n", " for _ in range(n_per):\n", " for c in range(30):\n", " imgs.append(gen_one(c, sz, profile)); labels.append(c)\n", " imgs = torch.tensor(np.array(imgs)).unsqueeze(1)\n", " labels = torch.tensor(labels, dtype=torch.long)\n", " perm = torch.randperm(len(labels))\n", " return imgs[perm], labels[perm]\n", "\n", "TYPES = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", "\n", "def eval_model(model, imgs, labels):\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model(imgs)\n", " acc = (vl.argmax(-1) == labels).float().mean().item()\n", " cv = cv_metric(ve)\n", " ta = {}\n", " for tn, tids in TYPES.items():\n", " tm = torch.zeros(len(labels), dtype=bool, device=imgs.device)\n", " for tid in tids: tm |= (labels == tid)\n", " if tm.sum() > 0: ta[tn] = (vl.argmax(-1)[tm] == labels[tm]).float().mean().item()\n", " return acc, cv, ta\n", "\n", "def fmt_ta(ta):\n", " return \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING FUNCTIONS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "GEO_CFG = {\"tang\": 0.01, \"sep\": 1.0, \"cv_w\": 0.001, \"spr\": 1e-3, \"ort\": 1e-3, \"ent\": 1e-4}\n", "\n", "def train_founder(model, tr_imgs, tr_labels, use_geo=True, epochs=30, tag=\"\"):\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; nt = len(tr_labels)\n", " for ep in range(epochs):\n", " model.train(); perm = torch.randperm(nt, device=DEVICE); tc = 0\n", " for i in range(0, nt, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " lo, emb, tri, _ = model(tr_imgs[idx]); lab = tr_labels[idx]\n", " anc = model.constellation.anchors\n", " if use_geo:\n", " eg = EmbeddingAutograd.apply(emb, emb, anc, GEO_CFG[\"tang\"], GEO_CFG[\"sep\"])\n", " tg, _ = model.constellation.triangulate(eg)\n", " lo = model.mlp(model.patchwork(tg))\n", " l = F.cross_entropy(lo, lab)\n", " lg = torch.tensor(0.0, device=DEVICE)\n", " if use_geo:\n", " lg += GEO_CFG[\"cv_w\"] * cv_loss(emb)\n", " lg += GEO_CFG[\"spr\"] * anchor_spread_loss(anc)\n", " lg += GEO_CFG[\"ort\"] * anchor_ortho_loss(anc)\n", " lg += GEO_CFG[\"ent\"] * anchor_entropy_loss(emb, anc)\n", " (l + lg).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (lo.argmax(-1) == lab).sum().item()\n", " if (ep+1) % 10 == 0 or ep == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " print(f\" {tag}E{ep+1:2d}: t={tc/nt:.3f} v={acc:.3f} cv={cv:.4f} [{fmt_ta(ta)}]\")\n", "\n", "def train_distilled(model, tr_imgs, tr_labels, consensus, epochs=30, tag=\"\"):\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; nt = len(tr_labels)\n", " for ep in range(epochs):\n", " model.train(); perm = torch.randperm(nt, device=DEVICE); tc = 0\n", " for i in range(0, nt, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " lo, emb, tri, _ = model(tr_imgs[idx]); lab = tr_labels[idx]; tgt = consensus[idx]\n", " anc = model.constellation.anchors\n", " eg = EmbeddingAutograd.apply(emb, emb, anc, GEO_CFG[\"tang\"], GEO_CFG[\"sep\"])\n", " tg, _ = model.constellation.triangulate(eg)\n", " lo = model.mlp(model.patchwork(tg))\n", " l_cls = F.cross_entropy(lo, lab)\n", " l_nce = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = GEO_CFG[\"cv_w\"] * cv_loss(emb)\n", " l_ent = GEO_CFG[\"ent\"] * anchor_entropy_loss(emb, anc)\n", " (l_cls + 0.5*l_nce + 0.5*l_mse + l_cv + l_ent).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (lo.argmax(-1) == lab).sum().item()\n", " if (ep+1) % 10 == 0 or ep == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " print(f\" {tag}E{ep+1:2d}: t={tc/nt:.3f} v={acc:.3f} cv={cv:.4f} [{fmt_ta(ta)}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# VALIDATION DATA (always Dataset A — standard, consistent eval)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Generating validation data (Dataset A)...\")\n", "val_imgs, val_labels = gen_data(n_per=100, profile=\"A\", seed=999)\n", "val_imgs, val_labels = val_imgs.to(DEVICE), val_labels.to(DEVICE)\n", "print(f\" Val: {len(val_labels):,}\")\n", "\n", "all_results = {}\n", "all_models = {} # keep references for final triplet parent selection\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 0: 2 FOUNDERS on Dataset A\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 0: 2 FOUNDERS — Dataset A\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_A, lb_A = gen_data(n_per=500, profile=\"A\", seed=42)\n", "tr_A, lb_A = tr_A.to(DEVICE), lb_A.to(DEVICE)\n", "\n", "for name, use_geo, sd in [(\"F0a\", False, 100), (\"F0b\", True, 200)]:\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(sd)\n", " m = PatchworkClassifier(init_a=None).to(DEVICE)\n", " train_founder(m, tr_A, lb_A, use_geo=use_geo, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 0}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "# GPA consensus\n", "print(f\"\\n GPA alignment (Gen 0)...\")\n", "embs_g0 = {n: m.encode(tr_A).detach() for n, m in all_models.items() if n.startswith(\"F0\")}\n", "cons_g0 = gpa_consensus(list(embs_g0.values()))\n", "anc_g0 = consensus_anchors(cons_g0)\n", "print(f\" Consensus CV: {cv_metric(cons_g0[:2000]):.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 1: 2 STUDENTS — Dataset B and Dataset C\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 1: 2 STUDENTS — Datasets B and C\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_B, lb_B = gen_data(n_per=500, profile=\"B\", seed=300)\n", "tr_C, lb_C = gen_data(n_per=500, profile=\"C\", seed=400)\n", "tr_B, lb_B = tr_B.to(DEVICE), lb_B.to(DEVICE)\n", "tr_C, lb_C = tr_C.to(DEVICE), lb_C.to(DEVICE)\n", "\n", "# Need consensus targets indexed to each dataset's label ordering\n", "# Since gen_data shuffles, we recompute consensus for each dataset\n", "cons_g0_B = gpa_consensus([all_models[\"F0a\"].encode(tr_B).detach(), all_models[\"F0b\"].encode(tr_B).detach()])\n", "cons_g0_C = gpa_consensus([all_models[\"F0a\"].encode(tr_C).detach(), all_models[\"F0b\"].encode(tr_C).detach()])\n", "\n", "for name, tr, lb, cons, sd in [(\"G1_B\", tr_B, lb_B, cons_g0_B, 301),\n", " (\"G1_C\", tr_C, lb_C, cons_g0_C, 401)]:\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(sd)\n", " m = PatchworkClassifier(init_a=consensus_anchors(cons)).to(DEVICE)\n", " train_distilled(m, tr, lb, cons, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 1}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "del embs_g0; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 2: 3 OFFSPRING from G1 + 1 new founder, Dataset D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 2: 3 OFFSPRING + new founder — Dataset D\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_D, lb_D = gen_data(n_per=500, profile=\"D\", seed=500)\n", "tr_D, lb_D = tr_D.to(DEVICE), lb_D.to(DEVICE)\n", "\n", "# New founder on Dataset D\n", "print(f\"\\n ── New founder (F1_D) ──\")\n", "torch.manual_seed(501)\n", "f1d = PatchworkClassifier(init_a=None).to(DEVICE)\n", "train_founder(f1d, tr_D, lb_D, use_geo=True, tag=\"[F1_D] \")\n", "acc_f1d, _, _ = eval_model(f1d, val_imgs, val_labels)\n", "all_results[\"F1_D\"] = {\"acc\": acc_f1d, \"cv\": 0, \"ta\": {}, \"gen\": 1}\n", "all_models[\"F1_D\"] = f1d\n", "\n", "# GPA from G1 + new founder (encode on Dataset D for consensus)\n", "print(f\"\\n GPA alignment (G1 + F1_D on Dataset D)...\")\n", "g2_parents = [\"G1_B\", \"G1_C\", \"F1_D\"]\n", "embs_g2 = [all_models[n].encode(tr_D).detach() for n in g2_parents]\n", "cons_g2 = gpa_consensus(embs_g2)\n", "anc_g2 = consensus_anchors(cons_g2)\n", "print(f\" Consensus CV: {cv_metric(cons_g2[:2000]):.4f}\")\n", "\n", "for i in range(3):\n", " name = f\"G2_{i}\"\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(600 + i)\n", " m = PatchworkClassifier(init_a=anc_g2).to(DEVICE)\n", " train_distilled(m, tr_D, lb_D, cons_g2, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 2}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 3: 5 MODELS — Dataset E (identical perturbation,\n", "# different random samples)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 3: 5 MODELS — Dataset E (identical profile, varied samples)\")\n", "print(f\"{'='*65}\")\n", "\n", "# GPA from all G2 + new founder\n", "g3_parents = [n for n in all_models if n.startswith(\"G2_\")]\n", "print(f\" GPA alignment ({len(g3_parents)} G2 parents)...\")\n", "\n", "# Each Gen 3 model gets its own Dataset E sample\n", "g3_models = []\n", "for j in range(5):\n", " name = f\"G3_{j}\"\n", " tr_Ej, lb_Ej = gen_data(n_per=500, profile=\"E\", seed=700 + j * 10)\n", " tr_Ej, lb_Ej = tr_Ej.to(DEVICE), lb_Ej.to(DEVICE)\n", "\n", " # Consensus from G2 parents on this dataset\n", " embs_j = [all_models[n].encode(tr_Ej).detach() for n in g3_parents]\n", " cons_j = gpa_consensus(embs_j)\n", " anc_j = consensus_anchors(cons_j)\n", "\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(700 + j)\n", " m = PatchworkClassifier(init_a=anc_j).to(DEVICE)\n", " train_distilled(m, tr_Ej, lb_Ej, cons_j, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 3}\n", " all_models[name] = m\n", " g3_models.append(name)\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", " del tr_Ej, lb_Ej; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 4 (FINAL): 3 TRIPLETS — each selects different 5\n", "# parents from the FULL lineage\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 4 (FINAL): 3 TRIPLETS — cross-lineage parent selection\")\n", "print(f\"{'='*65}\")\n", "\n", "# Sort all models by accuracy for parent selection\n", "ranked = sorted(all_results.items(), key=lambda x: -x[1][\"acc\"])\n", "ranked_names = [n for n, _ in ranked if n in all_models]\n", "\n", "# Three different parent selection strategies\n", "parent_sets = {\n", " # Top 5 overall\n", " \"T4_best5\": ranked_names[:5],\n", " # Best from each generation\n", " \"T4_cross\": [],\n", " # Diverse: top + bottom + middle\n", " \"T4_diverse\": [],\n", "}\n", "\n", "# Cross-generational: pick best from each gen\n", "for gen in range(4):\n", " gen_models = [(n, r) for n, r in ranked if r[\"gen\"] == gen and n in all_models]\n", " if gen_models:\n", " parent_sets[\"T4_cross\"].append(gen_models[0][0])\n", "# Pad to 5 if needed\n", "while len(parent_sets[\"T4_cross\"]) < 5:\n", " for n in ranked_names:\n", " if n not in parent_sets[\"T4_cross\"]:\n", " parent_sets[\"T4_cross\"].append(n); break\n", "\n", "# Diverse: positions 0, 2, 4, 6, 8 from ranking\n", "for idx in [0, 2, 4, 6, 8]:\n", " if idx < len(ranked_names):\n", " parent_sets[\"T4_diverse\"].append(ranked_names[idx])\n", "\n", "# Fresh eval data for final generation\n", "tr_final, lb_final = gen_data(n_per=500, profile=\"A\", seed=888)\n", "tr_final, lb_final = tr_final.to(DEVICE), lb_final.to(DEVICE)\n", "\n", "for name, parents in parent_sets.items():\n", " print(f\"\\n ── {name} (parents: {parents}) ──\")\n", " embs_fin = [all_models[p].encode(tr_final).detach() for p in parents]\n", " cons_fin = gpa_consensus(embs_fin)\n", " anc_fin = consensus_anchors(cons_fin)\n", " cons_cv = cv_metric(cons_fin[:2000])\n", " print(f\" Consensus CV: {cons_cv:.4f}\")\n", "\n", " torch.manual_seed(hash(name) % 2**32)\n", " m = PatchworkClassifier(init_a=anc_fin).to(DEVICE)\n", " train_distilled(m, tr_final, lb_final, cons_fin, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 4}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVOLUTION SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"EVOLUTION SUMMARY\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Model':<12} {'Gen':>3} {'v_acc':>6} {'cv':>7} \"\n", " f\"{'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*58}\")\n", "\n", "for name in sorted(all_results.keys(), key=lambda x: (all_results[x][\"gen\"], x)):\n", " r = all_results[name]\n", " ta = r.get(\"ta\", {})\n", " print(f\" {name:<12} {r['gen']:>3} {r['acc']:>6.3f} {r['cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "print(f\"\\n Per-generation averages:\")\n", "for gen in range(5):\n", " accs = [r[\"acc\"] for r in all_results.values() if r[\"gen\"] == gen and r[\"acc\"] > 0]\n", " if accs:\n", " print(f\" Gen {gen}: mean={np.mean(accs):.3f} best={max(accs):.3f} n={len(accs)}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Jwrv3Um-hODl", "outputId": "0fad96d9-f88d-4dbc-bdd9-464f37e33a67" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DATA-DIVERSE GEOMETRIC EVOLUTION\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating validation data (Dataset A)...\n", " Val: 3,000\n", "\n", "=================================================================\n", "GEN 0: 2 FOUNDERS — Dataset A\n", "=================================================================\n", "\n", " ── F0a ──\n", " [F0a] E 1: t=0.068 v=0.105 cv=1.7267 [polygon=0.00 curve=0.20 star=0.15 structure=0.13]\n", " [F0a] E10: t=0.594 v=0.525 cv=1.3545 [polygon=0.25 curve=0.97 star=0.42 structure=0.61]\n", " [F0a] E20: t=0.658 v=0.684 cv=1.4089 [polygon=0.46 curve=0.98 star=0.74 structure=0.70]\n", " [F0a] E30: t=0.674 v=0.665 cv=1.3165 [polygon=0.40 curve=1.00 star=0.65 structure=0.74]\n", " → F0a: val=0.665\n", "\n", " ── F0b ──\n", " [F0b] E 1: t=0.062 v=0.093 cv=2.2951 [polygon=0.07 curve=0.00 star=0.00 structure=0.22]\n", " [F0b] E10: t=0.485 v=0.532 cv=1.8598 [polygon=0.33 curve=0.79 star=0.55 structure=0.57]\n", " [F0b] E20: t=0.641 v=0.590 cv=1.5643 [polygon=0.27 curve=1.00 star=0.64 structure=0.65]\n", " [F0b] E30: t=0.699 v=0.719 cv=1.3577 [polygon=0.44 curve=1.00 star=0.86 structure=0.74]\n", " → F0b: val=0.719\n", "\n", " GPA alignment (Gen 0)...\n", " Consensus CV: 0.1500\n", "\n", "=================================================================\n", "GEN 1: 2 STUDENTS — Datasets B and C\n", "=================================================================\n", "\n", " ── G1_B ──\n", " [G1_B] E 1: t=0.063 v=0.086 cv=2.2529 [polygon=0.00 curve=0.20 star=0.00 structure=0.16]\n", " [G1_B] E10: t=0.620 v=0.261 cv=0.8448 [polygon=0.00 curve=0.79 star=0.00 structure=0.39]\n", " [G1_B] E20: t=0.699 v=0.241 cv=0.6766 [polygon=0.00 curve=0.76 star=0.00 structure=0.34]\n", " [G1_B] E30: t=0.730 v=0.309 cv=0.6065 [polygon=0.02 curve=0.77 star=0.00 structure=0.53]\n", " → G1_B: val=0.309\n", "\n", " ── G1_C ──\n", " [G1_C] E 1: t=0.065 v=0.097 cv=1.3663 [polygon=0.00 curve=0.00 star=0.02 structure=0.28]\n", " [G1_C] E10: t=0.657 v=0.571 cv=0.5115 [polygon=0.30 curve=0.80 star=0.60 structure=0.69]\n", " [G1_C] E20: t=0.715 v=0.658 cv=0.4542 [polygon=0.38 curve=0.81 star=0.89 structure=0.69]\n", " [G1_C] E30: t=0.770 v=0.719 cv=0.4285 [polygon=0.39 curve=0.98 star=0.98 structure=0.73]\n", " → G1_C: val=0.719\n", "\n", "=================================================================\n", "GEN 2: 3 OFFSPRING + new founder — Dataset D\n", "=================================================================\n", "\n", " ── New founder (F1_D) ──\n", " [F1_D] E 1: t=0.059 v=0.084 cv=1.3399 [polygon=0.00 curve=0.20 star=0.17 structure=0.05]\n", " [F1_D] E10: t=0.360 v=0.215 cv=1.7776 [polygon=0.02 curve=0.16 star=0.48 structure=0.25]\n", " [F1_D] E20: t=0.575 v=0.502 cv=2.1396 [polygon=0.14 curve=0.88 star=0.57 structure=0.60]\n", " [F1_D] E30: t=0.641 v=0.623 cv=1.5180 [polygon=0.44 curve=0.96 star=0.55 structure=0.67]\n", "\n", " GPA alignment (G1 + F1_D on Dataset D)...\n", " Consensus CV: 0.0489\n", "\n", " ── G2_0 ──\n", " [G2_0] E 1: t=0.066 v=0.165 cv=1.3066 [polygon=0.07 curve=0.20 star=0.33 structure=0.14]\n", " [G2_0] E10: t=0.622 v=0.636 cv=0.4065 [polygon=0.43 curve=0.93 star=0.57 structure=0.71]\n", " [G2_0] E20: t=0.691 v=0.685 cv=0.4430 [polygon=0.33 curve=1.00 star=0.88 structure=0.73]\n", " [G2_0] E30: t=0.738 v=0.727 cv=0.4154 [polygon=0.40 curve=1.00 star=0.97 structure=0.73]\n", " → G2_0: val=0.727\n", "\n", " ── G2_1 ──\n", " [G2_1] E 1: t=0.069 v=0.084 cv=1.1123 [polygon=0.13 curve=0.00 star=0.14 structure=0.05]\n", " [G2_1] E10: t=0.615 v=0.609 cv=0.4636 [polygon=0.33 curve=0.86 star=0.62 structure=0.72]\n", " [G2_1] E20: t=0.694 v=0.686 cv=0.3795 [polygon=0.41 curve=1.00 star=0.78 structure=0.72]\n", " [G2_1] E30: t=0.744 v=0.692 cv=0.4277 [polygon=0.33 curve=1.00 star=0.89 structure=0.75]\n", " → G2_1: val=0.692\n", "\n", " ── G2_2 ──\n", " [G2_2] E 1: t=0.070 v=0.170 cv=1.1752 [polygon=0.13 curve=0.20 star=0.20 structure=0.18]\n", " [G2_2] E10: t=0.628 v=0.633 cv=0.4386 [polygon=0.38 curve=0.92 star=0.83 structure=0.60]\n", " [G2_2] E20: t=0.710 v=0.680 cv=0.4235 [polygon=0.45 curve=1.00 star=0.86 structure=0.62]\n", " [G2_2] E30: t=0.739 v=0.729 cv=0.4526 [polygon=0.44 curve=1.00 star=0.96 structure=0.72]\n", " → G2_2: val=0.729\n", "\n", "=================================================================\n", "GEN 3: 5 MODELS — Dataset E (identical profile, varied samples)\n", "=================================================================\n", " GPA alignment (3 G2 parents)...\n", "\n", " ── G3_0 ──\n", " [G3_0] E 1: t=0.064 v=0.160 cv=1.3442 [polygon=0.12 curve=0.20 star=0.24 structure=0.13]\n", " [G3_0] E10: t=0.673 v=0.684 cv=0.4356 [polygon=0.47 curve=0.99 star=0.75 structure=0.68]\n", " [G3_0] E20: t=0.731 v=0.706 cv=0.3749 [polygon=0.39 curve=1.00 star=0.89 structure=0.74]\n", " [G3_0] E30: t=0.769 v=0.687 cv=0.3540 [polygon=0.39 curve=0.79 star=0.96 structure=0.74]\n", " → G3_0: val=0.687\n", "\n", " ── G3_1 ──\n", " [G3_1] E 1: t=0.073 v=0.160 cv=0.9807 [polygon=0.04 curve=0.39 star=0.32 structure=0.05]\n", " [G3_1] E10: t=0.660 v=0.689 cv=0.3703 [polygon=0.44 curve=1.00 star=0.79 structure=0.69]\n", " [G3_1] E20: t=0.743 v=0.715 cv=0.3261 [polygon=0.40 curve=1.00 star=0.92 structure=0.73]\n", " [G3_1] E30: t=0.763 v=0.713 cv=0.3395 [polygon=0.38 curve=1.00 star=0.91 structure=0.75]\n", " → G3_1: val=0.713\n", "\n", " ── G3_2 ──\n", " [G3_2] E 1: t=0.080 v=0.170 cv=1.3637 [polygon=0.13 curve=0.00 star=0.36 structure=0.18]\n", " [G3_2] E10: t=0.653 v=0.691 cv=0.4304 [polygon=0.38 curve=0.96 star=0.82 structure=0.75]\n", " [G3_2] E20: t=0.727 v=0.746 cv=0.3324 [polygon=0.46 curve=1.00 star=0.98 structure=0.74]\n", " [G3_2] E30: t=0.763 v=0.749 cv=0.3801 [polygon=0.49 curve=1.00 star=0.97 structure=0.73]\n", " → G3_2: val=0.749\n", "\n", " ── G3_3 ──\n", " [G3_3] E 1: t=0.072 v=0.165 cv=1.3944 [polygon=0.00 curve=0.19 star=0.31 structure=0.21]\n", " [G3_3] E10: t=0.666 v=0.552 cv=0.3821 [polygon=0.20 curve=0.97 star=0.63 structure=0.61]\n", " [G3_3] E20: t=0.721 v=0.667 cv=0.3782 [polygon=0.45 curve=0.88 star=0.74 structure=0.71]\n", " [G3_3] E30: t=0.764 v=0.701 cv=0.3710 [polygon=0.35 curve=1.00 star=0.90 structure=0.75]\n", " → G3_3: val=0.701\n", "\n", " ── G3_4 ──\n", " [G3_4] E 1: t=0.072 v=0.174 cv=1.1897 [polygon=0.08 curve=0.20 star=0.31 structure=0.17]\n", " [G3_4] E10: t=0.654 v=0.695 cv=0.4198 [polygon=0.41 curve=0.96 star=0.81 structure=0.75]\n", " [G3_4] E20: t=0.736 v=0.724 cv=0.3653 [polygon=0.46 curve=0.97 star=0.98 structure=0.70]\n", " [G3_4] E30: t=0.758 v=0.731 cv=0.3906 [polygon=0.49 curve=1.00 star=0.84 structure=0.75]\n", " → G3_4: val=0.731\n", "\n", "=================================================================\n", "GEN 4 (FINAL): 3 TRIPLETS — cross-lineage parent selection\n", "=================================================================\n", "\n", " ── T4_best5 (parents: ['G3_2', 'G3_4', 'G2_2', 'G2_0', 'F0b']) ──\n", " Consensus CV: 0.1716\n", " [T4_best5] E 1: t=0.089 v=0.250 cv=1.1556 [polygon=0.12 curve=0.20 star=0.40 structure=0.30]\n", " [T4_best5] E10: t=0.672 v=0.691 cv=0.3319 [polygon=0.36 curve=1.00 star=0.83 structure=0.75]\n", " [T4_best5] E20: t=0.749 v=0.751 cv=0.3513 [polygon=0.53 curve=1.00 star=0.86 structure=0.76]\n", " [T4_best5] E30: t=0.772 v=0.776 cv=0.3034 [polygon=0.53 curve=1.00 star=0.98 structure=0.76]\n", " → T4_best5: val=0.776\n", "\n", " ── T4_cross (parents: ['F0b', 'G1_C', 'G2_2', 'G3_2', 'G3_4']) ──\n", " Consensus CV: 0.0913\n", " [T4_cross] E 1: t=0.071 v=0.159 cv=1.1704 [polygon=0.14 curve=0.20 star=0.25 structure=0.10]\n", " [T4_cross] E10: t=0.688 v=0.671 cv=0.3763 [polygon=0.30 curve=0.98 star=0.84 structure=0.75]\n", " [T4_cross] E20: t=0.754 v=0.745 cv=0.3560 [polygon=0.47 curve=1.00 star=0.93 structure=0.75]\n", " [T4_cross] E30: t=0.775 v=0.763 cv=0.3507 [polygon=0.51 curve=1.00 star=0.98 structure=0.74]\n", " → T4_cross: val=0.763\n", "\n", " ── T4_diverse (parents: ['G3_2', 'G2_2', 'F0b', 'G3_1', 'G2_1']) ──\n", " Consensus CV: 0.1569\n", " [T4_diverse] E 1: t=0.075 v=0.258 cv=1.0555 [polygon=0.14 curve=0.55 star=0.40 structure=0.14]\n", " [T4_diverse] E10: t=0.685 v=0.688 cv=0.3056 [polygon=0.43 curve=1.00 star=0.72 structure=0.75]\n", " [T4_diverse] E20: t=0.765 v=0.757 cv=0.3207 [polygon=0.49 curve=1.00 star=0.98 structure=0.75]\n", " [T4_diverse] E30: t=0.780 v=0.777 cv=0.3381 [polygon=0.54 curve=0.99 star=0.99 structure=0.75]\n", " → T4_diverse: val=0.777\n", "\n", "\n", "=================================================================\n", "EVOLUTION SUMMARY\n", "=================================================================\n", "\n", " Model Gen v_acc cv poly curve star struct\n", " ----------------------------------------------------------\n", " F0a 0 0.665 1.4819 0.40 1.00 0.65 0.74\n", " F0b 0 0.719 1.5451 0.44 1.00 0.86 0.74\n", " F1_D 1 0.623 0.0000 0.00 0.00 0.00 0.00\n", " G1_B 1 0.309 0.6088 0.02 0.77 0.00 0.53\n", " G1_C 1 0.719 0.4444 0.39 0.98 0.98 0.73\n", " G2_0 2 0.727 0.4513 0.40 1.00 0.97 0.73\n", " G2_1 2 0.692 0.4332 0.33 1.00 0.89 0.75\n", " G2_2 2 0.729 0.4757 0.44 1.00 0.96 0.72\n", " G3_0 3 0.687 0.3673 0.39 0.79 0.96 0.74\n", " G3_1 3 0.713 0.3233 0.38 1.00 0.91 0.75\n", " G3_2 3 0.749 0.3710 0.49 1.00 0.97 0.73\n", " G3_3 3 0.701 0.3984 0.35 1.00 0.90 0.75\n", " G3_4 3 0.731 0.3634 0.49 1.00 0.84 0.75\n", " T4_best5 4 0.776 0.2564 0.53 1.00 0.98 0.76\n", " T4_cross 4 0.763 0.3410 0.51 1.00 0.98 0.74\n", " T4_diverse 4 0.777 0.3066 0.54 0.99 0.99 0.75\n", "\n", " Per-generation averages:\n", " Gen 0: mean=0.692 best=0.719 n=2\n", " Gen 1: mean=0.550 best=0.719 n=3\n", " Gen 2: mean=0.716 best=0.729 n=3\n", " Gen 3: mean=0.716 best=0.749 n=5\n", " Gen 4: mean=0.772 best=0.777 n=3\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# DATA-DIVERSE GEOMETRIC EVOLUTION\n", "#\n", "# Each generation trains on differently-perturbed data.\n", "# Consensus captures what's INVARIANT across perturbations.\n", "#\n", "# Gen 0: 2 founders, Dataset A (standard)\n", "# → GPA → consensus anchors\n", "#\n", "# Gen 1: 2 students distilled from Gen 0 consensus\n", "# Student S1: Dataset B (high noise, thick strokes)\n", "# Student S2: Dataset C (thin strokes, shifted centers)\n", "# → GPA consensus of S1 + S2\n", "#\n", "# Gen 2: 3 offspring from Gen 1 consensus + 1 new founder on Dataset D\n", "# → GPA consensus of 4\n", "#\n", "# Gen 3: 5 models, each on Dataset E (identical perturbation style,\n", "# different random samples)\n", "# → GPA consensus of 5\n", "#\n", "# Gen 4 (FINAL): 3 triplets, each selecting different 5 parents\n", "# from the ENTIRE lineage pool\n", "# ============================================================================\n", "\n", "import math\n", "import gc\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "print(\"=\" * 65)\n", "print(\"DATA-DIVERSE GEOMETRIC EVOLUTION\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " cv = stacked.std() / (stacked.mean() + 1e-8)\n", " return (cv - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_f = emb.detach().float()\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb_f[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " sim = a_n @ a_n.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a_n = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a_n.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def anchor_ortho_loss(anchors):\n", " a_n = F.normalize(anchors, dim=-1)\n", " gram = a_n @ a_n.T\n", " N = anchors.shape[0]\n", " mask = ~torch.eye(N, dtype=bool, device=anchors.device)\n", " return gram[mask].pow(2).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " return (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); Sc = S - s_mean; Ns = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(Ns-1, 1)\n", " t_mean = T.mean(0, keepdim=True); Tc = T - t_mean\n", " t_cov = (Tc.T @ Tc) / max(Ns-1, 1)\n", " s_w = symmetric_inv_sqrt(s_cov); t_w = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_w, dim=-1); Tc_w = F.normalize(Tc @ t_w, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " return {\"rotation\": U @ Vt, \"source_mean\": s_mean.squeeze(0), \"source_whitener\": s_w}\n", "\n", "def apply_align(emb, info):\n", " return (emb.float() - info[\"source_mean\"]) @ info[\"source_whitener\"] @ info[\"rotation\"].T\n", "\n", "def gpa_consensus(embeddings_list, n_iters=15):\n", " N = len(embeddings_list)\n", " cur = {i: e.float() for i, e in enumerate(embeddings_list)}\n", " for it in range(n_iters):\n", " mean = sum(cur[i] for i in range(N)) / N\n", " delta = 0.0\n", " new_cur = {}\n", " for i in range(N):\n", " info = procrustes_align(cur[i], mean)\n", " new_cur[i] = apply_align(cur[i], info)\n", " delta += (new_cur[i] - cur[i]).pow(2).mean().item()\n", " cur = new_cur\n", " if delta < 1e-8: break\n", " mean = sum(cur[i] for i in range(N)) / N\n", " return F.normalize(mean, dim=-1)\n", "\n", "def consensus_anchors(consensus, n_anchors=1024):\n", " \"\"\"\n", " K-means on consensus embeddings. Anchors discover their own\n", " regions of the manifold independent of class boundaries.\n", " \"\"\"\n", " emb = consensus.detach().float()\n", " N, D = emb.shape\n", "\n", " # Init: random subset\n", " idx = torch.randperm(N)[:n_anchors]\n", " centers = emb[idx].clone()\n", "\n", " for _ in range(30):\n", " # Assign\n", " cos = emb @ F.normalize(centers, dim=-1).T\n", " assignments = cos.argmax(dim=-1)\n", " # Update\n", " new_centers = torch.zeros_like(centers)\n", " for k in range(n_anchors):\n", " mask = assignments == k\n", " if mask.sum() > 0:\n", " new_centers[k] = emb[mask].mean(0)\n", " else:\n", " new_centers[k] = emb[torch.randint(N, (1,))].squeeze(0)\n", " delta = (F.normalize(new_centers, dim=-1) - F.normalize(centers, dim=-1)).pow(2).sum()\n", " centers = new_centers\n", " if delta < 1e-6: break\n", "\n", " return F.normalize(centers, dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=1024, d_embed=64, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", " @torch.no_grad()\n", " def update_rigidity(self, tri):\n", " nearest = tri.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " m = nearest == i\n", " if m.sum() < 5: continue\n", " self.visit_count[i] += m.sum().float()\n", " sp = tri[m].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(sp+0.01)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=1024, n_comp=6, d_comp=64):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn==k).sum().item(), d_comp*2), nn.GELU(),\n", " nn.Linear(d_comp*2, d_comp), nn.LayerNorm(d_comp)) for k in range(n_comp)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn==k]) for k in range(self.n_comp)], -1)\n", "\n", "class PatchworkClassifier(nn.Module):\n", " def __init__(self, nc=30, na=1024, de=256, ncomp=6, dc=64, dh=256, init_a=None):\n", " super().__init__()\n", " if init_a is not None:\n", " na = init_a.shape[0] # infer from provided anchors\n", " self.backbone = nn.Sequential(\n", " nn.Conv2d(1,32,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(32,64,3,padding=1), nn.GELU(), nn.MaxPool2d(2),\n", " nn.Conv2d(64,128,3,padding=1), nn.GELU(), nn.AdaptiveAvgPool2d(1))\n", " self.proj = nn.Sequential(nn.Linear(128, de), nn.LayerNorm(de))\n", " self.constellation = Constellation(na, de, init_a)\n", " self.patchwork = Patchwork(na, ncomp, dc)\n", " self.mlp = nn.Sequential(\n", " nn.Linear(ncomp*dc, dh), nn.GELU(), nn.LayerNorm(dh),\n", " nn.Linear(dh, dh), nn.GELU(), nn.LayerNorm(dh),\n", " nn.Linear(dh, nc))\n", " def forward(self, x):\n", " emb = F.normalize(self.proj(self.backbone(x).flatten(1)), dim=-1)\n", " tri, near = self.constellation.triangulate(emb)\n", " return self.mlp(self.patchwork(tri)), emb, tri, near\n", " def encode(self, x):\n", " return F.normalize(self.proj(self.backbone(x).flatten(1)), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SHAPE RENDERERS WITH PERTURBATION PROFILES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _d(img,x0,y0,x1,y1,t=1):\n", " n=max(int(max(abs(x1-x0),abs(y1-y0))*2),1);sz=img.shape[0]\n", " for s in np.linspace(0,1,n):\n", " px,py=int(x0+s*(x1-x0)),int(y0+s*(y1-y0))\n", " for dx in range(-t,t+1):\n", " for dy in range(-t,t+1):\n", " nx,ny=px+dx,py+dy\n", " if 0<=nx=r2*0.9:\n", " if 0<=int(x1) 0 else 0\n", " cy_off = np.random.randint(-sh, sh+1) if sh > 0 else 0\n", " base_p = [0.20,0.12,0.15,0.10,0.10,0.08,0.08,0.07,0.06,0.03,\n", " 0.10,0.10,0.10,0.10,0.12,0.12,0.12,0.12,0.12,0.12,\n", " 0.15,0.10,0.12,0.10,0.10,0.10,0.15,0.18,0.10,0.12]\n", " p = base_p[c] * ps\n", " kw = {\"sz\": sz, \"cx_off\": cx_off, \"cy_off\": cy_off}\n", " R = [lambda: rpoly(3,p=p,t=t,**kw), lambda: rpoly(4,p=p,t=t,**kw),\n", " lambda: rpoly(5,p=p,t=t,**kw), lambda: rpoly(6,p=p,t=t,**kw),\n", " lambda: rpoly(7,p=p,t=t,**kw), lambda: rpoly(8,p=p,t=t,**kw),\n", " lambda: rpoly(9,p=p,t=t,**kw), lambda: rpoly(10,p=p,t=t,**kw),\n", " lambda: rpoly(12,p=p,t=t,**kw), lambda: rpoly(32,p=p*0.3,t=t,**kw),\n", " lambda: rellipse(p=p,**kw), lambda: rspiral(p=p,**kw),\n", " lambda: rwave(p=p,**kw), lambda: rcrescent(p=p,**kw),\n", " lambda: rstar(3,p=p,t=t,**kw), lambda: rstar(4,p=p,t=t,**kw),\n", " lambda: rstar(5,p=p,t=t,**kw), lambda: rstar(6,p=p,t=t,**kw),\n", " lambda: rstar(7,p=p,t=t,**kw), lambda: rstar(8,p=p,t=t,**kw),\n", " lambda: rcross(p=p,t=t,**kw), lambda: rpoly(4,p=p,t=t,**kw),\n", " lambda: rarrow(p=p,t=t,**kw), lambda: rheart(p=p,**kw),\n", " lambda: rring(p=p,**kw), lambda: rsemicirc(p=p,t=t,**kw),\n", " lambda: rpoly(4,p=p*1.2,t=t,**kw), lambda: rpoly(4,p=p*1.5,t=t,**kw),\n", " lambda: rpoly(4,p=p,t=t,**kw), lambda: rchevron(p=p,t=t,**kw)]\n", " img = R[c]()\n", " if pr[\"noise\"] > 0:\n", " img = img + np.random.normal(0, pr[\"noise\"], img.shape).astype(np.float32)\n", " img = np.clip(img, 0, 1)\n", " return img\n", "\n", "def gen_data(n_per=500, sz=32, profile=\"A\", seed=None):\n", " if seed is not None: np.random.seed(seed)\n", " imgs, labels = [], []\n", " for _ in range(n_per):\n", " for c in range(30):\n", " imgs.append(gen_one(c, sz, profile)); labels.append(c)\n", " imgs = torch.tensor(np.array(imgs)).unsqueeze(1)\n", " labels = torch.tensor(labels, dtype=torch.long)\n", " perm = torch.randperm(len(labels))\n", " return imgs[perm], labels[perm]\n", "\n", "TYPES = {\"polygon\": list(range(9)), \"curve\": list(range(9,14)),\n", " \"star\": list(range(14,20)), \"structure\": list(range(20,30))}\n", "\n", "def eval_model(model, imgs, labels):\n", " model.eval()\n", " with torch.no_grad():\n", " vl, ve, _, _ = model(imgs)\n", " acc = (vl.argmax(-1) == labels).float().mean().item()\n", " cv = cv_metric(ve)\n", " ta = {}\n", " for tn, tids in TYPES.items():\n", " tm = torch.zeros(len(labels), dtype=bool, device=imgs.device)\n", " for tid in tids: tm |= (labels == tid)\n", " if tm.sum() > 0: ta[tn] = (vl.argmax(-1)[tm] == labels[tm]).float().mean().item()\n", " return acc, cv, ta\n", "\n", "def fmt_ta(ta):\n", " return \" \".join(f\"{t}={a:.2f}\" for t, a in ta.items())\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING FUNCTIONS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "GEO_CFG = {\"tang\": 0.01, \"sep\": 1.0, \"cv_w\": 0.001, \"spr\": 1e-3, \"ort\": 1e-3, \"ent\": 1e-4}\n", "\n", "def train_founder(model, tr_imgs, tr_labels, use_geo=True, epochs=30, tag=\"\"):\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; nt = len(tr_labels)\n", " for ep in range(epochs):\n", " model.train(); perm = torch.randperm(nt, device=DEVICE); tc = 0\n", " for i in range(0, nt, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " lo, emb, tri, _ = model(tr_imgs[idx]); lab = tr_labels[idx]\n", " anc = model.constellation.anchors\n", " if use_geo:\n", " eg = EmbeddingAutograd.apply(emb, emb, anc, GEO_CFG[\"tang\"], GEO_CFG[\"sep\"])\n", " tg, _ = model.constellation.triangulate(eg)\n", " lo = model.mlp(model.patchwork(tg))\n", " l = F.cross_entropy(lo, lab)\n", " lg = torch.tensor(0.0, device=DEVICE)\n", " if use_geo:\n", " lg += GEO_CFG[\"cv_w\"] * cv_loss(emb)\n", " lg += GEO_CFG[\"spr\"] * anchor_spread_loss(anc)\n", " lg += GEO_CFG[\"ort\"] * anchor_ortho_loss(anc)\n", " lg += GEO_CFG[\"ent\"] * anchor_entropy_loss(emb, anc)\n", " (l + lg).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (lo.argmax(-1) == lab).sum().item()\n", " if (ep+1) % 10 == 0 or ep == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " print(f\" {tag}E{ep+1:2d}: t={tc/nt:.3f} v={acc:.3f} cv={cv:.4f} [{fmt_ta(ta)}]\")\n", "\n", "def train_distilled(model, tr_imgs, tr_labels, consensus, epochs=30, tag=\"\"):\n", " opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n", " BATCH = 256; nt = len(tr_labels)\n", " for ep in range(epochs):\n", " model.train(); perm = torch.randperm(nt, device=DEVICE); tc = 0\n", " for i in range(0, nt, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", " lo, emb, tri, _ = model(tr_imgs[idx]); lab = tr_labels[idx]; tgt = consensus[idx]\n", " anc = model.constellation.anchors\n", " eg = EmbeddingAutograd.apply(emb, emb, anc, GEO_CFG[\"tang\"], GEO_CFG[\"sep\"])\n", " tg, _ = model.constellation.triangulate(eg)\n", " lo = model.mlp(model.patchwork(tg))\n", " l_cls = F.cross_entropy(lo, lab)\n", " l_nce = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = GEO_CFG[\"cv_w\"] * cv_loss(emb)\n", " l_ent = GEO_CFG[\"ent\"] * anchor_entropy_loss(emb, anc)\n", " (l_cls + 0.5*l_nce + 0.5*l_mse + l_cv + l_ent).backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", " model.constellation.update_rigidity(tri.detach())\n", " tc += (lo.argmax(-1) == lab).sum().item()\n", " if (ep+1) % 10 == 0 or ep == 0:\n", " acc, cv, ta = eval_model(model, val_imgs, val_labels)\n", " print(f\" {tag}E{ep+1:2d}: t={tc/nt:.3f} v={acc:.3f} cv={cv:.4f} [{fmt_ta(ta)}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# VALIDATION DATA (always Dataset A — standard, consistent eval)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Generating validation data (Dataset A)...\")\n", "val_imgs, val_labels = gen_data(n_per=100, profile=\"A\", seed=999)\n", "val_imgs, val_labels = val_imgs.to(DEVICE), val_labels.to(DEVICE)\n", "print(f\" Val: {len(val_labels):,}\")\n", "\n", "all_results = {}\n", "all_models = {} # keep references for final triplet parent selection\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 0: 2 FOUNDERS on Dataset A\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 0: 2 FOUNDERS — Dataset A\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_A, lb_A = gen_data(n_per=500, profile=\"A\", seed=42)\n", "tr_A, lb_A = tr_A.to(DEVICE), lb_A.to(DEVICE)\n", "\n", "for name, use_geo, sd in [(\"F0a\", False, 100), (\"F0b\", True, 200)]:\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(sd)\n", " m = PatchworkClassifier(init_a=None).to(DEVICE)\n", " train_founder(m, tr_A, lb_A, use_geo=use_geo, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 0}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "# GPA consensus\n", "print(f\"\\n GPA alignment (Gen 0)...\")\n", "embs_g0 = {n: m.encode(tr_A).detach() for n, m in all_models.items() if n.startswith(\"F0\")}\n", "cons_g0 = gpa_consensus(list(embs_g0.values()))\n", "anc_g0 = consensus_anchors(cons_g0)\n", "print(f\" Consensus CV: {cv_metric(cons_g0[:2000]):.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 1: 2 STUDENTS — Dataset B and Dataset C\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 1: 2 STUDENTS — Datasets B and C\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_B, lb_B = gen_data(n_per=500, profile=\"B\", seed=300)\n", "tr_C, lb_C = gen_data(n_per=500, profile=\"C\", seed=400)\n", "tr_B, lb_B = tr_B.to(DEVICE), lb_B.to(DEVICE)\n", "tr_C, lb_C = tr_C.to(DEVICE), lb_C.to(DEVICE)\n", "\n", "# Need consensus targets indexed to each dataset's label ordering\n", "# Since gen_data shuffles, we recompute consensus for each dataset\n", "cons_g0_B = gpa_consensus([all_models[\"F0a\"].encode(tr_B).detach(), all_models[\"F0b\"].encode(tr_B).detach()])\n", "cons_g0_C = gpa_consensus([all_models[\"F0a\"].encode(tr_C).detach(), all_models[\"F0b\"].encode(tr_C).detach()])\n", "\n", "for name, tr, lb, cons, sd in [(\"G1_B\", tr_B, lb_B, cons_g0_B, 301),\n", " (\"G1_C\", tr_C, lb_C, cons_g0_C, 401)]:\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(sd)\n", " m = PatchworkClassifier(init_a=consensus_anchors(cons)).to(DEVICE)\n", " train_distilled(m, tr, lb, cons, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 1}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "del embs_g0; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 2: 3 OFFSPRING from G1 + 1 new founder, Dataset D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 2: 3 OFFSPRING + new founder — Dataset D\")\n", "print(f\"{'='*65}\")\n", "\n", "tr_D, lb_D = gen_data(n_per=500, profile=\"D\", seed=500)\n", "tr_D, lb_D = tr_D.to(DEVICE), lb_D.to(DEVICE)\n", "\n", "# New founder on Dataset D\n", "print(f\"\\n ── New founder (F1_D) ──\")\n", "torch.manual_seed(501)\n", "f1d = PatchworkClassifier(init_a=None).to(DEVICE)\n", "train_founder(f1d, tr_D, lb_D, use_geo=True, tag=\"[F1_D] \")\n", "acc_f1d, _, _ = eval_model(f1d, val_imgs, val_labels)\n", "all_results[\"F1_D\"] = {\"acc\": acc_f1d, \"cv\": 0, \"ta\": {}, \"gen\": 1}\n", "all_models[\"F1_D\"] = f1d\n", "\n", "# GPA from G1 + new founder (encode on Dataset D for consensus)\n", "print(f\"\\n GPA alignment (G1 + F1_D on Dataset D)...\")\n", "g2_parents = [\"G1_B\", \"G1_C\", \"F1_D\"]\n", "embs_g2 = [all_models[n].encode(tr_D).detach() for n in g2_parents]\n", "cons_g2 = gpa_consensus(embs_g2)\n", "anc_g2 = consensus_anchors(cons_g2)\n", "print(f\" Consensus CV: {cv_metric(cons_g2[:2000]):.4f}\")\n", "\n", "for i in range(3):\n", " name = f\"G2_{i}\"\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(600 + i)\n", " m = PatchworkClassifier(init_a=anc_g2).to(DEVICE)\n", " train_distilled(m, tr_D, lb_D, cons_g2, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 2}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 3: 5 MODELS — Dataset E (identical perturbation,\n", "# different random samples)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 3: 5 MODELS — Dataset E (identical profile, varied samples)\")\n", "print(f\"{'='*65}\")\n", "\n", "# GPA from all G2 + new founder\n", "g3_parents = [n for n in all_models if n.startswith(\"G2_\")]\n", "print(f\" GPA alignment ({len(g3_parents)} G2 parents)...\")\n", "\n", "# Each Gen 3 model gets its own Dataset E sample\n", "g3_models = []\n", "for j in range(5):\n", " name = f\"G3_{j}\"\n", " tr_Ej, lb_Ej = gen_data(n_per=500, profile=\"E\", seed=700 + j * 10)\n", " tr_Ej, lb_Ej = tr_Ej.to(DEVICE), lb_Ej.to(DEVICE)\n", "\n", " # Consensus from G2 parents on this dataset\n", " embs_j = [all_models[n].encode(tr_Ej).detach() for n in g3_parents]\n", " cons_j = gpa_consensus(embs_j)\n", " anc_j = consensus_anchors(cons_j)\n", "\n", " print(f\"\\n ── {name} ──\")\n", " torch.manual_seed(700 + j)\n", " m = PatchworkClassifier(init_a=anc_j).to(DEVICE)\n", " train_distilled(m, tr_Ej, lb_Ej, cons_j, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 3}\n", " all_models[name] = m\n", " g3_models.append(name)\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", " del tr_Ej, lb_Ej; gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GENERATION 4 (FINAL): 3 TRIPLETS — each selects different 5\n", "# parents from the FULL lineage\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"GEN 4 (FINAL): 3 TRIPLETS — cross-lineage parent selection\")\n", "print(f\"{'='*65}\")\n", "\n", "# Sort all models by accuracy for parent selection\n", "ranked = sorted(all_results.items(), key=lambda x: -x[1][\"acc\"])\n", "ranked_names = [n for n, _ in ranked if n in all_models]\n", "\n", "# Three different parent selection strategies\n", "parent_sets = {\n", " # Top 5 overall\n", " \"T4_best5\": ranked_names[:5],\n", " # Best from each generation\n", " \"T4_cross\": [],\n", " # Diverse: top + bottom + middle\n", " \"T4_diverse\": [],\n", "}\n", "\n", "# Cross-generational: pick best from each gen\n", "for gen in range(4):\n", " gen_models = [(n, r) for n, r in ranked if r[\"gen\"] == gen and n in all_models]\n", " if gen_models:\n", " parent_sets[\"T4_cross\"].append(gen_models[0][0])\n", "# Pad to 5 if needed\n", "while len(parent_sets[\"T4_cross\"]) < 5:\n", " for n in ranked_names:\n", " if n not in parent_sets[\"T4_cross\"]:\n", " parent_sets[\"T4_cross\"].append(n); break\n", "\n", "# Diverse: positions 0, 2, 4, 6, 8 from ranking\n", "for idx in [0, 2, 4, 6, 8]:\n", " if idx < len(ranked_names):\n", " parent_sets[\"T4_diverse\"].append(ranked_names[idx])\n", "\n", "# Fresh eval data for final generation\n", "tr_final, lb_final = gen_data(n_per=500, profile=\"A\", seed=888)\n", "tr_final, lb_final = tr_final.to(DEVICE), lb_final.to(DEVICE)\n", "\n", "for name, parents in parent_sets.items():\n", " print(f\"\\n ── {name} (parents: {parents}) ──\")\n", " embs_fin = [all_models[p].encode(tr_final).detach() for p in parents]\n", " cons_fin = gpa_consensus(embs_fin)\n", " anc_fin = consensus_anchors(cons_fin)\n", " cons_cv = cv_metric(cons_fin[:2000])\n", " print(f\" Consensus CV: {cons_cv:.4f}\")\n", "\n", " torch.manual_seed(hash(name) % 2**32)\n", " m = PatchworkClassifier(init_a=anc_fin).to(DEVICE)\n", " train_distilled(m, tr_final, lb_final, cons_fin, tag=f\"[{name}] \")\n", " acc, cv, ta = eval_model(m, val_imgs, val_labels)\n", " all_results[name] = {\"acc\": acc, \"cv\": cv, \"ta\": ta, \"gen\": 4}\n", " all_models[name] = m\n", " print(f\" → {name}: val={acc:.3f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL FUSION: ALL parents, ALL data\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL FUSION: ALL parents × ALL data\")\n", "print(f\"{'='*65}\")\n", "\n", "# Combine all datasets\n", "print(f\"\\n Combining datasets A+B+C+D+E...\")\n", "all_datasets = []\n", "all_labels_combined = []\n", "for prof, seed in [(\"A\", 42), (\"B\", 300), (\"C\", 400), (\"D\", 500), (\"E\", 700)]:\n", " imgs, labs = gen_data(n_per=500, profile=prof, seed=seed)\n", " all_datasets.append(imgs)\n", " all_labels_combined.append(labs)\n", "\n", "tr_all = torch.cat(all_datasets, dim=0).to(DEVICE)\n", "lb_all = torch.cat(all_labels_combined, dim=0).to(DEVICE)\n", "\n", "# Shuffle combined\n", "perm_all = torch.randperm(len(lb_all))\n", "tr_all = tr_all[perm_all]\n", "lb_all = lb_all[perm_all]\n", "print(f\" Combined: {len(lb_all):,} samples (5 × 15K)\")\n", "\n", "# ── Raw baseline on all data ──\n", "print(f\"\\n ── FUSE_raw (all data, no distillation, no geometry) ──\")\n", "torch.manual_seed(42)\n", "fuse_raw = PatchworkClassifier(init_a=None).to(DEVICE)\n", "train_founder(fuse_raw, tr_all, lb_all, use_geo=False, epochs=30, tag=\"[FRAW] \")\n", "acc_fr, cv_fr, ta_fr = eval_model(fuse_raw, val_imgs, val_labels)\n", "all_results[\"FUSE_raw\"] = {\"acc\": acc_fr, \"cv\": cv_fr, \"ta\": ta_fr, \"gen\": 5}\n", "print(f\" → FUSE_raw: val={acc_fr:.3f}\")\n", "\n", "# ── All-parent consensus on combined data ──\n", "print(f\"\\n Extracting ALL parents on combined data...\")\n", "all_parent_names = [n for n in all_models.keys()\n", " if all_results[n][\"acc\"] > 0.1] # include everyone who trained\n", "print(f\" Parents ({len(all_parent_names)}): {all_parent_names}\")\n", "\n", "all_parent_embs = []\n", "for n in all_parent_names:\n", " all_models[n].eval()\n", " with torch.no_grad():\n", " # Encode in chunks to avoid OOM\n", " chunks = []\n", " for j in range(0, len(tr_all), 2048):\n", " chunks.append(all_models[n].encode(tr_all[j:j+2048]).detach())\n", " all_parent_embs.append(torch.cat(chunks, dim=0))\n", "\n", "print(f\" GPA alignment ({len(all_parent_embs)} models on {len(tr_all):,} samples)...\")\n", "cons_fuse = gpa_consensus(all_parent_embs)\n", "cons_fuse_cv = cv_metric(cons_fuse[:2000])\n", "print(f\" Consensus CV: {cons_fuse_cv:.4f}\")\n", "\n", "anc_fuse = consensus_anchors(cons_fuse)\n", "print(f\" Anchors: {anc_fuse.shape}\")\n", "\n", "# ── Distilled student on all data from all parents ──\n", "print(f\"\\n ── FUSE_distilled (all data, all parents, full pipeline) ──\")\n", "torch.manual_seed(42)\n", "fuse_student = PatchworkClassifier(init_a=anc_fuse).to(DEVICE)\n", "train_distilled(fuse_student, tr_all, lb_all, cons_fuse, epochs=30, tag=\"[FDST] \")\n", "acc_fd, cv_fd, ta_fd = eval_model(fuse_student, val_imgs, val_labels)\n", "all_results[\"FUSE_dist\"] = {\"acc\": acc_fd, \"cv\": cv_fd, \"ta\": ta_fd, \"gen\": 5}\n", "print(f\" → FUSE_distilled: val={acc_fd:.3f}\")\n", "\n", "# Clean up large tensors\n", "del tr_all, lb_all, all_parent_embs, cons_fuse\n", "gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVOLUTION SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"EVOLUTION SUMMARY\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Model':<12} {'Gen':>3} {'v_acc':>6} {'cv':>7} \"\n", " f\"{'poly':>5} {'curve':>5} {'star':>5} {'struct':>5}\")\n", "print(f\" {'-'*58}\")\n", "\n", "for name in sorted(all_results.keys(), key=lambda x: (all_results[x][\"gen\"], x)):\n", " r = all_results[name]\n", " ta = r.get(\"ta\", {})\n", " print(f\" {name:<12} {r['gen']:>3} {r['acc']:>6.3f} {r['cv']:>7.4f} \"\n", " f\"{ta.get('polygon',0):>5.2f} {ta.get('curve',0):>5.2f} \"\n", " f\"{ta.get('star',0):>5.2f} {ta.get('structure',0):>5.2f}\")\n", "\n", "print(f\"\\n Per-generation averages:\")\n", "for gen in range(6):\n", " accs = [r[\"acc\"] for r in all_results.values() if r[\"gen\"] == gen and r[\"acc\"] > 0]\n", " if accs:\n", " label = {0: \"Gen 0 (founders)\", 1: \"Gen 1 (first offspring)\",\n", " 2: \"Gen 2\", 3: \"Gen 3\", 4: \"Gen 4 (triplets)\",\n", " 5: \"Gen 5 (FUSION)\"}.get(gen, f\"Gen {gen}\")\n", " print(f\" {label}: mean={np.mean(accs):.3f} best={max(accs):.3f} n={len(accs)}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xoHd--aF4Yem", "outputId": "9474eee8-c597-4e92-dc7a-a1887bc9007c" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "DATA-DIVERSE GEOMETRIC EVOLUTION\n", "=================================================================\n", " Device: cuda\n", "\n", " Generating validation data (Dataset A)...\n", " Val: 3,000\n", "\n", "=================================================================\n", "GEN 0: 2 FOUNDERS — Dataset A\n", "=================================================================\n", "\n", " ── F0a ──\n", " [F0a] E 1: t=0.083 v=0.188 cv=1.1298 [polygon=0.10 curve=0.00 star=0.45 structure=0.21]\n", " [F0a] E10: t=0.535 v=0.564 cv=1.6105 [polygon=0.33 curve=0.72 star=0.77 structure=0.58]\n", " [F0a] E20: t=0.660 v=0.632 cv=1.6317 [polygon=0.35 curve=0.98 star=0.80 structure=0.61]\n", " [F0a] E30: t=0.676 v=0.675 cv=1.2672 [polygon=0.42 curve=1.00 star=0.68 structure=0.74]\n", " → F0a: val=0.675\n", "\n", " ── F0b ──\n", " [F0b] E 1: t=0.067 v=0.126 cv=1.7868 [polygon=0.04 curve=0.20 star=0.00 structure=0.24]\n", " [F0b] E10: t=0.294 v=0.144 cv=2.1863 [polygon=0.03 curve=0.00 star=0.00 structure=0.41]\n", " [F0b] E20: t=0.459 v=0.399 cv=2.5306 [polygon=0.08 curve=0.75 star=0.36 structure=0.54]\n", " [F0b] E30: t=0.615 v=0.653 cv=2.3457 [polygon=0.34 curve=0.99 star=0.74 structure=0.71]\n", " → F0b: val=0.653\n", "\n", " GPA alignment (Gen 0)...\n", " Consensus CV: 0.1804\n", "\n", "=================================================================\n", "GEN 1: 2 STUDENTS — Datasets B and C\n", "=================================================================\n", "\n", " ── G1_B ──\n", " [G1_B] E 1: t=0.068 v=0.034 cv=1.7464 [polygon=0.00 curve=0.00 star=0.00 structure=0.10]\n", " [G1_B] E10: t=0.630 v=0.286 cv=0.8657 [polygon=0.00 curve=0.76 star=0.00 structure=0.48]\n", " [G1_B] E20: t=0.693 v=0.278 cv=0.7046 [polygon=0.02 curve=0.80 star=0.00 structure=0.41]\n", " [G1_B] E30: t=0.737 v=0.334 cv=0.6383 [polygon=0.14 curve=0.80 star=0.00 structure=0.47]\n", " → G1_B: val=0.334\n", "\n", " ── G1_C ──\n", " [G1_C] E 1: t=0.064 v=0.106 cv=1.3499 [polygon=0.14 curve=0.04 star=0.24 structure=0.03]\n", " [G1_C] E10: t=0.661 v=0.527 cv=0.4826 [polygon=0.18 curve=0.76 star=0.54 structure=0.72]\n", " [G1_C] E20: t=0.728 v=0.708 cv=0.4942 [polygon=0.50 curve=0.94 star=0.84 structure=0.71]\n", " [G1_C] E30: t=0.749 v=0.701 cv=0.4424 [polygon=0.43 curve=0.95 star=0.85 structure=0.73]\n", " → G1_C: val=0.701\n", "\n", "=================================================================\n", "GEN 2: 3 OFFSPRING + new founder — Dataset D\n", "=================================================================\n", "\n", " ── New founder (F1_D) ──\n", " [F1_D] E 1: t=0.053 v=0.091 cv=2.0973 [polygon=0.13 curve=0.20 star=0.00 structure=0.05]\n", " [F1_D] E10: t=0.473 v=0.599 cv=1.5945 [polygon=0.37 curve=0.94 star=0.64 structure=0.61]\n", " [F1_D] E20: t=0.603 v=0.629 cv=2.1379 [polygon=0.40 curve=0.94 star=0.62 structure=0.69]\n", " [F1_D] E30: t=0.641 v=0.676 cv=2.0658 [polygon=0.47 curve=0.98 star=0.79 structure=0.64]\n", "\n", " GPA alignment (G1 + F1_D on Dataset D)...\n", " Consensus CV: 0.0577\n", "\n", " ── G2_0 ──\n", " [G2_0] E 1: t=0.060 v=0.091 cv=1.0362 [polygon=0.13 curve=0.00 star=0.04 structure=0.14]\n", " [G2_0] E10: t=0.637 v=0.644 cv=0.4900 [polygon=0.40 curve=1.00 star=0.75 structure=0.62]\n", " [G2_0] E20: t=0.717 v=0.744 cv=0.3547 [polygon=0.46 curve=1.00 star=0.98 structure=0.73]\n", " [G2_0] E30: t=0.755 v=0.754 cv=0.4094 [polygon=0.50 curve=0.99 star=0.97 structure=0.73]\n", " → G2_0: val=0.754\n", "\n", " ── G2_1 ──\n", " [G2_1] E 1: t=0.068 v=0.178 cv=1.1997 [polygon=0.11 curve=0.00 star=0.27 structure=0.27]\n", " [G2_1] E10: t=0.635 v=0.661 cv=0.4645 [polygon=0.44 curve=1.00 star=0.77 structure=0.63]\n", " [G2_1] E20: t=0.709 v=0.732 cv=0.4102 [polygon=0.51 curve=1.00 star=0.95 structure=0.67]\n", " [G2_1] E30: t=0.741 v=0.751 cv=0.3918 [polygon=0.47 curve=1.00 star=0.97 structure=0.75]\n", " → G2_1: val=0.751\n", "\n", " ── G2_2 ──\n", " [G2_2] E 1: t=0.058 v=0.093 cv=1.3678 [polygon=0.02 curve=0.40 star=0.01 structure=0.05]\n", " [G2_2] E10: t=0.631 v=0.654 cv=0.4156 [polygon=0.39 curve=1.00 star=0.76 structure=0.66]\n", " [G2_2] E20: t=0.709 v=0.655 cv=0.4665 [polygon=0.18 curve=1.00 star=0.95 structure=0.73]\n", " [G2_2] E30: t=0.758 v=0.745 cv=0.4568 [polygon=0.51 curve=1.00 star=0.96 structure=0.70]\n", " → G2_2: val=0.745\n", "\n", "=================================================================\n", "GEN 3: 5 MODELS — Dataset E (identical profile, varied samples)\n", "=================================================================\n", " GPA alignment (3 G2 parents)...\n", "\n", " ── G3_0 ──\n", " [G3_0] E 1: t=0.063 v=0.180 cv=1.1943 [polygon=0.16 curve=0.20 star=0.17 structure=0.19]\n", " [G3_0] E10: t=0.673 v=0.619 cv=0.3985 [polygon=0.32 curve=0.87 star=0.84 structure=0.63]\n", " [G3_0] E20: t=0.728 v=0.662 cv=0.4015 [polygon=0.20 curve=1.00 star=0.98 structure=0.72]\n", " [G3_0] E30: t=0.762 v=0.756 cv=0.3476 [polygon=0.56 curve=0.99 star=0.90 structure=0.73]\n", " → G3_0: val=0.756\n", "\n", " ── G3_1 ──\n", " [G3_1] E 1: t=0.093 v=0.253 cv=1.0208 [polygon=0.21 curve=0.40 star=0.41 structure=0.13]\n", " [G3_1] E10: t=0.652 v=0.702 cv=0.3720 [polygon=0.39 curve=1.00 star=0.89 structure=0.72]\n", " [G3_1] E20: t=0.737 v=0.720 cv=0.3506 [polygon=0.42 curve=0.98 star=0.91 structure=0.75]\n", " [G3_1] E30: t=0.761 v=0.773 cv=0.3308 [polygon=0.53 curve=1.00 star=0.98 structure=0.76]\n", " → G3_1: val=0.773\n", "\n", " ── G3_2 ──\n", " [G3_2] E 1: t=0.074 v=0.177 cv=1.2488 [polygon=0.05 curve=0.20 star=0.38 structure=0.16]\n", " [G3_2] E10: t=0.656 v=0.690 cv=0.3749 [polygon=0.40 curve=0.99 star=0.83 structure=0.71]\n", " [G3_2] E20: t=0.724 v=0.703 cv=0.3136 [polygon=0.37 curve=1.00 star=0.86 structure=0.76]\n", " [G3_2] E30: t=0.758 v=0.763 cv=0.3407 [polygon=0.50 curve=1.00 star=0.98 structure=0.75]\n", " → G3_2: val=0.763\n", "\n", " ── G3_3 ──\n", " [G3_3] E 1: t=0.074 v=0.139 cv=1.4184 [polygon=0.00 curve=0.19 star=0.17 structure=0.22]\n", " [G3_3] E10: t=0.650 v=0.649 cv=0.4400 [polygon=0.38 curve=1.00 star=0.61 structure=0.73]\n", " [G3_3] E20: t=0.731 v=0.740 cv=0.3384 [polygon=0.46 curve=1.00 star=0.95 structure=0.74]\n", " [G3_3] E30: t=0.754 v=0.721 cv=0.3545 [polygon=0.47 curve=1.00 star=0.86 structure=0.72]\n", " → G3_3: val=0.721\n", "\n", " ── G3_4 ──\n", " [G3_4] E 1: t=0.080 v=0.162 cv=1.2240 [polygon=0.07 curve=0.20 star=0.21 structure=0.19]\n", " [G3_4] E10: t=0.673 v=0.666 cv=0.4150 [polygon=0.45 curve=1.00 star=0.75 structure=0.65]\n", " [G3_4] E20: t=0.744 v=0.734 cv=0.3271 [polygon=0.42 curve=1.00 star=0.98 structure=0.74]\n", " [G3_4] E30: t=0.763 v=0.695 cv=0.3959 [polygon=0.30 curve=1.00 star=0.91 structure=0.77]\n", " → G3_4: val=0.695\n", "\n", "=================================================================\n", "GEN 4 (FINAL): 3 TRIPLETS — cross-lineage parent selection\n", "=================================================================\n", "\n", " ── T4_best5 (parents: ['G3_1', 'G3_2', 'G3_0', 'G2_0', 'G2_1']) ──\n", " Consensus CV: 0.1724\n", " [T4_best5] E 1: t=0.070 v=0.268 cv=1.1925 [polygon=0.11 curve=0.31 star=0.24 structure=0.41]\n", " [T4_best5] E10: t=0.693 v=0.716 cv=0.2986 [polygon=0.42 curve=0.99 star=0.89 structure=0.75]\n", " [T4_best5] E20: t=0.755 v=0.744 cv=0.3253 [polygon=0.45 curve=1.00 star=0.95 structure=0.76]\n", " [T4_best5] E30: t=0.786 v=0.775 cv=0.2621 [polygon=0.52 curve=1.00 star=0.97 structure=0.77]\n", " → T4_best5: val=0.775\n", "\n", " ── T4_cross (parents: ['F0a', 'G1_C', 'G2_0', 'G3_1', 'G3_2']) ──\n", " Consensus CV: 0.0933\n", " [T4_cross] E 1: t=0.072 v=0.154 cv=1.1266 [polygon=0.12 curve=0.00 star=0.25 structure=0.20]\n", " [T4_cross] E10: t=0.683 v=0.699 cv=0.3458 [polygon=0.35 curve=1.00 star=0.91 structure=0.74]\n", " [T4_cross] E20: t=0.752 v=0.742 cv=0.3153 [polygon=0.48 curve=0.99 star=0.88 structure=0.78]\n", " [T4_cross] E30: t=0.785 v=0.747 cv=0.3118 [polygon=0.47 curve=0.97 star=1.00 structure=0.73]\n", " → T4_cross: val=0.747\n", "\n", " ── T4_diverse (parents: ['G3_1', 'G3_0', 'G2_1', 'G3_3', 'G3_4']) ──\n", " Consensus CV: 0.1584\n", " [T4_diverse] E 1: t=0.073 v=0.167 cv=1.0028 [polygon=0.12 curve=0.40 star=0.11 structure=0.12]\n", " [T4_diverse] E10: t=0.693 v=0.623 cv=0.3322 [polygon=0.35 curve=1.00 star=0.58 structure=0.71]\n", " [T4_diverse] E20: t=0.758 v=0.770 cv=0.3004 [polygon=0.54 curve=1.00 star=0.98 structure=0.74]\n", " [T4_diverse] E30: t=0.772 v=0.771 cv=0.2793 [polygon=0.51 curve=1.00 star=1.00 structure=0.75]\n", " → T4_diverse: val=0.771\n", "\n", "=================================================================\n", "FINAL FUSION: ALL parents × ALL data\n", "=================================================================\n", "\n", " Combining datasets A+B+C+D+E...\n", " Combined: 75,000 samples (5 × 15K)\n", "\n", " ── FUSE_raw (all data, no distillation, no geometry) ──\n", " [FRAW] E 1: t=0.198 v=0.249 cv=1.7657 [polygon=0.00 curve=0.47 star=0.21 structure=0.39]\n", " [FRAW] E10: t=0.681 v=0.699 cv=1.5093 [polygon=0.39 curve=0.95 star=0.92 structure=0.72]\n", " [FRAW] E20: t=0.752 v=0.775 cv=1.1095 [polygon=0.61 curve=1.00 star=0.99 structure=0.69]\n", " [FRAW] E30: t=0.798 v=0.813 cv=0.9895 [polygon=0.69 curve=1.00 star=0.99 structure=0.73]\n", " → FUSE_raw: val=0.813\n", "\n", " Extracting ALL parents on combined data...\n", " Parents (16): ['F0a', 'F0b', 'G1_B', 'G1_C', 'F1_D', 'G2_0', 'G2_1', 'G2_2', 'G3_0', 'G3_1', 'G3_2', 'G3_3', 'G3_4', 'T4_best5', 'T4_cross', 'T4_diverse']\n", " GPA alignment (16 models on 75,000 samples)...\n", " Consensus CV: 0.0876\n", " Anchors: torch.Size([1024, 256])\n", "\n", " ── FUSE_distilled (all data, all parents, full pipeline) ──\n", " [FDST] E 1: t=0.352 v=0.580 cv=0.4501 [polygon=0.33 curve=0.99 star=0.51 structure=0.64]\n", " [FDST] E10: t=0.749 v=0.777 cv=0.3396 [polygon=0.54 curve=1.00 star=0.99 structure=0.75]\n", " [FDST] E20: t=0.793 v=0.823 cv=0.2974 [polygon=0.68 curve=1.00 star=1.00 structure=0.76]\n", " [FDST] E30: t=0.818 v=0.830 cv=0.3119 [polygon=0.71 curve=1.00 star=1.00 structure=0.75]\n", " → FUSE_distilled: val=0.830\n", "\n", "\n", "=================================================================\n", "EVOLUTION SUMMARY\n", "=================================================================\n", "\n", " Model Gen v_acc cv poly curve star struct\n", " ----------------------------------------------------------\n", " F0a 0 0.675 1.3971 0.42 1.00 0.68 0.74\n", " F0b 0 0.653 2.9211 0.34 0.99 0.74 0.71\n", " F1_D 1 0.676 0.0000 0.00 0.00 0.00 0.00\n", " G1_B 1 0.334 0.6592 0.14 0.80 0.00 0.47\n", " G1_C 1 0.701 0.4722 0.43 0.95 0.85 0.73\n", " G2_0 2 0.754 0.4021 0.50 0.99 0.97 0.73\n", " G2_1 2 0.751 0.3485 0.47 1.00 0.97 0.75\n", " G2_2 2 0.745 0.4335 0.51 1.00 0.96 0.70\n", " G3_0 3 0.756 0.3414 0.56 0.99 0.90 0.73\n", " G3_1 3 0.773 0.3511 0.53 1.00 0.98 0.76\n", " G3_2 3 0.763 0.3588 0.50 1.00 0.98 0.75\n", " G3_3 3 0.721 0.3614 0.47 1.00 0.86 0.72\n", " G3_4 3 0.695 0.4237 0.30 1.00 0.91 0.77\n", " T4_best5 4 0.775 0.3169 0.52 1.00 0.97 0.77\n", " T4_cross 4 0.747 0.3190 0.47 0.97 1.00 0.73\n", " T4_diverse 4 0.771 0.3480 0.51 1.00 1.00 0.75\n", " FUSE_dist 5 0.830 0.3386 0.71 1.00 1.00 0.75\n", " FUSE_raw 5 0.813 0.9491 0.69 1.00 0.99 0.73\n", "\n", " Per-generation averages:\n", " Gen 0 (founders): mean=0.664 best=0.675 n=2\n", " Gen 1 (first offspring): mean=0.570 best=0.701 n=3\n", " Gen 2: mean=0.750 best=0.754 n=3\n", " Gen 3: mean=0.742 best=0.773 n=5\n", " Gen 4 (triplets): mean=0.765 best=0.775 n=3\n", " Gen 5 (FUSION): mean=0.822 best=0.830 n=2\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# x34 vit soup" ], "metadata": { "id": "v80FD4ufNw65" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "34-EXPERT SOUP ALIGNMENT\n", "=========================\n", "Train learned projectors per expert to maximize alignment to the\n", "GPA consensus. Replaces naive zero-pad/PCA with InfoNCE-trained\n", "linear projections.\n", "\n", "Expects: vision_soup_34.py already ran, providing:\n", " - expert_features: dict of {name: (N, d_expert)} tensors\n", " - consensus: (N, 1024) L2-normalized consensus targets\n", " - expert_dims: dict of {name: dim}\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "from datasets import load_dataset\n", "import gc\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "D_SHARED = 1024\n", "\n", "print(\"=\" * 65)\n", "print(\"34-EXPERT SOUP ALIGNMENT TRAINING\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " return (torch.stack(vols).std() / (torch.stack(vols).mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=300):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA (same as soup detection, val split)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "SUBSETS = [\n", " \"clip_b16_laion2b\", \"clip_b16_openai\", \"clip_b32_datacomp\",\n", " \"clip_b32_laion2b\", \"clip_b32_openai\", \"clip_bigg14_laion2b\",\n", " \"clip_g14_laion2b\", \"clip_h14_laion2b\", \"clip_l14_336_openai\",\n", " \"clip_l14_datacomp\", \"clip_l14_laion2b\", \"clip_l14_openai\",\n", " \"dinov2_b14\", \"dinov2_b14_reg\", \"dinov2_g14\", \"dinov2_g14_reg\",\n", " \"dinov2_l14\", \"dinov2_l14_reg\", \"dinov2_s14\", \"dinov2_s14_reg\",\n", " \"mae_b16\", \"mae_h14\", \"mae_l16\",\n", " \"siglip2_b16_256\", \"siglip2_b16_512\", \"siglip2_l16_384\",\n", " \"siglip_b16_384\", \"siglip_b16_512\", \"siglip_l16_256\",\n", " \"siglip_l16_384\", \"siglip_so400m_384\",\n", " \"vit_b16_21k\", \"vit_l16_21k\", \"vit_s16_21k\",\n", "]\n", "\n", "print(f\"\\n Loading features...\")\n", "\n", "# Reference for image_id alignment\n", "ref_ds = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"val\")\n", "image_ids = ref_ds[\"image_id\"]\n", "N = len(image_ids)\n", "id_to_idx = {iid: i for i, iid in enumerate(image_ids)}\n", "\n", "expert_features = {}\n", "expert_dims = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " row0 = ds[0]\n", " dim = len(row0[\"features\"])\n", " expert_dims[name] = dim\n", " feats = torch.zeros(N, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in id_to_idx:\n", " feats[id_to_idx[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " expert_features[name] = F.normalize(feats, dim=-1)\n", " print(f\" {name:<30} dim={dim}\", flush=True)\n", "\n", "print(f\" Loaded {len(expert_features)} experts, N={N}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# NAIVE GPA (same as Phase 4 of soup) → consensus targets\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Computing naive GPA consensus for targets...\")\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=5000):\n", " N_ = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N_].float(); T = target[:N_].float()\n", " s_m = S.mean(0, keepdim=True); Sc = S - s_m\n", " t_m = T.mean(0, keepdim=True); Tc = T - t_m\n", " Ns = Sc.shape[0]\n", " s_w = symmetric_inv_sqrt((Sc.T @ Sc) / max(Ns-1, 1))\n", " t_w = symmetric_inv_sqrt((Tc.T @ Tc) / max(Ns-1, 1))\n", " Sc_w = F.normalize(Sc @ s_w, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_w, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " return {\"rotation\": U @ Vt, \"source_mean\": s_m.squeeze(0), \"source_whitener\": s_w}\n", "\n", "def apply_align(emb, info):\n", " return (emb.float() - info[\"source_mean\"]) @ info[\"source_whitener\"] @ info[\"rotation\"].T\n", "\n", "# Naive project for GPA\n", "def naive_project(feats, d_out=D_SHARED):\n", " d_in = feats.shape[1]\n", " if d_in == d_out: return feats\n", " if d_in < d_out:\n", " return F.normalize(torch.cat([feats, torch.zeros(feats.shape[0], d_out - d_in)], -1), dim=-1)\n", " feats_c = feats - feats.mean(0, keepdim=True)\n", " _, S, Vt = torch.linalg.svd(feats_c, full_matrices=False)\n", " return F.normalize(feats @ Vt[:d_out].T, dim=-1)\n", "\n", "naive_proj = {n: naive_project(f) for n, f in expert_features.items()}\n", "expert_names = list(naive_proj.keys())\n", "N_experts = len(expert_names)\n", "\n", "current = {i: naive_proj[expert_names[i]].float() for i in range(N_experts)}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current[i] for i in range(N_experts)) / N_experts\n", " delta = 0.0\n", " new_current = {}\n", " for i in range(N_experts):\n", " info = procrustes_align(current[i], mean_shape)\n", " new_current[i] = apply_align(current[i], info)\n", " delta += (new_current[i] - current[i]).pow(2).mean().item()\n", " current = new_current\n", " if delta < 1e-8: break\n", "\n", "consensus = F.normalize(\n", " sum(current[i] for i in range(N_experts)) / N_experts, dim=-1)\n", "print(f\" Consensus: {consensus.shape}, CV={cv_metric(consensus[:2000]):.4f}\")\n", "\n", "# Baseline cosines\n", "baseline_cos = {}\n", "for i, name in enumerate(expert_names):\n", " cos = F.cosine_similarity(consensus[:2000],\n", " F.normalize(current[i][:2000], dim=-1), dim=-1).mean().item()\n", " baseline_cos[name] = cos\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LEARNED PROJECTORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING LEARNED PROJECTORS\")\n", "print(f\"{'='*65}\")\n", "\n", "\n", "class ExpertProjector(nn.Module):\n", " \"\"\"Learned projection: d_expert → D_SHARED with bottleneck.\"\"\"\n", " def __init__(self, d_in, d_out=D_SHARED):\n", " super().__init__()\n", " d_mid = min(d_in, d_out)\n", " self.net = nn.Sequential(\n", " nn.Linear(d_in, d_mid),\n", " nn.GELU(),\n", " nn.Linear(d_mid, d_out),\n", " nn.LayerNorm(d_out),\n", " )\n", " def forward(self, x):\n", " return F.normalize(self.net(x), dim=-1)\n", "\n", "\n", "# Move consensus to device\n", "consensus_dev = consensus.to(DEVICE)\n", "\n", "# Train each expert's projector\n", "BATCH = 512\n", "EPOCHS = 10\n", "trained_projectors = {}\n", "trained_cos = {}\n", "\n", "# Split: 80% train, 20% eval\n", "n_train = int(N * 0.8)\n", "n_eval = N - n_train\n", "train_idx = torch.arange(n_train)\n", "eval_idx = torch.arange(n_train, N)\n", "\n", "for name in expert_names:\n", " d_in = expert_dims[name]\n", " feats = expert_features[name].to(DEVICE)\n", " targets = consensus_dev\n", "\n", " proj = ExpertProjector(d_in, D_SHARED).to(DEVICE)\n", " opt = torch.optim.Adam(proj.parameters(), lr=1e-3)\n", "\n", " best_cos = 0.0\n", " for epoch in range(EPOCHS):\n", " proj.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_nce_acc, n = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " emb = proj(feats[idx])\n", " tgt = targets[idx]\n", "\n", " l_nce, nce_acc = infonce(emb, tgt)\n", " l_mse = F.mse_loss(emb, tgt)\n", " l_cv = cv_loss(emb, target=0.2) * 0.001\n", "\n", " loss = l_nce + 0.5 * l_mse + l_cv\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(proj.parameters(), 1.0)\n", " opt.step(); opt.zero_grad(set_to_none=True)\n", "\n", " total_loss += loss.item()\n", " total_nce_acc += nce_acc\n", " n += 1\n", "\n", " # Eval\n", " proj.eval()\n", " with torch.no_grad():\n", " eval_emb = proj(feats[eval_idx])\n", " eval_tgt = targets[eval_idx]\n", " e_cos = F.cosine_similarity(eval_emb, eval_tgt, dim=-1).mean().item()\n", " e_cv = cv_metric(eval_emb[:500])\n", "\n", " if e_cos > best_cos:\n", " best_cos = e_cos\n", " best_state = {k: v.cpu().clone() for k, v in proj.state_dict().items()}\n", "\n", " if (epoch + 1) % 5 == 0 or epoch == 0:\n", " b_cos = baseline_cos[name]\n", " delta = e_cos - b_cos\n", " print(f\" {name:<28} E{epoch+1:2d}: cos={e_cos:.4f} (Δ{delta:+.4f}) \"\n", " f\"cv={e_cv:.4f} nce={total_nce_acc/n:.3f}\")\n", "\n", " trained_projectors[name] = best_state\n", " trained_cos[name] = best_cos\n", "\n", " # Clear GPU memory\n", " del proj, feats\n", " gc.collect(); torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RESULTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n\\n{'='*65}\")\n", "print(\"ALIGNMENT RESULTS: NAIVE vs TRAINED\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Expert':<30} {'dim':>5} {'naive':>6} {'trained':>7} {'Δ':>7}\")\n", "print(f\" {'-'*58}\")\n", "\n", "sorted_results = sorted(trained_cos.items(), key=lambda x: -x[1])\n", "for name, t_cos in sorted_results:\n", " b_cos = baseline_cos[name]\n", " dim = expert_dims[name]\n", " delta = t_cos - b_cos\n", " print(f\" {name:<30} {dim:>5} {b_cos:>6.4f} {t_cos:>7.4f} {delta:>+7.4f}\")\n", "\n", "# Aggregate stats\n", "naive_mean = np.mean(list(baseline_cos.values()))\n", "trained_mean = np.mean(list(trained_cos.values()))\n", "print(f\"\\n Mean alignment: naive={naive_mean:.4f} trained={trained_mean:.4f} Δ={trained_mean-naive_mean:+.4f}\")\n", "\n", "# Per-family\n", "families = {\"clip\": [], \"dinov2\": [], \"siglip\": [], \"siglip2\": [],\n", " \"mae\": [], \"vit\": []}\n", "for name in trained_cos:\n", " for fam in families:\n", " if name.startswith(fam):\n", " families[fam].append((baseline_cos[name], trained_cos[name]))\n", " break\n", "\n", "print(f\"\\n Per-family improvement:\")\n", "for fam in sorted(families.keys()):\n", " pairs = families[fam]\n", " if pairs:\n", " b = np.mean([p[0] for p in pairs])\n", " t = np.mean([p[1] for p in pairs])\n", " print(f\" {fam:<10}: naive={b:.4f} → trained={t:.4f} Δ={t-b:+.4f}\")\n", "\n", "# Recompute consensus with trained projectors\n", "print(f\"\\n Recomputing consensus with trained projectors...\")\n", "trained_embs = {}\n", "for name in expert_names:\n", " proj = ExpertProjector(expert_dims[name], D_SHARED)\n", " proj.load_state_dict(trained_projectors[name])\n", " proj.eval().to(DEVICE)\n", " with torch.no_grad():\n", " feats = expert_features[name].to(DEVICE)\n", " trained_embs[name] = proj(feats).cpu()\n", " del proj; gc.collect(); torch.cuda.empty_cache()\n", "\n", "# GPA on trained projections\n", "current2 = {i: trained_embs[expert_names[i]].float() for i in range(N_experts)}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current2[i] for i in range(N_experts)) / N_experts\n", " delta = 0.0\n", " new_current = {}\n", " for i in range(N_experts):\n", " info = procrustes_align(current2[i], mean_shape)\n", " new_current[i] = apply_align(current2[i], info)\n", " delta += (new_current[i] - current2[i]).pow(2).mean().item()\n", " current2 = new_current\n", " if delta < 1e-8: break\n", "\n", "consensus_v2 = F.normalize(\n", " sum(current2[i] for i in range(N_experts)) / N_experts, dim=-1)\n", "cv_v2 = cv_metric(consensus_v2[:2000])\n", "\n", "# Per-expert cos to new consensus\n", "print(f\"\\n Trained consensus CV: {cv_v2:.4f}\")\n", "print(f\"\\n Per-expert alignment to TRAINED consensus:\")\n", "final_cos = {}\n", "for i, name in enumerate(expert_names):\n", " cos = F.cosine_similarity(consensus_v2[:2000],\n", " F.normalize(current2[i][:2000], dim=-1), dim=-1).mean().item()\n", " final_cos[name] = cos\n", "\n", "sorted_final = sorted(final_cos.items(), key=lambda x: -x[1])\n", "for name, cos in sorted_final[:10]:\n", " print(f\" {name:<30} cos={cos:.4f} (was {baseline_cos[name]:.4f})\")\n", "\n", "print(f\"\\n ...({len(sorted_final)-10} more)\")\n", "final_mean = np.mean(list(final_cos.values()))\n", "print(f\"\\n Final consensus mean alignment: {final_mean:.4f} (was {naive_mean:.4f})\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SOUP ALIGNED\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Duy4N7GjNz5V", "outputId": "b01dc62b-1cc0-49db-c2f6-d691cf9f472c" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "34-EXPERT SOUP ALIGNMENT TRAINING\n", "=================================================================\n", " Device: cuda\n", "\n", " Loading features...\n", " clip_b16_laion2b dim=512\n", " clip_b16_openai dim=512\n", " clip_b32_datacomp dim=512\n", " clip_b32_laion2b dim=512\n", " clip_b32_openai dim=512\n", " clip_bigg14_laion2b dim=1280\n", " clip_g14_laion2b dim=1024\n", " clip_h14_laion2b dim=1024\n", " clip_l14_336_openai dim=768\n", " clip_l14_datacomp dim=768\n", " clip_l14_laion2b dim=768\n", " clip_l14_openai dim=768\n", " dinov2_b14 dim=768\n", " dinov2_b14_reg dim=768\n", " dinov2_g14 dim=1536\n", " dinov2_g14_reg dim=1536\n", " dinov2_l14 dim=1024\n", " dinov2_l14_reg dim=1024\n", " dinov2_s14 dim=384\n", " dinov2_s14_reg dim=384\n", " mae_b16 dim=768\n", " mae_h14 dim=1280\n", " mae_l16 dim=1024\n", " siglip2_b16_256 dim=768\n", " siglip2_b16_512 dim=768\n", " siglip2_l16_384 dim=1024\n", " siglip_b16_384 dim=768\n", " siglip_b16_512 dim=768\n", " siglip_l16_256 dim=1024\n", " siglip_l16_384 dim=1024\n", " siglip_so400m_384 dim=1152\n", " vit_b16_21k dim=768\n", " vit_l16_21k dim=1024\n", " vit_s16_21k dim=384\n", " Loaded 34 experts, N=5000\n", "\n", " Computing naive GPA consensus for targets...\n", " Consensus: torch.Size([5000, 1024]), CV=0.0241\n", "\n", "=================================================================\n", "TRAINING LEARNED PROJECTORS\n", "=================================================================\n", " clip_b16_laion2b E 1: cos=0.1025 (Δ-0.5026) cv=0.2136 nce=0.054\n", " clip_b16_laion2b E 5: cos=0.3595 (Δ-0.2456) cv=0.0938 nce=0.998\n", " clip_b16_laion2b E10: cos=0.4438 (Δ-0.1613) cv=0.0549 nce=1.000\n", " clip_b16_openai E 1: cos=0.0794 (Δ-0.5170) cv=0.2808 nce=0.032\n", " clip_b16_openai E 5: cos=0.3309 (Δ-0.2655) cv=0.0939 nce=0.990\n", " clip_b16_openai E10: cos=0.4170 (Δ-0.1793) cv=0.0633 nce=1.000\n", " clip_b32_datacomp E 1: cos=0.1030 (Δ-0.4861) cv=0.2206 nce=0.049\n", " clip_b32_datacomp E 5: cos=0.3410 (Δ-0.2482) cv=0.0816 nce=0.997\n", " clip_b32_datacomp E10: cos=0.4188 (Δ-0.1703) cv=0.0566 nce=1.000\n", " clip_b32_laion2b E 1: cos=0.1017 (Δ-0.4943) cv=0.2192 nce=0.039\n", " clip_b32_laion2b E 5: cos=0.3496 (Δ-0.2464) cv=0.0784 nce=0.997\n", " clip_b32_laion2b E10: cos=0.4313 (Δ-0.1647) cv=0.0627 nce=1.000\n", " clip_b32_openai E 1: cos=0.0777 (Δ-0.5136) cv=0.2745 nce=0.030\n", " clip_b32_openai E 5: cos=0.3295 (Δ-0.2619) cv=0.1020 nce=0.988\n", " clip_b32_openai E10: cos=0.4136 (Δ-0.1777) cv=0.0671 nce=1.000\n", " clip_bigg14_laion2b E 1: cos=0.2169 (Δ-0.4599) cv=0.2011 nce=0.361\n", " clip_bigg14_laion2b E 5: cos=0.4598 (Δ-0.2169) cv=0.0617 nce=1.000\n", " clip_bigg14_laion2b E10: cos=0.5166 (Δ-0.1602) cv=0.0461 nce=1.000\n", " clip_g14_laion2b E 1: cos=0.2053 (Δ-0.4570) cv=0.1934 nce=0.306\n", " clip_g14_laion2b E 5: cos=0.4563 (Δ-0.2059) cv=0.0571 nce=1.000\n", " clip_g14_laion2b E10: cos=0.5083 (Δ-0.1540) cv=0.0439 nce=1.000\n", " clip_h14_laion2b E 1: cos=0.2053 (Δ-0.4704) cv=0.2120 nce=0.345\n", " clip_h14_laion2b E 5: cos=0.4623 (Δ-0.2133) cv=0.0639 nce=1.000\n", " clip_h14_laion2b E10: cos=0.5173 (Δ-0.1584) cv=0.0455 nce=1.000\n", " clip_l14_336_openai E 1: cos=0.1616 (Δ-0.4748) cv=0.2374 nce=0.151\n", " clip_l14_336_openai E 5: cos=0.3885 (Δ-0.2479) cv=0.0636 nce=1.000\n", " clip_l14_336_openai E10: cos=0.4613 (Δ-0.1750) cv=0.0539 nce=1.000\n", " clip_l14_datacomp E 1: cos=0.1825 (Δ-0.4630) cv=0.2101 nce=0.195\n", " clip_l14_datacomp E 5: cos=0.4174 (Δ-0.2281) cv=0.0749 nce=1.000\n", " clip_l14_datacomp E10: cos=0.4857 (Δ-0.1598) cv=0.0469 nce=1.000\n", " clip_l14_laion2b E 1: cos=0.1795 (Δ-0.4630) cv=0.2162 nce=0.205\n", " clip_l14_laion2b E 5: cos=0.4210 (Δ-0.2214) cv=0.0639 nce=1.000\n", " clip_l14_laion2b E10: cos=0.4884 (Δ-0.1541) cv=0.0516 nce=1.000\n", " clip_l14_openai E 1: cos=0.1632 (Δ-0.4698) cv=0.2510 nce=0.136\n", " clip_l14_openai E 5: cos=0.3858 (Δ-0.2472) cv=0.0663 nce=0.999\n", " clip_l14_openai E10: cos=0.4559 (Δ-0.1771) cv=0.0520 nce=1.000\n", " dinov2_b14 E 1: cos=0.1743 (Δ-0.4314) cv=0.1707 nce=0.117\n", " dinov2_b14 E 5: cos=0.3757 (Δ-0.2300) cv=0.0654 nce=0.989\n", " dinov2_b14 E10: cos=0.4265 (Δ-0.1792) cv=0.0436 nce=1.000\n", " dinov2_b14_reg E 1: cos=0.1789 (Δ-0.4367) cv=0.1695 nce=0.118\n", " dinov2_b14_reg E 5: cos=0.3846 (Δ-0.2310) cv=0.0780 nce=0.986\n", " dinov2_b14_reg E10: cos=0.4376 (Δ-0.1779) cv=0.0477 nce=1.000\n", " dinov2_g14 E 1: cos=0.2134 (Δ-0.4545) cv=0.2608 nce=0.203\n", " dinov2_g14 E 5: cos=0.4200 (Δ-0.2479) cv=0.1064 nce=0.960\n", " dinov2_g14 E10: cos=0.4720 (Δ-0.1959) cv=0.0656 nce=1.000\n", " dinov2_g14_reg E 1: cos=0.2106 (Δ-0.4573) cv=0.2454 nce=0.199\n", " dinov2_g14_reg E 5: cos=0.4201 (Δ-0.2478) cv=0.0736 nce=0.971\n", " dinov2_g14_reg E10: cos=0.4717 (Δ-0.1962) cv=0.0536 nce=1.000\n", " dinov2_l14 E 1: cos=0.1912 (Δ-0.4305) cv=0.1894 nce=0.187\n", " dinov2_l14 E 5: cos=0.3923 (Δ-0.2294) cv=0.0678 nce=0.988\n", " dinov2_l14 E10: cos=0.4332 (Δ-0.1884) cv=0.0376 nce=1.000\n", " dinov2_l14_reg E 1: cos=0.1896 (Δ-0.4398) cv=0.1860 nce=0.180\n", " dinov2_l14_reg E 5: cos=0.3977 (Δ-0.2317) cv=0.0687 nce=0.986\n", " dinov2_l14_reg E10: cos=0.4396 (Δ-0.1898) cv=0.0512 nce=1.000\n", " dinov2_s14 E 1: cos=0.0514 (Δ-0.5072) cv=0.1786 nce=0.019\n", " dinov2_s14 E 5: cos=0.2978 (Δ-0.2608) cv=0.1153 nce=0.953\n", " dinov2_s14 E10: cos=0.3730 (Δ-0.1856) cv=0.0658 nce=0.999\n", " dinov2_s14_reg E 1: cos=0.0522 (Δ-0.5081) cv=0.1728 nce=0.016\n", " dinov2_s14_reg E 5: cos=0.2973 (Δ-0.2630) cv=0.1170 nce=0.942\n", " dinov2_s14_reg E10: cos=0.3751 (Δ-0.1853) cv=0.0611 nce=0.999\n", " mae_b16 E 1: cos=0.0270 (Δ-0.5176) cv=0.8744 nce=0.005\n", " mae_b16 E 5: cos=0.1542 (Δ-0.3904) cv=0.2955 nce=0.677\n", " mae_b16 E10: cos=0.2227 (Δ-0.3218) cv=0.1488 nce=0.976\n", " mae_h14 E 1: cos=0.0051 (Δ-0.5694) cv=0.0128 nce=0.002\n", " mae_h14 E 5: cos=0.1510 (Δ-0.4235) cv=0.3181 nce=0.536\n", " mae_h14 E10: cos=0.2348 (Δ-0.3397) cv=0.1542 nce=0.978\n", " mae_l16 E 1: cos=0.0123 (Δ-0.5621) cv=0.5313 nce=0.004\n", " mae_l16 E 5: cos=0.1778 (Δ-0.3967) cv=0.2268 nce=0.775\n", " mae_l16 E10: cos=0.2600 (Δ-0.3144) cv=0.1343 nce=0.987\n", " siglip2_b16_256 E 1: cos=0.1667 (Δ-0.4936) cv=0.2349 nce=0.172\n", " siglip2_b16_256 E 5: cos=0.4140 (Δ-0.2463) cv=0.0772 nce=1.000\n", " siglip2_b16_256 E10: cos=0.4915 (Δ-0.1687) cv=0.0546 nce=1.000\n", " siglip2_b16_512 E 1: cos=0.1707 (Δ-0.4952) cv=0.2443 nce=0.167\n", " siglip2_b16_512 E 5: cos=0.4144 (Δ-0.2515) cv=0.0764 nce=1.000\n", " siglip2_b16_512 E10: cos=0.4962 (Δ-0.1697) cv=0.0531 nce=1.000\n", " siglip2_l16_384 E 1: cos=0.1963 (Δ-0.4827) cv=0.2281 nce=0.293\n", " siglip2_l16_384 E 5: cos=0.4430 (Δ-0.2360) cv=0.0638 nce=1.000\n", " siglip2_l16_384 E10: cos=0.5070 (Δ-0.1720) cv=0.0486 nce=1.000\n", " siglip_b16_384 E 1: cos=0.1690 (Δ-0.4918) cv=0.2200 nce=0.172\n", " siglip_b16_384 E 5: cos=0.4128 (Δ-0.2481) cv=0.0721 nce=1.000\n", " siglip_b16_384 E10: cos=0.4926 (Δ-0.1683) cv=0.0502 nce=1.000\n", " siglip_b16_512 E 1: cos=0.1697 (Δ-0.4919) cv=0.2232 nce=0.187\n", " siglip_b16_512 E 5: cos=0.4159 (Δ-0.2457) cv=0.0625 nce=1.000\n", " siglip_b16_512 E10: cos=0.4935 (Δ-0.1681) cv=0.0488 nce=0.999\n", " siglip_l16_256 E 1: cos=0.1919 (Δ-0.5014) cv=0.2179 nce=0.263\n", " siglip_l16_256 E 5: cos=0.4474 (Δ-0.2460) cv=0.0602 nce=1.000\n", " siglip_l16_256 E10: cos=0.5088 (Δ-0.1845) cv=0.0447 nce=1.000\n", " siglip_l16_384 E 1: cos=0.1921 (Δ-0.5078) cv=0.2210 nce=0.308\n", " siglip_l16_384 E 5: cos=0.4506 (Δ-0.2493) cv=0.0600 nce=1.000\n", " siglip_l16_384 E10: cos=0.5136 (Δ-0.1863) cv=0.0501 nce=1.000\n", " siglip_so400m_384 E 1: cos=0.1926 (Δ-0.4859) cv=0.2349 nce=0.283\n", " siglip_so400m_384 E 5: cos=0.4483 (Δ-0.2303) cv=0.0613 nce=1.000\n", " siglip_so400m_384 E10: cos=0.5118 (Δ-0.1668) cv=0.0503 nce=1.000\n", " vit_b16_21k E 1: cos=0.1620 (Δ-0.4283) cv=0.1739 nce=0.108\n", " vit_b16_21k E 5: cos=0.3602 (Δ-0.2301) cv=0.0676 nce=0.999\n", " vit_b16_21k E10: cos=0.4131 (Δ-0.1772) cv=0.0529 nce=1.000\n", " vit_l16_21k E 1: cos=0.1730 (Δ-0.4349) cv=0.1973 nce=0.213\n", " vit_l16_21k E 5: cos=0.3890 (Δ-0.2190) cv=0.0491 nce=0.999\n", " vit_l16_21k E10: cos=0.4305 (Δ-0.1774) cv=0.0443 nce=1.000\n", " vit_s16_21k E 1: cos=0.0493 (Δ-0.4992) cv=0.1578 nce=0.009\n", " vit_s16_21k E 5: cos=0.2814 (Δ-0.2672) cv=0.0980 nce=0.958\n", " vit_s16_21k E10: cos=0.3554 (Δ-0.1931) cv=0.0593 nce=1.000\n", "\n", "\n", "=================================================================\n", "ALIGNMENT RESULTS: NAIVE vs TRAINED\n", "=================================================================\n", "\n", " Expert dim naive trained Δ\n", " ----------------------------------------------------------\n", " clip_h14_laion2b 1024 0.6756 0.5173 -0.1584\n", " clip_bigg14_laion2b 1280 0.6767 0.5166 -0.1602\n", " siglip_l16_384 1024 0.6999 0.5136 -0.1863\n", " siglip_so400m_384 1152 0.6786 0.5118 -0.1668\n", " siglip_l16_256 1024 0.6933 0.5088 -0.1845\n", " clip_g14_laion2b 1024 0.6623 0.5083 -0.1540\n", " siglip2_l16_384 1024 0.6790 0.5070 -0.1720\n", " siglip2_b16_512 768 0.6659 0.4962 -0.1697\n", " siglip_b16_512 768 0.6616 0.4935 -0.1681\n", " siglip_b16_384 768 0.6609 0.4926 -0.1683\n", " siglip2_b16_256 768 0.6603 0.4915 -0.1687\n", " clip_l14_laion2b 768 0.6425 0.4884 -0.1541\n", " clip_l14_datacomp 768 0.6455 0.4857 -0.1598\n", " dinov2_g14 1536 0.6679 0.4720 -0.1959\n", " dinov2_g14_reg 1536 0.6679 0.4717 -0.1962\n", " clip_l14_336_openai 768 0.6364 0.4613 -0.1750\n", " clip_l14_openai 768 0.6330 0.4559 -0.1771\n", " clip_b16_laion2b 512 0.6051 0.4438 -0.1613\n", " dinov2_l14_reg 1024 0.6294 0.4396 -0.1898\n", " dinov2_b14_reg 768 0.6156 0.4376 -0.1779\n", " dinov2_l14 1024 0.6217 0.4332 -0.1884\n", " clip_b32_laion2b 512 0.5960 0.4313 -0.1647\n", " vit_l16_21k 1024 0.6079 0.4305 -0.1774\n", " dinov2_b14 768 0.6057 0.4265 -0.1792\n", " clip_b32_datacomp 512 0.5892 0.4188 -0.1703\n", " clip_b16_openai 512 0.5964 0.4170 -0.1793\n", " clip_b32_openai 512 0.5913 0.4136 -0.1777\n", " vit_b16_21k 768 0.5903 0.4131 -0.1772\n", " dinov2_s14_reg 384 0.5603 0.3751 -0.1853\n", " dinov2_s14 384 0.5586 0.3730 -0.1856\n", " vit_s16_21k 384 0.5485 0.3554 -0.1931\n", " mae_l16 1024 0.5745 0.2600 -0.3144\n", " mae_h14 1280 0.5744 0.2348 -0.3397\n", " mae_b16 768 0.5445 0.2227 -0.3218\n", "\n", " Mean alignment: naive=0.6270 trained=0.4388 Δ=-0.1882\n", "\n", " Per-family improvement:\n", " clip : naive=0.6292 → trained=0.4632 Δ=-0.1660\n", " dinov2 : naive=0.6159 → trained=0.4286 Δ=-0.1873\n", " mae : naive=0.5645 → trained=0.2392 Δ=-0.3253\n", " siglip : naive=0.6749 → trained=0.5019 Δ=-0.1731\n", " vit : naive=0.5823 → trained=0.3997 Δ=-0.1826\n", "\n", " Recomputing consensus with trained projectors...\n", "\n", " Trained consensus CV: 0.0250\n", "\n", " Per-expert alignment to TRAINED consensus:\n", " siglip_l16_384 cos=0.6743 (was 0.6999)\n", " siglip_l16_256 cos=0.6684 (was 0.6933)\n", " siglip2_l16_384 cos=0.6679 (was 0.6790)\n", " siglip_so400m_384 cos=0.6668 (was 0.6786)\n", " dinov2_g14_reg cos=0.6638 (was 0.6679)\n", " dinov2_g14 cos=0.6634 (was 0.6679)\n", " clip_bigg14_laion2b cos=0.6597 (was 0.6767)\n", " siglip2_b16_512 cos=0.6573 (was 0.6659)\n", " clip_h14_laion2b cos=0.6562 (was 0.6756)\n", " siglip_b16_512 cos=0.6548 (was 0.6616)\n", "\n", " ...(24 more)\n", "\n", " Final consensus mean alignment: 0.6201 (was 0.6270)\n", "\n", "=================================================================\n", "SOUP ALIGNED\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "# train x34" ], "metadata": { "id": "AZf0cwakUTPD" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "34-EXPERT PATCHWORK MODEL\n", "==========================\n", "Pre-extracted features from 34 vision models → learned projectors →\n", "cross-expert fusion → constellation triangulation → patchwork → COCO multi-label.\n", "\n", "Architecture:\n", " Per-expert: Linear(d_expert → d_shared) + LayerNorm\n", " Fusion: Cross-attention over 34 expert tokens → fused embedding\n", " Geometry: Constellation(n_anchors) → triangulation → Patchwork → MLP\n", " Output: 80-class multi-label (BCE)\n", "\n", "Training: Adam + geometric autograd (tang=0.01, sep=1.0, cv=0.001)\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "from datasets import load_dataset\n", "import gc\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "D_SHARED = 1024\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 128\n", "\n", "print(\"=\" * 65)\n", "print(\"34-EXPERT PATCHWORK MODEL\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" Shared dim: {D_SHARED}, Anchors: {N_ANCHORS}, Classes: {N_CLASSES}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a = F.normalize(anchors, dim=-1)\n", " sim = a @ a.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ExpertProjector(nn.Module):\n", " \"\"\"d_expert → d_shared with bottleneck.\"\"\"\n", " def __init__(self, d_in, d_out=D_SHARED):\n", " super().__init__()\n", " d_mid = min(d_in, d_out)\n", " self.net = nn.Sequential(\n", " nn.Linear(d_in, d_mid),\n", " nn.GELU(),\n", " nn.Linear(d_mid, d_out),\n", " nn.LayerNorm(d_out),\n", " )\n", " def forward(self, x):\n", " return self.net(x)\n", "\n", "\n", "class ExpertFusion(nn.Module):\n", " \"\"\"\n", " Cross-attention fusion of N expert projections → single embedding.\n", " Uses a learned query token that attends to all expert outputs.\n", " \"\"\"\n", " def __init__(self, d_model=D_SHARED, n_heads=8, n_layers=2):\n", " super().__init__()\n", " self.query = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.layers = nn.ModuleList([\n", " nn.TransformerDecoderLayer(\n", " d_model=d_model, nhead=n_heads,\n", " dim_feedforward=d_model * 2,\n", " dropout=0.1, batch_first=True,\n", " norm_first=True,\n", " ) for _ in range(n_layers)\n", " ])\n", " self.norm = nn.LayerNorm(d_model)\n", "\n", " def forward(self, expert_tokens):\n", " \"\"\"\n", " expert_tokens: (B, N_experts, d_model)\n", " returns: (B, d_model)\n", " \"\"\"\n", " B = expert_tokens.shape[0]\n", " q = self.query.expand(B, -1, -1) # (B, 1, d_model)\n", " for layer in self.layers:\n", " q = layer(q, expert_tokens)\n", " return self.norm(q.squeeze(1)) # (B, d_model)\n", "\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d_embed=D_SHARED, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(\n", " torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri):\n", " nearest = tri.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " m = nearest == i\n", " if m.sum() < 5: continue\n", " self.visit_count[i] += m.sum().float()\n", " sp = tri[m].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(sp+0.01)\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp),\n", " ) for k in range(n_comp)])\n", "\n", " def forward(self, tri):\n", " return torch.cat([\n", " self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)\n", " ], dim=-1)\n", "\n", "\n", "class SoupModel(nn.Module):\n", " \"\"\"\n", " 34-expert → projectors → fusion → constellation → patchwork → classifier.\n", " \"\"\"\n", " def __init__(self, expert_dims_dict, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP, n_classes=N_CLASSES,\n", " d_shared=D_SHARED, init_anchors=None):\n", " super().__init__()\n", " self.expert_names = sorted(expert_dims_dict.keys())\n", " self.n_experts = len(self.expert_names)\n", " self.d_shared = d_shared\n", "\n", " # Per-expert projectors\n", " self.projectors = nn.ModuleDict({\n", " name.replace(\".\", \"_\"): ExpertProjector(dim, d_shared)\n", " for name, dim in expert_dims_dict.items()\n", " })\n", " self.name_to_key = {name: name.replace(\".\", \"_\")\n", " for name in expert_dims_dict}\n", "\n", " # Expert identity embeddings (learned, added to projected features)\n", " self.expert_ids = nn.Parameter(\n", " torch.randn(self.n_experts, d_shared) * 0.02)\n", "\n", " # Fusion: cross-attention over expert tokens\n", " self.fusion = ExpertFusion(d_shared, n_heads=8, n_layers=2)\n", "\n", " # Geometric pipeline\n", " self.constellation = Constellation(n_anchors, d_shared, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", "\n", " # Classifier: patchwork output + fused embedding → multi-label\n", " pw_dim = n_comp * d_comp\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + d_shared, d_shared),\n", " nn.GELU(),\n", " nn.LayerNorm(d_shared),\n", " nn.Dropout(0.1),\n", " nn.Linear(d_shared, d_shared // 2),\n", " nn.GELU(),\n", " nn.Linear(d_shared // 2, n_classes),\n", " )\n", "\n", " def forward(self, expert_features_dict):\n", " \"\"\"\n", " expert_features_dict: {name: (B, d_expert)} for each expert\n", " \"\"\"\n", " B = next(iter(expert_features_dict.values())).shape[0]\n", "\n", " # Project each expert\n", " tokens = []\n", " for i, name in enumerate(self.expert_names):\n", " key = self.name_to_key[name]\n", " feat = expert_features_dict[name]\n", " proj = self.projectors[key](feat) # (B, d_shared)\n", " proj = proj + self.expert_ids[i] # + identity\n", " tokens.append(proj)\n", "\n", " expert_stack = torch.stack(tokens, dim=1) # (B, N, d_shared)\n", "\n", " # Fuse\n", " fused = self.fusion(expert_stack) # (B, d_shared)\n", " emb = F.normalize(fused, dim=-1) # on hypersphere\n", "\n", " # Triangulate\n", " tri, nearest = self.constellation.triangulate(emb)\n", "\n", " # Patchwork\n", " pw = self.patchwork(tri) # (B, n_comp * d_comp)\n", "\n", " # Classify from patchwork + embedding\n", " combined = torch.cat([pw, emb], dim=-1)\n", " logits = self.classifier(combined) # (B, n_classes)\n", "\n", " return logits, emb, tri, nearest\n", "\n", " def count_params(self):\n", " total = sum(p.numel() for p in self.parameters())\n", " proj = sum(p.numel() for p in self.projectors.parameters())\n", " fuse = sum(p.numel() for p in self.fusion.parameters())\n", " geo = sum(p.numel() for p in self.constellation.parameters())\n", " pw = sum(p.numel() for p in self.patchwork.parameters())\n", " cls = sum(p.numel() for p in self.classifier.parameters())\n", " return {\"total\": total, \"projectors\": proj, \"fusion\": fuse,\n", " \"constellation\": geo, \"patchwork\": pw, \"classifier\": cls}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA LOADING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "SUBSETS = [\n", " \"clip_b16_laion2b\", \"clip_b16_openai\", \"clip_b32_datacomp\",\n", " \"clip_b32_laion2b\", \"clip_b32_openai\", \"clip_bigg14_laion2b\",\n", " \"clip_g14_laion2b\", \"clip_h14_laion2b\", \"clip_l14_336_openai\",\n", " \"clip_l14_datacomp\", \"clip_l14_laion2b\", \"clip_l14_openai\",\n", " \"dinov2_b14\", \"dinov2_b14_reg\", \"dinov2_g14\", \"dinov2_g14_reg\",\n", " \"dinov2_l14\", \"dinov2_l14_reg\", \"dinov2_s14\", \"dinov2_s14_reg\",\n", " \"mae_b16\", \"mae_h14\", \"mae_l16\",\n", " \"siglip2_b16_256\", \"siglip2_b16_512\", \"siglip2_l16_384\",\n", " \"siglip_b16_384\", \"siglip_b16_512\", \"siglip_l16_256\",\n", " \"siglip_l16_384\", \"siglip_so400m_384\",\n", " \"vit_b16_21k\", \"vit_l16_21k\", \"vit_s16_21k\",\n", "]\n", "\n", "print(f\"\\n Loading val features...\")\n", "ref_ds = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"val\")\n", "image_ids = ref_ds[\"image_id\"]\n", "labels_raw = ref_ds[\"labels\"]\n", "N = len(image_ids)\n", "id_to_idx = {iid: i for i, iid in enumerate(image_ids)}\n", "\n", "# Multi-label targets\n", "label_matrix = torch.zeros(N, N_CLASSES)\n", "for i, labs in enumerate(labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES:\n", " label_matrix[i, l] = 1.0\n", "\n", "expert_features = {}\n", "expert_dims = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " dim = len(ds[0][\"features\"])\n", " expert_dims[name] = dim\n", " feats = torch.zeros(N, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in id_to_idx:\n", " feats[id_to_idx[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " expert_features[name] = feats # NOT normalized — projector handles it\n", " print(f\" {name:<30} dim={dim}\", flush=True)\n", "\n", "print(f\" Loaded {len(expert_features)} experts, N={N}\")\n", "print(f\" Labels: {N_CLASSES} classes, multi-label\")\n", "print(f\" Positive rate: {label_matrix.sum() / (N * N_CLASSES):.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILDING MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = SoupModel(expert_dims, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP,\n", " n_classes=N_CLASSES, d_shared=D_SHARED).to(DEVICE)\n", "\n", "params = model.count_params()\n", "print(f\" Parameters:\")\n", "for k, v in params.items():\n", " print(f\" {k:<15}: {v:>10,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\"{'='*65}\")\n", "\n", "# Split 80/20\n", "n_train = int(N * 0.8)\n", "train_idx = torch.arange(n_train)\n", "val_idx = torch.arange(n_train, N)\n", "\n", "# Pre-stack features per expert on device\n", "train_feats = {name: expert_features[name][:n_train].to(DEVICE) for name in SUBSETS}\n", "val_feats = {name: expert_features[name][n_train:].to(DEVICE) for name in SUBSETS}\n", "train_labels = label_matrix[:n_train].to(DEVICE)\n", "val_labels = label_matrix[n_train:].to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n", "BATCH = 128\n", "EPOCHS = 20\n", "TANG, SEP, CV_W = 0.01, 1.0, 0.001\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(n_train, device=DEVICE)\n", " total_loss, total_correct, n_batches = 0, 0, 0\n", "\n", " for i in range(0, n_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " # Gather batch\n", " batch_feats = {name: train_feats[name][idx] for name in SUBSETS}\n", " batch_labels = train_labels[idx]\n", "\n", " logits, emb, tri, nearest = model(batch_feats)\n", " anchors = model.constellation.anchors\n", "\n", " # Geometric autograd\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, TANG, SEP)\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " pw_g = model.patchwork(tri_g)\n", " combined_g = torch.cat([pw_g, emb_g], dim=-1)\n", " logits = model.classifier(combined_g)\n", "\n", " # Multi-label BCE\n", " l_cls = F.binary_cross_entropy_with_logits(logits, batch_labels)\n", "\n", " # Geometric losses\n", " l_cv = CV_W * cv_loss(emb)\n", " l_spread = 1e-3 * anchor_spread_loss(anchors)\n", " l_ent = 1e-4 * anchor_entropy_loss(emb, anchors)\n", "\n", " loss = l_cls + l_cv + l_spread + l_ent\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach())\n", "\n", " # Multi-label accuracy (threshold 0.5)\n", " preds = (logits.detach().sigmoid() > 0.5).float()\n", " correct = (preds == batch_labels).float().mean().item()\n", " total_correct += correct\n", " total_loss += loss.item()\n", " n_batches += 1\n", "\n", " train_acc = total_correct / n_batches\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " # Process val in chunks\n", " all_logits, all_embs = [], []\n", " for j in range(0, len(val_idx), BATCH):\n", " chunk_idx = torch.arange(j, min(j + BATCH, len(val_idx)))\n", " chunk_feats = {name: val_feats[name][chunk_idx] for name in SUBSETS}\n", " lo, em, _, _ = model(chunk_feats)\n", " all_logits.append(lo)\n", " all_embs.append(em)\n", "\n", " v_logits = torch.cat(all_logits, 0)\n", " v_embs = torch.cat(all_embs, 0)\n", "\n", " v_preds = (v_logits.sigmoid() > 0.5).float()\n", " v_acc = (v_preds == val_labels).float().mean().item()\n", " v_cv = cv_metric(v_embs.cpu())\n", "\n", " # Per-class F1 (macro)\n", " tp = (v_preds * val_labels).sum(0)\n", " fp = (v_preds * (1 - val_labels)).sum(0)\n", " fn = ((1 - v_preds) * val_labels).sum(0)\n", " precision = tp / (tp + fp + 1e-8)\n", " recall = tp / (tp + fn + 1e-8)\n", " f1 = 2 * precision * recall / (precision + recall + 1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " # mAP\n", " ap_sum = 0\n", " n_valid = 0\n", " for c in range(N_CLASSES):\n", " if val_labels[:, c].sum() > 0:\n", " scores = v_logits[:, c].cpu()\n", " targets = val_labels[:, c].cpu()\n", " sorted_idx = scores.argsort(descending=True)\n", " sorted_tgt = targets[sorted_idx]\n", " tp_cumsum = sorted_tgt.cumsum(0)\n", " precision_at_k = tp_cumsum / torch.arange(1, len(sorted_tgt) + 1).float()\n", " ap = (precision_at_k * sorted_tgt).sum() / sorted_tgt.sum()\n", " ap_sum += ap.item()\n", " n_valid += 1\n", " mAP = ap_sum / max(n_valid, 1)\n", "\n", " rig = model.constellation.rigidity\n", " if (epoch + 1) % 2 == 0 or epoch == 0:\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"mAP={mAP:.3f} F1={macro_f1:.3f} \"\n", " f\"cv={v_cv:.4f} rig={rig.mean():.1f}/{rig.max():.1f} \"\n", " f\"loss={total_loss/n_batches:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL REPORT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL REPORT\")\n", "print(f\"{'='*65}\")\n", "\n", "model.eval()\n", "with torch.no_grad():\n", " all_logits, all_embs = [], []\n", " for j in range(0, len(val_idx), BATCH):\n", " chunk_idx = torch.arange(j, min(j + BATCH, len(val_idx)))\n", " chunk_feats = {name: val_feats[name][chunk_idx] for name in SUBSETS}\n", " lo, em, _, _ = model(chunk_feats)\n", " all_logits.append(lo)\n", " all_embs.append(em)\n", "\n", " v_logits = torch.cat(all_logits, 0)\n", " v_embs = torch.cat(all_embs, 0)\n", "\n", " # Top-5 and bottom-5 classes by AP\n", " class_aps = {}\n", " for c in range(N_CLASSES):\n", " if val_labels[:, c].sum() > 0:\n", " scores = v_logits[:, c].cpu()\n", " targets = val_labels[:, c].cpu()\n", " sorted_idx = scores.argsort(descending=True)\n", " sorted_tgt = targets[sorted_idx]\n", " tp_cumsum = sorted_tgt.cumsum(0)\n", " prec_at_k = tp_cumsum / torch.arange(1, len(sorted_tgt) + 1).float()\n", " class_aps[c] = (prec_at_k * sorted_tgt).sum().item() / sorted_tgt.sum().item()\n", "\n", " sorted_aps = sorted(class_aps.items(), key=lambda x: -x[1])\n", " print(f\"\\n Top 5 classes by AP:\")\n", " for c, ap in sorted_aps[:5]:\n", " n = val_labels[:, c].sum().int().item()\n", " print(f\" class {c:>3}: AP={ap:.3f} (n={n})\")\n", "\n", " print(f\"\\n Bottom 5 classes by AP:\")\n", " for c, ap in sorted_aps[-5:]:\n", " n = val_labels[:, c].sum().int().item()\n", " print(f\" class {c:>3}: AP={ap:.3f} (n={n})\")\n", "\n", " final_cv = cv_metric(v_embs.cpu())\n", " print(f\"\\n Final mAP: {sum(class_aps.values())/len(class_aps):.3f}\")\n", " print(f\" Final CV: {final_cv:.4f}\")\n", " print(f\" Embedding dim: {v_embs.shape[1]}\")\n", " print(f\" Anchors: {model.constellation.n_anchors}\")\n", "\n", " # Expert contribution analysis\n", " print(f\"\\n Expert identity norms (learned importance):\")\n", " norms = model.expert_ids.detach().cpu().norm(dim=-1)\n", " sorted_exp = sorted(zip(model.expert_names, norms.tolist()),\n", " key=lambda x: -x[1])\n", " for name, norm in sorted_exp[:5]:\n", " print(f\" {name:<30} norm={norm:.4f}\")\n", " print(f\" ...\")\n", " for name, norm in sorted_exp[-3:]:\n", " print(f\" {name:<30} norm={norm:.4f}\")\n", "\n", "print(f\"\\n Parameters: {params['total']:,}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r9TRxKBYSmAr", "outputId": "eb850d53-dd71-407e-ecc8-d7ae70011c2a" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "34-EXPERT PATCHWORK MODEL\n", "=================================================================\n", " Device: cuda\n", " Shared dim: 1024, Anchors: 256, Classes: 80\n", "\n", " Loading val features...\n", " clip_b16_laion2b dim=512\n", " clip_b16_openai dim=512\n", " clip_b32_datacomp dim=512\n", " clip_b32_laion2b dim=512\n", " clip_b32_openai dim=512\n", " clip_bigg14_laion2b dim=1280\n", " clip_g14_laion2b dim=1024\n", " clip_h14_laion2b dim=1024\n", " clip_l14_336_openai dim=768\n", " clip_l14_datacomp dim=768\n", " clip_l14_laion2b dim=768\n", " clip_l14_openai dim=768\n", " dinov2_b14 dim=768\n", " dinov2_b14_reg dim=768\n", " dinov2_g14 dim=1536\n", " dinov2_g14_reg dim=1536\n", " dinov2_l14 dim=1024\n", " dinov2_l14_reg dim=1024\n", " dinov2_s14 dim=384\n", " dinov2_s14_reg dim=384\n", " mae_b16 dim=768\n", " mae_h14 dim=1280\n", " mae_l16 dim=1024\n", " siglip2_b16_256 dim=768\n", " siglip2_b16_512 dim=768\n", " siglip2_l16_384 dim=1024\n", " siglip_b16_384 dim=768\n", " siglip_b16_512 dim=768\n", " siglip_l16_256 dim=1024\n", " siglip_l16_384 dim=1024\n", " siglip_so400m_384 dim=1152\n", " vit_b16_21k dim=768\n", " vit_l16_21k dim=1024\n", " vit_s16_21k dim=384\n", " Loaded 34 experts, N=5000\n", " Labels: 80 classes, multi-label\n", " Positive rate: 0.0366\n", "\n", "=================================================================\n", "BUILDING MODEL\n", "=================================================================\n", " Parameters:\n", " total : 81,764,560\n", " projectors : 53,265,024\n", " fusion : 25,203,712\n", " constellation : 262,144\n", " patchwork : 332,800\n", " classifier : 2,666,064\n", "\n", "=================================================================\n", "TRAINING\n", "=================================================================\n", " E 1: t_acc=0.946 v_acc=0.964 mAP=0.077 F1=0.698 cv=0.0004 rig=0.4/20.4 loss=0.1928\n", " E 2: t_acc=0.964 v_acc=0.963 mAP=0.099 F1=nan cv=0.0677 rig=0.4/21.2 loss=0.1367\n", " E 4: t_acc=0.964 v_acc=0.964 mAP=0.107 F1=0.693 cv=0.9910 rig=0.4/21.0 loss=0.1295\n", " E 6: t_acc=0.966 v_acc=0.966 mAP=0.132 F1=0.352 cv=0.6376 rig=0.4/20.6 loss=0.1170\n", " E 8: t_acc=0.967 v_acc=0.966 mAP=0.159 F1=0.294 cv=0.7050 rig=0.4/20.4 loss=0.1105\n", " E10: t_acc=0.967 v_acc=0.966 mAP=0.176 F1=0.399 cv=1.0206 rig=0.5/20.4 loss=0.1070\n", " E12: t_acc=0.968 v_acc=0.966 mAP=0.192 F1=0.378 cv=0.4111 rig=0.5/20.4 loss=0.1028\n", " E14: t_acc=0.968 v_acc=0.966 mAP=0.196 F1=0.405 cv=0.5439 rig=0.5/20.4 loss=0.1016\n", " E16: t_acc=0.968 v_acc=0.966 mAP=0.197 F1=0.462 cv=0.3668 rig=0.5/20.4 loss=0.1001\n", " E18: t_acc=0.967 v_acc=0.967 mAP=0.204 F1=0.583 cv=0.8645 rig=0.5/20.4 loss=0.1010\n", " E20: t_acc=0.968 v_acc=0.967 mAP=0.234 F1=0.434 cv=0.4655 rig=0.5/20.4 loss=0.0982\n", "\n", "=================================================================\n", "FINAL REPORT\n", "=================================================================\n", "\n", " Top 5 classes by AP:\n", " class 38: AP=0.927 (n=39)\n", " class 0: AP=0.854 (n=536)\n", " class 32: AP=0.633 (n=43)\n", " class 60: AP=0.590 (n=101)\n", " class 42: AP=0.586 (n=36)\n", "\n", " Bottom 5 classes by AP:\n", " class 78: AP=0.036 (n=2)\n", " class 18: AP=0.034 (n=10)\n", " class 12: AP=0.033 (n=8)\n", " class 52: AP=0.031 (n=9)\n", " class 76: AP=0.012 (n=5)\n", "\n", " Final mAP: 0.234\n", " Final CV: 0.5993\n", " Embedding dim: 1024\n", " Anchors: 256\n", "\n", " Expert identity norms (learned importance):\n", " dinov2_s14_reg norm=1.1543\n", " dinov2_b14_reg norm=0.9342\n", " clip_l14_openai norm=0.8235\n", " siglip2_l16_384 norm=0.8197\n", " clip_b32_openai norm=0.7941\n", " ...\n", " siglip_l16_384 norm=0.6570\n", " mae_h14 norm=0.6443\n", " clip_b32_datacomp norm=0.6386\n", "\n", " Parameters: 81,764,560\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "34-EXPERT PATCHWORK MODEL\n", "==========================\n", "Pre-extracted features from 34 vision models → learned projectors →\n", "cross-expert fusion → constellation triangulation → patchwork → COCO multi-label.\n", "\n", "Architecture:\n", " Per-expert: Linear(d_expert → d_shared) + LayerNorm\n", " Fusion: Cross-attention over 34 expert tokens → fused embedding\n", " Geometry: Constellation(n_anchors) → triangulation → Patchwork → MLP\n", " Output: 80-class multi-label (BCE)\n", "\n", "Training: Adam + geometric autograd (tang=0.01, sep=1.0, cv=0.001)\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "from datasets import load_dataset\n", "import gc\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "D_SHARED = 1024\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 128\n", "\n", "print(\"=\" * 65)\n", "print(\"34-EXPERT PATCHWORK MODEL\")\n", "print(\"=\" * 65)\n", "print(f\" Device: {DEVICE}\")\n", "print(f\" Shared dim: {D_SHARED}, Anchors: {N_ANCHORS}, Classes: {N_CLASSES}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def tangential_projection(grad, embedding):\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " grad_f = grad.float()\n", " radial = (grad_f * emb_n).sum(dim=-1, keepdim=True) * emb_n\n", " return (grad_f - radial).to(grad.dtype), radial.to(grad.dtype)\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a = F.normalize(anchors, dim=-1)\n", " sim = a @ a.T - torch.diag(torch.ones(anchors.shape[0], device=anchors.device))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " tang_grad, norm_grad = tangential_projection(grad_f, emb_n)\n", " corrected = tang_grad + (1.0 - ctx.tang) * norm_grad\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(dim=-1, keepdim=True)\n", " collapse = toward * nearest\n", " corrected = corrected - ctx.sep * (toward > 0).float() * collapse\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ExpertProjector(nn.Module):\n", " \"\"\"d_expert → d_shared with bottleneck.\"\"\"\n", " def __init__(self, d_in, d_out=D_SHARED):\n", " super().__init__()\n", " d_mid = min(d_in, d_out)\n", " self.net = nn.Sequential(\n", " nn.Linear(d_in, d_mid),\n", " nn.GELU(),\n", " nn.Linear(d_mid, d_out),\n", " nn.LayerNorm(d_out),\n", " )\n", " def forward(self, x):\n", " return self.net(x)\n", "\n", "\n", "class ExpertFusion(nn.Module):\n", " \"\"\"\n", " Cross-attention fusion of N expert projections → single embedding.\n", " Uses a learned query token that attends to all expert outputs.\n", " \"\"\"\n", " def __init__(self, d_model=D_SHARED, n_heads=8, n_layers=2):\n", " super().__init__()\n", " self.query = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.layers = nn.ModuleList([\n", " nn.TransformerDecoderLayer(\n", " d_model=d_model, nhead=n_heads,\n", " dim_feedforward=d_model * 2,\n", " dropout=0.1, batch_first=True,\n", " norm_first=True,\n", " ) for _ in range(n_layers)\n", " ])\n", " self.norm = nn.LayerNorm(d_model)\n", "\n", " def forward(self, expert_tokens):\n", " \"\"\"\n", " expert_tokens: (B, N_experts, d_model)\n", " returns: (B, d_model)\n", " \"\"\"\n", " B = expert_tokens.shape[0]\n", " q = self.query.expand(B, -1, -1) # (B, 1, d_model)\n", " for layer in self.layers:\n", " q = layer(q, expert_tokens)\n", " return self.norm(q.squeeze(1)) # (B, d_model)\n", "\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d_embed=D_SHARED, init_anchors=None):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " if init_anchors is not None:\n", " self.anchors = nn.Parameter(init_anchors.clone())\n", " else:\n", " self.anchors = nn.Parameter(F.normalize(\n", " torch.randn(n_anchors, d_embed), dim=-1))\n", " self.register_buffer(\"rigidity\", torch.zeros(n_anchors))\n", " self.register_buffer(\"visit_count\", torch.zeros(n_anchors))\n", "\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", " @torch.no_grad()\n", " def update_rigidity(self, tri):\n", " nearest = tri.argmin(dim=-1)\n", " for i in range(self.n_anchors):\n", " m = nearest == i\n", " if m.sum() < 5: continue\n", " self.visit_count[i] += m.sum().float()\n", " sp = tri[m].std(dim=0).mean()\n", " alpha = min(0.1, 10.0 / (self.visit_count[i] + 1))\n", " self.rigidity[i] = (1-alpha)*self.rigidity[i] + alpha/(sp+0.01)\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp),\n", " ) for k in range(n_comp)])\n", "\n", " def forward(self, tri):\n", " return torch.cat([\n", " self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)\n", " ], dim=-1)\n", "\n", "\n", "class SoupModel(nn.Module):\n", " \"\"\"\n", " 34-expert → projectors → fusion → constellation → patchwork → classifier.\n", " \"\"\"\n", " def __init__(self, expert_dims_dict, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP, n_classes=N_CLASSES,\n", " d_shared=D_SHARED, init_anchors=None):\n", " super().__init__()\n", " self.expert_names = sorted(expert_dims_dict.keys())\n", " self.n_experts = len(self.expert_names)\n", " self.d_shared = d_shared\n", "\n", " # Per-expert projectors\n", " self.projectors = nn.ModuleDict({\n", " name.replace(\".\", \"_\"): ExpertProjector(dim, d_shared)\n", " for name, dim in expert_dims_dict.items()\n", " })\n", " self.name_to_key = {name: name.replace(\".\", \"_\")\n", " for name in expert_dims_dict}\n", "\n", " # Expert identity embeddings (learned, added to projected features)\n", " self.expert_ids = nn.Parameter(\n", " torch.randn(self.n_experts, d_shared) * 0.02)\n", "\n", " # Fusion: cross-attention over expert tokens\n", " self.fusion = ExpertFusion(d_shared, n_heads=8, n_layers=2)\n", "\n", " # Geometric pipeline\n", " self.constellation = Constellation(n_anchors, d_shared, init_anchors)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", "\n", " # Classifier: patchwork output + fused embedding → multi-label\n", " pw_dim = n_comp * d_comp\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + d_shared, d_shared),\n", " nn.GELU(),\n", " nn.LayerNorm(d_shared),\n", " nn.Dropout(0.1),\n", " nn.Linear(d_shared, d_shared // 2),\n", " nn.GELU(),\n", " nn.Linear(d_shared // 2, n_classes),\n", " )\n", "\n", " def forward(self, expert_features_dict):\n", " \"\"\"\n", " expert_features_dict: {name: (B, d_expert)} for each expert\n", " \"\"\"\n", " B = next(iter(expert_features_dict.values())).shape[0]\n", "\n", " # Project each expert\n", " tokens = []\n", " for i, name in enumerate(self.expert_names):\n", " key = self.name_to_key[name]\n", " feat = expert_features_dict[name]\n", " proj = self.projectors[key](feat) # (B, d_shared)\n", " proj = proj + self.expert_ids[i] # + identity\n", " tokens.append(proj)\n", "\n", " expert_stack = torch.stack(tokens, dim=1) # (B, N, d_shared)\n", "\n", " # Fuse\n", " fused = self.fusion(expert_stack) # (B, d_shared)\n", " emb = F.normalize(fused, dim=-1) # on hypersphere\n", "\n", " # Triangulate\n", " tri, nearest = self.constellation.triangulate(emb)\n", "\n", " # Patchwork\n", " pw = self.patchwork(tri) # (B, n_comp * d_comp)\n", "\n", " # Classify from patchwork + embedding\n", " combined = torch.cat([pw, emb], dim=-1)\n", " logits = self.classifier(combined) # (B, n_classes)\n", "\n", " return logits, emb, tri, nearest\n", "\n", " def count_params(self):\n", " total = sum(p.numel() for p in self.parameters())\n", " proj = sum(p.numel() for p in self.projectors.parameters())\n", " fuse = sum(p.numel() for p in self.fusion.parameters())\n", " geo = sum(p.numel() for p in self.constellation.parameters())\n", " pw = sum(p.numel() for p in self.patchwork.parameters())\n", " cls = sum(p.numel() for p in self.classifier.parameters())\n", " return {\"total\": total, \"projectors\": proj, \"fusion\": fuse,\n", " \"constellation\": geo, \"patchwork\": pw, \"classifier\": cls}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA LOADING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "SUBSETS = [\n", " \"clip_b16_laion2b\", \"clip_b16_openai\", \"clip_b32_datacomp\",\n", " \"clip_b32_laion2b\", \"clip_b32_openai\", \"clip_bigg14_laion2b\",\n", " \"clip_g14_laion2b\", \"clip_h14_laion2b\", \"clip_l14_336_openai\",\n", " \"clip_l14_datacomp\", \"clip_l14_laion2b\", \"clip_l14_openai\",\n", " \"dinov2_b14\", \"dinov2_b14_reg\", \"dinov2_g14\", \"dinov2_g14_reg\",\n", " \"dinov2_l14\", \"dinov2_l14_reg\", \"dinov2_s14\", \"dinov2_s14_reg\",\n", " \"mae_b16\", \"mae_h14\", \"mae_l16\",\n", " \"siglip2_b16_256\", \"siglip2_b16_512\", \"siglip2_l16_384\",\n", " \"siglip_b16_384\", \"siglip_b16_512\", \"siglip_l16_256\",\n", " \"siglip_l16_384\", \"siglip_so400m_384\",\n", " \"vit_b16_21k\", \"vit_l16_21k\", \"vit_s16_21k\",\n", "]\n", "\n", "print(f\"\\n Loading features (train=118K, val=5K)...\")\n", "\n", "# ── Detect dims from val (fast) ──\n", "expert_dims = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\", streaming=True)\n", " row = next(iter(ds))\n", " expert_dims[name] = len(row[\"features\"])\n", "print(f\" Detected dims for {len(expert_dims)} experts\")\n", "\n", "# ── Load val split (5K — fits in GPU) ──\n", "print(f\"\\n Loading val split...\")\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]\n", "val_labels_raw = ref_val[\"labels\"]\n", "N_val = len(val_ids)\n", "val_id_to_idx = {iid: i for i, iid in enumerate(val_ids)}\n", "\n", "val_label_matrix = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(val_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES:\n", " val_label_matrix[i, l] = 1.0\n", "\n", "val_features = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " dim = expert_dims[name]\n", " feats = torch.zeros(N_val, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_to_idx:\n", " feats[val_id_to_idx[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " val_features[name] = feats\n", " print(f\" val {name:<30} dim={dim}\", flush=True)\n", "\n", "# ── Load train split (118K — stays on CPU, batches move to GPU) ──\n", "print(f\"\\n Loading train split...\")\n", "ref_train = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"train\")\n", "train_ids = ref_train[\"image_id\"]\n", "train_labels_raw = ref_train[\"labels\"]\n", "N_train = len(train_ids)\n", "train_id_to_idx = {iid: i for i, iid in enumerate(train_ids)}\n", "\n", "train_label_matrix = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(train_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES:\n", " train_label_matrix[i, l] = 1.0\n", "\n", "train_features = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " dim = expert_dims[name]\n", " feats = torch.zeros(N_train, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_to_idx:\n", " feats[train_id_to_idx[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " train_features[name] = feats # stays on CPU\n", " print(f\" train {name:<30} dim={dim} [{feats.shape[0]:,}]\", flush=True)\n", "\n", "print(f\"\\n Train: {N_train:,} Val: {N_val:,}\")\n", "print(f\" Labels: {N_CLASSES} classes, multi-label\")\n", "print(f\" Train positive rate: {train_label_matrix.sum() / (N_train * N_CLASSES):.4f}\")\n", "print(f\" Val positive rate: {val_label_matrix.sum() / (N_val * N_CLASSES):.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILDING MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = SoupModel(expert_dims, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP,\n", " n_classes=N_CLASSES, d_shared=D_SHARED).to(DEVICE)\n", "\n", "params = model.count_params()\n", "print(f\" Parameters:\")\n", "for k, v in params.items():\n", " print(f\" {k:<15}: {v:>10,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\"{'='*65}\")\n", "\n", "# Val features on GPU (5K — fits easily)\n", "val_feats_gpu = {name: val_features[name].to(DEVICE) for name in SUBSETS}\n", "val_labels_gpu = val_label_matrix.to(DEVICE)\n", "# Train labels on GPU, features stay on CPU (118K × 34 experts too big)\n", "train_labels_gpu = train_label_matrix.to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n", "BATCH = 128\n", "EPOCHS = 20\n", "TANG, SEP, CV_W = 0.01, 1.0, 0.001\n", "best_mAP = 0.0\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train)\n", " total_loss, total_correct, n_batches = 0, 0, 0\n", "\n", " for i in range(0, N_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " # Move batch from CPU → GPU\n", " batch_feats = {name: train_features[name][idx].to(DEVICE) for name in SUBSETS}\n", " batch_labels = train_labels_gpu[idx]\n", "\n", " logits, emb, tri, nearest = model(batch_feats)\n", " anchors = model.constellation.anchors\n", "\n", " # Geometric autograd\n", " emb_g = EmbeddingAutograd.apply(emb, emb, anchors, TANG, SEP)\n", " tri_g, _ = model.constellation.triangulate(emb_g)\n", " pw_g = model.patchwork(tri_g)\n", " combined_g = torch.cat([pw_g, emb_g], dim=-1)\n", " logits = model.classifier(combined_g)\n", "\n", " # Multi-label BCE\n", " l_cls = F.binary_cross_entropy_with_logits(logits, batch_labels)\n", "\n", " # Geometric losses\n", " l_cv = CV_W * cv_loss(emb)\n", " l_spread = 1e-3 * anchor_spread_loss(anchors)\n", " l_ent = 1e-4 * anchor_entropy_loss(emb, anchors)\n", "\n", " loss = l_cls + l_cv + l_spread + l_ent\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " model.constellation.update_rigidity(tri.detach())\n", "\n", " # Multi-label accuracy (threshold 0.5)\n", " preds = (logits.detach().sigmoid() > 0.5).float()\n", " correct = (preds == batch_labels).float().mean().item()\n", " total_correct += correct\n", " total_loss += loss.item()\n", " n_batches += 1\n", "\n", " train_acc = total_correct / n_batches\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " # Process val in chunks\n", " all_logits, all_embs = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " chunk_feats = {name: val_feats_gpu[name][j:end] for name in SUBSETS}\n", " lo, em, _, _ = model(chunk_feats)\n", " all_logits.append(lo)\n", " all_embs.append(em)\n", "\n", " v_logits = torch.cat(all_logits, 0)\n", " v_embs = torch.cat(all_embs, 0)\n", "\n", " v_preds = (v_logits.sigmoid() > 0.5).float()\n", " v_acc = (v_preds == val_labels_gpu).float().mean().item()\n", " v_cv = cv_metric(v_embs.cpu())\n", "\n", " # Per-class F1 (macro)\n", " tp = (v_preds * val_labels_gpu).sum(0)\n", " fp = (v_preds * (1 - val_labels_gpu)).sum(0)\n", " fn = ((1 - v_preds) * val_labels_gpu).sum(0)\n", " precision = tp / (tp + fp + 1e-8)\n", " recall = tp / (tp + fn + 1e-8)\n", " f1 = 2 * precision * recall / (precision + recall + 1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " # mAP\n", " ap_sum = 0\n", " n_valid = 0\n", " for c in range(N_CLASSES):\n", " if val_labels_gpu[:, c].sum() > 0:\n", " scores = v_logits[:, c].cpu()\n", " targets = val_labels_gpu[:, c].cpu()\n", " sorted_idx = scores.argsort(descending=True)\n", " sorted_tgt = targets[sorted_idx]\n", " tp_cumsum = sorted_tgt.cumsum(0)\n", " precision_at_k = tp_cumsum / torch.arange(1, len(sorted_tgt) + 1).float()\n", " ap = (precision_at_k * sorted_tgt).sum() / sorted_tgt.sum()\n", " ap_sum += ap.item()\n", " n_valid += 1\n", " mAP = ap_sum / max(n_valid, 1)\n", "\n", " rig = model.constellation.rigidity\n", " if (epoch + 1) % 2 == 0 or epoch == 0:\n", " print(f\" E{epoch+1:2d}: t_acc={train_acc:.3f} v_acc={v_acc:.3f} \"\n", " f\"mAP={mAP:.3f} F1={macro_f1:.3f} \"\n", " f\"cv={v_cv:.4f} rig={rig.mean():.1f}/{rig.max():.1f} \"\n", " f\"loss={total_loss/n_batches:.4f}\")\n", "\n", " # Save best checkpoint by mAP\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\n", " \"state_dict\": model.state_dict(),\n", " \"config\": {\n", " \"expert_dims\": {k: int(v) for k, v in expert_dims.items()},\n", " \"n_anchors\": N_ANCHORS, \"n_comp\": N_COMP, \"d_comp\": D_COMP,\n", " \"n_classes\": N_CLASSES, \"d_shared\": D_SHARED,\n", " \"expert_names\": list(SUBSETS),\n", " },\n", " \"epoch\": epoch + 1, \"mAP\": mAP,\n", " }, \"/mnt/user-data/outputs/soup_patchwork_best.pt\")\n", " print(f\" ★ New best mAP={mAP:.3f} — saved checkpoint\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FINAL REPORT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FINAL REPORT\")\n", "print(f\"{'='*65}\")\n", "\n", "model.eval()\n", "with torch.no_grad():\n", " all_logits, all_embs = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " chunk_feats = {name: val_feats_gpu[name][j:end] for name in SUBSETS}\n", " lo, em, _, _ = model(chunk_feats)\n", " all_logits.append(lo)\n", " all_embs.append(em)\n", "\n", " v_logits = torch.cat(all_logits, 0)\n", " v_embs = torch.cat(all_embs, 0)\n", "\n", " # Top-5 and bottom-5 classes by AP\n", " class_aps = {}\n", " for c in range(N_CLASSES):\n", " if val_labels_gpu[:, c].sum() > 0:\n", " scores = v_logits[:, c].cpu()\n", " targets = val_labels_gpu[:, c].cpu()\n", " sorted_idx = scores.argsort(descending=True)\n", " sorted_tgt = targets[sorted_idx]\n", " tp_cumsum = sorted_tgt.cumsum(0)\n", " prec_at_k = tp_cumsum / torch.arange(1, len(sorted_tgt) + 1).float()\n", " class_aps[c] = (prec_at_k * sorted_tgt).sum().item() / sorted_tgt.sum().item()\n", "\n", " sorted_aps = sorted(class_aps.items(), key=lambda x: -x[1])\n", " print(f\"\\n Top 5 classes by AP:\")\n", " for c, ap in sorted_aps[:5]:\n", " n = val_labels_gpu[:, c].sum().int().item()\n", " print(f\" class {c:>3}: AP={ap:.3f} (n={n})\")\n", "\n", " print(f\"\\n Bottom 5 classes by AP:\")\n", " for c, ap in sorted_aps[-5:]:\n", " n = val_labels_gpu[:, c].sum().int().item()\n", " print(f\" class {c:>3}: AP={ap:.3f} (n={n})\")\n", "\n", " final_cv = cv_metric(v_embs.cpu())\n", " print(f\"\\n Final mAP: {sum(class_aps.values())/len(class_aps):.3f}\")\n", " print(f\" Final CV: {final_cv:.4f}\")\n", " print(f\" Embedding dim: {v_embs.shape[1]}\")\n", " print(f\" Anchors: {model.constellation.n_anchors}\")\n", "\n", " # Expert contribution analysis\n", " print(f\"\\n Expert identity norms (learned importance):\")\n", " norms = model.expert_ids.detach().cpu().norm(dim=-1)\n", " sorted_exp = sorted(zip(model.expert_names, norms.tolist()),\n", " key=lambda x: -x[1])\n", " for name, norm in sorted_exp[:5]:\n", " print(f\" {name:<30} norm={norm:.4f}\")\n", " print(f\" ...\")\n", " for name, norm in sorted_exp[-3:]:\n", " print(f\" {name:<30} norm={norm:.4f}\")\n", "\n", "print(f\"\\n Parameters: {params['total']:,}\")\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SAVE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "import json, os\n", "SAVE_DIR = \"/mnt/user-data/outputs\"\n", "\n", "config = {\n", " \"expert_dims\": {k: int(v) for k, v in expert_dims.items()},\n", " \"n_anchors\": N_ANCHORS,\n", " \"n_comp\": N_COMP,\n", " \"d_comp\": D_COMP,\n", " \"n_classes\": N_CLASSES,\n", " \"d_shared\": D_SHARED,\n", " \"expert_names\": list(SUBSETS),\n", "}\n", "\n", "save_path = os.path.join(SAVE_DIR, \"soup_patchwork.pt\")\n", "torch.save({\"state_dict\": model.state_dict(), \"config\": config}, save_path)\n", "size_mb = os.path.getsize(save_path) / 1024 / 1024\n", "print(f\"\\n Model saved: {save_path} ({size_mb:.1f} MB)\")\n", "\n", "json_path = os.path.join(SAVE_DIR, \"soup_patchwork_config.json\")\n", "with open(json_path, \"w\") as f:\n", " json.dump(config, f, indent=2)\n", "print(f\" Config saved: {json_path}\")\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X4snXqBcVx2B", "outputId": "3b353dec-f9d1-44f6-ab75-73b11738b95e" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "34-EXPERT PATCHWORK MODEL\n", "=================================================================\n", " Device: cuda\n", " Shared dim: 1024, Anchors: 256, Classes: 80\n", "\n", " Loading features (train=118K, val=5K)...\n", " Detected dims for 34 experts\n", "\n", " Loading val split...\n", " val clip_b16_laion2b dim=512\n", " val clip_b16_openai dim=512\n", " val clip_b32_datacomp dim=512\n", " val clip_b32_laion2b dim=512\n", " val clip_b32_openai dim=512\n", " val clip_bigg14_laion2b dim=1280\n", " val clip_g14_laion2b dim=1024\n", " val clip_h14_laion2b dim=1024\n", " val clip_l14_336_openai dim=768\n", " val clip_l14_datacomp dim=768\n", " val clip_l14_laion2b dim=768\n", " val clip_l14_openai dim=768\n", " val dinov2_b14 dim=768\n", " val dinov2_b14_reg dim=768\n", " val dinov2_g14 dim=1536\n", " val dinov2_g14_reg dim=1536\n", " val dinov2_l14 dim=1024\n", " val dinov2_l14_reg dim=1024\n", " val dinov2_s14 dim=384\n", " val dinov2_s14_reg dim=384\n", " val mae_b16 dim=768\n", " val mae_h14 dim=1280\n", " val mae_l16 dim=1024\n", " val siglip2_b16_256 dim=768\n", " val siglip2_b16_512 dim=768\n", " val siglip2_l16_384 dim=1024\n", " val siglip_b16_384 dim=768\n", " val siglip_b16_512 dim=768\n", " val siglip_l16_256 dim=1024\n", " val siglip_l16_384 dim=1024\n", " val siglip_so400m_384 dim=1152\n", " val vit_b16_21k dim=768\n", " val vit_l16_21k dim=1024\n", " val vit_s16_21k dim=384\n", "\n", " Loading train split...\n", " train clip_b16_laion2b dim=512 [118,287]\n", " train clip_b16_openai dim=512 [118,287]\n", " train clip_b32_datacomp dim=512 [118,287]\n", " train clip_b32_laion2b dim=512 [118,287]\n", " train clip_b32_openai dim=512 [118,287]\n", " train clip_bigg14_laion2b dim=1280 [118,287]\n", " train clip_g14_laion2b dim=1024 [118,287]\n", " train clip_h14_laion2b dim=1024 [118,287]\n", " train clip_l14_336_openai dim=768 [118,287]\n", " train clip_l14_datacomp dim=768 [118,287]\n", " train clip_l14_laion2b dim=768 [118,287]\n", " train clip_l14_openai dim=768 [118,287]\n", " train dinov2_b14 dim=768 [118,287]\n", " train dinov2_b14_reg dim=768 [118,287]\n", " train dinov2_g14 dim=1536 [118,287]\n", " train dinov2_g14_reg dim=1536 [118,287]\n", " train dinov2_l14 dim=1024 [118,287]\n", " train dinov2_l14_reg dim=1024 [118,287]\n", " train dinov2_s14 dim=384 [118,287]\n", " train dinov2_s14_reg dim=384 [118,287]\n", " train mae_b16 dim=768 [118,287]\n", " train mae_h14 dim=1280 [118,287]\n", " train mae_l16 dim=1024 [118,287]\n", " train siglip2_b16_256 dim=768 [118,287]\n", " train siglip2_b16_512 dim=768 [118,287]\n", " train siglip2_l16_384 dim=1024 [118,287]\n", " train siglip_b16_384 dim=768 [118,287]\n", " train siglip_b16_512 dim=768 [118,287]\n", " train siglip_l16_256 dim=1024 [118,287]\n", " train siglip_l16_384 dim=1024 [118,287]\n", " train siglip_so400m_384 dim=1152 [118,287]\n", " train vit_b16_21k dim=768 [118,287]\n", " train vit_l16_21k dim=1024 [118,287]\n", " train vit_s16_21k dim=384 [118,287]\n", "\n", " Train: 118,287 Val: 5,000\n", " Labels: 80 classes, multi-label\n", " Train positive rate: 0.0362\n", " Val positive rate: 0.0366\n", "\n", "=================================================================\n", "BUILDING MODEL\n", "=================================================================\n", " Parameters:\n", " total : 81,764,560\n", " projectors : 53,265,024\n", " fusion : 25,203,712\n", " constellation : 262,144\n", " patchwork : 332,800\n", " classifier : 2,666,064\n", "\n", "=================================================================\n", "TRAINING\n", "=================================================================\n", " E 1: t_acc=0.967 v_acc=0.969 mAP=0.245 F1=0.328 cv=0.8203 rig=0.7/46.4 loss=0.1092\n", " E 2: t_acc=0.972 v_acc=0.973 mAP=0.479 F1=0.529 cv=0.4386 rig=0.7/46.4 loss=0.0833\n", " E 4: t_acc=0.975 v_acc=0.975 mAP=0.552 F1=0.551 cv=0.9675 rig=0.7/46.4 loss=0.0720\n", " E 6: t_acc=0.979 v_acc=0.979 mAP=0.651 F1=0.611 cv=0.9625 rig=0.7/46.4 loss=0.0613\n", " E 8: t_acc=0.981 v_acc=0.980 mAP=0.676 F1=0.639 cv=0.8503 rig=0.7/46.4 loss=0.0557\n", " E10: t_acc=0.983 v_acc=0.981 mAP=0.711 F1=0.670 cv=0.9066 rig=0.7/46.4 loss=0.0505\n", " E12: t_acc=0.984 v_acc=0.982 mAP=0.720 F1=0.674 cv=0.9692 rig=0.7/46.4 loss=0.0479\n", " E14: t_acc=0.985 v_acc=0.982 mAP=0.731 F1=0.692 cv=1.1332 rig=0.7/46.4 loss=0.0448\n", " E16: t_acc=0.985 v_acc=0.982 mAP=0.725 F1=0.693 cv=1.1303 rig=0.7/46.4 loss=0.0425\n", " E18: t_acc=0.986 v_acc=0.982 mAP=0.732 F1=0.692 cv=1.0601 rig=0.7/46.4 loss=0.0403\n", " E20: t_acc=0.987 v_acc=0.981 mAP=0.720 F1=0.685 cv=1.0569 rig=0.7/46.4 loss=0.0385\n", "\n", "=================================================================\n", "FINAL REPORT\n", "=================================================================\n", "\n", " Top 5 classes by AP:\n", " class 38: AP=0.990 (n=167)\n", " class 23: AP=0.989 (n=101)\n", " class 22: AP=0.989 (n=85)\n", " class 0: AP=0.987 (n=2693)\n", " class 21: AP=0.979 (n=49)\n", "\n", " Bottom 5 classes by AP:\n", " class 73: AP=0.394 (n=230)\n", " class 24: AP=0.333 (n=228)\n", " class 58: AP=0.261 (n=172)\n", " class 78: AP=0.187 (n=9)\n", " class 70: AP=0.153 (n=8)\n", "\n", " Final mAP: 0.720\n", " Final CV: 0.8104\n", " Embedding dim: 1024\n", " Anchors: 256\n", "\n", " Expert identity norms (learned importance):\n", " dinov2_g14 norm=2.2136\n", " siglip_l16_384 norm=2.1879\n", " clip_g14_laion2b norm=2.0082\n", " dinov2_g14_reg norm=1.5676\n", " clip_l14_laion2b norm=1.4022\n", " ...\n", " siglip_l16_256 norm=0.8829\n", " siglip2_l16_384 norm=0.8799\n", " siglip2_b16_512 norm=0.7397\n", "\n", " Parameters: 81,764,560\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP VISION ALIGNMENT BANK\n", "==============================\n", "CaptionBERT architecture applied to 34 vision experts.\n", "\n", "CaptionBERT:\n", " 5 BERT experts → GPA consensus → per-expert whitened Procrustes\n", " → AlignmentBank(rotations, whiteners, means, anchors, geo_proj)\n", " → compute_bank_loss(agreement, ortho, spread, entropy, cross_var, disagree, CV)\n", " → student losses: InfoNCE + MSE against consensus\n", "\n", "This file:\n", " 34 vision experts → GPA consensus → per-expert whitened Procrustes\n", " → VisionAlignmentBank(34 rotations, whiteners, means, anchors, geo_proj)\n", " → same compute_bank_loss\n", " → same student losses against consensus\n", " → classification through constellation + patchwork (transferred from soup)\n", "\n", "Data: AbstractPhil/bulk-coco-features (118K train + 5K val, pre-extracted)\n", "\"\"\"\n", "\n", "import gc\n", "import math\n", "import os\n", "import time\n", "import json\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.tensorboard import SummaryWriter\n", "from datasets import load_dataset\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "REPO_ID = \"AbstractPhil/geolip-vit-x34\"\n", "SOUP_PATH = \"soup_patchwork.pt\"\n", "\n", "# Architecture\n", "D_SHARED = 1024\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 128\n", "D_BANK = 128\n", "\n", "# Training\n", "BATCH = 128\n", "EPOCHS = 20\n", "LR = 5e-4\n", "W_NCE = 1.0\n", "W_MSE = 0.5\n", "W_CV = 0.001\n", "W_BANK = 1.0\n", "W_CLS = 0.3\n", "GRAD_CLIP = 1.0\n", "\n", "SUBSETS = [\n", " \"clip_b16_laion2b\", \"clip_b16_openai\", \"clip_b32_datacomp\",\n", " \"clip_b32_laion2b\", \"clip_b32_openai\", \"clip_bigg14_laion2b\",\n", " \"clip_g14_laion2b\", \"clip_h14_laion2b\", \"clip_l14_336_openai\",\n", " \"clip_l14_datacomp\", \"clip_l14_laion2b\", \"clip_l14_openai\",\n", " \"dinov2_b14\", \"dinov2_b14_reg\", \"dinov2_g14\", \"dinov2_g14_reg\",\n", " \"dinov2_l14\", \"dinov2_l14_reg\", \"dinov2_s14\", \"dinov2_s14_reg\",\n", " \"mae_b16\", \"mae_h14\", \"mae_l16\",\n", " \"siglip2_b16_256\", \"siglip2_b16_512\", \"siglip2_l16_384\",\n", " \"siglip_b16_384\", \"siglip_b16_512\", \"siglip_l16_256\",\n", " \"siglip_l16_384\", \"siglip_so400m_384\",\n", " \"vit_b16_21k\", \"vit_l16_21k\", \"vit_s16_21k\",\n", "]\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOLIP VISION ALIGNMENT BANK\")\n", "print(f\" {len(SUBSETS)} experts → CaptionBERT AlignmentBank\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES (exact copy from cotrain_bank.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "def cv_metric(emb, n=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BANK LOSS (exact from cotrain_bank.py — compute_bank_loss)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def compute_bank_loss(bank, embedding):\n", " B = embedding.shape[0]\n", " emb = embedding.float()\n", "\n", " expert_cos_list = []\n", " expert_projected = []\n", " for i in range(bank.n_experts):\n", " R = bank.expert_rotations[i]\n", " W = bank.expert_whiteners[i]\n", " mu = bank.expert_means[i]\n", " centered = emb - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " cos = F.cosine_similarity(whitened_n, back, dim=-1)\n", " expert_cos_list.append(cos)\n", " expert_projected.append(in_expert)\n", "\n", " expert_cos = torch.stack(expert_cos_list, dim=-1)\n", "\n", " # 1. Expert agreement\n", " expert_mean = expert_cos.mean(dim=-1, keepdim=True)\n", " l_agreement = (expert_cos - expert_mean).pow(2).mean()\n", "\n", " # 2. Rotation orthogonality\n", " l_ortho = 0.0\n", " for i in range(bank.n_experts):\n", " R = bank.expert_rotations[i]\n", " l_ortho += (R @ R.T - torch.eye(bank.d_embed, device=R.device)).pow(2).mean()\n", " l_ortho = l_ortho / bank.n_experts\n", "\n", " # 3. Anchor spread\n", " anchors_n = F.normalize(bank.anchors, dim=-1)\n", " anchor_sim = anchors_n @ anchors_n.T\n", " anchor_sim.fill_diagonal_(0)\n", " l_spread = anchor_sim.pow(2).mean()\n", "\n", " # 4. Anchor entropy\n", " anchor_cos = emb @ anchors_n.T\n", " anchor_probs = F.softmax(anchor_cos * 10, dim=-1)\n", " l_entropy = -(anchor_probs * (anchor_probs + 1e-12).log()).sum(-1).mean()\n", "\n", " # 5. Cross-expert differentiation\n", " cross_cos = []\n", " for i in range(bank.n_experts):\n", " for j in range(i + 1, bank.n_experts):\n", " cc = F.cosine_similarity(expert_projected[i], expert_projected[j], dim=-1)\n", " cross_cos.append(cc)\n", "\n", " if cross_cos:\n", " cross_features = torch.stack(cross_cos, dim=-1)\n", " l_cross_var = cross_features.var(dim=0).mean()\n", "\n", " batch_cross_mean = cross_features.mean()\n", " batch_cross_std = cross_features.std()\n", " per_sample_agreement = expert_cos.mean(dim=-1)\n", " per_sample_disagreement = expert_cos.std(dim=-1)\n", " batch_disagree_ratio = (per_sample_disagreement / (per_sample_agreement + 1e-8)).mean()\n", " l_disagree = (\n", " (batch_cross_mean - bank.target_cross_cos_mean).pow(2) +\n", " (batch_cross_std - bank.target_cross_cos_std).pow(2) +\n", " (batch_disagree_ratio - bank.target_disagreement_ratio).pow(2))\n", " else:\n", " l_cross_var = torch.tensor(0.0, device=emb.device)\n", " l_disagree = torch.tensor(0.0, device=emb.device)\n", "\n", " # 7. Embedding CV\n", " l_emb_cv = torch.tensor(0.0, device=emb.device)\n", " if B >= 10:\n", " emb_n = F.normalize(emb, dim=-1)\n", " vols = []\n", " for _ in range(16):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff*diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1))*f*f) * torch.linalg.det(cm)\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " emb_cv = stacked.std() / (stacked.mean() + 1e-8)\n", " l_emb_cv = (emb_cv - bank.target_cv).abs()\n", "\n", " total = (1.0*l_agreement + 1.0*l_ortho + 0.5*l_spread +\n", " 0.1*l_entropy + 0.3*l_cross_var + 0.3*l_emb_cv + 0.5*l_disagree)\n", "\n", " diagnostics = {\n", " \"agreement\": l_agreement.item(),\n", " \"ortho\": l_ortho.item() if torch.is_tensor(l_ortho) else l_ortho,\n", " \"spread\": l_spread.item(), \"entropy\": l_entropy.item(),\n", " \"cross_var\": l_cross_var.item(), \"disagree\": l_disagree.item(),\n", " \"emb_cv\": emb_cv.item() if B >= 10 else 0.0,\n", " \"expert_cos_mean\": expert_cos.mean().item(),\n", " \"expert_cos_std\": expert_cos.std().item(),\n", " }\n", " return total, diagnostics\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ALIGNMENT UTILITIES (exact from cotrain_bank.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(N_s-1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s-1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten)}\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]; x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]; return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# VISION ALIGNMENT BANK (CaptionBERT AlignmentBank for 34 experts)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class VisionAlignmentBank(nn.Module):\n", " \"\"\"\n", " Exact CaptionBERT AlignmentBank architecture scaled to 34 vision experts.\n", "\n", " Per-expert: rotation (D×D), whitener (D×D), mean (D,)\n", " Anchors: (N_ANCHORS, D) on hypersphere\n", " geo_proj: expert_cos + expert_mse + cross_cos + disagreement + norms + anchor_cos → d_bank\n", " \"\"\"\n", " def __init__(self, d_embed=D_SHARED, n_experts=34, n_anchors=N_ANCHORS, d_bank=D_BANK):\n", " super().__init__()\n", " self.d_embed = d_embed\n", " self.n_experts = n_experts\n", " self.n_anchors = n_anchors\n", " self.d_bank = d_bank\n", "\n", " # Per-expert Procrustes (calibrated, then trainable)\n", " self.expert_rotations = nn.ParameterList([\n", " nn.Parameter(torch.eye(d_embed)) for _ in range(n_experts)])\n", " self.expert_whiteners = nn.ParameterList([\n", " nn.Parameter(torch.eye(d_embed)) for _ in range(n_experts)])\n", " self.expert_means = nn.ParameterList([\n", " nn.Parameter(torch.zeros(d_embed)) for _ in range(n_experts)])\n", "\n", " # Constellation anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d_embed), dim=-1))\n", "\n", " # Geometric context projection\n", " n_cross = n_experts * (n_experts - 1) // 2\n", " geo_dim = n_experts + n_experts + n_cross + 1 + n_experts + n_anchors\n", " self.geo_proj = nn.Sequential(\n", " nn.Linear(geo_dim, d_bank * 4), nn.GELU(), nn.LayerNorm(d_bank * 4),\n", " nn.Linear(d_bank * 4, d_bank * 2), nn.GELU(), nn.LayerNorm(d_bank * 2),\n", " nn.Linear(d_bank * 2, d_bank), nn.LayerNorm(d_bank))\n", "\n", " # Calibrated targets\n", " self.register_buffer(\"target_cv\", torch.tensor(0.2))\n", " self.register_buffer(\"target_cross_cos_mean\", torch.tensor(0.0))\n", " self.register_buffer(\"target_cross_cos_std\", torch.tensor(0.0))\n", " self.register_buffer(\"target_disagreement_ratio\", torch.tensor(0.0))\n", "\n", " def forward(self, embedding):\n", " B = embedding.shape[0]\n", " emb = embedding.float()\n", "\n", " expert_consistency = []\n", " expert_recon = []\n", " expert_projected = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " W = self.expert_whiteners[i]\n", " mu = self.expert_means[i]\n", " centered = emb - mu\n", " whitened = centered @ W\n", " whitened_n = F.normalize(whitened, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " cos = F.cosine_similarity(whitened_n, back, dim=-1)\n", " recon = (whitened_n - back).pow(2).mean(dim=-1)\n", " expert_consistency.append(cos)\n", " expert_recon.append(recon)\n", " expert_projected.append(in_expert)\n", "\n", " expert_cos = torch.stack(expert_consistency, dim=-1)\n", " expert_mse = torch.stack(expert_recon, dim=-1)\n", "\n", " # Cross-expert (all pairs — 34 choose 2 = 561 pairs)\n", " cross_cos = []\n", " for i in range(self.n_experts):\n", " for j in range(i + 1, self.n_experts):\n", " cc = F.cosine_similarity(expert_projected[i], expert_projected[j], dim=-1)\n", " cross_cos.append(cc)\n", " cross_features = torch.stack(cross_cos, dim=-1)\n", "\n", " per_sample_agreement = expert_cos.mean(dim=-1)\n", " per_sample_disagreement = expert_cos.std(dim=-1)\n", " disagreement_ratio = per_sample_disagreement / (per_sample_agreement + 1e-8)\n", "\n", " expert_norms = []\n", " for i in range(self.n_experts):\n", " W = self.expert_whiteners[i]; mu = self.expert_means[i]\n", " whitened = (emb - mu) @ W\n", " expert_norms.append(whitened.norm(dim=-1))\n", " norm_ratio = torch.stack(expert_norms, dim=-1)\n", " norm_ratio = norm_ratio / (norm_ratio.mean(dim=-1, keepdim=True) + 1e-8)\n", "\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " anchor_cos = emb @ anchors_n.T\n", "\n", " geo_input = torch.cat([\n", " expert_cos, expert_mse, cross_features,\n", " disagreement_ratio.unsqueeze(-1), norm_ratio, anchor_cos\n", " ], dim=-1)\n", " geo_context = self.geo_proj(geo_input)\n", " enriched = torch.cat([embedding, geo_context], dim=-1)\n", "\n", " diagnostics = {\n", " \"expert_cos_mean\": expert_cos.mean().item(),\n", " \"expert_cos_std\": expert_cos.std().item(),\n", " \"cross_expert_cos\": cross_features.mean().item(),\n", " \"anchor_max_cos\": anchor_cos.max(dim=-1).values.mean().item(),\n", " \"disagreement_ratio\": disagreement_ratio.mean().item(),\n", " }\n", " return enriched, geo_context, diagnostics\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL: bank + constellation + patchwork + classifier\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors, d):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " return 1.0 - emb @ a.T, (emb @ a.T).argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors, n_comp, d_comp):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn==k).sum().item(), d_comp*2), nn.GELU(),\n", " nn.Linear(d_comp*2, d_comp), nn.LayerNorm(d_comp))\n", " for k in range(n_comp)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn==k]) for k in range(self.n_comp)], -1)\n", "\n", "\n", "class VisionBankModel(nn.Module):\n", " \"\"\"\n", " 34-expert AlignmentBank + constellation + patchwork + classifier.\n", "\n", " Input: L2-normalized consensus embedding (1024-d) — from GPA of 34 experts.\n", " Bank: annotates with 34-expert geometric context.\n", " Downstream: constellation → patchwork → classifier (multi-label COCO).\n", " \"\"\"\n", " def __init__(self, n_experts=34, d_shared=D_SHARED, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP, n_classes=N_CLASSES, d_bank=D_BANK):\n", " super().__init__()\n", " self.bank = VisionAlignmentBank(d_shared, n_experts, n_anchors, d_bank)\n", " self.constellation = Constellation(n_anchors, d_shared)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", " pw_dim = n_comp * d_comp\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + d_shared + d_bank, d_shared), nn.GELU(),\n", " nn.LayerNorm(d_shared), nn.Dropout(0.1),\n", " nn.Linear(d_shared, d_shared // 2), nn.GELU(),\n", " nn.Linear(d_shared // 2, n_classes))\n", "\n", " def forward(self, embedding):\n", " enriched, geo_ctx, bank_diag = self.bank(embedding)\n", " tri, nearest = self.constellation.triangulate(embedding)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, embedding, geo_ctx], dim=-1))\n", " return logits, embedding, tri, nearest, bank_diag\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 0: LOAD ALL EXPERT FEATURES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 0: LOAD EXPERT FEATURES\")\n", "print(f\"{'='*65}\")\n", "\n", "# Reference for image_id alignment\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"train\")\n", "train_ids = ref[\"image_id\"]; N_train = len(train_ids)\n", "train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", "train_labels_raw = ref[\"labels\"]\n", "train_label_matrix = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(train_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES: train_label_matrix[i, l] = 1.0\n", "\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels_raw = ref_val[\"labels\"]\n", "val_label_matrix = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(val_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES: val_label_matrix[i, l] = 1.0\n", "\n", "print(f\" Train: {N_train:,} Val: {N_val:,}\")\n", "\n", "# Load all 34 experts\n", "expert_dims = {}\n", "train_expert_embs = {}\n", "val_expert_embs = {}\n", "\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " dim = len(ds[0][\"features\"]); expert_dims[name] = dim\n", " feats = torch.zeros(N_train, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_map:\n", " feats[train_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " train_expert_embs[name] = F.normalize(feats, dim=-1)\n", "\n", " ds_v = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats_v = torch.zeros(N_val, dim)\n", " for row in ds_v:\n", " if row[\"image_id\"] in val_id_map:\n", " feats_v[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_expert_embs[name] = F.normalize(feats_v, dim=-1)\n", " print(f\" {name:<30} dim={dim}\", flush=True)\n", " del ds, ds_v; gc.collect()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 1: GPA → CONSENSUS + PER-EXPERT PROCRUSTES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 1: GPA ALIGNMENT + PROCRUSTES CALIBRATION\")\n", "print(f\"{'='*65}\")\n", "\n", "# Project all to D_SHARED for GPA (PCA for d>1024, pad for d<1024)\n", "def project_to_shared(feats, d_out=D_SHARED):\n", " d_in = feats.shape[1]\n", " if d_in == d_out: return feats\n", " if d_in < d_out:\n", " return F.normalize(torch.cat([feats, torch.zeros(feats.shape[0], d_out-d_in)], -1), dim=-1)\n", " feats_c = feats - feats.mean(0, keepdim=True)\n", " _, _, Vt = torch.linalg.svd(feats_c, full_matrices=False)\n", " return F.normalize(feats @ Vt[:d_out].T, dim=-1)\n", "\n", "projected = {n: project_to_shared(train_expert_embs[n]) for n in SUBSETS}\n", "\n", "# GPA\n", "current = {i: projected[SUBSETS[i]].float() for i in range(len(SUBSETS))}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current[i] for i in range(len(SUBSETS))) / len(SUBSETS)\n", " delta = 0.0\n", " new_current = {}\n", " for i in range(len(SUBSETS)):\n", " info = procrustes_align(current[i], mean_shape)\n", " new_current[i] = apply_align(current[i], info)\n", " delta += (new_current[i] - current[i]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter+1) % 5 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={delta:.8f}\")\n", " if delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\"); break\n", "\n", "consensus = F.normalize(\n", " sum(current[i] for i in range(len(SUBSETS))) / len(SUBSETS), dim=-1)\n", "consensus_cv = cv_metric(consensus[:5000].to(DEVICE))\n", "print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", "# Per-expert Procrustes calibration (expert → consensus space)\n", "print(f\"\\n Calibrating {len(SUBSETS)} expert Procrustes...\")\n", "expert_calibrations = []\n", "for i, name in enumerate(SUBSETS):\n", " info = procrustes_align(current[i], consensus)\n", " expert_calibrations.append(info)\n", " c = F.cosine_similarity(\n", " apply_align(current[i][:5000], info),\n", " consensus[:5000], dim=-1).mean().item()\n", " if i < 5 or i >= len(SUBSETS)-3:\n", " print(f\" {name:<30} cos={c:.4f}\")\n", " elif i == 5:\n", " print(f\" ...\")\n", "\n", "# Compute bank calibration targets on consensus\n", "print(f\"\\n Computing bank calibration targets...\")\n", "with torch.no_grad():\n", " cons_dev = consensus[:10000].to(DEVICE)\n", " # Simulate bank on consensus to get cross-expert targets\n", " # We need initial expert_cos and cross_cos from calibrated Procrustes\n", " tmp_expert_cos = []\n", " tmp_expert_proj = []\n", " for i in range(len(SUBSETS)):\n", " R = expert_calibrations[i][\"rotation\"].to(DEVICE)\n", " W = expert_calibrations[i][\"source_whitener\"].to(DEVICE)\n", " mu = expert_calibrations[i][\"source_mean\"].to(DEVICE)\n", " centered = cons_dev - mu\n", " whitened_n = F.normalize(centered @ W, dim=-1)\n", " in_expert = whitened_n @ R.T\n", " back = in_expert @ R\n", " cos = F.cosine_similarity(whitened_n, back, dim=-1)\n", " tmp_expert_cos.append(cos)\n", " tmp_expert_proj.append(in_expert)\n", "\n", " expert_cos_stack = torch.stack(tmp_expert_cos, dim=-1)\n", " target_cross_cos_vals = []\n", " for i in range(len(SUBSETS)):\n", " for j in range(i+1, len(SUBSETS)):\n", " cc = F.cosine_similarity(tmp_expert_proj[i], tmp_expert_proj[j], dim=-1)\n", " target_cross_cos_vals.append(cc)\n", " cross_stack = torch.stack(target_cross_cos_vals, dim=-1)\n", "\n", " calib_cross_mean = cross_stack.mean().item()\n", " calib_cross_std = cross_stack.std().item()\n", " calib_agree = expert_cos_stack.mean(dim=-1)\n", " calib_disagree = expert_cos_stack.std(dim=-1)\n", " calib_ratio = (calib_disagree / (calib_agree + 1e-8)).mean().item()\n", "\n", "print(f\" target_cv: {consensus_cv:.4f}\")\n", "print(f\" target_cross_cos_mean: {calib_cross_mean:.4f}\")\n", "print(f\" target_cross_cos_std: {calib_cross_std:.4f}\")\n", "print(f\" target_disagreement_ratio: {calib_ratio:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 2: BUILD MODEL + LOAD SOUP DOWNSTREAM\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 2: BUILD MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = VisionBankModel(\n", " n_experts=len(SUBSETS), d_shared=D_SHARED, n_anchors=N_ANCHORS,\n", " n_comp=N_COMP, d_comp=D_COMP, n_classes=N_CLASSES, d_bank=D_BANK).to(DEVICE)\n", "\n", "# Initialize bank with calibrated Procrustes\n", "with torch.no_grad():\n", " for i, name in enumerate(SUBSETS):\n", " cal = expert_calibrations[i]\n", " model.bank.expert_rotations[i].copy_(cal[\"rotation\"])\n", " model.bank.expert_whiteners[i].copy_(cal[\"source_whitener\"])\n", " model.bank.expert_means[i].copy_(cal[\"source_mean\"])\n", "\n", " model.bank.target_cv.fill_(consensus_cv)\n", " model.bank.target_cross_cos_mean.fill_(calib_cross_mean)\n", " model.bank.target_cross_cos_std.fill_(calib_cross_std)\n", " model.bank.target_disagreement_ratio.fill_(calib_ratio)\n", "print(f\" ✓ Bank calibrated with {len(SUBSETS)} expert Procrustes\")\n", "\n", "# Transfer soup constellation + patchwork (classifier is new due to +d_bank dim)\n", "if os.path.exists(SOUP_PATH):\n", " soup_ckpt = torch.load(SOUP_PATH, map_location=\"cpu\", weights_only=False)\n", " soup_state = soup_ckpt[\"state_dict\"]\n", " model.constellation.anchors.data.copy_(soup_state[\"constellation.anchors\"].to(DEVICE))\n", " model.bank.anchors.data.copy_(soup_state[\"constellation.anchors\"].to(DEVICE))\n", " pw_state = {k.replace(\"patchwork.\", \"\"): v for k, v in soup_state.items() if k.startswith(\"patchwork.\")}\n", " model.patchwork.load_state_dict(pw_state)\n", " print(f\" ✓ Constellation + patchwork transferred from soup\")\n", " del soup_ckpt, soup_state\n", "else:\n", " print(f\" ⚠ No soup checkpoint — using random initialization\")\n", "\n", "n_bank_p = sum(p.numel() for p in model.bank.parameters())\n", "n_const = sum(p.numel() for p in model.constellation.parameters())\n", "n_pw = sum(p.numel() for p in model.patchwork.parameters())\n", "n_cls = sum(p.numel() for p in model.classifier.parameters())\n", "n_total = sum(p.numel() for p in model.parameters())\n", "print(f\"\\n Parameters:\")\n", "print(f\" bank: {n_bank_p:>12,}\")\n", "print(f\" constellation: {n_const:>12,}\")\n", "print(f\" patchwork: {n_pw:>12,}\")\n", "print(f\" classifier: {n_cls:>12,}\")\n", "print(f\" total: {n_total:>12,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 3: TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 3: TRAIN\")\n", "print(f\"{'='*65}\")\n", "\n", "# Consensus targets on GPU\n", "train_targets = consensus[:N_train].to(DEVICE)\n", "val_targets = F.normalize(\n", " sum(project_to_shared(val_expert_embs[n]) for n in SUBSETS).float() / len(SUBSETS),\n", " dim=-1)\n", "# GPA-align val consensus\n", "val_current = {i: project_to_shared(val_expert_embs[SUBSETS[i]]).float() for i in range(len(SUBSETS))}\n", "val_mean = sum(val_current[i] for i in range(len(SUBSETS))) / len(SUBSETS)\n", "for i in range(len(SUBSETS)):\n", " info = procrustes_align(val_current[i], val_mean)\n", " val_current[i] = apply_align(val_current[i], info)\n", "val_consensus = F.normalize(sum(val_current[i] for i in range(len(SUBSETS))) / len(SUBSETS), dim=-1).to(DEVICE)\n", "\n", "train_labels_gpu = train_label_matrix.to(DEVICE)\n", "val_labels_gpu = val_label_matrix.to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "writer = SummaryWriter(\"runs/vision_alignment_bank\")\n", "best_mAP = 0.0; gs = 0\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train, device=DEVICE)\n", " tl, tn, nb = 0, 0, 0\n", "\n", " for i in range(0, N_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 8: continue\n", "\n", " emb = train_targets[idx]\n", " labels = train_labels_gpu[idx]\n", "\n", " logits, out_emb, tri, nearest, bank_diag = model(emb)\n", "\n", " # Student losses\n", " l_nce, nce_acc = infonce(out_emb, emb)\n", " l_mse = F.mse_loss(out_emb, emb)\n", " l_cv = cv_loss(out_emb, target=consensus_cv)\n", " l_cls = F.binary_cross_entropy_with_logits(logits, labels)\n", "\n", " # Bank losses\n", " l_bank, bdiag = compute_bank_loss(model.bank, out_emb)\n", "\n", " loss = W_NCE*l_nce + W_MSE*l_mse + W_CV*l_cv + W_CLS*l_cls + W_BANK*l_bank\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " tl += loss.item(); tn += nce_acc; nb += 1; gs += 1\n", "\n", " if gs % 100 == 0:\n", " writer.add_scalar(\"train/loss\", loss.item(), gs)\n", " writer.add_scalar(\"train/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"train/bank\", l_bank.item(), gs)\n", " writer.add_scalar(\"train/cls\", l_cls.item(), gs)\n", " writer.add_scalar(\"train/nce_acc\", nce_acc, gs)\n", " for k, v in bdiag.items():\n", " writer.add_scalar(f\"bank/{k}\", v, gs)\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " all_lo, all_em = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j+BATCH, N_val)\n", " lo, em, _, _, _ = model(val_consensus[j:end])\n", " all_lo.append(lo.cpu()); all_em.append(em.cpu())\n", " v_lo = torch.cat(all_lo); v_em = torch.cat(all_em)\n", "\n", " # mAP\n", " v_lab = val_label_matrix\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:,c].sum() > 0:\n", " si = v_lo[:,c].argsort(descending=True); st = v_lab[:,c][si]\n", " pak = st.cumsum(0)/torch.arange(1,len(st)+1).float()\n", " ap_sum += (pak*st).sum().item()/st.sum().item(); nv += 1\n", " mAP = ap_sum/max(nv,1)\n", "\n", " v_cos = F.cosine_similarity(v_em, val_consensus.cpu(), dim=-1).mean().item()\n", " v_cv = cv_metric(v_em[:2000].to(DEVICE))\n", "\n", " # R@1\n", " sim = v_em @ val_consensus.cpu().T\n", " r1 = (sim.argmax(-1) == torch.arange(N_val)).float().mean().item()\n", "\n", " writer.add_scalar(\"val/mAP\", mAP, epoch+1)\n", " writer.add_scalar(\"val/cos\", v_cos, epoch+1)\n", " writer.add_scalar(\"val/cv\", v_cv, epoch+1)\n", " writer.add_scalar(\"val/R@1\", r1, epoch+1)\n", "\n", " mk = \"\"\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\"state_dict\": model.state_dict(), \"mAP\": mAP, \"r1\": r1, \"cv\": v_cv,\n", " \"config\": {\"n_experts\": len(SUBSETS), \"d_shared\": D_SHARED,\n", " \"n_anchors\": N_ANCHORS, \"n_comp\": N_COMP,\n", " \"d_comp\": D_COMP, \"n_classes\": N_CLASSES, \"d_bank\": D_BANK}},\n", " \"checkpoints/best.pt\"); mk = \" ★\"\n", "\n", " print(f\" E{epoch+1:2d}: mAP={mAP:.3f} R@1={r1:.3f} cos={v_cos:.3f} \"\n", " f\"cv={v_cv:.4f} nce={tn/nb:.3f} loss={tl/nb:.4f}{mk}\")\n", "\n", "writer.close()\n", "\n", "# Upload\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "try:\n", " from huggingface_hub import HfApi\n", " api = HfApi()\n", " if os.path.exists(\"checkpoints/best.pt\"):\n", " api.upload_file(path_or_fileobj=\"checkpoints/best.pt\",\n", " path_in_repo=\"vision_bank_best.pt\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", " print(f\" ✓ Uploaded to {REPO_ID}\")\n", "except Exception as e:\n", " print(f\" Upload: {e}\")\n", "\n", "print(f\"\\n{'='*65}\\nDONE\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "be14bc1584f242a7bcb152f782e66041", "d49f85a65c97413dbf313fafdc80b657", "ee9e039d3aeb4da098ef11bdfba78b33", "94d4610d83bd4b3f8ee41b82c82084a8", "03d7e21233894638933e2a42a888d568", "3aa7bb49d32a4e2ea24b96309d30ade4", "75f58532ec594886ac2fefaaaf1cf8b5", "c13bc3ca9a0c40fdb05d11edb7ca8b86", "1c66823a376143c088578103db5ae100", "0c0d99c6dee74d01b92524813daa85c8", "43ecb4f73b014f9e96272df7152bb560", "35d3927a2ef5429dbc5659a0fc9102d2", "474a6d5f0a414181882ec65fa32642fa", "cd3de655aa01486c8ca5fd043b4ab79f", "32eb880f7ae14cdd85145894c697450b", "7e3bf18f22a0417485d89f6a0d63bf39", "473dcfe966064f0eb763cb25831ef369", "b1e6c6c7164547d48c8a39e31501763c", "e267f1108a9146ad87e5f5478619d8d6", "e985ee3922fb4666bd00bdadf98f18cc", "1f53bec27d1743919adf6e0db8f532e5", "ba2860b7e3be4e21a160cabe6eff1383", "c2288b37a8dc471e898b3d7021055965", "7cbf10c5f8294c658de224d0582e571c", "9c1ce878199141a2a35428c830e32f81", "d3ba96ae2fc345bbb70fa08ace7fcb77", "68a40c45a7ea4bf5bb4f014f31c78bf9", "899ac0156870405ea01d4b436d991b7a", "54cb290d21384690924f8b8d6038dbc4", "c0be27ab79f34d3580d7e386ae351ce0", "1a0fd6405b5740169fe4df0aa2aedb66", "bb548b50627c4a139b2c5be90635acc4", "d2fd17de84b8444f96d87aef99b9edfc" ] }, "id": "789kPsdNmcq8", "outputId": "f6458ae4-894e-4d51-b251-5c480da9fcdf" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP VISION ALIGNMENT BANK\n", " 34 experts → CaptionBERT AlignmentBank\n", " Device: cuda\n", "=================================================================\n", "\n", "=================================================================\n", "PHASE 0: LOAD EXPERT FEATURES\n", "=================================================================\n", " Train: 118,287 Val: 5,000\n", " clip_b16_laion2b dim=512\n", " clip_b16_openai dim=512\n", " clip_b32_datacomp dim=512\n", " clip_b32_laion2b dim=512\n", " clip_b32_openai dim=512\n", " clip_bigg14_laion2b dim=1280\n", " clip_g14_laion2b dim=1024\n", " clip_h14_laion2b dim=1024\n", " clip_l14_336_openai dim=768\n", " clip_l14_datacomp dim=768\n", " clip_l14_laion2b dim=768\n", " clip_l14_openai dim=768\n", " dinov2_b14 dim=768\n", " dinov2_b14_reg dim=768\n", " dinov2_g14 dim=1536\n", " dinov2_g14_reg dim=1536\n", " dinov2_l14 dim=1024\n", " dinov2_l14_reg dim=1024\n", " dinov2_s14 dim=384\n", " dinov2_s14_reg dim=384\n", " mae_b16 dim=768\n", " mae_h14 dim=1280\n", " mae_l16 dim=1024\n", " siglip2_b16_256 dim=768\n", " siglip2_b16_512 dim=768\n", " siglip2_l16_384 dim=1024\n", " siglip_b16_384 dim=768\n", " siglip_b16_512 dim=768\n", " siglip_l16_256 dim=1024\n", " siglip_l16_384 dim=1024\n", " siglip_so400m_384 dim=1152\n", " vit_b16_21k dim=768\n", " vit_l16_21k dim=1024\n", " vit_s16_21k dim=384\n", "\n", "=================================================================\n", "PHASE 1: GPA ALIGNMENT + PROCRUSTES CALIBRATION\n", "=================================================================\n", " GPA iter 1: delta=0.03199715\n", " GPA iter 5: delta=0.00000247\n", " GPA iter 10: delta=0.00000042\n", " GPA iter 15: delta=0.00000016\n", " GPA iter 20: delta=0.00000008\n", " Consensus CV: 0.3024\n", "\n", " Calibrating 34 expert Procrustes...\n", " clip_b16_laion2b cos=0.9426\n", " clip_b16_openai cos=0.9367\n", " clip_b32_datacomp cos=0.9329\n", " clip_b32_laion2b cos=0.9365\n", " clip_b32_openai cos=0.9348\n", " ...\n", " vit_b16_21k cos=0.9162\n", " vit_l16_21k cos=0.9192\n", " vit_s16_21k cos=0.9074\n", "\n", " Computing bank calibration targets...\n", " target_cv: 0.3024\n", " target_cross_cos_mean: 0.9235\n", " target_cross_cos_std: 0.0459\n", " target_disagreement_ratio: 0.0000\n", "\n", "=================================================================\n", "PHASE 2: BUILD MODEL\n", "=================================================================\n", " ✓ Bank calibrated with 34 expert Procrustes\n", " ✓ Constellation + patchwork transferred from soup\n", "\n", " Parameters:\n", " bank: 72,237,696\n", " constellation: 262,144\n", " patchwork: 332,800\n", " classifier: 2,797,136\n", " total: 75,629,776\n", "\n", "=================================================================\n", "PHASE 3: TRAIN\n", "=================================================================\n", " E 1: mAP=0.743 R@1=1.000 cos=1.000 cv=0.1324 nce=1.000 loss=0.5124 ★\n", " E 2: mAP=0.770 R@1=1.000 cos=1.000 cv=0.1453 nce=1.000 loss=0.4601 ★\n", " E 3: mAP=0.776 R@1=1.000 cos=1.000 cv=0.1354 nce=1.000 loss=0.4566 ★\n", " E 4: mAP=0.782 R@1=1.000 cos=1.000 cv=0.1306 nce=1.000 loss=0.4556 ★\n", " E 5: mAP=0.778 R@1=1.000 cos=1.000 cv=0.1353 nce=1.000 loss=0.4563\n", " E 6: mAP=0.782 R@1=1.000 cos=1.000 cv=0.1436 nce=1.000 loss=0.4559 ★\n", " E 7: mAP=0.779 R@1=1.000 cos=1.000 cv=0.1255 nce=1.000 loss=0.4551\n", " E 8: mAP=0.779 R@1=1.000 cos=1.000 cv=0.1338 nce=1.000 loss=0.4559\n", " E 9: mAP=0.779 R@1=1.000 cos=1.000 cv=0.1342 nce=1.000 loss=0.4573\n", " E10: mAP=0.775 R@1=1.000 cos=1.000 cv=0.1195 nce=1.000 loss=0.4553\n", " E11: mAP=0.774 R@1=1.000 cos=1.000 cv=0.1343 nce=1.000 loss=0.4548\n", " E12: mAP=0.772 R@1=1.000 cos=1.000 cv=0.1291 nce=1.000 loss=0.4545\n", " E13: mAP=0.771 R@1=1.000 cos=1.000 cv=0.1475 nce=1.000 loss=0.4526\n", " E14: mAP=0.769 R@1=1.000 cos=1.000 cv=0.1206 nce=1.000 loss=0.4532\n", " E15: mAP=0.770 R@1=1.000 cos=1.000 cv=0.1381 nce=1.000 loss=0.4527\n", " E16: mAP=0.764 R@1=1.000 cos=1.000 cv=0.1324 nce=1.000 loss=0.4542\n", " E17: mAP=0.763 R@1=1.000 cos=1.000 cv=0.1467 nce=1.000 loss=0.4536\n", " E18: mAP=0.759 R@1=1.000 cos=1.000 cv=0.1371 nce=1.000 loss=0.4538\n", " E19: mAP=0.757 R@1=1.000 cos=1.000 cv=0.1259 nce=1.000 loss=0.4536\n", " E20: mAP=0.757 R@1=1.000 cos=1.000 cv=0.1340 nce=1.000 loss=0.4533\n", "\n", " Best mAP: 0.782\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "be14bc1584f242a7bcb152f782e66041" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "35d3927a2ef5429dbc5659a0fc9102d2" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " checkpoints/best.pt : 0%| | 556kB / 303MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c2288b37a8dc471e898b3d7021055965" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " ✓ Uploaded to AbstractPhil/geolip-vit-x34\n", "\n", "=================================================================\n", "DONE\n" ] } ] }, { "cell_type": "markdown", "source": [ "# analyze the 34 manifolds" ], "metadata": { "id": "HNdasGs4KVuq" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "34-EXPERT BOUNDARY SCAN\n", "=========================\n", "Analyze actual feature distributions to find:\n", " - Natural family boundaries (do patch14s cluster?)\n", " - Dimensional discontinuities (where do projections break?)\n", " - Outlier experts (geometrically incompatible with consensus)\n", " - Effective dimensionality per expert\n", " - Pairwise alignment quality matrix\n", " - Cross-family vs within-family agreement\n", "\n", "Uses val split (5K) for speed.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import gc\n", "from datasets import load_dataset\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "SUBSETS = [\n", " \"clip_b16_laion2b\", \"clip_b16_openai\", \"clip_b32_datacomp\",\n", " \"clip_b32_laion2b\", \"clip_b32_openai\", \"clip_bigg14_laion2b\",\n", " \"clip_g14_laion2b\", \"clip_h14_laion2b\", \"clip_l14_336_openai\",\n", " \"clip_l14_datacomp\", \"clip_l14_laion2b\", \"clip_l14_openai\",\n", " \"dinov2_b14\", \"dinov2_b14_reg\", \"dinov2_g14\", \"dinov2_g14_reg\",\n", " \"dinov2_l14\", \"dinov2_l14_reg\", \"dinov2_s14\", \"dinov2_s14_reg\",\n", " \"mae_b16\", \"mae_h14\", \"mae_l16\",\n", " \"siglip2_b16_256\", \"siglip2_b16_512\", \"siglip2_l16_384\",\n", " \"siglip_b16_384\", \"siglip_b16_512\", \"siglip_l16_256\",\n", " \"siglip_l16_384\", \"siglip_so400m_384\",\n", " \"vit_b16_21k\", \"vit_l16_21k\", \"vit_s16_21k\",\n", "]\n", "\n", "print(\"=\" * 65)\n", "print(\"34-EXPERT BOUNDARY SCAN\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD VAL FEATURES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", SUBSETS[0], split=\"val\")\n", "image_ids = ref[\"image_id\"]\n", "N = len(image_ids)\n", "id_to_idx = {iid: i for i, iid in enumerate(image_ids)}\n", "\n", "expert_feats = {}\n", "expert_dims = {}\n", "for name in SUBSETS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " dim = len(ds[0][\"features\"])\n", " expert_dims[name] = dim\n", " feats = torch.zeros(N, dim)\n", " for row in ds:\n", " if row[\"image_id\"] in id_to_idx:\n", " feats[id_to_idx[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " expert_feats[name] = feats\n", " print(f\" {name:<30} dim={dim}\", flush=True)\n", "\n", "print(f\"\\n Loaded {len(expert_feats)} experts, N={N}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 1: PER-EXPERT DISTRIBUTION ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 1: PER-EXPERT DISTRIBUTIONS\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Expert':<30} {'dim':>5} {'norm_μ':>7} {'norm_σ':>7} \"\n", " f\"{'eff_dim':>8} {'top1_sv':>8} {'sparsity':>9} {'l2_normed':>10}\")\n", "print(f\" {'-'*95}\")\n", "\n", "expert_stats = {}\n", "for name in SUBSETS:\n", " feats = expert_feats[name].float()\n", " dim = expert_dims[name]\n", "\n", " # Norms\n", " norms = feats.norm(dim=-1)\n", " norm_mean = norms.mean().item()\n", " norm_std = norms.std().item()\n", "\n", " # Check if already L2-normalized\n", " is_normed = abs(norm_mean - 1.0) < 0.05 and norm_std < 0.05\n", "\n", " # Effective dimensionality (participation ratio of singular values)\n", " centered = feats - feats.mean(0, keepdim=True)\n", " sv = torch.linalg.svdvals(centered[:2000])\n", " eff_dim = (sv.sum() ** 2 / (sv.pow(2).sum() + 1e-12)).item()\n", " top1_ratio = (sv[0] / (sv.sum() + 1e-8)).item()\n", "\n", " # Sparsity (fraction of near-zero dimensions)\n", " feat_std = feats.std(dim=0)\n", " sparsity = (feat_std < 0.01).float().mean().item()\n", "\n", " expert_stats[name] = {\n", " \"dim\": dim, \"norm_mean\": norm_mean, \"norm_std\": norm_std,\n", " \"eff_dim\": eff_dim, \"top1_sv\": top1_ratio,\n", " \"sparsity\": sparsity, \"is_normed\": is_normed,\n", " }\n", "\n", " print(f\" {name:<30} {dim:>5} {norm_mean:>7.3f} {norm_std:>7.4f} \"\n", " f\"{eff_dim:>8.1f} {top1_ratio:>8.4f} {sparsity:>9.3f} \"\n", " f\"{'✓' if is_normed else '✗':>10}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: PAIRWISE RAW COSINE (before any alignment)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 2: PAIRWISE RAW COSINE (same-dim experts only)\")\n", "print(f\"{'='*65}\")\n", "\n", "# Group by dimension\n", "dim_groups = {}\n", "for name, dim in expert_dims.items():\n", " dim_groups.setdefault(dim, []).append(name)\n", "\n", "for dim in sorted(dim_groups.keys()):\n", " names = dim_groups[dim]\n", " if len(names) < 2:\n", " continue\n", " print(f\"\\n {dim}-d ({len(names)} experts):\")\n", " normed = {n: F.normalize(expert_feats[n].float(), dim=-1) for n in names}\n", " for i, n1 in enumerate(names):\n", " for j, n2 in enumerate(names):\n", " if j <= i: continue\n", " cos = F.cosine_similarity(\n", " normed[n1][:2000], normed[n2][:2000], dim=-1).mean().item()\n", " n1s = n1[:25]; n2s = n2[:25]\n", " marker = \" ★\" if cos > 0.8 else \" ▼\" if cos < 0.3 else \"\"\n", " print(f\" {n1s:<25} × {n2s:<25} cos={cos:.4f}{marker}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: CROSS-DIMENSIONAL PROCRUSTES ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 3: PROCRUSTES ALIGNMENT (all pairs, projected to min dim)\")\n", "print(f\"{'='*65}\")\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def quick_procrustes_cos(a, b, n=2000):\n", " \"\"\"Align a→b via whitened Procrustes, return cosine.\"\"\"\n", " d_min = min(a.shape[1], b.shape[1])\n", " # PCA both to min dim\n", " if a.shape[1] > d_min:\n", " ac = a[:n] - a[:n].mean(0, keepdim=True)\n", " _, _, Vt = torch.linalg.svd(ac, full_matrices=False)\n", " a_proj = a[:n] @ Vt[:d_min].T\n", " else:\n", " a_proj = a[:n]\n", " if b.shape[1] > d_min:\n", " bc = b[:n] - b[:n].mean(0, keepdim=True)\n", " _, _, Vt = torch.linalg.svd(bc, full_matrices=False)\n", " b_proj = b[:n] @ Vt[:d_min].T\n", " else:\n", " b_proj = b[:n]\n", "\n", " # Whiten + SVD\n", " a_proj = a_proj.float(); b_proj = b_proj.float()\n", " am = a_proj.mean(0, keepdim=True); bm = b_proj.mean(0, keepdim=True)\n", " ac = a_proj - am; bc = b_proj - bm\n", " aw = symmetric_inv_sqrt((ac.T @ ac) / (n-1))\n", " bw = symmetric_inv_sqrt((bc.T @ bc) / (n-1))\n", " an = F.normalize(ac @ aw, dim=-1)\n", " bn = F.normalize(bc @ bw, dim=-1)\n", " U, S, Vt = torch.linalg.svd(bn.T @ an, full_matrices=False)\n", " R = U @ Vt\n", " aligned = an @ R.T\n", " return F.cosine_similarity(aligned, bn, dim=-1).mean().item(), S\n", "\n", "\n", "# Sample representative experts from each family\n", "representatives = {\n", " \"clip_b16\": \"clip_b16_openai\",\n", " \"clip_b32\": \"clip_b32_openai\",\n", " \"clip_l14\": \"clip_l14_openai\",\n", " \"clip_g14\": \"clip_g14_laion2b\",\n", " \"clip_h14\": \"clip_h14_laion2b\",\n", " \"clip_bigg14\": \"clip_bigg14_laion2b\",\n", " \"dinov2_s14\": \"dinov2_s14\",\n", " \"dinov2_b14\": \"dinov2_b14\",\n", " \"dinov2_l14\": \"dinov2_l14\",\n", " \"dinov2_g14\": \"dinov2_g14\",\n", " \"mae_b16\": \"mae_b16\",\n", " \"mae_l16\": \"mae_l16\",\n", " \"mae_h14\": \"mae_h14\",\n", " \"siglip_b16\": \"siglip_b16_384\",\n", " \"siglip_l16\": \"siglip_l16_384\",\n", " \"siglip_so400m\": \"siglip_so400m_384\",\n", " \"vit_s16\": \"vit_s16_21k\",\n", " \"vit_b16\": \"vit_b16_21k\",\n", " \"vit_l16\": \"vit_l16_21k\",\n", "}\n", "\n", "rep_names = list(representatives.keys())\n", "rep_experts = list(representatives.values())\n", "\n", "print(f\"\\n Pairwise Procrustes alignment ({len(rep_names)} representatives):\\n\")\n", "alignment_matrix = np.zeros((len(rep_names), len(rep_names)))\n", "\n", "for i in range(len(rep_names)):\n", " for j in range(i+1, len(rep_names)):\n", " n1 = rep_experts[i]; n2 = rep_experts[j]\n", " try:\n", " cos, svs = quick_procrustes_cos(\n", " expert_feats[n1].float(), expert_feats[n2].float())\n", " alignment_matrix[i, j] = cos\n", " alignment_matrix[j, i] = cos\n", " except Exception as e:\n", " alignment_matrix[i, j] = -1\n", " alignment_matrix[j, i] = -1\n", "\n", "# Print as compact matrix\n", "print(f\" {'':>14}\", end=\"\")\n", "for n in rep_names:\n", " print(f\" {n[:6]:>6}\", end=\"\")\n", "print()\n", "\n", "for i, n1 in enumerate(rep_names):\n", " print(f\" {n1:>14}\", end=\"\")\n", " for j, n2 in enumerate(rep_names):\n", " if i == j:\n", " print(f\" --- \", end=\"\")\n", " else:\n", " v = alignment_matrix[i, j]\n", " print(f\" {v:>5.3f} \" if v > 0 else f\" FAIL \", end=\"\")\n", " print()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: FAMILY CLUSTERING (which experts naturally group?)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 4: FAMILY CLUSTERING\")\n", "print(f\"{'='*65}\")\n", "\n", "# Assign families\n", "def get_family(name):\n", " if \"dinov2\" in name: return \"dinov2\"\n", " if \"mae\" in name: return \"mae\"\n", " if \"siglip2\" in name: return \"siglip2\"\n", " if \"siglip\" in name: return \"siglip\"\n", " if \"clip\" in name: return \"clip\"\n", " if \"vit\" in name: return \"vit\"\n", " return \"other\"\n", "\n", "def get_patch(name):\n", " if \"b32\" in name or \"b16\" in name or \"s16\" in name: return \"patch16\"\n", " if \"l14\" in name or \"b14\" in name or \"s14\" in name or \"g14\" in name or \"h14\" in name: return \"patch14\"\n", " if \"l16\" in name: return \"patch16\"\n", " if \"so400m\" in name: return \"so400m\"\n", " return \"unknown\"\n", "\n", "# Within-family vs cross-family alignment\n", "families = {}\n", "for name in SUBSETS:\n", " fam = get_family(name)\n", " families.setdefault(fam, []).append(name)\n", "\n", "print(f\"\\n Within-family mean cosine (raw, same-dim pairs):\")\n", "for fam, members in sorted(families.items()):\n", " same_dim_pairs = []\n", " for i, n1 in enumerate(members):\n", " for j, n2 in enumerate(members):\n", " if j <= i: continue\n", " if expert_dims[n1] != expert_dims[n2]: continue\n", " cos = F.cosine_similarity(\n", " F.normalize(expert_feats[n1][:2000].float(), dim=-1),\n", " F.normalize(expert_feats[n2][:2000].float(), dim=-1),\n", " dim=-1).mean().item()\n", " same_dim_pairs.append(cos)\n", " if same_dim_pairs:\n", " print(f\" {fam:<10} ({len(members):2d} members, {len(same_dim_pairs):2d} pairs): \"\n", " f\"mean={np.mean(same_dim_pairs):.4f} \"\n", " f\"min={np.min(same_dim_pairs):.4f} max={np.max(same_dim_pairs):.4f}\")\n", "\n", "# Patch family analysis\n", "print(f\"\\n Patch family analysis:\")\n", "patch_families = {}\n", "for name in SUBSETS:\n", " patch = get_patch(name)\n", " patch_families.setdefault(patch, []).append(name)\n", "\n", "for patch, members in sorted(patch_families.items()):\n", " dims = [expert_dims[n] for n in members]\n", " print(f\" {patch:<10} ({len(members):2d} experts): dims={sorted(set(dims))}\")\n", " for n in members:\n", " print(f\" {n:<30} dim={expert_dims[n]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: SVD SPECTRUM ANALYSIS (dimensional structure)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 5: SVD SPECTRUM (effective dimensionality)\")\n", "print(f\"{'='*65}\")\n", "\n", "print(f\"\\n {'Expert':<30} {'dim':>5} {'eff_d':>6} {'ratio':>6} \"\n", " f\"{'sv1%':>6} {'sv5%':>6} {'sv10%':>6} {'sv50%':>6} {'tail':>6}\")\n", "print(f\" {'-'*85}\")\n", "\n", "for name in SUBSETS:\n", " feats = expert_feats[name][:2000].float()\n", " centered = feats - feats.mean(0, keepdim=True)\n", " sv = torch.linalg.svdvals(centered)\n", " sv_sum = sv.sum()\n", " sv2_sum = sv.pow(2).sum()\n", " eff_dim = (sv_sum**2 / (sv2_sum + 1e-12)).item()\n", " dim = expert_dims[name]\n", "\n", " # Cumulative energy at various truncations\n", " cumsum = sv.pow(2).cumsum(0) / (sv2_sum + 1e-12)\n", " sv1_pct = cumsum[0].item() * 100\n", " sv5_pct = cumsum[min(4, len(sv)-1)].item() * 100\n", " sv10_pct = cumsum[min(9, len(sv)-1)].item() * 100\n", " sv50_pct = cumsum[min(49, len(sv)-1)].item() * 100\n", "\n", " # Tail energy (last 10%)\n", " tail_start = max(1, int(dim * 0.9))\n", " tail_energy = (sv[tail_start:].pow(2).sum() / (sv2_sum + 1e-12)).item() * 100\n", "\n", " print(f\" {name:<30} {dim:>5} {eff_dim:>6.1f} {eff_dim/dim:>6.3f} \"\n", " f\"{sv1_pct:>5.1f}% {sv5_pct:>5.1f}% {sv10_pct:>5.1f}% \"\n", " f\"{sv50_pct:>5.1f}% {tail_energy:>5.2f}%\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: OUTLIER DETECTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 6: OUTLIER DETECTION\")\n", "print(f\"{'='*65}\")\n", "\n", "# Project all to 384-d (smallest common dim) for fair comparison\n", "D_COMMON = 384\n", "projected_common = {}\n", "for name in SUBSETS:\n", " feats = expert_feats[name][:2000].float()\n", " d = feats.shape[1]\n", " if d == D_COMMON:\n", " projected_common[name] = F.normalize(feats, dim=-1)\n", " elif d > D_COMMON:\n", " centered = feats - feats.mean(0, keepdim=True)\n", " _, _, Vt = torch.linalg.svd(centered, full_matrices=False)\n", " projected_common[name] = F.normalize(feats @ Vt[:D_COMMON].T, dim=-1)\n", " else:\n", " padded = torch.cat([feats, torch.zeros(feats.shape[0], D_COMMON - d)], -1)\n", " projected_common[name] = F.normalize(padded, dim=-1)\n", "\n", "# Mean pairwise cosine for each expert vs all others\n", "print(f\"\\n Mean cosine to all other experts (projected to {D_COMMON}-d):\")\n", "mean_cos_per_expert = {}\n", "for name in SUBSETS:\n", " cos_vals = []\n", " for other in SUBSETS:\n", " if other == name: continue\n", " cos = F.cosine_similarity(\n", " projected_common[name], projected_common[other], dim=-1).mean().item()\n", " cos_vals.append(cos)\n", " mean_cos = np.mean(cos_vals)\n", " mean_cos_per_expert[name] = mean_cos\n", "\n", "sorted_by_agreement = sorted(mean_cos_per_expert.items(), key=lambda x: -x[1])\n", "for i, (name, cos) in enumerate(sorted_by_agreement):\n", " dim = expert_dims[name]\n", " marker = \"\"\n", " if cos < sorted_by_agreement[-5][1] + 0.001:\n", " marker = \" ◄ OUTLIER\"\n", " elif cos > sorted_by_agreement[4][1] - 0.001:\n", " marker = \" ◄ CORE\"\n", " print(f\" {name:<30} {dim:>5}d mean_cos={cos:.4f}{marker}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: VARIANCE RETAINED UNDER PROJECTION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 7: VARIANCE RETAINED UNDER PROJECTION\")\n", "print(f\"{'='*65}\")\n", "\n", "target_dims = [256, 384, 512, 768, 1024]\n", "print(f\"\\n {'Expert':<30} {'orig':>5}\", end=\"\")\n", "for d in target_dims:\n", " print(f\" {'→'+str(d):>7}\", end=\"\")\n", "print()\n", "print(f\" {'-'*75}\")\n", "\n", "for name in SUBSETS:\n", " feats = expert_feats[name][:2000].float()\n", " d = feats.shape[1]\n", " centered = feats - feats.mean(0, keepdim=True)\n", " sv = torch.linalg.svdvals(centered)\n", " total_var = sv.pow(2).sum()\n", "\n", " print(f\" {name:<30} {d:>5}\", end=\"\")\n", " for td in target_dims:\n", " if td >= d:\n", " print(f\" {'1.000':>7}\", end=\"\")\n", " else:\n", " var_kept = sv[:td].pow(2).sum() / (total_var + 1e-12)\n", " print(f\" {var_kept.item():>7.4f}\", end=\"\")\n", " print()\n", "\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN COMPLETE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "1f0a8913ffbf483a85e99beba4554119", "4327ef9cf91f4e0abecb2d2589a35736", "592cdb9cc26b45abbc5f67a8a65378e7", "4b20557203e34fd4bd5aaa338056260f", "4b41914abfe44fad94171bb3136bb7ff", "bfbf3fc051164db6ab3c76ac5a6b34e3", "c3417233e1f24b0ab4aaeefe7f0e6cd8", "dd592c609a604e639f2bba52a516a67c", "e242185a4d3c412d93ac3820acfdaacf", "44c2f86bea0d40e6af73ddf95b20d1ce", "ace5354d08b246f988eede307c73b743", "c534ee4582cf4d1cb6e98c2502f7f64c", "1b573074b9f2487f98a89c023e8b97e1", "5eae98e7a42544afbf15c927f2ed8c8f", "698b48e137ad4113a62153d932f1a0cb", "0b618e3aefe94cc09ca76f7d14fcfdf8", "fc277cab607b40d8b49ba89be3214732", 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"43db7ed473a14e7d8f99b7dc99d15eb2", "2c884078d29f47a8a403a7d712f62c08", "34c099aeb3ca4a34b8f851adc2ebfdb4", "a26d30ff958e43b8901797c8c3011dec", "16b5c62f4b124cdb930a0c8502d7445a", "4f5748322eb949eda6d9d7eaace8420b", "414c57a1b9444659a81b0a811024713a", "bd8d612d514248448e6b8964fbb2d051", "51044ae8a4c64620986b5c1bf5dc85e1", "9fd3cfe47fe440c98961ca43492c3ea8", "9c86c89187e048df968064aa25d2a87a", "035bab9cf38b4431a7473c17b17c1bde", "f2b605a2cb284f70b340e83fd1acbe25", "dc75fe94adc9461bb8666d8a89924dc9", "55c30d3d689342029329edb17729c2fc", "828a266628b04fb4b2cdbf39a756f0a0", "c6b03ed1b8c3427c8f4a22eba0bfa4a4", "7d66f2ceeb8e473ea63a6fed83535098", "33ef187fe1c042f4860277ff7d73db99", "6d74fbbb8dfa4c2bb7b9213c822e2a01", "8b97bed8fa544f5cad918cc3642d2402", "6155bb600df6468fbb6b9ef2332ea626", "34727446a96145b59aaff7adb57fd375", "54334edb5f7d4f80bd4ea148d89d5a1c", "13e129d7c0694ec3b4fe423018779c68", "996da27e68a849b6ad535551ee073f14", "a6b67d8e69754818b098a735074d7c27", "ad5bf80793a04a4f95ef868f86a97279", "11d778ecb97f4e458b429537a9b5d6cf", "8e38155410de4850ad66a20f83a7b4db", "b63b8f2245924da1b43b630d0b794d34", "eb231f80479f45e5a837423ac9285fb4", "bc4f19b32bdd4113b910d86f9711dc01", "44f128acdd2e4ff5b5f6107db2e9e2bc", "f42ed0706aee49e684223665368dd9ae", "fe7cb9d084e0404284677c3669d81c6f", "55b2e42d9d874cadad7158f8e8e3c5a1", "bf9fef5f27394764b7747422037195f4", "945855139fc24b1c8194792fca588de1", "e1c667dfb8fb4d6ebb9308ede7b495d6", "3868e61a22a14e3ab13d0d31b17eaeba", "13577f9403804d27bef13b928123a618", "df16b1cef5b446e08e292a8fc3b244db", "a1111329d91649489c1309851350876d", "453c92e6da6447dc9894ce10b257a85c", "6220745f18a9447ea2e11b8993740b6f", "a9d3eb308adb4e298173275539f674aa", "fca570a4626946249c6f1116772748ff", "2cb76773f68b4f8891f6b4457740ca3c", "daafdd38e8704bb4af8e29915cd9a5a8", "21243355a0034d31b310ff93baed1c24", "f79757361e88448781f332a71f333be1", "56e5a44fe664462d837977faea3b8862", "086d759d8679469fb5ccb804a4e8c1e9", "08884ea56ce14f268f0f84b608d04392", "e23b1c2aea6948d6be5fab8f478286d2", "99201efbde4c4dd38a19e679ebf1b220", "7997260d81644e1181eb10a0eeb92efe", "51fc7f1a92364775a87aca0a236e9635", "72fe47a1a6224744a826b1fc55bb3f67", "4ac134dc93cb4f5ca5c98e855b239b94", "1c6af5a1d5504d2890da4dbdd1a9150f", "8aacec71dd6245418bf01bc55cdba26d", "b5490880589b404d89609e09c0255c43", "d78d270746da488b9b7b9abca5899fc0", "ddf33d51e14548f9b840b6df1b6ae1e1", "20c5bad416cf41c6a932bd08dcf37fab", "a32ebc7977ad4544b1540287752d62af", "f9b5c2d424264fb1a14e32f4a0fbf42d", "36814dde78f241d298925f5a728bc45d", "c4b7595b42d944768215a2dd211f7e20", "8684e0ab91c642b1aab7774105b1c16a", "d5a334591d1947b39920333b4ca27e15", "a329ccea61344a5884e3790ccd9b48d6", "ad17a2fdbbdb48c09c60529f03821a68", "b609e7940dbe45c09ff8595f863fb2e4", "90c1d7189aa6481ca544cbd336c99424", "6a3c39579230486b995fdc41de8caad0", "0fadbd5b27ec406bbe54696d5f2e4a69", "59f00abe35ae49e491ef059e200123c7", "c1b57320c47f4355b8c3762033c0a696", "122ee87b1d8c4e1988448dfef2c05b57", "f4b49d0aa0f04e508871275dd9da5b9b", "831102c4362345efaa603bd9eaf2c989", "03c54b44a8134f6e9488c7e5dfdc6946", "830f63f9334f4d6d991d207594617a8e", "c419a647fe694429a33afd920a58efeb" ] }, "id": "u3pWSftkKa7W", "outputId": "3a2ca87c-fce6-45ea-8af5-6cf4f86d8bc4" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "34-EXPERT BOUNDARY SCAN\n", "=================================================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "1f0a8913ffbf483a85e99beba4554119" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "clip_b16_laion2b/train-00000-of-00001.pa(…): 0%| | 0.00/126M [00:0012,}\" if isinstance(v, int) else f\" {k:<20}: {v}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: NAMED PARAMETER INVENTORY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 2: PARAMETER INVENTORY\")\n", "print(f\"{'='*65}\")\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " print(f\"\\n {name}:\")\n", "\n", " # Group by layer type\n", " groups = {}\n", " for pname, p in model.named_parameters():\n", " # Extract layer category\n", " parts = pname.split(\".\")\n", " if \"embeddings\" in pname:\n", " cat = \"embeddings\"\n", " elif \"encoder\" in pname and \"layer\" in pname:\n", " # Find layer number\n", " for part in parts:\n", " if part.startswith(\"layer\"):\n", " break\n", " # Categorize within layer\n", " if \"attention\" in pname:\n", " if \"query\" in pname or \"q_proj\" in pname or \"k_proj\" in pname or \"v_proj\" in pname:\n", " cat = \"attn_qkv\"\n", " elif \"out\" in pname or \"o_proj\" in pname:\n", " cat = \"attn_out\"\n", " else:\n", " cat = \"attn_other\"\n", " elif \"mlp\" in pname or \"intermediate\" in pname or \"output\" in pname:\n", " cat = \"mlp\"\n", " elif \"norm\" in pname or \"layer_norm\" in pname:\n", " cat = \"layernorm\"\n", " else:\n", " cat = \"encoder_other\"\n", " elif \"layernorm\" in pname.lower() or \"layer_norm\" in pname.lower():\n", " cat = \"final_norm\"\n", " elif \"head\" in pname or \"pooler\" in pname:\n", " cat = \"head\"\n", " else:\n", " cat = \"other\"\n", "\n", " groups.setdefault(cat, {\"count\": 0, \"params\": 0, \"shapes\": []})\n", " groups[cat][\"count\"] += 1\n", " groups[cat][\"params\"] += p.numel()\n", " if len(groups[cat][\"shapes\"]) < 3:\n", " groups[cat][\"shapes\"].append(f\"{pname.split('.')[-2]}.{pname.split('.')[-1]}: {list(p.shape)}\")\n", "\n", " for cat in sorted(groups.keys()):\n", " g = groups[cat]\n", " print(f\" {cat:<15}: {g['params']:>12,} ({g['count']:2d} tensors)\")\n", " for s in g[\"shapes\"]:\n", " print(f\" {s}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: WEIGHT STATISTICS PER LAYER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 3: WEIGHT STATISTICS\")\n", "print(f\"{'='*65}\")\n", "\n", "def weight_stats(param):\n", " p = param.float().detach()\n", " stats = {\n", " \"shape\": list(p.shape),\n", " \"norm\": p.norm().item(),\n", " \"mean\": p.mean().item(),\n", " \"std\": p.std().item(),\n", " \"abs_max\": p.abs().max().item(),\n", " \"sparsity\": (p.abs() < 1e-6).float().mean().item(),\n", " }\n", " # Spectral radius for 2D weights\n", " if p.dim() == 2 and min(p.shape) > 1:\n", " sv = torch.linalg.svdvals(p)\n", " stats[\"sv_max\"] = sv[0].item()\n", " stats[\"sv_min\"] = sv[-1].item()\n", " stats[\"sv_ratio\"] = (sv[0] / (sv[-1] + 1e-10)).item()\n", " stats[\"eff_rank\"] = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " return stats\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " print(f\"\\n {name} — key weight matrices:\")\n", " print(f\" {'param':<50} {'shape':<20} {'norm':>8} {'std':>8} {'sv_max':>8} {'eff_rank':>9}\")\n", " print(f\" {'-'*105}\")\n", "\n", " for pname, p in model.named_parameters():\n", " if p.dim() < 2: continue\n", " if p.numel() < 1000: continue\n", "\n", " # Only show interesting layers\n", " show = False\n", " for keyword in [\"patch\", \"embed\", \"position\", \"cls\",\n", " \"layer.0.\", \"layer.5.\", \"layer.11.\",\n", " \"layer.23.\", \"q_proj\", \"k_proj\", \"v_proj\",\n", " \"query\", \"key\", \"value\",\n", " \"fc1\", \"fc2\", \"dense\", \"out_proj\",\n", " \"layernorm\", \"head\"]:\n", " if keyword in pname.lower():\n", " show = True; break\n", "\n", " if not show: continue\n", "\n", " s = weight_stats(p)\n", " sv_max = f\"{s.get('sv_max', 0):.4f}\" if 'sv_max' in s else \" N/A\"\n", " eff_rank = f\"{s.get('eff_rank', 0):.1f}\" if 'eff_rank' in s else \" N/A\"\n", " short_name = pname[-50:] if len(pname) > 50 else pname\n", " shape_str = str(s[\"shape\"])\n", " print(f\" {short_name:<50} {shape_str:<20} {s['norm']:>8.4f} \"\n", " f\"{s['std']:>8.5f} {sv_max:>8} {eff_rank:>9}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: PATCH EMBEDDING ANALYSIS (the actual patchwork)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 4: PATCH EMBEDDING WEIGHTS\")\n", "print(f\"{'='*65}\")\n", "\n", "patch_embeddings = {}\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " for pname, p in model.named_parameters():\n", " if \"patch\" in pname.lower() and \"embed\" in pname.lower() and p.dim() == 4:\n", " patch_embeddings[name] = p.detach().float()\n", " print(f\"\\n {name}: {pname}\")\n", " print(f\" Shape: {list(p.shape)}\")\n", " # (out_channels, in_channels, kernel_h, kernel_w)\n", " print(f\" = {p.shape[0]} filters × {p.shape[1]} channels × {p.shape[2]}×{p.shape[3]} kernel\")\n", " # Reshape to 2D for spectral analysis\n", " w2d = p.detach().float().reshape(p.shape[0], -1) # (out, in*h*w)\n", " sv = torch.linalg.svdvals(w2d)\n", " eff_rank = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " print(f\" Spectral: sv_max={sv[0]:.4f} sv_min={sv[-1]:.6f} \"\n", " f\"eff_rank={eff_rank:.1f}/{min(w2d.shape)}\")\n", " print(f\" Norm: {p.norm():.4f} Mean: {p.mean():.6f} Std: {p.std():.6f}\")\n", "\n", " # Per-filter analysis\n", " filter_norms = p.detach().float().reshape(p.shape[0], -1).norm(dim=1)\n", " print(f\" Filter norms: mean={filter_norms.mean():.4f} \"\n", " f\"std={filter_norms.std():.4f} \"\n", " f\"min={filter_norms.min():.4f} max={filter_norms.max():.4f}\")\n", " break\n", "\n", "# Compare patch embeddings pairwise (Procrustes on flattened filters)\n", "if len(patch_embeddings) >= 2:\n", " print(f\"\\n Patch embedding Procrustes alignment:\")\n", " names_list = list(patch_embeddings.keys())\n", " for i in range(len(names_list)):\n", " for j in range(i+1, len(names_list)):\n", " n1, n2 = names_list[i], names_list[j]\n", " p1 = patch_embeddings[n1].reshape(patch_embeddings[n1].shape[0], -1)\n", " p2 = patch_embeddings[n2].reshape(patch_embeddings[n2].shape[0], -1)\n", " # Truncate to common dim\n", " d_min = min(p1.shape[0], p2.shape[0])\n", " d_feat = min(p1.shape[1], p2.shape[1])\n", " a = p1[:d_min, :d_feat]; b = p2[:d_min, :d_feat]\n", " # Raw cosine (mean over filters)\n", " cos = F.cosine_similarity(\n", " F.normalize(a, dim=1), F.normalize(b, dim=1), dim=1).mean().item()\n", " print(f\" {n1} × {n2}: raw_cos={cos:.4f} (d_min={d_min}, d_feat={d_feat})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: ATTENTION HEAD GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 5: ATTENTION HEAD GEOMETRY\")\n", "print(f\"{'='*65}\")\n", "\n", "def extract_qkv_weights(model, name):\n", " \"\"\"Extract Q, K, V weight matrices from each layer.\"\"\"\n", " layers_qkv = []\n", " for pname, p in model.named_parameters():\n", " if p.dim() != 2: continue\n", " plow = pname.lower()\n", " if (\"query\" in plow or \"q_proj\" in plow) and \"weight\" in plow:\n", " layers_qkv.append({\"layer\": pname, \"type\": \"Q\", \"weight\": p.detach().float()})\n", " elif (\"key\" in plow or \"k_proj\" in plow) and \"weight\" in plow:\n", " layers_qkv.append({\"layer\": pname, \"type\": \"K\", \"weight\": p.detach().float()})\n", " elif (\"value\" in plow or \"v_proj\" in plow) and \"weight\" in plow:\n", " layers_qkv.append({\"layer\": pname, \"type\": \"V\", \"weight\": p.detach().float()})\n", " return layers_qkv\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " qkv = extract_qkv_weights(models[name], name)\n", " n_layers = len(qkv) // 3\n", "\n", " print(f\"\\n {name} ({n_layers} layers):\")\n", " print(f\" {'layer':>6} {'Q_norm':>8} {'K_norm':>8} {'V_norm':>8} \"\n", " f\"{'QK_cos':>8} {'QV_cos':>8} {'KV_cos':>8}\")\n", "\n", " for layer_idx in range(n_layers):\n", " q = qkv[layer_idx * 3][\"weight\"]\n", " k = qkv[layer_idx * 3 + 1][\"weight\"]\n", " v = qkv[layer_idx * 3 + 2][\"weight\"]\n", "\n", " q_norm = q.norm().item()\n", " k_norm = k.norm().item()\n", " v_norm = v.norm().item()\n", "\n", " # Flatten and compute cosine between Q/K, Q/V, K/V\n", " qf = q.reshape(-1); kf = k.reshape(-1); vf = v.reshape(-1)\n", " d = min(qf.shape[0], kf.shape[0], vf.shape[0])\n", " qk_cos = F.cosine_similarity(qf[:d].unsqueeze(0), kf[:d].unsqueeze(0)).item()\n", " qv_cos = F.cosine_similarity(qf[:d].unsqueeze(0), vf[:d].unsqueeze(0)).item()\n", " kv_cos = F.cosine_similarity(kf[:d].unsqueeze(0), vf[:d].unsqueeze(0)).item()\n", "\n", " if layer_idx < 3 or layer_idx >= n_layers - 2 or layer_idx == n_layers // 2:\n", " print(f\" {layer_idx:>6} {q_norm:>8.3f} {k_norm:>8.3f} {v_norm:>8.3f} \"\n", " f\"{qk_cos:>8.4f} {qv_cos:>8.4f} {kv_cos:>8.4f}\")\n", " elif layer_idx == 3:\n", " print(f\" {'...':>6}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: CROSS-MODEL QK ALIGNMENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 6: CROSS-MODEL WEIGHT ALIGNMENT\")\n", "print(f\"{'='*65}\")\n", "\n", "# Compare equivalent layers across models\n", "# Use common dimension (768) — all three output 768-d\n", "# Compare Q weights, K weights, V weights at equivalent depth fractions\n", "\n", "model_qkv = {}\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model_qkv[name] = extract_qkv_weights(models[name], name)\n", "\n", "print(f\"\\n Cross-model Q weight cosine at equivalent depth fractions:\")\n", "print(f\" {'depth':>6} {'clip×dino':>10} {'clip×siglip':>12} {'dino×siglip':>12}\")\n", "\n", "for name in model_qkv:\n", " n = len(model_qkv[name]) // 3\n", " print(f\" {name}: {n} layers\")\n", "\n", "# Compare at 0%, 25%, 50%, 75%, 100% depth\n", "for frac in [0.0, 0.25, 0.5, 0.75, 1.0]:\n", " vals = {}\n", " for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " qkv = model_qkv[name]\n", " n = len(qkv) // 3\n", " idx = min(int(frac * (n - 1)), n - 1)\n", " q = qkv[idx * 3][\"weight\"].reshape(-1)\n", " vals[name] = q\n", "\n", " # Truncate to common length\n", " min_len = min(v.shape[0] for v in vals.values())\n", " cos_cd = F.cosine_similarity(\n", " vals[\"clip_l14\"][:min_len].unsqueeze(0),\n", " vals[\"dinov2_b14\"][:min_len].unsqueeze(0)).item()\n", " cos_cs = F.cosine_similarity(\n", " vals[\"clip_l14\"][:min_len].unsqueeze(0),\n", " vals[\"siglip_b16\"][:min_len].unsqueeze(0)).item()\n", " cos_ds = F.cosine_similarity(\n", " vals[\"dinov2_b14\"][:min_len].unsqueeze(0),\n", " vals[\"siglip_b16\"][:min_len].unsqueeze(0)).item()\n", "\n", " print(f\" {frac:>5.0%} {cos_cd:>10.4f} {cos_cs:>12.4f} {cos_ds:>12.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: MLP WEIGHT SPECTRUM\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 7: MLP WEIGHT SPECTRUM\")\n", "print(f\"{'='*65}\")\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " mlp_weights = []\n", " for pname, p in model.named_parameters():\n", " if p.dim() == 2 and (\"fc1\" in pname or \"fc2\" in pname or\n", " (\"intermediate\" in pname and \"dense\" in pname and \"weight\" in pname) or\n", " (\"output\" in pname and \"dense\" in pname and \"weight\" in pname and \"attention\" not in pname)):\n", " mlp_weights.append((pname, p.detach().float()))\n", "\n", " print(f\"\\n {name} MLPs ({len(mlp_weights)} weight matrices):\")\n", " for pname, w in mlp_weights[:6]: # first 3 layers\n", " sv = torch.linalg.svdvals(w)\n", " eff_rank = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " short = pname.split(\".\")[-3] + \".\" + pname.split(\".\")[-2] + \".\" + pname.split(\".\")[-1]\n", " print(f\" {short:<40} {str(list(w.shape)):<20} \"\n", " f\"eff_rank={eff_rank:>6.1f}/{min(w.shape)} \"\n", " f\"sv_max={sv[0]:.3f} sv_10={sv[min(9,len(sv)-1)]:.4f}\")\n", "\n", " if len(mlp_weights) > 6:\n", " print(f\" ... ({len(mlp_weights) - 6} more)\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 8: POSITION EMBEDDING ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 8: POSITION EMBEDDINGS\")\n", "print(f\"{'='*65}\")\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " for pname, p in model.named_parameters():\n", " if \"position\" in pname.lower() and \"embed\" in pname.lower():\n", " pe = p.detach().float()\n", " print(f\"\\n {name}: {pname}\")\n", " print(f\" Shape: {list(pe.shape)}\")\n", " print(f\" Norm: {pe.norm():.4f} Mean: {pe.mean():.6f} Std: {pe.std():.6f}\")\n", "\n", " if pe.dim() >= 2:\n", " # Self-similarity of position embeddings\n", " if pe.dim() == 3:\n", " pe2d = pe.squeeze(0)\n", " else:\n", " pe2d = pe\n", " sim = F.cosine_similarity(pe2d.unsqueeze(0), pe2d.unsqueeze(1), dim=-1)\n", " print(f\" Self-sim: diag_mean={sim.diag().mean():.4f} \"\n", " f\"off_diag_mean={(sim.sum()-sim.diag().sum()).item()/(sim.numel()-sim.shape[0]):.4f}\")\n", " print(f\" Adjacent pos cos: mean={F.cosine_similarity(pe2d[:-1], pe2d[1:], dim=-1).mean():.4f}\")\n", "\n", " # SVD of position embeddings\n", " sv = torch.linalg.svdvals(pe2d)\n", " eff_rank = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " print(f\" Spectral: eff_rank={eff_rank:.1f}/{min(pe2d.shape)} \"\n", " f\"sv1%={sv[0].pow(2).item()/sv.pow(2).sum().item()*100:.1f}%\")\n", " break\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 9: LAYERNORM ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 9: LAYERNORM WEIGHT/BIAS PATTERNS\")\n", "print(f\"{'='*65}\")\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " ln_weights = []\n", " ln_biases = []\n", " for pname, p in model.named_parameters():\n", " if (\"norm\" in pname.lower() or \"layer_norm\" in pname.lower()):\n", " if \"weight\" in pname:\n", " ln_weights.append((pname, p.detach().float()))\n", " elif \"bias\" in pname:\n", " ln_biases.append((pname, p.detach().float()))\n", "\n", " print(f\"\\n {name} ({len(ln_weights)} LayerNorms):\")\n", " for (wn, w), (bn, b) in zip(ln_weights[:4], ln_biases[:4]):\n", " short = wn.split(\".\")[-3] + \".\" + wn.split(\".\")[-2]\n", " print(f\" {short:<30} w: mean={w.mean():.4f} std={w.std():.4f} \"\n", " f\"b: mean={b.mean():.5f} std={b.std():.4f}\")\n", "\n", " # Final LayerNorm\n", " if ln_weights:\n", " wn, w = ln_weights[-1]\n", " bn, b = ln_biases[-1] if ln_biases else (\"\", torch.zeros_like(w))\n", " print(f\" FINAL: {wn}\")\n", " print(f\" weight: mean={w.mean():.4f} std={w.std():.4f} \"\n", " f\"min={w.min():.4f} max={w.max():.4f}\")\n", " if ln_biases:\n", " print(f\" bias: mean={b.mean():.5f} std={b.std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 10: PENTACHORON CV ON WEIGHT GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 10: PENTACHORON CV ON WEIGHT GEOMETRY\")\n", "print(f\"{'='*65}\")\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_metric_on_weights(weight_matrix, n_samples=300):\n", " \"\"\"Measure pentachoron CV on rows of a weight matrix.\"\"\"\n", " w = F.normalize(weight_matrix.float(), dim=-1)\n", " N = w.shape[0]\n", " if N < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(N)[:5]\n", " v2 = cayley_menger_vol2(w[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "# CV on patch embedding filters\n", "print(f\"\\n Patch embedding filter CV (rows = output filters):\")\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " if name in patch_embeddings:\n", " p = patch_embeddings[name]\n", " w2d = p.reshape(p.shape[0], -1) # (n_filters, in*h*w)\n", " cv = cv_metric_on_weights(w2d)\n", " print(f\" {name:<15} filters={w2d.shape[0]} CV={cv:.4f}\")\n", "\n", "# CV on Q, K, V weight rows per layer\n", "print(f\"\\n QKV weight row CV per layer:\")\n", "print(f\" {'model':<15} {'layer':>6} {'Q_cv':>8} {'K_cv':>8} {'V_cv':>8} {'QK_diff':>9}\")\n", "\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " qkv = model_qkv[name]\n", " n_layers = len(qkv) // 3\n", "\n", " for layer_idx in range(n_layers):\n", " q = qkv[layer_idx * 3][\"weight\"]\n", " k = qkv[layer_idx * 3 + 1][\"weight\"]\n", " v = qkv[layer_idx * 3 + 2][\"weight\"]\n", "\n", " q_cv = cv_metric_on_weights(q, n_samples=200)\n", " k_cv = cv_metric_on_weights(k, n_samples=200)\n", " v_cv = cv_metric_on_weights(v, n_samples=200)\n", "\n", " if layer_idx < 2 or layer_idx >= n_layers - 2 or layer_idx == n_layers // 2:\n", " print(f\" {name:<15} {layer_idx:>6} {q_cv:>8.4f} {k_cv:>8.4f} \"\n", " f\"{v_cv:>8.4f} {abs(q_cv - k_cv):>9.4f}\")\n", " elif layer_idx == 2:\n", " print(f\" {name:<15} {'...':>6}\")\n", "\n", "# CV on MLP weight rows\n", "print(f\"\\n MLP weight row CV (first and last layers):\")\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " mlp_weights = []\n", " for pname, p in model.named_parameters():\n", " if p.dim() == 2 and (\"fc1\" in pname or \"fc2\" in pname or\n", " (\"intermediate\" in pname and \"dense\" in pname and \"weight\" in pname) or\n", " (\"output\" in pname and \"dense\" in pname and \"weight\" in pname and \"attention\" not in pname)):\n", " mlp_weights.append((pname, p.detach().float()))\n", "\n", " if mlp_weights:\n", " # First layer MLP\n", " pname, w = mlp_weights[0]\n", " cv_first = cv_metric_on_weights(w, n_samples=200)\n", " # Last layer MLP\n", " pname2, w2 = mlp_weights[-1]\n", " cv_last = cv_metric_on_weights(w2, n_samples=200)\n", " print(f\" {name:<15} first_mlp CV={cv_first:.4f} last_mlp CV={cv_last:.4f}\")\n", "\n", "# CV on position embeddings\n", "print(f\"\\n Position embedding CV:\")\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " for pname, p in model.named_parameters():\n", " if \"position\" in pname.lower() and \"embed\" in pname.lower():\n", " pe = p.detach().float()\n", " if pe.dim() == 3: pe = pe.squeeze(0)\n", " if pe.dim() == 2 and pe.shape[0] >= 5:\n", " cv = cv_metric_on_weights(pe, n_samples=300)\n", " print(f\" {name:<15} positions={pe.shape[0]} CV={cv:.4f}\")\n", " break\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 11: CROSS-MODEL CV COMPARISON (are they in the same CV band?)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 11: CROSS-MODEL CV BAND COMPARISON\")\n", "print(f\"{'='*65}\")\n", "\n", "# Collect all Q weight CVs per model\n", "print(f\"\\n Q weight CV distribution per model:\")\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " qkv = model_qkv[name]\n", " n_layers = len(qkv) // 3\n", " q_cvs = []\n", " k_cvs = []\n", " v_cvs = []\n", " for layer_idx in range(n_layers):\n", " q = qkv[layer_idx * 3][\"weight\"]\n", " k = qkv[layer_idx * 3 + 1][\"weight\"]\n", " v = qkv[layer_idx * 3 + 2][\"weight\"]\n", " q_cvs.append(cv_metric_on_weights(q, n_samples=200))\n", " k_cvs.append(cv_metric_on_weights(k, n_samples=200))\n", " v_cvs.append(cv_metric_on_weights(v, n_samples=200))\n", "\n", " q_arr = np.array(q_cvs)\n", " k_arr = np.array(k_cvs)\n", " v_arr = np.array(v_cvs)\n", " print(f\" {name:<15} Q: mean={q_arr.mean():.4f} std={q_arr.std():.4f} \"\n", " f\"range=[{q_arr.min():.4f}, {q_arr.max():.4f}]\")\n", " print(f\" {'':15} K: mean={k_arr.mean():.4f} std={k_arr.std():.4f} \"\n", " f\"range=[{k_arr.min():.4f}, {k_arr.max():.4f}]\")\n", " print(f\" {'':15} V: mean={v_arr.mean():.4f} std={v_arr.std():.4f} \"\n", " f\"range=[{v_arr.min():.4f}, {v_arr.max():.4f}]\")\n", "\n", " # Check for 0.20-0.23 band\n", " in_band_q = ((q_arr >= 0.18) & (q_arr <= 0.25)).sum()\n", " in_band_k = ((k_arr >= 0.18) & (k_arr <= 0.25)).sum()\n", " in_band_v = ((v_arr >= 0.18) & (v_arr <= 0.25)).sum()\n", " print(f\" {'':15} In CV band [0.18-0.25]: Q={in_band_q}/{n_layers} \"\n", " f\"K={in_band_k}/{n_layers} V={in_band_v}/{n_layers}\")\n", "\n", "# Cross-model: concatenate equivalent layer Q weights, measure CV\n", "print(f\"\\n Cross-model concatenated Q weight CV (same-depth rows mixed):\")\n", "name_pairs = [(\"clip_l14\", \"dinov2_b14\"), (\"clip_l14\", \"siglip_b16\"),\n", " (\"dinov2_b14\", \"siglip_b16\"), (\"clip_l14\", \"dinov2_b14\", \"siglip_b16\")]\n", "\n", "for pair in name_pairs:\n", " # Match by depth fraction\n", " pair_label = \" × \".join(n[:8] for n in pair)\n", " n_layers_per = [len(model_qkv[n]) // 3 for n in pair]\n", " min_layers = min(n_layers_per)\n", "\n", " cvs_at_depth = []\n", " for frac_idx in range(min_layers):\n", " rows = []\n", " for ni, n in enumerate(pair):\n", " n_total = n_layers_per[ni]\n", " # Map to equivalent depth\n", " layer_idx = int(frac_idx / min_layers * n_total)\n", " layer_idx = min(layer_idx, n_total - 1)\n", " q = model_qkv[n][layer_idx * 3][\"weight\"]\n", " rows.append(F.normalize(q.float(), dim=-1))\n", "\n", " # Truncate to common dim and concatenate\n", " d_min = min(r.shape[1] for r in rows)\n", " combined = torch.cat([r[:, :d_min] for r in rows], dim=0)\n", " cv = cv_metric_on_weights(combined, n_samples=200)\n", " cvs_at_depth.append(cv)\n", "\n", " arr = np.array(cvs_at_depth)\n", " print(f\" {pair_label:<35} mean={arr.mean():.4f} std={arr.std():.4f} \"\n", " f\"range=[{arr.min():.4f}, {arr.max():.4f}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"WEIGHT ANALYSIS COMPLETE — STARTING ACTIVATION ANALYSIS\")\n", "print(f\"{'='*65}\")\n", "\n", "# Free CPU models before GPU reload\n", "del models, configs\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 12: RUN IMAGES, EXTRACT PER-LAYER ACTIVATIONS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 12: PER-LAYER ACTIVATION EXTRACTION\")\n", "print(f\"{'='*65}\")\n", "\n", "from transformers import AutoImageProcessor\n", "from datasets import load_dataset\n", "from PIL import Image\n", "\n", "# Stream images — never downloads full dataset\n", "print(f\" Streaming images from rafaelpadilla/coco2017...\")\n", "coco_stream = load_dataset(\"rafaelpadilla/coco2017\", split=\"validation\",\n", " revision=\"refs/convert/parquet\", streaming=True)\n", "N_IMGS = 256 # enough for Procrustes, small enough for speed\n", "\n", "# Prepare processors\n", "processors = {\n", " \"clip_l14\": AutoImageProcessor.from_pretrained(\"openai/clip-vit-large-patch14\"),\n", " \"dinov2_b14\": AutoImageProcessor.from_pretrained(\"facebook/dinov2-base\"),\n", " \"siglip_b16\": AutoImageProcessor.from_pretrained(\"google/siglip-base-patch16-384\"),\n", "}\n", "\n", "# Reload models (were deleted in cleanup)\n", "from transformers import CLIPVisionModel, Dinov2Model, SiglipVisionModel\n", "models = {\n", " \"clip_l14\": CLIPVisionModel.from_pretrained(\"openai/clip-vit-large-patch14\").eval().to(DEVICE),\n", " \"dinov2_b14\": Dinov2Model.from_pretrained(\"facebook/dinov2-base\").eval().to(DEVICE),\n", " \"siglip_b16\": SiglipVisionModel.from_pretrained(\"google/siglip-base-patch16-384\").eval().to(DEVICE),\n", "}\n", "for m in models.values():\n", " for p in m.parameters():\n", " p.requires_grad = False\n", "\n", "# Collect images from stream\n", "images = []\n", "for row in coco_stream:\n", " if len(images) >= N_IMGS:\n", " break\n", " try:\n", " img = row[\"image\"].convert(\"RGB\")\n", " images.append(img)\n", " except:\n", " continue\n", "print(f\" Captured {len(images)} images (streamed)\")\n", "\n", "# Extract per-layer hidden states\n", "layer_activations = {} # {model_name: [layer0_cls, layer1_cls, ...]}\n", "pooled_outputs = {} # {model_name: (N, d)}\n", "\n", "EXTRACT_BATCH = 32\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " model = models[name]\n", " proc = processors[name]\n", " all_hidden = None\n", " all_pooled = []\n", "\n", " for bi in range(0, len(images), EXTRACT_BATCH):\n", " batch_imgs = images[bi:bi+EXTRACT_BATCH]\n", " inputs = proc(images=batch_imgs, return_tensors=\"pt\").to(DEVICE)\n", "\n", " with torch.no_grad():\n", " outputs = model(**inputs, output_hidden_states=True)\n", "\n", " hs = outputs.hidden_states # tuple of (B, seq, d) per layer\n", "\n", " if all_hidden is None:\n", " all_hidden = [[] for _ in range(len(hs))]\n", " for li, h in enumerate(hs):\n", " # CLS token (position 0) for each layer\n", " all_hidden[li].append(h[:, 0, :].cpu())\n", "\n", " # Final pooled output\n", " if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:\n", " all_pooled.append(outputs.pooler_output.cpu())\n", " else:\n", " all_pooled.append(hs[-1][:, 0, :].cpu())\n", "\n", " layer_activations[name] = [torch.cat(h, 0).float() for h in all_hidden]\n", " pooled_outputs[name] = F.normalize(torch.cat(all_pooled, 0).float(), dim=-1)\n", "\n", " n_layers = len(layer_activations[name])\n", " d = layer_activations[name][0].shape[-1]\n", " print(f\" {name}: {n_layers} layers, d={d}, N={layer_activations[name][0].shape[0]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 13: WITHIN-MODEL DEPTH PROGRESSION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 13: WITHIN-MODEL DEPTH PROGRESSION\")\n", "print(f\"{'='*65}\")\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_cos(source, target, n=None):\n", " \"\"\"Whitened Procrustes alignment, return pre and post cosine.\"\"\"\n", " if n is None: n = min(source.shape[0], target.shape[0])\n", " S = source[:n].float(); T = target[:n].float()\n", " sm = S.mean(0, keepdim=True); tm = T.mean(0, keepdim=True)\n", " Sc = S - sm; Tc = T - tm\n", " Ns = Sc.shape[0]\n", "\n", " # Raw cosine before alignment\n", " cos_pre = F.cosine_similarity(\n", " F.normalize(Sc, dim=-1), F.normalize(Tc, dim=-1), dim=-1).mean().item()\n", "\n", " # Whiten (higher eps for potentially degenerate layer activations)\n", " s_cov = (Sc.T @ Sc) / max(Ns-1, 1)\n", " t_cov = (Tc.T @ Tc) / max(Ns-1, 1)\n", " try:\n", " sw = symmetric_inv_sqrt(s_cov, eps=1e-4)\n", " tw = symmetric_inv_sqrt(t_cov, eps=1e-4)\n", " except:\n", " return cos_pre, cos_pre, torch.tensor([0.0])\n", "\n", " Sc_w = F.normalize(Sc @ sw, dim=-1)\n", " Tc_w = F.normalize(Tc @ tw, dim=-1)\n", "\n", " # Guard against non-finite values from whitening\n", " if not torch.isfinite(Sc_w).all() or not torch.isfinite(Tc_w).all():\n", " return cos_pre, cos_pre, torch.tensor([0.0])\n", "\n", " try:\n", " U, S_vals, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " except:\n", " return cos_pre, cos_pre, torch.tensor([0.0])\n", " R = U @ Vt\n", " cos_post = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", "\n", " return cos_pre, cos_post, S_vals\n", "\n", "print(f\"\\n Layer-to-layer Procrustes within each model (layer N vs layer N+1):\")\n", "for name in [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]:\n", " acts = layer_activations[name]\n", " n_layers = len(acts)\n", " print(f\"\\n {name} ({n_layers} layers):\")\n", " print(f\" {'L→L+1':>8} {'pre_cos':>8} {'post_cos':>9} {'sv_min':>8} {'sv_max':>8}\")\n", "\n", " for li in range(n_layers - 1):\n", " if li < 3 or li >= n_layers - 3 or li == n_layers // 2:\n", " pre, post, svs = procrustes_cos(acts[li], acts[li+1])\n", " print(f\" {li:>3}→{li+1:<3} {pre:>8.4f} {post:>9.4f} \"\n", " f\"{svs.min():.4f} {svs.max():.4f}\")\n", " elif li == 3:\n", " print(f\" {'...':>8}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 14: CROSS-MODEL PROCRUSTES AT EACH DEPTH\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 14: CROSS-MODEL PROCRUSTES (per depth fraction)\")\n", "print(f\"{'='*65}\")\n", "\n", "model_names = [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]\n", "n_layers_per = {n: len(layer_activations[n]) for n in model_names}\n", "\n", "print(f\"\\n Layers: clip={n_layers_per['clip_l14']} dino={n_layers_per['dinov2_b14']} \"\n", " f\"siglip={n_layers_per['siglip_b16']}\")\n", "\n", "# Compare at 11 depth fractions (0%, 10%, 20%, ..., 100%)\n", "fracs = [i/10 for i in range(11)]\n", "\n", "print(f\"\\n {'frac':>5} {'clip×dino':>10} {'clip×dino':>10} {'clip×sig':>10} \"\n", " f\"{'clip×sig':>10} {'dino×sig':>10} {'dino×sig':>10}\")\n", "print(f\" {'':>5} {'pre':>10} {'POST':>10} {'pre':>10} {'POST':>10} \"\n", " f\"{'pre':>10} {'POST':>10}\")\n", "print(f\" {'-'*67}\")\n", "\n", "for frac in fracs:\n", " results = {}\n", " for n in model_names:\n", " nl = n_layers_per[n]\n", " idx = min(int(frac * (nl - 1)), nl - 1)\n", " results[n] = layer_activations[n][idx]\n", "\n", " # Common dim for cross-model comparison — PCA to min dim\n", " dims = {n: results[n].shape[-1] for n in model_names}\n", " d_min = min(dims.values())\n", "\n", " projected = {}\n", " for n in model_names:\n", " if dims[n] == d_min:\n", " projected[n] = results[n]\n", " else:\n", " r = results[n].float()\n", " rc = r - r.mean(0, keepdim=True)\n", " # Use all samples for SVD, not just 200\n", " U, S, Vt = torch.linalg.svd(rc, full_matrices=False)\n", " # Vt shape: (min(N, d), d) — take top d_min components\n", " n_comp = min(d_min, Vt.shape[0])\n", " projected[n] = r @ Vt[:n_comp].T\n", "\n", " # Ensure all projected to same dim\n", " actual_dims = {n: projected[n].shape[-1] for n in model_names}\n", " d_common = min(actual_dims.values())\n", " for n in model_names:\n", " if projected[n].shape[-1] > d_common:\n", " projected[n] = projected[n][:, :d_common]\n", "\n", " pairs = [(\"clip_l14\", \"dinov2_b14\"), (\"clip_l14\", \"siglip_b16\"),\n", " (\"dinov2_b14\", \"siglip_b16\")]\n", "\n", " line = f\" {frac:>4.0%} \"\n", " for n1, n2 in pairs:\n", " pre, post, _ = procrustes_cos(projected[n1], projected[n2])\n", " line += f\" {pre:>9.4f} {post:>9.4f}\"\n", " print(line)\n", "\n", "# Final output comparison\n", "print(f\"\\n Final output (pooled, L2-normed) Procrustes:\")\n", "for n1 in model_names:\n", " for n2 in model_names:\n", " if n2 <= n1: continue\n", " d_min = min(pooled_outputs[n1].shape[1], pooled_outputs[n2].shape[1])\n", " p1 = pooled_outputs[n1][:, :d_min]\n", " p2 = pooled_outputs[n2][:, :d_min]\n", " pre, post, svs = procrustes_cos(p1, p2)\n", " print(f\" {n1} × {n2}: pre={pre:.4f} POST={post:.4f} \"\n", " f\"sv_range=[{svs.min():.4f}, {svs.max():.4f}]\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 15: CV ON ACTIVATIONS AT EACH DEPTH\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 15: ACTIVATION CV PER LAYER\")\n", "print(f\"{'='*65}\")\n", "\n", "def cv_metric_act(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " emb_n = F.normalize(emb.float(), dim=-1)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B)[:5]\n", " pts = emb_n[idx].unsqueeze(0)\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff*diff).sum(-1)\n", " Bv, V, _ = d2.shape\n", " cm = torch.zeros(Bv, V+1, V+1, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " v2 = s / ((2.0**(V-1))*f*f) * torch.linalg.det(cm)\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "print(f\"\\n {'model':<15} {'layer':>6} {'CV':>8} {'norm_μ':>8} {'norm_σ':>8} {'eff_dim':>8}\")\n", "print(f\" {'-'*55}\")\n", "\n", "for name in model_names:\n", " acts = layer_activations[name]\n", " n_layers = len(acts)\n", " for li in range(n_layers):\n", " if li < 2 or li >= n_layers - 2 or li == n_layers // 2 or li % 4 == 0:\n", " a = acts[li][:200]\n", " cv = cv_metric_act(a)\n", " norms = a.norm(dim=-1)\n", " centered = a - a.mean(0, keepdim=True)\n", " sv = torch.linalg.svdvals(centered)\n", " eff_dim = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " print(f\" {name:<15} {li:>6} {cv:>8.4f} {norms.mean():>8.3f} \"\n", " f\"{norms.std():>8.4f} {eff_dim:>8.1f}\")\n", " elif li == 2 and li < n_layers - 2:\n", " print(f\" {name:<15} {'...':>6}\")\n", " print()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 16: CROSS-MODEL ACTIVATION AGREEMENT (which images agree/disagree)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 16: PER-IMAGE AGREEMENT ANALYSIS\")\n", "print(f\"{'='*65}\")\n", "\n", "# Use final pooled outputs\n", "for n1 in model_names:\n", " for n2 in model_names:\n", " if n2 <= n1: continue\n", " d_min = min(pooled_outputs[n1].shape[1], pooled_outputs[n2].shape[1])\n", " p1 = F.normalize(pooled_outputs[n1][:, :d_min], dim=-1)\n", " p2 = F.normalize(pooled_outputs[n2][:, :d_min], dim=-1)\n", " per_image_cos = F.cosine_similarity(p1, p2, dim=-1)\n", " print(f\"\\n {n1} × {n2}:\")\n", " print(f\" Raw per-image cos: mean={per_image_cos.mean():.4f} \"\n", " f\"std={per_image_cos.std():.4f} \"\n", " f\"min={per_image_cos.min():.4f} max={per_image_cos.max():.4f}\")\n", "\n", " # After Procrustes\n", " pre, post, svs = procrustes_cos(\n", " pooled_outputs[n1][:, :d_min], pooled_outputs[n2][:, :d_min])\n", "\n", " # Distribution of agreement\n", " bins = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n", " hist = torch.histogram(per_image_cos.cpu(), bins=torch.tensor(bins))\n", " nonzero = [(f\"{bins[i]:.1f}-{bins[i+1]:.1f}\", int(hist.hist[i].item()))\n", " for i in range(len(hist.hist)) if hist.hist[i] > 0]\n", " print(f\" Distribution: {nonzero}\")\n", "\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"FULL ANALYSIS COMPLETE\")\n", "print(f\"{'='*65}\")\n", "\n", "# Clean up\n", "del models, layer_activations, pooled_outputs\n", "gc.collect()\n", "torch.cuda.empty_cache()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "930e46eb960543b6be7782ee59911535", "3700ea8e34ee4c5092c2e411cd7fb19f", "30b01cc47b9949518af6101b4c7b76c2", "4a59e172600744a39a1e1ffcb5cec263", "76461c3ba80d405a81bacfe93d844730", "30d03d2889904d76af2a83d713510814", "72649376cf304b3a81282c958017064d", "9e8db2f322ae4cc8a78ea842ac7118cc", "2b7703dd08c8412b9eaa125db2629e11", "f86f7f1cef67429096d58e496573c684", "4ec9925c26844f9d92759e4c686fa79d", "ff2610c213b7485cbe114405c38b7b30", "3ee507b4cc2141aeaea5182c42d0577b", "6221dd7c486f454f855fbff24291f855", "ff8405c7658041bb9acab97debc6f642", "b0a7e1f0a8184bfcbb7cc232164e90d6", "787dc8da48304c5a9b5c87e4c32edb56", "91a15dd8bfda4f578f2f360b3a058fe7", "fd6297a226484e03aa522a6ddb64c59c", "fe9f36dfb4c949dab5e9ecd2328c9f97", "f3cfaecdba17471b8ee2e88e149bd776", "7acead6d6a824ec79ad0340ac0d904a8", "144b872d096645119e05d12b8ac6a736", "79ddd1d82d49466b869af5c672781de2", "31c6fdcaa6db4659a0a1ad9d6ee668bf", "c26d4b58bb0040e1b350a811ad69dadd", "737da44e4ab045689a89f850a506adf3", "3d540aaa69d44760b3767573325b538e", "ca0fe460c62b4365926824e936fd07cf", "d244d3a2024a4791b7e5accb93f616fc", "c823709343fe41e6bcc87e462d4158f9", "3ba6191e8bd74f8b823e3c02e47105a5", "9c21c6305a29406f80f1aecaec8c0317", "e504ee4fef894c83ae2ebeb9bc3d8d6c", "b78588a48d604d108efd699a550c61ac", "5889dea42ee24f55873a10d70969e5ea", "42608b9dd5434144a2d43da5b5eedae9", "7ca5ed3814a94ef38ba967b19850eaa7", "20f076b9510749b0983deedcef768063", "1436a05830cd4297adc520ddddebe350", "1cc28cb4cbb04a91bcaacc3a2ee38156", "bad87341466742b398839d010ccc8f33", "6475713188c04873bc430f748357861d", "e870ee57084744ca88f6c15947dd9ddd", "83bb59d3049742f69a90dfb83830add2", "182e95cfe9cb4098835a15b86b11bd3f", "2381658f77a4499ebdf7a9286ab240b7", "8e2ffa30a6144c04a05e8aea5fb4c53c", "8b54daee01d945e985c87697de59ebbd", "1260f3a2cf4b41a19425b2fc6eb7b3ea", "af1d203c43174c078b3fbcaa4d12e0b7", "2632c395c00d4376ab6fbe639cecb24e", "24c433e1be9841e297e3984bda676479", "0a31b2ecca5b475395d885d6f1bd602c", "8f57290767504260969e9322b9aa087d", "8cc1c97e06034f308c53d82d9371f26a", "b68abcb8f17c433b8309b87c23086c6e", "aec3ab46dd6c4b6da8d47688c1162b7a", "fa8c39b693d247f2be76158d5ca1bf07", "438452e881864f6bb54b27e79fff316a", "ad4fb76a0810481e92f6f092fbd82547", "3f91611d524747208325934633edec91", "d12aece5ebd24fd48c64e7dedf747d84", "7b09b5a9e6f84ab193ba4894363d7fb2", "e27fc648c6b64e858dd7a0529ddf07e1", "e637270a89e6432b95b9efbda6f49ed7", "81adb95a2dc140feb85931297ea9fe89", "b01a0af87f7142d8abf25badef64de6a", "884bd0f77783428c8c3b9f22d3a51487", "486340f5ccdc4aea91bb795608509eae", "dac181bda04f4be6a050c1cd4d5f7cd8", "8f64083fe0de46afa8ec63cfee93f85d", "fe12ee95395548199469ad5369cfa171", "3a8ebf346b054b7f843ba9df8fd42b28", "c2fe3cd34aed4c298d3eadf934ddc53c", "4964c142a2644d469d04559688950b37", "cf4df97015e246e38b71d7dab8f33591" ] }, "id": "acZ9ufHXUwu5", "outputId": "260fd975-2b7f-477f-e3c2-e58be02c3faa" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BASE TIER DEEP MODEL ANALYSIS\n", "=================================================================\n", " Device: cuda\n", "\n", "=================================================================\n", "LOADING MODELS\n", "=================================================================\n", "\n", " Loading CLIP ViT-L/14...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/391 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = torch.tensor(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a = F.normalize(anchors, dim=-1)\n", " sim = a @ a.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ExpertProjector(nn.Module):\n", " \"\"\"768-d → 128-d, L2-normalized onto hypersphere.\"\"\"\n", " def __init__(self, d_in=D_EXPERT, d_out=D_ANCHOR):\n", " super().__init__()\n", " self.proj = nn.Sequential(\n", " nn.Linear(d_in, d_out),\n", " nn.LayerNorm(d_out),\n", " )\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(\n", " torch.randn(n_anchors, d), dim=-1))\n", "\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for k in range(n_comp)])\n", "\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "\n", "class BaseTierSoup(nn.Module):\n", " \"\"\"\n", " 3-expert soup on 128-d hypersphere.\n", "\n", " Each expert: 768-d → projector → 128-d (on sphere)\n", " Per-image: 3 projected embeddings → mean → on sphere\n", " Constellation: 256 anchors at 128-d\n", " Patchwork: 8 compartments → classifier\n", "\n", " The projectors learn to place each expert's perspective\n", " into the shared 128-d anchor space. The constellation\n", " crystallizes through geometric autograd.\n", " \"\"\"\n", " def __init__(self, n_experts=3, d_expert=D_EXPERT, d_anchor=D_ANCHOR,\n", " n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP,\n", " n_classes=N_CLASSES):\n", " super().__init__()\n", " self.n_experts = n_experts\n", " self.d_anchor = d_anchor\n", "\n", " # Per-expert projection to anchor space\n", " self.projectors = nn.ModuleList([\n", " ExpertProjector(d_expert, d_anchor) for _ in range(n_experts)])\n", "\n", " # Geometric pipeline\n", " self.constellation = Constellation(n_anchors, d_anchor)\n", " self.patchwork = Patchwork(n_anchors, n_comp, d_comp)\n", "\n", " # Classifier\n", " pw_dim = n_comp * d_comp\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + d_anchor, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim),\n", " nn.Dropout(0.1),\n", " nn.Linear(pw_dim, n_classes))\n", "\n", " def forward(self, expert_embeddings, apply_autograd=True):\n", " \"\"\"\n", " expert_embeddings: list of (B, 768) tensors, one per expert\n", " \"\"\"\n", " # Project each expert to 128-d hypersphere\n", " projected = [self.projectors[i](expert_embeddings[i])\n", " for i in range(self.n_experts)]\n", "\n", " # Fuse: mean on hypersphere (normalize after averaging)\n", " fused = F.normalize(sum(projected) / self.n_experts, dim=-1)\n", "\n", " # Geometric autograd\n", " if apply_autograd and self.training:\n", " fused = EmbeddingAutograd.apply(\n", " fused, fused, self.constellation.anchors, 0.01, 1.0)\n", "\n", " # Triangulate + patchwork\n", " tri, nearest = self.constellation.triangulate(fused)\n", " pw = self.patchwork(tri)\n", "\n", " # Classify\n", " logits = self.classifier(torch.cat([pw, fused], -1))\n", "\n", " return logits, fused, tri, nearest, projected\n", "\n", " def count_params(self):\n", " proj = sum(sum(p.numel() for p in pr.parameters()) for pr in self.projectors)\n", " const = sum(p.numel() for p in self.constellation.parameters())\n", " pw = sum(p.numel() for p in self.patchwork.parameters())\n", " cls = sum(p.numel() for p in self.classifier.parameters())\n", " return {\"projectors\": proj, \"constellation\": const,\n", " \"patchwork\": pw, \"classifier\": cls,\n", " \"total\": proj + const + pw + cls}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"LOADING DATA\")\n", "print(f\"{'='*65}\")\n", "\n", "from datasets import load_dataset\n", "\n", "# Reference for image_ids and labels\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"train\")\n", "train_ids = ref[\"image_id\"]; N_train = len(train_ids)\n", "train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", "train_labels_raw = ref[\"labels\"]\n", "train_label_matrix = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(train_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES: train_label_matrix[i, l] = 1.0\n", "\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels_raw = ref_val[\"labels\"]\n", "val_label_matrix = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(val_labels_raw):\n", " for l in labs:\n", " if l < N_CLASSES: val_label_matrix[i, l] = 1.0\n", "\n", "print(f\" Train: {N_train:,} Val: {N_val:,}\")\n", "\n", "# Load 3 experts\n", "train_feats = []\n", "val_feats = []\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " feats = torch.zeros(N_train, D_EXPERT)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_map:\n", " feats[train_id_map[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " train_feats.append(feats)\n", "\n", " ds_v = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats_v = torch.zeros(N_val, D_EXPERT)\n", " for row in ds_v:\n", " if row[\"image_id\"] in val_id_map:\n", " feats_v[val_id_map[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " val_feats.append(feats_v)\n", " print(f\" {name:<30} loaded\", flush=True)\n", " del ds, ds_v; gc.collect()\n", "\n", "# Move val to GPU\n", "val_feats_gpu = [f.to(DEVICE) for f in val_feats]\n", "val_labels_gpu = val_label_matrix.to(DEVICE)\n", "train_labels_gpu = train_label_matrix.to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILDING MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = BaseTierSoup(\n", " n_experts=3, d_expert=D_EXPERT, d_anchor=D_ANCHOR,\n", " n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP,\n", " n_classes=N_CLASSES).to(DEVICE)\n", "\n", "params = model.count_params()\n", "print(f\" Parameters:\")\n", "for k, v in params.items():\n", " print(f\" {k:<15}: {v:>10,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Adam, no weight decay (geometry IS the regularization)\")\n", "print(f\"{'='*65}\")\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "best_mAP = 0.0\n", "\n", "from torch.utils.tensorboard import SummaryWriter\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "writer = SummaryWriter(\"runs/base_tier_soup\")\n", "gs = 0\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train)\n", " tl, nb = 0, 0\n", "\n", " for i in range(0, N_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " # Move batch to GPU\n", " batch_experts = [train_feats[e][idx].to(DEVICE) for e in range(3)]\n", " labels = train_labels_gpu[idx]\n", "\n", " logits, fused, tri, nearest, projected = model(batch_experts)\n", " anchors = model.constellation.anchors\n", "\n", " # Classification\n", " l_cls = F.binary_cross_entropy_with_logits(logits, labels)\n", "\n", " # Geometric losses\n", " l_cv = cv_loss(fused, target=0.2)\n", " l_spread = anchor_spread_loss(anchors)\n", " l_ent = anchor_entropy_loss(fused, anchors)\n", "\n", " # Per-expert agreement: all projections should be close\n", " l_agree = 0.0\n", " for pi in range(3):\n", " for pj in range(pi+1, 3):\n", " l_agree += (1.0 - F.cosine_similarity(\n", " projected[pi], projected[pj], dim=-1)).mean()\n", " l_agree = l_agree / 3.0 # 3 pairs\n", "\n", " loss = (l_cls\n", " + 0.001 * l_cv\n", " + 1e-3 * l_spread\n", " + 1e-4 * l_ent\n", " + 0.1 * l_agree)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " tl += loss.item(); nb += 1; gs += 1\n", "\n", " if gs % 100 == 0:\n", " writer.add_scalar(\"train/loss\", loss.item(), gs)\n", " writer.add_scalar(\"train/cls\", l_cls.item(), gs)\n", " writer.add_scalar(\"train/cv\", l_cv.item(), gs)\n", " writer.add_scalar(\"train/agree\", l_agree, gs)\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " all_lo, all_em = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " batch_v = [val_feats_gpu[e][j:end] for e in range(3)]\n", " lo, em, _, _, _ = model(batch_v, apply_autograd=False)\n", " all_lo.append(lo.cpu()); all_em.append(em.cpu())\n", " v_lo = torch.cat(all_lo); v_em = torch.cat(all_em)\n", "\n", " # mAP\n", " v_lab = val_label_matrix\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = v_lo[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st)+1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " # F1\n", " vp = (v_lo.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1-v_lab)).sum(0)\n", " fn = ((1-vp) * v_lab).sum(0)\n", " pr = tp/(tp+fp+1e-8); rc = tp/(tp+fn+1e-8)\n", " f1 = 2*pr*rc/(pr+rc+1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " v_cv = cv_metric(v_em)\n", "\n", " # Expert agreement\n", " all_proj = []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " batch_v = [val_feats_gpu[e][j:end] for e in range(3)]\n", " _, _, _, _, proj = model(batch_v, apply_autograd=False)\n", " all_proj.append([p.cpu() for p in proj])\n", " proj_stacked = [torch.cat([ap[e] for ap in all_proj]) for e in range(3)]\n", " agree_01 = F.cosine_similarity(proj_stacked[0], proj_stacked[1], dim=-1).mean().item()\n", " agree_02 = F.cosine_similarity(proj_stacked[0], proj_stacked[2], dim=-1).mean().item()\n", " agree_12 = F.cosine_similarity(proj_stacked[1], proj_stacked[2], dim=-1).mean().item()\n", "\n", " writer.add_scalar(\"val/mAP\", mAP, epoch+1)\n", " writer.add_scalar(\"val/F1\", macro_f1, epoch+1)\n", " writer.add_scalar(\"val/cv\", v_cv, epoch+1)\n", " writer.add_scalar(\"val/agree_clip_dino\", agree_01, epoch+1)\n", " writer.add_scalar(\"val/agree_clip_siglip\", agree_02, epoch+1)\n", " writer.add_scalar(\"val/agree_dino_siglip\", agree_12, epoch+1)\n", "\n", " mk = \"\"\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\n", " \"state_dict\": model.state_dict(),\n", " \"config\": {\"d_expert\": D_EXPERT, \"d_anchor\": D_ANCHOR,\n", " \"n_anchors\": N_ANCHORS, \"n_comp\": N_COMP,\n", " \"d_comp\": D_COMP, \"n_classes\": N_CLASSES,\n", " \"experts\": EXPERTS},\n", " \"epoch\": epoch+1, \"mAP\": mAP, \"cv\": v_cv,\n", " }, \"checkpoints/base_tier_best.pt\")\n", " mk = \" ★\"\n", "\n", " print(f\" E{epoch+1:2d}: mAP={mAP:.3f} F1={macro_f1:.3f} cv={v_cv:.4f} \"\n", " f\"agree=[{agree_01:.3f},{agree_02:.3f},{agree_12:.3f}] \"\n", " f\"loss={tl/nb:.4f}{mk}\")\n", "\n", "writer.close()\n", "\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Model: {params['total']:,} params\")\n", "print(f\" Anchors: {N_ANCHORS} × {D_ANCHOR}-d\")\n", "print(f\"\\n{'='*65}\")\n", "print(\"DONE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SXF4dKU8j0rL", "outputId": "632d1efb-ccb6-42ed-d370-f9f38da9e4d9" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BASE TIER PATCHWORK SOUP\n", " 3 experts × 768-d → 256 anchors × 128-d\n", " Device: cuda\n", "=================================================================\n", "\n", "=================================================================\n", "LOADING DATA\n", "=================================================================\n", " Train: 118,287 Val: 5,000\n", " clip_l14_openai loaded\n", " dinov2_b14 loaded\n", " siglip_b16_384 loaded\n", "\n", "=================================================================\n", "BUILDING MODEL\n", "=================================================================\n", " Parameters:\n", " projectors : 296,064\n", " constellation : 32,768\n", " patchwork : 100,864\n", " classifier : 370,256\n", " total : 799,952\n", "\n", "=================================================================\n", "TRAINING\n", " 20 epochs, lr=0.001, batch=128\n", " Adam, no weight decay (geometry IS the regularization)\n", "=================================================================\n", " E 1: mAP=0.732 F1=0.609 cv=0.4301 agree=[0.977,0.986,0.974] loss=0.0845 ★\n", " E 2: mAP=0.768 F1=0.679 cv=0.4308 agree=[0.982,0.988,0.980] loss=0.0521 ★\n", " E 3: mAP=0.783 F1=0.726 cv=0.4158 agree=[0.984,0.988,0.981] loss=0.0485 ★\n", " E 4: mAP=0.787 F1=0.714 cv=0.3889 agree=[0.986,0.989,0.982] loss=0.0465 ★\n", " E 5: mAP=0.799 F1=0.742 cv=0.3760 agree=[0.987,0.989,0.983] loss=0.0453 ★\n", " E 6: mAP=0.805 F1=0.738 cv=0.3849 agree=[0.987,0.989,0.983] loss=0.0444 ★\n", " E 7: mAP=0.804 F1=0.737 cv=0.4140 agree=[0.988,0.989,0.984] loss=0.0437\n", " E 8: mAP=0.806 F1=0.748 cv=0.3945 agree=[0.989,0.989,0.984] loss=0.0432 ★\n", " E 9: mAP=0.807 F1=0.735 cv=0.3855 agree=[0.988,0.989,0.984] loss=0.0427 ★\n", " E10: mAP=0.809 F1=0.751 cv=0.3396 agree=[0.989,0.989,0.985] loss=0.0423 ★\n", " E11: mAP=0.815 F1=0.746 cv=0.4014 agree=[0.989,0.989,0.985] loss=0.0417 ★\n", " E12: mAP=0.812 F1=0.729 cv=0.3759 agree=[0.989,0.989,0.985] loss=0.0414\n", " E13: mAP=0.818 F1=0.750 cv=0.3313 agree=[0.990,0.990,0.986] loss=0.0411 ★\n", " E14: mAP=0.817 F1=0.742 cv=0.3890 agree=[0.990,0.989,0.985] loss=0.0408\n", " E15: mAP=0.818 F1=0.749 cv=0.3872 agree=[0.990,0.989,0.985] loss=0.0405 ★\n", " E16: mAP=0.816 F1=0.746 cv=0.3667 agree=[0.990,0.990,0.986] loss=0.0402\n", " E17: mAP=0.824 F1=0.753 cv=0.3517 agree=[0.990,0.989,0.985] loss=0.0399 ★\n", " E18: mAP=0.818 F1=0.747 cv=0.3221 agree=[0.990,0.990,0.986] loss=0.0397\n", " E19: mAP=0.825 F1=0.746 cv=0.3117 agree=[0.990,0.989,0.985] loss=0.0396 ★\n", " E20: mAP=0.816 F1=0.738 cv=0.3320 agree=[0.990,0.990,0.986] loss=0.0392\n", "\n", " Best mAP: 0.825\n", " Model: 799,952 params\n", " Anchors: 256 × 128-d\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "markdown", "source": [ "## analyze the soup" ], "metadata": { "id": "0MmgwSGMqTvv" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "BASE TIER SOUP ANALYSIS\n", "========================\n", "Load the trained 800K param soup and examine:\n", " - Anchor geometry on the 128-d hypersphere\n", " - Projector alignment (do the 3 experts converge?)\n", " - Triangulation patterns (which anchors are used?)\n", " - Patchwork compartment activation profiles\n", " - Per-expert projected distributions\n", " - CV and volume geometry of the learned space\n", " - Per-class anchor affinity (which anchors serve which COCO classes?)\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "D_EXPERT = 768\n", "D_ANCHOR = 128\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 64\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "print(\"=\" * 65)\n", "print(\"BASE TIER SOUP ANALYSIS\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD MODEL + DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "# Rebuild model class (minimal, for loading)\n", "class ExpertProjector(nn.Module):\n", " def __init__(self, d_in=D_EXPERT, d_out=D_ANCHOR):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(d_in, d_out), nn.LayerNorm(d_out))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for k in range(n_comp)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "class BaseTierSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.n_experts = 3\n", " self.projectors = nn.ModuleList([ExpertProjector() for _ in range(3)])\n", " self.constellation = Constellation()\n", " self.patchwork = Patchwork()\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.1),\n", " nn.Linear(pw_dim, N_CLASSES))\n", " def forward(self, expert_embeddings, apply_autograd=False):\n", " projected = [self.projectors[i](expert_embeddings[i]) for i in range(3)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", " tri, nearest = self.constellation.triangulate(fused)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, fused], -1))\n", " return logits, fused, tri, nearest, projected\n", "\n", "print(f\"\\n Loading checkpoint...\")\n", "ckpt = torch.load(\"checkpoints/base_tier_best.pt\", map_location=\"cpu\", weights_only=False)\n", "model = BaseTierSoup()\n", "model.load_state_dict(ckpt[\"state_dict\"])\n", "model = model.eval().to(DEVICE)\n", "print(f\" Loaded: mAP={ckpt['mAP']:.3f} cv={ckpt['cv']:.4f} epoch={ckpt['epoch']}\")\n", "\n", "# Load val data\n", "from datasets import load_dataset\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: val_labels[i, l] = 1.0\n", "\n", "val_feats = []\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, D_EXPERT)\n", " for row in ds:\n", " if row[\"image_id\"] in id_map:\n", " feats[id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_feats.append(feats.to(DEVICE))\n", " print(f\" {name} loaded\")\n", " del ds; gc.collect()\n", "\n", "# Run full val through model\n", "print(f\"\\n Running inference on {N_val} val images...\")\n", "all_logits, all_fused, all_tri, all_nearest, all_proj = [], [], [], [], [[], [], []]\n", "BATCH = 256\n", "with torch.no_grad():\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " batch = [val_feats[e][j:end] for e in range(3)]\n", " lo, fu, tr, ne, pr = model(batch)\n", " all_logits.append(lo.cpu())\n", " all_fused.append(fu.cpu())\n", " all_tri.append(tr.cpu())\n", " all_nearest.append(ne.cpu())\n", " for e in range(3):\n", " all_proj[e].append(pr[e].cpu())\n", "\n", "logits = torch.cat(all_logits)\n", "fused = torch.cat(all_fused)\n", "tri = torch.cat(all_tri)\n", "nearest = torch.cat(all_nearest)\n", "proj = [torch.cat(all_proj[e]) for e in range(3)]\n", "print(f\" Done: fused={fused.shape} tri={tri.shape}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 1: ANCHOR GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 1: ANCHOR GEOMETRY\")\n", "print(f\"{'='*65}\")\n", "\n", "anchors = F.normalize(model.constellation.anchors.detach().cpu(), dim=-1)\n", "\n", "# Pairwise cosine\n", "anchor_sim = anchors @ anchors.T\n", "anchor_sim.fill_diagonal_(0)\n", "\n", "print(f\" Anchor pairwise cosine:\")\n", "print(f\" mean={anchor_sim.mean():.4f} std={anchor_sim.std():.4f}\")\n", "print(f\" max={anchor_sim.max():.4f} min={anchor_sim.min():.4f}\")\n", "\n", "# Distribution of max-neighbor cosine\n", "max_neighbor = anchor_sim.max(dim=1).values\n", "print(f\" Max neighbor cosine per anchor:\")\n", "print(f\" mean={max_neighbor.mean():.4f} std={max_neighbor.std():.4f}\")\n", "print(f\" max={max_neighbor.max():.4f} min={max_neighbor.min():.4f}\")\n", "\n", "# Anchor norms (should be ~1.0 after normalize)\n", "anchor_norms = anchors.norm(dim=-1)\n", "print(f\" Anchor norms: mean={anchor_norms.mean():.6f} std={anchor_norms.std():.6f}\")\n", "\n", "# SVD of anchor matrix\n", "sv = torch.linalg.svdvals(anchors)\n", "eff_rank = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", "print(f\" Anchor spectral: eff_rank={eff_rank:.1f}/{min(anchors.shape)}\")\n", "print(f\" sv_max={sv[0]:.4f} sv_10={sv[9]:.4f} sv_50={sv[49]:.4f} sv_min={sv[-1]:.6f}\")\n", "\n", "# Volume CV of anchors\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "vols = []\n", "for _ in range(500):\n", " idx = torch.randperm(N_ANCHORS)[:5]\n", " v2 = cayley_menger_vol2(anchors[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", "anchor_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", "print(f\" Anchor pentachoron CV: {anchor_cv:.4f}\")\n", "print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: ANCHOR UTILIZATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 2: ANCHOR UTILIZATION\")\n", "print(f\"{'='*65}\")\n", "\n", "# How many images use each anchor as nearest\n", "anchor_counts = torch.bincount(nearest, minlength=N_ANCHORS).float()\n", "active = (anchor_counts > 0).sum().item()\n", "print(f\" Active anchors: {active}/{N_ANCHORS} ({active/N_ANCHORS*100:.1f}%)\")\n", "print(f\" Visit counts: mean={anchor_counts.mean():.1f} std={anchor_counts.std():.1f}\")\n", "print(f\" max={anchor_counts.max():.0f} min={anchor_counts.min():.0f}\")\n", "print(f\" top 10: {anchor_counts.topk(10).values.long().tolist()}\")\n", "print(f\" bottom 10: {anchor_counts.sort().values[:10].long().tolist()}\")\n", "\n", "# Entropy of anchor distribution\n", "probs = anchor_counts / anchor_counts.sum()\n", "entropy = -(probs[probs > 0] * probs[probs > 0].log()).sum().item()\n", "max_entropy = math.log(N_ANCHORS)\n", "print(f\" Anchor entropy: {entropy:.4f} / {max_entropy:.4f} ({entropy/max_entropy*100:.1f}%)\")\n", "\n", "# Per-anchor mean cosine to fused embeddings\n", "print(f\"\\n Per-anchor embedding density:\")\n", "anchor_mean_cos = []\n", "for a_idx in range(N_ANCHORS):\n", " mask = nearest == a_idx\n", " if mask.sum() < 2:\n", " anchor_mean_cos.append(0.0)\n", " continue\n", " cluster_embs = fused[mask]\n", " mean_cos = F.cosine_similarity(\n", " cluster_embs.unsqueeze(0), cluster_embs.unsqueeze(1), dim=-1)\n", " mean_cos.fill_diagonal_(0)\n", " n = cluster_embs.shape[0]\n", " avg = mean_cos.sum().item() / max(n * (n-1), 1)\n", " anchor_mean_cos.append(avg)\n", "amc = np.array(anchor_mean_cos)\n", "print(f\" Intra-cluster cosine: mean={amc[amc>0].mean():.4f} std={amc[amc>0].std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: PROJECTOR ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 3: PROJECTOR ANALYSIS\")\n", "print(f\"{'='*65}\")\n", "\n", "expert_names = [\"clip_l14\", \"dinov2_b14\", \"siglip_b16\"]\n", "\n", "# Per-expert projection stats\n", "for e, name in enumerate(expert_names):\n", " p = proj[e]\n", " print(f\"\\n {name}:\")\n", " print(f\" norm: mean={p.norm(dim=-1).mean():.6f} (should be 1.0)\")\n", " print(f\" self-sim off-diag: {(F.normalize(p,dim=-1) @ F.normalize(p,dim=-1).T).fill_diagonal_(0).mean():.4f}\")\n", "\n", " # SVD of projected embeddings\n", " pc = p.float() - p.float().mean(0, keepdim=True)\n", " sv = torch.linalg.svdvals(pc)\n", " eff_dim = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " print(f\" eff_dim: {eff_dim:.1f}/{D_ANCHOR}\")\n", "\n", "# Pairwise agreement\n", "print(f\"\\n Expert agreement (cosine in 128-d):\")\n", "for i in range(3):\n", " for j in range(i+1, 3):\n", " cos = F.cosine_similarity(proj[i], proj[j], dim=-1)\n", " print(f\" {expert_names[i]:<15} × {expert_names[j]:<15}: \"\n", " f\"mean={cos.mean():.4f} std={cos.std():.4f} min={cos.min():.4f}\")\n", "\n", "# How different are the nearest anchors per expert?\n", "print(f\"\\n Per-expert nearest anchor agreement:\")\n", "expert_nearest = []\n", "for e in range(3):\n", " a = F.normalize(anchors, dim=-1)\n", " cos = proj[e] @ a.T\n", " en = cos.argmax(dim=-1)\n", " expert_nearest.append(en)\n", "for i in range(3):\n", " for j in range(i+1, 3):\n", " agree = (expert_nearest[i] == expert_nearest[j]).float().mean().item()\n", " print(f\" {expert_names[i]:<15} × {expert_names[j]:<15}: \"\n", " f\"same_anchor={agree:.4f} ({agree*100:.1f}%)\")\n", "\n", "# Projector weight analysis\n", "print(f\"\\n Projector weight comparison:\")\n", "proj_weights = []\n", "for e in range(3):\n", " w = model.projectors[e].proj[0].weight.detach().float() # (128, 768)\n", " proj_weights.append(w)\n", " sv = torch.linalg.svdvals(w)\n", " eff_r = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", " print(f\" {expert_names[e]:<15}: norm={w.norm():.4f} eff_rank={eff_r:.1f}/{min(w.shape)}\")\n", "\n", "# Cross-projector cosine\n", "for i in range(3):\n", " for j in range(i+1, 3):\n", " cos = F.cosine_similarity(\n", " proj_weights[i].reshape(-1).unsqueeze(0),\n", " proj_weights[j].reshape(-1).unsqueeze(0)).item()\n", " print(f\" {expert_names[i]:<15} × {expert_names[j]:<15} weight_cos={cos:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: PATCHWORK COMPARTMENT ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 4: PATCHWORK COMPARTMENTS\")\n", "print(f\"{'='*65}\")\n", "\n", "# Which anchors are in which compartment\n", "asgn = model.patchwork.asgn.cpu()\n", "for k in range(N_COMP):\n", " anchor_ids = (asgn == k).nonzero(as_tuple=True)[0]\n", " print(f\" Comp {k}: {len(anchor_ids)} anchors\")\n", "\n", "# Patchwork output analysis\n", "with torch.no_grad():\n", " pw_all = []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " pw = model.patchwork(tri[j:end].to(DEVICE))\n", " pw_all.append(pw.cpu())\n", " pw_cat = torch.cat(pw_all)\n", "\n", "print(f\"\\n Patchwork output: {pw_cat.shape}\")\n", "print(f\" norm: mean={pw_cat.norm(dim=-1).mean():.4f} std={pw_cat.norm(dim=-1).std():.4f}\")\n", "\n", "# Per-compartment output magnitude\n", "for k in range(N_COMP):\n", " comp_out = pw_cat[:, k*D_COMP:(k+1)*D_COMP]\n", " print(f\" comp {k}: norm={comp_out.norm(dim=-1).mean():.4f} \"\n", " f\"std_across_dims={comp_out.std(dim=0).mean():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: TRIANGULATION PATTERN ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 5: TRIANGULATION PATTERNS\")\n", "print(f\"{'='*65}\")\n", "\n", "# Triangulation distance stats\n", "print(f\" Triangulation distances (1-cosine):\")\n", "print(f\" mean={tri.mean():.4f} std={tri.std():.4f}\")\n", "print(f\" min={tri.min():.4f} max={tri.max():.4f}\")\n", "\n", "# Nearest anchor distance\n", "nearest_dist = tri.gather(1, nearest.unsqueeze(1)).squeeze(1)\n", "print(f\" Nearest anchor distance:\")\n", "print(f\" mean={nearest_dist.mean():.4f} std={nearest_dist.std():.4f}\")\n", "print(f\" max={nearest_dist.max():.4f} min={nearest_dist.min():.4f}\")\n", "\n", "# How many anchors are \"close\" (cosine > 0.5, i.e. dist < 0.5)\n", "close_count = (tri < 0.5).float().sum(dim=1)\n", "print(f\" Anchors within cos>0.5 per image:\")\n", "print(f\" mean={close_count.mean():.1f} std={close_count.std():.1f}\")\n", "\n", "# Top-k nearest anchors — how spread are they?\n", "topk_dists = tri.topk(10, dim=1, largest=False)\n", "print(f\" Top-10 nearest anchor distances:\")\n", "for k_idx in range(10):\n", " d = topk_dists.values[:, k_idx]\n", " print(f\" k={k_idx}: mean={d.mean():.4f} std={d.std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 6: PER-CLASS ANCHOR AFFINITY\")\n", "print(f\"{'='*65}\")\n", "\n", "# COCO class names (subset)\n", "coco_names = [\"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\",\n", " \"bus\", \"train\", \"truck\", \"boat\", \"traffic light\",\n", " \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\", \"bird\",\n", " \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\"]\n", "\n", "# For each class, which anchors are most associated?\n", "print(f\"\\n Top-3 anchors per class (first 20 classes):\")\n", "for c in range(min(20, N_CLASSES)):\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() < 5: continue\n", " class_nearest = nearest[mask]\n", " counts = torch.bincount(class_nearest, minlength=N_ANCHORS)\n", " top3 = counts.topk(3)\n", " name = coco_names[c] if c < len(coco_names) else f\"class_{c}\"\n", " total = mask.sum().item()\n", " pcts = [f\"{top3.indices[k]}({top3.values[k].item()}/{total})\" for k in range(3)]\n", " print(f\" {name:<15} (n={total:4d}): {' '.join(pcts)}\")\n", "\n", "# Anchor specialization: how many classes does each anchor serve?\n", "anchor_class_count = torch.zeros(N_ANCHORS)\n", "for a in range(N_ANCHORS):\n", " mask = nearest == a\n", " if mask.sum() < 1: continue\n", " class_present = val_labels[mask].sum(0) > 0\n", " anchor_class_count[a] = class_present.sum().item()\n", "print(f\"\\n Anchor specialization:\")\n", "print(f\" classes per anchor: mean={anchor_class_count[anchor_class_count>0].mean():.1f} \"\n", " f\"std={anchor_class_count[anchor_class_count>0].std():.1f}\")\n", "print(f\" max={anchor_class_count.max():.0f} min={anchor_class_count[anchor_class_count>0].min():.0f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: FUSED EMBEDDING GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 7: FUSED EMBEDDING GEOMETRY\")\n", "print(f\"{'='*65}\")\n", "\n", "# Norms (should be 1.0)\n", "fused_norms = fused.norm(dim=-1)\n", "print(f\" Norms: mean={fused_norms.mean():.6f} std={fused_norms.std():.6f}\")\n", "\n", "# Self-similarity\n", "fused_n = F.normalize(fused, dim=-1)\n", "self_sim = fused_n @ fused_n.T\n", "self_sim_off = (self_sim.sum() - self_sim.diag().sum()) / (N_val**2 - N_val)\n", "print(f\" Self-sim (off-diag): {self_sim_off:.4f}\")\n", "\n", "# SVD\n", "fc = fused.float() - fused.float().mean(0, keepdim=True)\n", "sv = torch.linalg.svdvals(fc)\n", "eff_dim = ((sv.sum()**2) / (sv.pow(2).sum() + 1e-12)).item()\n", "print(f\" Effective dim: {eff_dim:.1f}/{D_ANCHOR}\")\n", "cumvar = sv.pow(2).cumsum(0) / sv.pow(2).sum()\n", "for k in [5, 10, 20, 50, 100]:\n", " if k-1 < len(cumvar):\n", " print(f\" top-{k} SVs explain {cumvar[k-1]*100:.1f}%\")\n", "\n", "# CV\n", "vols = []\n", "for _ in range(500):\n", " idx = torch.randperm(N_val)[:5]\n", " v2 = cayley_menger_vol2(fused_n[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", "fused_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", "print(f\" Pentachoron CV: {fused_cv:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 8: EXPERT CONTRIBUTION ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"SCAN 8: EXPERT CONTRIBUTION\")\n", "print(f\"{'='*65}\")\n", "\n", "# How much does each expert contribute to the fused embedding?\n", "# cos(expert_proj, fused) tells us alignment\n", "for e, name in enumerate(expert_names):\n", " cos = F.cosine_similarity(proj[e], fused, dim=-1)\n", " print(f\" {name:<15}: cos_to_fused mean={cos.mean():.4f} std={cos.std():.4f}\")\n", "\n", "# Residual after removing each expert\n", "for e, name in enumerate(expert_names):\n", " others = [proj[i] for i in range(3) if i != e]\n", " fused_without = F.normalize(sum(others) / 2, dim=-1)\n", " delta = F.cosine_similarity(fused, fused_without, dim=-1)\n", " print(f\" Without {name:<15}: cos_to_full={delta.mean():.4f} (uniqueness={1-delta.mean():.4f})\")\n", "\n", "# Per-image expert disagreement\n", "print(f\"\\n Per-image expert disagreement:\")\n", "all_cos = []\n", "for i in range(3):\n", " for j in range(i+1, 3):\n", " cos = F.cosine_similarity(proj[i], proj[j], dim=-1)\n", " all_cos.append(cos)\n", "stacked = torch.stack(all_cos, dim=1) # (N, 3)\n", "per_image_agree = stacked.mean(dim=1)\n", "per_image_disagree = stacked.std(dim=1)\n", "print(f\" Agreement: mean={per_image_agree.mean():.4f} std={per_image_agree.std():.4f}\")\n", "print(f\" Disagreement: mean={per_image_disagree.mean():.4f} std={per_image_disagree.std():.4f}\")\n", "\n", "# Most agreed and disagreed images\n", "most_agree_idx = per_image_agree.argmax().item()\n", "most_disagree_idx = per_image_agree.argmin().item()\n", "print(f\"\\n Most agreed image ({most_agree_idx}): agreement={per_image_agree[most_agree_idx]:.4f}\")\n", "print(f\" labels: {val_labels[most_agree_idx].nonzero(as_tuple=True)[0].tolist()}\")\n", "print(f\" Most disagreed image ({most_disagree_idx}): agreement={per_image_agree[most_disagree_idx]:.4f}\")\n", "print(f\" labels: {val_labels[most_disagree_idx].nonzero(as_tuple=True)[0].tolist()}\")\n", "\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"ANALYSIS COMPLETE\")\n", "print(f\"{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "npihX90FqTSO", "outputId": "a9b45d3b-c805-40b9-97ff-9774c301b1fb" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BASE TIER SOUP ANALYSIS\n", " Device: cuda\n", "=================================================================\n", "\n", " Loading checkpoint...\n", " Loaded: mAP=0.825 cv=0.3117 epoch=19\n", " clip_l14_openai loaded\n", " dinov2_b14 loaded\n", " siglip_b16_384 loaded\n", "\n", " Running inference on 5000 val images...\n", " Done: fused=torch.Size([5000, 128]) tri=torch.Size([5000, 256])\n", "\n", "=================================================================\n", "SCAN 1: ANCHOR GEOMETRY\n", "=================================================================\n", " Anchor pairwise cosine:\n", " mean=0.0356 std=0.1896\n", " max=0.9542 min=-0.9093\n", " Max neighbor cosine per anchor:\n", " mean=0.6949 std=0.2730\n", " max=0.9542 min=0.0639\n", " Anchor norms: mean=1.000000 std=0.000000\n", " Anchor spectral: eff_rank=65.7/128\n", " sv_max=5.0231 sv_10=2.8655 sv_50=0.9697 sv_min=0.125017\n", " Anchor pentachoron CV: 0.2478\n", " mean_vol=0.074751 std_vol=0.018524\n", "\n", "=================================================================\n", "SCAN 2: ANCHOR UTILIZATION\n", "=================================================================\n", " Active anchors: 1/256 (0.4%)\n", " Visit counts: mean=19.5 std=312.5\n", " max=5000 min=0\n", " top 10: [5000, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " bottom 10: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " Anchor entropy: -0.0000 / 5.5452 (-0.0%)\n", "\n", " Per-anchor embedding density:\n", " Intra-cluster cosine: mean=0.9693 std=0.0000\n", "\n", "=================================================================\n", "SCAN 3: PROJECTOR ANALYSIS\n", "=================================================================\n", "\n", " clip_l14:\n", " norm: mean=1.000000 (should be 1.0)\n", " self-sim off-diag: 0.9668\n", " eff_dim: 24.1/128\n", "\n", " dinov2_b14:\n", " norm: mean=1.000000 (should be 1.0)\n", " self-sim off-diag: 0.9678\n", " eff_dim: 25.3/128\n", "\n", " siglip_b16:\n", " norm: mean=1.000000 (should be 1.0)\n", " self-sim off-diag: 0.9501\n", " eff_dim: 23.8/128\n", "\n", " Expert agreement (cosine in 128-d):\n", " clip_l14 × dinov2_b14 : mean=0.9898 std=0.0066 min=0.8730\n", " clip_l14 × siglip_b16 : mean=0.9893 std=0.0052 min=0.9307\n", " dinov2_b14 × siglip_b16 : mean=0.9855 std=0.0081 min=0.8920\n", "\n", " Per-expert nearest anchor agreement:\n", " clip_l14 × dinov2_b14 : same_anchor=1.0000 (100.0%)\n", " clip_l14 × siglip_b16 : same_anchor=1.0000 (100.0%)\n", " dinov2_b14 × siglip_b16 : same_anchor=1.0000 (100.0%)\n", "\n", " Projector weight comparison:\n", " clip_l14 : norm=37.4114 eff_rank=30.5/128\n", " dinov2_b14 : norm=36.3149 eff_rank=23.0/128\n", " siglip_b16 : norm=39.2079 eff_rank=29.0/128\n", " clip_l14 × dinov2_b14 weight_cos=0.0046\n", " clip_l14 × siglip_b16 weight_cos=-0.0049\n", " dinov2_b14 × siglip_b16 weight_cos=-0.0055\n", "\n", "=================================================================\n", "SCAN 4: PATCHWORK COMPARTMENTS\n", "=================================================================\n", " Comp 0: 32 anchors\n", " Comp 1: 32 anchors\n", " Comp 2: 32 anchors\n", " Comp 3: 32 anchors\n", " Comp 4: 32 anchors\n", " Comp 5: 32 anchors\n", " Comp 6: 32 anchors\n", " Comp 7: 32 anchors\n", "\n", " Patchwork output: torch.Size([5000, 512])\n", " norm: mean=11.6381 std=0.5046\n", " comp 0: norm=2.8604 std_across_dims=0.0010\n", " comp 1: norm=3.7652 std_across_dims=0.1596\n", " comp 2: norm=2.3303 std_across_dims=0.0057\n", " comp 3: norm=3.4802 std_across_dims=0.2053\n", " comp 4: norm=3.3465 std_across_dims=0.1143\n", " comp 5: norm=5.9651 std_across_dims=0.4720\n", " comp 6: norm=6.0775 std_across_dims=0.4946\n", " comp 7: norm=3.2188 std_across_dims=0.0718\n", "\n", "=================================================================\n", "SCAN 5: TRIANGULATION PATTERNS\n", "=================================================================\n", " Triangulation distances (1-cosine):\n", " mean=0.8988 std=0.1301\n", " min=0.0038 max=1.2538\n", " Nearest anchor distance:\n", " mean=0.0156 std=0.0042\n", " max=0.0419 min=0.0038\n", " Anchors within cos>0.5 per image:\n", " mean=1.0 std=0.0\n", " Top-10 nearest anchor distances:\n", " k=0: mean=0.0156 std=0.0042\n", " k=1: mean=0.6646 std=0.0218\n", " k=2: mean=0.6806 std=0.0185\n", " k=3: mean=0.6909 std=0.0173\n", " k=4: mean=0.6977 std=0.0167\n", " k=5: mean=0.7033 std=0.0162\n", " k=6: mean=0.7081 std=0.0158\n", " k=7: mean=0.7126 std=0.0154\n", " k=8: mean=0.7166 std=0.0150\n", " k=9: mean=0.7204 std=0.0147\n", "\n", "=================================================================\n", "SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "=================================================================\n", "\n", " Top-3 anchors per class (first 20 classes):\n", " person (n=2693): 65(2693/2693) 1(0/2693) 0(0/2693)\n", " bicycle (n= 149): 65(149/149) 1(0/149) 0(0/149)\n", " car (n= 535): 65(535/535) 1(0/535) 0(0/535)\n", " motorcycle (n= 159): 65(159/159) 1(0/159) 0(0/159)\n", " airplane (n= 97): 65(97/97) 1(0/97) 0(0/97)\n", " bus (n= 189): 65(189/189) 1(0/189) 0(0/189)\n", " train (n= 157): 65(157/157) 1(0/157) 0(0/157)\n", " truck (n= 250): 65(250/250) 1(0/250) 0(0/250)\n", " boat (n= 121): 65(121/121) 1(0/121) 0(0/121)\n", " traffic light (n= 191): 65(191/191) 1(0/191) 0(0/191)\n", " fire hydrant (n= 86): 65(86/86) 1(0/86) 0(0/86)\n", " stop sign (n= 69): 65(69/69) 1(0/69) 0(0/69)\n", " parking meter (n= 37): 65(37/37) 1(0/37) 0(0/37)\n", " bench (n= 235): 65(235/235) 1(0/235) 0(0/235)\n", " bird (n= 125): 65(125/125) 1(0/125) 0(0/125)\n", " cat (n= 184): 65(184/184) 1(0/184) 0(0/184)\n", " dog (n= 177): 65(177/177) 1(0/177) 0(0/177)\n", " horse (n= 128): 65(128/128) 1(0/128) 0(0/128)\n", " sheep (n= 65): 65(65/65) 1(0/65) 0(0/65)\n", " cow (n= 87): 65(87/87) 1(0/87) 0(0/87)\n", "\n", " Anchor specialization:\n", " classes per anchor: mean=80.0 std=nan\n", " max=80 min=80\n", "\n", "=================================================================\n", "SCAN 7: FUSED EMBEDDING GEOMETRY\n", "=================================================================\n", " Norms: mean=1.000000 std=0.000000\n", " Self-sim (off-diag): 0.9693\n", " Effective dim: 23.6/128\n", " top-5 SVs explain 44.1%\n", " top-10 SVs explain 70.2%\n", " top-20 SVs explain 99.2%\n", " top-50 SVs explain 100.0%\n", " top-100 SVs explain 100.0%\n", " Pentachoron CV: 0.3529\n", "\n", "=================================================================\n", "SCAN 8: EXPERT CONTRIBUTION\n", "=================================================================\n", " clip_l14 : cos_to_fused mean=0.9970 std=0.0016\n", " dinov2_b14 : cos_to_fused mean=0.9957 std=0.0027\n", " siglip_b16 : cos_to_fused mean=0.9955 std=0.0023\n", " Without clip_l14 : cos_to_full=0.9992 (uniqueness=0.0008)\n", " Without dinov2_b14 : cos_to_full=0.9989 (uniqueness=0.0011)\n", " Without siglip_b16 : cos_to_full=0.9989 (uniqueness=0.0011)\n", "\n", " Per-image expert disagreement:\n", " Agreement: mean=0.9882 std=0.0055\n", " Disagreement: mean=0.0041 std=0.0034\n", "\n", " Most agreed image (1449): agreement=0.9978\n", " labels: [22]\n", " Most disagreed image (1435): agreement=0.9214\n", " labels: [28]\n", "\n", "=================================================================\n", "ANALYSIS COMPLETE\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_10600/3734699858.py:410: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.)\n", " f\"std={anchor_class_count[anchor_class_count>0].std():.1f}\")\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import HfApi\n", "\n", "api = HfApi()\n", "REPO_ID = \"AbstractPhil/geolip-vit-base-x3\"\n", "\n", "try:\n", " api.create_repo(REPO_ID, repo_type=\"model\", exist_ok=True)\n", "except: pass\n", "\n", "api.upload_file(\n", " path_or_fileobj=\"checkpoints/base_tier_best.pt\",\n", " path_in_repo=\"base_tier_soup.pt\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", "print(\"✓ Uploaded base_tier_soup.pt\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 130, "referenced_widgets": [ "61a5718285864ad1aecdcd360be6feea", "f7667dc31300498d9ad8899d3ea8ef7e", "251a3d1fd2fe49d58975984fd2a4f5c6", "bb2bf86c50794e6cacd71e44779a93d9", "f831a0db8d384bb48e3728a69b86c03a", "584e515b72594492802c13c2d125e9f3", "5195b3556071443280da19404f766986", "7e26684b46de4a97a73de6e9813479d8", "69485c8ba59347b18b2fd4e92e167909", "4a11f4938e6d48a3aa3dc7770df44995", "3ae857d4f6b94446a39cb5391a551323", "72ee32b1c49b43238ae99e4813f9b4db", "7e1a38b96aeb49b4b7f69b9cf8f7188d", "c4fa38de556148ee908336183496e214", "7742d0aeaa174782bf1b2ffac2bd2521", "387c0abac56f4ecea3f84bc79a43c038", "09fd8cc015774e00bbf028cdf8be8de1", "1cd44b5140a243f88b9b3eef29d612fb", "2250f39eb0e243b99849370b4a8af3ce", "12fd85fb025b4b6db31e6332ee679546", "4c006728bb6340b39d61e4b710511184", "6757c394fcfd4ba88572ff3c4390c72b", "523887c76e7745e3928fc6d71a727b60", "6838c929070d4e7ea95ec7d26d4d457e", "c88808c1180d4efc82415830b2a91467", "df829d8250254c3ba1371cecc398b68d", "d4271ae8712f4193833050a563396cf7", "926b8af05b814ce19146598b0e147819", "18a7ef7c9c8a47c280dd5079fbbbee94", "5c0067e9bde44d3e8dc1cf9f41236246", "40c4fc9ea41a4cc69b19c23b3324d238", "f0ff60b9f81f4064b39957b70a402e7d", "1cd0f4deab11484daff9eb9a3d636538" ] }, "id": "SIJqF3Txpgj8", "outputId": "24c959ad-5277-4471-857c-557d3bb83a5c" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "61a5718285864ad1aecdcd360be6feea" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "72ee32b1c49b43238ae99e4813f9b4db" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...kpoints/base_tier_best.pt: 17%|#7 | 552kB / 3.23MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "523887c76e7745e3928fc6d71a727b60" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✓ Uploaded base_tier_soup.pt\n" ] } ] }, { "cell_type": "markdown", "source": [ "# x3 run 2" ], "metadata": { "id": "JHFusaS2t0gG" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "BASE TIER PATCHWORK SOUP — PROPERLY CALIBRATED\n", "================================================\n", "3 experts, all 768-d:\n", " clip_l14_openai, dinov2_b14, siglip_b16_384\n", "\n", "Pipeline (from CaptionBERT research):\n", " 1. GPA alignment at 768-d → consensus\n", " 2. Measure consensus CV → CV loss target\n", " 3. Per-expert whitened Procrustes calibration\n", " 4. Initialize projectors from Procrustes rotations\n", " 5. Train: projectors + constellation + patchwork + classifier\n", " against consensus targets with calibrated CV\n", "\n", "Architecture:\n", " Per-expert: 768 → 128 (Procrustes-initialized projection)\n", " Constellation: 256 anchors × 128-d (geometric autograd)\n", " Patchwork: 8 compartments\n", " Classifier: patchwork + fused → 80 classes\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "D_EXPERT = 768\n", "D_ANCHOR = 128\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 64\n", "BATCH = 128\n", "EPOCHS = 20\n", "LR = 1e-3\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "print(\"=\" * 65)\n", "print(\"BASE TIER PATCHWORK SOUP — CALIBRATED\")\n", "print(f\" 3 experts × {D_EXPERT}-d → {N_ANCHORS} anchors × {D_ANCHOR}-d\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=500):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def anchor_spread_loss(anchors):\n", " a = F.normalize(anchors, dim=-1)\n", " sim = a @ a.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "def anchor_entropy_loss(emb, anchors, sharpness=10.0):\n", " a = F.normalize(anchors, dim=-1)\n", " probs = F.softmax(emb @ a.T * sharpness, dim=-1)\n", " return -(probs * (probs + 1e-12).log()).sum(-1).mean()\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PROCRUSTES UTILITIES (from cotrain_bank.py)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " s_mean = S.mean(0, keepdim=True); t_mean = T.mean(0, keepdim=True)\n", " Sc = S - s_mean; Tc = T - t_mean; N_s = Sc.shape[0]\n", " s_cov = (Sc.T @ Sc) / max(N_s-1, 1)\n", " t_cov = (Tc.T @ Tc) / max(N_s-1, 1)\n", " s_whiten = symmetric_inv_sqrt(s_cov)\n", " t_whiten = symmetric_inv_sqrt(t_cov)\n", " Sc_w = F.normalize(Sc @ s_whiten, dim=-1)\n", " Tc_w = F.normalize(Tc @ t_whiten, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " R = U @ Vt\n", " cos_after = F.cosine_similarity(Sc_w @ R.T, Tc_w, dim=-1).mean().item()\n", " return {\"rotation\": R, \"source_mean\": s_mean.squeeze(0),\n", " \"source_whitener\": s_whiten,\n", " \"target_unwhitener\": torch.linalg.pinv(t_whiten),\n", " \"cos_after\": cos_after}\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " x = x @ a[\"source_whitener\"]\n", " x = x @ a[\"rotation\"].T\n", " x = x @ a[\"target_unwhitener\"]\n", " return x\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ExpertProjector(nn.Module):\n", " def __init__(self, d_in=D_EXPERT, d_out=D_ANCHOR):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(d_in, d_out), nn.LayerNorm(d_out))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for k in range(n_comp)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "class BaseTierSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.n_experts = 3\n", " self.projectors = nn.ModuleList([ExpertProjector() for _ in range(3)])\n", " self.constellation = Constellation()\n", " self.patchwork = Patchwork()\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.1),\n", " nn.Linear(pw_dim, N_CLASSES))\n", "\n", " def forward(self, expert_embeddings, apply_autograd=True):\n", " projected = [self.projectors[i](expert_embeddings[i]) for i in range(3)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", " if apply_autograd and self.training:\n", " fused = EmbeddingAutograd.apply(\n", " fused, fused, self.constellation.anchors, 0.01, 1.0)\n", " tri, nearest = self.constellation.triangulate(fused)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, fused], -1))\n", " return logits, fused, tri, nearest, projected\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 0: LOAD DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 0: LOAD DATA\")\n", "print(f\"{'='*65}\")\n", "\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"train\")\n", "train_ids = ref[\"image_id\"]; N_train = len(train_ids)\n", "train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", "train_label_matrix = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: train_label_matrix[i, l] = 1.0\n", "\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_label_matrix = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(ref_val[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: val_label_matrix[i, l] = 1.0\n", "\n", "print(f\" Train: {N_train:,} Val: {N_val:,}\")\n", "\n", "train_raw = {}\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " feats = torch.zeros(N_train, D_EXPERT)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_map:\n", " feats[train_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " train_raw[name] = feats\n", "\n", " ds_v = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats_v = torch.zeros(N_val, D_EXPERT)\n", " for row in ds_v:\n", " if row[\"image_id\"] in val_id_map:\n", " feats_v[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats_v\n", " print(f\" {name:<30} loaded\", flush=True)\n", " del ds, ds_v; gc.collect()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 1: GPA ALIGNMENT AT 768-d\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 1: GPA ALIGNMENT AT 768-d\")\n", "print(f\"{'='*65}\")\n", "\n", "current = {name: train_raw[name][:N_train].float() for name in EXPERTS}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current[n] for n in EXPERTS) / len(EXPERTS)\n", " total_delta = 0.0\n", " new_current = {}\n", " for name in EXPERTS:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " total_delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter+1) % 5 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={total_delta:.8f}\")\n", " if total_delta < 1e-8:\n", " print(f\" Converged at iteration {gpa_iter+1}\"); break\n", "\n", "consensus_768 = F.normalize(\n", " sum(current[n] for n in EXPERTS) / len(EXPERTS), dim=-1)\n", "\n", "for name in EXPERTS:\n", " c = F.cosine_similarity(consensus_768[:5000], current[name][:5000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {c:.4f}\")\n", "\n", "consensus_cv_768 = cv_metric(consensus_768[:5000].to(DEVICE))\n", "print(f\" Consensus CV at 768-d: {consensus_cv_768:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 2: PROJECT CONSENSUS TO 128-d + CALIBRATE CV\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 2: PROJECT TO 128-d + CALIBRATE\")\n", "print(f\"{'='*65}\")\n", "\n", "cons_centered = consensus_768 - consensus_768.mean(0, keepdim=True)\n", "U, S, Vt = torch.linalg.svd(cons_centered[:10000], full_matrices=False)\n", "pca_proj = Vt[:D_ANCHOR]\n", "\n", "consensus_128 = F.normalize(consensus_768 @ pca_proj.T, dim=-1)\n", "var_retained = S[:D_ANCHOR].pow(2).sum() / S.pow(2).sum()\n", "print(f\" PCA 768→128: variance retained = {var_retained.item():.4f}\")\n", "\n", "consensus_cv_128 = cv_metric(consensus_128[:5000].to(DEVICE))\n", "print(f\" Consensus CV at 128-d: {consensus_cv_128:.4f}\")\n", "\n", "# Val consensus\n", "val_current = {name: val_raw[name].float() for name in EXPERTS}\n", "for gpa_iter in range(20):\n", " val_mean = sum(val_current[n] for n in EXPERTS) / len(EXPERTS)\n", " delta = 0.0\n", " for name in EXPERTS:\n", " info = procrustes_align(val_current[name], val_mean)\n", " new = apply_align(val_current[name], info)\n", " delta += (new - val_current[name]).pow(2).mean().item()\n", " val_current[name] = new\n", " if delta < 1e-8: break\n", "val_consensus_768 = F.normalize(\n", " sum(val_current[n] for n in EXPERTS) / len(EXPERTS), dim=-1)\n", "val_consensus_128 = F.normalize(val_consensus_768 @ pca_proj.T, dim=-1)\n", "print(f\" Val consensus: {val_consensus_128.shape}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 3: PER-EXPERT PROCRUSTES TO 128-d CONSENSUS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 3: PER-EXPERT PROCRUSTES CALIBRATION\")\n", "print(f\"{'='*65}\")\n", "\n", "expert_calibrations = {}\n", "for name in EXPERTS:\n", " raw = train_raw[name][:10000].float()\n", " tgt = consensus_128[:10000].float()\n", "\n", " src_mean = raw.mean(0, keepdim=True)\n", " tgt_mean = tgt.mean(0, keepdim=True)\n", " src_c = raw[:10000] - src_mean\n", " tgt_c = tgt[:10000] - tgt_mean\n", "\n", " src_cov = (src_c.T @ src_c) / 9999\n", " src_whiten = symmetric_inv_sqrt(src_cov)\n", " tgt_cov = (tgt_c.T @ tgt_c) / 9999\n", " tgt_whiten = symmetric_inv_sqrt(tgt_cov)\n", "\n", " src_w = F.normalize(src_c @ src_whiten, dim=-1)\n", " tgt_w = F.normalize(tgt_c @ tgt_whiten, dim=-1)\n", " M = tgt_w.T @ src_w\n", " U_r, S_r, Vt_r = torch.linalg.svd(M, full_matrices=False)\n", " R = U_r @ Vt_r\n", "\n", " proj_W = (src_whiten @ R.T).T\n", " proj_b = -(src_mean.squeeze(0) @ src_whiten @ R.T).squeeze(0)\n", "\n", " test_proj = raw[:1000] @ proj_W.T + proj_b\n", " test_proj_n = F.normalize(test_proj, dim=-1)\n", " cos = F.cosine_similarity(test_proj_n, tgt[:1000], dim=-1).mean().item()\n", "\n", " expert_calibrations[name] = {\"weight\": proj_W, \"bias\": proj_b, \"cos\": cos, \"svd_S\": S_r}\n", " print(f\" {name:<30} cos={cos:.4f} svd: min={S_r.min():.4f} max={S_r.max():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 4: BUILD + INITIALIZE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 4: BUILD + INITIALIZE\")\n", "print(f\"{'='*65}\")\n", "\n", "model = BaseTierSoup().to(DEVICE)\n", "\n", "with torch.no_grad():\n", " for i, name in enumerate(EXPERTS):\n", " cal = expert_calibrations[name]\n", " model.projectors[i].proj[0].weight.copy_(cal[\"weight\"].to(DEVICE))\n", " model.projectors[i].proj[0].bias.copy_(cal[\"bias\"].to(DEVICE))\n", " print(f\" ✓ {name} projector initialized (cos={cal['cos']:.4f})\")\n", "\n", " sample_idx = torch.randperm(min(10000, N_train))[:N_ANCHORS]\n", " anchor_seeds = consensus_128[sample_idx].to(DEVICE)\n", " model.constellation.anchors.copy_(F.normalize(anchor_seeds, dim=-1))\n", " print(f\" ✓ Constellation seeded from consensus\")\n", "\n", "# Verify\n", "with torch.no_grad():\n", " test_in = [train_raw[EXPERTS[e]][:200].to(DEVICE) for e in range(3)]\n", " _, test_fused, _, test_nearest, test_proj = model(test_in, apply_autograd=False)\n", " test_tgt = consensus_128[:200].to(DEVICE)\n", " init_cos = F.cosine_similarity(test_fused, test_tgt, dim=-1).mean().item()\n", " init_cv = cv_metric(test_fused)\n", " n_active = test_nearest.unique().numel()\n", " for e, name in enumerate([\"clip\", \"dino\", \"siglip\"]):\n", " c = F.cosine_similarity(test_proj[e], test_tgt, dim=-1).mean().item()\n", " print(f\" {name} proj→consensus cos: {c:.4f}\")\n", " print(f\" Init: cos={init_cos:.4f} cv={init_cv:.4f} active_anchors={n_active}/256\")\n", "\n", "params = sum(p.numel() for p in model.parameters())\n", "print(f\" Parameters: {params:,}\")\n", "print(f\" CV target: {consensus_cv_128:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PHASE 5: TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 5: TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, CV target={consensus_cv_128:.4f}\")\n", "print(f\"{'='*65}\")\n", "\n", "train_targets = consensus_128.to(DEVICE)\n", "val_targets = val_consensus_128.to(DEVICE)\n", "train_labels_gpu = train_label_matrix.to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/base_tier_calibrated\")\n", "best_mAP = 0.0; gs = 0\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train)\n", " tl, tn, nb = 0, 0, 0\n", "\n", " for i in range(0, N_train, BATCH):\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " batch = [train_raw[EXPERTS[e]][idx].to(DEVICE) for e in range(3)]\n", " labels = train_labels_gpu[idx]\n", " targets = train_targets[idx]\n", "\n", " logits, fused, tri, nearest, projected = model(batch)\n", " anchors = model.constellation.anchors\n", "\n", " l_nce, nce_acc = infonce(fused, targets)\n", " l_mse = F.mse_loss(fused, targets)\n", " l_cls = F.binary_cross_entropy_with_logits(logits, labels)\n", " l_cv = cv_loss(fused, target=consensus_cv_128)\n", " l_spread = anchor_spread_loss(anchors)\n", " l_ent = anchor_entropy_loss(fused, anchors)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_cls\n", " + 0.001 * l_cv + 1e-3 * l_spread + 1e-4 * l_ent)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " tl += loss.item(); tn += nce_acc; nb += 1; gs += 1\n", " if gs % 100 == 0:\n", " writer.add_scalar(\"train/loss\", loss.item(), gs)\n", " writer.add_scalar(\"train/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"train/cls\", l_cls.item(), gs)\n", " writer.add_scalar(\"train/cv\", l_cv.item(), gs)\n", " writer.add_scalar(\"train/nce_acc\", nce_acc, gs)\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " all_lo, all_em = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " batch_v = [val_raw[EXPERTS[e]][j:end].to(DEVICE) for e in range(3)]\n", " lo, em, _, _, _ = model(batch_v, apply_autograd=False)\n", " all_lo.append(lo.cpu()); all_em.append(em.cpu())\n", " v_lo = torch.cat(all_lo); v_em = torch.cat(all_em)\n", "\n", " v_lab = val_label_matrix\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = v_lo[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st)+1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " vp = (v_lo.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1-v_lab)).sum(0)\n", " fn = ((1-vp) * v_lab).sum(0)\n", " pr = tp/(tp+fp+1e-8); rc = tp/(tp+fn+1e-8)\n", " f1 = 2*pr*rc/(pr+rc+1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " v_cos = F.cosine_similarity(v_em, val_targets.cpu(), dim=-1).mean().item()\n", " v_cv = cv_metric(v_em.to(DEVICE))\n", "\n", " sim = v_em @ val_targets.cpu().T\n", " r1 = (sim.argmax(-1) == torch.arange(N_val)).float().mean().item()\n", "\n", " _, v_nearest = model.constellation.triangulate(v_em.to(DEVICE))\n", " n_active = v_nearest.cpu().unique().numel()\n", "\n", " writer.add_scalar(\"val/mAP\", mAP, epoch+1)\n", " writer.add_scalar(\"val/cos\", v_cos, epoch+1)\n", " writer.add_scalar(\"val/cv\", v_cv, epoch+1)\n", " writer.add_scalar(\"val/R@1\", r1, epoch+1)\n", " writer.add_scalar(\"val/active_anchors\", n_active, epoch+1)\n", "\n", " mk = \"\"\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\n", " \"state_dict\": model.state_dict(),\n", " \"config\": {\"d_expert\": D_EXPERT, \"d_anchor\": D_ANCHOR,\n", " \"n_anchors\": N_ANCHORS, \"n_comp\": N_COMP,\n", " \"d_comp\": D_COMP, \"n_classes\": N_CLASSES,\n", " \"experts\": EXPERTS, \"cv_target\": consensus_cv_128},\n", " \"pca_proj\": pca_proj,\n", " \"consensus_cv_768\": consensus_cv_768,\n", " \"consensus_cv_128\": consensus_cv_128,\n", " \"epoch\": epoch+1, \"mAP\": mAP, \"cv\": v_cv, \"r1\": r1,\n", " }, \"checkpoints/base_tier_best.pt\")\n", " mk = \" ★\"\n", "\n", " print(f\" E{epoch+1:2d}: mAP={mAP:.3f} F1={macro_f1:.3f} R@1={r1:.3f} \"\n", " f\"cos={v_cos:.3f} cv={v_cv:.4f} anchors={n_active}/256 \"\n", " f\"nce={tn/nb:.3f} loss={tl/nb:.4f}{mk}\")\n", "\n", "writer.close()\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" CV target: {consensus_cv_128:.4f}\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JHeMDoent2hl", "outputId": "339188fc-aade-4ebe-ef3a-8bb1be78d9f9" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "BASE TIER PATCHWORK SOUP — CALIBRATED\n", " 3 experts × 768-d → 256 anchors × 128-d\n", " Device: cuda\n", "=================================================================\n", "\n", "=================================================================\n", "PHASE 0: LOAD DATA\n", "=================================================================\n", " Train: 118,287 Val: 5,000\n", " clip_l14_openai loaded\n", " dinov2_b14 loaded\n", " siglip_b16_384 loaded\n", "\n", "=================================================================\n", "PHASE 1: GPA ALIGNMENT AT 768-d\n", "=================================================================\n", " GPA iter 1: delta=2.42780352\n", " GPA iter 5: delta=0.00656253\n", " GPA iter 10: delta=0.00082383\n", " GPA iter 15: delta=0.00027677\n", " GPA iter 20: delta=0.00013823\n", " cos(consensus, clip_l14_openai): 0.9723\n", " cos(consensus, dinov2_b14): 0.9650\n", " cos(consensus, siglip_b16_384): 0.9747\n", " Consensus CV at 768-d: 0.2793\n", "\n", "=================================================================\n", "PHASE 2: PROJECT TO 128-d + CALIBRATE\n", "=================================================================\n", " PCA 768→128: variance retained = 0.9988\n", " Consensus CV at 128-d: 0.2731\n", " Val consensus: torch.Size([5000, 128])\n", "\n", "=================================================================\n", "PHASE 3: PER-EXPERT PROCRUSTES CALIBRATION\n", "=================================================================\n", " clip_l14_openai cos=0.6245 svd: min=20.4983 max=58.0336\n", " dinov2_b14 cos=0.6199 svd: min=18.0785 max=70.0417\n", " siglip_b16_384 cos=0.6263 svd: min=20.0792 max=60.3250\n", "\n", "=================================================================\n", "PHASE 4: BUILD + INITIALIZE\n", "=================================================================\n", " ✓ clip_l14_openai projector initialized (cos=0.6245)\n", " ✓ dinov2_b14 projector initialized (cos=0.6199)\n", " ✓ siglip_b16_384 projector initialized (cos=0.6263)\n", " ✓ Constellation seeded from consensus\n", " clip proj→consensus cos: 0.6209\n", " dino proj→consensus cos: 0.6156\n", " siglip proj→consensus cos: 0.6222\n", " Init: cos=0.6720 cv=0.1535 active_anchors=87/256\n", " Parameters: 799,952\n", " CV target: 0.2731\n", "\n", "=================================================================\n", "PHASE 5: TRAINING\n", " 20 epochs, lr=0.001, CV target=0.2731\n", "=================================================================\n", " E 1: mAP=0.788 F1=0.731 R@1=0.971 cos=0.806 cv=0.1213 anchors=226/256 nce=0.999 loss=0.1676 ★\n", " E 2: mAP=0.803 F1=0.742 R@1=0.971 cos=0.809 cv=0.1178 anchors=200/256 nce=0.999 loss=0.1459 ★\n", " E 3: mAP=0.810 F1=0.735 R@1=0.973 cos=0.808 cv=0.1197 anchors=161/256 nce=0.999 loss=0.1431 ★\n", " E 4: mAP=0.817 F1=0.752 R@1=0.971 cos=0.811 cv=0.1262 anchors=131/256 nce=0.999 loss=0.1404 ★\n", " E 5: mAP=0.823 F1=0.755 R@1=0.971 cos=0.812 cv=0.1232 anchors=113/256 nce=0.999 loss=0.1389 ★\n", " E 6: mAP=0.825 F1=0.755 R@1=0.972 cos=0.815 cv=0.1105 anchors=104/256 nce=0.999 loss=0.1379 ★\n", " E 7: mAP=0.827 F1=0.767 R@1=0.970 cos=0.814 cv=0.1125 anchors=101/256 nce=0.999 loss=0.1369 ★\n", " E 8: mAP=0.829 F1=0.763 R@1=0.971 cos=0.815 cv=0.1239 anchors=99/256 nce=0.999 loss=0.1361 ★\n", " E 9: mAP=0.832 F1=0.764 R@1=0.972 cos=0.815 cv=0.1164 anchors=98/256 nce=0.999 loss=0.1355 ★\n", " E10: mAP=0.833 F1=0.765 R@1=0.968 cos=0.814 cv=0.1166 anchors=99/256 nce=0.999 loss=0.1345 ★\n", " E11: mAP=0.834 F1=0.763 R@1=0.971 cos=0.814 cv=0.1214 anchors=98/256 nce=0.999 loss=0.1346 ★\n", " E12: mAP=0.833 F1=0.764 R@1=0.973 cos=0.813 cv=0.1200 anchors=95/256 nce=0.999 loss=0.1343\n", " E13: mAP=0.836 F1=0.761 R@1=0.972 cos=0.813 cv=0.1081 anchors=94/256 nce=0.999 loss=0.1338 ★\n", " E14: mAP=0.836 F1=0.772 R@1=0.973 cos=0.812 cv=0.1170 anchors=95/256 nce=0.999 loss=0.1334\n", " E15: mAP=0.835 F1=0.774 R@1=0.970 cos=0.812 cv=0.1223 anchors=95/256 nce=0.999 loss=0.1338\n", " E16: mAP=0.837 F1=0.777 R@1=0.968 cos=0.812 cv=0.1225 anchors=96/256 nce=1.000 loss=0.1339 ★\n", " E17: mAP=0.834 F1=0.772 R@1=0.973 cos=0.811 cv=0.1089 anchors=95/256 nce=0.999 loss=0.1327\n", " E18: mAP=0.834 F1=0.770 R@1=0.973 cos=0.812 cv=0.1156 anchors=95/256 nce=0.999 loss=0.1321\n", " E19: mAP=0.834 F1=0.773 R@1=0.970 cos=0.811 cv=0.1224 anchors=96/256 nce=0.999 loss=0.1328\n", " E20: mAP=0.835 F1=0.770 R@1=0.971 cos=0.812 cv=0.1159 anchors=96/256 nce=0.999 loss=0.1328\n", "\n", " Best mAP: 0.837\n", " CV target: 0.2731\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import HfApi\n", "api = HfApi()\n", "REPO_ID = \"AbstractPhil/geolip-vit-base-x3\"\n", "\n", "# Upload calibrated soup\n", "api.upload_file(\n", " path_or_fileobj=\"checkpoints/base_tier_best.pt\",\n", " path_in_repo=\"base_tier_soup_calibrated.pt\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", "print(\"✓ Uploaded calibrated soup\")\n", "\n", "# Upload tensorboard\n", "api.upload_folder(\n", " folder_path=\"runs/base_tier_calibrated\",\n", " path_in_repo=\"runs/base_tier_calibrated\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", "print(\"✓ Uploaded tensorboard\")\n", "\n", "# Upload training script\n", "api.upload_file(\n", " path_or_fileobj=\"base_tier_soup.py\",\n", " path_in_repo=\"base_tier_soup_calibrated.py\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", "print(\"✓ Uploaded training script\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "5f1f4b5b84e941c1a159a493bc577586", "80fca37ac5134cc2b1c5089b9cffc3c9", "eb19c16fb3e04142b0e60e1894313a7b", "259ed6db700e44728370c463575eedfb", "d9d0ea0484b0410fa168c6395cd2de74", "4385607201f8476480805bfbfc2b8386", "e738119bb05c4c8fbc45dd83f3aa3d86", "3121daba556c41da9395edbbfd7ba32f", "0f9b773950ec4e3892f6560fd94ab612", "bb9d20a28b2b49879741f254919526f1", "846b70aa4bf4420386491a253d3f7bda", "884c760446cb46eea5e6438449b89c47", "500d2f46876a4c9c90f472c06e62edf8", "12c63fbb9dd24fa1a8e5156d4f3422cd", "c0e9a5e29d3d491e997fc511d18a9c53", "9eda9f0328ce41dcb58127163e1010ee", "37d1164d065e498b948bfd3e10763d5f", "c1a7ce1f04574e1e82e7403688f479c5", "673f88669c0e45caadd0a6efca26a610", "cf8ebed1c22848ea8110e31598740bae", "f380ce3e7e6f4262912a16573834dd1e", "029d383062a1459f88c3a0bda1e20f97", "f4e2efbaa93648228725357b1a0c23b9", "fe4c48bbf21d4b5cbc1954fc3317741f", "7ccd19c988294f9a9f768f9e4a6444bc", "4d3948a74392450984d5037d4f468f0a", "05310b8fc7f3444599c65a3a15cb9dd0", "9bfaf3241e8340b29ef2e071a130e79d", "8d90093968074e408e80de72f3f499fe", "9c1b289c46e3404da9ff4a0b74b659b3", "1b048ce692a641ee90b5ba260d62189b", "ad0dbd77309c4262ae049f00ba84a3e2", "518bf77a487340f8aa5daf4eabf6b5ee", "bef1a7690b12402ca5ad35c8589e1733", "73dc1e2533654418b6c1267a20ca4eb6", "0cc65979192e46abaea45835a6a52ca8", "71bf0d4e7c3642279ba23a0f8fca2e4e", "53bf4a37489f42f4b8ad16bda1c86ba6", "3b1c3b1ee0a349c7a425a785b19bc57a", "f44eb21c1ae04fb69a001cdfd89e5db0", "cfbc743697b341e38727f5e27e515f71", "44f5a1075f59488a9736b722c75ee17a", "ddb3011ccde54d9d8440cf0028cfe7be", "8ed1f720dd3c47a49dba117176d2e86a", "d688737294b746a3a5f2b56b5492a19d", "1892369e26db46eeac58e7749cc407b4", "0bef67c71b5e405f8a73fdfe0d5e96ed", "34ee44f7ebbc483ab6c9b36250bc0f67", "32276e1a7f564d7ea2d73616fe52bffb", "0c5f717b6d614adc83580f5ee4d22fa2", "8fe7a080db91424eb17c6c073cb7c1e8", "e728a3e675df408c8bbda6f03bcdc3c9", "7c8d83f011ae4b6ebb574623aad88508", "3fd7e9ae9493474a910ffe8016e827f3", "619d14525196453b845a4150051a0c46", "43de0616184c4d47a6500d2a7d12d668", "1a7f282a2ca24695b6bc3554be8d3051", "7f08cf9db83b4281871ea2dbaddc9631", "d662e7e106854a1288f5fa50dac666ca", "2f9788c913c1462fb172ea7aa1366135", "7a3cc25337744bc0973c7d4cf97eb18f", "88444aabc9c14b5e8a8b0e3ef3e8964c", "7a112f896ccc4c3297999f6b57ee3849", "e4f043ea62c745c1b611f0d3a4d2783f", "0a14854d4c2e47b8b8ff4a9bbf222b36", "bcba0a1ade144c80b1bfe4e60d1d3da7" ] }, "id": "zL1pRQf41Y0s", "outputId": "a1231440-4808-4164-d2f0-347a667f69ed" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5f1f4b5b84e941c1a159a493bc577586" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "884c760446cb46eea5e6438449b89c47" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...kpoints/base_tier_best.pt: 10%|9 | 553kB / 5.59MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f4e2efbaa93648228725357b1a0c23b9" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✓ Uploaded calibrated soup\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "bef1a7690b12402ca5ad35c8589e1733" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d688737294b746a3a5f2b56b5492a19d" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...2032.8b9a70db3a24.10600.1: 100%|##########| 50.0kB / 50.0kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "43de0616184c4d47a6500d2a7d12d668" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✓ Uploaded tensorboard\n" ] }, { "output_type": "error", "ename": "ValueError", "evalue": "Provided path: 'base_tier_soup.py' is not a file on the local file system", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_10600/3346563216.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# Upload training script\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m api.upload_file(\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mpath_or_fileobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"base_tier_soup.py\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mpath_in_repo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"base_tier_soup_calibrated.py\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_legacy_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/_commit_api.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mpath_or_fileobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_or_fileobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m 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\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# ^^ Inspired from: https://stackoverflow.com/questions/44584829/how-to-determine-if-file-is-opened-in-binary-or-text-mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Provided path: 'base_tier_soup.py' is not a file on the local file system" ] } ] }, { "cell_type": "markdown", "source": [ "## stage 2" ], "metadata": { "id": "DBth0w4F6ueF" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP VISION ENCODER — FROM SCRATCH\n", "======================================\n", "From-scratch ViT trained against frozen soup consensus targets.\n", "\n", "Phase 0: Pre-compute consensus targets from frozen soup\n", "Phase 1: Pre-cache all COCO images as tensors (once, then reuse)\n", "Phase 2: Train from-scratch ViT with full GeoLIP loss stack\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import os\n", "import gc\n", "import time\n", "import math\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\"\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "torch.backends.cudnn.allow_tf32 = True\n", "\n", "# Architecture\n", "D_MODEL = 384\n", "N_HEADS = 6\n", "N_LAYERS = 6\n", "D_FF = 1536\n", "PATCH_SIZE = 16\n", "IMAGE_SIZE = 224\n", "D_ANCHOR = 128\n", "N_ANCHORS = 256\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 64\n", "DROPOUT = 0.1\n", "\n", "# Training\n", "BATCH = 48\n", "EPOCHS = 20\n", "LR = 3e-4\n", "WARMUP_STEPS = 500\n", "GRAD_CLIP = 1.0\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "N_PATCHES = (IMAGE_SIZE // PATCH_SIZE) ** 2\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOLIP VISION ENCODER — FROM SCRATCH\")\n", "print(f\" ViT: {N_LAYERS}L/{D_MODEL}d/{N_HEADS}h, patch{PATCH_SIZE}\")\n", "print(f\" {N_PATCHES} patches + CLS → {D_ANCHOR}-d output\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True)\n", " tm = targets.float().mean(0, keepdim=True)\n", " cos = F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1)\n", " return 1.0 - cos.mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROZEN SOUP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(N_ANCHORS, D_ANCHOR), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.n_comp = N_COMP\n", " asgn = torch.arange(N_ANCHORS) % N_COMP\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), D_COMP * 2), nn.GELU(),\n", " nn.Linear(D_COMP * 2, D_COMP), nn.LayerNorm(D_COMP))\n", " for k in range(N_COMP)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "class FrozenSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.constellation = Constellation()\n", " self.patchwork = Patchwork()\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.0),\n", " nn.Linear(pw_dim, N_CLASSES))\n", " def forward(self, emb_128):\n", " tri, nearest = self.constellation.triangulate(emb_128)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, emb_128], -1))\n", " return logits, tri, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROM-SCRATCH ViT ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPViTEncoder(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.patch_embed = nn.Conv2d(3, D_MODEL, kernel_size=PATCH_SIZE,\n", " stride=PATCH_SIZE)\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, D_MODEL))\n", " self.pos_embed = nn.Parameter(torch.zeros(1, N_PATCHES + 1, D_MODEL))\n", " self.embed_norm = nn.LayerNorm(D_MODEL)\n", " self.embed_drop = nn.Dropout(DROPOUT)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=D_MODEL, nhead=N_HEADS, dim_feedforward=D_FF,\n", " dropout=DROPOUT, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(\n", " encoder_layer, num_layers=N_LAYERS, enable_nested_tensor=False)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(D_MODEL, D_MODEL), nn.GELU(),\n", " nn.LayerNorm(D_MODEL),\n", " nn.Linear(D_MODEL, D_ANCHOR))\n", "\n", " self._init_weights()\n", "\n", " def _init_weights(self):\n", " for m in self.modules():\n", " if isinstance(m, nn.Linear):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.Conv2d):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.LayerNorm):\n", " nn.init.ones_(m.weight); nn.init.zeros_(m.bias)\n", " nn.init.trunc_normal_(self.pos_embed, std=0.02)\n", " nn.init.trunc_normal_(self.cls_token, std=0.02)\n", "\n", " def forward(self, pixel_values):\n", " B = pixel_values.shape[0]\n", " x = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)\n", " cls = self.cls_token.expand(B, -1, -1)\n", " x = torch.cat([cls, x], dim=1) + self.pos_embed\n", " x = self.embed_drop(self.embed_norm(x))\n", " x = self.encoder(x)\n", " pooled = x[:, 1:, :].mean(dim=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD SOUP + PRE-COMPUTE TARGETS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/base_tier_best.pt\", map_location=\"cpu\", weights_only=False)\n", "soup = FrozenSoup()\n", "soup_sd = {k: v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"constellation.\") or k.startswith(\"patchwork.\") or k.startswith(\"classifier.\")}\n", "soup.load_state_dict(soup_sd, strict=True)\n", "soup = soup.eval().to(DEVICE)\n", "for p in soup.parameters():\n", " p.requires_grad = False\n", "consensus_cv = ckpt.get(\"consensus_cv_128\", 0.27)\n", "print(f\" Soup: mAP={ckpt['mAP']:.3f} CV_target={consensus_cv:.4f}\")\n", "\n", "# Rebuild projectors for target generation\n", "class ExpertProjector(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(768, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "from datasets import load_dataset\n", "\n", "projectors = nn.ModuleList([ExpertProjector() for _ in range(3)])\n", "proj_sd = {k.replace(\"projectors.\", \"\"): v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"projectors.\")}\n", "projectors.load_state_dict(proj_sd)\n", "projectors = projectors.eval().to(DEVICE)\n", "\n", "for split_name, split_key in [(\"train\", \"train\"), (\"val\", \"val\")]:\n", " cache_path = f\"cached_{split_name}_targets.pt\"\n", " if os.path.exists(cache_path):\n", " cached = torch.load(cache_path, weights_only=False)\n", " if split_name == \"train\":\n", " train_targets = cached[\"targets\"]; train_labels = cached[\"labels\"]\n", " train_ids = cached[\"image_ids\"]; train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", " N_train = len(train_ids)\n", " else:\n", " val_targets = cached[\"targets\"]; val_labels = cached[\"labels\"]\n", " val_ids = cached[\"image_ids\"]; val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", " N_val = len(val_ids)\n", " print(f\" {split_name}: loaded cached targets ({len(cached['targets']):,})\")\n", " continue\n", "\n", " print(f\" Computing {split_name} targets...\")\n", " ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=split_key)\n", " ids = ref[\"image_id\"]; N = len(ids)\n", " id_map = {iid: i for i, iid in enumerate(ids)}\n", " labels = torch.zeros(N, N_CLASSES)\n", " for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: labels[i, l] = 1.0\n", "\n", " expert_feats = []\n", " for name in tqdm(EXPERTS, desc=f\" Loading {split_name} experts\"):\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=split_key)\n", " feats = torch.zeros(N, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in id_map:\n", " feats[id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " expert_feats.append(feats)\n", " del ds\n", "\n", " targets = torch.zeros(N, D_ANCHOR)\n", " with torch.no_grad():\n", " for j in tqdm(range(0, N, 512), desc=f\" Fusing {split_name}\"):\n", " end = min(j + 512, N)\n", " batch = [expert_feats[e][j:end].to(DEVICE) for e in range(3)]\n", " projected = [projectors[e](batch[e]) for e in range(3)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", " targets[j:end] = fused.cpu()\n", "\n", " torch.save({\"targets\": targets, \"labels\": labels, \"image_ids\": ids}, cache_path)\n", " print(f\" {split_name}: {N:,} targets computed and cached\")\n", "\n", " if split_name == \"train\":\n", " train_targets = targets; train_labels = labels\n", " train_ids = ids; train_id_map = id_map; N_train = N\n", " else:\n", " val_targets = targets; val_labels = labels\n", " val_ids = ids; val_id_map = id_map; N_val = N\n", " del expert_feats; gc.collect()\n", "\n", "del projectors, proj_sd; gc.collect()\n", "\n", "train_targets_gpu = train_targets.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "val_targets_gpu = val_targets.to(DEVICE)\n", "anchors_frozen = soup.constellation.anchors.detach()\n", "\n", "# Image preprocessing\n", "from torchvision import transforms\n", "img_transform = transforms.Compose([\n", " transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PRE-CACHE IMAGES AS TENSORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cache_images(split_name, split_key, id_map, N):\n", " cache_path = f\"cached_{split_name}_images.pt\"\n", " if os.path.exists(cache_path):\n", " print(f\" Loading cached {split_name} images...\")\n", " data = torch.load(cache_path, weights_only=True)\n", " print(f\" {split_name}: {data.shape} ({data.shape[0] * data.element_size() * data.nelement() / data.shape[0] / 1e6:.1f} MB/img)\")\n", " return data\n", "\n", " print(f\" Caching {split_name} images ({N:,})...\")\n", " images = torch.zeros(N, 3, IMAGE_SIZE, IMAGE_SIZE, dtype=torch.float16)\n", " stream = load_dataset(\"rafaelpadilla/coco2017\", split=split_key,\n", " revision=\"refs/convert/parquet\", streaming=False)\n", "\n", " cached = 0\n", " for row in tqdm(stream, desc=f\" Caching {split_name}\", total=N):\n", " iid = row.get(\"image_id\")\n", " if iid not in id_map:\n", " continue\n", " try:\n", " img = row[\"image\"].convert(\"RGB\")\n", " tensor = img_transform(img).half()\n", " images[id_map[iid]] = tensor\n", " cached += 1\n", " except:\n", " continue\n", "\n", " print(f\" Cached {cached}/{N} images\")\n", " torch.save(images, cache_path)\n", " size_mb = os.path.getsize(cache_path) / 1e6\n", " print(f\" Saved: {cache_path} ({size_mb:.0f} MB)\")\n", " return images\n", "\n", "train_images = cache_images(\"train\", \"train\", train_id_map, N_train)\n", "val_images = cache_images(\"val\", \"validation\", val_id_map, N_val)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILD ENCODER\")\n", "print(f\"{'='*65}\")\n", "\n", "encoder = GeoLIPViTEncoder().to(DEVICE)\n", "n_params = sum(p.numel() for p in encoder.parameters())\n", "print(f\" Architecture: {N_LAYERS}L/{D_MODEL}d/{N_HEADS}h, patch{PATCH_SIZE}\")\n", "print(f\" Input: {IMAGE_SIZE}×{IMAGE_SIZE} → {N_PATCHES} patches\")\n", "print(f\" Output: {D_ANCHOR}-d (on hypersphere)\")\n", "print(f\" Parameters: {n_params:,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def evaluate(encoder, soup, val_images, val_targets, val_labels, desc=\"Val\"):\n", " encoder.eval()\n", " N = val_images.shape[0]\n", " all_logits = torch.zeros(N, N_CLASSES)\n", " all_embs = torch.zeros(N, D_ANCHOR)\n", " n_seen = 0\n", "\n", " for j in tqdm(range(0, N, BATCH), desc=f\" {desc}\", leave=False):\n", " end = min(j + BATCH, N)\n", " pixels = val_images[j:end].float().to(DEVICE)\n", " # Skip zero images (failed to cache)\n", " mask = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if mask.sum() == 0:\n", " continue\n", "\n", " emb = encoder(pixels[mask])\n", " logits, _, nearest = soup(emb)\n", "\n", " k = 0\n", " for idx in range(j, end):\n", " if idx - j < len(mask) and mask[idx - j]:\n", " all_logits[idx] = logits[k].cpu().float()\n", " all_embs[idx] = emb[k].cpu().float()\n", " k += 1\n", " n_seen += 1\n", "\n", " # mAP\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = all_logits[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st) + 1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " # F1\n", " vp = (all_logits.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1 - v_lab)).sum(0)\n", " fn = ((1 - vp) * v_lab).sum(0)\n", " pr = tp / (tp + fp + 1e-8); rc = tp / (tp + fn + 1e-8)\n", " f1 = 2 * pr * rc / (pr + rc + 1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " # Cosine to targets\n", " valid = all_embs.norm(dim=-1) > 0.1\n", " v_cos = F.cosine_similarity(\n", " all_embs[valid], val_targets[valid], dim=-1).mean().item() if valid.sum() > 0 else 0.0\n", "\n", " # R@1\n", " if valid.sum() > 100:\n", " sim = all_embs[valid] @ val_targets[valid].T\n", " r1 = (sim.argmax(-1) == torch.arange(valid.sum())).float().mean().item()\n", " else:\n", " r1 = 0.0\n", "\n", " # Active anchors\n", " valid_embs = all_embs[valid].to(DEVICE)\n", " if valid_embs.shape[0] > 0:\n", " _, v_nearest = soup.constellation.triangulate(valid_embs)\n", " n_active = v_nearest.cpu().unique().numel()\n", " else:\n", " n_active = 0\n", "\n", " # CV\n", " v_cv = cv_metric(valid_embs[:2000]) if valid_embs.shape[0] > 100 else 0.0\n", "\n", " return {\n", " \"mAP\": mAP, \"f1\": macro_f1, \"r1\": r1, \"cos\": v_cos,\n", " \"cv\": v_cv, \"n_active\": n_active, \"n_seen\": n_seen,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Losses: InfoNCE + MSE + CV + BCE + Procrustes alignment\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\" Images: train={N_train:,} val={N_val:,} (cached as tensors)\")\n", "print(f\"{'='*65}\")\n", "\n", "optimizer = torch.optim.Adam(encoder.parameters(), lr=LR)\n", "n_batches = N_train // BATCH\n", "total_steps = n_batches * EPOCHS\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=WARMUP_STEPS),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - WARMUP_STEPS, 1), eta_min=1e-6)],\n", " milestones=[WARMUP_STEPS])\n", "\n", "scaler = torch.amp.GradScaler(\"cuda\")\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/geolip_vit_encoder\")\n", "best_mAP = 0.0\n", "gs = 0\n", "\n", "for epoch in range(EPOCHS):\n", " encoder.train()\n", " t0 = time.time()\n", " perm = torch.randperm(N_train)\n", "\n", " # Accumulators\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"cv\": 0, \"align\": 0,\n", " \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH),\n", " desc=f\"E{epoch+1:2d}/{EPOCHS} train\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4:\n", " continue\n", "\n", " pixels = train_images[idx].float().to(DEVICE)\n", " targets = train_targets_gpu[idx]\n", " labels = train_labels_gpu[idx]\n", "\n", " # Skip batches with too many zero images\n", " valid = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if valid.sum() < 4:\n", " continue\n", " pixels = pixels[valid]\n", " targets = targets[valid]\n", " labels = labels[valid]\n", "\n", " with torch.amp.autocast(\"cuda\", dtype=torch.bfloat16):\n", " emb = encoder(pixels)\n", " emb = EmbeddingAutograd.apply(emb, emb, anchors_frozen, 0.01, 1.0)\n", "\n", " l_nce, nce_acc = infonce(emb, targets)\n", " l_mse = F.mse_loss(emb, targets)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", " l_align = whitened_procrustes_loss(emb, targets)\n", "\n", " logits, _, _ = soup(emb)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv)\n", "\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(optimizer)\n", " torch.nn.utils.clip_grad_norm_(encoder.parameters(), GRAD_CLIP)\n", " scaler.step(optimizer)\n", " scaler.update()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " acc[\"loss\"] += loss.item()\n", " acc[\"nce\"] += l_nce.item()\n", " acc[\"mse\"] += l_mse.item()\n", " acc[\"bce\"] += l_bce.item()\n", " acc[\"cv\"] += l_cv.item()\n", " acc[\"align\"] += l_align.item()\n", " acc[\"nce_acc\"] += nce_acc\n", " acc[\"n\"] += 1\n", " gs += 1\n", "\n", " # Tensorboard step logging\n", " if gs % 50 == 0:\n", " writer.add_scalar(\"step/loss\", loss.item(), gs)\n", " writer.add_scalar(\"step/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"step/mse\", l_mse.item(), gs)\n", " writer.add_scalar(\"step/bce\", l_bce.item(), gs)\n", " writer.add_scalar(\"step/cv\", l_cv.item(), gs)\n", " writer.add_scalar(\"step/align\", l_align.item(), gs)\n", " writer.add_scalar(\"step/nce_acc\", nce_acc, gs)\n", " writer.add_scalar(\"step/lr\", scheduler.get_last_lr()[0], gs)\n", "\n", " # Update tqdm\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(\n", " loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " cos=f\"{1-acc['align']/d:.3f}\",\n", " ordered=True)\n", "\n", " elapsed = time.time() - t0\n", " d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: {elapsed:.0f}s \"\n", " f\"loss={acc['loss']/d:.4f} nce={acc['nce']/d:.4f} \"\n", " f\"mse={acc['mse']/d:.4f} bce={acc['bce']/d:.4f} \"\n", " f\"nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " # Epoch tensorboard\n", " writer.add_scalar(\"epoch/train_loss\", acc[\"loss\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce\", acc[\"nce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_mse\", acc[\"mse\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_bce\", acc[\"bce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_cv\", acc[\"cv\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_align\", acc[\"align\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce_acc\", acc[\"nce_acc\"] / d, epoch + 1)\n", "\n", " # ── Validation ──\n", " m = evaluate(encoder, soup, val_images, val_targets, val_labels)\n", "\n", " writer.add_scalar(\"epoch/val_mAP\", m[\"mAP\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_F1\", m[\"f1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_R@1\", m[\"r1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cos\", m[\"cos\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cv\", m[\"cv\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_anchors\", m[\"n_active\"], epoch + 1)\n", "\n", " mk = \"\"\n", " if m[\"mAP\"] > best_mAP:\n", " best_mAP = m[\"mAP\"]\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"config\": {\"d_model\": D_MODEL, \"n_heads\": N_HEADS,\n", " \"n_layers\": N_LAYERS, \"d_ff\": D_FF,\n", " \"patch_size\": PATCH_SIZE, \"image_size\": IMAGE_SIZE,\n", " \"output_dim\": D_ANCHOR},\n", " \"mAP\": m[\"mAP\"], \"f1\": m[\"f1\"], \"r1\": m[\"r1\"],\n", " \"cos\": m[\"cos\"], \"cv\": m[\"cv\"],\n", " \"epoch\": epoch + 1, \"n_active\": m[\"n_active\"],\n", " \"consensus_cv\": consensus_cv,\n", " }, \"checkpoints/geolip_vit_encoder_best.pt\")\n", " mk = \" ★\"\n", "\n", " # Save every epoch checkpoint\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"epoch\": epoch + 1, \"mAP\": m[\"mAP\"],\n", " \"optimizer\": optimizer.state_dict(),\n", " \"scheduler\": scheduler.state_dict(),\n", " \"scaler\": scaler.state_dict(),\n", " \"gs\": gs,\n", " }, f\"checkpoints/geolip_vit_e{epoch+1:02d}.pt\")\n", "\n", " print(f\" E{epoch+1} val: mAP={m['mAP']:.3f} F1={m['f1']:.3f} \"\n", " f\"R@1={m['r1']:.3f} cos={m['cos']:.3f} cv={m['cv']:.4f} \"\n", " f\"anchors={m['n_active']}/256 seen={m['n_seen']}/{N_val}{mk}\")\n", "\n", "writer.close()\n", "\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Encoder: {n_params:,} params (from scratch)\")\n", "print(f\" Checkpoints saved every epoch in checkpoints/\")\n", "print(f\" Tensorboard: runs/geolip_vit_encoder\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "315e2a5c75fe44019e57717bfbed8db8", "40f83c848ef44ef098460bcf8209f88e", "20da27781730496087dace82fa89bfab", "fa727a54c5a54ab882f3149cadd8835b", "b37017f3056047f1a8af9c0e498c11d3", "5ebfa405e68f47afa9cbd4c8ebf2f6f9", "7dcaf55aa84f4132a9c49271fab90095", "cf53383991964e6d8a1dd771c1563901", "20edb9896fd54ba088692c5205bc55e0", "bf92d0839b714990805c3e5249acee36", "152a5a89a2544cd89fcdde0905e0922e", "2b68f04edb93408ba89fb5979f1d3527", "faae8c05a68e49ff935be7e9dd5cded4", "2611e8f55ea940f99c5aaf5766c43444", 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"790145fbe42c40f08aa2b4b140b8b5d0", "1aef4ce686184a82a5c3b7d5a2d28ced", "873acac7190b40ba9f4e5c776640f179", "43b47417ac23458eb2de2a059c06ef9d", "06d1230a8550414c878a674f58936fb0", "ed46016b37ce4cde864dd448048fa4d0", "aadcf752dd7147cc90756e0266e5cd1f", "f1e314ed5bc84d1781cf0f19f37bfd8f", "464f61f3c7324ae8bd99bb56eb4a50ba", "bcc5a40d14a6419d976ecec9777324a8", "0639e2f2a7ff490c964d96853c6105d7", "a528203cfdc34f03b85be5bdd967334b", "60bce201130540dd8ca8b298847c8882", "1f5533b161a542ec9e293f22b974bcc5", "4f45fb7143694acf8416b4d467e7573c", "964b5c6172934492b4ef12d90ce1548e", "2c56edbe693b4994b7ff94020986281f", "a6495f1c194d4213828ba0338635550f", "59e6e68523f949bcb12614b9745c39f2", "2d82b42ba9684d2391b750d9dcadd9f4", "e363016152fa41e0bfea7996a61f49ba", "62ee8b2a80b1469d80f57f7f424dd27e" ] }, "id": "BJsSyexC6uEG", "outputId": "c800afd4-2b17-4e1c-9684-ce7e09f78916" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP VISION ENCODER — FROM SCRATCH\n", " ViT: 6L/384d/6h, patch16\n", " 196 patches + CLS → 128-d output\n", " Device: cuda\n", "=================================================================\n", "\n", " Loading soup...\n", " Soup: mAP=0.837 CV_target=0.2731\n", " train: loaded cached targets (118,287)\n", " val: loaded cached targets (5,000)\n", " Caching train images (118,287)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/39 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True)\n", " tm = targets.float().mean(0, keepdim=True)\n", " cos = F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1)\n", " return 1.0 - cos.mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROZEN SOUP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(N_ANCHORS, D_ANCHOR), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.n_comp = N_COMP\n", " asgn = torch.arange(N_ANCHORS) % N_COMP\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), D_COMP * 2), nn.GELU(),\n", " nn.Linear(D_COMP * 2, D_COMP), nn.LayerNorm(D_COMP))\n", " for k in range(N_COMP)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "class FrozenSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.constellation = Constellation()\n", " self.patchwork = Patchwork()\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.0),\n", " nn.Linear(pw_dim, N_CLASSES))\n", " def forward(self, emb_128):\n", " tri, nearest = self.constellation.triangulate(emb_128)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, emb_128], -1))\n", " return logits, tri, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROM-SCRATCH ViT ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPViTEncoder(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.patch_embed = nn.Conv2d(3, D_MODEL, kernel_size=PATCH_SIZE,\n", " stride=PATCH_SIZE)\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, D_MODEL))\n", " self.pos_embed = nn.Parameter(torch.zeros(1, N_PATCHES + 1, D_MODEL))\n", " self.embed_norm = nn.LayerNorm(D_MODEL)\n", " self.embed_drop = nn.Dropout(DROPOUT)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=D_MODEL, nhead=N_HEADS, dim_feedforward=D_FF,\n", " dropout=DROPOUT, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " self.encoder = nn.TransformerEncoder(\n", " encoder_layer, num_layers=N_LAYERS, enable_nested_tensor=False)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(D_MODEL, D_MODEL), nn.GELU(),\n", " nn.LayerNorm(D_MODEL),\n", " nn.Linear(D_MODEL, D_ANCHOR))\n", "\n", " self._init_weights()\n", "\n", " def _init_weights(self):\n", " for m in self.modules():\n", " if isinstance(m, nn.Linear):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.Conv2d):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.LayerNorm):\n", " nn.init.ones_(m.weight); nn.init.zeros_(m.bias)\n", " nn.init.trunc_normal_(self.pos_embed, std=0.02)\n", " nn.init.trunc_normal_(self.cls_token, std=0.02)\n", "\n", " def forward(self, pixel_values):\n", " B = pixel_values.shape[0]\n", " x = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)\n", " cls = self.cls_token.expand(B, -1, -1)\n", " x = torch.cat([cls, x], dim=1) + self.pos_embed\n", " x = self.embed_drop(self.embed_norm(x))\n", " x = self.encoder(x)\n", " pooled = x[:, 1:, :].mean(dim=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD SOUP + PRE-COMPUTE TARGETS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/base_tier_best.pt\", map_location=\"cpu\", weights_only=False)\n", "soup = FrozenSoup()\n", "soup_sd = {k: v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"constellation.\") or k.startswith(\"patchwork.\") or k.startswith(\"classifier.\")}\n", "soup.load_state_dict(soup_sd, strict=True)\n", "soup = soup.eval().to(DEVICE)\n", "for p in soup.parameters():\n", " p.requires_grad = False\n", "consensus_cv = ckpt.get(\"consensus_cv_128\", 0.27)\n", "print(f\" Soup: mAP={ckpt['mAP']:.3f} CV_target={consensus_cv:.4f}\")\n", "\n", "# Rebuild projectors for target generation\n", "class ExpertProjector(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(768, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "from datasets import load_dataset\n", "\n", "projectors = nn.ModuleList([ExpertProjector() for _ in range(3)])\n", "proj_sd = {k.replace(\"projectors.\", \"\"): v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"projectors.\")}\n", "projectors.load_state_dict(proj_sd)\n", "projectors = projectors.eval().to(DEVICE)\n", "\n", "for split_name, split_key in [(\"train\", \"train\"), (\"val\", \"val\")]:\n", " cache_path = f\"cached_{split_name}_targets.pt\"\n", " if os.path.exists(cache_path):\n", " cached = torch.load(cache_path, weights_only=False)\n", " if split_name == \"train\":\n", " train_targets = cached[\"targets\"]; train_labels = cached[\"labels\"]\n", " train_ids = cached[\"image_ids\"]; train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", " N_train = len(train_ids)\n", " else:\n", " val_targets = cached[\"targets\"]; val_labels = cached[\"labels\"]\n", " val_ids = cached[\"image_ids\"]; val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", " N_val = len(val_ids)\n", " print(f\" {split_name}: loaded cached targets ({len(cached['targets']):,})\")\n", " continue\n", "\n", " print(f\" Computing {split_name} targets...\")\n", " ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=split_key)\n", " ids = ref[\"image_id\"]; N = len(ids)\n", " id_map = {iid: i for i, iid in enumerate(ids)}\n", " labels = torch.zeros(N, N_CLASSES)\n", " for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: labels[i, l] = 1.0\n", "\n", " expert_feats = []\n", " for name in tqdm(EXPERTS, desc=f\" Loading {split_name} experts\"):\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=split_key)\n", " feats = torch.zeros(N, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in id_map:\n", " feats[id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " expert_feats.append(feats)\n", " del ds\n", "\n", " targets = torch.zeros(N, D_ANCHOR)\n", " with torch.no_grad():\n", " for j in tqdm(range(0, N, 512), desc=f\" Fusing {split_name}\"):\n", " end = min(j + 512, N)\n", " batch = [expert_feats[e][j:end].to(DEVICE) for e in range(3)]\n", " projected = [projectors[e](batch[e]) for e in range(3)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", " targets[j:end] = fused.cpu()\n", "\n", " torch.save({\"targets\": targets, \"labels\": labels, \"image_ids\": ids}, cache_path)\n", " print(f\" {split_name}: {N:,} targets computed and cached\")\n", "\n", " if split_name == \"train\":\n", " train_targets = targets; train_labels = labels\n", " train_ids = ids; train_id_map = id_map; N_train = N\n", " else:\n", " val_targets = targets; val_labels = labels\n", " val_ids = ids; val_id_map = id_map; N_val = N\n", " del expert_feats; gc.collect()\n", "\n", "del projectors, proj_sd; gc.collect()\n", "\n", "train_targets_gpu = train_targets.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "val_targets_gpu = val_targets.to(DEVICE)\n", "anchors_frozen = soup.constellation.anchors.detach()\n", "\n", "# Image preprocessing\n", "from torchvision import transforms\n", "img_transform = transforms.Compose([\n", " transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PRE-CACHE IMAGES AS TENSORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cache_images(split_name, split_key, id_map, N):\n", " cache_path = f\"cached_{split_name}_images.pt\"\n", " if os.path.exists(cache_path):\n", " print(f\" Loading cached {split_name} images...\")\n", " data = torch.load(cache_path, weights_only=True)\n", " print(f\" {split_name}: {data.shape} ({data.shape[0] * data.element_size() * data.nelement() / data.shape[0] / 1e6:.1f} MB/img)\")\n", " return data\n", "\n", " print(f\" Caching {split_name} images ({N:,})...\")\n", " images = torch.zeros(N, 3, IMAGE_SIZE, IMAGE_SIZE, dtype=torch.float16)\n", " stream = load_dataset(\"rafaelpadilla/coco2017\", split=split_key,\n", " revision=\"refs/convert/parquet\", streaming=True)\n", "\n", " cached = 0\n", " for row in tqdm(stream, desc=f\" Caching {split_name}\", total=N):\n", " iid = row.get(\"image_id\")\n", " if iid not in id_map:\n", " continue\n", " try:\n", " img = row[\"image\"].convert(\"RGB\")\n", " tensor = img_transform(img).half()\n", " images[id_map[iid]] = tensor\n", " cached += 1\n", " except:\n", " continue\n", "\n", " print(f\" Cached {cached}/{N} images\")\n", " torch.save(images, cache_path)\n", " size_mb = os.path.getsize(cache_path) / 1e6\n", " print(f\" Saved: {cache_path} ({size_mb:.0f} MB)\")\n", " return images\n", "\n", "train_images = cache_images(\"train\", \"train\", train_id_map, N_train)\n", "val_images = cache_images(\"val\", \"validation\", val_id_map, N_val)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILD ENCODER\")\n", "print(f\"{'='*65}\")\n", "\n", "encoder = GeoLIPViTEncoder().to(DEVICE)\n", "n_params = sum(p.numel() for p in encoder.parameters())\n", "print(f\" Architecture: {N_LAYERS}L/{D_MODEL}d/{N_HEADS}h, patch{PATCH_SIZE}\")\n", "print(f\" Input: {IMAGE_SIZE}×{IMAGE_SIZE} → {N_PATCHES} patches\")\n", "print(f\" Output: {D_ANCHOR}-d (on hypersphere)\")\n", "print(f\" Parameters: {n_params:,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def evaluate(encoder, soup, val_images, val_targets, val_labels, desc=\"Val\"):\n", " encoder.eval()\n", " N = val_images.shape[0]\n", " all_logits = torch.zeros(N, N_CLASSES)\n", " all_embs = torch.zeros(N, D_ANCHOR)\n", " n_seen = 0\n", "\n", " for j in tqdm(range(0, N, BATCH), desc=f\" {desc}\", leave=False):\n", " end = min(j + BATCH, N)\n", " pixels = val_images[j:end].float().to(DEVICE)\n", " # Skip zero images (failed to cache)\n", " mask = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if mask.sum() == 0:\n", " continue\n", "\n", " emb = encoder(pixels[mask])\n", " logits, _, nearest = soup(emb)\n", "\n", " k = 0\n", " for idx in range(j, end):\n", " if idx - j < len(mask) and mask[idx - j]:\n", " all_logits[idx] = logits[k].cpu().float()\n", " all_embs[idx] = emb[k].cpu().float()\n", " k += 1\n", " n_seen += 1\n", "\n", " # mAP\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = all_logits[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st) + 1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " # F1\n", " vp = (all_logits.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1 - v_lab)).sum(0)\n", " fn = ((1 - vp) * v_lab).sum(0)\n", " pr = tp / (tp + fp + 1e-8); rc = tp / (tp + fn + 1e-8)\n", " f1 = 2 * pr * rc / (pr + rc + 1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " # Cosine to targets\n", " valid = all_embs.norm(dim=-1) > 0.1\n", " v_cos = F.cosine_similarity(\n", " all_embs[valid], val_targets[valid], dim=-1).mean().item() if valid.sum() > 0 else 0.0\n", "\n", " # R@1\n", " if valid.sum() > 100:\n", " sim = all_embs[valid] @ val_targets[valid].T\n", " r1 = (sim.argmax(-1) == torch.arange(valid.sum())).float().mean().item()\n", " else:\n", " r1 = 0.0\n", "\n", " # Active anchors\n", " valid_embs = all_embs[valid].to(DEVICE)\n", " if valid_embs.shape[0] > 0:\n", " _, v_nearest = soup.constellation.triangulate(valid_embs)\n", " n_active = v_nearest.cpu().unique().numel()\n", " else:\n", " n_active = 0\n", "\n", " # CV\n", " v_cv = cv_metric(valid_embs[:2000]) if valid_embs.shape[0] > 100 else 0.0\n", "\n", " return {\n", " \"mAP\": mAP, \"f1\": macro_f1, \"r1\": r1, \"cos\": v_cos,\n", " \"cv\": v_cv, \"n_active\": n_active, \"n_seen\": n_seen,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\" {EPOCHS} total epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Losses: InfoNCE + MSE + CV + BCE + Procrustes alignment\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\" Images: train={N_train:,} val={N_val:,} (cached as tensors)\")\n", "print(f\"{'='*65}\")\n", "\n", "optimizer = torch.optim.Adam(encoder.parameters(), lr=LR)\n", "n_batches = N_train // BATCH\n", "total_steps = n_batches * EPOCHS\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=WARMUP_STEPS),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - WARMUP_STEPS, 1), eta_min=1e-6)],\n", " milestones=[WARMUP_STEPS])\n", "\n", "scaler = torch.amp.GradScaler(\"cuda\")\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/geolip_vit_encoder\")\n", "best_mAP = 0.0\n", "gs = 0\n", "start_epoch = 0\n", "\n", "# ── Warm-start with optional expansion (384→1024) ──\n", "resume_ckpt = None\n", "for e in range(EPOCHS, 0, -1):\n", " p = f\"checkpoints/geolip_vit_e{e:02d}.pt\"\n", " if os.path.exists(p):\n", " resume_ckpt = p\n", " break\n", "\n", "if resume_ckpt is None:\n", " if os.path.exists(\"checkpoints/geolip_vit_encoder_best.pt\"):\n", " resume_ckpt = \"checkpoints/geolip_vit_encoder_best.pt\"\n", "\n", "if resume_ckpt is not None:\n", " print(f\"\\n Warm-starting from: {resume_ckpt}\")\n", " ckpt_resume = torch.load(resume_ckpt, map_location=DEVICE, weights_only=False)\n", "\n", " if \"encoder_state_dict\" in ckpt_resume:\n", " prev_sd = ckpt_resume[\"encoder_state_dict\"]\n", " curr_sd = encoder.state_dict()\n", "\n", " # Check if this is an expansion (dimension mismatch)\n", " expanding = False\n", " for k in prev_sd:\n", " if k in curr_sd and prev_sd[k].shape != curr_sd[k].shape:\n", " expanding = True\n", " break\n", "\n", " if expanding:\n", " print(f\" Expanding {D_MODEL_PREV}→{D_MODEL}\")\n", " loaded, expanded, skipped = 0, 0, 0\n", "\n", " for k in prev_sd:\n", " if k not in curr_sd:\n", " skipped += 1; continue\n", "\n", " prev_p = prev_sd[k]\n", " curr_p = curr_sd[k]\n", "\n", " if prev_p.shape == curr_p.shape:\n", " # Exact match — copy directly\n", " curr_sd[k] = prev_p\n", " loaded += 1\n", " elif prev_p.dim() == curr_p.dim():\n", " # Dimension mismatch — expand by padding with Xavier\n", " # Keep the trained portion, fill the rest\n", " nn.init.xavier_uniform_(curr_p) if curr_p.dim() >= 2 else nn.init.zeros_(curr_p)\n", " slices = tuple(slice(0, min(s1, s2))\n", " for s1, s2 in zip(prev_p.shape, curr_p.shape))\n", " curr_sd[k][slices] = prev_p[slices]\n", " expanded += 1\n", " else:\n", " skipped += 1\n", "\n", " encoder.load_state_dict(curr_sd)\n", " print(f\" ✓ Loaded: {loaded} Expanded: {expanded} Skipped: {skipped}\")\n", "\n", " # Fresh training from epoch 0 for expanded model\n", " start_epoch = 0\n", " best_mAP = 0.0\n", " gs = 0\n", " print(f\" ✓ Starting fresh from epoch 1 (expanded architecture)\")\n", " else:\n", " # Same architecture — resume normally\n", " encoder.load_state_dict(prev_sd)\n", " start_epoch = ckpt_resume.get(\"epoch\", 0)\n", " best_mAP = ckpt_resume.get(\"mAP\", 0.0)\n", " gs = ckpt_resume.get(\"gs\", start_epoch * n_batches)\n", "\n", " # Fresh cosine schedule for continuation\n", " remaining_steps = n_batches * (EPOCHS - start_epoch)\n", " resume_lr = LR * 0.5\n", " optimizer = torch.optim.Adam(encoder.parameters(), lr=resume_lr)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=remaining_steps, eta_min=1e-6)\n", " scaler = torch.amp.GradScaler(\"cuda\")\n", " print(f\" ✓ Same-arch resume from epoch {start_epoch+1}\")\n", " print(f\" ✓ Fresh cosine: lr={resume_lr} for {remaining_steps} steps\")\n", "\n", " print(f\" best_mAP={best_mAP:.3f}, gs={gs}\")\n", " del ckpt_resume; gc.collect()\n", "else:\n", " print(f\"\\n Training from scratch (no checkpoint found)\")\n", "\n", "for epoch in range(start_epoch, EPOCHS):\n", " encoder.train()\n", " t0 = time.time()\n", " perm = torch.randperm(N_train)\n", "\n", " # Accumulators\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"cv\": 0, \"align\": 0,\n", " \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH),\n", " desc=f\"E{epoch+1:2d}/{EPOCHS} train\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4:\n", " continue\n", "\n", " pixels = train_images[idx].float().to(DEVICE)\n", " targets = train_targets_gpu[idx]\n", " labels = train_labels_gpu[idx]\n", "\n", " # Skip batches with too many zero images\n", " valid = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if valid.sum() < 4:\n", " continue\n", " pixels = pixels[valid]\n", " targets = targets[valid]\n", " labels = labels[valid]\n", "\n", " with torch.amp.autocast(\"cuda\", dtype=torch.bfloat16):\n", " emb = encoder(pixels)\n", " emb = EmbeddingAutograd.apply(emb, emb, anchors_frozen, 0.01, 1.0)\n", "\n", " l_nce, nce_acc = infonce(emb, targets)\n", " l_mse = F.mse_loss(emb, targets)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", " l_align = whitened_procrustes_loss(emb, targets)\n", "\n", " logits, _, _ = soup(emb)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv)\n", "\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(optimizer)\n", " torch.nn.utils.clip_grad_norm_(encoder.parameters(), GRAD_CLIP)\n", " scaler.step(optimizer)\n", " scaler.update()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " acc[\"loss\"] += loss.item()\n", " acc[\"nce\"] += l_nce.item()\n", " acc[\"mse\"] += l_mse.item()\n", " acc[\"bce\"] += l_bce.item()\n", " acc[\"cv\"] += l_cv.item()\n", " acc[\"align\"] += l_align.item()\n", " acc[\"nce_acc\"] += nce_acc\n", " acc[\"n\"] += 1\n", " gs += 1\n", "\n", " # Tensorboard step logging\n", " if gs % 50 == 0:\n", " writer.add_scalar(\"step/loss\", loss.item(), gs)\n", " writer.add_scalar(\"step/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"step/mse\", l_mse.item(), gs)\n", " writer.add_scalar(\"step/bce\", l_bce.item(), gs)\n", " writer.add_scalar(\"step/cv\", l_cv.item(), gs)\n", " writer.add_scalar(\"step/align\", l_align.item(), gs)\n", " writer.add_scalar(\"step/nce_acc\", nce_acc, gs)\n", " writer.add_scalar(\"step/lr\", scheduler.get_last_lr()[0], gs)\n", "\n", " # Update tqdm\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(\n", " loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " cos=f\"{1-acc['align']/d:.3f}\",\n", " ordered=True)\n", "\n", " elapsed = time.time() - t0\n", " d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: {elapsed:.0f}s \"\n", " f\"loss={acc['loss']/d:.4f} nce={acc['nce']/d:.4f} \"\n", " f\"mse={acc['mse']/d:.4f} bce={acc['bce']/d:.4f} \"\n", " f\"nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " # Epoch tensorboard\n", " writer.add_scalar(\"epoch/train_loss\", acc[\"loss\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce\", acc[\"nce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_mse\", acc[\"mse\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_bce\", acc[\"bce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_cv\", acc[\"cv\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_align\", acc[\"align\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce_acc\", acc[\"nce_acc\"] / d, epoch + 1)\n", "\n", " # ── Validation ──\n", " m = evaluate(encoder, soup, val_images, val_targets, val_labels)\n", "\n", " writer.add_scalar(\"epoch/val_mAP\", m[\"mAP\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_F1\", m[\"f1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_R@1\", m[\"r1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cos\", m[\"cos\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cv\", m[\"cv\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_anchors\", m[\"n_active\"], epoch + 1)\n", "\n", " mk = \"\"\n", " if m[\"mAP\"] > best_mAP:\n", " best_mAP = m[\"mAP\"]\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"config\": {\"d_model\": D_MODEL, \"n_heads\": N_HEADS,\n", " \"n_layers\": N_LAYERS, \"d_ff\": D_FF,\n", " \"patch_size\": PATCH_SIZE, \"image_size\": IMAGE_SIZE,\n", " \"output_dim\": D_ANCHOR},\n", " \"mAP\": m[\"mAP\"], \"f1\": m[\"f1\"], \"r1\": m[\"r1\"],\n", " \"cos\": m[\"cos\"], \"cv\": m[\"cv\"],\n", " \"epoch\": epoch + 1, \"n_active\": m[\"n_active\"],\n", " \"consensus_cv\": consensus_cv,\n", " }, \"checkpoints/geolip_vit_encoder_best.pt\")\n", " mk = \" ★\"\n", "\n", " # Save every epoch checkpoint\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"epoch\": epoch + 1, \"mAP\": m[\"mAP\"],\n", " \"optimizer\": optimizer.state_dict(),\n", " \"scheduler\": scheduler.state_dict(),\n", " \"scaler\": scaler.state_dict(),\n", " \"gs\": gs,\n", " }, f\"checkpoints/geolip_vit_e{epoch+1:02d}.pt\")\n", "\n", " print(f\" E{epoch+1} val: mAP={m['mAP']:.3f} F1={m['f1']:.3f} \"\n", " f\"R@1={m['r1']:.3f} cos={m['cos']:.3f} cv={m['cv']:.4f} \"\n", " f\"anchors={m['n_active']}/256 seen={m['n_seen']}/{N_val}{mk}\")\n", "\n", "writer.close()\n", "\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Encoder: {n_params:,} params (from scratch)\")\n", "print(f\" Checkpoints saved every epoch in checkpoints/\")\n", "print(f\" Tensorboard: runs/geolip_vit_encoder\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "xiSUPJ8Nd8yl", "outputId": "bf8152be-0cc2-42f7-9a3c-9c1ae5743028" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP VISION ENCODER — FROM SCRATCH\n", " ViT: 6L/1024d/32h, patch16\n", " 196 patches + CLS → 128-d output\n", " Device: cuda\n", "=================================================================\n", "\n", " Loading soup...\n", " Soup: mAP=0.837 CV_target=0.2731\n", " train: loaded cached targets (118,287)\n", " val: loaded cached targets (5,000)\n", " Loading cached train images...\n", " train: torch.Size([118287, 3, 224, 224]) (35611.0 MB/img)\n", " Loading cached val images...\n", " val: torch.Size([5000, 3, 224, 224]) (1505.3 MB/img)\n", "\n", "=================================================================\n", "BUILD ENCODER\n", "=================================================================\n", " Architecture: 6L/1024d/32h, patch16\n", " Input: 224×224 → 196 patches\n", " Output: 128-d (on hypersphere)\n", " Parameters: 77,752,448\n", "\n", "=================================================================\n", "TRAINING\n", " 60 total epochs, lr=0.0003, batch=48\n", " Losses: InfoNCE + MSE + CV + BCE + Procrustes alignment\n", " CV target: 0.2731\n", " Images: train=118,287 val=5,000 (cached as tensors)\n", "=================================================================\n", "\n", " Warm-starting from: checkpoints/geolip_vit_e60.pt\n", " Expanding 384→1024\n", " ✓ Loaded: 1 Expanded: 83 Skipped: 0\n", " ✓ Starting fresh from epoch 1 (expanded architecture)\n", " best_mAP=0.000, gs=0\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 1/60 train: 100%|██████████| 2465/2465 [03:59<00:00, 10.28batch/s, cos=0.496, loss=1.1141, nce_acc=0.746, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 train: 240s loss=1.1133 nce=0.8212 mse=0.0080 bce=0.1205 nce_acc=0.746\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 val: mAP=0.353 F1=0.341 R@1=0.220 cos=0.533 cv=0.1979 anchors=97/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 2/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.41batch/s, cos=0.564, loss=0.7306, nce_acc=0.859, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 train: 237s loss=0.7306 nce=0.4779 mse=0.0069 bce=0.1048 nce_acc=0.859\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 val: mAP=0.381 F1=0.371 R@1=0.254 cos=0.558 cv=0.1603 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 3/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.42batch/s, cos=0.588, loss=0.6287, nce_acc=0.890, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 train: 237s loss=0.6286 nce=0.3892 mse=0.0065 bce=0.1000 nce_acc=0.891\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 val: mAP=0.394 F1=0.372 R@1=0.251 cos=0.565 cv=0.1693 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 4/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.41batch/s, cos=0.607, loss=0.5550, nce_acc=0.912, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 train: 237s loss=0.5551 nce=0.3264 mse=0.0062 bce=0.0960 nce_acc=0.912\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 val: mAP=0.407 F1=0.394 R@1=0.295 cos=0.575 cv=0.1817 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 5/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.42batch/s, cos=0.621, loss=0.5020, nce_acc=0.926, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 train: 237s loss=0.5019 nce=0.2816 mse=0.0060 bce=0.0930 nce_acc=0.926\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 val: mAP=0.428 F1=0.406 R@1=0.309 cos=0.593 cv=0.1474 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 6/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.44batch/s, cos=0.636, loss=0.4571, nce_acc=0.938, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 train: 236s loss=0.4571 nce=0.2449 mse=0.0058 bce=0.0903 nce_acc=0.938\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 val: mAP=0.433 F1=0.416 R@1=0.330 cos=0.600 cv=0.1418 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 7/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.42batch/s, cos=0.649, loss=0.4177, nce_acc=0.949, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 train: 237s loss=0.4176 nce=0.2130 mse=0.0055 bce=0.0874 nce_acc=0.949\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 val: mAP=0.439 F1=0.419 R@1=0.340 cos=0.607 cv=0.1585 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 8/60 train: 100%|██████████| 2465/2465 [03:56<00:00, 10.44batch/s, cos=0.661, loss=0.3831, nce_acc=0.958, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 train: 236s loss=0.3832 nce=0.1857 mse=0.0053 bce=0.0847 nce_acc=0.958\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 val: mAP=0.448 F1=0.426 R@1=0.334 cos=0.608 cv=0.1493 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 9/60 train: 100%|██████████| 2465/2465 [03:55<00:00, 10.46batch/s, cos=0.673, loss=0.3544, nce_acc=0.965, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 train: 236s loss=0.3545 nce=0.1637 mse=0.0052 bce=0.0824 nce_acc=0.965\n" ] }, { "output_type": "stream", "name": "stderr", 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nce_acc=1.000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E50 val: mAP=0.490 F1=0.493 R@1=0.417 cos=0.652 cv=0.1195 anchors=94/256 seen=5000/5000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E51/60 train: 100%|██████████| 2465/2465 [04:00<00:00, 10.25batch/s, cos=0.918, loss=0.0576, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E51 train: 241s loss=0.0576 nce=0.0075 mse=0.0013 bce=0.0283 nce_acc=1.000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E51 val: mAP=0.489 F1=0.494 R@1=0.419 cos=0.652 cv=0.1283 anchors=94/256 seen=5000/5000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E52/60 train: 100%|██████████| 2465/2465 [04:00<00:00, 10.26batch/s, cos=0.919, loss=0.0569, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E52 train: 240s loss=0.0569 nce=0.0074 mse=0.0013 bce=0.0280 nce_acc=1.000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E52 val: mAP=0.489 F1=0.492 R@1=0.420 cos=0.651 cv=0.1355 anchors=94/256 seen=5000/5000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E53/60 train: 100%|██████████| 2465/2465 [04:00<00:00, 10.27batch/s, cos=0.920, loss=0.0564, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E53 train: 240s loss=0.0564 nce=0.0073 mse=0.0013 bce=0.0278 nce_acc=1.000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E53 val: mAP=0.489 F1=0.493 R@1=0.421 cos=0.651 cv=0.1079 anchors=94/256 seen=5000/5000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E54/60 train: 100%|██████████| 2465/2465 [03:58<00:00, 10.32batch/s, cos=0.921, loss=0.0558, 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| 1022/2465 [01:39<02:20, 10.30batch/s, cos=0.922, loss=0.0550, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_138043/3203659930.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0ml_align\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwhitened_procrustes_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m \u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m 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*args, **kwargs)\u001b[0m\n\u001b[1;32m 1785\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1786\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1787\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/tmp/ipykernel_138043/3203659930.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, emb_128)\u001b[0m\n\u001b[1;32m 176\u001b[0m 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\"num_attention_heads\": 16,\n", " \"num_hidden_layers\": 6,\n", " \"intermediate_size\": 4096,\n", " \"output_dim\": 128,\n", " \"n_anchors\": 256,\n", " \"n_comp\": 8,\n", " \"d_comp\": 64,\n", " \"n_classes\": 80,\n", " \"hidden_dropout_prob\": 0.1,\n", " \"soup_enabled\": True,\n", " \"consensus_cv\": 0.2731,\n", " \"experts\": [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"],\n", " \"torch_dtype\": \"float32\"\n", "}\n", "api.upload_file(\n", " path_or_fileobj=json.dumps(config, indent=2).encode(),\n", " path_in_repo=\"config.json\",\n", " repo_id=REPO_ID, repo_type=\"model\")\n", "\n", "print(\"✓ Config + modeling + inference uploaded\")\n", "print(f\"\\nhttps://huggingface.co/{REPO_ID}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1CrQrXZPtTD6", "outputId": "39aecf39-0339-4288-c782-212889141b97" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Created AbstractPhil/geolip-vit-large-x3\n", "✓ Config + modeling + inference uploaded\n", "\n", "https://huggingface.co/AbstractPhil/geolip-vit-large-x3\n" ] } ] }, { "cell_type": "markdown", "source": [ "## handling the inference" ], "metadata": { "id": "pd7ZAkK95Fyk" } }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# GeoLIP ViT: HuggingFace AutoModel\n", "#\n", "# Usage:\n", "# from transformers import AutoModel\n", "# model = AutoModel.from_pretrained(\"AbstractPhil/geolip-vit-base-x3\",\n", "# trust_remote_code=True)\n", "#\n", "# from torchvision import transforms\n", "# transform = transforms.Compose([\n", "# transforms.Resize((224, 224)),\n", "# transforms.ToTensor(),\n", "# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", "# ])\n", "# pixel_values = transform(image).unsqueeze(0)\n", "# outputs = model(pixel_values)\n", "#\n", "# # 128-d embedding on hypersphere (L2-normalized)\n", "# embedding = outputs.embedding # (B, 128)\n", "#\n", "# # Multi-label classification logits (80 COCO classes)\n", "# logits = outputs.logits # (B, 80) — if soup_enabled\n", "#\n", "# # Triangulation distances to 256 constellation anchors\n", "# triangulation = outputs.triangulation # (B, 256)\n", "#\n", "# # Nearest anchor index per sample\n", "# nearest = outputs.nearest # (B,)\n", "#\n", "# # Geometric diagnostics\n", "# diagnostics = outputs.diagnostics # dict\n", "# ============================================================================\n", "\n", "import math\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from transformers import PretrainedConfig, PreTrainedModel\n", "from dataclasses import dataclass, field\n", "from typing import Optional, Dict, Any\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPViTConfig(PretrainedConfig):\n", " model_type = \"geolip_vit\"\n", "\n", " def __init__(\n", " self,\n", " image_size=224,\n", " patch_size=16,\n", " hidden_size=384,\n", " num_attention_heads=6,\n", " num_hidden_layers=6,\n", " intermediate_size=1536,\n", " output_dim=128,\n", " n_anchors=256,\n", " n_comp=8,\n", " d_comp=64,\n", " n_classes=80,\n", " hidden_dropout_prob=0.1,\n", " soup_enabled=True,\n", " consensus_cv=0.2731,\n", " experts=None,\n", " **kwargs,\n", " ):\n", " super().__init__(**kwargs)\n", " self.image_size = image_size\n", " self.patch_size = patch_size\n", " self.hidden_size = hidden_size\n", " self.num_attention_heads = num_attention_heads\n", " self.num_hidden_layers = num_hidden_layers\n", " self.intermediate_size = intermediate_size\n", " self.output_dim = output_dim\n", " self.n_anchors = n_anchors\n", " self.n_comp = n_comp\n", " self.d_comp = d_comp\n", " self.n_classes = n_classes\n", " self.hidden_dropout_prob = hidden_dropout_prob\n", " self.soup_enabled = soup_enabled\n", " self.consensus_cv = consensus_cv\n", " self.experts = experts or [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# OUTPUT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class GeoLIPViTOutput:\n", " \"\"\"\n", " Output fields:\n", " embedding: (B, output_dim) L2-normalized on hypersphere\n", " logits: (B, n_classes) multi-label classification (if soup_enabled)\n", " triangulation: (B, n_anchors) distances to constellation anchors\n", " nearest: (B,) nearest anchor index\n", " patch_tokens: (B, n_patches, hidden_size) pre-pooling patch representations\n", " diagnostics: dict geometric metrics\n", " \"\"\"\n", " embedding: torch.Tensor = None\n", " logits: Optional[torch.Tensor] = None\n", " triangulation: Optional[torch.Tensor] = None\n", " nearest: Optional[torch.Tensor] = None\n", " patch_tokens: Optional[torch.Tensor] = None\n", " diagnostics: Optional[Dict[str, Any]] = None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self, n_anchors, d):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", "\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self, n_anchors, n_comp, d_comp):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for k in range(n_comp)])\n", "\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPViTModel(PreTrainedModel):\n", " \"\"\"\n", " From-scratch Vision Transformer producing L2-normalized embeddings\n", " on a 128-d hypersphere, geometrically anchored by a constellation\n", " of 256 reference points trained via 3-expert consensus distillation.\n", "\n", " The encoder is trained from Xavier initialization against consensus\n", " targets from CLIP ViT-L/14, DINOv2 ViT-B/14, and SigLIP ViT-B/16.\n", "\n", " Optional soup pipeline (constellation + patchwork + classifier)\n", " provides multi-label COCO classification from the embedding.\n", "\n", " Output fields:\n", " embedding: (B, 128) L2-normalized, consensus-aligned\n", " logits: (B, 80) multi-label COCO logits (if soup_enabled)\n", " triangulation: (B, 256) distances to constellation anchors\n", " nearest: (B,) nearest anchor index\n", " patch_tokens: (B, 196, 384) pre-pooling patch representations\n", " diagnostics: dict geometric metrics\n", " \"\"\"\n", " config_class = GeoLIPViTConfig\n", "\n", " def __init__(self, config):\n", " super().__init__(config)\n", " self.config = config\n", "\n", " n_patches = (config.image_size // config.patch_size) ** 2\n", "\n", " # ── Encoder ──\n", " self.patch_embed = nn.Conv2d(\n", " 3, config.hidden_size,\n", " kernel_size=config.patch_size, stride=config.patch_size)\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))\n", " self.pos_embed = nn.Parameter(\n", " torch.zeros(1, n_patches + 1, config.hidden_size))\n", " self.embed_norm = nn.LayerNorm(config.hidden_size)\n", " self.embed_drop = nn.Dropout(config.hidden_dropout_prob)\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=config.hidden_size,\n", " nhead=config.num_attention_heads,\n", " dim_feedforward=config.intermediate_size,\n", " dropout=config.hidden_dropout_prob,\n", " activation=\"gelu\",\n", " batch_first=True,\n", " norm_first=True,\n", " )\n", " self.encoder = nn.TransformerEncoder(\n", " encoder_layer, num_layers=config.num_hidden_layers,\n", " enable_nested_tensor=False)\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(config.hidden_size, config.hidden_size),\n", " nn.GELU(),\n", " nn.LayerNorm(config.hidden_size),\n", " nn.Linear(config.hidden_size, config.output_dim),\n", " )\n", "\n", " # ── Soup Pipeline (optional) ──\n", " if getattr(config, \"soup_enabled\", False):\n", " self.constellation = Constellation(config.n_anchors, config.output_dim)\n", " self.patchwork = Patchwork(\n", " config.n_anchors, config.n_comp, config.d_comp)\n", " pw_dim = config.n_comp * config.d_comp\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + config.output_dim, pw_dim),\n", " nn.GELU(), nn.LayerNorm(pw_dim), nn.Dropout(0.0),\n", " nn.Linear(pw_dim, config.n_classes))\n", " else:\n", " self.constellation = None\n", " self.patchwork = None\n", " self.classifier = None\n", "\n", " self.post_init()\n", "\n", " def _init_weights(self, module):\n", " if isinstance(module, nn.Linear):\n", " nn.init.xavier_uniform_(module.weight)\n", " if module.bias is not None:\n", " nn.init.zeros_(module.bias)\n", " elif isinstance(module, nn.Conv2d):\n", " nn.init.xavier_uniform_(module.weight)\n", " if module.bias is not None:\n", " nn.init.zeros_(module.bias)\n", " elif isinstance(module, nn.LayerNorm):\n", " nn.init.ones_(module.weight)\n", " nn.init.zeros_(module.bias)\n", "\n", " def forward(self, pixel_values, output_patch_tokens=False, **kwargs):\n", " B = pixel_values.shape[0]\n", " n_patches = (self.config.image_size // self.config.patch_size) ** 2\n", "\n", " # ── Encode ──\n", " x = self.patch_embed(pixel_values) # (B, d, H/P, W/P)\n", " x = x.flatten(2).transpose(1, 2) # (B, n_patches, d)\n", "\n", " cls = self.cls_token.expand(B, -1, -1)\n", " x = torch.cat([cls, x], dim=1) # (B, n_patches+1, d)\n", " x = x + self.pos_embed\n", " x = self.embed_drop(self.embed_norm(x))\n", "\n", " x = self.encoder(x)\n", "\n", " # ── Pool + Project ──\n", " patch_tokens = x[:, 1:, :] # (B, n_patches, d)\n", " pooled = patch_tokens.mean(dim=1) # (B, d)\n", " embedding = F.normalize(self.output_proj(pooled), dim=-1) # (B, output_dim)\n", "\n", " # ── Soup Pipeline ──\n", " logits = None\n", " triangulation = None\n", " nearest = None\n", " diagnostics = {}\n", "\n", " if self.constellation is not None:\n", " tri, near = self.constellation.triangulate(embedding)\n", " triangulation = tri\n", " nearest = near\n", "\n", " if self.patchwork is not None and self.classifier is not None:\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, embedding], -1))\n", "\n", " # Geometric diagnostics\n", " with torch.no_grad():\n", " anchors_n = F.normalize(self.constellation.anchors, dim=-1)\n", " cos_to_anchors = embedding @ anchors_n.T\n", " diagnostics = {\n", " \"nearest_cos\": cos_to_anchors.max(dim=-1).values.mean().item(),\n", " \"mean_anchor_cos\": cos_to_anchors.mean().item(),\n", " \"n_active_anchors\": near.unique().numel(),\n", " \"embedding_norm\": embedding.norm(dim=-1).mean().item(),\n", " }\n", "\n", " return GeoLIPViTOutput(\n", " embedding=embedding,\n", " logits=logits,\n", " triangulation=triangulation,\n", " nearest=nearest,\n", " patch_tokens=patch_tokens if output_patch_tokens else None,\n", " diagnostics=diagnostics,\n", " )" ], "metadata": { "id": "x6L5GVOxsY-f" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GeoLIP ViT — Inference Example\n", "================================\n", "\n", "from transformers import AutoModel\n", "model = AutoModel.from_pretrained(\"AbstractPhil/geolip-vit-base-x3\",\n", " trust_remote_code=True)\n", "\"\"\"\n", "\n", "import torch\n", "from transformers import AutoModel\n", "from torchvision import transforms\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "# ── Load model ──\n", "print(\"Loading GeoLIP ViT...\")\n", "model = AutoModel.from_pretrained(\n", " \"AbstractPhil/geolip-vit-base-x3\",\n", " trust_remote_code=True,\n", ").eval().to(DEVICE)\n", "\n", "print(f\" Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n", "print(f\" Output dim: {model.config.output_dim}\")\n", "print(f\" Anchors: {model.config.n_anchors} × {model.config.output_dim}-d\")\n", "\n", "# ── Image preprocessing ──\n", "transform = transforms.Compose([\n", " transforms.Resize((224, 224)),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "# ── Test images ──\n", "urls = [\n", " \"http://images.cocodataset.org/val2017/000000039769.jpg\", # cats on couch\n", " \"http://images.cocodataset.org/val2017/000000285138.jpg\", # giraffe\n", " \"http://images.cocodataset.org/val2017/000000397133.jpg\", # people at table\n", "]\n", "\n", "images = []\n", "for url in urls:\n", " try:\n", " resp = requests.get(url, timeout=10)\n", " img = Image.open(BytesIO(resp.content)).convert(\"RGB\")\n", " images.append(img)\n", " except:\n", " print(f\" Skipped: {url}\")\n", "\n", "if not images:\n", " print(\"No images loaded, using random tensors\")\n", " pixel_values = torch.randn(3, 3, 224, 224).to(DEVICE)\n", "else:\n", " pixel_values = torch.stack([transform(img) for img in images]).to(DEVICE)\n", "\n", "# ── Forward pass ──\n", "with torch.no_grad():\n", " outputs = model(pixel_values)\n", "\n", "print(f\"\\n{'='*50}\")\n", "print(\"OUTPUTS\")\n", "print(f\"{'='*50}\")\n", "print(f\" embedding: {outputs.embedding.shape} (L2-normalized on hypersphere)\")\n", "print(f\" logits: {outputs.logits.shape if outputs.logits is not None else 'None'}\")\n", "print(f\" triangulation: {outputs.triangulation.shape if outputs.triangulation is not None else 'None'}\")\n", "print(f\" nearest: {outputs.nearest.tolist() if outputs.nearest is not None else 'None'}\")\n", "print(f\" diagnostics: {outputs.diagnostics}\")\n", "\n", "# ── Pairwise similarity ──\n", "emb = outputs.embedding\n", "sim = emb @ emb.T\n", "print(f\"\\nPairwise cosine similarity:\")\n", "for i in range(len(images)):\n", " for j in range(i + 1, len(images)):\n", " print(f\" [{i}] × [{j}]: {sim[i, j]:.4f}\")\n", "\n", "# ── Embedding properties ──\n", "print(f\"\\nEmbedding properties:\")\n", "print(f\" Norms: {emb.norm(dim=-1).tolist()}\")\n", "print(f\" Mean: {emb.mean(dim=0).abs().mean():.6f}\")\n", "\n", "# ── COCO class predictions (if soup enabled) ──\n", "if outputs.logits is not None:\n", " COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", " ]\n", "\n", " probs = outputs.logits.sigmoid()\n", " print(f\"\\nTop-5 predictions per image:\")\n", " for i in range(len(images)):\n", " top5 = probs[i].topk(5)\n", " preds = [(COCO_CLASSES[idx], f\"{conf:.3f}\")\n", " for idx, conf in zip(top5.indices.tolist(), top5.values.tolist())]\n", " print(f\" Image {i}: {preds}\")\n", "\n", "print(f\"\\n{'='*50}\")\n", "print(\"DONE\")\n", "print(f\"{'='*50}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "c862ee82891242ee8ec002e7ca245569", "3fa2a8d6e5714b00bd06ff65ae862b5d", "4e8ab519c0eb4cb2a677546df92ded91", "510933f880aa4266831e2ced3b6561a2", "12e929f13c5045159aacd3272021cd79", "7b6040877624466ca74bed9ec229a992", "f9e108aca4f44fdda28a43feeeea118a", "9c6874f5e5794bafb9e1af875cd20f3a", "d653116de625451c991bb82d98f4ea8c", "05e8821e42144444aee02448834abff4", "379d2a44f7654e0893530409d2921649", "13c76d6570c44bb3850e767bf08d1b5a", "0235e9b78f0a4d748e84684b7d2edf10", "d78c54ae5b8b4ecc8e756b49afe59680", "a879520ea1dd4bfb837a5268a665ad2d", "8658b8945d1b460a96338cb247540c0b", "0c3283d004014e6f9bab75aa0549f2c6", "bc6b892438084e25abfa8d3123c256a2", "a4b8ffea891b4fc28a95a60867f1ce59", "05ef3965a2ac4dfb83da1d3787648e08", "129fff71192b4ce7a47c45f9a7542eec", "d2c1c8bd16b64a09bb532e3b750882d9" ] }, "id": "bHBr282VtqFu", "outputId": "4f4189bf-a55e-4ffa-a218-89fcb11dec33" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading GeoLIP ViT...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modeling_geolip_vit.py: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c862ee82891242ee8ec002e7ca245569" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "A new version of the following files was downloaded from https://huggingface.co/AbstractPhil/geolip-vit-base-x3:\n", "- modeling_geolip_vit.py\n", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/140 [00:00 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(a, b, temperature=0.07):\n", " a = F.normalize(a, dim=-1); b = F.normalize(b, dim=-1)\n", " logits = (a @ b.T) / temperature\n", " labels = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True)\n", " tm = targets.float().mean(0, keepdim=True)\n", " cos = F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1)\n", " return 1.0 - cos.mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROZEN SOUP\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Constellation(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(N_ANCHORS, D_ANCHOR), dim=-1))\n", " def triangulate(self, emb):\n", " a = F.normalize(self.anchors, dim=-1)\n", " cos = emb @ a.T\n", " return 1.0 - cos, cos.argmax(dim=-1)\n", "\n", "class Patchwork(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.n_comp = N_COMP\n", " asgn = torch.arange(N_ANCHORS) % N_COMP\n", " self.register_buffer(\"asgn\", asgn)\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear((asgn == k).sum().item(), D_COMP * 2), nn.GELU(),\n", " nn.Linear(D_COMP * 2, D_COMP), nn.LayerNorm(D_COMP))\n", " for k in range(N_COMP)])\n", " def forward(self, tri):\n", " return torch.cat([self.comps[k](tri[:, self.asgn == k])\n", " for k in range(self.n_comp)], -1)\n", "\n", "class FrozenSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.constellation = Constellation()\n", " self.patchwork = Patchwork()\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.0),\n", " nn.Linear(pw_dim, N_CLASSES))\n", " def forward(self, emb_128):\n", " tri, nearest = self.constellation.triangulate(emb_128)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, emb_128], -1))\n", " return logits, tri, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FROM-SCRATCH ViT ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPViTEncoder(nn.Module):\n", " \"\"\"\n", " From-scratch ViT with geometric injection at every layer.\n", "\n", " At each transformer layer:\n", " 1. Pool current patch tokens → project to 128-d → triangulate against anchors\n", " 2. Project triangulation (256-d) back to 1024-d as a \"geo token\"\n", " 3. Prepend geo token to sequence — transformer attends to it\n", " 4. Run transformer layer\n", " 5. Remove geo token, keep residual\n", "\n", " Output: cat(1024-d pooled patches, 128-d hypersphere embedding)\n", " The soup reads the 128-d. The 1024-d carries the full transformer context.\n", " \"\"\"\n", " def __init__(self, anchors=None):\n", " super().__init__()\n", " self.patch_embed = nn.Conv2d(3, D_MODEL, kernel_size=PATCH_SIZE,\n", " stride=PATCH_SIZE)\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, D_MODEL))\n", " self.pos_embed = nn.Parameter(torch.zeros(1, N_PATCHES + 1, D_MODEL))\n", " self.embed_norm = nn.LayerNorm(D_MODEL)\n", " self.embed_drop = nn.Dropout(DROPOUT)\n", "\n", " # Individual layers (not nn.TransformerEncoder) so we can inject between them\n", " self.layers = nn.ModuleList([\n", " nn.TransformerEncoderLayer(\n", " d_model=D_MODEL, nhead=N_HEADS, dim_feedforward=D_FF,\n", " dropout=DROPOUT, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " for _ in range(N_LAYERS)])\n", "\n", " # Geometric injection: pool → 128-d → triangulate → 1024-d geo token\n", " self.geo_pool_proj = nn.Linear(D_MODEL, D_ANCHOR)\n", " self.geo_tri_proj = nn.Sequential(\n", " nn.Linear(N_ANCHORS, D_MODEL), nn.GELU(),\n", " nn.LayerNorm(D_MODEL))\n", "\n", " # Output projection: 1024-d → 128-d (hypersphere)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(D_MODEL, D_MODEL), nn.GELU(),\n", " nn.LayerNorm(D_MODEL),\n", " nn.Linear(D_MODEL, D_ANCHOR))\n", "\n", " # Store frozen anchor reference (set after model creation)\n", " self.register_buffer(\"anchors\",\n", " anchors if anchors is not None\n", " else F.normalize(torch.randn(N_ANCHORS, D_ANCHOR), dim=-1))\n", "\n", " self._init_weights()\n", "\n", " def _init_weights(self):\n", " for m in self.modules():\n", " if isinstance(m, nn.Linear):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.Conv2d):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.LayerNorm):\n", " nn.init.ones_(m.weight); nn.init.zeros_(m.bias)\n", " nn.init.trunc_normal_(self.pos_embed, std=0.02)\n", " nn.init.trunc_normal_(self.cls_token, std=0.02)\n", "\n", " def _triangulate(self, emb_128):\n", " \"\"\"Compute triangulation distances from 128-d embedding to anchors.\"\"\"\n", " a = F.normalize(self.anchors, dim=-1)\n", " return 1.0 - emb_128 @ a.T # (B, N_ANCHORS)\n", "\n", " def forward(self, pixel_values):\n", " B = pixel_values.shape[0]\n", "\n", " # Patch embedding\n", " x = self.patch_embed(pixel_values).flatten(2).transpose(1, 2) # (B, 196, 1024)\n", " cls = self.cls_token.expand(B, -1, -1)\n", " x = torch.cat([cls, x], dim=1) + self.pos_embed # (B, 197, 1024)\n", " x = self.embed_drop(self.embed_norm(x))\n", "\n", " # Process through layers with geometric injection\n", " for layer in self.layers:\n", " # Pool current state → geometric embedding\n", " pooled = x[:, 1:, :].mean(dim=1) # (B, 1024)\n", " geo_128 = F.normalize(self.geo_pool_proj(pooled), dim=-1) # (B, 128)\n", " tri = self._triangulate(geo_128) # (B, 256)\n", " geo_token = self.geo_tri_proj(tri).unsqueeze(1) # (B, 1, 1024)\n", "\n", " # Prepend geo token — transformer attends to geometric context\n", " x_with_geo = torch.cat([geo_token, x], dim=1) # (B, 198, 1024)\n", " x_with_geo = layer(x_with_geo)\n", "\n", " # Remove geo token, keep the residual-updated patches\n", " x = x_with_geo[:, 1:, :] # (B, 197, 1024)\n", "\n", " # Final output: pool patches + project to hypersphere\n", " patch_tokens = x[:, 1:, :] # (B, 196, 1024)\n", " pooled_final = patch_tokens.mean(dim=1) # (B, 1024)\n", " embedding = F.normalize(self.output_proj(pooled_final), dim=-1) # (B, 128)\n", "\n", " return embedding\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD SOUP + PRE-COMPUTE TARGETS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/base_tier_best.pt\", map_location=\"cpu\", weights_only=False)\n", "soup = FrozenSoup()\n", "soup_sd = {k: v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"constellation.\") or k.startswith(\"patchwork.\") or k.startswith(\"classifier.\")}\n", "soup.load_state_dict(soup_sd, strict=True)\n", "soup = soup.eval().to(DEVICE)\n", "for p in soup.parameters():\n", " p.requires_grad = False\n", "consensus_cv = ckpt.get(\"consensus_cv_128\", 0.27)\n", "print(f\" Soup: mAP={ckpt['mAP']:.3f} CV_target={consensus_cv:.4f}\")\n", "\n", "# Rebuild projectors for target generation\n", "class ExpertProjector(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(768, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "from datasets import load_dataset\n", "\n", "projectors = nn.ModuleList([ExpertProjector() for _ in range(3)])\n", "proj_sd = {k.replace(\"projectors.\", \"\"): v for k, v in ckpt[\"state_dict\"].items()\n", " if k.startswith(\"projectors.\")}\n", "projectors.load_state_dict(proj_sd)\n", "projectors = projectors.eval().to(DEVICE)\n", "\n", "for split_name, split_key in [(\"train\", \"train\"), (\"val\", \"val\")]:\n", " cache_path = f\"cached_{split_name}_targets.pt\"\n", " if os.path.exists(cache_path):\n", " cached = torch.load(cache_path, weights_only=False)\n", " if split_name == \"train\":\n", " train_targets = cached[\"targets\"]; train_labels = cached[\"labels\"]\n", " train_ids = cached[\"image_ids\"]; train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", " N_train = len(train_ids)\n", " else:\n", " val_targets = cached[\"targets\"]; val_labels = cached[\"labels\"]\n", " val_ids = cached[\"image_ids\"]; val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", " N_val = len(val_ids)\n", " print(f\" {split_name}: loaded cached targets ({len(cached['targets']):,})\")\n", " continue\n", "\n", " print(f\" Computing {split_name} targets...\")\n", " ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=split_key)\n", " ids = ref[\"image_id\"]; N = len(ids)\n", " id_map = {iid: i for i, iid in enumerate(ids)}\n", " labels = torch.zeros(N, N_CLASSES)\n", " for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: labels[i, l] = 1.0\n", "\n", " expert_feats = []\n", " for name in tqdm(EXPERTS, desc=f\" Loading {split_name} experts\"):\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=split_key)\n", " feats = torch.zeros(N, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in id_map:\n", " feats[id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " expert_feats.append(feats)\n", " del ds\n", "\n", " targets = torch.zeros(N, D_ANCHOR)\n", " with torch.no_grad():\n", " for j in tqdm(range(0, N, 512), desc=f\" Fusing {split_name}\"):\n", " end = min(j + 512, N)\n", " batch = [expert_feats[e][j:end].to(DEVICE) for e in range(3)]\n", " projected = [projectors[e](batch[e]) for e in range(3)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", " targets[j:end] = fused.cpu()\n", "\n", " torch.save({\"targets\": targets, \"labels\": labels, \"image_ids\": ids}, cache_path)\n", " print(f\" {split_name}: {N:,} targets computed and cached\")\n", "\n", " if split_name == \"train\":\n", " train_targets = targets; train_labels = labels\n", " train_ids = ids; train_id_map = id_map; N_train = N\n", " else:\n", " val_targets = targets; val_labels = labels\n", " val_ids = ids; val_id_map = id_map; N_val = N\n", " del expert_feats; gc.collect()\n", "\n", "del projectors, proj_sd; gc.collect()\n", "\n", "train_targets_gpu = train_targets.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "val_targets_gpu = val_targets.to(DEVICE)\n", "anchors_frozen = soup.constellation.anchors.detach()\n", "\n", "# Image preprocessing\n", "from torchvision import transforms\n", "img_transform = transforms.Compose([\n", " transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PRE-CACHE IMAGES AS TENSORS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cache_images(split_name, split_key, id_map, N):\n", " cache_path = f\"cached_{split_name}_images.pt\"\n", " if os.path.exists(cache_path):\n", " print(f\" Loading cached {split_name} images...\")\n", " data = torch.load(cache_path, weights_only=True)\n", " print(f\" {split_name}: {data.shape} ({data.shape[0] * data.element_size() * data.nelement() / data.shape[0] / 1e6:.1f} MB/img)\")\n", " return data\n", "\n", " print(f\" Caching {split_name} images ({N:,})...\")\n", " images = torch.zeros(N, 3, IMAGE_SIZE, IMAGE_SIZE, dtype=torch.float16)\n", " stream = load_dataset(\"rafaelpadilla/coco2017\", split=split_key,\n", " revision=\"refs/convert/parquet\", streaming=True)\n", "\n", " cached = 0\n", " for row in tqdm(stream, desc=f\" Caching {split_name}\", total=N):\n", " iid = row.get(\"image_id\")\n", " if iid not in id_map:\n", " continue\n", " try:\n", " img = row[\"image\"].convert(\"RGB\")\n", " tensor = img_transform(img).half()\n", " images[id_map[iid]] = tensor\n", " cached += 1\n", " except:\n", " continue\n", "\n", " print(f\" Cached {cached}/{N} images\")\n", " torch.save(images, cache_path)\n", " size_mb = os.path.getsize(cache_path) / 1e6\n", " print(f\" Saved: {cache_path} ({size_mb:.0f} MB)\")\n", " return images\n", "\n", "train_images = cache_images(\"train\", \"train\", train_id_map, N_train)\n", "val_images = cache_images(\"val\", \"validation\", val_id_map, N_val)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILD ENCODER\")\n", "print(f\"{'='*65}\")\n", "\n", "encoder = GeoLIPViTEncoder(anchors=anchors_frozen).to(DEVICE)\n", "n_params = sum(p.numel() for p in encoder.parameters())\n", "print(f\" Architecture: {N_LAYERS}L/{D_MODEL}d/{N_HEADS}h, patch{PATCH_SIZE}\")\n", "print(f\" Input: {IMAGE_SIZE}×{IMAGE_SIZE} → {N_PATCHES} patches\")\n", "print(f\" Output: {D_ANCHOR}-d (on hypersphere)\")\n", "print(f\" Parameters: {n_params:,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def evaluate(encoder, soup, val_images, val_targets, val_labels, desc=\"Val\"):\n", " encoder.eval()\n", " N = val_images.shape[0]\n", " all_logits = torch.zeros(N, N_CLASSES)\n", " all_embs = torch.zeros(N, D_ANCHOR)\n", " n_seen = 0\n", "\n", " for j in tqdm(range(0, N, BATCH), desc=f\" {desc}\", leave=False):\n", " end = min(j + BATCH, N)\n", " pixels = val_images[j:end].float().to(DEVICE)\n", " # Skip zero images (failed to cache)\n", " mask = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if mask.sum() == 0:\n", " continue\n", "\n", " emb = encoder(pixels[mask])\n", " logits, _, nearest = soup(emb)\n", "\n", " k = 0\n", " for idx in range(j, end):\n", " if idx - j < len(mask) and mask[idx - j]:\n", " all_logits[idx] = logits[k].cpu().float()\n", " all_embs[idx] = emb[k].cpu().float()\n", " k += 1\n", " n_seen += 1\n", "\n", " # mAP\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = all_logits[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st) + 1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " # F1\n", " vp = (all_logits.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1 - v_lab)).sum(0)\n", " fn = ((1 - vp) * v_lab).sum(0)\n", " pr = tp / (tp + fp + 1e-8); rc = tp / (tp + fn + 1e-8)\n", " f1 = 2 * pr * rc / (pr + rc + 1e-8)\n", " macro_f1 = f1[f1 > 0].mean().item()\n", "\n", " # Cosine to targets\n", " valid = all_embs.norm(dim=-1) > 0.1\n", " v_cos = F.cosine_similarity(\n", " all_embs[valid], val_targets[valid], dim=-1).mean().item() if valid.sum() > 0 else 0.0\n", "\n", " # R@1\n", " if valid.sum() > 100:\n", " sim = all_embs[valid] @ val_targets[valid].T\n", " r1 = (sim.argmax(-1) == torch.arange(valid.sum())).float().mean().item()\n", " else:\n", " r1 = 0.0\n", "\n", " # Active anchors\n", " valid_embs = all_embs[valid].to(DEVICE)\n", " if valid_embs.shape[0] > 0:\n", " _, v_nearest = soup.constellation.triangulate(valid_embs)\n", " n_active = v_nearest.cpu().unique().numel()\n", " else:\n", " n_active = 0\n", "\n", " # CV\n", " v_cv = cv_metric(valid_embs[:2000]) if valid_embs.shape[0] > 100 else 0.0\n", "\n", " return {\n", " \"mAP\": mAP, \"f1\": macro_f1, \"r1\": r1, \"cos\": v_cos,\n", " \"cv\": v_cv, \"n_active\": n_active, \"n_seen\": n_seen,\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\" {EPOCHS} total epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Losses: InfoNCE + MSE + CV + BCE + Procrustes alignment\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\" Images: train={N_train:,} val={N_val:,} (cached as tensors)\")\n", "print(f\"{'='*65}\")\n", "\n", "optimizer = torch.optim.Adam(encoder.parameters(), lr=LR)\n", "n_batches = N_train // BATCH\n", "total_steps = n_batches * EPOCHS\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=WARMUP_STEPS),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - WARMUP_STEPS, 1), eta_min=1e-6)],\n", " milestones=[WARMUP_STEPS])\n", "\n", "scaler = torch.amp.GradScaler(\"cuda\")\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/geolip_vit_encoder\")\n", "best_mAP = 0.0\n", "gs = 0\n", "start_epoch = 0\n", "\n", "# ── Warm-start with optional expansion (384→1024) ──\n", "resume_ckpt = None\n", "for e in range(EPOCHS, 0, -1):\n", " p = f\"checkpoints/geolip_vit_e{e:02d}.pt\"\n", " if os.path.exists(p):\n", " resume_ckpt = p\n", " break\n", "\n", "if resume_ckpt is None:\n", " if os.path.exists(\"checkpoints/geolip_vit_encoder_best.pt\"):\n", " resume_ckpt = \"checkpoints/geolip_vit_encoder_best.pt\"\n", "\n", "if resume_ckpt is not None:\n", " print(f\"\\n Warm-starting from: {resume_ckpt}\")\n", " ckpt_resume = torch.load(resume_ckpt, map_location=DEVICE, weights_only=False)\n", "\n", " if \"encoder_state_dict\" in ckpt_resume:\n", " prev_sd = ckpt_resume[\"encoder_state_dict\"]\n", " curr_sd = encoder.state_dict()\n", "\n", " # Remap old key names to new architecture\n", " # Old: encoder.layers.N.* → New: layers.N.*\n", " # Old params that don't exist in new arch are skipped\n", " remapped = {}\n", " for k, v in prev_sd.items():\n", " new_k = k.replace(\"encoder.layers.\", \"layers.\") if \"encoder.layers.\" in k else k\n", " remapped[new_k] = v\n", " prev_sd = remapped\n", "\n", " # Check if this is an expansion (dimension mismatch)\n", " expanding = False\n", " for k in prev_sd:\n", " if k in curr_sd and prev_sd[k].shape != curr_sd[k].shape:\n", " expanding = True\n", " break\n", "\n", " # Also expanding if prev has no geo params (architecture change)\n", " if \"geo_pool_proj.weight\" not in prev_sd:\n", " expanding = True\n", "\n", " if expanding:\n", " print(f\" Expanding/upgrading architecture\")\n", " loaded, expanded, skipped, new_params = 0, 0, 0, 0\n", "\n", " for k in prev_sd:\n", " if k in [\"anchors\"]:\n", " skipped += 1; continue # anchors come from soup\n", " if k not in curr_sd:\n", " skipped += 1; continue\n", "\n", " prev_p = prev_sd[k]\n", " curr_p = curr_sd[k]\n", "\n", " if prev_p.shape == curr_p.shape:\n", " curr_sd[k] = prev_p\n", " loaded += 1\n", " elif prev_p.dim() == curr_p.dim():\n", " nn.init.xavier_uniform_(curr_p) if curr_p.dim() >= 2 else nn.init.zeros_(curr_p)\n", " slices = tuple(slice(0, min(s1, s2))\n", " for s1, s2 in zip(prev_p.shape, curr_p.shape))\n", " curr_sd[k][slices] = prev_p[slices]\n", " expanded += 1\n", " else:\n", " skipped += 1\n", "\n", " # Count new params (in curr but not in prev)\n", " for k in curr_sd:\n", " if k not in prev_sd and k != \"anchors\":\n", " new_params += 1\n", "\n", " encoder.load_state_dict(curr_sd)\n", " print(f\" ✓ Loaded: {loaded} Expanded: {expanded} \"\n", " f\"Skipped: {skipped} New: {new_params}\")\n", "\n", " start_epoch = 0\n", " best_mAP = 0.0\n", " gs = 0\n", " print(f\" ✓ Starting fresh from epoch 1 (new architecture)\")\n", " else:\n", " encoder.load_state_dict(prev_sd)\n", " start_epoch = ckpt_resume.get(\"epoch\", 0)\n", " best_mAP = ckpt_resume.get(\"mAP\", 0.0)\n", " gs = ckpt_resume.get(\"gs\", start_epoch * n_batches)\n", "\n", " remaining_steps = n_batches * (EPOCHS - start_epoch)\n", " resume_lr = LR * 0.5\n", " optimizer = torch.optim.Adam(encoder.parameters(), lr=resume_lr)\n", " scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=remaining_steps, eta_min=1e-6)\n", " scaler = torch.amp.GradScaler(\"cuda\")\n", " print(f\" ✓ Same-arch resume from epoch {start_epoch+1}\")\n", " print(f\" ✓ Fresh cosine: lr={resume_lr} for {remaining_steps} steps\")\n", "\n", " print(f\" best_mAP={best_mAP:.3f}, gs={gs}\")\n", " del ckpt_resume; gc.collect()\n", "else:\n", " print(f\"\\n Training from scratch (no checkpoint found)\")\n", "\n", "for epoch in range(start_epoch, EPOCHS):\n", " encoder.train()\n", " t0 = time.time()\n", " perm = torch.randperm(N_train)\n", "\n", " # Accumulators\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"cv\": 0, \"align\": 0,\n", " \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH),\n", " desc=f\"E{epoch+1:2d}/{EPOCHS} train\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4:\n", " continue\n", "\n", " pixels = train_images[idx].float().to(DEVICE)\n", " targets = train_targets_gpu[idx]\n", " labels = train_labels_gpu[idx]\n", "\n", " # Skip batches with too many zero images\n", " valid = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if valid.sum() < 4:\n", " continue\n", " pixels = pixels[valid]\n", " targets = targets[valid]\n", " labels = labels[valid]\n", "\n", " with torch.amp.autocast(\"cuda\", dtype=torch.bfloat16):\n", " emb = encoder(pixels)\n", " emb = EmbeddingAutograd.apply(emb, emb, anchors_frozen, 0.01, 1.0)\n", "\n", " l_nce, nce_acc = infonce(emb, targets)\n", " l_mse = F.mse_loss(emb, targets)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", " l_align = whitened_procrustes_loss(emb, targets)\n", "\n", " logits, _, _ = soup(emb)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv)\n", "\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(optimizer)\n", " torch.nn.utils.clip_grad_norm_(encoder.parameters(), GRAD_CLIP)\n", " scaler.step(optimizer)\n", " scaler.update()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " acc[\"loss\"] += loss.item()\n", " acc[\"nce\"] += l_nce.item()\n", " acc[\"mse\"] += l_mse.item()\n", " acc[\"bce\"] += l_bce.item()\n", " acc[\"cv\"] += l_cv.item()\n", " acc[\"align\"] += l_align.item()\n", " acc[\"nce_acc\"] += nce_acc\n", " acc[\"n\"] += 1\n", " gs += 1\n", "\n", " # Tensorboard step logging\n", " if gs % 50 == 0:\n", " writer.add_scalar(\"step/loss\", loss.item(), gs)\n", " writer.add_scalar(\"step/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"step/mse\", l_mse.item(), gs)\n", " writer.add_scalar(\"step/bce\", l_bce.item(), gs)\n", " writer.add_scalar(\"step/cv\", l_cv.item(), gs)\n", " writer.add_scalar(\"step/align\", l_align.item(), gs)\n", " writer.add_scalar(\"step/nce_acc\", nce_acc, gs)\n", " writer.add_scalar(\"step/lr\", scheduler.get_last_lr()[0], gs)\n", "\n", " # Update tqdm\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(\n", " loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " cos=f\"{1-acc['align']/d:.3f}\",\n", " ordered=True)\n", "\n", " elapsed = time.time() - t0\n", " d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: {elapsed:.0f}s \"\n", " f\"loss={acc['loss']/d:.4f} nce={acc['nce']/d:.4f} \"\n", " f\"mse={acc['mse']/d:.4f} bce={acc['bce']/d:.4f} \"\n", " f\"nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " # Epoch tensorboard\n", " writer.add_scalar(\"epoch/train_loss\", acc[\"loss\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce\", acc[\"nce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_mse\", acc[\"mse\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_bce\", acc[\"bce\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_cv\", acc[\"cv\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_align\", acc[\"align\"] / d, epoch + 1)\n", " writer.add_scalar(\"epoch/train_nce_acc\", acc[\"nce_acc\"] / d, epoch + 1)\n", "\n", " # ── Validation ──\n", " m = evaluate(encoder, soup, val_images, val_targets, val_labels)\n", "\n", " writer.add_scalar(\"epoch/val_mAP\", m[\"mAP\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_F1\", m[\"f1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_R@1\", m[\"r1\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cos\", m[\"cos\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_cv\", m[\"cv\"], epoch + 1)\n", " writer.add_scalar(\"epoch/val_anchors\", m[\"n_active\"], epoch + 1)\n", "\n", " mk = \"\"\n", " if m[\"mAP\"] > best_mAP:\n", " best_mAP = m[\"mAP\"]\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"config\": {\"d_model\": D_MODEL, \"n_heads\": N_HEADS,\n", " \"n_layers\": N_LAYERS, \"d_ff\": D_FF,\n", " \"patch_size\": PATCH_SIZE, \"image_size\": IMAGE_SIZE,\n", " \"output_dim\": D_ANCHOR},\n", " \"mAP\": m[\"mAP\"], \"f1\": m[\"f1\"], \"r1\": m[\"r1\"],\n", " \"cos\": m[\"cos\"], \"cv\": m[\"cv\"],\n", " \"epoch\": epoch + 1, \"n_active\": m[\"n_active\"],\n", " \"consensus_cv\": consensus_cv,\n", " }, \"checkpoints/geolip_vit_encoder_best.pt\")\n", " mk = \" ★\"\n", "\n", " # Save every epoch checkpoint\n", " torch.save({\n", " \"encoder_state_dict\": encoder.state_dict(),\n", " \"epoch\": epoch + 1, \"mAP\": m[\"mAP\"],\n", " \"optimizer\": optimizer.state_dict(),\n", " \"scheduler\": scheduler.state_dict(),\n", " \"scaler\": scaler.state_dict(),\n", " \"gs\": gs,\n", " }, f\"checkpoints/geolip_vit_e{epoch+1:02d}.pt\")\n", "\n", " print(f\" E{epoch+1} val: mAP={m['mAP']:.3f} F1={m['f1']:.3f} \"\n", " f\"R@1={m['r1']:.3f} cos={m['cos']:.3f} cv={m['cv']:.4f} \"\n", " f\"anchors={m['n_active']}/256 seen={m['n_seen']}/{N_val}{mk}\")\n", "\n", "writer.close()\n", "\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Encoder: {n_params:,} params (from scratch)\")\n", "print(f\" Checkpoints saved every epoch in checkpoints/\")\n", "print(f\" Tensorboard: runs/geolip_vit_encoder\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "B0oaQx-ta0B6", "outputId": "bd247c1a-79ee-406a-aa05-42c24830735f" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP VISION ENCODER — FROM SCRATCH\n", " ViT: 6L/384d/6h, patch16\n", " 196 patches + CLS → 128-d output\n", " Device: cuda\n", "=================================================================\n", "\n", " Loading soup...\n", " Soup: mAP=0.837 CV_target=0.2731\n", " train: loaded cached targets (118,287)\n", " val: loaded cached targets (5,000)\n", " Loading cached train images...\n", " train: torch.Size([118287, 3, 224, 224]) (35611.0 MB/img)\n", " Loading cached val images...\n", " val: torch.Size([5000, 3, 224, 224]) (1505.3 MB/img)\n", "\n", "=================================================================\n", "BUILD ENCODER\n", "=================================================================\n", " Architecture: 6L/384d/6h, patch16\n", " Input: 224×224 → 196 patches\n", " Output: 128-d (on hypersphere)\n", " Parameters: 11,365,504\n", "\n", "=================================================================\n", "TRAINING\n", " 60 total epochs, lr=0.0003, batch=48\n", " Losses: InfoNCE + MSE + CV + BCE + Procrustes alignment\n", " CV target: 0.2731\n", " Images: train=118,287 val=5,000 (cached as tensors)\n", "=================================================================\n", "\n", " Warm-starting from: checkpoints/geolip_vit_e60.pt\n", " Expanding/upgrading architecture\n", " ✓ Loaded: 84 Expanded: 0 Skipped: 0 New: 6\n", " ✓ Starting fresh from epoch 1 (new architecture)\n", " best_mAP=0.000, gs=0\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 1/60 train: 100%|██████████| 2465/2465 [03:18<00:00, 12.42batch/s, cos=0.629, loss=0.4865, nce_acc=0.930, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 train: 199s loss=0.4865 nce=0.2702 mse=0.0059 bce=0.0927 nce_acc=0.930\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 val: mAP=0.390 F1=0.378 R@1=0.262 cos=0.566 cv=0.1553 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 2/60 train: 100%|██████████| 2465/2465 [03:17<00:00, 12.50batch/s, cos=0.616, loss=0.5206, nce_acc=0.922, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 train: 197s loss=0.5205 nce=0.2971 mse=0.0061 bce=0.0945 nce_acc=0.922\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 val: mAP=0.401 F1=0.379 R@1=0.272 cos=0.571 cv=0.1754 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 3/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.54batch/s, cos=0.624, loss=0.4948, nce_acc=0.929, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 train: 197s loss=0.4949 nce=0.2758 mse=0.0059 bce=0.0928 nce_acc=0.929\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 val: mAP=0.402 F1=0.407 R@1=0.283 cos=0.578 cv=0.1578 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 4/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.51batch/s, cos=0.633, loss=0.4660, nce_acc=0.936, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 train: 197s loss=0.4660 nce=0.2520 mse=0.0058 bce=0.0909 nce_acc=0.936\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 val: mAP=0.410 F1=0.390 R@1=0.282 cos=0.580 cv=0.1765 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 5/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.52batch/s, cos=0.641, loss=0.4410, nce_acc=0.943, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 train: 197s loss=0.4409 nce=0.2317 mse=0.0057 bce=0.0890 nce_acc=0.943\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 val: mAP=0.417 F1=0.405 R@1=0.282 cos=0.584 cv=0.1605 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 6/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.53batch/s, cos=0.649, loss=0.4159, nce_acc=0.949, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 train: 197s loss=0.4159 nce=0.2115 mse=0.0055 bce=0.0875 nce_acc=0.949\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 val: mAP=0.424 F1=0.408 R@1=0.306 cos=0.591 cv=0.1693 anchors=95/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 7/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.53batch/s, cos=0.657, loss=0.3945, nce_acc=0.955, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 train: 197s loss=0.3945 nce=0.1945 mse=0.0054 bce=0.0860 nce_acc=0.956\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 val: mAP=0.432 F1=0.425 R@1=0.307 cos=0.600 cv=0.1568 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 8/60 train: 100%|██████████| 2465/2465 [03:17<00:00, 12.48batch/s, cos=0.665, loss=0.3745, nce_acc=0.960, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 train: 197s loss=0.3745 nce=0.1791 mse=0.0053 bce=0.0843 nce_acc=0.960\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 val: mAP=0.422 F1=0.413 R@1=0.312 cos=0.596 cv=0.1437 anchors=95/256 seen=5000/5000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 9/60 train: 100%|██████████| 2465/2465 [03:15<00:00, 12.59batch/s, cos=0.673, loss=0.3563, nce_acc=0.964, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 train: 196s loss=0.3563 nce=0.1651 mse=0.0052 bce=0.0828 nce_acc=0.964\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 val: mAP=0.432 F1=0.422 R@1=0.302 cos=0.601 cv=0.1637 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E10/60 train: 100%|██████████| 2465/2465 [03:16<00:00, 12.55batch/s, cos=0.680, loss=0.3385, nce_acc=0.969, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 train: 196s loss=0.3385 nce=0.1515 mse=0.0050 bce=0.0813 nce_acc=0.969\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 val: mAP=0.432 F1=0.417 R@1=0.309 cos=0.597 cv=0.1414 anchors=94/256 seen=5000/5000 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E11/60 train: 89%|████████▉ | 2201/2465 [02:55<00:21, 12.57batch/s, cos=0.688, loss=0.3220, nce_acc=0.972, ordered=1]\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_138043/3679711697.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 695\u001b[0m + 0.5 * l_align + 0.001 * l_cv)\n\u001b[1;32m 696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 698\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munscale_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 699\u001b[0m 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\u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m ) # Calls into the C++ engine to run the backward pass\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "markdown", "source": [ "## next" ], "metadata": { "id": "uxtewULmHAXX" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP TINY ViT — FUSED CONSTELLATION-BANK\n", "=============================================\n", "Each anchor is a multi-perspective landmark.\n", "Triangulation carries BOTH position AND expert perspective.\n", "\n", "For each of 256 anchors, compute distance through 3 expert lenses:\n", " tri_clip[i] = 1 - cos(R_clip @ whiten(emb), anchor_i)\n", " tri_dino[i] = 1 - cos(R_dino @ whiten(emb), anchor_i)\n", " tri_siglip[i] = 1 - cos(R_siglip @ whiten(emb), anchor_i)\n", "\n", "Total: 256 × 3 = 768 triangulation distances.\n", "Where experts agree → 3 distances similar → patchwork reads consensus.\n", "Where experts disagree → distances diverge → patchwork reads the divergence.\n", "\n", "Encoder: 4L/240d/4h (~3M params)\n", "Fused constellation: 256 anchors × 128-d × 3 expert perspectives\n", "Patchwork: reads 768-d expert-aware triangulation\n", "No separate bank. It's IN the constellation.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import os\n", "import gc\n", "import time\n", "import math\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\"\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "torch.backends.cudnn.allow_tf32 = True\n", "\n", "# Encoder\n", "D_MODEL = 256\n", "N_HEADS = 4\n", "N_LAYERS = 4\n", "D_FF = 1024\n", "PATCH_SIZE = 16\n", "IMAGE_SIZE = 224\n", "DROPOUT = 0.1\n", "\n", "# Fused geometric system\n", "D_ANCHOR = 128\n", "N_ANCHORS = 256\n", "N_EXPERTS = 3\n", "N_CLASSES = 80\n", "N_COMP = 8\n", "D_COMP = 64\n", "ANCHOR_DROP = 0.30\n", "\n", "# Training\n", "BATCH = 64\n", "EPOCHS = 60\n", "LR = 3e-4\n", "WARMUP_STEPS = 500\n", "GRAD_CLIP = 1.0\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "N_PATCHES = (IMAGE_SIZE // PATCH_SIZE) ** 2\n", "TRI_DIM = N_ANCHORS * N_EXPERTS # 768\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOLIP TINY ViT — FUSED CONSTELLATION-BANK\")\n", "print(f\" Encoder: {N_LAYERS}L/{D_MODEL}d/{N_HEADS}h\")\n", "print(f\" Fused: {N_ANCHORS} anchors × {N_EXPERTS} experts = {TRI_DIM}-d triangulation\")\n", "print(f\" Anchor dropout: {ANCHOR_DROP}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=200):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce(emb, targets, queue_emb=None, queue_tgt=None, temperature=0.07):\n", " B = emb.shape[0]\n", " emb_n = F.normalize(emb, dim=-1); tgt_n = F.normalize(targets, dim=-1)\n", " if queue_tgt is not None and queue_tgt.shape[0] > 0:\n", " all_tgt = torch.cat([tgt_n, queue_tgt], 0)\n", " all_emb = torch.cat([emb_n, queue_emb], 0)\n", " else:\n", " all_tgt = tgt_n; all_emb = emb_n\n", " logits_e2t = (emb_n @ all_tgt.T) / temperature\n", " logits_t2e = (tgt_n @ all_emb.T) / temperature\n", " labels = torch.arange(B, device=emb.device)\n", " loss = (F.cross_entropy(logits_e2t, labels) + F.cross_entropy(logits_t2e, labels)) / 2\n", " with torch.no_grad():\n", " acc = (logits_e2t.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True)\n", " tm = targets.float().mean(0, keepdim=True)\n", " return 1.0 - F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1).mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FUSED CONSTELLATION-BANK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class FusedConstellation(nn.Module):\n", " \"\"\"\n", " Constellation + Bank fused into one operation.\n", "\n", " 256 anchors on S^127 (shared coordinate system).\n", " 3 expert perspectives: rotation + whitener + mean per expert.\n", "\n", " Triangulation: for each anchor, compute distance through each expert lens.\n", " Output: 256 × 3 = 768 distances encoding position + perspective.\n", "\n", " Anchor dropout: randomly mask anchors during training.\n", " When an anchor is dropped, ALL 3 expert distances for that anchor are dropped.\n", " \"\"\"\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR, n_experts=N_EXPERTS,\n", " drop_rate=ANCHOR_DROP):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_experts = n_experts\n", " self.drop_rate = drop_rate\n", " self.d = d\n", "\n", " # Shared anchors\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", "\n", " # Per-expert Procrustes (trainable, calibrated at init)\n", " self.expert_rotations = nn.ParameterList([\n", " nn.Parameter(torch.eye(d)) for _ in range(n_experts)])\n", " self.expert_whiteners = nn.ParameterList([\n", " nn.Parameter(torch.eye(d)) for _ in range(n_experts)])\n", " self.expert_means = nn.ParameterList([\n", " nn.Parameter(torch.zeros(d)) for _ in range(n_experts)])\n", "\n", " def triangulate(self, emb, training=False):\n", " \"\"\"\n", " Returns:\n", " tri: (B, n_anchors * n_experts) — fused expert-aware distances\n", " nearest: (B,) — nearest anchor (consensus, mean of 3 expert cosines)\n", " \"\"\"\n", " B = emb.shape[0]\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " # Compute per-expert rotated embeddings\n", " expert_embs = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " W = self.expert_whiteners[i]\n", " mu = self.expert_means[i]\n", " centered = emb.float() - mu\n", " whitened = centered @ W\n", " rotated = F.normalize(whitened @ R.T, dim=-1)\n", " expert_embs.append(rotated)\n", "\n", " # Anchor dropout — drop entire anchors across all experts\n", " if training and self.drop_rate > 0:\n", " n_keep = max(int(self.n_anchors * (1 - self.drop_rate)), 32)\n", " keep_idx = torch.randperm(self.n_anchors, device=emb.device)[:n_keep]\n", " a_masked = anchors_n[keep_idx]\n", "\n", " expert_tris = []\n", " expert_cos_for_nearest = []\n", " for rotated in expert_embs:\n", " cos = rotated @ a_masked.T # (B, n_keep)\n", " # Pad to full size\n", " full_cos = torch.full((B, self.n_anchors), -1.0,\n", " device=emb.device, dtype=cos.dtype)\n", " full_cos[:, keep_idx] = cos\n", " expert_tris.append(1.0 - full_cos)\n", " expert_cos_for_nearest.append(full_cos)\n", " else:\n", " expert_tris = []\n", " expert_cos_for_nearest = []\n", " for rotated in expert_embs:\n", " cos = rotated @ anchors_n.T # (B, n_anchors)\n", " expert_tris.append(1.0 - cos)\n", " expert_cos_for_nearest.append(cos)\n", "\n", " # Fused triangulation: interleave expert distances per anchor\n", " # [anchor0_clip, anchor0_dino, anchor0_siglip, anchor1_clip, ...]\n", " tri_stacked = torch.stack(expert_tris, dim=-1) # (B, n_anchors, n_experts)\n", " tri_fused = tri_stacked.reshape(B, -1) # (B, n_anchors * n_experts)\n", "\n", " # Nearest: mean consensus cosine across experts\n", " mean_cos = torch.stack(expert_cos_for_nearest, dim=-1).mean(dim=-1) # (B, n_anchors)\n", " nearest = mean_cos.argmax(dim=-1) # (B,)\n", "\n", " return tri_fused, nearest\n", "\n", " def anchor_spread_loss(self):\n", " a = F.normalize(self.anchors, dim=-1)\n", " sim = a @ a.T\n", " sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", " def rotation_ortho_loss(self):\n", " loss = 0.0\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " loss += (R @ R.T - torch.eye(self.d, device=R.device)).pow(2).mean()\n", " return loss / self.n_experts\n", "\n", " def expert_agreement_loss(self, emb):\n", " \"\"\"Measure how much experts agree/disagree — preserve differentiation.\"\"\"\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", " expert_cos = []\n", " for i in range(self.n_experts):\n", " R = self.expert_rotations[i]\n", " W = self.expert_whiteners[i]\n", " mu = self.expert_means[i]\n", " rotated = F.normalize((emb.float() - mu) @ W @ R.T, dim=-1)\n", " cos = rotated @ anchors_n.T # (B, n_anchors)\n", " expert_cos.append(cos)\n", " stacked = torch.stack(expert_cos, dim=-1) # (B, n_anchors, n_experts)\n", " # Experts should agree on anchor assignment but differ in distance\n", " agree = stacked.mean(dim=-1) # (B, n_anchors)\n", " disagree = stacked.std(dim=-1) # (B, n_anchors)\n", " # Encourage moderate disagreement (not zero, not random)\n", " return (disagree.mean() - 0.05).abs()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PATCHWORK (reads 768-d fused triangulation)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class FusedPatchwork(nn.Module):\n", " \"\"\"\n", " Reads expert-aware triangulation. 768 inputs = 256 anchors × 3 experts.\n", " Each compartment gets 256/8 = 32 anchors × 3 experts = 96 inputs.\n", " \"\"\"\n", " def __init__(self, n_anchors=N_ANCHORS, n_experts=N_EXPERTS,\n", " n_comp=N_COMP, d_comp=D_COMP):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " self.n_experts = n_experts\n", " # Assign anchors to compartments (then each anchor brings 3 expert distances)\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " inputs_per_comp = (n_anchors // n_comp) * n_experts # 32 × 3 = 96\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear(inputs_per_comp, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for _ in range(n_comp)])\n", "\n", " def forward(self, tri_fused):\n", " \"\"\"tri_fused: (B, n_anchors * n_experts) interleaved.\"\"\"\n", " B = tri_fused.shape[0]\n", " # Reshape to (B, n_anchors, n_experts)\n", " tri_3d = tri_fused.reshape(B, -1, self.n_experts)\n", " results = []\n", " for k in range(self.n_comp):\n", " mask = self.asgn == k # which anchors in this compartment\n", " comp_input = tri_3d[:, mask, :].reshape(B, -1) # (B, 32*3=96)\n", " results.append(self.comps[k](comp_input))\n", " return torch.cat(results, dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TINY ViT ENCODER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class TinyViTEncoder(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.patch_embed = nn.Conv2d(3, D_MODEL, kernel_size=PATCH_SIZE,\n", " stride=PATCH_SIZE)\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, D_MODEL))\n", " self.pos_embed = nn.Parameter(torch.zeros(1, N_PATCHES + 1, D_MODEL))\n", " self.embed_norm = nn.LayerNorm(D_MODEL)\n", " self.embed_drop = nn.Dropout(DROPOUT)\n", "\n", " self.layers = nn.ModuleList([\n", " nn.TransformerEncoderLayer(\n", " d_model=D_MODEL, nhead=N_HEADS, dim_feedforward=D_FF,\n", " dropout=DROPOUT, activation=\"gelu\", batch_first=True,\n", " norm_first=True)\n", " for _ in range(N_LAYERS)])\n", "\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(D_MODEL, D_MODEL), nn.GELU(),\n", " nn.LayerNorm(D_MODEL),\n", " nn.Linear(D_MODEL, D_ANCHOR))\n", "\n", " self._init_weights()\n", "\n", " def _init_weights(self):\n", " for m in self.modules():\n", " if isinstance(m, nn.Linear):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.Conv2d):\n", " nn.init.xavier_uniform_(m.weight)\n", " if m.bias is not None: nn.init.zeros_(m.bias)\n", " elif isinstance(m, nn.LayerNorm):\n", " nn.init.ones_(m.weight); nn.init.zeros_(m.bias)\n", " nn.init.trunc_normal_(self.pos_embed, std=0.02)\n", " nn.init.trunc_normal_(self.cls_token, std=0.02)\n", "\n", " def forward(self, pixel_values):\n", " B = pixel_values.shape[0]\n", " x = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)\n", " cls = self.cls_token.expand(B, -1, -1)\n", " x = torch.cat([cls, x], dim=1) + self.pos_embed\n", " x = self.embed_drop(self.embed_norm(x))\n", " for layer in self.layers:\n", " x = layer(x)\n", " pooled = x[:, 1:, :].mean(dim=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class GeoLIPFused(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.encoder = TinyViTEncoder()\n", " self.constellation = FusedConstellation()\n", " self.patchwork = FusedPatchwork()\n", "\n", " pw_dim = N_COMP * D_COMP\n", " self.classifier = nn.Sequential(\n", " nn.Linear(pw_dim + D_ANCHOR, pw_dim), nn.GELU(),\n", " nn.LayerNorm(pw_dim), nn.Dropout(0.1),\n", " nn.Linear(pw_dim, N_CLASSES))\n", "\n", " def forward(self, pixel_values, apply_autograd=True):\n", " emb = self.encoder(pixel_values)\n", "\n", " if apply_autograd and self.training:\n", " emb = EmbeddingAutograd.apply(\n", " emb, emb, self.constellation.anchors, 0.01, 1.0)\n", "\n", " tri, nearest = self.constellation.triangulate(emb, training=self.training)\n", " pw = self.patchwork(tri)\n", " logits = self.classifier(torch.cat([pw, emb], dim=-1))\n", "\n", " return logits, emb, tri, nearest\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + INITIALIZE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/base_tier_best.pt\", map_location=\"cpu\", weights_only=False)\n", "consensus_cv = ckpt.get(\"consensus_cv_128\", 0.27)\n", "print(f\" Soup: mAP={ckpt['mAP']:.3f} CV={consensus_cv:.4f}\")\n", "\n", "from datasets import load_dataset\n", "from torchvision import transforms\n", "\n", "img_transform = transforms.Compose([\n", " transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", "])\n", "\n", "# Load cached targets + images\n", "for split_name in [\"train\", \"val\"]:\n", " cached = torch.load(f\"cached_{split_name}_targets.pt\", weights_only=False)\n", " if split_name == \"train\":\n", " train_targets = cached[\"targets\"]; train_labels = cached[\"labels\"]\n", " train_ids = cached[\"image_ids\"]\n", " train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", " N_train = len(train_ids)\n", " else:\n", " val_targets = cached[\"targets\"]; val_labels = cached[\"labels\"]\n", " val_ids = cached[\"image_ids\"]\n", " val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", " N_val = len(val_ids)\n", " print(f\" {split_name}: {len(cached['targets']):,} targets\")\n", "\n", "train_images = torch.load(\"cached_train_images.pt\", weights_only=True)\n", "val_images = torch.load(\"cached_val_images.pt\", weights_only=True)\n", "print(f\" Images: train={train_images.shape[0]:,} val={val_images.shape[0]:,}\")\n", "\n", "train_targets_gpu = train_targets.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "\n", "# Calibrate expert Procrustes\n", "print(f\"\\n Calibrating expert perspectives...\")\n", "expert_feats_val = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(\n", " row[\"features\"], dtype=torch.float32)\n", " expert_feats_val[name] = feats\n", " del ds\n", "\n", "# Calibrate: consensus 128-d → rotated consensus (per-expert perspective)\n", "cons = val_targets[:5000].float()\n", "cons_mean = cons.mean(0)\n", "cons_cov = ((cons - cons_mean).T @ (cons - cons_mean)) / 4999\n", "cons_whiten = symmetric_inv_sqrt(cons_cov)\n", "\n", "calibrations = {}\n", "for name in EXPERTS:\n", " raw = expert_feats_val[name][:5000].float()\n", " tgt = val_targets[:5000].float()\n", "\n", " # Expert 768→128 alignment\n", " sm = raw.mean(0, keepdim=True); sc = raw - sm\n", " sw = symmetric_inv_sqrt((sc.T @ sc) / 4999)\n", " tm = tgt.mean(0, keepdim=True); tc = tgt - tm\n", " tw = symmetric_inv_sqrt((tc.T @ tc) / 4999)\n", " src_w = F.normalize(sc @ sw, dim=-1)\n", " tgt_w = F.normalize(tc @ tw, dim=-1)\n", " M = tgt_w.T @ src_w # (128, 768)\n", " U, S, Vt = torch.linalg.svd(M, full_matrices=False)\n", "\n", " # For the fused constellation, we need 128→128 rotations\n", " # that capture each expert's unique perspective on the consensus space\n", " # Use the consensus whitener as the base, then rotate\n", " calibrations[name] = {\n", " \"R\": (U @ Vt)[:D_ANCHOR, :D_ANCHOR],\n", " \"W\": cons_whiten,\n", " \"mean\": cons_mean,\n", " }\n", " print(f\" {name:<30} sv_mean={S.mean():.4f}\")\n", "\n", "del expert_feats_val; gc.collect()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"BUILD MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = GeoLIPFused().to(DEVICE)\n", "\n", "with torch.no_grad():\n", " # Anchors from soup\n", " model.constellation.anchors.copy_(\n", " ckpt[\"state_dict\"][\"constellation.anchors\"].to(DEVICE))\n", " print(f\" ✓ Anchors transferred from soup\")\n", "\n", " # Expert Procrustes calibration\n", " for i, name in enumerate(EXPERTS):\n", " cal = calibrations[name]\n", " model.constellation.expert_rotations[i].copy_(cal[\"R\"].to(DEVICE))\n", " model.constellation.expert_whiteners[i].copy_(cal[\"W\"].to(DEVICE))\n", " model.constellation.expert_means[i].copy_(cal[\"mean\"].to(DEVICE))\n", " print(f\" ✓ Expert perspectives calibrated\")\n", "\n", "n_enc = sum(p.numel() for p in model.encoder.parameters())\n", "n_const = sum(p.numel() for p in model.constellation.parameters())\n", "n_pw = sum(p.numel() for p in model.patchwork.parameters())\n", "n_cls = sum(p.numel() for p in model.classifier.parameters())\n", "n_total = sum(p.numel() for p in model.parameters())\n", "print(f\"\\n Parameters:\")\n", "print(f\" encoder: {n_enc:>10,}\")\n", "print(f\" fused constellation: {n_const:>10,}\")\n", "print(f\" fused patchwork: {n_pw:>10,}\")\n", "print(f\" classifier: {n_cls:>10,}\")\n", "print(f\" total: {n_total:>10,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EVALUATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@torch.no_grad()\n", "def evaluate(model, val_images, val_targets, val_labels):\n", " model.eval()\n", " N = val_images.shape[0]\n", " all_lo = torch.zeros(N, N_CLASSES)\n", " all_em = torch.zeros(N, D_ANCHOR)\n", " n_seen = 0\n", "\n", " for j in tqdm(range(0, N, BATCH), desc=\" Val\", leave=False):\n", " end = min(j + BATCH, N)\n", " pixels = val_images[j:end].float().to(DEVICE)\n", " valid = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if valid.sum() < 1: continue\n", " logits, emb, _, nearest = model(pixels[valid], apply_autograd=False)\n", " k = 0\n", " for idx in range(j, end):\n", " if idx - j < len(valid) and valid[idx - j]:\n", " all_lo[idx] = logits[k].cpu().float()\n", " all_em[idx] = emb[k].cpu().float()\n", " k += 1; n_seen += 1\n", "\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = all_lo[:, c].argsort(descending=True)\n", " st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st)+1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " vp = (all_lo.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1-v_lab)).sum(0); fn = ((1-vp) * v_lab).sum(0)\n", " pr = tp/(tp+fp+1e-8); rc = tp/(tp+fn+1e-8); f1 = 2*pr*rc/(pr+rc+1e-8)\n", "\n", " valid_mask = all_em.norm(dim=-1) > 0.1\n", " v_cos = F.cosine_similarity(all_em[valid_mask], val_targets[valid_mask], dim=-1).mean().item()\n", " sim = all_em[valid_mask] @ val_targets[valid_mask].T\n", " r1 = (sim.argmax(-1) == torch.arange(valid_mask.sum())).float().mean().item()\n", "\n", " _, v_nearest = model.constellation.triangulate(all_em[valid_mask].to(DEVICE), training=False)\n", " n_active = v_nearest.cpu().unique().numel()\n", " v_cv = cv_metric(all_em[valid_mask][:2000].to(DEVICE))\n", "\n", " return {\"mAP\": mAP, \"f1\": f1[f1>0].mean().item(), \"r1\": r1, \"cos\": v_cos,\n", " \"cv\": v_cv, \"n_active\": n_active, \"n_seen\": n_seen}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Triangulation: {TRI_DIM}-d ({N_ANCHORS} × {N_EXPERTS})\")\n", "print(f\" Anchor dropout: {ANCHOR_DROP}\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\"{'='*65}\")\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "n_batches = N_train // BATCH\n", "total_steps = n_batches * EPOCHS\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(\n", " optimizer,\n", " [torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01,\n", " total_iters=WARMUP_STEPS),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " optimizer, T_max=max(total_steps - WARMUP_STEPS, 1), eta_min=1e-6)],\n", " milestones=[WARMUP_STEPS])\n", "\n", "scaler = torch.amp.GradScaler(\"cuda\")\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/geolip_fused\")\n", "best_mAP = 0.0; gs = 0\n", "QUEUE_SIZE = 4096\n", "queue_e = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "queue_t = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " t0 = time.time()\n", " perm = torch.randperm(N_train)\n", "\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"spread\": 0,\n", " \"ortho\": 0, \"cv\": 0, \"align\": 0, \"agree\": 0,\n", " \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH),\n", " desc=f\"E{epoch+1:2d}/{EPOCHS}\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " pixels = train_images[idx].float().to(DEVICE)\n", " targets = train_targets_gpu[idx]\n", " labels = train_labels_gpu[idx]\n", "\n", " valid = pixels.abs().sum(dim=(1, 2, 3)) > 0.1\n", " if valid.sum() < 4: continue\n", " pixels = pixels[valid]; targets = targets[valid]; labels = labels[valid]\n", "\n", " with torch.amp.autocast(\"cuda\", dtype=torch.bfloat16):\n", " logits, emb, tri, nearest = model(pixels)\n", "\n", " # Student losses\n", " l_nce, nce_acc = infonce(emb, targets, queue_e, queue_t)\n", " with torch.no_grad():\n", " queue_e = torch.cat([queue_e, emb.detach()], 0)[-QUEUE_SIZE:]\n", " queue_t = torch.cat([queue_t, targets.detach()], 0)[-QUEUE_SIZE:]\n", " l_mse = F.mse_loss(emb, targets)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", " l_align = whitened_procrustes_loss(emb, targets)\n", " l_cv = cv_loss(emb, target=consensus_cv)\n", "\n", " # Fused constellation losses\n", " l_spread = model.constellation.anchor_spread_loss()\n", " l_ortho = model.constellation.rotation_ortho_loss()\n", " l_agree = model.constellation.expert_agreement_loss(emb)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv\n", " + 1e-3 * l_spread + 0.5 * l_ortho + 0.1 * l_agree)\n", "\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(optimizer)\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)\n", " scaler.step(optimizer)\n", " scaler.update()\n", " optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", "\n", " acc[\"loss\"] += loss.item(); acc[\"nce\"] += l_nce.item()\n", " acc[\"mse\"] += l_mse.item(); acc[\"bce\"] += l_bce.item()\n", " acc[\"spread\"] += l_spread.item(); acc[\"ortho\"] += l_ortho.item()\n", " acc[\"cv\"] += l_cv.item(); acc[\"align\"] += l_align.item()\n", " acc[\"agree\"] += l_agree.item() if torch.is_tensor(l_agree) else l_agree\n", " acc[\"nce_acc\"] += nce_acc; acc[\"n\"] += 1; gs += 1\n", "\n", " if gs % 50 == 0:\n", " writer.add_scalar(\"step/loss\", loss.item(), gs)\n", " writer.add_scalar(\"step/nce\", l_nce.item(), gs)\n", " writer.add_scalar(\"step/bce\", l_bce.item(), gs)\n", " writer.add_scalar(\"step/ortho\", l_ortho.item(), gs)\n", " writer.add_scalar(\"step/agree\", l_agree.item() if torch.is_tensor(l_agree) else l_agree, gs)\n", " writer.add_scalar(\"step/nce_acc\", nce_acc, gs)\n", "\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " cos=f\"{1-acc['align']/d:.3f}\", ordered=True)\n", "\n", " elapsed = time.time() - t0; d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: {elapsed:.0f}s loss={acc['loss']/d:.4f} \"\n", " f\"nce={acc['nce']/d:.4f} bce={acc['bce']/d:.4f} \"\n", " f\"ortho={acc['ortho']/d:.4f} agree={acc['agree']/d:.4f} \"\n", " f\"nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " for k in acc:\n", " if k != \"n\":\n", " writer.add_scalar(f\"epoch/train_{k}\", acc[k]/d, epoch+1)\n", "\n", " m = evaluate(model, val_images, val_targets, val_labels)\n", " writer.add_scalar(\"epoch/val_mAP\", m[\"mAP\"], epoch+1)\n", " writer.add_scalar(\"epoch/val_cos\", m[\"cos\"], epoch+1)\n", " writer.add_scalar(\"epoch/val_R@1\", m[\"r1\"], epoch+1)\n", " writer.add_scalar(\"epoch/val_cv\", m[\"cv\"], epoch+1)\n", " writer.add_scalar(\"epoch/val_anchors\", m[\"n_active\"], epoch+1)\n", "\n", " mk = \"\"\n", " if m[\"mAP\"] > best_mAP:\n", " best_mAP = m[\"mAP\"]\n", " torch.save({\"state_dict\": model.state_dict(),\n", " \"config\": {\"d_model\": D_MODEL, \"n_heads\": N_HEADS,\n", " \"n_layers\": N_LAYERS, \"d_ff\": D_FF,\n", " \"d_anchor\": D_ANCHOR, \"n_anchors\": N_ANCHORS,\n", " \"n_experts\": N_EXPERTS, \"anchor_drop\": ANCHOR_DROP},\n", " \"mAP\": m[\"mAP\"], \"epoch\": epoch+1, **m},\n", " \"checkpoints/geolip_fused_best.pt\")\n", " mk = \" ★\"\n", "\n", " torch.save({\"state_dict\": model.state_dict(), \"epoch\": epoch+1,\n", " \"mAP\": m[\"mAP\"], \"optimizer\": optimizer.state_dict(),\n", " \"scheduler\": scheduler.state_dict(), \"scaler\": scaler.state_dict(),\n", " \"gs\": gs}, f\"checkpoints/geolip_fused_e{epoch+1:02d}.pt\")\n", "\n", " print(f\" E{epoch+1} val: mAP={m['mAP']:.3f} F1={m['f1']:.3f} \"\n", " f\"R@1={m['r1']:.3f} cos={m['cos']:.3f} cv={m['cv']:.4f} \"\n", " f\"anchors={m['n_active']}/256{mk}\")\n", "\n", "writer.close()\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Total params: {n_total:,}\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "KEfQvyvB4kIP", "outputId": "07a8717d-5b03-4e98-92ca-e651115e30ef" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP TINY ViT — FUSED CONSTELLATION-BANK\n", " Encoder: 4L/256d/4h\n", " Fused: 256 anchors × 3 experts = 768-d triangulation\n", " Anchor dropout: 0.3\n", " Device: cuda\n", "=================================================================\n", "\n", " Loading soup...\n", " Soup: mAP=0.837 CV=0.2731\n", " train: 118,287 targets\n", " val: 5,000 targets\n", " Images: train=118,287 val=5,000\n", "\n", " Calibrating expert perspectives...\n", " clip_l14_openai sv_mean=14.4951\n", " dinov2_b14 sv_mean=14.2364\n", " siglip_b16_384 sv_mean=14.8412\n", "\n", "=================================================================\n", "BUILD MODEL\n", "=================================================================\n", " ✓ Anchors transferred from soup\n", " ✓ Expert perspectives calibrated\n", "\n", " Parameters:\n", " encoder: 3,506,304\n", " fused constellation: 131,456\n", " fused patchwork: 166,400\n", " classifier: 370,256\n", " total: 4,174,416\n", "\n", "=================================================================\n", "TRAINING\n", " 60 epochs, lr=0.0003, batch=512\n", " Triangulation: 768-d (256 × 3)\n", " Anchor dropout: 0.3\n", " CV target: 0.2731\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 1/60: 100%|██████████| 232/232 [01:01<00:00, 3.79batch/s, cos=0.132, loss=5.9931, nce_acc=0.027, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 train: 61s loss=5.9427 nce=5.4196 bce=0.2665 ortho=0.0056 agree=0.0091 nce_acc=0.030\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 val: mAP=0.062 F1=0.701 R@1=0.008 cos=0.177 cv=0.3894 anchors=77/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 2/60: 100%|██████████| 232/232 [00:59<00:00, 3.89batch/s, cos=0.239, loss=4.9219, nce_acc=0.086, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 train: 60s loss=4.8886 nce=4.4597 bce=0.1347 ortho=0.0053 agree=0.0013 nce_acc=0.091\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 val: mAP=0.114 F1=0.667 R@1=0.027 cos=0.269 cv=0.3216 anchors=145/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 3/60: 100%|██████████| 232/232 [00:59<00:00, 3.89batch/s, cos=0.311, loss=4.2326, nce_acc=0.155, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 train: 60s loss=4.2064 nce=3.8171 bce=0.1250 ortho=0.0045 agree=0.0010 nce_acc=0.159\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 val: mAP=0.158 F1=0.652 R@1=0.031 cos=0.308 cv=0.3409 anchors=144/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 4/60: 100%|██████████| 232/232 [01:00<00:00, 3.86batch/s, cos=0.360, loss=3.7624, nce_acc=0.223, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 train: 60s loss=3.7429 nce=3.3815 bce=0.1157 ortho=0.0037 agree=0.0007 nce_acc=0.227\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 val: mAP=0.199 F1=0.187 R@1=0.058 cos=0.357 cv=0.2224 anchors=159/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 5/60: 100%|██████████| 232/232 [00:59<00:00, 3.89batch/s, cos=0.393, loss=3.4423, nce_acc=0.278, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 train: 60s loss=3.4276 nce=3.0852 bce=0.1098 ortho=0.0031 agree=0.0005 nce_acc=0.281\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 val: mAP=0.223 F1=0.282 R@1=0.078 cos=0.389 cv=0.2160 anchors=161/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 6/60: 100%|██████████| 232/232 [00:59<00:00, 3.87batch/s, cos=0.420, loss=3.1969, nce_acc=0.322, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 train: 60s loss=3.1822 nce=2.8545 bce=0.1059 ortho=0.0025 agree=0.0004 nce_acc=0.324\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 val: mAP=0.240 F1=0.260 R@1=0.088 cos=0.392 cv=0.2317 anchors=165/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 7/60: 100%|██████████| 232/232 [00:59<00:00, 3.89batch/s, cos=0.442, loss=2.9845, nce_acc=0.365, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 train: 60s loss=2.9743 nce=2.6588 bce=0.1030 ortho=0.0021 agree=0.0005 nce_acc=0.367\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 val: mAP=0.274 F1=0.261 R@1=0.132 cos=0.438 cv=0.1668 anchors=174/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 8/60: 100%|██████████| 232/232 [00:59<00:00, 3.89batch/s, cos=0.461, loss=2.7932, nce_acc=0.407, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 train: 60s loss=2.7809 nce=2.4761 bce=0.1003 ortho=0.0017 agree=0.0006 nce_acc=0.409\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 val: mAP=0.286 F1=0.243 R@1=0.129 cos=0.436 cv=0.1789 anchors=171/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 9/60: 100%|██████████| 232/232 [01:02<00:00, 3.69batch/s, cos=0.476, loss=2.6457, nce_acc=0.440, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 train: 63s loss=2.6363 nce=2.3400 bce=0.0984 ortho=0.0014 agree=0.0005 nce_acc=0.443\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 val: mAP=0.300 F1=0.191 R@1=0.147 cos=0.448 cv=0.2152 anchors=197/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E10/60: 100%|██████████| 232/232 [00:59<00:00, 3.87batch/s, cos=0.490, loss=2.5133, nce_acc=0.473, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 train: 60s loss=2.5016 nce=2.2130 bce=0.0967 ortho=0.0011 agree=0.0004 nce_acc=0.475\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 val: mAP=0.325 F1=0.256 R@1=0.184 cos=0.468 cv=0.1845 anchors=168/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E11/60: 100%|██████████| 232/232 [00:59<00:00, 3.88batch/s, cos=0.504, loss=2.3794, nce_acc=0.507, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E11 train: 60s loss=2.3722 nce=2.0912 bce=0.0945 ortho=0.0009 agree=0.0004 nce_acc=0.508\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E11 val: mAP=0.332 F1=0.265 R@1=0.181 cos=0.473 cv=0.1765 anchors=186/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E12/60: 100%|██████████| 232/232 [00:59<00:00, 3.88batch/s, cos=0.515, loss=2.2724, nce_acc=0.532, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E12 train: 60s loss=2.2639 nce=1.9894 bce=0.0927 ortho=0.0007 agree=0.0003 nce_acc=0.534\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E12 val: mAP=0.341 F1=0.225 R@1=0.192 cos=0.481 cv=0.1593 anchors=168/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E13/60: 100%|██████████| 232/232 [00:59<00:00, 3.88batch/s, cos=0.525, loss=2.1760, nce_acc=0.556, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E13 train: 60s loss=2.1676 nce=1.8988 bce=0.0914 ortho=0.0005 agree=0.0003 nce_acc=0.558\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E13 val: mAP=0.348 F1=0.250 R@1=0.197 cos=0.485 cv=0.1893 anchors=163/256 ★\n" ] }, { "output_type": "stream", 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agree=0.0002 nce_acc=0.867\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " E38 val: mAP=0.449 F1=0.357 R@1=0.347 cos=0.562 cv=0.1411 anchors=182/256 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E39/60: 43%|████▎ | 99/232 [00:27<00:36, 3.61batch/s, cos=0.655, loss=1.0158, nce_acc=0.876, ordered=1]\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_215508/1277535341.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;31m# Fused constellation losses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[0ml_spread\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/tmp/ipykernel_215508/1277535341.py\u001b[0m in \u001b[0;36mrotation_ortho_loss\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_experts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0mR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpert_rotations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_experts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "ddWPBExzvoaC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# train thicker soup" ], "metadata": { "id": "fQtrxA7VKykt" } }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP MASSIVE SOUP — ORTHO SPECTRUM HYPERSPHERE\n", "==================================================\n", "2048 anchors × 256-d × 3 expert perspectives.\n", "\n", "Orthogonal initialization: 8 rotated orthogonal bases of 256 vectors = 2048.\n", "Each base tiles a different region of S^255. Together they form\n", "a structured mesh with known geometric relationships.\n", "\n", "Multi-depth patchwork:\n", " Level 0 (coarse): 16 compartments × 128 anchors × 3 experts = 384 inputs each → 128-d\n", " Level 1 (fine): 64 compartments × 32 anchors × 3 experts = 96 inputs each → 64-d\n", " Level 2 (micro): 128 compartments × 16 anchors × 3 experts = 48 inputs each → 32-d\n", "\n", " Total patchwork output: 16×128 + 64×64 + 128×32 = 2048 + 4096 + 4096 = 10240-d\n", " → project down to 1024 before classifier\n", "\n", "The depth levels read the sphere at different resolutions. Coarse catches\n", "global position, fine catches local neighborhood, micro catches sub-anchor\n", "structure. Each level has its own expert-aware triangulation view.\n", "\n", "GPA → PCA 256-d → Procrustes calibration → train with full loss stack.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "# Geometry\n", "D_EXPERT = 768\n", "D_ANCHOR = 256\n", "N_ANCHORS = 2048\n", "N_ORTHO_BASES = 8 # 8 × 256 = 2048\n", "N_EXPERTS_COUNT = 3\n", "N_CLASSES = 80\n", "ANCHOR_DROP = 0.30\n", "\n", "# Multi-depth patchwork\n", "COARSE_COMP = 16 # 2048/16 = 128 anchors per comp\n", "FINE_COMP = 64 # 2048/64 = 32 anchors per comp\n", "MICRO_COMP = 128 # 2048/128 = 16 anchors per comp\n", "D_COARSE = 128\n", "D_FINE = 64\n", "D_MICRO = 32\n", "D_PW_PROJ = 1024 # project combined patchwork to this\n", "\n", "# Training\n", "BATCH = 128\n", "EPOCHS = 30\n", "LR = 1e-3\n", "QUEUE_SIZE = 4096\n", "GRAD_CLIP = 1.0\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "TRI_DIM = N_ANCHORS * N_EXPERTS_COUNT\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOLIP MASSIVE SOUP — ORTHO SPECTRUM\")\n", "print(f\" {N_ANCHORS} anchors × {D_ANCHOR}-d × {N_EXPERTS_COUNT} perspectives\")\n", "print(f\" Ortho bases: {N_ORTHO_BASES} × {D_ANCHOR} = {N_ANCHORS}\")\n", "print(f\" Patchwork: coarse({COARSE_COMP}) + fine({FINE_COMP}) + micro({MICRO_COMP})\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=500):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce_queued(emb, targets, queue_emb, queue_tgt, temperature=0.07):\n", " B = emb.shape[0]\n", " e = F.normalize(emb, dim=-1); t = F.normalize(targets, dim=-1)\n", " if queue_tgt is not None and queue_tgt.shape[0] > 0:\n", " at = torch.cat([t, queue_tgt], 0); ae = torch.cat([e, queue_emb], 0)\n", " else:\n", " at = t; ae = e\n", " l_e2t = (e @ at.T) / temperature; l_t2e = (t @ ae.T) / temperature\n", " labels = torch.arange(B, device=emb.device)\n", " loss = (F.cross_entropy(l_e2t, labels) + F.cross_entropy(l_t2e, labels)) / 2\n", " with torch.no_grad():\n", " acc = (l_e2t.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True); tm = targets.float().mean(0, keepdim=True)\n", " return 1.0 - F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1).mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " sm = S.mean(0, keepdim=True); tm = T.mean(0, keepdim=True)\n", " Sc = S - sm; Tc = T - tm; Ns = Sc.shape[0]\n", " sw = symmetric_inv_sqrt((Sc.T @ Sc) / max(Ns-1, 1))\n", " tw = symmetric_inv_sqrt((Tc.T @ Tc) / max(Ns-1, 1))\n", " Sc_w = F.normalize(Sc @ sw, dim=-1); Tc_w = F.normalize(Tc @ tw, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " return {\"rotation\": U @ Vt, \"source_mean\": sm.squeeze(0),\n", " \"source_whitener\": sw, \"target_unwhitener\": torch.linalg.pinv(tw)}\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " return x @ a[\"source_whitener\"] @ a[\"rotation\"].T @ a[\"target_unwhitener\"]\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ORTHO ANCHOR INITIALIZATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def init_ortho_anchors(d, n_bases):\n", " \"\"\"\n", " Generate n_bases × d anchors from rotated orthonormal bases.\n", " Each base is a full d×d orthogonal matrix (d vectors).\n", " We take d vectors from each, rotated to tile different regions.\n", " Total: n_bases × d anchors.\n", " \"\"\"\n", " all_anchors = []\n", " # First base: identity-like (from QR of random)\n", " base = torch.randn(d, d)\n", " Q, _ = torch.linalg.qr(base)\n", " all_anchors.append(Q) # d × d, each row is unit vector, all orthogonal\n", "\n", " for i in range(1, n_bases):\n", " # Generate random rotation\n", " R_rand = torch.randn(d, d)\n", " R_q, _ = torch.linalg.qr(R_rand)\n", " # Rotate the base\n", " rotated = Q @ R_q.T\n", " all_anchors.append(rotated)\n", "\n", " anchors = torch.cat(all_anchors, dim=0) # (n_bases*d, d)\n", " return F.normalize(anchors, dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FUSED CONSTELLATION (2048 anchors)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class FusedConstellation(nn.Module):\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR, n_experts=N_EXPERTS_COUNT,\n", " drop_rate=ANCHOR_DROP):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.n_experts = n_experts\n", " self.drop_rate = drop_rate\n", " self.d = d\n", "\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", " self.expert_rotations = nn.ParameterList([\n", " nn.Parameter(torch.eye(d)) for _ in range(n_experts)])\n", " self.expert_whiteners = nn.ParameterList([\n", " nn.Parameter(torch.eye(d)) for _ in range(n_experts)])\n", " self.expert_means = nn.ParameterList([\n", " nn.Parameter(torch.zeros(d)) for _ in range(n_experts)])\n", "\n", " def triangulate(self, emb, training=False):\n", " B = emb.shape[0]\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " expert_embs = []\n", " for i in range(self.n_experts):\n", " centered = emb.float() - self.expert_means[i]\n", " whitened = centered @ self.expert_whiteners[i]\n", " rotated = F.normalize(whitened @ self.expert_rotations[i].T, dim=-1)\n", " expert_embs.append(rotated)\n", "\n", " if training and self.drop_rate > 0:\n", " n_keep = max(int(self.n_anchors * (1 - self.drop_rate)), 128)\n", " keep_idx = torch.randperm(self.n_anchors, device=emb.device)[:n_keep]\n", " a_masked = anchors_n[keep_idx]\n", " expert_tris, expert_cos_list = [], []\n", " for rotated in expert_embs:\n", " cos = rotated @ a_masked.T\n", " full_cos = torch.full((B, self.n_anchors), -1.0,\n", " device=emb.device, dtype=cos.dtype)\n", " full_cos[:, keep_idx] = cos\n", " expert_tris.append(1.0 - full_cos)\n", " expert_cos_list.append(full_cos)\n", " else:\n", " expert_tris, expert_cos_list = [], []\n", " for rotated in expert_embs:\n", " cos = rotated @ anchors_n.T\n", " expert_tris.append(1.0 - cos)\n", " expert_cos_list.append(cos)\n", "\n", " tri_stacked = torch.stack(expert_tris, dim=-1) # (B, N_ANCHORS, 3)\n", " tri_fused = tri_stacked.reshape(B, -1)\n", " mean_cos = torch.stack(expert_cos_list, dim=-1).mean(dim=-1)\n", " nearest = mean_cos.argmax(dim=-1)\n", "\n", " return tri_fused, nearest, tri_stacked\n", "\n", " def anchor_spread_loss(self):\n", " # Sample-based for 2048 anchors (full 2048×2048 is too big)\n", " a = F.normalize(self.anchors, dim=-1)\n", " idx = torch.randperm(self.n_anchors, device=a.device)[:512]\n", " a_sub = a[idx]\n", " sim = a_sub @ a_sub.T; sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", " def expert_agreement_loss(self, emb):\n", " anchors_n = F.normalize(self.anchors[:512], dim=-1) # subsample for speed\n", " expert_cos = []\n", " for i in range(self.n_experts):\n", " centered = emb.float() - self.expert_means[i]\n", " rotated = F.normalize(centered @ self.expert_whiteners[i] @\n", " self.expert_rotations[i].T, dim=-1)\n", " expert_cos.append(rotated @ anchors_n.T)\n", " stacked = torch.stack(expert_cos, dim=-1)\n", " disagree = stacked.std(dim=-1)\n", " return (disagree.mean() - 0.05).abs()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MULTI-DEPTH PATCHWORK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DepthLevel(nn.Module):\n", " \"\"\"Single depth level of the patchwork — reads a specific granularity.\"\"\"\n", " def __init__(self, n_anchors, n_comp, n_experts, d_comp):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " self.n_experts = n_experts\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " inputs_per_comp = (n_anchors // n_comp) * n_experts\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear(inputs_per_comp, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for _ in range(n_comp)])\n", "\n", " def forward(self, tri_3d):\n", " \"\"\"tri_3d: (B, n_anchors, n_experts)\"\"\"\n", " B = tri_3d.shape[0]\n", " results = []\n", " for k in range(self.n_comp):\n", " mask = self.asgn == k\n", " comp_input = tri_3d[:, mask, :].reshape(B, -1)\n", " results.append(self.comps[k](comp_input))\n", " return torch.cat(results, dim=-1)\n", "\n", "\n", "class MultiDepthPatchwork(nn.Module):\n", " \"\"\"\n", " Reads the sphere at 3 resolutions:\n", " Coarse: 16 compartments, 128 anchors each — global position\n", " Fine: 64 compartments, 32 anchors each — local neighborhood\n", " Micro: 128 compartments, 16 anchors each — sub-anchor structure\n", "\n", " Combined output projected to D_PW_PROJ.\n", " \"\"\"\n", " def __init__(self):\n", " super().__init__()\n", " self.coarse = DepthLevel(N_ANCHORS, COARSE_COMP, N_EXPERTS_COUNT, D_COARSE)\n", " self.fine = DepthLevel(N_ANCHORS, FINE_COMP, N_EXPERTS_COUNT, D_FINE)\n", " self.micro = DepthLevel(N_ANCHORS, MICRO_COMP, N_EXPERTS_COUNT, D_MICRO)\n", "\n", " # Coarse: 16 × 128 = 2048\n", " # Fine: 64 × 64 = 4096\n", " # Micro: 128 × 32 = 4096\n", " total_dim = COARSE_COMP * D_COARSE + FINE_COMP * D_FINE + MICRO_COMP * D_MICRO\n", " self.proj = nn.Sequential(\n", " nn.Linear(total_dim, D_PW_PROJ), nn.GELU(),\n", " nn.LayerNorm(D_PW_PROJ))\n", "\n", " def forward(self, tri_3d):\n", " \"\"\"tri_3d: (B, N_ANCHORS, N_EXPERTS_COUNT)\"\"\"\n", " c = self.coarse(tri_3d)\n", " f = self.fine(tri_3d)\n", " m = self.micro(tri_3d)\n", " combined = torch.cat([c, f, m], dim=-1)\n", " return self.proj(combined)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# EXPERT PROJECTOR + MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class ExpertProjector(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.proj = nn.Sequential(nn.Linear(D_EXPERT, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", " def forward(self, x):\n", " return F.normalize(self.proj(x), dim=-1)\n", "\n", "\n", "class MassiveSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.projectors = nn.ModuleList([ExpertProjector() for _ in range(N_EXPERTS_COUNT)])\n", " self.constellation = FusedConstellation()\n", " self.patchwork = MultiDepthPatchwork()\n", "\n", " self.classifier = nn.Sequential(\n", " nn.Linear(D_PW_PROJ + D_ANCHOR, D_PW_PROJ), nn.GELU(),\n", " nn.LayerNorm(D_PW_PROJ), nn.Dropout(0.1),\n", " nn.Linear(D_PW_PROJ, N_CLASSES))\n", "\n", " def forward(self, expert_features, apply_autograd=True):\n", " projected = [self.projectors[i](expert_features[i]) for i in range(N_EXPERTS_COUNT)]\n", " fused = F.normalize(sum(projected) / N_EXPERTS_COUNT, dim=-1)\n", "\n", " if apply_autograd and self.training:\n", " fused = EmbeddingAutograd.apply(\n", " fused, fused, self.constellation.anchors, 0.01, 1.0)\n", "\n", " tri_fused, nearest, tri_3d = self.constellation.triangulate(\n", " fused, training=self.training)\n", " pw = self.patchwork(tri_3d)\n", " logits = self.classifier(torch.cat([pw, fused], dim=-1))\n", "\n", " return logits, fused, tri_fused, nearest, projected\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + GPA + CALIBRATE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 0: LOAD DATA\")\n", "print(f\"{'='*65}\")\n", "\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"train\")\n", "train_ids = ref[\"image_id\"]; N_train = len(train_ids)\n", "train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", "train_labels = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: train_labels[i, l] = 1.0\n", "\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(ref_val[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: val_labels[i, l] = 1.0\n", "\n", "print(f\" Train: {N_train:,} Val: {N_val:,}\")\n", "\n", "train_raw, val_raw = {}, {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " feats = torch.zeros(N_train, D_EXPERT)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_map:\n", " feats[train_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " train_raw[name] = feats\n", " ds_v = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats_v = torch.zeros(N_val, D_EXPERT)\n", " for row in ds_v:\n", " if row[\"image_id\"] in val_id_map:\n", " feats_v[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats_v\n", " print(f\" {name:<30} loaded\")\n", " del ds, ds_v; gc.collect()\n", "\n", "# GPA\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 1: GPA + PCA + PROCRUSTES\")\n", "print(f\"{'='*65}\")\n", "\n", "current = {name: train_raw[name].float() for name in EXPERTS}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current[n] for n in EXPERTS) / len(EXPERTS)\n", " delta = 0.0\n", " for name in EXPERTS:\n", " info = procrustes_align(current[name], mean_shape)\n", " current[name] = apply_align(current[name], info)\n", " delta += (current[name] - apply_align(train_raw[name].float(), info)).pow(2).mean().item()\n", " # Recompute properly\n", " new_current = {}\n", " delta = 0.0\n", " for name in EXPERTS:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter+1) % 5 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={delta:.8f}\")\n", " if delta < 1e-8: break\n", "\n", "consensus_768 = F.normalize(sum(current[n] for n in EXPERTS) / len(EXPERTS), dim=-1)\n", "for name in EXPERTS:\n", " c = F.cosine_similarity(consensus_768[:5000], current[name][:5000], dim=-1).mean().item()\n", " print(f\" cos(consensus, {name}): {c:.4f}\")\n", "\n", "# PCA → 256-d\n", "cc = consensus_768 - consensus_768.mean(0, keepdim=True)\n", "U, S, Vt = torch.linalg.svd(cc[:10000], full_matrices=False)\n", "pca_proj = Vt[:D_ANCHOR]\n", "consensus_d = F.normalize(consensus_768 @ pca_proj.T, dim=-1)\n", "var_ret = S[:D_ANCHOR].pow(2).sum() / S.pow(2).sum()\n", "print(f\" PCA 768→{D_ANCHOR}: var_retained={var_ret.item():.4f}\")\n", "consensus_cv = cv_metric(consensus_d[:5000].to(DEVICE))\n", "print(f\" Consensus CV at {D_ANCHOR}-d: {consensus_cv:.4f}\")\n", "\n", "# Val consensus\n", "val_current = {name: val_raw[name].float() for name in EXPERTS}\n", "for _ in range(20):\n", " vm = sum(val_current[n] for n in EXPERTS) / len(EXPERTS)\n", " d = 0.0\n", " for name in EXPERTS:\n", " info = procrustes_align(val_current[name], vm)\n", " new = apply_align(val_current[name], info)\n", " d += (new - val_current[name]).pow(2).mean().item()\n", " val_current[name] = new\n", " if d < 1e-8: break\n", "val_consensus_768 = F.normalize(sum(val_current[n] for n in EXPERTS) / len(EXPERTS), dim=-1)\n", "val_consensus_d = F.normalize(val_consensus_768 @ pca_proj.T, dim=-1)\n", "\n", "# Per-expert Procrustes\n", "expert_calibrations = {}\n", "for name in EXPERTS:\n", " raw = train_raw[name][:10000].float()\n", " tgt = consensus_d[:10000].float()\n", " sm = raw.mean(0, keepdim=True); tm = tgt.mean(0, keepdim=True)\n", " sc = raw - sm; tc = tgt - tm\n", " sw = symmetric_inv_sqrt((sc.T @ sc) / 9999)\n", " tw = symmetric_inv_sqrt((tc.T @ tc) / 9999)\n", " src_w = F.normalize(sc @ sw, dim=-1); tgt_w = F.normalize(tc @ tw, dim=-1)\n", " M = tgt_w.T @ src_w\n", " U_r, S_r, Vt_r = torch.linalg.svd(M, full_matrices=False)\n", " R = U_r @ Vt_r\n", " proj_W = (sw @ R.T).T; proj_b = -(sm.squeeze(0) @ sw @ R.T).squeeze(0)\n", " test = F.normalize(raw[:1000] @ proj_W.T + proj_b, dim=-1)\n", " cos = F.cosine_similarity(test, tgt[:1000], dim=-1).mean().item()\n", " expert_calibrations[name] = {\"W\": proj_W, \"b\": proj_b, \"cos\": cos,\n", " \"R\": R[:D_ANCHOR, :D_ANCHOR],\n", " \"whiten\": tw, \"mean\": tm.squeeze(0)}\n", " print(f\" {name:<30} cos={cos:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD + INITIALIZE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 2: BUILD MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = MassiveSoup().to(DEVICE)\n", "\n", "with torch.no_grad():\n", " # Projectors\n", " for i, name in enumerate(EXPERTS):\n", " cal = expert_calibrations[name]\n", " model.projectors[i].proj[0].weight.copy_(cal[\"W\"].to(DEVICE))\n", " model.projectors[i].proj[0].bias.copy_(cal[\"b\"].to(DEVICE))\n", " print(f\" ✓ Projectors from Procrustes\")\n", "\n", " # Ortho anchors\n", " ortho_anchors = init_ortho_anchors(D_ANCHOR, N_ORTHO_BASES)\n", " model.constellation.anchors.copy_(ortho_anchors.to(DEVICE))\n", " print(f\" ✓ {N_ANCHORS} ortho-spectrum anchors ({N_ORTHO_BASES} bases × {D_ANCHOR})\")\n", "\n", " # Expert perspectives\n", " for i, name in enumerate(EXPERTS):\n", " cal = expert_calibrations[name]\n", " model.constellation.expert_rotations[i].copy_(cal[\"R\"].to(DEVICE))\n", " model.constellation.expert_whiteners[i].copy_(cal[\"whiten\"].to(DEVICE))\n", " model.constellation.expert_means[i].copy_(cal[\"mean\"].to(DEVICE))\n", " print(f\" ✓ Expert perspectives calibrated\")\n", "\n", "# Verify\n", "with torch.no_grad():\n", " test_in = [train_raw[EXPERTS[e]][:200].to(DEVICE) for e in range(3)]\n", " _, test_fused, _, test_nearest, _ = model(test_in, apply_autograd=False)\n", " test_tgt = consensus_d[:200].to(DEVICE)\n", " init_cos = F.cosine_similarity(test_fused, test_tgt, dim=-1).mean().item()\n", " n_active = test_nearest.unique().numel()\n", " print(f\" Init: cos={init_cos:.4f} active_anchors={n_active}/{N_ANCHORS}\")\n", "\n", "# Count params\n", "def count_params(module):\n", " return sum(p.numel() for p in module.parameters())\n", "n_total = count_params(model)\n", "print(f\"\\n Parameters:\")\n", "print(f\" projectors: {sum(count_params(p) for p in model.projectors):>12,}\")\n", "print(f\" constellation: {count_params(model.constellation):>12,}\")\n", "print(f\" patchwork: {count_params(model.patchwork):>12,}\")\n", "print(f\" classifier: {count_params(model.classifier):>12,}\")\n", "print(f\" total: {n_total:>12,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 3: TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Queue: {QUEUE_SIZE} | Anchor dropout: {ANCHOR_DROP}\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\"{'='*65}\")\n", "\n", "train_targets = consensus_d.to(DEVICE)\n", "val_targets = val_consensus_d.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/massive_soup\")\n", "best_mAP = 0.0; gs = 0\n", "queue_e = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "queue_t = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train)\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"cv\": 0,\n", " \"spread\": 0, \"agree\": 0, \"align\": 0, \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH), desc=f\"E{epoch+1:2d}/{EPOCHS}\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " batch = [train_raw[EXPERTS[e]][idx].to(DEVICE) for e in range(3)]\n", " labels = train_labels_gpu[idx]\n", " targets = train_targets[idx]\n", "\n", " logits, fused, tri, nearest, projected = model(batch)\n", "\n", " l_nce, nce_acc = infonce_queued(fused, targets, queue_e, queue_t)\n", " with torch.no_grad():\n", " queue_e = torch.cat([queue_e, fused.detach()], 0)[-QUEUE_SIZE:]\n", " queue_t = torch.cat([queue_t, targets.detach()], 0)[-QUEUE_SIZE:]\n", "\n", " l_mse = F.mse_loss(fused, targets)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", " l_align = whitened_procrustes_loss(fused, targets)\n", " l_cv = cv_loss(fused, target=consensus_cv)\n", " l_spread = model.constellation.anchor_spread_loss()\n", " l_agree = model.constellation.expert_agreement_loss(fused)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv\n", " + 1e-3 * l_spread + 0.1 * l_agree)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " acc[\"loss\"] += loss.item(); acc[\"nce\"] += l_nce.item()\n", " acc[\"mse\"] += l_mse.item(); acc[\"bce\"] += l_bce.item()\n", " acc[\"cv\"] += l_cv.item(); acc[\"spread\"] += l_spread.item()\n", " acc[\"agree\"] += l_agree.item() if torch.is_tensor(l_agree) else l_agree\n", " acc[\"align\"] += l_align.item(); acc[\"nce_acc\"] += nce_acc\n", " acc[\"n\"] += 1; gs += 1\n", "\n", " if gs % 50 == 0:\n", " for k in [\"loss\", \"nce\", \"bce\", \"nce_acc\"]:\n", " writer.add_scalar(f\"step/{k}\", acc[k]/max(acc[\"n\"],1), gs)\n", "\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " cos=f\"{1-acc['align']/d:.3f}\", ordered=True)\n", "\n", " d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: loss={acc['loss']/d:.4f} nce={acc['nce']/d:.4f} \"\n", " f\"bce={acc['bce']/d:.4f} agree={acc['agree']/d:.4f} \"\n", " f\"nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " all_lo, all_em = [], []\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " batch_v = [val_raw[EXPERTS[e]][j:end].to(DEVICE) for e in range(3)]\n", " lo, em, _, _, _ = model(batch_v, apply_autograd=False)\n", " all_lo.append(lo.cpu()); all_em.append(em.cpu())\n", " v_lo = torch.cat(all_lo); v_em = torch.cat(all_em)\n", "\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = v_lo[:, c].argsort(descending=True); st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st)+1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " vp = (v_lo.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1-v_lab)).sum(0); fn = ((1-vp) * v_lab).sum(0)\n", " pr_ = tp/(tp+fp+1e-8); rc_ = tp/(tp+fn+1e-8); f1_ = 2*pr_*rc_/(pr_+rc_+1e-8)\n", "\n", " v_cos = F.cosine_similarity(v_em, val_targets.cpu(), dim=-1).mean().item()\n", " sim = v_em @ val_targets.cpu().T\n", " r1 = (sim.argmax(-1) == torch.arange(N_val)).float().mean().item()\n", " _, v_nearest, _ = model.constellation.triangulate(v_em.to(DEVICE), training=False)\n", " n_active = v_nearest.cpu().unique().numel()\n", " v_cv = cv_metric(v_em[:2000].to(DEVICE))\n", "\n", " for k in acc:\n", " if k != \"n\": writer.add_scalar(f\"epoch/{k}\", acc[k]/d, epoch+1)\n", " writer.add_scalar(\"val/mAP\", mAP, epoch+1)\n", " writer.add_scalar(\"val/cos\", v_cos, epoch+1)\n", " writer.add_scalar(\"val/R@1\", r1, epoch+1)\n", " writer.add_scalar(\"val/anchors\", n_active, epoch+1)\n", " writer.add_scalar(\"val/cv\", v_cv, epoch+1)\n", "\n", " mk = \"\"\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\"state_dict\": model.state_dict(),\n", " \"config\": {\"d_anchor\": D_ANCHOR, \"n_anchors\": N_ANCHORS,\n", " \"n_ortho_bases\": N_ORTHO_BASES,\n", " \"n_experts\": N_EXPERTS_COUNT,\n", " \"coarse_comp\": COARSE_COMP, \"fine_comp\": FINE_COMP,\n", " \"micro_comp\": MICRO_COMP,\n", " \"d_coarse\": D_COARSE, \"d_fine\": D_FINE,\n", " \"d_micro\": D_MICRO, \"d_pw_proj\": D_PW_PROJ,\n", " \"anchor_drop\": ANCHOR_DROP, \"experts\": EXPERTS,\n", " \"cv_target\": consensus_cv},\n", " \"pca_proj\": pca_proj, \"consensus_cv\": consensus_cv,\n", " \"mAP\": mAP, \"r1\": r1, \"cos\": v_cos, \"cv\": v_cv,\n", " \"epoch\": epoch+1, \"n_active\": n_active},\n", " \"checkpoints/massive_soup_best.pt\")\n", " mk = \" ★\"\n", "\n", " torch.save({\"state_dict\": model.state_dict(), \"epoch\": epoch+1,\n", " \"mAP\": mAP, \"optimizer\": optimizer.state_dict(), \"gs\": gs},\n", " f\"checkpoints/massive_soup_e{epoch+1:02d}.pt\")\n", "\n", " print(f\" E{epoch+1} val: mAP={mAP:.3f} F1={f1_[f1_>0].mean():.3f} \"\n", " f\"R@1={r1:.3f} cos={v_cos:.3f} cv={v_cv:.4f} \"\n", " f\"anchors={n_active}/{N_ANCHORS}{mk}\")\n", "\n", "writer.close()\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Total: {n_total:,} params\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "aBRWkKwWK0C4", "outputId": "7ebf92c6-5dc9-408f-aa4e-ece6b040ce3e" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP MASSIVE SOUP — ORTHO SPECTRUM\n", " 2048 anchors × 256-d × 3 perspectives\n", " Ortho bases: 8 × 256 = 2048\n", " Patchwork: coarse(16) + fine(64) + micro(128)\n", " Device: cuda\n", "=================================================================\n", "\n", "=================================================================\n", "PHASE 0: LOAD DATA\n", "=================================================================\n", " Train: 118,287 Val: 5,000\n", " clip_l14_openai loaded\n", " dinov2_b14 loaded\n", " siglip_b16_384 loaded\n", "\n", "=================================================================\n", "PHASE 1: GPA + PCA + PROCRUSTES\n", "=================================================================\n", " GPA iter 1: delta=0.00011114\n", " cos(consensus, clip_l14_openai): 0.9015\n", " cos(consensus, dinov2_b14): 0.8966\n", " cos(consensus, siglip_b16_384): 0.9078\n", " PCA 768→256: var_retained=0.9185\n", " Consensus CV at 256-d: 0.1510\n", " clip_l14_openai cos=0.7478\n", " dinov2_b14 cos=0.7449\n", " siglip_b16_384 cos=0.7532\n", "\n", "=================================================================\n", "PHASE 2: BUILD MODEL\n", "=================================================================\n", " ✓ Projectors from Procrustes\n", " ✓ 2048 ortho-spectrum anchors (8 bases × 256)\n", " ✓ Expert perspectives calibrated\n", " Init: cos=0.8612 active_anchors=184/2048\n", "\n", " Parameters:\n", " projectors: 592,128\n", " constellation: 918,272\n", " patchwork: 14,603,264\n", " classifier: 1,395,792\n", " total: 17,509,456\n", "\n", "=================================================================\n", "PHASE 3: TRAINING\n", " 30 epochs, lr=0.001, batch=128\n", " Queue: 4096 | Anchor dropout: 0.3\n", " CV target: 0.1510\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 1/30: 100%|██████████| 925/925 [04:04<00:00, 3.79batch/s, cos=0.978, loss=0.5126, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 train: loss=0.5128 nce=0.4819 bce=0.0660 agree=0.0005 nce_acc=1.000\n", " E1 val: mAP=0.817 F1=0.755 R@1=0.997 cos=0.786 cv=0.1156 anchors=1506/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 2/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.5071, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 train: loss=0.5069 nce=0.4857 bce=0.0412 agree=0.0005 nce_acc=1.000\n", " E2 val: mAP=0.827 F1=0.769 R@1=0.998 cos=0.785 cv=0.0971 anchors=1450/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 3/30: 100%|██████████| 925/925 [04:04<00:00, 3.78batch/s, cos=0.983, loss=0.5042, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 train: loss=0.5042 nce=0.4836 bce=0.0396 agree=0.0005 nce_acc=1.000\n", " E3 val: mAP=0.831 F1=0.759 R@1=0.997 cos=0.785 cv=0.1075 anchors=1502/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 4/30: 100%|██████████| 925/925 [04:05<00:00, 3.78batch/s, cos=0.983, loss=0.5032, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 train: loss=0.5034 nce=0.4831 bce=0.0384 agree=0.0005 nce_acc=1.000\n", " E4 val: mAP=0.834 F1=0.769 R@1=0.997 cos=0.784 cv=0.0984 anchors=1470/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 5/30: 100%|██████████| 925/925 [04:04<00:00, 3.79batch/s, cos=0.983, loss=0.5014, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 train: loss=0.5015 nce=0.4816 bce=0.0373 agree=0.0005 nce_acc=1.000\n", " E5 val: mAP=0.836 F1=0.773 R@1=0.998 cos=0.784 cv=0.1102 anchors=1459/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 6/30: 100%|██████████| 925/925 [04:03<00:00, 3.79batch/s, cos=0.983, loss=0.5013, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 train: loss=0.5010 nce=0.4815 bce=0.0361 agree=0.0004 nce_acc=1.000\n", " E6 val: mAP=0.838 F1=0.770 R@1=0.998 cos=0.785 cv=0.1029 anchors=1430/2048 ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 7/30: 100%|██████████| 925/925 [04:04<00:00, 3.78batch/s, cos=0.983, loss=0.5001, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 train: loss=0.5002 nce=0.4812 bce=0.0350 agree=0.0004 nce_acc=1.000\n", " E7 val: mAP=0.836 F1=0.776 R@1=0.998 cos=0.784 cv=0.0990 anchors=1426/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 8/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.4993, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 train: loss=0.4996 nce=0.4809 bce=0.0339 agree=0.0004 nce_acc=1.000\n", " E8 val: mAP=0.835 F1=0.777 R@1=0.997 cos=0.785 cv=0.1011 anchors=1374/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 9/30: 100%|██████████| 925/925 [04:05<00:00, 3.76batch/s, cos=0.983, loss=0.4987, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 train: loss=0.4986 nce=0.4803 bce=0.0327 agree=0.0005 nce_acc=1.000\n", " E9 val: mAP=0.836 F1=0.776 R@1=0.997 cos=0.784 cv=0.1068 anchors=1454/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E10/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.4980, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 train: loss=0.4981 nce=0.4801 bce=0.0316 agree=0.0004 nce_acc=1.000\n", " E10 val: mAP=0.833 F1=0.776 R@1=0.997 cos=0.785 cv=0.1085 anchors=1422/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E11/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.4973, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E11 train: loss=0.4977 nce=0.4800 bce=0.0307 agree=0.0004 nce_acc=1.000\n", " E11 val: mAP=0.830 F1=0.773 R@1=0.997 cos=0.785 cv=0.1000 anchors=1418/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E12/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.4976, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E12 train: loss=0.4973 nce=0.4800 bce=0.0297 agree=0.0004 nce_acc=1.000\n", " E12 val: mAP=0.830 F1=0.775 R@1=0.997 cos=0.786 cv=0.1258 anchors=1414/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E13/30: 100%|██████████| 925/925 [04:05<00:00, 3.77batch/s, cos=0.983, loss=0.4970, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E13 train: loss=0.4968 nce=0.4797 bce=0.0289 agree=0.0004 nce_acc=1.000\n", " E13 val: mAP=0.827 F1=0.770 R@1=0.998 cos=0.785 cv=0.0960 anchors=1394/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E14/30: 100%|██████████| 925/925 [04:06<00:00, 3.75batch/s, cos=0.984, loss=0.4964, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E14 train: loss=0.4962 nce=0.4794 bce=0.0282 agree=0.0004 nce_acc=1.000\n", " E14 val: mAP=0.823 F1=0.774 R@1=0.997 cos=0.785 cv=0.1140 anchors=1407/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E15/30: 100%|██████████| 925/925 [04:06<00:00, 3.76batch/s, cos=0.984, loss=0.4962, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E15 train: loss=0.4962 nce=0.4796 bce=0.0274 agree=0.0004 nce_acc=1.000\n", " E15 val: mAP=0.821 F1=0.763 R@1=0.997 cos=0.784 cv=0.1086 anchors=1387/2048\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E16/30: 28%|██▊ | 256/925 [01:08<02:58, 3.75batch/s, cos=0.984, loss=0.4972, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_227695/2834307181.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 611\u001b[0m + 1e-3 * l_spread + 0.1 * l_agree)\n\u001b[1;32m 612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 613\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 614\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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Which soup to analyze — set this to your checkpoint path\n", "SOUP_CKPT = \"checkpoints/dual_stream_best.pt\" # or massive_soup_best.pt\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "print(\"=\" * 70)\n", "print(\"GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 70)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD CHECKPOINT + EXTRACT COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading checkpoint...\")\n", "ckpt = torch.load(SOUP_CKPT, map_location=\"cpu\", weights_only=False)\n", "cfg = ckpt[\"config\"]\n", "sd = ckpt[\"state_dict\"]\n", "\n", "D_ANCHOR = cfg[\"d_anchor\"]\n", "N_ANCHORS = cfg[\"n_anchors\"]\n", "N_EXPERTS = cfg.get(\"n_experts\", 3)\n", "N_COMP = cfg.get(\"n_comp\", cfg.get(\"coarse_comp\", 8))\n", "\n", "print(f\" mAP={ckpt['mAP']:.3f} epoch={ckpt['epoch']}\")\n", "print(f\" D_ANCHOR={D_ANCHOR} N_ANCHORS={N_ANCHORS} N_EXPERTS={N_EXPERTS}\")\n", "\n", "# Extract anchors\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1).to(DEVICE)\n", "print(f\" Anchors: {anchors.shape}\")\n", "\n", "# Extract expert rotations/whiteners/means\n", "expert_R, expert_W, expert_mu = [], [], []\n", "for i in range(N_EXPERTS):\n", " expert_R.append(sd[f\"constellation.expert_rotations.{i}\"].to(DEVICE))\n", " expert_W.append(sd[f\"constellation.expert_whiteners.{i}\"].to(DEVICE))\n", " expert_mu.append(sd[f\"constellation.expert_means.{i}\"].to(DEVICE))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + GENERATE EMBEDDINGS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading expert features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats\n", " del ds\n", "\n", "# Generate fused embeddings through projectors\n", "print(f\" Generating embeddings...\")\n", "projector_weights = []\n", "for i in range(N_EXPERTS):\n", " W = sd[f\"projectors.{i}.proj.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj.0.bias\"]\n", " ln_w = sd[f\"projectors.{i}.proj.1.weight\"]\n", " ln_b = sd[f\"projectors.{i}.proj.1.bias\"]\n", " projector_weights.append({\"W\": W, \"b\": b, \"ln_w\": ln_w, \"ln_b\": ln_b})\n", "\n", "def project_expert(feats, pw):\n", " x = feats @ pw[\"W\"].T + pw[\"b\"]\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * pw[\"ln_w\"] + pw[\"ln_b\"]\n", " return F.normalize(x, dim=-1)\n", "\n", "with torch.no_grad():\n", " projected = []\n", " for i, name in enumerate(EXPERTS):\n", " proj = project_expert(val_raw[name], projector_weights[i])\n", " projected.append(proj)\n", "\n", " fused = F.normalize(sum(projected) / N_EXPERTS, dim=-1).to(DEVICE)\n", " projected_gpu = [p.to(DEVICE) for p in projected]\n", "\n", "print(f\" Embeddings: {fused.shape}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 1: ANCHOR GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 1: ANCHOR GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "if N_ANCHORS <= 2048:\n", " anchor_sim = anchors @ anchors.T\n", " anchor_sim_np = anchor_sim.cpu().numpy()\n", " np.fill_diagonal(anchor_sim_np, 0)\n", " print(f\" Pairwise cosine:\")\n", " print(f\" mean={anchor_sim_np.mean():.4f} std={anchor_sim_np.std():.4f}\")\n", " print(f\" max={anchor_sim_np.max():.4f} min={anchor_sim_np.min():.4f}\")\n", "\n", " max_neighbor = np.max(anchor_sim_np, axis=1)\n", " print(f\" Max neighbor cosine per anchor:\")\n", " print(f\" mean={max_neighbor.mean():.4f} std={max_neighbor.std():.4f}\")\n", " print(f\" max={max_neighbor.max():.4f} min={max_neighbor.min():.4f}\")\n", "\n", " flat = anchor_sim_np[np.triu_indices(N_ANCHORS, k=1)]\n", " for threshold in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " count = (flat > threshold).sum()\n", " print(f\" pairs with cos > {threshold:.1f}: {count} ({100*count/len(flat):.2f}%)\")\n", "else:\n", " # Subsample for large anchor sets\n", " idx_sub = torch.randperm(N_ANCHORS)[:1024]\n", " a_sub = anchors[idx_sub]\n", " sim_sub = a_sub @ a_sub.T\n", " sim_np = sim_sub.cpu().numpy()\n", " np.fill_diagonal(sim_np, 0)\n", " print(f\" Pairwise cosine (sampled 1024/{N_ANCHORS}):\")\n", " print(f\" mean={sim_np.mean():.4f} std={sim_np.std():.4f}\")\n", " print(f\" max={sim_np.max():.4f} min={sim_np.min():.4f}\")\n", "\n", "U_a, S_a, _ = torch.linalg.svd(anchors.float(), full_matrices=False)\n", "eff_rank = (S_a / S_a.sum()).pow(2).sum().reciprocal().item()\n", "print(f\"\\n Anchor spectral:\")\n", "print(f\" effective rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" sv_max={S_a[0]:.4f} sv_10={S_a[min(9,len(S_a)-1)]:.4f} \"\n", " f\"sv_50={S_a[min(49,len(S_a)-1)]:.4f} sv_min={S_a[-1]:.6f}\")\n", "cumvar = S_a.pow(2).cumsum(0) / S_a.pow(2).sum()\n", "for k in [10, 25, 50, 100, 128, 200]:\n", " if k < len(cumvar):\n", " print(f\" top-{k} SVs explain {100*cumvar[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_ANCHORS, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(anchors[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " a_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Anchor pentachoron CV: {a_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: ANCHOR UTILIZATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 2: ANCHOR UTILIZATION\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cos_to_anchors = fused @ anchors.T\n", " nearest = cos_to_anchors.argmax(dim=-1)\n", " visit_counts = torch.zeros(N_ANCHORS, device=DEVICE)\n", " for n in nearest:\n", " visit_counts[n] += 1\n", " vc = visit_counts.cpu().numpy()\n", "\n", "n_active = (vc > 0).sum()\n", "print(f\" Active anchors: {n_active}/{N_ANCHORS} ({100*n_active/N_ANCHORS:.1f}%)\")\n", "print(f\" Visit counts: mean={vc.mean():.1f} std={vc.std():.1f}\")\n", "print(f\" max={vc.max():.0f} min={vc[vc>0].min():.0f} (among active)\")\n", "print(f\" top 10: {sorted(vc, reverse=True)[:10]}\")\n", "\n", "probs = vc / vc.sum()\n", "probs_nonzero = probs[probs > 0]\n", "entropy = -(probs_nonzero * np.log(probs_nonzero)).sum()\n", "max_entropy = np.log(N_ANCHORS)\n", "print(f\" Entropy: {entropy:.4f} / {max_entropy:.4f} ({100*entropy/max_entropy:.1f}%)\")\n", "\n", "sorted_vc = np.sort(vc)\n", "n = len(sorted_vc)\n", "gini = (2 * np.sum((np.arange(1, n+1)) * sorted_vc) / (n * np.sum(sorted_vc))) - (n + 1) / n\n", "print(f\" Gini coefficient: {gini:.4f} (0=equal, 1=one anchor gets all)\")\n", "\n", "for bucket in [(1, 5), (5, 20), (20, 50), (50, 100), (100, 500), (500, 5000)]:\n", " count = ((vc >= bucket[0]) & (vc < bucket[1])).sum()\n", " print(f\" anchors with {bucket[0]}-{bucket[1]} visits: {count}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: EMBEDDING MANIFOLD GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 3: EMBEDDING MANIFOLD GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "emb_cpu = fused.cpu().float()\n", "emb_centered = emb_cpu - emb_cpu.mean(0, keepdim=True)\n", "U_e, S_e, Vt_e = torch.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "\n", "eff_dim = (S_e / S_e.sum()).pow(2).sum().reciprocal().item()\n", "print(f\" Effective dimensionality: {eff_dim:.1f}/{D_ANCHOR}\")\n", "\n", "cumvar_e = S_e.pow(2).cumsum(0) / S_e.pow(2).sum()\n", "for k in [5, 10, 20, 50, 100, 128, 200]:\n", " if k < len(cumvar_e):\n", " print(f\" top-{k} SVs explain {100*cumvar_e[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " sample = fused[:2000]\n", " selfsim = sample @ sample.T\n", " mask = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag = selfsim[mask]\n", " print(f\"\\n Self-similarity (off-diagonal):\")\n", " print(f\" mean={offdiag.mean():.4f} std={offdiag.std():.4f}\")\n", " print(f\" max={offdiag.max():.4f} min={offdiag.min():.4f}\")\n", "\n", "norms = fused.norm(dim=-1)\n", "print(f\"\\n Norms: mean={norms.mean():.6f} std={norms.std():.6f}\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_val, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(fused[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " global_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Global pentachoron CV: {global_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " expert_rotated = []\n", " for i in range(N_EXPERTS):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_rotated.append(rotated)\n", "\n", " expert_tri = []\n", " for rotated in expert_rotated:\n", " cos = rotated @ anchors.T\n", " expert_tri.append(1.0 - cos)\n", "\n", " print(f\"\\n Per-image expert agreement:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(expert_rotated[i], expert_rotated[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"mean={cos_ij.mean():.4f} std={cos_ij.std():.4f} \"\n", " f\"min={cos_ij.min():.4f}\")\n", "\n", " print(f\"\\n Per-anchor expert divergence:\")\n", " tri_stack = torch.stack(expert_tri, dim=-1)\n", " per_anchor_std = tri_stack.std(dim=-1).mean(dim=0)\n", " pas = per_anchor_std.cpu().numpy()\n", " print(f\" mean divergence: {pas.mean():.4f} std: {pas.std():.4f}\")\n", " print(f\" max divergence: {pas.max():.4f} (anchor {pas.argmax()})\")\n", " print(f\" min divergence: {pas.min():.4f} (anchor {pas.argmin()})\")\n", "\n", " top_div = np.argsort(pas)[::-1][:10]\n", " print(f\"\\n Top 10 most contentious anchors:\")\n", " for rank, aidx in enumerate(top_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " bot_div = np.argsort(pas)[:10]\n", " print(f\"\\n Top 10 most unanimous anchors:\")\n", " for rank, aidx in enumerate(bot_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " print(f\"\\n Expert rotation eigenspectra:\")\n", " for i in range(N_EXPERTS):\n", " R = expert_R[i]\n", " RRT = R @ R.T\n", " identity_err = (RRT - torch.eye(D_ANCHOR, device=DEVICE)).pow(2).mean().item()\n", " evals = torch.linalg.eigvalsh(RRT)\n", " print(f\" {EXPERTS[i][:20]:<20}: ortho_err={identity_err:.6f} \"\n", " f\"eval_min={evals.min():.4f} eval_max={evals.max():.4f}\")\n", "\n", " print(f\"\\n Expert whitener condition:\")\n", " for i in range(N_EXPERTS):\n", " W = expert_W[i]\n", " s = torch.linalg.svdvals(W)\n", " cond = s.max() / s.min()\n", " print(f\" {EXPERTS[i][:20]:<20}: cond={cond:.2f} \"\n", " f\"sv_max={s.max():.4f} sv_min={s.min():.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: NEAREST ANCHOR DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 5: NEAREST ANCHOR DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " sorted_cos, sorted_idx = cos_to_anchors.sort(dim=-1, descending=True)\n", "\n", " for k in [0, 1, 2, 4, 9, 19, 49, 99]:\n", " if k < N_ANCHORS:\n", " dist = (1 - sorted_cos[:, k])\n", " print(f\" k={k:3d}: mean_dist={dist.mean():.4f} std={dist.std():.4f} \"\n", " f\"max={dist.max():.4f} min={dist.min():.4f}\")\n", "\n", " for thresh in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " within = (cos_to_anchors > thresh).sum(dim=-1).float()\n", " print(f\" anchors with cos > {thresh:.1f}: mean={within.mean():.1f} \"\n", " f\"max={within.max():.0f} min={within.min():.0f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 6: PER-CLASS ANCHOR AFFINITY\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " nearest_cpu = nearest.cpu()\n", " labels_np = val_labels.numpy()\n", "\n", " class_anchor_counts = np.zeros((80, N_ANCHORS))\n", " for img_idx in range(N_val):\n", " anchor_id = nearest_cpu[img_idx].item()\n", " for c in range(80):\n", " if labels_np[img_idx, c] > 0:\n", " class_anchor_counts[c, anchor_id] += 1\n", "\n", " anchor_class_count = (class_anchor_counts > 0).sum(axis=0)\n", " active_anchor_classes = anchor_class_count[anchor_class_count > 0]\n", " print(f\" Anchor specialization:\")\n", " print(f\" classes per active anchor: mean={active_anchor_classes.mean():.1f} \"\n", " f\"std={active_anchor_classes.std():.1f}\")\n", " print(f\" max={active_anchor_classes.max()} min={active_anchor_classes.min()}\")\n", "\n", " class_anchor_spread = (class_anchor_counts > 0).sum(axis=1)\n", " print(f\"\\n Class spread (anchors per class):\")\n", " print(f\" mean={class_anchor_spread.mean():.1f} std={class_anchor_spread.std():.1f}\")\n", " print(f\" max={class_anchor_spread.max()} ({COCO_CLASSES[class_anchor_spread.argmax()]})\")\n", " print(f\" min={class_anchor_spread.min()} ({COCO_CLASSES[class_anchor_spread.argmin()]})\")\n", "\n", " sorted_classes = np.argsort(class_anchor_spread)[::-1]\n", " print(f\"\\n Top 10 by anchor spread:\")\n", " for c in sorted_classes[:10]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", " print(f\" Bottom 10 by anchor spread:\")\n", " for c in sorted_classes[-10:]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " class_centroids = torch.zeros(80, D_ANCHOR, device=DEVICE)\n", " class_counts = torch.zeros(80, device=DEVICE)\n", " for c in range(80):\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 0:\n", " class_centroids[c] = fused[mask.to(DEVICE)].mean(dim=0)\n", " class_counts[c] = mask.sum()\n", " class_centroids = F.normalize(class_centroids, dim=-1)\n", "\n", " valid_classes = class_counts > 10\n", " vc_idx = valid_classes.nonzero(as_tuple=True)[0]\n", " n_vc = len(vc_idx)\n", " class_sim = class_centroids[vc_idx] @ class_centroids[vc_idx].T\n", " cs_np = class_sim.cpu().numpy()\n", " np.fill_diagonal(cs_np, 0)\n", "\n", " print(f\" Inter-class cosine ({n_vc} classes with >10 images):\")\n", " print(f\" mean={cs_np[np.triu_indices(n_vc, k=1)].mean():.4f}\")\n", " print(f\" max={cs_np.max():.4f} min={cs_np[np.triu_indices(n_vc, k=1)].min():.4f}\")\n", "\n", " upper = np.triu_indices(n_vc, k=1)\n", " pair_sims = cs_np[upper]\n", " top_pairs = np.argsort(pair_sims)[::-1][:15]\n", " print(f\"\\n Most similar class pairs:\")\n", " for rank, pidx in enumerate(top_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " bot_pairs = np.argsort(pair_sims)[:15]\n", " print(f\"\\n Most distant class pairs:\")\n", " for rank, pidx in enumerate(bot_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " print(f\"\\n Intra-class spread:\")\n", " class_spreads = []\n", " for c in vc_idx:\n", " c = c.item()\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 10:\n", " cls_embs = fused[mask.to(DEVICE)]\n", " centroid = cls_embs.mean(dim=0, keepdim=True)\n", " cos_to_cent = F.cosine_similarity(cls_embs, centroid, dim=-1)\n", " spread = 1 - cos_to_cent.mean().item()\n", " class_spreads.append((c, spread, mask.sum().item()))\n", " class_spreads.sort(key=lambda x: x[1])\n", " print(f\" Tightest 10:\")\n", " for c, spread, n in class_spreads[:10]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", " print(f\" Loosest 10:\")\n", " for c, spread, n in class_spreads[-10:]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 8: LOCAL PENTACHORON CV\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 8: LOCAL PENTACHORON CV\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cluster_cvs = []\n", " for a_idx in tqdm(range(N_ANCHORS), desc=\" Local CV\", leave=False):\n", " mask = nearest == a_idx\n", " if mask.sum() >= 10:\n", " cluster_embs = fused[mask]\n", " vols = []\n", " for _ in range(100):\n", " if cluster_embs.shape[0] < 5: break\n", " idx = torch.randperm(cluster_embs.shape[0], device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(cluster_embs[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) > 5:\n", " cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " cluster_cvs.append((a_idx, cv, mask.sum().item(), np.mean(vols)))\n", "\n", " if cluster_cvs:\n", " cvs = [c[1] for c in cluster_cvs]\n", " print(f\" Clusters with 10+ members: {len(cluster_cvs)}\")\n", " print(f\" Local CV: mean={np.mean(cvs):.4f} std={np.std(cvs):.4f}\")\n", " print(f\" max={np.max(cvs):.4f} min={np.min(cvs):.4f}\")\n", " print(f\" Global CV: {global_cv:.4f}\")\n", " print(f\" Ratio (local/global): {np.mean(cvs)/global_cv:.4f}\")\n", "\n", " cluster_cvs.sort(key=lambda x: x[1], reverse=True)\n", " print(f\"\\n Highest local CV (most diverse clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[:10]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", " print(f\" Lowest local CV (most uniform clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[-10:]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 9: PROJECTOR ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 9: PROJECTOR ANALYSIS\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " proj_emb = projected_gpu[i]\n", " ss = proj_emb[:2000] @ proj_emb[:2000].T\n", " mask_ss = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag_ss = ss[mask_ss]\n", "\n", " pc = proj_emb[:5000] - proj_emb[:5000].mean(0, keepdim=True)\n", " _, s_p, _ = torch.linalg.svd(pc, full_matrices=False)\n", " ed = (s_p / s_p.sum()).pow(2).sum().reciprocal().item()\n", "\n", " cos_fused = F.cosine_similarity(proj_emb, fused, dim=-1)\n", "\n", " print(f\"\\n {EXPERTS[i]}:\")\n", " print(f\" self-sim: mean={offdiag_ss.mean():.4f} std={offdiag_ss.std():.4f}\")\n", " print(f\" eff_dim: {ed:.1f}/{D_ANCHOR}\")\n", " print(f\" cos→fused: mean={cos_fused.mean():.4f} std={cos_fused.std():.4f}\")\n", "\n", " print(f\"\\n Cross-expert agreement (projected):\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(projected_gpu[i], projected_gpu[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"cos={cos_ij.mean():.4f} std={cos_ij.std():.4f}\")\n", "\n", " print(f\"\\n Expert uniqueness (leave-one-out):\")\n", " for i in range(N_EXPERTS):\n", " others = [projected_gpu[j] for j in range(N_EXPERTS) if j != i]\n", " fused_without = F.normalize(sum(others) / len(others), dim=-1)\n", " cos_full = F.cosine_similarity(fused_without, fused, dim=-1).mean().item()\n", " print(f\" Without {EXPERTS[i][:20]:<20}: cos_to_full={cos_full:.4f} \"\n", " f\"(uniqueness={1-cos_full:.4f})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 10: TRIANGULATION STRUCTURE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 10: TRIANGULATION STRUCTURE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " tri = expert_tri[i]\n", " print(f\"\\n {EXPERTS[i]} triangulation:\")\n", " print(f\" mean={tri.mean():.4f} std={tri.std():.4f}\")\n", " print(f\" min={tri.min():.4f} max={tri.max():.4f}\")\n", " nearest_dist = tri.min(dim=-1).values\n", " print(f\" nearest: mean={nearest_dist.mean():.4f} std={nearest_dist.std():.4f}\")\n", "\n", " print(f\"\\n Expert triangulation correlation:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " per_img = F.cosine_similarity(expert_tri[i][:1000], expert_tri[j][:1000], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"per_img_cos mean={per_img.mean():.4f} std={per_img.std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SUMMARY\")\n", "print(f\"{'='*70}\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" mAP: {ckpt['mAP']:.3f}\")\n", "print(f\" Anchors: {N_ANCHORS} × {D_ANCHOR}-d, {n_active} active ({100*n_active/N_ANCHORS:.0f}%)\")\n", "print(f\" Embedding eff_dim: {eff_dim:.1f}/{D_ANCHOR}\")\n", "print(f\" Anchor eff_rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" Global CV: {global_cv:.4f}\")\n", "print(f\" Anchor CV: {a_cv:.4f}\")\n", "if cluster_cvs:\n", " print(f\" Local CV (mean): {np.mean(cvs):.4f}\")\n", "print(f\" Utilization entropy: {100*entropy/max_entropy:.1f}%\")\n", "print(f\" Utilization Gini: {gini:.4f}\")\n", "print(f\"\\n{'='*70}\")\n", "print(\"ANALYSIS COMPLETE\")\n", "print(f\"{'='*70}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 384 }, "id": "12UCGm1vk3pk", "outputId": "8eb25da6-000e-40dc-d41e-c6367a44f94c" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", "GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\n", " Checkpoint: checkpoints/dual_stream_best.pt\n", " Device: cuda\n", "======================================================================\n", "\n", " Loading checkpoint...\n", " mAP=0.838 epoch=17\n", " D_ANCHOR=256 N_ANCHORS=512 N_EXPERTS=3\n", " Anchors: torch.Size([512, 256])\n" ] }, { "output_type": "error", "ename": "KeyError", "evalue": "'constellation.expert_rotations.0'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_255833/4133772767.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mexpert_R\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpert_W\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpert_mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_EXPERTS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mexpert_R\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_rotations.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0mexpert_W\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_whiteners.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mexpert_mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_means.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'constellation.expert_rotations.0'" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn.functional as F\n", "import math\n", "\n", "D = 256\n", "N_ANCHORS = 512\n", "\n", "# Load the soup\n", "ckpt = torch.load(\"checkpoints/heavy_soup_best.pt\", map_location=\"cuda\", weights_only=False)\n", "anchors = F.normalize(ckpt[\"state_dict\"][\"constellation.anchors\"], dim=-1).cuda()\n", "\n", "# 1. Are we on the sphere?\n", "norms = anchors.norm(dim=-1)\n", "print(f\"Norms: mean={norms.mean():.10f} std={norms.std():.2e} max_dev={( norms-1).abs().max():.2e}\")\n", "\n", "# 2. Cayley-Menger on 5 random anchors\n", "def cm_vol(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "# Run 1000 random pentachoron samples\n", "vols_pos, vols_neg, vols_zero = 0, 0, 0\n", "vol_values = []\n", "for _ in range(1000):\n", " idx = torch.randperm(N_ANCHORS, device=\"cuda\")[:5]\n", " v2 = cm_vol(anchors[idx].unsqueeze(0))\n", " val = v2[0].item()\n", " if val > 1e-20: vols_pos += 1\n", " elif val < -1e-20: vols_neg += 1\n", " else: vols_zero += 1\n", " vol_values.append(val)\n", "\n", "v = torch.tensor(vol_values)\n", "print(f\"\\nPentachoron squared volumes (1000 samples):\")\n", "print(f\" positive: {vols_pos} negative: {vols_neg} zero: {vols_zero}\")\n", "print(f\" mean={v.mean():.8f} std={v.std():.8f}\")\n", "print(f\" min={v.min():.8f} max={v.max():.8f}\")\n", "\n", "# 3. Are distances consistent with S^255?\n", "# On a unit sphere, max possible distance^2 between two points = 4 (antipodal)\n", "# For near-orthogonal points in high-d, expect d^2 ≈ 2\n", "dists_sq = 2 - 2 * (anchors[:100] @ anchors[:100].T)\n", "mask = ~torch.eye(100, dtype=torch.bool, device=\"cuda\")\n", "d2_vals = dists_sq[mask]\n", "print(f\"\\nPairwise squared distances (100 anchors):\")\n", "print(f\" mean={d2_vals.mean():.6f} (expect ~2.0 for orthogonal on S^d)\")\n", "print(f\" std={d2_vals.std():.6f}\")\n", "print(f\" min={d2_vals.min():.6f} max={d2_vals.max():.6f}\")\n", "\n", "# 4. Theoretical check: expected volume of random simplex on S^{d-1}\n", "# For 5 points on S^255, the squared distances should cluster around 2.0\n", "# and the CM determinant should be strictly positive\n", "print(f\"\\nVerdict: {'PERFECT HYPERSPHERE' if vols_neg == 0 else 'DEGENERATE — negative volumes detected'}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XrSyVmrP0oc1", "outputId": "9836fd5a-292d-478a-b566-8dc2ccfdc22a" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Norms: mean=1.0000000000 std=2.54e-08 max_dev=1.19e-07\n", "\n", "Pentachoron squared volumes (1000 samples):\n", " positive: 1000 negative: 0 zero: 0\n", " mean=0.00834175 std=0.00101716\n", " min=0.00392169 max=0.01096440\n", "\n", "Pairwise squared distances (100 anchors):\n", " mean=1.992599 (expect ~2.0 for orthogonal on S^d)\n", " std=0.162944\n", " min=0.574060 max=2.415142\n", "\n", "Verdict: PERFECT HYPERSPHERE\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE MANIFOLD VISUALIZATION\n", "==========================================\n", "6-panel manifold view + 3-panel expert perspective divergence.\n", "S^255 projected to S^2 via PCA.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import math\n", "\n", "DEVICE = \"cpu\"\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD + EMBED\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/heavy_soup_best.pt\", map_location=\"cpu\", weights_only=False)\n", "sd = ckpt[\"state_dict\"]\n", "D_ANCHOR = ckpt[\"config\"][\"d_anchor\"]\n", "N_ANCHORS = ckpt[\"config\"][\"n_anchors\"]\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1)\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "print(\"Loading features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats; del ds\n", "\n", "def project_expert(feats, i):\n", " W = sd[f\"projectors.{i}.proj.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", "\n", "print(\"Generating embeddings...\")\n", "with torch.no_grad():\n", " projected = [project_expert(val_raw[name], i) for i, name in enumerate(EXPERTS)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PCA → 3D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "emb = fused.numpy()\n", "emb_centered = emb - emb.mean(axis=0, keepdims=True)\n", "U, S, Vt = np.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "pca3 = Vt[:3]\n", "\n", "emb_3d = emb @ pca3.T\n", "anchors_3d = anchors.numpy() @ pca3.T\n", "\n", "var_explained = S[:3]**2 / (S**2).sum()\n", "print(f\"PCA 3D variance: {var_explained.sum()*100:.1f}% \"\n", " f\"({var_explained[0]*100:.1f}%, {var_explained[1]*100:.1f}%, {var_explained[2]*100:.1f}%)\")\n", "\n", "def to_sphere(pts):\n", " norms = np.linalg.norm(pts, axis=-1, keepdims=True)\n", " return pts / (norms + 1e-8)\n", "\n", "emb_s = to_sphere(emb_3d)\n", "anchors_s = to_sphere(anchors_3d)\n", "\n", "# Reference sphere wireframe\n", "phi = np.linspace(0, 2*np.pi, 60)\n", "theta = np.linspace(0, np.pi, 30)\n", "xs = np.outer(np.cos(phi), np.sin(theta))\n", "ys = np.outer(np.sin(phi), np.sin(theta))\n", "zs = np.outer(np.ones_like(phi), np.cos(theta))\n", "\n", "# Primary class per image (most specific)\n", "class_freq = val_labels.sum(0).numpy()\n", "primary_class = np.zeros(N_val, dtype=int)\n", "for i in range(N_val):\n", " present = np.where(val_labels[i].numpy() > 0)[0]\n", " if len(present) > 0:\n", " primary_class[i] = present[class_freq[present].argmin()]\n", "\n", "cmap20 = plt.cm.tab20(np.linspace(0, 1, 20))\n", "class_colors = np.array([cmap20[primary_class[i] % 20] for i in range(N_val)])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# HELPER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def setup_ax(ax, title):\n", " ax.set_facecolor('black')\n", " ax.xaxis.pane.fill = False; ax.yaxis.pane.fill = False; ax.zaxis.pane.fill = False\n", " ax.xaxis.pane.set_edgecolor('gray'); ax.yaxis.pane.set_edgecolor('gray')\n", " ax.zaxis.pane.set_edgecolor('gray')\n", " ax.set_xlabel('PC1', color='gray', fontsize=8)\n", " ax.set_ylabel('PC2', color='gray', fontsize=8)\n", " ax.set_zlabel('PC3', color='gray', fontsize=8)\n", " ax.tick_params(colors='gray', labelsize=6)\n", " ax.set_title(title, color='white', fontsize=11, pad=10)\n", " ax.plot_wireframe(xs*0.98, ys*0.98, zs*0.98, alpha=0.03, color='white', linewidth=0.3)\n", " ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_zlim(-1.3, 1.3)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 1: 6-PANEL MANIFOLD VIEW\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 1...\")\n", "fig = plt.figure(figsize=(24, 16), facecolor='black')\n", "fig.suptitle(\n", " 'GeoLIP Hypersphere Manifold — S²⁵⁵ projected to S²\\n'\n", " f'{N_ANCHORS} anchors × {D_ANCHOR}-d × 3 experts | mAP={ckpt[\"mAP\"]:.3f} | eff_dim=76.9',\n", " color='white', fontsize=16, y=0.98)\n", "\n", "# Panel 1: Full manifold\n", "ax1 = fig.add_subplot(231, projection='3d')\n", "setup_ax(ax1, f'Full Manifold — {N_val} embeddings + {N_ANCHORS} anchors')\n", "ax1.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=class_colors, s=1, alpha=0.3)\n", "ax1.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=8, alpha=0.6, marker='^')\n", "\n", "# Panel 2: Class centroids\n", "ax2 = fig.add_subplot(232, projection='3d')\n", "setup_ax(ax2, '80 COCO Class Centroids')\n", "centroids = np.zeros((80, emb.shape[1]))\n", "for c in range(80):\n", " mask = val_labels[:, c].numpy() > 0\n", " if mask.sum() > 0:\n", " centroids[c] = emb[mask].mean(0)\n", "centroids_3d = to_sphere(centroids @ pca3.T)\n", "sizes = val_labels.sum(0).numpy()\n", "sizes_scaled = 20 + 200 * (sizes / sizes.max())\n", "colors80 = plt.cm.hsv(np.linspace(0, 0.95, 80))\n", "ax2.scatter(centroids_3d[:, 0], centroids_3d[:, 1], centroids_3d[:, 2],\n", " c=colors80, s=sizes_scaled, alpha=0.8, edgecolors='white', linewidth=0.3)\n", "for c in [0, 2, 14, 15, 16, 22, 23, 56, 62]:\n", " if sizes[c] > 30:\n", " ax2.text(centroids_3d[c, 0]*1.15, centroids_3d[c, 1]*1.15,\n", " centroids_3d[c, 2]*1.15,\n", " COCO_CLASSES[c], color='white', fontsize=7, ha='center')\n", "\n", "# Panel 3: 50 random with anchor connections\n", "ax3 = fig.add_subplot(233, projection='3d')\n", "setup_ax(ax3, '50 Random — nearest anchor connections')\n", "np.random.seed(42)\n", "idx50 = np.random.choice(N_val, 50, replace=False)\n", "emb_50 = emb_s[idx50]\n", "colors_50 = class_colors[idx50]\n", "with torch.no_grad():\n", " cos_50 = fused[idx50] @ anchors.T\n", " nearest_50 = cos_50.argmax(-1).numpy()\n", "ax3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=4, alpha=0.2, marker='^')\n", "ax3.scatter(emb_50[:, 0], emb_50[:, 1], emb_50[:, 2],\n", " c=colors_50, s=40, alpha=0.9, edgecolors='white', linewidth=0.5)\n", "for i in range(50):\n", " a = nearest_50[i]\n", " ax3.plot([emb_50[i, 0], anchors_s[a, 0]],\n", " [emb_50[i, 1], anchors_s[a, 1]],\n", " [emb_50[i, 2], anchors_s[a, 2]],\n", " color='yellow', alpha=0.3, linewidth=0.5)\n", "\n", "# Panel 4: 10 random — triangulation heatmap\n", "ax4 = fig.add_subplot(234, projection='3d')\n", "setup_ax(ax4, '10 Random — anchor affinity heatmap')\n", "idx10 = np.random.choice(N_val, 10, replace=False)\n", "emb_10 = emb_s[idx10]\n", "with torch.no_grad():\n", " cos_10 = (fused[idx10] @ anchors.T).numpy()\n", " mean_cos = cos_10.mean(0)\n", "anchor_heat = (mean_cos - mean_cos.min()) / (mean_cos.max() - mean_cos.min() + 1e-8)\n", "anchor_colors = plt.cm.hot(anchor_heat)\n", "ax4.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=anchor_colors, s=10, alpha=0.6)\n", "ax4.scatter(emb_10[:, 0], emb_10[:, 1], emb_10[:, 2],\n", " c='cyan', s=80, alpha=1.0, edgecolors='white', linewidth=1, zorder=10)\n", "\n", "# Panel 5: Single encoding\n", "ax5 = fig.add_subplot(235, projection='3d')\n", "single_idx = 42\n", "single_class = primary_class[single_idx]\n", "setup_ax(ax5, f'Single Encoding: \"{COCO_CLASSES[single_class]}\" — top 5 anchors')\n", "with torch.no_grad():\n", " cos_single = (fused[single_idx] @ anchors.T).numpy()\n", "single_heat = (cos_single - cos_single.min()) / (cos_single.max() - cos_single.min() + 1e-8)\n", "single_colors = plt.cm.plasma(single_heat)\n", "single_sizes = 2 + 50 * single_heat**3\n", "ax5.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=single_colors, s=single_sizes, alpha=0.7)\n", "single_pt = emb_s[single_idx]\n", "ax5.scatter([single_pt[0]], [single_pt[1]], [single_pt[2]],\n", " c='lime', s=150, alpha=1.0, edgecolors='white', linewidth=2,\n", " zorder=10, marker='*')\n", "top5 = np.argsort(cos_single)[::-1][:5]\n", "for a in top5:\n", " ax5.plot([single_pt[0], anchors_s[a, 0]],\n", " [single_pt[1], anchors_s[a, 1]],\n", " [single_pt[2], anchors_s[a, 2]],\n", " color='lime', alpha=0.6, linewidth=1.5)\n", "\n", "# Panel 6: Radial deviation\n", "ax6 = fig.add_subplot(236, projection='3d')\n", "radii = np.linalg.norm(emb_3d, axis=-1)\n", "setup_ax(ax6, f'PCA Projection Radii — mean={radii.mean():.4f} std={radii.std():.4f}')\n", "radius_dev = radii - radii.mean()\n", "dev_norm = (radius_dev - radius_dev.min()) / (radius_dev.max() - radius_dev.min() + 1e-8)\n", "dev_colors = plt.cm.coolwarm(dev_norm)\n", "scale = 1.0 / radii.max()\n", "ax6.scatter(emb_3d[:, 0]*scale, emb_3d[:, 1]*scale, emb_3d[:, 2]*scale,\n", " c=dev_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"hypersphere_manifold.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: hypersphere_manifold.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 2: EXPERT PERSPECTIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 2...\")\n", "fig2 = plt.figure(figsize=(21, 7), facecolor='black')\n", "fig2.suptitle('Expert Perspective Divergence — Same sphere, three lenses',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "expert_R = [sd[f\"constellation.expert_rotations.{i}\"] for i in range(3)]\n", "expert_W = [sd[f\"constellation.expert_whiteners.{i}\"] for i in range(3)]\n", "expert_mu = [sd[f\"constellation.expert_means.{i}\"] for i in range(3)]\n", "\n", "with torch.no_grad():\n", " for i, name in enumerate(EXPERTS):\n", " ax = fig2.add_subplot(1, 3, i+1, projection='3d')\n", "\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", "\n", " rot_np = rotated.numpy()\n", " rot_c = rot_np - rot_np.mean(axis=0, keepdims=True)\n", " _, S_r, Vt_r = np.linalg.svd(rot_c[:5000], full_matrices=False)\n", " rot_3d = to_sphere(rot_np @ Vt_r[:3].T)\n", "\n", " var_exp = S_r[:3]**2 / (S_r**2).sum()\n", " setup_ax(ax, f'{name[:25]}\\nPC variance: {var_exp.sum()*100:.1f}%')\n", " ax.scatter(rot_3d[:, 0], rot_3d[:, 1], rot_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"expert_perspectives.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: expert_perspectives.png\")\n", "plt.close()\n", "\n", "print(\"\\nDone.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "81Gwk7qz46M8", "outputId": "62f6f0bf-1dff-46eb-c33c-cd5399b7e2ae" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading soup...\n", "Loading features...\n", "Generating embeddings...\n", "PCA 3D variance: 21.5% (8.4%, 6.7%, 6.4%)\n", "Rendering figure 1...\n", "Saved: hypersphere_manifold.png\n", "Rendering figure 2...\n", "Saved: expert_perspectives.png\n", "\n", "Done.\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE MANIFOLD VISUALIZATION\n", "==========================================\n", "6-panel manifold view + 3-panel expert perspective divergence.\n", "S^255 projected to S^2 via PCA.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import math\n", "\n", "DEVICE = \"cpu\"\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD + EMBED\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/dual_stream_best.pt\", map_location=\"cpu\", weights_only=False)\n", "sd = ckpt[\"state_dict\"]\n", "D_ANCHOR = ckpt[\"config\"][\"d_anchor\"]\n", "N_ANCHORS = ckpt[\"config\"][\"n_anchors\"]\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1)\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "print(\"Loading features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats; del ds\n", "\n", "def project_expert(feats, i):\n", " W = sd[f\"projectors.{i}.proj.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", "\n", "print(\"Generating embeddings...\")\n", "with torch.no_grad():\n", " projected = [project_expert(val_raw[name], i) for i, name in enumerate(EXPERTS)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PCA → 3D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "emb = fused.numpy()\n", "emb_centered = emb - emb.mean(axis=0, keepdims=True)\n", "U, S, Vt = np.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "pca3 = Vt[:3]\n", "\n", "emb_3d = emb @ pca3.T\n", "anchors_3d = anchors.numpy() @ pca3.T\n", "\n", "var_explained = S[:3]**2 / (S**2).sum()\n", "print(f\"PCA 3D variance: {var_explained.sum()*100:.1f}% \"\n", " f\"({var_explained[0]*100:.1f}%, {var_explained[1]*100:.1f}%, {var_explained[2]*100:.1f}%)\")\n", "\n", "def to_sphere(pts):\n", " norms = np.linalg.norm(pts, axis=-1, keepdims=True)\n", " return pts / (norms + 1e-8)\n", "\n", "emb_s = to_sphere(emb_3d)\n", "anchors_s = to_sphere(anchors_3d)\n", "\n", "# Reference sphere wireframe\n", "phi = np.linspace(0, 2*np.pi, 60)\n", "theta = np.linspace(0, np.pi, 30)\n", "xs = np.outer(np.cos(phi), np.sin(theta))\n", "ys = np.outer(np.sin(phi), np.sin(theta))\n", "zs = np.outer(np.ones_like(phi), np.cos(theta))\n", "\n", "# Primary class per image (most specific)\n", "class_freq = val_labels.sum(0).numpy()\n", "primary_class = np.zeros(N_val, dtype=int)\n", "for i in range(N_val):\n", " present = np.where(val_labels[i].numpy() > 0)[0]\n", " if len(present) > 0:\n", " primary_class[i] = present[class_freq[present].argmin()]\n", "\n", "cmap20 = plt.cm.tab20(np.linspace(0, 1, 20))\n", "class_colors = np.array([cmap20[primary_class[i] % 20] for i in range(N_val)])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# HELPER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def setup_ax(ax, title):\n", " ax.set_facecolor('black')\n", " ax.xaxis.pane.fill = False; ax.yaxis.pane.fill = False; ax.zaxis.pane.fill = False\n", " ax.xaxis.pane.set_edgecolor('gray'); ax.yaxis.pane.set_edgecolor('gray')\n", " ax.zaxis.pane.set_edgecolor('gray')\n", " ax.set_xlabel('PC1', color='gray', fontsize=8)\n", " ax.set_ylabel('PC2', color='gray', fontsize=8)\n", " ax.set_zlabel('PC3', color='gray', fontsize=8)\n", " ax.tick_params(colors='gray', labelsize=6)\n", " ax.set_title(title, color='white', fontsize=11, pad=10)\n", " ax.plot_wireframe(xs*0.98, ys*0.98, zs*0.98, alpha=0.03, color='white', linewidth=0.3)\n", " ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_zlim(-1.3, 1.3)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 1: 6-PANEL MANIFOLD VIEW\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 1...\")\n", "fig = plt.figure(figsize=(24, 16), facecolor='black')\n", "fig.suptitle(\n", " 'GeoLIP Hypersphere Manifold — S²⁵⁵ projected to S²\\n'\n", " f'{N_ANCHORS} anchors × {D_ANCHOR}-d × 3 experts | mAP={ckpt[\"mAP\"]:.3f} | eff_dim=76.9',\n", " color='white', fontsize=16, y=0.98)\n", "\n", "# Panel 1: Full manifold\n", "ax1 = fig.add_subplot(231, projection='3d')\n", "setup_ax(ax1, f'Full Manifold — {N_val} embeddings + {N_ANCHORS} anchors')\n", "ax1.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=class_colors, s=1, alpha=0.3)\n", "ax1.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=8, alpha=0.6, marker='^')\n", "\n", "# Panel 2: Class centroids\n", "ax2 = fig.add_subplot(232, projection='3d')\n", "setup_ax(ax2, '80 COCO Class Centroids')\n", "centroids = np.zeros((80, emb.shape[1]))\n", "for c in range(80):\n", " mask = val_labels[:, c].numpy() > 0\n", " if mask.sum() > 0:\n", " centroids[c] = emb[mask].mean(0)\n", "centroids_3d = to_sphere(centroids @ pca3.T)\n", "sizes = val_labels.sum(0).numpy()\n", "sizes_scaled = 20 + 200 * (sizes / sizes.max())\n", "colors80 = plt.cm.hsv(np.linspace(0, 0.95, 80))\n", "ax2.scatter(centroids_3d[:, 0], centroids_3d[:, 1], centroids_3d[:, 2],\n", " c=colors80, s=sizes_scaled, alpha=0.8, edgecolors='white', linewidth=0.3)\n", "for c in [0, 2, 14, 15, 16, 22, 23, 56, 62]:\n", " if sizes[c] > 30:\n", " ax2.text(centroids_3d[c, 0]*1.15, centroids_3d[c, 1]*1.15,\n", " centroids_3d[c, 2]*1.15,\n", " COCO_CLASSES[c], color='white', fontsize=7, ha='center')\n", "\n", "# Panel 3: 50 random with anchor connections\n", "ax3 = fig.add_subplot(233, projection='3d')\n", "setup_ax(ax3, '50 Random — nearest anchor connections')\n", "np.random.seed(42)\n", "idx50 = np.random.choice(N_val, 50, replace=False)\n", "emb_50 = emb_s[idx50]\n", "colors_50 = class_colors[idx50]\n", "with torch.no_grad():\n", " cos_50 = fused[idx50] @ anchors.T\n", " nearest_50 = cos_50.argmax(-1).numpy()\n", "ax3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=4, alpha=0.2, marker='^')\n", "ax3.scatter(emb_50[:, 0], emb_50[:, 1], emb_50[:, 2],\n", " c=colors_50, s=40, alpha=0.9, edgecolors='white', linewidth=0.5)\n", "for i in range(50):\n", " a = nearest_50[i]\n", " ax3.plot([emb_50[i, 0], anchors_s[a, 0]],\n", " [emb_50[i, 1], anchors_s[a, 1]],\n", " [emb_50[i, 2], anchors_s[a, 2]],\n", " color='yellow', alpha=0.3, linewidth=0.5)\n", "\n", "# Panel 4: 10 random — triangulation heatmap\n", "ax4 = fig.add_subplot(234, projection='3d')\n", "setup_ax(ax4, '10 Random — anchor affinity heatmap')\n", "idx10 = np.random.choice(N_val, 10, replace=False)\n", "emb_10 = emb_s[idx10]\n", "with torch.no_grad():\n", " cos_10 = (fused[idx10] @ anchors.T).numpy()\n", " mean_cos = cos_10.mean(0)\n", "anchor_heat = (mean_cos - mean_cos.min()) / (mean_cos.max() - mean_cos.min() + 1e-8)\n", "anchor_colors = plt.cm.hot(anchor_heat)\n", "ax4.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=anchor_colors, s=10, alpha=0.6)\n", "ax4.scatter(emb_10[:, 0], emb_10[:, 1], emb_10[:, 2],\n", " c='cyan', s=80, alpha=1.0, edgecolors='white', linewidth=1, zorder=10)\n", "\n", "# Panel 5: Single encoding\n", "ax5 = fig.add_subplot(235, projection='3d')\n", "single_idx = 42\n", "single_class = primary_class[single_idx]\n", "setup_ax(ax5, f'Single Encoding: \"{COCO_CLASSES[single_class]}\" — top 5 anchors')\n", "with torch.no_grad():\n", " cos_single = (fused[single_idx] @ anchors.T).numpy()\n", "single_heat = (cos_single - cos_single.min()) / (cos_single.max() - cos_single.min() + 1e-8)\n", "single_colors = plt.cm.plasma(single_heat)\n", "single_sizes = 2 + 50 * single_heat**3\n", "ax5.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=single_colors, s=single_sizes, alpha=0.7)\n", "single_pt = emb_s[single_idx]\n", "ax5.scatter([single_pt[0]], [single_pt[1]], [single_pt[2]],\n", " c='lime', s=150, alpha=1.0, edgecolors='white', linewidth=2,\n", " zorder=10, marker='*')\n", "top5 = np.argsort(cos_single)[::-1][:5]\n", "for a in top5:\n", " ax5.plot([single_pt[0], anchors_s[a, 0]],\n", " [single_pt[1], anchors_s[a, 1]],\n", " [single_pt[2], anchors_s[a, 2]],\n", " color='lime', alpha=0.6, linewidth=1.5)\n", "\n", "# Panel 6: Radial deviation\n", "ax6 = fig.add_subplot(236, projection='3d')\n", "radii = np.linalg.norm(emb_3d, axis=-1)\n", "setup_ax(ax6, f'PCA Projection Radii — mean={radii.mean():.4f} std={radii.std():.4f}')\n", "radius_dev = radii - radii.mean()\n", "dev_norm = (radius_dev - radius_dev.min()) / (radius_dev.max() - radius_dev.min() + 1e-8)\n", "dev_colors = plt.cm.coolwarm(dev_norm)\n", "scale = 1.0 / radii.max()\n", "ax6.scatter(emb_3d[:, 0]*scale, emb_3d[:, 1]*scale, emb_3d[:, 2]*scale,\n", " c=dev_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"hypersphere_manifold.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: hypersphere_manifold.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 2: EXPERT PERSPECTIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 2...\")\n", "fig2 = plt.figure(figsize=(21, 7), facecolor='black')\n", "fig2.suptitle('Expert Perspective Divergence — Same sphere, three lenses',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "expert_R = [sd[f\"constellation.expert_rotations.{i}\"] for i in range(3)]\n", "expert_W = [sd[f\"constellation.expert_whiteners.{i}\"] for i in range(3)]\n", "expert_mu = [sd[f\"constellation.expert_means.{i}\"] for i in range(3)]\n", "\n", "with torch.no_grad():\n", " for i, name in enumerate(EXPERTS):\n", " ax = fig2.add_subplot(1, 3, i+1, projection='3d')\n", "\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", "\n", " rot_np = rotated.numpy()\n", " rot_c = rot_np - rot_np.mean(axis=0, keepdims=True)\n", " _, S_r, Vt_r = np.linalg.svd(rot_c[:5000], full_matrices=False)\n", " rot_3d = to_sphere(rot_np @ Vt_r[:3].T)\n", "\n", " var_exp = S_r[:3]**2 / (S_r**2).sum()\n", " setup_ax(ax, f'{name[:25]}\\nPC variance: {var_exp.sum()*100:.1f}%')\n", " ax.scatter(rot_3d[:, 0], rot_3d[:, 1], rot_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"expert_perspectives.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: expert_perspectives.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 3: ANCHORS ONLY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 3 — anchors only...\")\n", "\n", "# Anchor visit counts for coloring\n", "with torch.no_grad():\n", " cos_all = fused @ anchors.T\n", " nearest_all = cos_all.argmax(dim=-1)\n", " vc = torch.zeros(N_ANCHORS)\n", " for n in nearest_all:\n", " vc[n] += 1\n", " vc_np = vc.numpy()\n", "\n", "fig3 = plt.figure(figsize=(24, 8), facecolor='black')\n", "fig3.suptitle(f'Constellation — {N_ANCHORS} anchors × {D_ANCHOR}-d on S²⁵⁵',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "# Panel 1: Anchors colored by visit count\n", "ax_a1 = fig3.add_subplot(131, projection='3d')\n", "setup_ax(ax_a1, f'Anchor Utilization — {int((vc_np>0).sum())}/{N_ANCHORS} active')\n", "heat = np.zeros(N_ANCHORS)\n", "active_mask = vc_np > 0\n", "heat[active_mask] = np.log1p(vc_np[active_mask])\n", "heat = heat / (heat.max() + 1e-8)\n", "a_colors = plt.cm.inferno(heat)\n", "a_sizes = 5 + 60 * heat\n", "# Dead anchors in blue\n", "dead_mask = vc_np == 0\n", "ax_a1.scatter(anchors_s[dead_mask, 0], anchors_s[dead_mask, 1], anchors_s[dead_mask, 2],\n", " c='dodgerblue', s=8, alpha=0.4, marker='x', label=f'dead ({int(dead_mask.sum())})')\n", "ax_a1.scatter(anchors_s[active_mask, 0], anchors_s[active_mask, 1], anchors_s[active_mask, 2],\n", " c=a_colors[active_mask], s=a_sizes[active_mask], alpha=0.8)\n", "\n", "# Panel 2: Anchors colored by nearest neighbor distance\n", "ax_a2 = fig3.add_subplot(132, projection='3d')\n", "anchor_sim = (anchors.numpy() @ anchors.numpy().T)\n", "np.fill_diagonal(anchor_sim, -1)\n", "max_neighbor_cos = anchor_sim.max(axis=1)\n", "nn_heat = (max_neighbor_cos - max_neighbor_cos.min()) / (max_neighbor_cos.max() - max_neighbor_cos.min() + 1e-8)\n", "nn_colors = plt.cm.viridis(nn_heat)\n", "setup_ax(ax_a2, f'Anchor Isolation — nearest neighbor cosine\\n'\n", " f'mean={max_neighbor_cos.mean():.3f} max={max_neighbor_cos.max():.3f}')\n", "ax_a2.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=nn_colors, s=15, alpha=0.8)\n", "\n", "# Panel 3: Anchors colored by expert divergence at that anchor\n", "ax_a3 = fig3.add_subplot(133, projection='3d')\n", "with torch.no_grad():\n", " expert_rotated_list = []\n", " for i in range(3):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_rotated_list.append(rotated)\n", " expert_tri_stack = []\n", " for rotated in expert_rotated_list:\n", " cos_r = rotated @ anchors.T\n", " expert_tri_stack.append(1.0 - cos_r)\n", " tri_stack = torch.stack(expert_tri_stack, dim=-1) # (N_val, N_ANCHORS, 3)\n", " per_anchor_div = tri_stack.std(dim=-1).mean(dim=0).numpy() # (N_ANCHORS,)\n", "\n", "div_heat = (per_anchor_div - per_anchor_div.min()) / (per_anchor_div.max() - per_anchor_div.min() + 1e-8)\n", "div_colors = plt.cm.coolwarm(div_heat)\n", "setup_ax(ax_a3, f'Expert Divergence per Anchor\\n'\n", " f'mean={per_anchor_div.mean():.4f} range=[{per_anchor_div.min():.4f}, {per_anchor_div.max():.4f}]')\n", "ax_a3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=div_colors, s=15, alpha=0.8)\n", "\n", "# Add connections between closest anchor pairs (top 20)\n", "flat_sim = anchor_sim.copy()\n", "np.fill_diagonal(flat_sim, -999)\n", "for panel_ax in [ax_a1, ax_a2]:\n", " for _ in range(20):\n", " idx_flat = np.argmax(flat_sim)\n", " i_a, j_a = np.unravel_index(idx_flat, flat_sim.shape)\n", " flat_sim[i_a, j_a] = -999; flat_sim[j_a, i_a] = -999\n", " panel_ax.plot([anchors_s[i_a, 0], anchors_s[j_a, 0]],\n", " [anchors_s[i_a, 1], anchors_s[j_a, 1]],\n", " [anchors_s[i_a, 2], anchors_s[j_a, 2]],\n", " color='white', alpha=0.15, linewidth=0.5)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"anchors_only.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: anchors_only.png\")\n", "plt.close()\n", "\n", "print(\"\\nDone.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 384 }, "id": "YuwXio7x7k5P", "outputId": "e3929e32-dbf2-458c-c1d6-9581d639061f" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading soup...\n", "Loading features...\n", "Generating embeddings...\n" ] }, { "output_type": "error", "ename": "KeyError", "evalue": "'projectors.0.proj.0.weight'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_255833/4254688628.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating embeddings...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mwith\u001b[0m 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\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"projectors.{i}.proj.0.weight\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"projectors.{i}.proj.0.bias\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mlw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"projectors.{i}.proj.1.weight\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'projectors.0.proj.0.weight'" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE MANIFOLD VISUALIZATION\n", "==========================================\n", "6-panel manifold view + 3-panel expert perspective divergence.\n", "S^255 projected to S^2 via PCA.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import math\n", "\n", "DEVICE = \"cpu\"\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD + EMBED\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/heavy_soup_best.pt\", map_location=\"cpu\", weights_only=False)\n", "sd = ckpt[\"state_dict\"]\n", "D_ANCHOR = ckpt[\"config\"][\"d_anchor\"]\n", "N_ANCHORS = ckpt[\"config\"][\"n_anchors\"]\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1)\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "print(\"Loading features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats; del ds\n", "\n", "def project_expert(feats, i):\n", " W = sd[f\"projectors.{i}.proj.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", "\n", "print(\"Generating embeddings...\")\n", "with torch.no_grad():\n", " projected = [project_expert(val_raw[name], i) for i, name in enumerate(EXPERTS)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PCA → 3D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "emb = fused.numpy()\n", "emb_centered = emb - emb.mean(axis=0, keepdims=True)\n", "U, S, Vt = np.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "pca3 = Vt[:3]\n", "\n", "emb_3d = emb @ pca3.T\n", "anchors_3d = anchors.numpy() @ pca3.T\n", "\n", "var_explained = S[:3]**2 / (S**2).sum()\n", "print(f\"PCA 3D variance: {var_explained.sum()*100:.1f}% \"\n", " f\"({var_explained[0]*100:.1f}%, {var_explained[1]*100:.1f}%, {var_explained[2]*100:.1f}%)\")\n", "\n", "def to_sphere(pts):\n", " norms = np.linalg.norm(pts, axis=-1, keepdims=True)\n", " return pts / (norms + 1e-8)\n", "\n", "emb_s = to_sphere(emb_3d)\n", "anchors_s = to_sphere(anchors_3d)\n", "\n", "# Reference sphere wireframe\n", "phi = np.linspace(0, 2*np.pi, 60)\n", "theta = np.linspace(0, np.pi, 30)\n", "xs = np.outer(np.cos(phi), np.sin(theta))\n", "ys = np.outer(np.sin(phi), np.sin(theta))\n", "zs = np.outer(np.ones_like(phi), np.cos(theta))\n", "\n", "# Primary class per image (most specific)\n", "class_freq = val_labels.sum(0).numpy()\n", "primary_class = np.zeros(N_val, dtype=int)\n", "for i in range(N_val):\n", " present = np.where(val_labels[i].numpy() > 0)[0]\n", " if len(present) > 0:\n", " primary_class[i] = present[class_freq[present].argmin()]\n", "\n", "cmap20 = plt.cm.tab20(np.linspace(0, 1, 20))\n", "class_colors = np.array([cmap20[primary_class[i] % 20] for i in range(N_val)])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# HELPER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def setup_ax(ax, title):\n", " ax.set_facecolor('black')\n", " ax.xaxis.pane.fill = False; ax.yaxis.pane.fill = False; ax.zaxis.pane.fill = False\n", " ax.xaxis.pane.set_edgecolor('gray'); ax.yaxis.pane.set_edgecolor('gray')\n", " ax.zaxis.pane.set_edgecolor('gray')\n", " ax.set_xlabel('PC1', color='gray', fontsize=8)\n", " ax.set_ylabel('PC2', color='gray', fontsize=8)\n", " ax.set_zlabel('PC3', color='gray', fontsize=8)\n", " ax.tick_params(colors='gray', labelsize=6)\n", " ax.set_title(title, color='white', fontsize=11, pad=10)\n", " ax.plot_wireframe(xs*0.98, ys*0.98, zs*0.98, alpha=0.03, color='white', linewidth=0.3)\n", " ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_zlim(-1.3, 1.3)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 1: 6-PANEL MANIFOLD VIEW\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 1...\")\n", "fig = plt.figure(figsize=(24, 16), facecolor='black')\n", "fig.suptitle(\n", " 'GeoLIP Hypersphere Manifold — S²⁵⁵ projected to S²\\n'\n", " f'{N_ANCHORS} anchors × {D_ANCHOR}-d × 3 experts | mAP={ckpt[\"mAP\"]:.3f} | eff_dim=76.9',\n", " color='white', fontsize=16, y=0.98)\n", "\n", "# Panel 1: Full manifold\n", "ax1 = fig.add_subplot(231, projection='3d')\n", "setup_ax(ax1, f'Full Manifold — {N_val} embeddings + {N_ANCHORS} anchors')\n", "ax1.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=class_colors, s=1, alpha=0.3)\n", "ax1.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=8, alpha=0.6, marker='^')\n", "\n", "# Panel 2: Class centroids\n", "ax2 = fig.add_subplot(232, projection='3d')\n", "setup_ax(ax2, '80 COCO Class Centroids')\n", "centroids = np.zeros((80, emb.shape[1]))\n", "for c in range(80):\n", " mask = val_labels[:, c].numpy() > 0\n", " if mask.sum() > 0:\n", " centroids[c] = emb[mask].mean(0)\n", "centroids_3d = to_sphere(centroids @ pca3.T)\n", "sizes = val_labels.sum(0).numpy()\n", "sizes_scaled = 20 + 200 * (sizes / sizes.max())\n", "colors80 = plt.cm.hsv(np.linspace(0, 0.95, 80))\n", "ax2.scatter(centroids_3d[:, 0], centroids_3d[:, 1], centroids_3d[:, 2],\n", " c=colors80, s=sizes_scaled, alpha=0.8, edgecolors='white', linewidth=0.3)\n", "for c in [0, 2, 14, 15, 16, 22, 23, 56, 62]:\n", " if sizes[c] > 30:\n", " ax2.text(centroids_3d[c, 0]*1.15, centroids_3d[c, 1]*1.15,\n", " centroids_3d[c, 2]*1.15,\n", " COCO_CLASSES[c], color='white', fontsize=7, ha='center')\n", "\n", "# Panel 3: 50 random with anchor connections\n", "ax3 = fig.add_subplot(233, projection='3d')\n", "setup_ax(ax3, '50 Random — nearest anchor connections')\n", "np.random.seed(42)\n", "idx50 = np.random.choice(N_val, 50, replace=False)\n", "emb_50 = emb_s[idx50]\n", "colors_50 = class_colors[idx50]\n", "with torch.no_grad():\n", " cos_50 = fused[idx50] @ anchors.T\n", " nearest_50 = cos_50.argmax(-1).numpy()\n", "ax3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=4, alpha=0.2, marker='^')\n", "ax3.scatter(emb_50[:, 0], emb_50[:, 1], emb_50[:, 2],\n", " c=colors_50, s=40, alpha=0.9, edgecolors='white', linewidth=0.5)\n", "for i in range(50):\n", " a = nearest_50[i]\n", " ax3.plot([emb_50[i, 0], anchors_s[a, 0]],\n", " [emb_50[i, 1], anchors_s[a, 1]],\n", " [emb_50[i, 2], anchors_s[a, 2]],\n", " color='yellow', alpha=0.3, linewidth=0.5)\n", "\n", "# Panel 4: 10 random — triangulation heatmap\n", "ax4 = fig.add_subplot(234, projection='3d')\n", "setup_ax(ax4, '10 Random — anchor affinity heatmap')\n", "idx10 = np.random.choice(N_val, 10, replace=False)\n", "emb_10 = emb_s[idx10]\n", "with torch.no_grad():\n", " cos_10 = (fused[idx10] @ anchors.T).numpy()\n", " mean_cos = cos_10.mean(0)\n", "anchor_heat = (mean_cos - mean_cos.min()) / (mean_cos.max() - mean_cos.min() + 1e-8)\n", "anchor_colors = plt.cm.hot(anchor_heat)\n", "ax4.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=anchor_colors, s=10, alpha=0.6)\n", "ax4.scatter(emb_10[:, 0], emb_10[:, 1], emb_10[:, 2],\n", " c='cyan', s=80, alpha=1.0, edgecolors='white', linewidth=1, zorder=10)\n", "\n", "# Panel 5: Single encoding\n", "ax5 = fig.add_subplot(235, projection='3d')\n", "single_idx = 42\n", "single_class = primary_class[single_idx]\n", "setup_ax(ax5, f'Single Encoding: \"{COCO_CLASSES[single_class]}\" — top 5 anchors')\n", "with torch.no_grad():\n", " cos_single = (fused[single_idx] @ anchors.T).numpy()\n", "single_heat = (cos_single - cos_single.min()) / (cos_single.max() - cos_single.min() + 1e-8)\n", "single_colors = plt.cm.plasma(single_heat)\n", "single_sizes = 2 + 50 * single_heat**3\n", "ax5.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=single_colors, s=single_sizes, alpha=0.7)\n", "single_pt = emb_s[single_idx]\n", "ax5.scatter([single_pt[0]], [single_pt[1]], [single_pt[2]],\n", " c='lime', s=150, alpha=1.0, edgecolors='white', linewidth=2,\n", " zorder=10, marker='*')\n", "top5 = np.argsort(cos_single)[::-1][:5]\n", "for a in top5:\n", " ax5.plot([single_pt[0], anchors_s[a, 0]],\n", " [single_pt[1], anchors_s[a, 1]],\n", " [single_pt[2], anchors_s[a, 2]],\n", " color='lime', alpha=0.6, linewidth=1.5)\n", "\n", "# Panel 6: Radial deviation\n", "ax6 = fig.add_subplot(236, projection='3d')\n", "radii = np.linalg.norm(emb_3d, axis=-1)\n", "setup_ax(ax6, f'PCA Projection Radii — mean={radii.mean():.4f} std={radii.std():.4f}')\n", "radius_dev = radii - radii.mean()\n", "dev_norm = (radius_dev - radius_dev.min()) / (radius_dev.max() - radius_dev.min() + 1e-8)\n", "dev_colors = plt.cm.coolwarm(dev_norm)\n", "scale = 1.0 / radii.max()\n", "ax6.scatter(emb_3d[:, 0]*scale, emb_3d[:, 1]*scale, emb_3d[:, 2]*scale,\n", " c=dev_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"hypersphere_manifold.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: hypersphere_manifold.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 2: EXPERT PERSPECTIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 2...\")\n", "fig2 = plt.figure(figsize=(21, 7), facecolor='black')\n", "fig2.suptitle('Expert Perspective Divergence — Same sphere, three lenses',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "expert_R = [sd[f\"constellation.expert_rotations.{i}\"] for i in range(3)]\n", "expert_W = [sd[f\"constellation.expert_whiteners.{i}\"] for i in range(3)]\n", "expert_mu = [sd[f\"constellation.expert_means.{i}\"] for i in range(3)]\n", "\n", "with torch.no_grad():\n", " for i, name in enumerate(EXPERTS):\n", " ax = fig2.add_subplot(1, 3, i+1, projection='3d')\n", "\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", "\n", " rot_np = rotated.numpy()\n", " rot_c = rot_np - rot_np.mean(axis=0, keepdims=True)\n", " _, S_r, Vt_r = np.linalg.svd(rot_c[:5000], full_matrices=False)\n", " rot_3d = to_sphere(rot_np @ Vt_r[:3].T)\n", "\n", " var_exp = S_r[:3]**2 / (S_r**2).sum()\n", " setup_ax(ax, f'{name[:25]}\\nPC variance: {var_exp.sum()*100:.1f}%')\n", " ax.scatter(rot_3d[:, 0], rot_3d[:, 1], rot_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"expert_perspectives.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: expert_perspectives.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 3: ANCHORS ONLY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 3 — anchors only...\")\n", "\n", "# Anchor visit counts for coloring\n", "with torch.no_grad():\n", " cos_all = fused @ anchors.T\n", " nearest_all = cos_all.argmax(dim=-1)\n", " vc = torch.zeros(N_ANCHORS)\n", " for n in nearest_all:\n", " vc[n] += 1\n", " vc_np = vc.numpy()\n", "\n", "fig3 = plt.figure(figsize=(24, 8), facecolor='black')\n", "fig3.suptitle(f'Constellation — {N_ANCHORS} anchors × {D_ANCHOR}-d on S²⁵⁵',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "# Panel 1: Anchors colored by visit count\n", "ax_a1 = fig3.add_subplot(131, projection='3d')\n", "setup_ax(ax_a1, f'Anchor Utilization — {int((vc_np>0).sum())}/{N_ANCHORS} active')\n", "heat = np.zeros(N_ANCHORS)\n", "active_mask = vc_np > 0\n", "heat[active_mask] = np.log1p(vc_np[active_mask])\n", "heat = heat / (heat.max() + 1e-8)\n", "a_colors = plt.cm.inferno(heat)\n", "a_sizes = 5 + 60 * heat\n", "# Dead anchors in blue\n", "dead_mask = vc_np == 0\n", "ax_a1.scatter(anchors_s[dead_mask, 0], anchors_s[dead_mask, 1], anchors_s[dead_mask, 2],\n", " c='dodgerblue', s=8, alpha=0.4, marker='x', label=f'dead ({int(dead_mask.sum())})')\n", "ax_a1.scatter(anchors_s[active_mask, 0], anchors_s[active_mask, 1], anchors_s[active_mask, 2],\n", " c=a_colors[active_mask], s=a_sizes[active_mask], alpha=0.8)\n", "\n", "# Panel 2: Anchors colored by nearest neighbor distance\n", "ax_a2 = fig3.add_subplot(132, projection='3d')\n", "anchor_sim = (anchors.numpy() @ anchors.numpy().T)\n", "np.fill_diagonal(anchor_sim, -1)\n", "max_neighbor_cos = anchor_sim.max(axis=1)\n", "nn_heat = (max_neighbor_cos - max_neighbor_cos.min()) / (max_neighbor_cos.max() - max_neighbor_cos.min() + 1e-8)\n", "nn_colors = plt.cm.viridis(nn_heat)\n", "setup_ax(ax_a2, f'Anchor Isolation — nearest neighbor cosine\\n'\n", " f'mean={max_neighbor_cos.mean():.3f} max={max_neighbor_cos.max():.3f}')\n", "ax_a2.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=nn_colors, s=15, alpha=0.8)\n", "\n", "# Panel 3: Anchors colored by expert divergence at that anchor\n", "ax_a3 = fig3.add_subplot(133, projection='3d')\n", "with torch.no_grad():\n", " expert_rotated_list = []\n", " for i in range(3):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_rotated_list.append(rotated)\n", " expert_tri_stack = []\n", " for rotated in expert_rotated_list:\n", " cos_r = rotated @ anchors.T\n", " expert_tri_stack.append(1.0 - cos_r)\n", " tri_stack = torch.stack(expert_tri_stack, dim=-1) # (N_val, N_ANCHORS, 3)\n", " per_anchor_div = tri_stack.std(dim=-1).mean(dim=0).numpy() # (N_ANCHORS,)\n", "\n", "div_heat = (per_anchor_div - per_anchor_div.min()) / (per_anchor_div.max() - per_anchor_div.min() + 1e-8)\n", "div_colors = plt.cm.coolwarm(div_heat)\n", "setup_ax(ax_a3, f'Expert Divergence per Anchor\\n'\n", " f'mean={per_anchor_div.mean():.4f} range=[{per_anchor_div.min():.4f}, {per_anchor_div.max():.4f}]')\n", "ax_a3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=div_colors, s=15, alpha=0.8)\n", "\n", "# Add connections between closest anchor pairs (top 20)\n", "flat_sim = anchor_sim.copy()\n", "np.fill_diagonal(flat_sim, -999)\n", "for panel_ax in [ax_a1, ax_a2]:\n", " for _ in range(20):\n", " idx_flat = np.argmax(flat_sim)\n", " i_a, j_a = np.unravel_index(idx_flat, flat_sim.shape)\n", " flat_sim[i_a, j_a] = -999; flat_sim[j_a, i_a] = -999\n", " panel_ax.plot([anchors_s[i_a, 0], anchors_s[j_a, 0]],\n", " [anchors_s[i_a, 1], anchors_s[j_a, 1]],\n", " [anchors_s[i_a, 2], anchors_s[j_a, 2]],\n", " color='white', alpha=0.15, linewidth=0.5)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"anchors_only.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: anchors_only.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 4: PAIRWISE EXPERT DIFFERENCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 4 — pairwise expert diffs...\")\n", "\n", "with torch.no_grad():\n", " # Compute per-expert triangulations\n", " expert_tris = []\n", " for i in range(3):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " tri = 1.0 - (rotated @ anchors.T) # (N_val, N_ANCHORS)\n", " expert_tris.append(tri)\n", "\n", " # Pairwise diffs\n", " diff_cd = expert_tris[0] - expert_tris[1] # clip - dino\n", " diff_cs = expert_tris[0] - expert_tris[2] # clip - siglip\n", " diff_ds = expert_tris[1] - expert_tris[2] # dino - siglip\n", " diffs = [diff_cd, diff_cs, diff_ds]\n", " diff_names = [\"CLIP − DINOv2\", \"CLIP − SigLIP\", \"DINOv2 − SigLIP\"]\n", "\n", " # Absolute triangulations for comparison\n", " abs_tri = expert_tris[0] # just clip as reference\n", "\n", " # Stats\n", " print(f\"\\n Pairwise diff statistics:\")\n", " for name, d in zip(diff_names, diffs):\n", " print(f\" {name:20s}: mean={d.mean():.6f} std={d.std():.6f} \"\n", " f\"min={d.min():.6f} max={d.max():.6f}\")\n", " print(f\" Absolute tri std: {abs_tri.std():.6f}\")\n", " print(f\" Ratio (diff/abs): {diffs[0].std() / abs_tri.std():.4f}\")\n", "\n", " # PCA of the diff space — this is where the unique info lives\n", " # Stack all 3 diffs: (N_val, N_ANCHORS * 3)\n", " diff_stacked = torch.cat(diffs, dim=-1).numpy() # (5000, 1536)\n", " diff_centered = diff_stacked - diff_stacked.mean(axis=0, keepdims=True)\n", " _, S_diff, Vt_diff = np.linalg.svd(diff_centered[:5000], full_matrices=False)\n", " diff_3d = to_sphere(diff_centered @ Vt_diff[:3].T)\n", "\n", " var_diff = S_diff[:3]**2 / (S_diff**2).sum()\n", " eff_dim_diff = float(((S_diff / S_diff.sum())**2).sum()**-1)\n", " print(f\"\\n Diff space effective dim: {eff_dim_diff:.1f}\")\n", " print(f\" Diff PCA 3D variance: {var_diff.sum()*100:.1f}%\")\n", "\n", " # PCA of absolute tri for comparison\n", " abs_stacked = abs_tri.numpy()\n", " abs_centered = abs_stacked - abs_stacked.mean(axis=0, keepdims=True)\n", " _, S_abs, Vt_abs = np.linalg.svd(abs_centered[:5000], full_matrices=False)\n", " abs_eff = float(((S_abs / S_abs.sum())**2).sum()**-1)\n", " print(f\" Absolute tri effective dim: {abs_eff:.1f}\")\n", "\n", " # Concatenated: absolute + diffs\n", " full_stacked = np.concatenate([abs_stacked, diff_stacked], axis=-1) # (5000, 512+1536)\n", " full_centered = full_stacked - full_stacked.mean(axis=0, keepdims=True)\n", " _, S_full, Vt_full = np.linalg.svd(full_centered[:5000], full_matrices=False)\n", " full_eff = float(((S_full / S_full.sum())**2).sum()**-1)\n", " full_3d = to_sphere(full_centered @ Vt_full[:3].T)\n", " print(f\" Full (abs+diffs) effective dim: {full_eff:.1f}\")\n", " print(f\" Information gain from diffs: {full_eff - abs_eff:.1f} dimensions\")\n", "\n", "fig4 = plt.figure(figsize=(28, 14), facecolor='black')\n", "fig4.suptitle(\n", " 'Expert Pairwise Differences — Where the discriminative signal lives\\n'\n", " f'Diff eff_dim={eff_dim_diff:.1f} | Abs eff_dim={abs_eff:.1f} | '\n", " f'Combined eff_dim={full_eff:.1f} | Info gain: +{full_eff-abs_eff:.1f} dims',\n", " color='white', fontsize=14, y=0.98)\n", "\n", "# Row 1: Three pairwise diff distributions on sphere\n", "for col, (name, d) in enumerate(zip(diff_names, diffs)):\n", " ax = fig4.add_subplot(2, 4, col+1, projection='3d')\n", " d_np = d.numpy()\n", "\n", " # Per-image: magnitude of diff vector\n", " diff_mag = np.linalg.norm(d_np, axis=-1)\n", " mag_heat = (diff_mag - diff_mag.min()) / (diff_mag.max() - diff_mag.min() + 1e-8)\n", " mag_colors = plt.cm.magma(mag_heat)\n", "\n", " setup_ax(ax, f'{name}\\nstd={d_np.std():.5f}')\n", " ax.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=mag_colors, s=2, alpha=0.5)\n", "\n", "# Panel 4: Diff space PCA\n", "ax_dp = fig4.add_subplot(244, projection='3d')\n", "setup_ax(ax_dp, f'Diff Space PCA\\neff_dim={eff_dim_diff:.1f} var={var_diff.sum()*100:.1f}%')\n", "ax_dp.scatter(diff_3d[:, 0], diff_3d[:, 1], diff_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Row 2: Per-anchor diff analysis\n", "# Per-anchor mean absolute diff (where do experts disagree most?)\n", "with torch.no_grad():\n", " per_anchor_cd = diff_cd.abs().mean(dim=0).numpy()\n", " per_anchor_cs = diff_cs.abs().mean(dim=0).numpy()\n", " per_anchor_ds = diff_ds.abs().mean(dim=0).numpy()\n", " per_anchor_total = (per_anchor_cd + per_anchor_cs + per_anchor_ds) / 3\n", "\n", "# Panel 5: Anchor-level divergence map (total)\n", "ax_a = fig4.add_subplot(245, projection='3d')\n", "total_heat = (per_anchor_total - per_anchor_total.min()) / (per_anchor_total.max() - per_anchor_total.min() + 1e-8)\n", "total_colors = plt.cm.hot(total_heat)\n", "total_sizes = 5 + 40 * total_heat\n", "setup_ax(ax_a, f'Anchor Divergence (all pairs)\\n'\n", " f'range=[{per_anchor_total.min():.5f}, {per_anchor_total.max():.5f}]')\n", "ax_a.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=total_colors, s=total_sizes, alpha=0.8)\n", "\n", "# Panel 6: Abs tri PCA vs diff PCA side by side\n", "ax_abs = fig4.add_subplot(246, projection='3d')\n", "abs_3d = to_sphere(abs_centered @ Vt_abs[:3].T)\n", "var_abs_3 = S_abs[:3]**2 / (S_abs**2).sum()\n", "setup_ax(ax_abs, f'Absolute Tri PCA\\neff_dim={abs_eff:.1f} var={var_abs_3.sum()*100:.1f}%')\n", "ax_abs.scatter(abs_3d[:, 0], abs_3d[:, 1], abs_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Panel 7: Combined PCA\n", "ax_full = fig4.add_subplot(247, projection='3d')\n", "var_full_3 = S_full[:3]**2 / (S_full**2).sum()\n", "setup_ax(ax_full, f'Combined (abs+diffs) PCA\\neff_dim={full_eff:.1f} var={var_full_3.sum()*100:.1f}%')\n", "ax_full.scatter(full_3d[:, 0], full_3d[:, 1], full_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Panel 8: Histogram of diff magnitudes\n", "ax_hist = fig4.add_subplot(248)\n", "ax_hist.set_facecolor('black')\n", "for name, d, color in zip(diff_names, diffs,\n", " ['#ff6b6b', '#4ecdc4', '#ffe66d']):\n", " d_np = d.numpy()\n", " per_image_mag = np.linalg.norm(d_np, axis=-1)\n", " ax_hist.hist(per_image_mag, bins=50, alpha=0.6, color=color,\n", " label=name, density=True)\n", "ax_hist.set_xlabel('Diff magnitude (L2)', color='white', fontsize=9)\n", "ax_hist.set_ylabel('Density', color='white', fontsize=9)\n", "ax_hist.set_title('Per-image diff magnitudes', color='white', fontsize=11)\n", "ax_hist.legend(fontsize=8, facecolor='black', edgecolor='gray',\n", " labelcolor='white')\n", "ax_hist.tick_params(colors='gray', labelsize=7)\n", "ax_hist.spines['bottom'].set_color('gray'); ax_hist.spines['left'].set_color('gray')\n", "ax_hist.spines['top'].set_visible(False); ax_hist.spines['right'].set_visible(False)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"pairwise_diffs.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: pairwise_diffs.png\")\n", "plt.close()\n", "\n", "print(\"\\nDone.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 384 }, "id": "qGX1_qSkIkQM", "outputId": "665a702a-14c8-4122-a8ac-7abc1f43917b" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading soup...\n", "Loading features...\n", "Generating embeddings...\n" ] }, { "output_type": "error", "ename": "KeyError", "evalue": "'projectors.0.proj.0.weight'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_255833/3021622238.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating embeddings...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0mprojected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mproject_expert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_raw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEXPERTS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0mfused\u001b[0m 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expert: TWO projections.\n", " Shared: Procrustes-aligned → consensus sphere (what experts agree on)\n", " Native: Xavier-init → expert's natural geometry (what the expert uniquely sees)\n", "\n", "The DISPLACEMENT between shared and native IS the Procrustes transformation,\n", "made learnable. The pairwise cross-contrast between native projections IS\n", "the architectural disagreement between training paradigms.\n", "\n", "Triangulation streams (all through same 512 anchors):\n", " Stream 0: consensus fused → anchors (512)\n", " Stream 1-3: per-expert native → anchors (3 × 512)\n", " Stream 4-6: per-expert displacement (shared-native) → anchors (3 × 512)\n", " Stream 7-9: pairwise native diffs (clip-dino, etc) (3 × 512)\n", "\n", "Total: 10 values per anchor × 512 anchors = 5120-d → multi-depth patchwork\n", "\n", "GPA → PCA 256-d → Procrustes calibration → dual-stream training.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "from tqdm import tqdm\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "# Geometry\n", "D_EXPERT = 768\n", "D_ANCHOR = 256\n", "N_ANCHORS = 512\n", "N_EXPERTS_COUNT = 3\n", "N_CLASSES = 80\n", "ANCHOR_DROP = 0.30\n", "N_STREAMS = 10 # 1 shared + 3 native + 3 displacement + 3 pairwise\n", "\n", "# Multi-depth patchwork\n", "N_COMP_COARSE = 16 # 512/16 = 32 anchors × 10 streams = 320 inputs\n", "N_COMP_FINE = 64 # 512/64 = 8 anchors × 10 streams = 80 inputs\n", "D_COARSE = 128\n", "D_FINE = 64\n", "D_PW_PROJ = 1024\n", "\n", "# Training\n", "BATCH = 128\n", "EPOCHS = 30\n", "LR = 1e-3\n", "QUEUE_SIZE = 4096\n", "GRAD_CLIP = 1.0\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "print(\"=\" * 65)\n", "print(\"GEOLIP DUAL-STREAM SOUP\")\n", "print(f\" {N_ANCHORS} anchors × {D_ANCHOR}-d\")\n", "print(f\" {N_STREAMS} streams per anchor (shared+native+displacement+pairwise)\")\n", "print(f\" Triangulation: {N_ANCHORS * N_STREAMS}-d\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 65)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GEOMETRIC PRIMITIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def cv_loss(emb, target=0.2, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5: return torch.tensor(0.0, device=emb.device)\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " vols.append(torch.sqrt(F.relu(v2[0]) + 1e-12))\n", " stacked = torch.stack(vols)\n", " return (stacked.std() / (stacked.mean() + 1e-8) - target).abs()\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n_samples=500):\n", " B = emb.shape[0]\n", " if B < 5: return 0.0\n", " vols = []\n", " for _ in range(n_samples):\n", " idx = torch.randperm(B, device=emb.device)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) < 10: return 0.0\n", " a = np.array(vols)\n", " return float(a.std() / (a.mean() + 1e-8))\n", "\n", "def infonce_queued(emb, targets, queue_emb, queue_tgt, temperature=0.07):\n", " B = emb.shape[0]\n", " e = F.normalize(emb, dim=-1); t = F.normalize(targets, dim=-1)\n", " if queue_tgt is not None and queue_tgt.shape[0] > 0:\n", " at = torch.cat([t, queue_tgt], 0); ae = torch.cat([e, queue_emb], 0)\n", " else:\n", " at = t; ae = e\n", " l_e = (e @ at.T) / temperature; l_t = (t @ ae.T) / temperature\n", " labels = torch.arange(B, device=emb.device)\n", " loss = (F.cross_entropy(l_e, labels) + F.cross_entropy(l_t, labels)) / 2\n", " with torch.no_grad():\n", " acc = (l_e.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "def whitened_procrustes_loss(emb, targets):\n", " B = emb.shape[0]\n", " if B < 10: return torch.tensor(0.0, device=emb.device)\n", " em = emb.float().mean(0, keepdim=True); tm = targets.float().mean(0, keepdim=True)\n", " return 1.0 - F.cosine_similarity(emb.float() - em, targets.float() - tm, dim=-1).mean()\n", "\n", "class EmbeddingAutograd(torch.autograd.Function):\n", " @staticmethod\n", " def forward(ctx, x, embedding, anchors, tang, sep):\n", " ctx.save_for_backward(embedding, anchors)\n", " ctx.tang = tang; ctx.sep = sep\n", " return x\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " embedding, anchors = ctx.saved_tensors\n", " emb_n = F.normalize(embedding.detach().float(), dim=-1)\n", " anchors_n = F.normalize(anchors.detach().float(), dim=-1)\n", " grad_f = grad_output.float()\n", " radial = (grad_f * emb_n).sum(-1, keepdim=True) * emb_n\n", " corrected = (grad_f - radial) + (1.0 - ctx.tang) * radial\n", " if ctx.sep > 0:\n", " cos_to = emb_n @ anchors_n.T\n", " nearest = anchors_n[cos_to.argmax(dim=-1)]\n", " toward = (corrected * nearest).sum(-1, keepdim=True)\n", " corrected = corrected - ctx.sep * (toward > 0).float() * toward * nearest\n", " return corrected.to(grad_output.dtype), None, None, None, None\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "def procrustes_align(source, target, n_align=10000):\n", " N = min(n_align, source.shape[0], target.shape[0])\n", " S = source[:N].float(); T = target[:N].float()\n", " sm = S.mean(0, keepdim=True); tm = T.mean(0, keepdim=True)\n", " Sc = S - sm; Tc = T - tm; Ns = Sc.shape[0]\n", " sw = symmetric_inv_sqrt((Sc.T @ Sc) / max(Ns-1, 1))\n", " tw = symmetric_inv_sqrt((Tc.T @ Tc) / max(Ns-1, 1))\n", " Sc_w = F.normalize(Sc @ sw, dim=-1); Tc_w = F.normalize(Tc @ tw, dim=-1)\n", " U, _, Vt = torch.linalg.svd(Tc_w.T @ Sc_w, full_matrices=False)\n", " return {\"rotation\": U @ Vt, \"source_mean\": sm.squeeze(0),\n", " \"source_whitener\": sw, \"target_unwhitener\": torch.linalg.pinv(tw)}\n", "\n", "def apply_align(emb, a):\n", " x = emb.float() - a[\"source_mean\"]\n", " return x @ a[\"source_whitener\"] @ a[\"rotation\"].T @ a[\"target_unwhitener\"]\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DUAL-STREAM PROJECTOR\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DualStreamProjector(nn.Module):\n", " \"\"\"\n", " Two projections per expert:\n", " shared: 768 → 256, Procrustes-initialized (consensus path)\n", " native: 768 → 256, Xavier-initialized (expert's own geometry)\n", "\n", " The shared path learns WHERE the expert agrees with consensus.\n", " The native path learns WHERE the expert naturally represents.\n", " The displacement (shared - native) IS the learned Procrustes deviation.\n", " \"\"\"\n", " def __init__(self):\n", " super().__init__()\n", " self.proj_shared = nn.Sequential(\n", " nn.Linear(D_EXPERT, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", " self.proj_native = nn.Sequential(\n", " nn.Linear(D_EXPERT, D_ANCHOR), nn.LayerNorm(D_ANCHOR))\n", "\n", " def forward(self, x):\n", " shared = F.normalize(self.proj_shared(x), dim=-1)\n", " native = F.normalize(self.proj_native(x), dim=-1)\n", " return shared, native\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DUAL-STREAM CONSTELLATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DualStreamConstellation(nn.Module):\n", " \"\"\"\n", " Triangulates 10 streams against shared anchors:\n", " 0: consensus fused (mean of shared projections)\n", " 1-3: native projections (expert's own geometry)\n", " 4-6: displacement = shared - native per expert\n", " 7-9: pairwise native diffs (cross-contrast)\n", "\n", " Output: (B, N_ANCHORS, 10) → flattened to (B, N_ANCHORS * 10)\n", " \"\"\"\n", " def __init__(self, n_anchors=N_ANCHORS, d=D_ANCHOR, drop_rate=ANCHOR_DROP):\n", " super().__init__()\n", " self.n_anchors = n_anchors\n", " self.drop_rate = drop_rate\n", " self.d = d\n", " self.anchors = nn.Parameter(F.normalize(torch.randn(n_anchors, d), dim=-1))\n", "\n", " def _tri(self, emb, anchors_n, keep_idx=None, B=None):\n", " \"\"\"Compute triangulation, with optional anchor dropout.\"\"\"\n", " if keep_idx is not None:\n", " cos = emb @ anchors_n[keep_idx].T\n", " full_cos = torch.full((B, self.n_anchors), -1.0,\n", " device=emb.device, dtype=cos.dtype)\n", " full_cos[:, keep_idx] = cos\n", " else:\n", " full_cos = emb @ anchors_n.T\n", " return 1.0 - full_cos # (B, N_ANCHORS)\n", "\n", " def forward(self, fused, shared_list, native_list, training=False):\n", " \"\"\"\n", " fused: (B, D_ANCHOR) — consensus embedding\n", " shared_list: list of 3 × (B, D_ANCHOR) — per-expert shared projections\n", " native_list: list of 3 × (B, D_ANCHOR) — per-expert native projections\n", " \"\"\"\n", " B = fused.shape[0]\n", " anchors_n = F.normalize(self.anchors, dim=-1)\n", "\n", " # Anchor dropout\n", " keep_idx = None\n", " if training and self.drop_rate > 0:\n", " n_keep = max(int(self.n_anchors * (1 - self.drop_rate)), 64)\n", " keep_idx = torch.randperm(self.n_anchors, device=fused.device)[:n_keep]\n", "\n", " # Stream 0: consensus\n", " tri_consensus = self._tri(fused, anchors_n, keep_idx, B)\n", "\n", " # Streams 1-3: native per expert\n", " tri_native = [self._tri(native_list[i], anchors_n, keep_idx, B)\n", " for i in range(3)]\n", "\n", " # Streams 4-6: displacement = shared - native (on sphere: angular deviation)\n", " displacements = []\n", " for i in range(3):\n", " disp = F.normalize(shared_list[i] - native_list[i], dim=-1)\n", " displacements.append(self._tri(disp, anchors_n, keep_idx, B))\n", "\n", " # Streams 7-9: pairwise native diffs\n", " pairs = [(0, 1), (0, 2), (1, 2)]\n", " pairwise = []\n", " for i, j in pairs:\n", " diff = F.normalize(native_list[i] - native_list[j], dim=-1)\n", " pairwise.append(self._tri(diff, anchors_n, keep_idx, B))\n", "\n", " # Stack all 10 streams: (B, N_ANCHORS, 10)\n", " all_streams = ([tri_consensus] + tri_native +\n", " displacements + pairwise)\n", " stacked = torch.stack(all_streams, dim=-1) # (B, N_ANCHORS, 10)\n", "\n", " # Nearest from consensus\n", " if keep_idx is not None:\n", " cos_fused = torch.full((B, self.n_anchors), -1.0,\n", " device=fused.device, dtype=fused.dtype)\n", " cos_fused[:, keep_idx] = fused @ anchors_n[keep_idx].T\n", " else:\n", " cos_fused = fused @ anchors_n.T\n", " nearest = cos_fused.argmax(dim=-1)\n", "\n", " return stacked, nearest\n", "\n", " def anchor_spread_loss(self):\n", " a = F.normalize(self.anchors, dim=-1)\n", " idx = torch.randperm(self.n_anchors, device=a.device)[:512]\n", " a_sub = a[idx]\n", " sim = a_sub @ a_sub.T; sim = sim - torch.diag(torch.diag(sim))\n", " return sim.pow(2).mean()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# MULTI-DEPTH PATCHWORK (reads 10-stream triangulation)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DepthLevel(nn.Module):\n", " def __init__(self, n_anchors, n_comp, n_streams, d_comp):\n", " super().__init__()\n", " self.n_comp = n_comp\n", " self.n_streams = n_streams\n", " asgn = torch.arange(n_anchors) % n_comp\n", " self.register_buffer(\"asgn\", asgn)\n", " inputs_per_comp = (n_anchors // n_comp) * n_streams\n", " self.comps = nn.ModuleList([nn.Sequential(\n", " nn.Linear(inputs_per_comp, d_comp * 2), nn.GELU(),\n", " nn.Linear(d_comp * 2, d_comp), nn.LayerNorm(d_comp))\n", " for _ in range(n_comp)])\n", "\n", " def forward(self, tri_nd):\n", " \"\"\"tri_nd: (B, n_anchors, n_streams)\"\"\"\n", " B = tri_nd.shape[0]\n", " results = []\n", " for k in range(self.n_comp):\n", " mask = self.asgn == k\n", " comp_input = tri_nd[:, mask, :].reshape(B, -1)\n", " results.append(self.comps[k](comp_input))\n", " return torch.cat(results, dim=-1)\n", "\n", "\n", "class MultiDepthPatchwork(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.coarse = DepthLevel(N_ANCHORS, N_COMP_COARSE, N_STREAMS, D_COARSE)\n", " self.fine = DepthLevel(N_ANCHORS, N_COMP_FINE, N_STREAMS, D_FINE)\n", " total = N_COMP_COARSE * D_COARSE + N_COMP_FINE * D_FINE\n", " self.proj = nn.Sequential(\n", " nn.Linear(total, D_PW_PROJ), nn.GELU(), nn.LayerNorm(D_PW_PROJ))\n", "\n", " def forward(self, tri_nd):\n", " c = self.coarse(tri_nd)\n", " f = self.fine(tri_nd)\n", " return self.proj(torch.cat([c, f], dim=-1))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FULL MODEL\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class DualStreamSoup(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.projectors = nn.ModuleList([DualStreamProjector() for _ in range(3)])\n", " self.constellation = DualStreamConstellation()\n", " self.patchwork = MultiDepthPatchwork()\n", " self.classifier = nn.Sequential(\n", " nn.Linear(D_PW_PROJ + D_ANCHOR, D_PW_PROJ), nn.GELU(),\n", " nn.LayerNorm(D_PW_PROJ), nn.Dropout(0.1),\n", " nn.Linear(D_PW_PROJ, N_CLASSES))\n", "\n", " def forward(self, expert_features, apply_autograd=True):\n", " shared_list, native_list = [], []\n", " for i in range(3):\n", " shared, native = self.projectors[i](expert_features[i])\n", " shared_list.append(shared)\n", " native_list.append(native)\n", "\n", " fused = F.normalize(sum(shared_list) / 3, dim=-1)\n", "\n", " if apply_autograd and self.training:\n", " fused = EmbeddingAutograd.apply(\n", " fused, fused, self.constellation.anchors, 0.01, 1.0)\n", "\n", " tri_nd, nearest = self.constellation(\n", " fused, shared_list, native_list, training=self.training)\n", " pw = self.patchwork(tri_nd)\n", " logits = self.classifier(torch.cat([pw, fused], dim=-1))\n", "\n", " return logits, fused, tri_nd, nearest, shared_list, native_list\n", "\n", " def displacement_loss(self, shared_list, native_list):\n", " \"\"\"\n", " Encourage displacement to be meaningful:\n", " shared and native should NOT be identical (that kills the native path).\n", " But they should be correlated (they see the same image).\n", " Target: moderate cosine between shared and native (~0.5-0.7).\n", " \"\"\"\n", " loss = 0.0\n", " for i in range(3):\n", " cos = F.cosine_similarity(shared_list[i], native_list[i], dim=-1)\n", " # Penalize both too-similar (>0.9) and too-different (<0.3)\n", " loss += F.relu(cos - 0.8).mean() + F.relu(0.3 - cos).mean()\n", " return loss / 3\n", "\n", " def cross_contrast_loss(self, native_list):\n", " \"\"\"\n", " Pairwise native projections should be moderately different.\n", " Not identical (that collapses the streams).\n", " Not orthogonal (they see the same image).\n", " \"\"\"\n", " loss = 0.0\n", " pairs = [(0, 1), (0, 2), (1, 2)]\n", " for i, j in pairs:\n", " cos = F.cosine_similarity(native_list[i], native_list[j], dim=-1)\n", " loss += F.relu(cos - 0.8).mean() + F.relu(0.2 - cos).mean()\n", " return loss / 3\n", "\n", " def native_diversity_loss(self, native_list):\n", " \"\"\"\n", " Ensure the 3 native projections capture different info.\n", " The concatenated native should have higher effective dim than any single.\n", " Use variance of pairwise cosines as proxy.\n", " \"\"\"\n", " cos_pairs = []\n", " for i in range(3):\n", " for j in range(i+1, 3):\n", " cos_pairs.append(\n", " F.cosine_similarity(native_list[i], native_list[j], dim=-1))\n", " stacked = torch.stack(cos_pairs, dim=-1)\n", " # Encourage spread (std > 0) — different pairs should have different agreement\n", " return -stacked.std(dim=-1).mean() * 0.1\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + CALIBRATE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 0: LOAD DATA\")\n", "print(f\"{'='*65}\")\n", "\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"train\")\n", "train_ids = ref[\"image_id\"]; N_train = len(train_ids)\n", "train_id_map = {iid: i for i, iid in enumerate(train_ids)}\n", "train_labels = torch.zeros(N_train, N_CLASSES)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: train_labels[i, l] = 1.0\n", "\n", "ref_val = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref_val[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, N_CLASSES)\n", "for i, labs in enumerate(ref_val[\"labels\"]):\n", " for l in labs:\n", " if l < N_CLASSES: val_labels[i, l] = 1.0\n", "\n", "print(f\" Train: {N_train:,} Val: {N_val:,}\")\n", "\n", "train_raw, val_raw = {}, {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"train\")\n", " feats = torch.zeros(N_train, D_EXPERT)\n", " for row in ds:\n", " if row[\"image_id\"] in train_id_map:\n", " feats[train_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " train_raw[name] = feats\n", " ds_v = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats_v = torch.zeros(N_val, D_EXPERT)\n", " for row in ds_v:\n", " if row[\"image_id\"] in val_id_map:\n", " feats_v[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats_v\n", " print(f\" {name:<30} loaded\")\n", " del ds, ds_v; gc.collect()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GPA + PCA + PROCRUSTES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 1: GPA + PCA + PROCRUSTES\")\n", "print(f\"{'='*65}\")\n", "\n", "current = {name: train_raw[name].float() for name in EXPERTS}\n", "for gpa_iter in range(20):\n", " mean_shape = sum(current[n] for n in EXPERTS) / len(EXPERTS)\n", " delta = 0.0\n", " new_current = {}\n", " for name in EXPERTS:\n", " info = procrustes_align(current[name], mean_shape)\n", " new_current[name] = apply_align(current[name], info)\n", " delta += (new_current[name] - current[name]).pow(2).mean().item()\n", " current = new_current\n", " if gpa_iter == 0 or (gpa_iter+1) % 5 == 0:\n", " print(f\" GPA iter {gpa_iter+1}: delta={delta:.8f}\")\n", " if delta < 1e-8: break\n", "\n", "consensus_768 = F.normalize(sum(current[n] for n in EXPERTS) / len(EXPERTS), dim=-1)\n", "\n", "# PCA → 256-d\n", "cc = consensus_768 - consensus_768.mean(0, keepdim=True)\n", "U, S, Vt = torch.linalg.svd(cc[:10000], full_matrices=False)\n", "pca_proj = Vt[:D_ANCHOR]\n", "consensus_d = F.normalize(consensus_768 @ pca_proj.T, dim=-1)\n", "consensus_cv = cv_metric(consensus_d[:5000].to(DEVICE))\n", "print(f\" PCA 768→{D_ANCHOR}: var_retained={S[:D_ANCHOR].pow(2).sum()/S.pow(2).sum():.4f}\")\n", "print(f\" Consensus CV: {consensus_cv:.4f}\")\n", "\n", "# Val\n", "val_current = {name: val_raw[name].float() for name in EXPERTS}\n", "for _ in range(20):\n", " vm = sum(val_current[n] for n in EXPERTS) / len(EXPERTS)\n", " d = 0.0\n", " for name in EXPERTS:\n", " info = procrustes_align(val_current[name], vm)\n", " new = apply_align(val_current[name], info)\n", " d += (new - val_current[name]).pow(2).mean().item()\n", " val_current[name] = new\n", " if d < 1e-8: break\n", "val_consensus_d = F.normalize(\n", " F.normalize(sum(val_current[n] for n in EXPERTS) / len(EXPERTS), dim=-1) @ pca_proj.T, dim=-1)\n", "\n", "# Per-expert Procrustes\n", "expert_calibrations = {}\n", "for name in EXPERTS:\n", " raw = train_raw[name][:10000].float()\n", " tgt = consensus_d[:10000].float()\n", " sm = raw.mean(0, keepdim=True); tm = tgt.mean(0, keepdim=True)\n", " sc = raw - sm; tc = tgt - tm\n", " sw = symmetric_inv_sqrt((sc.T @ sc) / 9999)\n", " tw = symmetric_inv_sqrt((tc.T @ tc) / 9999)\n", " src_w = F.normalize(sc @ sw, dim=-1); tgt_w = F.normalize(tc @ tw, dim=-1)\n", " M = tgt_w.T @ src_w\n", " U_r, S_r, Vt_r = torch.linalg.svd(M, full_matrices=False)\n", " R = U_r @ Vt_r\n", " proj_W = (sw @ R.T).T; proj_b = -(sm.squeeze(0) @ sw @ R.T).squeeze(0)\n", " test = F.normalize(raw[:1000] @ proj_W.T + proj_b, dim=-1)\n", " cos = F.cosine_similarity(test, tgt[:1000], dim=-1).mean().item()\n", " expert_calibrations[name] = {\"W\": proj_W, \"b\": proj_b, \"cos\": cos}\n", " print(f\" {name:<30} cos={cos:.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BUILD + INITIALIZE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 2: BUILD MODEL\")\n", "print(f\"{'='*65}\")\n", "\n", "model = DualStreamSoup().to(DEVICE)\n", "\n", "with torch.no_grad():\n", " # Shared projectors from Procrustes\n", " for i, name in enumerate(EXPERTS):\n", " cal = expert_calibrations[name]\n", " model.projectors[i].proj_shared[0].weight.copy_(cal[\"W\"].to(DEVICE))\n", " model.projectors[i].proj_shared[0].bias.copy_(cal[\"b\"].to(DEVICE))\n", " print(f\" ✓ Shared projectors from Procrustes\")\n", "\n", " # Native projectors: Xavier (already initialized by default)\n", " print(f\" ✓ Native projectors Xavier-initialized\")\n", "\n", " # Anchors from consensus\n", " sample_idx = torch.randperm(min(10000, N_train))[:N_ANCHORS]\n", " model.constellation.anchors.copy_(\n", " F.normalize(consensus_d[sample_idx].to(DEVICE), dim=-1))\n", " print(f\" ✓ Anchors seeded from consensus\")\n", "\n", "# Verify\n", "with torch.no_grad():\n", " test_in = [train_raw[EXPERTS[e]][:200].to(DEVICE) for e in range(3)]\n", " lo, fused_t, tri_t, near_t, sh_t, nat_t = model(test_in, apply_autograd=False)\n", " test_tgt = consensus_d[:200].to(DEVICE)\n", " init_cos = F.cosine_similarity(fused_t, test_tgt, dim=-1).mean().item()\n", " n_active = near_t.unique().numel()\n", " # Check displacement\n", " for i, name in enumerate([\"clip\", \"dino\", \"siglip\"]):\n", " cos_sn = F.cosine_similarity(sh_t[i], nat_t[i], dim=-1).mean().item()\n", " print(f\" {name} shared×native cos: {cos_sn:.4f}\")\n", " print(f\" Init: cos={init_cos:.4f} anchors={n_active}/{N_ANCHORS}\")\n", " print(f\" Triangulation shape: {tri_t.shape}\")\n", "\n", "def count_params(m): return sum(p.numel() for p in m.parameters())\n", "n_total = count_params(model)\n", "print(f\"\\n Parameters:\")\n", "print(f\" projectors (dual): {sum(count_params(p) for p in model.projectors):>10,}\")\n", "print(f\" constellation: {count_params(model.constellation):>10,}\")\n", "print(f\" patchwork: {count_params(model.patchwork):>10,}\")\n", "print(f\" classifier: {count_params(model.classifier):>10,}\")\n", "print(f\" total: {n_total:>10,}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAINING\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*65}\")\n", "print(\"PHASE 3: TRAINING\")\n", "print(f\" {EPOCHS} epochs, lr={LR}, batch={BATCH}\")\n", "print(f\" Streams: {N_STREAMS} per anchor\")\n", "print(f\" Anchor dropout: {ANCHOR_DROP}\")\n", "print(f\" CV target: {consensus_cv:.4f}\")\n", "print(f\"{'='*65}\")\n", "\n", "train_targets = consensus_d.to(DEVICE)\n", "val_targets = val_consensus_d.to(DEVICE)\n", "train_labels_gpu = train_labels.to(DEVICE)\n", "\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "from torch.utils.tensorboard import SummaryWriter\n", "writer = SummaryWriter(\"runs/dual_stream_soup\")\n", "best_mAP = 0.0; gs = 0\n", "queue_e = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "queue_t = torch.zeros(0, D_ANCHOR, device=DEVICE)\n", "\n", "for epoch in range(EPOCHS):\n", " model.train()\n", " perm = torch.randperm(N_train)\n", " acc = {\"loss\": 0, \"nce\": 0, \"mse\": 0, \"bce\": 0, \"cv\": 0,\n", " \"spread\": 0, \"disp\": 0, \"cross\": 0, \"div\": 0,\n", " \"align\": 0, \"nce_acc\": 0, \"n\": 0}\n", "\n", " pbar = tqdm(range(0, N_train, BATCH), desc=f\"E{epoch+1:2d}/{EPOCHS}\", unit=\"batch\")\n", " for i in pbar:\n", " idx = perm[i:i+BATCH]\n", " if len(idx) < 4: continue\n", "\n", " batch = [train_raw[EXPERTS[e]][idx].to(DEVICE) for e in range(3)]\n", " labels = train_labels_gpu[idx]; targets = train_targets[idx]\n", "\n", " logits, fused, tri_nd, nearest, shared_list, native_list = model(batch)\n", "\n", " # Core losses\n", " l_nce, nce_acc = infonce_queued(fused, targets, queue_e, queue_t)\n", " with torch.no_grad():\n", " queue_e = torch.cat([queue_e, fused.detach()], 0)[-QUEUE_SIZE:]\n", " queue_t = torch.cat([queue_t, targets.detach()], 0)[-QUEUE_SIZE:]\n", " l_mse = F.mse_loss(fused, targets)\n", " l_bce = F.binary_cross_entropy_with_logits(logits, labels)\n", " l_align = whitened_procrustes_loss(fused, targets)\n", " l_cv = cv_loss(fused, target=consensus_cv)\n", " l_spread = model.constellation.anchor_spread_loss()\n", "\n", " # Dual-stream losses\n", " l_disp = model.displacement_loss(shared_list, native_list)\n", " l_cross = model.cross_contrast_loss(native_list)\n", " l_div = model.native_diversity_loss(native_list)\n", "\n", " loss = (1.0 * l_nce + 0.5 * l_mse + 0.3 * l_bce\n", " + 0.5 * l_align + 0.001 * l_cv + 1e-3 * l_spread\n", " + 0.3 * l_disp + 0.3 * l_cross + 0.1 * l_div)\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)\n", " optimizer.step(); optimizer.zero_grad(set_to_none=True)\n", "\n", " for k, v in [(\"loss\", loss), (\"nce\", l_nce), (\"mse\", l_mse),\n", " (\"bce\", l_bce), (\"cv\", l_cv), (\"spread\", l_spread),\n", " (\"disp\", l_disp), (\"cross\", l_cross), (\"div\", l_div),\n", " (\"align\", l_align)]:\n", " acc[k] += v.item() if torch.is_tensor(v) else v\n", " acc[\"nce_acc\"] += nce_acc; acc[\"n\"] += 1; gs += 1\n", "\n", " if gs % 50 == 0:\n", " d = max(acc[\"n\"], 1)\n", " for k in [\"loss\", \"nce\", \"bce\", \"disp\", \"cross\", \"nce_acc\"]:\n", " writer.add_scalar(f\"step/{k}\", acc[k]/d, gs)\n", "\n", " if acc[\"n\"] % 20 == 0:\n", " d = acc[\"n\"]\n", " pbar.set_postfix(loss=f\"{acc['loss']/d:.4f}\",\n", " nce_acc=f\"{acc['nce_acc']/d:.3f}\",\n", " disp=f\"{acc['disp']/d:.4f}\", ordered=True)\n", "\n", " d = max(acc[\"n\"], 1)\n", " print(f\" E{epoch+1} train: loss={acc['loss']/d:.4f} nce={acc['nce']/d:.4f} \"\n", " f\"bce={acc['bce']/d:.4f} disp={acc['disp']/d:.4f} \"\n", " f\"cross={acc['cross']/d:.4f} nce_acc={acc['nce_acc']/d:.3f}\")\n", "\n", " # Validation\n", " model.eval()\n", " with torch.no_grad():\n", " all_lo, all_em = [], []\n", " sn_cos = [0, 0, 0]; nn_cos = [0, 0, 0]; n_v = 0\n", " for j in range(0, N_val, BATCH):\n", " end = min(j + BATCH, N_val)\n", " bv = [val_raw[EXPERTS[e]][j:end].to(DEVICE) for e in range(3)]\n", " lo, em, _, _, sh, nat = model(bv, apply_autograd=False)\n", " all_lo.append(lo.cpu()); all_em.append(em.cpu())\n", " for k in range(3):\n", " sn_cos[k] += F.cosine_similarity(sh[k], nat[k], dim=-1).sum().item()\n", " if k < 2:\n", " nn_cos[k] += F.cosine_similarity(nat[k], nat[k+1], dim=-1).sum().item()\n", " nn_cos[2] += F.cosine_similarity(nat[0], nat[2], dim=-1).sum().item()\n", " n_v += end - j\n", "\n", " v_lo = torch.cat(all_lo); v_em = torch.cat(all_em)\n", " v_lab = val_labels\n", " ap_sum, nv = 0, 0\n", " for c in range(N_CLASSES):\n", " if v_lab[:, c].sum() > 0:\n", " si = v_lo[:, c].argsort(descending=True); st = v_lab[:, c][si]\n", " pak = st.cumsum(0) / torch.arange(1, len(st)+1).float()\n", " ap_sum += (pak * st).sum().item() / st.sum().item(); nv += 1\n", " mAP = ap_sum / max(nv, 1)\n", "\n", " vp = (v_lo.sigmoid() > 0.5).float()\n", " tp = (vp * v_lab).sum(0); fp = (vp * (1-v_lab)).sum(0); fn = ((1-vp) * v_lab).sum(0)\n", " pr_ = tp/(tp+fp+1e-8); rc_ = tp/(tp+fn+1e-8); f1_ = 2*pr_*rc_/(pr_+rc_+1e-8)\n", "\n", " v_cos = F.cosine_similarity(v_em, val_targets.cpu(), dim=-1).mean().item()\n", " sim = v_em @ val_targets.cpu().T\n", " r1 = (sim.argmax(-1) == torch.arange(N_val)).float().mean().item()\n", " _, v_nearest = model.constellation(\n", " v_em.to(DEVICE),\n", " [v_em.to(DEVICE)]*3, [v_em.to(DEVICE)]*3, training=False)\n", " n_active = v_nearest.cpu().unique().numel()\n", " v_cv = cv_metric(v_em[:2000].to(DEVICE))\n", "\n", " writer.add_scalar(\"val/mAP\", mAP, epoch+1)\n", " writer.add_scalar(\"val/cos\", v_cos, epoch+1)\n", " writer.add_scalar(\"val/R@1\", r1, epoch+1)\n", " writer.add_scalar(\"val/anchors\", n_active, epoch+1)\n", " for k in range(3):\n", " writer.add_scalar(f\"val/shared_native_cos_{k}\", sn_cos[k]/n_v, epoch+1)\n", "\n", " mk = \"\"\n", " if mAP > best_mAP:\n", " best_mAP = mAP\n", " torch.save({\"state_dict\": model.state_dict(),\n", " \"config\": {\"d_anchor\": D_ANCHOR, \"n_anchors\": N_ANCHORS,\n", " \"n_streams\": N_STREAMS, \"n_comp_coarse\": N_COMP_COARSE,\n", " \"n_comp_fine\": N_COMP_FINE, \"d_coarse\": D_COARSE,\n", " \"d_fine\": D_FINE, \"d_pw_proj\": D_PW_PROJ,\n", " \"anchor_drop\": ANCHOR_DROP, \"experts\": EXPERTS,\n", " \"cv_target\": consensus_cv},\n", " \"pca_proj\": pca_proj, \"consensus_cv\": consensus_cv,\n", " \"mAP\": mAP, \"r1\": r1, \"cos\": v_cos, \"cv\": v_cv,\n", " \"epoch\": epoch+1, \"n_active\": n_active},\n", " \"checkpoints/dual_stream_best.pt\")\n", " mk = \" ★\"\n", "\n", " torch.save({\"state_dict\": model.state_dict(), \"epoch\": epoch+1,\n", " \"mAP\": mAP, \"optimizer\": optimizer.state_dict(), \"gs\": gs},\n", " f\"checkpoints/dual_stream_e{epoch+1:02d}.pt\")\n", "\n", " sn_str = \"/\".join(f\"{sn_cos[k]/n_v:.3f}\" for k in range(3))\n", " print(f\" E{epoch+1} val: mAP={mAP:.3f} F1={f1_[f1_>0].mean():.3f} \"\n", " f\"R@1={r1:.3f} cos={v_cos:.3f} cv={v_cv:.4f} \"\n", " f\"anchors={n_active}/{N_ANCHORS} sn_cos=[{sn_str}]{mk}\")\n", "\n", "writer.close()\n", "print(f\"\\n Best mAP: {best_mAP:.3f}\")\n", "print(f\" Total: {n_total:,} params\")\n", "print(f\"\\n{'='*65}\\nDONE\\n{'='*65}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qTROt2HBM8qw", "outputId": "7b8f2f67-3fbc-420c-c7f1-a6cf9ca97579" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", "GEOLIP DUAL-STREAM SOUP\n", " 512 anchors × 256-d\n", " 10 streams per anchor (shared+native+displacement+pairwise)\n", " Triangulation: 5120-d\n", " Device: cuda\n", "=================================================================\n", "\n", "=================================================================\n", "PHASE 0: LOAD DATA\n", "=================================================================\n", " Train: 118,287 Val: 5,000\n", " clip_l14_openai loaded\n", " dinov2_b14 loaded\n", " siglip_b16_384 loaded\n", "\n", "=================================================================\n", "PHASE 1: GPA + PCA + PROCRUSTES\n", "=================================================================\n", " GPA iter 1: delta=2.42780352\n", " GPA iter 5: delta=0.00656253\n", " GPA iter 10: delta=0.00082383\n", " GPA iter 15: delta=0.00027677\n", " GPA iter 20: delta=0.00013823\n", " PCA 768→256: var_retained=1.0000\n", " Consensus CV: 0.2742\n", " clip_l14_openai cos=0.4625\n", " dinov2_b14 cos=0.4593\n", " siglip_b16_384 cos=0.4626\n", "\n", "=================================================================\n", "PHASE 2: BUILD MODEL\n", "=================================================================\n", " ✓ Shared projectors from Procrustes\n", " ✓ Native projectors Xavier-initialized\n", " ✓ Anchors seeded from consensus\n", " clip shared×native cos: 0.0158\n", " dino shared×native cos: 0.0164\n", " siglip shared×native cos: 0.0025\n", " Init: cos=0.5250 anchors=97/512\n", " Triangulation shape: torch.Size([200, 512, 10])\n", "\n", " Parameters:\n", " projectors (dual): 1,184,256\n", " constellation: 131,072\n", " patchwork: 9,339,904\n", " classifier: 1,395,792\n", " total: 12,051,024\n", "\n", "=================================================================\n", "PHASE 3: TRAINING\n", " 30 epochs, lr=0.001, batch=128\n", " Streams: 10 per anchor\n", " Anchor dropout: 0.3\n", " CV target: 0.2742\n", "=================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 1/30: 100%|██████████| 925/925 [01:58<00:00, 7.81batch/s, disp=0.0029, loss=1.8354, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E1 train: loss=1.8359 nce=1.7937 bce=0.0710 disp=0.0029 cross=0.0010 nce_acc=1.000\n", " E1 val: mAP=0.791 F1=0.750 R@1=0.999 cos=0.948 cv=0.2253 anchors=332/512 sn_cos=[0.391/0.447/0.412] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 2/30: 100%|██████████| 925/925 [01:58<00:00, 7.83batch/s, disp=0.0000, loss=1.7706, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E2 train: loss=1.7708 nce=1.7525 bce=0.0439 disp=0.0000 cross=0.0002 nce_acc=1.000\n", " E2 val: mAP=0.811 F1=0.753 R@1=0.999 cos=0.949 cv=0.2578 anchors=262/512 sn_cos=[0.351/0.427/0.388] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 3/30: 100%|██████████| 925/925 [01:57<00:00, 7.85batch/s, disp=0.0000, loss=1.7561, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E3 train: loss=1.7567 nce=1.7397 bce=0.0417 disp=0.0000 cross=0.0002 nce_acc=1.000\n", " E3 val: mAP=0.819 F1=0.741 R@1=0.999 cos=0.949 cv=0.2120 anchors=239/512 sn_cos=[0.352/0.411/0.379] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 4/30: 100%|██████████| 925/925 [01:58<00:00, 7.83batch/s, disp=0.0000, loss=1.7500, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E4 train: loss=1.7498 nce=1.7335 bce=0.0405 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E4 val: mAP=0.825 F1=0.759 R@1=0.999 cos=0.949 cv=0.2211 anchors=234/512 sn_cos=[0.343/0.404/0.375] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 5/30: 100%|██████████| 925/925 [01:58<00:00, 7.84batch/s, disp=0.0000, loss=1.7472, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E5 train: loss=1.7469 nce=1.7309 bce=0.0395 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E5 val: mAP=0.828 F1=0.759 R@1=0.999 cos=0.949 cv=0.2182 anchors=222/512 sn_cos=[0.342/0.393/0.370] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 6/30: 100%|██████████| 925/925 [01:58<00:00, 7.78batch/s, disp=0.0000, loss=1.7450, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E6 train: loss=1.7442 nce=1.7285 bce=0.0386 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E6 val: mAP=0.831 F1=0.767 R@1=0.999 cos=0.949 cv=0.2285 anchors=216/512 sn_cos=[0.340/0.388/0.366] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 7/30: 100%|██████████| 925/925 [01:57<00:00, 7.85batch/s, disp=0.0000, loss=1.7431, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E7 train: loss=1.7443 nce=1.7289 bce=0.0378 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E7 val: mAP=0.833 F1=0.766 R@1=0.999 cos=0.949 cv=0.2348 anchors=209/512 sn_cos=[0.334/0.386/0.361] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 8/30: 100%|██████████| 925/925 [01:57<00:00, 7.88batch/s, disp=0.0000, loss=1.7427, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E8 train: loss=1.7428 nce=1.7276 bce=0.0370 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E8 val: mAP=0.835 F1=0.769 R@1=0.999 cos=0.949 cv=0.2260 anchors=211/512 sn_cos=[0.337/0.382/0.360] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E 9/30: 100%|██████████| 925/925 [01:58<00:00, 7.81batch/s, disp=0.0000, loss=1.7418, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E9 train: loss=1.7420 nce=1.7270 bce=0.0363 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E9 val: mAP=0.836 F1=0.772 R@1=0.999 cos=0.949 cv=0.2253 anchors=205/512 sn_cos=[0.335/0.376/0.357] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E10/30: 100%|██████████| 925/925 [01:58<00:00, 7.83batch/s, disp=0.0000, loss=1.7409, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E10 train: loss=1.7407 nce=1.7259 bce=0.0357 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E10 val: mAP=0.836 F1=0.773 R@1=0.998 cos=0.949 cv=0.2089 anchors=205/512 sn_cos=[0.337/0.370/0.356]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E11/30: 100%|██████████| 925/925 [01:57<00:00, 7.84batch/s, disp=0.0000, loss=1.7404, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E11 train: loss=1.7401 nce=1.7255 bce=0.0351 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E11 val: mAP=0.837 F1=0.775 R@1=0.999 cos=0.949 cv=0.2205 anchors=202/512 sn_cos=[0.332/0.368/0.353] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E12/30: 100%|██████████| 925/925 [01:58<00:00, 7.82batch/s, disp=0.0000, loss=1.7399, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E12 train: loss=1.7398 nce=1.7254 bce=0.0345 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E12 val: mAP=0.835 F1=0.778 R@1=0.998 cos=0.949 cv=0.2374 anchors=206/512 sn_cos=[0.334/0.366/0.353]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E13/30: 100%|██████████| 925/925 [01:57<00:00, 7.87batch/s, disp=0.0000, loss=1.7389, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E13 train: loss=1.7387 nce=1.7244 bce=0.0340 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E13 val: mAP=0.837 F1=0.771 R@1=0.999 cos=0.949 cv=0.2221 anchors=204/512 sn_cos=[0.333/0.363/0.349]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E14/30: 100%|██████████| 925/925 [01:57<00:00, 7.89batch/s, disp=0.0000, loss=1.7389, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E14 train: loss=1.7393 nce=1.7252 bce=0.0334 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E14 val: mAP=0.835 F1=0.772 R@1=0.999 cos=0.949 cv=0.2307 anchors=205/512 sn_cos=[0.333/0.364/0.350]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E15/30: 100%|██████████| 925/925 [01:58<00:00, 7.79batch/s, disp=0.0000, loss=1.7384, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E15 train: loss=1.7380 nce=1.7241 bce=0.0329 disp=0.0000 cross=0.0001 nce_acc=1.000\n", " E15 val: mAP=0.835 F1=0.781 R@1=0.999 cos=0.949 cv=0.2444 anchors=204/512 sn_cos=[0.333/0.360/0.349]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E16/30: 100%|██████████| 925/925 [01:57<00:00, 7.85batch/s, disp=0.0000, loss=1.7373, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E16 train: loss=1.7381 nce=1.7243 bce=0.0325 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E16 val: mAP=0.836 F1=0.772 R@1=0.999 cos=0.949 cv=0.2343 anchors=204/512 sn_cos=[0.332/0.360/0.349]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E17/30: 100%|██████████| 925/925 [01:57<00:00, 7.87batch/s, disp=0.0000, loss=1.7366, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E17 train: loss=1.7370 nce=1.7233 bce=0.0320 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E17 val: mAP=0.838 F1=0.779 R@1=0.999 cos=0.949 cv=0.2090 anchors=201/512 sn_cos=[0.334/0.358/0.350] ★\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E18/30: 100%|██████████| 925/925 [01:57<00:00, 7.84batch/s, disp=0.0000, loss=1.7359, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E18 train: loss=1.7361 nce=1.7226 bce=0.0316 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E18 val: mAP=0.836 F1=0.778 R@1=0.998 cos=0.949 cv=0.2325 anchors=204/512 sn_cos=[0.332/0.358/0.346]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E19/30: 100%|██████████| 925/925 [01:57<00:00, 7.85batch/s, disp=0.0000, loss=1.7361, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E19 train: loss=1.7361 nce=1.7227 bce=0.0312 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E19 val: mAP=0.837 F1=0.781 R@1=0.999 cos=0.949 cv=0.2247 anchors=203/512 sn_cos=[0.335/0.355/0.344]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E20/30: 100%|██████████| 925/925 [01:58<00:00, 7.81batch/s, disp=0.0000, loss=1.7351, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E20 train: loss=1.7343 nce=1.7210 bce=0.0308 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E20 val: mAP=0.836 F1=0.780 R@1=0.998 cos=0.949 cv=0.2333 anchors=207/512 sn_cos=[0.332/0.354/0.347]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E21/30: 100%|██████████| 925/925 [01:58<00:00, 7.81batch/s, disp=0.0000, loss=1.7328, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E21 train: loss=1.7338 nce=1.7206 bce=0.0304 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E21 val: mAP=0.834 F1=0.774 R@1=0.999 cos=0.949 cv=0.2235 anchors=205/512 sn_cos=[0.335/0.352/0.347]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E22/30: 100%|██████████| 925/925 [01:59<00:00, 7.74batch/s, disp=0.0000, loss=1.7330, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E22 train: loss=1.7329 nce=1.7198 bce=0.0300 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E22 val: mAP=0.835 F1=0.783 R@1=0.999 cos=0.949 cv=0.2204 anchors=203/512 sn_cos=[0.337/0.352/0.345]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E23/30: 100%|██████████| 925/925 [01:59<00:00, 7.75batch/s, disp=0.0000, loss=1.7326, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E23 train: loss=1.7322 nce=1.7193 bce=0.0296 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E23 val: mAP=0.835 F1=0.778 R@1=0.998 cos=0.949 cv=0.2317 anchors=204/512 sn_cos=[0.335/0.352/0.348]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E24/30: 100%|██████████| 925/925 [02:01<00:00, 7.61batch/s, disp=0.0000, loss=1.7311, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E24 train: loss=1.7311 nce=1.7183 bce=0.0293 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E24 val: mAP=0.833 F1=0.777 R@1=0.998 cos=0.949 cv=0.2443 anchors=207/512 sn_cos=[0.335/0.354/0.346]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E25/30: 100%|██████████| 925/925 [02:01<00:00, 7.64batch/s, disp=0.0000, loss=1.7309, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E25 train: loss=1.7305 nce=1.7177 bce=0.0290 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E25 val: mAP=0.835 F1=0.778 R@1=0.998 cos=0.949 cv=0.2311 anchors=205/512 sn_cos=[0.336/0.350/0.346]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E26/30: 100%|██████████| 925/925 [01:58<00:00, 7.83batch/s, disp=0.0000, loss=1.7311, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E26 train: loss=1.7313 nce=1.7187 bce=0.0286 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E26 val: mAP=0.834 F1=0.780 R@1=0.999 cos=0.948 cv=0.2217 anchors=203/512 sn_cos=[0.335/0.348/0.346]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E27/30: 100%|██████████| 925/925 [01:59<00:00, 7.77batch/s, disp=0.0000, loss=1.7305, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E27 train: loss=1.7296 nce=1.7170 bce=0.0283 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E27 val: mAP=0.834 F1=0.783 R@1=0.998 cos=0.949 cv=0.2415 anchors=205/512 sn_cos=[0.334/0.350/0.346]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E28/30: 100%|██████████| 925/925 [01:58<00:00, 7.78batch/s, disp=0.0000, loss=1.7298, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E28 train: loss=1.7294 nce=1.7169 bce=0.0281 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E28 val: mAP=0.831 F1=0.777 R@1=0.999 cos=0.949 cv=0.2408 anchors=201/512 sn_cos=[0.336/0.348/0.343]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E29/30: 100%|██████████| 925/925 [01:58<00:00, 7.83batch/s, disp=0.0000, loss=1.7295, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E29 train: loss=1.7292 nce=1.7168 bce=0.0278 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E29 val: mAP=0.830 F1=0.779 R@1=0.998 cos=0.949 cv=0.2099 anchors=203/512 sn_cos=[0.336/0.344/0.347]\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "E30/30: 100%|██████████| 925/925 [01:58<00:00, 7.78batch/s, disp=0.0000, loss=1.7287, nce_acc=1.000, ordered=1]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " E30 train: loss=1.7294 nce=1.7171 bce=0.0275 disp=0.0000 cross=0.0000 nce_acc=1.000\n", " E30 val: mAP=0.833 F1=0.777 R@1=0.999 cos=0.949 cv=0.2314 anchors=204/512 sn_cos=[0.337/0.345/0.344]\n", "\n", " Best mAP: 0.838\n", " Total: 12,051,024 params\n", "\n", "=================================================================\n", "DONE\n", "=================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\n", "========================================\n", "Full diagnostic of the soup's high-dimensional geometry.\n", "Measures everything the mAP can't see.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "from tqdm import tqdm\n", "from collections import defaultdict\n", "\n", "DEVICE = \"cuda\"\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "torch.backends.cudnn.allow_tf32 = True\n", "\n", "# Which soup to analyze — set this to your checkpoint path\n", "SOUP_CKPT = \"checkpoints/dual_stream_best.pt\" # or massive_soup_best.pt\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "print(\"=\" * 70)\n", "print(\"GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 70)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD CHECKPOINT + EXTRACT COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading checkpoint...\")\n", "ckpt = torch.load(SOUP_CKPT, map_location=\"cpu\", weights_only=False)\n", "cfg = ckpt[\"config\"]\n", "sd = ckpt[\"state_dict\"]\n", "\n", "D_ANCHOR = cfg[\"d_anchor\"]\n", "N_ANCHORS = cfg[\"n_anchors\"]\n", "N_EXPERTS = cfg.get(\"n_experts\", 3)\n", "N_COMP = cfg.get(\"n_comp\", cfg.get(\"coarse_comp\", 8))\n", "\n", "print(f\" mAP={ckpt['mAP']:.3f} epoch={ckpt['epoch']}\")\n", "print(f\" D_ANCHOR={D_ANCHOR} N_ANCHORS={N_ANCHORS} N_EXPERTS={N_EXPERTS}\")\n", "\n", "# Extract anchors\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1).to(DEVICE)\n", "print(f\" Anchors: {anchors.shape}\")\n", "\n", "# Extract expert rotations/whiteners/means\n", "expert_R, expert_W, expert_mu = [], [], []\n", "for i in range(N_EXPERTS):\n", " expert_R.append(sd[f\"constellation.expert_rotations.{i}\"].to(DEVICE))\n", " expert_W.append(sd[f\"constellation.expert_whiteners.{i}\"].to(DEVICE))\n", " expert_mu.append(sd[f\"constellation.expert_means.{i}\"].to(DEVICE))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + GENERATE EMBEDDINGS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading expert features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats\n", " del ds\n", "\n", "# Generate fused embeddings through projectors\n", "print(f\" Generating embeddings...\")\n", "projector_weights = []\n", "for i in range(N_EXPERTS):\n", " prefix = f\"projectors.{i}.proj_shared\" if f\"projectors.{i}.proj_shared.0.weight\" in sd else f\"projectors.{i}.proj\"\n", " W = sd[f\"{prefix}.0.weight\"]\n", " b = sd[f\"{prefix}.0.bias\"]\n", " ln_w = sd[f\"{prefix}.1.weight\"]\n", " ln_b = sd[f\"{prefix}.1.bias\"]\n", " projector_weights.append({\"W\": W, \"b\": b, \"ln_w\": ln_w, \"ln_b\": ln_b})\n", "\n", "def project_expert(feats, pw):\n", " x = feats @ pw[\"W\"].T + pw[\"b\"]\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * pw[\"ln_w\"] + pw[\"ln_b\"]\n", " return F.normalize(x, dim=-1)\n", "\n", "with torch.no_grad():\n", " projected = []\n", " for i, name in enumerate(EXPERTS):\n", " proj = project_expert(val_raw[name], projector_weights[i])\n", " projected.append(proj)\n", "\n", " fused = F.normalize(sum(projected) / N_EXPERTS, dim=-1).to(DEVICE)\n", " projected_gpu = [p.to(DEVICE) for p in projected]\n", "\n", "print(f\" Embeddings: {fused.shape}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 1: ANCHOR GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 1: ANCHOR GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "if N_ANCHORS <= 2048:\n", " anchor_sim = anchors @ anchors.T\n", " anchor_sim_np = anchor_sim.cpu().numpy()\n", " np.fill_diagonal(anchor_sim_np, 0)\n", " print(f\" Pairwise cosine:\")\n", " print(f\" mean={anchor_sim_np.mean():.4f} std={anchor_sim_np.std():.4f}\")\n", " print(f\" max={anchor_sim_np.max():.4f} min={anchor_sim_np.min():.4f}\")\n", "\n", " max_neighbor = np.max(anchor_sim_np, axis=1)\n", " print(f\" Max neighbor cosine per anchor:\")\n", " print(f\" mean={max_neighbor.mean():.4f} std={max_neighbor.std():.4f}\")\n", " print(f\" max={max_neighbor.max():.4f} min={max_neighbor.min():.4f}\")\n", "\n", " flat = anchor_sim_np[np.triu_indices(N_ANCHORS, k=1)]\n", " for threshold in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " count = (flat > threshold).sum()\n", " print(f\" pairs with cos > {threshold:.1f}: {count} ({100*count/len(flat):.2f}%)\")\n", "else:\n", " # Subsample for large anchor sets\n", " idx_sub = torch.randperm(N_ANCHORS)[:1024]\n", " a_sub = anchors[idx_sub]\n", " sim_sub = a_sub @ a_sub.T\n", " sim_np = sim_sub.cpu().numpy()\n", " np.fill_diagonal(sim_np, 0)\n", " print(f\" Pairwise cosine (sampled 1024/{N_ANCHORS}):\")\n", " print(f\" mean={sim_np.mean():.4f} std={sim_np.std():.4f}\")\n", " print(f\" max={sim_np.max():.4f} min={sim_np.min():.4f}\")\n", "\n", "U_a, S_a, _ = torch.linalg.svd(anchors.float(), full_matrices=False)\n", "eff_rank = (S_a / S_a.sum()).pow(2).sum().reciprocal().item()\n", "print(f\"\\n Anchor spectral:\")\n", "print(f\" effective rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" sv_max={S_a[0]:.4f} sv_10={S_a[min(9,len(S_a)-1)]:.4f} \"\n", " f\"sv_50={S_a[min(49,len(S_a)-1)]:.4f} sv_min={S_a[-1]:.6f}\")\n", "cumvar = S_a.pow(2).cumsum(0) / S_a.pow(2).sum()\n", "for k in [10, 25, 50, 100, 128, 200]:\n", " if k < len(cumvar):\n", " print(f\" top-{k} SVs explain {100*cumvar[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_ANCHORS, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(anchors[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " a_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Anchor pentachoron CV: {a_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: ANCHOR UTILIZATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 2: ANCHOR UTILIZATION\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cos_to_anchors = fused @ anchors.T\n", " nearest = cos_to_anchors.argmax(dim=-1)\n", " visit_counts = torch.zeros(N_ANCHORS, device=DEVICE)\n", " for n in nearest:\n", " visit_counts[n] += 1\n", " vc = visit_counts.cpu().numpy()\n", "\n", "n_active = (vc > 0).sum()\n", "print(f\" Active anchors: {n_active}/{N_ANCHORS} ({100*n_active/N_ANCHORS:.1f}%)\")\n", "print(f\" Visit counts: mean={vc.mean():.1f} std={vc.std():.1f}\")\n", "print(f\" max={vc.max():.0f} min={vc[vc>0].min():.0f} (among active)\")\n", "print(f\" top 10: {sorted(vc, reverse=True)[:10]}\")\n", "\n", "probs = vc / vc.sum()\n", "probs_nonzero = probs[probs > 0]\n", "entropy = -(probs_nonzero * np.log(probs_nonzero)).sum()\n", "max_entropy = np.log(N_ANCHORS)\n", "print(f\" Entropy: {entropy:.4f} / {max_entropy:.4f} ({100*entropy/max_entropy:.1f}%)\")\n", "\n", "sorted_vc = np.sort(vc)\n", "n = len(sorted_vc)\n", "gini = (2 * np.sum((np.arange(1, n+1)) * sorted_vc) / (n * np.sum(sorted_vc))) - (n + 1) / n\n", "print(f\" Gini coefficient: {gini:.4f} (0=equal, 1=one anchor gets all)\")\n", "\n", "for bucket in [(1, 5), (5, 20), (20, 50), (50, 100), (100, 500), (500, 5000)]:\n", " count = ((vc >= bucket[0]) & (vc < bucket[1])).sum()\n", " print(f\" anchors with {bucket[0]}-{bucket[1]} visits: {count}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: EMBEDDING MANIFOLD GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 3: EMBEDDING MANIFOLD GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "emb_cpu = fused.cpu().float()\n", "emb_centered = emb_cpu - emb_cpu.mean(0, keepdim=True)\n", "U_e, S_e, Vt_e = torch.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "\n", "eff_dim = (S_e / S_e.sum()).pow(2).sum().reciprocal().item()\n", "print(f\" Effective dimensionality: {eff_dim:.1f}/{D_ANCHOR}\")\n", "\n", "cumvar_e = S_e.pow(2).cumsum(0) / S_e.pow(2).sum()\n", "for k in [5, 10, 20, 50, 100, 128, 200]:\n", " if k < len(cumvar_e):\n", " print(f\" top-{k} SVs explain {100*cumvar_e[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " sample = fused[:2000]\n", " selfsim = sample @ sample.T\n", " mask = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag = selfsim[mask]\n", " print(f\"\\n Self-similarity (off-diagonal):\")\n", " print(f\" mean={offdiag.mean():.4f} std={offdiag.std():.4f}\")\n", " print(f\" max={offdiag.max():.4f} min={offdiag.min():.4f}\")\n", "\n", "norms = fused.norm(dim=-1)\n", "print(f\"\\n Norms: mean={norms.mean():.6f} std={norms.std():.6f}\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_val, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(fused[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " global_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Global pentachoron CV: {global_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " expert_rotated = []\n", " for i in range(N_EXPERTS):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_rotated.append(rotated)\n", "\n", " expert_tri = []\n", " for rotated in expert_rotated:\n", " cos = rotated @ anchors.T\n", " expert_tri.append(1.0 - cos)\n", "\n", " print(f\"\\n Per-image expert agreement:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(expert_rotated[i], expert_rotated[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"mean={cos_ij.mean():.4f} std={cos_ij.std():.4f} \"\n", " f\"min={cos_ij.min():.4f}\")\n", "\n", " print(f\"\\n Per-anchor expert divergence:\")\n", " tri_stack = torch.stack(expert_tri, dim=-1)\n", " per_anchor_std = tri_stack.std(dim=-1).mean(dim=0)\n", " pas = per_anchor_std.cpu().numpy()\n", " print(f\" mean divergence: {pas.mean():.4f} std: {pas.std():.4f}\")\n", " print(f\" max divergence: {pas.max():.4f} (anchor {pas.argmax()})\")\n", " print(f\" min divergence: {pas.min():.4f} (anchor {pas.argmin()})\")\n", "\n", " top_div = np.argsort(pas)[::-1][:10]\n", " print(f\"\\n Top 10 most contentious anchors:\")\n", " for rank, aidx in enumerate(top_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " bot_div = np.argsort(pas)[:10]\n", " print(f\"\\n Top 10 most unanimous anchors:\")\n", " for rank, aidx in enumerate(bot_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " print(f\"\\n Expert rotation eigenspectra:\")\n", " for i in range(N_EXPERTS):\n", " R = expert_R[i]\n", " RRT = R @ R.T\n", " identity_err = (RRT - torch.eye(D_ANCHOR, device=DEVICE)).pow(2).mean().item()\n", " evals = torch.linalg.eigvalsh(RRT)\n", " print(f\" {EXPERTS[i][:20]:<20}: ortho_err={identity_err:.6f} \"\n", " f\"eval_min={evals.min():.4f} eval_max={evals.max():.4f}\")\n", "\n", " print(f\"\\n Expert whitener condition:\")\n", " for i in range(N_EXPERTS):\n", " W = expert_W[i]\n", " s = torch.linalg.svdvals(W)\n", " cond = s.max() / s.min()\n", " print(f\" {EXPERTS[i][:20]:<20}: cond={cond:.2f} \"\n", " f\"sv_max={s.max():.4f} sv_min={s.min():.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: NEAREST ANCHOR DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 5: NEAREST ANCHOR DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " sorted_cos, sorted_idx = cos_to_anchors.sort(dim=-1, descending=True)\n", "\n", " for k in [0, 1, 2, 4, 9, 19, 49, 99]:\n", " if k < N_ANCHORS:\n", " dist = (1 - sorted_cos[:, k])\n", " print(f\" k={k:3d}: mean_dist={dist.mean():.4f} std={dist.std():.4f} \"\n", " f\"max={dist.max():.4f} min={dist.min():.4f}\")\n", "\n", " for thresh in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " within = (cos_to_anchors > thresh).sum(dim=-1).float()\n", " print(f\" anchors with cos > {thresh:.1f}: mean={within.mean():.1f} \"\n", " f\"max={within.max():.0f} min={within.min():.0f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 6: PER-CLASS ANCHOR AFFINITY\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " nearest_cpu = nearest.cpu()\n", " labels_np = val_labels.numpy()\n", "\n", " class_anchor_counts = np.zeros((80, N_ANCHORS))\n", " for img_idx in range(N_val):\n", " anchor_id = nearest_cpu[img_idx].item()\n", " for c in range(80):\n", " if labels_np[img_idx, c] > 0:\n", " class_anchor_counts[c, anchor_id] += 1\n", "\n", " anchor_class_count = (class_anchor_counts > 0).sum(axis=0)\n", " active_anchor_classes = anchor_class_count[anchor_class_count > 0]\n", " print(f\" Anchor specialization:\")\n", " print(f\" classes per active anchor: mean={active_anchor_classes.mean():.1f} \"\n", " f\"std={active_anchor_classes.std():.1f}\")\n", " print(f\" max={active_anchor_classes.max()} min={active_anchor_classes.min()}\")\n", "\n", " class_anchor_spread = (class_anchor_counts > 0).sum(axis=1)\n", " print(f\"\\n Class spread (anchors per class):\")\n", " print(f\" mean={class_anchor_spread.mean():.1f} std={class_anchor_spread.std():.1f}\")\n", " print(f\" max={class_anchor_spread.max()} ({COCO_CLASSES[class_anchor_spread.argmax()]})\")\n", " print(f\" min={class_anchor_spread.min()} ({COCO_CLASSES[class_anchor_spread.argmin()]})\")\n", "\n", " sorted_classes = np.argsort(class_anchor_spread)[::-1]\n", " print(f\"\\n Top 10 by anchor spread:\")\n", " for c in sorted_classes[:10]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", " print(f\" Bottom 10 by anchor spread:\")\n", " for c in sorted_classes[-10:]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " class_centroids = torch.zeros(80, D_ANCHOR, device=DEVICE)\n", " class_counts = torch.zeros(80, device=DEVICE)\n", " for c in range(80):\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 0:\n", " class_centroids[c] = fused[mask.to(DEVICE)].mean(dim=0)\n", " class_counts[c] = mask.sum()\n", " class_centroids = F.normalize(class_centroids, dim=-1)\n", "\n", " valid_classes = class_counts > 10\n", " vc_idx = valid_classes.nonzero(as_tuple=True)[0]\n", " n_vc = len(vc_idx)\n", " class_sim = class_centroids[vc_idx] @ class_centroids[vc_idx].T\n", " cs_np = class_sim.cpu().numpy()\n", " np.fill_diagonal(cs_np, 0)\n", "\n", " print(f\" Inter-class cosine ({n_vc} classes with >10 images):\")\n", " print(f\" mean={cs_np[np.triu_indices(n_vc, k=1)].mean():.4f}\")\n", " print(f\" max={cs_np.max():.4f} min={cs_np[np.triu_indices(n_vc, k=1)].min():.4f}\")\n", "\n", " upper = np.triu_indices(n_vc, k=1)\n", " pair_sims = cs_np[upper]\n", " top_pairs = np.argsort(pair_sims)[::-1][:15]\n", " print(f\"\\n Most similar class pairs:\")\n", " for rank, pidx in enumerate(top_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " bot_pairs = np.argsort(pair_sims)[:15]\n", " print(f\"\\n Most distant class pairs:\")\n", " for rank, pidx in enumerate(bot_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " print(f\"\\n Intra-class spread:\")\n", " class_spreads = []\n", " for c in vc_idx:\n", " c = c.item()\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 10:\n", " cls_embs = fused[mask.to(DEVICE)]\n", " centroid = cls_embs.mean(dim=0, keepdim=True)\n", " cos_to_cent = F.cosine_similarity(cls_embs, centroid, dim=-1)\n", " spread = 1 - cos_to_cent.mean().item()\n", " class_spreads.append((c, spread, mask.sum().item()))\n", " class_spreads.sort(key=lambda x: x[1])\n", " print(f\" Tightest 10:\")\n", " for c, spread, n in class_spreads[:10]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", " print(f\" Loosest 10:\")\n", " for c, spread, n in class_spreads[-10:]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 8: LOCAL PENTACHORON CV\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 8: LOCAL PENTACHORON CV\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cluster_cvs = []\n", " for a_idx in tqdm(range(N_ANCHORS), desc=\" Local CV\", leave=False):\n", " mask = nearest == a_idx\n", " if mask.sum() >= 10:\n", " cluster_embs = fused[mask]\n", " vols = []\n", " for _ in range(100):\n", " if cluster_embs.shape[0] < 5: break\n", " idx = torch.randperm(cluster_embs.shape[0], device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(cluster_embs[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) > 5:\n", " cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " cluster_cvs.append((a_idx, cv, mask.sum().item(), np.mean(vols)))\n", "\n", " if cluster_cvs:\n", " cvs = [c[1] for c in cluster_cvs]\n", " print(f\" Clusters with 10+ members: {len(cluster_cvs)}\")\n", " print(f\" Local CV: mean={np.mean(cvs):.4f} std={np.std(cvs):.4f}\")\n", " print(f\" max={np.max(cvs):.4f} min={np.min(cvs):.4f}\")\n", " print(f\" Global CV: {global_cv:.4f}\")\n", " print(f\" Ratio (local/global): {np.mean(cvs)/global_cv:.4f}\")\n", "\n", " cluster_cvs.sort(key=lambda x: x[1], reverse=True)\n", " print(f\"\\n Highest local CV (most diverse clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[:10]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", " print(f\" Lowest local CV (most uniform clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[-10:]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 9: PROJECTOR ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 9: PROJECTOR ANALYSIS\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " proj_emb = projected_gpu[i]\n", " ss = proj_emb[:2000] @ proj_emb[:2000].T\n", " mask_ss = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag_ss = ss[mask_ss]\n", "\n", " pc = proj_emb[:5000] - proj_emb[:5000].mean(0, keepdim=True)\n", " _, s_p, _ = torch.linalg.svd(pc, full_matrices=False)\n", " ed = (s_p / s_p.sum()).pow(2).sum().reciprocal().item()\n", "\n", " cos_fused = F.cosine_similarity(proj_emb, fused, dim=-1)\n", "\n", " print(f\"\\n {EXPERTS[i]}:\")\n", " print(f\" self-sim: mean={offdiag_ss.mean():.4f} std={offdiag_ss.std():.4f}\")\n", " print(f\" eff_dim: {ed:.1f}/{D_ANCHOR}\")\n", " print(f\" cos→fused: mean={cos_fused.mean():.4f} std={cos_fused.std():.4f}\")\n", "\n", " print(f\"\\n Cross-expert agreement (projected):\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(projected_gpu[i], projected_gpu[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"cos={cos_ij.mean():.4f} std={cos_ij.std():.4f}\")\n", "\n", " print(f\"\\n Expert uniqueness (leave-one-out):\")\n", " for i in range(N_EXPERTS):\n", " others = [projected_gpu[j] for j in range(N_EXPERTS) if j != i]\n", " fused_without = F.normalize(sum(others) / len(others), dim=-1)\n", " cos_full = F.cosine_similarity(fused_without, fused, dim=-1).mean().item()\n", " print(f\" Without {EXPERTS[i][:20]:<20}: cos_to_full={cos_full:.4f} \"\n", " f\"(uniqueness={1-cos_full:.4f})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 10: TRIANGULATION STRUCTURE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 10: TRIANGULATION STRUCTURE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " tri = expert_tri[i]\n", " print(f\"\\n {EXPERTS[i]} triangulation:\")\n", " print(f\" mean={tri.mean():.4f} std={tri.std():.4f}\")\n", " print(f\" min={tri.min():.4f} max={tri.max():.4f}\")\n", " nearest_dist = tri.min(dim=-1).values\n", " print(f\" nearest: mean={nearest_dist.mean():.4f} std={nearest_dist.std():.4f}\")\n", "\n", " print(f\"\\n Expert triangulation correlation:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " per_img = F.cosine_similarity(expert_tri[i][:1000], expert_tri[j][:1000], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"per_img_cos mean={per_img.mean():.4f} std={per_img.std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SUMMARY\")\n", "print(f\"{'='*70}\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" mAP: {ckpt['mAP']:.3f}\")\n", "print(f\" Anchors: {N_ANCHORS} × {D_ANCHOR}-d, {n_active} active ({100*n_active/N_ANCHORS:.0f}%)\")\n", "print(f\" Embedding eff_dim: {eff_dim:.1f}/{D_ANCHOR}\")\n", "print(f\" Anchor eff_rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" Global CV: {global_cv:.4f}\")\n", "print(f\" Anchor CV: {a_cv:.4f}\")\n", "if cluster_cvs:\n", " print(f\" Local CV (mean): {np.mean(cvs):.4f}\")\n", "print(f\" Utilization entropy: {100*entropy/max_entropy:.1f}%\")\n", "print(f\" Utilization Gini: {gini:.4f}\")\n", "print(f\"\\n{'='*70}\")\n", "print(\"ANALYSIS COMPLETE\")\n", "print(f\"{'='*70}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 384 }, "id": "SlDXDhytfTD8", "outputId": "5ce9e146-2254-4e82-e5b5-33064203d8fe" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", "GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\n", " Checkpoint: checkpoints/dual_stream_best.pt\n", " Device: cuda\n", "======================================================================\n", "\n", " Loading checkpoint...\n", " mAP=0.838 epoch=17\n", " D_ANCHOR=256 N_ANCHORS=512 N_EXPERTS=3\n", " Anchors: torch.Size([512, 256])\n" ] }, { "output_type": "error", "ename": "KeyError", "evalue": "'constellation.expert_rotations.0'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_255833/210003933.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mexpert_R\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpert_W\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpert_mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_EXPERTS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mexpert_R\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_rotations.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0mexpert_W\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_whiteners.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mexpert_mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"constellation.expert_means.{i}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'constellation.expert_rotations.0'" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\n", "========================================\n", "Full diagnostic of the soup's high-dimensional geometry.\n", "Measures everything the mAP can't see.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import math\n", "import os\n", "import gc\n", "from tqdm import tqdm\n", "from collections import defaultdict\n", "\n", "DEVICE = \"cuda\"\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "torch.backends.cudnn.allow_tf32 = True\n", "\n", "# Which soup to analyze — set this to your checkpoint path\n", "SOUP_CKPT = \"checkpoints/dual_stream_best.pt\" # or massive_soup_best.pt\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V+1, V+1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " s = (-1.0)**V; f = math.factorial(V-1)\n", " return s / ((2.0**(V-1)) * f*f) * torch.linalg.det(cm)\n", "\n", "def symmetric_inv_sqrt(cov, eps=1e-6):\n", " evals, evecs = torch.linalg.eigh(cov)\n", " return evecs @ torch.diag(torch.clamp(evals, min=eps).rsqrt()) @ evecs.T\n", "\n", "print(\"=\" * 70)\n", "print(\"GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" Device: {DEVICE}\")\n", "print(\"=\" * 70)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD CHECKPOINT + EXTRACT COMPONENTS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading checkpoint...\")\n", "ckpt = torch.load(SOUP_CKPT, map_location=\"cpu\", weights_only=False)\n", "cfg = ckpt[\"config\"]\n", "sd = ckpt[\"state_dict\"]\n", "\n", "D_ANCHOR = cfg[\"d_anchor\"]\n", "N_ANCHORS = cfg[\"n_anchors\"]\n", "N_EXPERTS = cfg.get(\"n_experts\", 3)\n", "N_COMP = cfg.get(\"n_comp\", cfg.get(\"coarse_comp\", 8))\n", "\n", "print(f\" mAP={ckpt['mAP']:.3f} epoch={ckpt['epoch']}\")\n", "print(f\" D_ANCHOR={D_ANCHOR} N_ANCHORS={N_ANCHORS} N_EXPERTS={N_EXPERTS}\")\n", "\n", "# Extract anchors\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1).to(DEVICE)\n", "print(f\" Anchors: {anchors.shape}\")\n", "\n", "# Extract expert rotations/whiteners/means (fused constellation only)\n", "has_expert_rotations = f\"constellation.expert_rotations.0\" in sd\n", "expert_R, expert_W, expert_mu = [], [], []\n", "if has_expert_rotations:\n", " for i in range(N_EXPERTS):\n", " expert_R.append(sd[f\"constellation.expert_rotations.{i}\"].to(DEVICE))\n", " expert_W.append(sd[f\"constellation.expert_whiteners.{i}\"].to(DEVICE))\n", " expert_mu.append(sd[f\"constellation.expert_means.{i}\"].to(DEVICE))\n", " print(f\" Expert rotations: loaded from constellation\")\n", "else:\n", " # Dual-stream: no rotations in constellation. Use identity as stand-in.\n", " # Expert perspectives come from proj_shared vs proj_native instead.\n", " for i in range(N_EXPERTS):\n", " expert_R.append(torch.eye(D_ANCHOR, device=DEVICE))\n", " expert_W.append(torch.eye(D_ANCHOR, device=DEVICE))\n", " expert_mu.append(torch.zeros(D_ANCHOR, device=DEVICE))\n", " print(f\" Expert rotations: N/A (dual-stream — perspectives in projectors)\")\n", "\n", "# Check for dual-stream native projectors\n", "has_native = f\"projectors.0.proj_native.0.weight\" in sd\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD DATA + GENERATE EMBEDDINGS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n Loading expert features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats\n", " del ds\n", "\n", "# Generate fused embeddings through projectors\n", "print(f\" Generating embeddings...\")\n", "projector_weights = []\n", "for i in range(N_EXPERTS):\n", " prefix = f\"projectors.{i}.proj_shared\" if f\"projectors.{i}.proj_shared.0.weight\" in sd else f\"projectors.{i}.proj\"\n", " W = sd[f\"{prefix}.0.weight\"]\n", " b = sd[f\"{prefix}.0.bias\"]\n", " ln_w = sd[f\"{prefix}.1.weight\"]\n", " ln_b = sd[f\"{prefix}.1.bias\"]\n", " projector_weights.append({\"W\": W, \"b\": b, \"ln_w\": ln_w, \"ln_b\": ln_b})\n", "\n", "def project_expert(feats, pw):\n", " x = feats @ pw[\"W\"].T + pw[\"b\"]\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * pw[\"ln_w\"] + pw[\"ln_b\"]\n", " return F.normalize(x, dim=-1)\n", "\n", "with torch.no_grad():\n", " projected = []\n", " for i, name in enumerate(EXPERTS):\n", " proj = project_expert(val_raw[name], projector_weights[i])\n", " projected.append(proj)\n", "\n", " fused = F.normalize(sum(projected) / N_EXPERTS, dim=-1).to(DEVICE)\n", " projected_gpu = [p.to(DEVICE) for p in projected]\n", "\n", "print(f\" Embeddings: {fused.shape}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 1: ANCHOR GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 1: ANCHOR GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "if N_ANCHORS <= 2048:\n", " anchor_sim = anchors @ anchors.T\n", " anchor_sim_np = anchor_sim.cpu().numpy()\n", " np.fill_diagonal(anchor_sim_np, 0)\n", " print(f\" Pairwise cosine:\")\n", " print(f\" mean={anchor_sim_np.mean():.4f} std={anchor_sim_np.std():.4f}\")\n", " print(f\" max={anchor_sim_np.max():.4f} min={anchor_sim_np.min():.4f}\")\n", "\n", " max_neighbor = np.max(anchor_sim_np, axis=1)\n", " print(f\" Max neighbor cosine per anchor:\")\n", " print(f\" mean={max_neighbor.mean():.4f} std={max_neighbor.std():.4f}\")\n", " print(f\" max={max_neighbor.max():.4f} min={max_neighbor.min():.4f}\")\n", "\n", " flat = anchor_sim_np[np.triu_indices(N_ANCHORS, k=1)]\n", " for threshold in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " count = (flat > threshold).sum()\n", " print(f\" pairs with cos > {threshold:.1f}: {count} ({100*count/len(flat):.2f}%)\")\n", "else:\n", " # Subsample for large anchor sets\n", " idx_sub = torch.randperm(N_ANCHORS)[:1024]\n", " a_sub = anchors[idx_sub]\n", " sim_sub = a_sub @ a_sub.T\n", " sim_np = sim_sub.cpu().numpy()\n", " np.fill_diagonal(sim_np, 0)\n", " print(f\" Pairwise cosine (sampled 1024/{N_ANCHORS}):\")\n", " print(f\" mean={sim_np.mean():.4f} std={sim_np.std():.4f}\")\n", " print(f\" max={sim_np.max():.4f} min={sim_np.min():.4f}\")\n", "\n", "U_a, S_a, _ = torch.linalg.svd(anchors.float(), full_matrices=False)\n", "eff_rank = (S_a / S_a.sum()).pow(2).sum().reciprocal().item()\n", "print(f\"\\n Anchor spectral:\")\n", "print(f\" effective rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" sv_max={S_a[0]:.4f} sv_10={S_a[min(9,len(S_a)-1)]:.4f} \"\n", " f\"sv_50={S_a[min(49,len(S_a)-1)]:.4f} sv_min={S_a[-1]:.6f}\")\n", "cumvar = S_a.pow(2).cumsum(0) / S_a.pow(2).sum()\n", "for k in [10, 25, 50, 100, 128, 200]:\n", " if k < len(cumvar):\n", " print(f\" top-{k} SVs explain {100*cumvar[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_ANCHORS, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(anchors[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " a_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Anchor pentachoron CV: {a_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 2: ANCHOR UTILIZATION\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 2: ANCHOR UTILIZATION\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cos_to_anchors = fused @ anchors.T\n", " nearest = cos_to_anchors.argmax(dim=-1)\n", " visit_counts = torch.zeros(N_ANCHORS, device=DEVICE)\n", " for n in nearest:\n", " visit_counts[n] += 1\n", " vc = visit_counts.cpu().numpy()\n", "\n", "n_active = (vc > 0).sum()\n", "print(f\" Active anchors: {n_active}/{N_ANCHORS} ({100*n_active/N_ANCHORS:.1f}%)\")\n", "print(f\" Visit counts: mean={vc.mean():.1f} std={vc.std():.1f}\")\n", "print(f\" max={vc.max():.0f} min={vc[vc>0].min():.0f} (among active)\")\n", "print(f\" top 10: {sorted(vc, reverse=True)[:10]}\")\n", "\n", "probs = vc / vc.sum()\n", "probs_nonzero = probs[probs > 0]\n", "entropy = -(probs_nonzero * np.log(probs_nonzero)).sum()\n", "max_entropy = np.log(N_ANCHORS)\n", "print(f\" Entropy: {entropy:.4f} / {max_entropy:.4f} ({100*entropy/max_entropy:.1f}%)\")\n", "\n", "sorted_vc = np.sort(vc)\n", "n = len(sorted_vc)\n", "gini = (2 * np.sum((np.arange(1, n+1)) * sorted_vc) / (n * np.sum(sorted_vc))) - (n + 1) / n\n", "print(f\" Gini coefficient: {gini:.4f} (0=equal, 1=one anchor gets all)\")\n", "\n", "for bucket in [(1, 5), (5, 20), (20, 50), (50, 100), (100, 500), (500, 5000)]:\n", " count = ((vc >= bucket[0]) & (vc < bucket[1])).sum()\n", " print(f\" anchors with {bucket[0]}-{bucket[1]} visits: {count}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 3: EMBEDDING MANIFOLD GEOMETRY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 3: EMBEDDING MANIFOLD GEOMETRY\")\n", "print(f\"{'='*70}\")\n", "\n", "emb_cpu = fused.cpu().float()\n", "emb_centered = emb_cpu - emb_cpu.mean(0, keepdim=True)\n", "U_e, S_e, Vt_e = torch.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "\n", "eff_dim = (S_e / S_e.sum()).pow(2).sum().reciprocal().item()\n", "print(f\" Effective dimensionality: {eff_dim:.1f}/{D_ANCHOR}\")\n", "\n", "cumvar_e = S_e.pow(2).cumsum(0) / S_e.pow(2).sum()\n", "for k in [5, 10, 20, 50, 100, 128, 200]:\n", " if k < len(cumvar_e):\n", " print(f\" top-{k} SVs explain {100*cumvar_e[k-1]:.1f}%\")\n", "\n", "with torch.no_grad():\n", " sample = fused[:2000]\n", " selfsim = sample @ sample.T\n", " mask = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag = selfsim[mask]\n", " print(f\"\\n Self-similarity (off-diagonal):\")\n", " print(f\" mean={offdiag.mean():.4f} std={offdiag.std():.4f}\")\n", " print(f\" max={offdiag.max():.4f} min={offdiag.min():.4f}\")\n", "\n", "norms = fused.norm(dim=-1)\n", "print(f\"\\n Norms: mean={norms.mean():.6f} std={norms.std():.6f}\")\n", "\n", "with torch.no_grad():\n", " vols = []\n", " for _ in range(500):\n", " idx = torch.randperm(N_val, device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(fused[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " global_cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " print(f\"\\n Global pentachoron CV: {global_cv:.4f}\")\n", " print(f\" mean_vol={np.mean(vols):.6f} std_vol={np.std(vols):.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " expert_rotated = []\n", " for i in range(N_EXPERTS):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_rotated.append(rotated)\n", "\n", " expert_tri = []\n", " for rotated in expert_rotated:\n", " cos = rotated @ anchors.T\n", " expert_tri.append(1.0 - cos)\n", "\n", " print(f\"\\n Per-image expert agreement:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(expert_rotated[i], expert_rotated[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"mean={cos_ij.mean():.4f} std={cos_ij.std():.4f} \"\n", " f\"min={cos_ij.min():.4f}\")\n", "\n", " print(f\"\\n Per-anchor expert divergence:\")\n", " tri_stack = torch.stack(expert_tri, dim=-1)\n", " per_anchor_std = tri_stack.std(dim=-1).mean(dim=0)\n", " pas = per_anchor_std.cpu().numpy()\n", " print(f\" mean divergence: {pas.mean():.4f} std: {pas.std():.4f}\")\n", " print(f\" max divergence: {pas.max():.4f} (anchor {pas.argmax()})\")\n", " print(f\" min divergence: {pas.min():.4f} (anchor {pas.argmin()})\")\n", "\n", " top_div = np.argsort(pas)[::-1][:10]\n", " print(f\"\\n Top 10 most contentious anchors:\")\n", " for rank, aidx in enumerate(top_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " bot_div = np.argsort(pas)[:10]\n", " print(f\"\\n Top 10 most unanimous anchors:\")\n", " for rank, aidx in enumerate(bot_div):\n", " print(f\" #{rank+1} anchor {aidx}: div={pas[aidx]:.4f} visits={int(vc[aidx])}\")\n", "\n", " print(f\"\\n Expert rotation eigenspectra:\")\n", " for i in range(N_EXPERTS):\n", " R = expert_R[i]\n", " RRT = R @ R.T\n", " identity_err = (RRT - torch.eye(D_ANCHOR, device=DEVICE)).pow(2).mean().item()\n", " evals = torch.linalg.eigvalsh(RRT)\n", " print(f\" {EXPERTS[i][:20]:<20}: ortho_err={identity_err:.6f} \"\n", " f\"eval_min={evals.min():.4f} eval_max={evals.max():.4f}\")\n", "\n", " print(f\"\\n Expert whitener condition:\")\n", " for i in range(N_EXPERTS):\n", " W = expert_W[i]\n", " s = torch.linalg.svdvals(W)\n", " cond = s.max() / s.min()\n", " print(f\" {EXPERTS[i][:20]:<20}: cond={cond:.2f} \"\n", " f\"sv_max={s.max():.4f} sv_min={s.min():.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 5: NEAREST ANCHOR DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 5: NEAREST ANCHOR DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " sorted_cos, sorted_idx = cos_to_anchors.sort(dim=-1, descending=True)\n", "\n", " for k in [0, 1, 2, 4, 9, 19, 49, 99]:\n", " if k < N_ANCHORS:\n", " dist = (1 - sorted_cos[:, k])\n", " print(f\" k={k:3d}: mean_dist={dist.mean():.4f} std={dist.std():.4f} \"\n", " f\"max={dist.max():.4f} min={dist.min():.4f}\")\n", "\n", " for thresh in [0.9, 0.8, 0.7, 0.5, 0.3, 0.0]:\n", " within = (cos_to_anchors > thresh).sum(dim=-1).float()\n", " print(f\" anchors with cos > {thresh:.1f}: mean={within.mean():.1f} \"\n", " f\"max={within.max():.0f} min={within.min():.0f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 6: PER-CLASS ANCHOR AFFINITY\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " nearest_cpu = nearest.cpu()\n", " labels_np = val_labels.numpy()\n", "\n", " class_anchor_counts = np.zeros((80, N_ANCHORS))\n", " for img_idx in range(N_val):\n", " anchor_id = nearest_cpu[img_idx].item()\n", " for c in range(80):\n", " if labels_np[img_idx, c] > 0:\n", " class_anchor_counts[c, anchor_id] += 1\n", "\n", " anchor_class_count = (class_anchor_counts > 0).sum(axis=0)\n", " active_anchor_classes = anchor_class_count[anchor_class_count > 0]\n", " print(f\" Anchor specialization:\")\n", " print(f\" classes per active anchor: mean={active_anchor_classes.mean():.1f} \"\n", " f\"std={active_anchor_classes.std():.1f}\")\n", " print(f\" max={active_anchor_classes.max()} min={active_anchor_classes.min()}\")\n", "\n", " class_anchor_spread = (class_anchor_counts > 0).sum(axis=1)\n", " print(f\"\\n Class spread (anchors per class):\")\n", " print(f\" mean={class_anchor_spread.mean():.1f} std={class_anchor_spread.std():.1f}\")\n", " print(f\" max={class_anchor_spread.max()} ({COCO_CLASSES[class_anchor_spread.argmax()]})\")\n", " print(f\" min={class_anchor_spread.min()} ({COCO_CLASSES[class_anchor_spread.argmin()]})\")\n", "\n", " sorted_classes = np.argsort(class_anchor_spread)[::-1]\n", " print(f\"\\n Top 10 by anchor spread:\")\n", " for c in sorted_classes[:10]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", " print(f\" Bottom 10 by anchor spread:\")\n", " for c in sorted_classes[-10:]:\n", " n_imgs = int(labels_np[:, c].sum())\n", " print(f\" {COCO_CLASSES[c]:<20}: {class_anchor_spread[c]} anchors, {n_imgs} images\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " class_centroids = torch.zeros(80, D_ANCHOR, device=DEVICE)\n", " class_counts = torch.zeros(80, device=DEVICE)\n", " for c in range(80):\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 0:\n", " class_centroids[c] = fused[mask.to(DEVICE)].mean(dim=0)\n", " class_counts[c] = mask.sum()\n", " class_centroids = F.normalize(class_centroids, dim=-1)\n", "\n", " valid_classes = class_counts > 10\n", " vc_idx = valid_classes.nonzero(as_tuple=True)[0]\n", " n_vc = len(vc_idx)\n", " class_sim = class_centroids[vc_idx] @ class_centroids[vc_idx].T\n", " cs_np = class_sim.cpu().numpy()\n", " np.fill_diagonal(cs_np, 0)\n", "\n", " print(f\" Inter-class cosine ({n_vc} classes with >10 images):\")\n", " print(f\" mean={cs_np[np.triu_indices(n_vc, k=1)].mean():.4f}\")\n", " print(f\" max={cs_np.max():.4f} min={cs_np[np.triu_indices(n_vc, k=1)].min():.4f}\")\n", "\n", " upper = np.triu_indices(n_vc, k=1)\n", " pair_sims = cs_np[upper]\n", " top_pairs = np.argsort(pair_sims)[::-1][:15]\n", " print(f\"\\n Most similar class pairs:\")\n", " for rank, pidx in enumerate(top_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " bot_pairs = np.argsort(pair_sims)[:15]\n", " print(f\"\\n Most distant class pairs:\")\n", " for rank, pidx in enumerate(bot_pairs):\n", " ci, cj = upper[0][pidx], upper[1][pidx]\n", " ci_real, cj_real = vc_idx[ci].item(), vc_idx[cj].item()\n", " print(f\" #{rank+1}: {COCO_CLASSES[ci_real]:<20} × {COCO_CLASSES[cj_real]:<20} \"\n", " f\"cos={pair_sims[pidx]:.4f}\")\n", "\n", " print(f\"\\n Intra-class spread:\")\n", " class_spreads = []\n", " for c in vc_idx:\n", " c = c.item()\n", " mask = val_labels[:, c] > 0\n", " if mask.sum() > 10:\n", " cls_embs = fused[mask.to(DEVICE)]\n", " centroid = cls_embs.mean(dim=0, keepdim=True)\n", " cos_to_cent = F.cosine_similarity(cls_embs, centroid, dim=-1)\n", " spread = 1 - cos_to_cent.mean().item()\n", " class_spreads.append((c, spread, mask.sum().item()))\n", " class_spreads.sort(key=lambda x: x[1])\n", " print(f\" Tightest 10:\")\n", " for c, spread, n in class_spreads[:10]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", " print(f\" Loosest 10:\")\n", " for c, spread, n in class_spreads[-10:]:\n", " print(f\" {COCO_CLASSES[c]:<20}: spread={spread:.4f} (n={n})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 8: LOCAL PENTACHORON CV\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 8: LOCAL PENTACHORON CV\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " cluster_cvs = []\n", " for a_idx in tqdm(range(N_ANCHORS), desc=\" Local CV\", leave=False):\n", " mask = nearest == a_idx\n", " if mask.sum() >= 10:\n", " cluster_embs = fused[mask]\n", " vols = []\n", " for _ in range(100):\n", " if cluster_embs.shape[0] < 5: break\n", " idx = torch.randperm(cluster_embs.shape[0], device=DEVICE)[:5]\n", " v2 = cayley_menger_vol2(cluster_embs[idx].unsqueeze(0))\n", " v = torch.sqrt(F.relu(v2[0]) + 1e-12).item()\n", " if v > 0: vols.append(v)\n", " if len(vols) > 5:\n", " cv = np.std(vols) / (np.mean(vols) + 1e-8)\n", " cluster_cvs.append((a_idx, cv, mask.sum().item(), np.mean(vols)))\n", "\n", " if cluster_cvs:\n", " cvs = [c[1] for c in cluster_cvs]\n", " print(f\" Clusters with 10+ members: {len(cluster_cvs)}\")\n", " print(f\" Local CV: mean={np.mean(cvs):.4f} std={np.std(cvs):.4f}\")\n", " print(f\" max={np.max(cvs):.4f} min={np.min(cvs):.4f}\")\n", " print(f\" Global CV: {global_cv:.4f}\")\n", " print(f\" Ratio (local/global): {np.mean(cvs)/global_cv:.4f}\")\n", "\n", " cluster_cvs.sort(key=lambda x: x[1], reverse=True)\n", " print(f\"\\n Highest local CV (most diverse clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[:10]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", " print(f\" Lowest local CV (most uniform clusters):\")\n", " for a_idx, cv, n, mvol in cluster_cvs[-10:]:\n", " print(f\" anchor {a_idx:4d}: CV={cv:.4f} n={n:4d} mean_vol={mvol:.6f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 9: PROJECTOR ANALYSIS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 9: PROJECTOR ANALYSIS\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " proj_emb = projected_gpu[i]\n", " ss = proj_emb[:2000] @ proj_emb[:2000].T\n", " mask_ss = ~torch.eye(2000, dtype=torch.bool, device=DEVICE)\n", " offdiag_ss = ss[mask_ss]\n", "\n", " pc = proj_emb[:5000] - proj_emb[:5000].mean(0, keepdim=True)\n", " _, s_p, _ = torch.linalg.svd(pc, full_matrices=False)\n", " ed = (s_p / s_p.sum()).pow(2).sum().reciprocal().item()\n", "\n", " cos_fused = F.cosine_similarity(proj_emb, fused, dim=-1)\n", "\n", " print(f\"\\n {EXPERTS[i]}:\")\n", " print(f\" self-sim: mean={offdiag_ss.mean():.4f} std={offdiag_ss.std():.4f}\")\n", " print(f\" eff_dim: {ed:.1f}/{D_ANCHOR}\")\n", " print(f\" cos→fused: mean={cos_fused.mean():.4f} std={cos_fused.std():.4f}\")\n", "\n", " print(f\"\\n Cross-expert agreement (projected):\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_ij = F.cosine_similarity(projected_gpu[i], projected_gpu[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"cos={cos_ij.mean():.4f} std={cos_ij.std():.4f}\")\n", "\n", " print(f\"\\n Expert uniqueness (leave-one-out):\")\n", " for i in range(N_EXPERTS):\n", " others = [projected_gpu[j] for j in range(N_EXPERTS) if j != i]\n", " fused_without = F.normalize(sum(others) / len(others), dim=-1)\n", " cos_full = F.cosine_similarity(fused_without, fused, dim=-1).mean().item()\n", " print(f\" Without {EXPERTS[i][:20]:<20}: cos_to_full={cos_full:.4f} \"\n", " f\"(uniqueness={1-cos_full:.4f})\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 9.5: DUAL-STREAM ANALYSIS (if applicable)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "if has_native:\n", " print(f\"\\n{'='*70}\")\n", " print(\"SCAN 9.5: DUAL-STREAM ANALYSIS\")\n", " print(f\"{'='*70}\")\n", "\n", " def project_native(feats, i):\n", " W = sd[f\"projectors.{i}.proj_native.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj_native.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj_native.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj_native.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", "\n", " with torch.no_grad():\n", " native_embs = [project_native(val_raw[name], i).to(DEVICE)\n", " for i, name in enumerate(EXPERTS)]\n", "\n", " # Shared vs Native per expert\n", " print(f\"\\n Shared × Native cosine per expert:\")\n", " for i, name in enumerate(EXPERTS):\n", " cos_sn = F.cosine_similarity(projected_gpu[i], native_embs[i], dim=-1)\n", " print(f\" {name[:25]:<25}: mean={cos_sn.mean():.4f} std={cos_sn.std():.4f} \"\n", " f\"min={cos_sn.min():.4f} max={cos_sn.max():.4f}\")\n", "\n", " # Displacement analysis\n", " print(f\"\\n Displacement (shared - native):\")\n", " for i, name in enumerate(EXPERTS):\n", " disp = projected_gpu[i] - native_embs[i]\n", " disp_norm = disp.norm(dim=-1)\n", " print(f\" {name[:25]:<25}: L2_mean={disp_norm.mean():.4f} \"\n", " f\"std={disp_norm.std():.4f}\")\n", "\n", " # Native effective dimensionality\n", " print(f\"\\n Native effective dimensionality:\")\n", " for i, name in enumerate(EXPERTS):\n", " nc = native_embs[i][:5000] - native_embs[i][:5000].mean(0, keepdim=True)\n", " _, s_n, _ = torch.linalg.svd(nc, full_matrices=False)\n", " ed = (s_n / s_n.sum()).pow(2).sum().reciprocal().item()\n", " print(f\" {name[:25]:<25}: eff_dim={ed:.1f}/{D_ANCHOR}\")\n", "\n", " # Cross-expert native agreement\n", " print(f\"\\n Cross-expert native agreement:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " cos_nn = F.cosine_similarity(native_embs[i], native_embs[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"mean={cos_nn.mean():.4f} std={cos_nn.std():.4f}\")\n", "\n", " # Cross-expert shared vs other's native\n", " print(f\"\\n Shared × Other's Native (cross-stream):\")\n", " for i in range(N_EXPERTS):\n", " for j in range(N_EXPERTS):\n", " if i == j: continue\n", " cos_cross = F.cosine_similarity(projected_gpu[i], native_embs[j], dim=-1)\n", " print(f\" {EXPERTS[i][:12]:>12}_shared × {EXPERTS[j][:12]:<12}_native: \"\n", " f\"mean={cos_cross.mean():.4f}\")\n", "\n", " # Native triangulation vs shared triangulation\n", " print(f\"\\n Native triangulation divergence from shared:\")\n", " shared_tri = (fused @ anchors.T)\n", " for i, name in enumerate(EXPERTS):\n", " native_tri = (native_embs[i] @ anchors.T)\n", " tri_cos = F.cosine_similarity(shared_tri, native_tri, dim=-1)\n", " tri_diff = (shared_tri - native_tri).abs().mean()\n", " print(f\" {name[:25]:<25}: tri_cos={tri_cos.mean():.4f} \"\n", " f\"tri_diff={tri_diff:.4f}\")\n", "\n", " # Pairwise native triangulation\n", " print(f\"\\n Native pairwise triangulation correlation:\")\n", " native_tris = [(native_embs[i] @ anchors.T) for i in range(N_EXPERTS)]\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " tc = F.cosine_similarity(native_tris[i], native_tris[j], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"mean={tc.mean():.4f} std={tc.std():.4f}\")\n", "\n", " # Information gain from native streams\n", " shared_flat = shared_tri.cpu().numpy()\n", " all_native_flat = torch.cat([nt.cpu() for nt in native_tris], dim=-1).numpy()\n", " combined = np.concatenate([shared_flat, all_native_flat], axis=-1)\n", "\n", " sc = shared_flat - shared_flat.mean(axis=0, keepdims=True)\n", " _, s_s, _ = np.linalg.svd(sc[:5000], full_matrices=False)\n", " shared_eff = float(((s_s / s_s.sum())**2).sum()**-1)\n", "\n", " cc = combined - combined.mean(axis=0, keepdims=True)\n", " _, s_c, _ = np.linalg.svd(cc[:5000], full_matrices=False)\n", " combined_eff = float(((s_c / s_c.sum())**2).sum()**-1)\n", "\n", " print(f\"\\n Information content:\")\n", " print(f\" Shared tri eff_dim: {shared_eff:.1f}\")\n", " print(f\" Combined eff_dim: {combined_eff:.1f}\")\n", " print(f\" Info gain from native: +{combined_eff - shared_eff:.1f} dims\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SCAN 10: TRIANGULATION STRUCTURE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SCAN 10: TRIANGULATION STRUCTURE\")\n", "print(f\"{'='*70}\")\n", "\n", "with torch.no_grad():\n", " for i in range(N_EXPERTS):\n", " tri = expert_tri[i]\n", " print(f\"\\n {EXPERTS[i]} triangulation:\")\n", " print(f\" mean={tri.mean():.4f} std={tri.std():.4f}\")\n", " print(f\" min={tri.min():.4f} max={tri.max():.4f}\")\n", " nearest_dist = tri.min(dim=-1).values\n", " print(f\" nearest: mean={nearest_dist.mean():.4f} std={nearest_dist.std():.4f}\")\n", "\n", " print(f\"\\n Expert triangulation correlation:\")\n", " for i in range(N_EXPERTS):\n", " for j in range(i+1, N_EXPERTS):\n", " per_img = F.cosine_similarity(expert_tri[i][:1000], expert_tri[j][:1000], dim=-1)\n", " print(f\" {EXPERTS[i][:15]:>15} × {EXPERTS[j][:15]:<15}: \"\n", " f\"per_img_cos mean={per_img.mean():.4f} std={per_img.std():.4f}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# SUMMARY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(f\"\\n{'='*70}\")\n", "print(\"SUMMARY\")\n", "print(f\"{'='*70}\")\n", "print(f\" Checkpoint: {SOUP_CKPT}\")\n", "print(f\" mAP: {ckpt['mAP']:.3f}\")\n", "print(f\" Anchors: {N_ANCHORS} × {D_ANCHOR}-d, {n_active} active ({100*n_active/N_ANCHORS:.0f}%)\")\n", "print(f\" Embedding eff_dim: {eff_dim:.1f}/{D_ANCHOR}\")\n", "print(f\" Anchor eff_rank: {eff_rank:.1f}/{D_ANCHOR}\")\n", "print(f\" Global CV: {global_cv:.4f}\")\n", "print(f\" Anchor CV: {a_cv:.4f}\")\n", "if cluster_cvs:\n", " print(f\" Local CV (mean): {np.mean(cvs):.4f}\")\n", "print(f\" Utilization entropy: {100*entropy/max_entropy:.1f}%\")\n", "print(f\" Utilization Gini: {gini:.4f}\")\n", "print(f\"\\n{'='*70}\")\n", "print(\"ANALYSIS COMPLETE\")\n", "print(f\"{'='*70}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CngR43ryfcBZ", "outputId": "d65de842-a50a-476d-ee68-146639b94cce" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", "GEOLIP HYPERSPHERE STRUCTURAL ANALYSIS\n", " Checkpoint: checkpoints/dual_stream_best.pt\n", " Device: cuda\n", "======================================================================\n", "\n", " Loading checkpoint...\n", " mAP=0.838 epoch=17\n", " D_ANCHOR=256 N_ANCHORS=512 N_EXPERTS=3\n", " Anchors: torch.Size([512, 256])\n", " Expert rotations: N/A (dual-stream — perspectives in projectors)\n", "\n", " Loading expert features...\n", " Generating embeddings...\n", " Embeddings: torch.Size([5000, 256])\n", "\n", "======================================================================\n", "SCAN 1: ANCHOR GEOMETRY\n", "======================================================================\n", " Pairwise cosine:\n", " mean=0.0001 std=0.0442\n", " max=0.5800 min=-0.5539\n", " Max neighbor cosine per anchor:\n", " mean=0.2330 std=0.0823\n", " max=0.5800 min=0.1204\n", " pairs with cos > 0.9: 0 (0.00%)\n", " pairs with cos > 0.8: 0 (0.00%)\n", " pairs with cos > 0.7: 0 (0.00%)\n", " pairs with cos > 0.5: 3 (0.00%)\n", " pairs with cos > 0.3: 47 (0.04%)\n", " pairs with cos > 0.0: 64430 (49.25%)\n", "\n", " Anchor spectral:\n", " effective rank: 256.0/256\n", " sv_max=1.4151 sv_10=1.4146 sv_50=1.4145 sv_min=1.413725\n", " top-10 SVs explain 3.9%\n", " top-25 SVs explain 9.8%\n", " top-50 SVs explain 19.5%\n", " top-100 SVs explain 39.1%\n", " top-128 SVs explain 50.0%\n", " top-200 SVs explain 78.1%\n", "\n", " Anchor pentachoron CV: 0.0286\n", " mean_vol=0.092596 std_vol=0.002649\n", "\n", "======================================================================\n", "SCAN 2: ANCHOR UTILIZATION\n", "======================================================================\n", " Active anchors: 201/512 (39.3%)\n", " Visit counts: mean=9.8 std=25.6\n", " max=264 min=1 (among active)\n", " top 10: [np.float32(264.0), np.float32(194.0), np.float32(161.0), np.float32(140.0), np.float32(129.0), np.float32(114.0), np.float32(111.0), np.float32(103.0), np.float32(96.0), np.float32(90.0)]\n", " Entropy: 4.5776 / 6.2383 (73.4%)\n", " Gini coefficient: 0.8568 (0=equal, 1=one anchor gets all)\n", " anchors with 1-5 visits: 66\n", " anchors with 5-20 visits: 64\n", " anchors with 20-50 visits: 39\n", " anchors with 50-100 visits: 24\n", " anchors with 100-500 visits: 8\n", " anchors with 500-5000 visits: 0\n", "\n", "======================================================================\n", "SCAN 3: EMBEDDING MANIFOLD GEOMETRY\n", "======================================================================\n", " Effective dimensionality: 71.6/256\n", " top-5 SVs explain 33.5%\n", " top-10 SVs explain 51.1%\n", " top-20 SVs explain 72.6%\n", " top-50 SVs explain 94.2%\n", " top-100 SVs explain 99.4%\n", " top-128 SVs explain 99.8%\n", " top-200 SVs explain 100.0%\n", "\n", " Self-similarity (off-diagonal):\n", " mean=0.0025 std=0.1920\n", " max=0.9931 min=-0.5248\n", "\n", " Norms: mean=1.000000 std=0.000000\n", "\n", " Global pentachoron CV: 0.2199\n", " mean_vol=0.080570 std_vol=0.017715\n", "\n", "======================================================================\n", "SCAN 4: EXPERT PERSPECTIVE DIVERGENCE\n", "======================================================================\n", "\n", " Per-image expert agreement:\n", " clip_l14_openai × dinov2_b14 : mean=1.0000 std=0.0000 min=1.0000\n", " clip_l14_openai × siglip_b16_384 : mean=1.0000 std=0.0000 min=1.0000\n", " dinov2_b14 × siglip_b16_384 : mean=1.0000 std=0.0000 min=1.0000\n", "\n", " Per-anchor expert divergence:\n", " mean divergence: 0.0000 std: 0.0000\n", " max divergence: 0.0000 (anchor 0)\n", " min divergence: 0.0000 (anchor 0)\n", "\n", " Top 10 most contentious anchors:\n", " #1 anchor 39: div=0.0000 visits=0\n", " #2 anchor 38: div=0.0000 visits=0\n", " #3 anchor 37: div=0.0000 visits=0\n", " #4 anchor 36: div=0.0000 visits=0\n", " #5 anchor 35: div=0.0000 visits=0\n", " #6 anchor 34: div=0.0000 visits=0\n", " #7 anchor 33: div=0.0000 visits=0\n", " #8 anchor 32: div=0.0000 visits=2\n", " #9 anchor 47: div=0.0000 visits=0\n", " #10 anchor 46: div=0.0000 visits=1\n", "\n", " Top 10 most unanimous anchors:\n", " #1 anchor 504: div=0.0000 visits=0\n", " #2 anchor 505: div=0.0000 visits=2\n", " #3 anchor 506: div=0.0000 visits=27\n", " #4 anchor 507: div=0.0000 visits=17\n", " #5 anchor 508: div=0.0000 visits=4\n", " #6 anchor 509: div=0.0000 visits=0\n", " #7 anchor 510: div=0.0000 visits=0\n", " #8 anchor 511: div=0.0000 visits=0\n", " #9 anchor 496: div=0.0000 visits=0\n", " #10 anchor 497: div=0.0000 visits=0\n", "\n", " Expert rotation eigenspectra:\n", " clip_l14_openai : ortho_err=0.000000 eval_min=1.0000 eval_max=1.0000\n", " dinov2_b14 : ortho_err=0.000000 eval_min=1.0000 eval_max=1.0000\n", " siglip_b16_384 : ortho_err=0.000000 eval_min=1.0000 eval_max=1.0000\n", "\n", " Expert whitener condition:\n", " clip_l14_openai : cond=1.00 sv_max=1.0000 sv_min=1.000000\n", " dinov2_b14 : cond=1.00 sv_max=1.0000 sv_min=1.000000\n", " siglip_b16_384 : cond=1.00 sv_max=1.0000 sv_min=1.000000\n", "\n", "======================================================================\n", "SCAN 5: NEAREST ANCHOR DISTANCES\n", "======================================================================\n", " k= 0: mean_dist=0.5721 std=0.0978 max=0.8074 min=0.2505\n", " k= 1: mean_dist=0.6521 std=0.0692 max=0.8282 min=0.4311\n", " k= 2: mean_dist=0.6931 std=0.0559 max=0.8344 min=0.5052\n", " k= 4: mean_dist=0.7466 std=0.0436 max=0.8548 min=0.5865\n", " k= 9: mean_dist=0.8172 std=0.0243 max=0.8985 min=0.7391\n", " k= 19: mean_dist=0.8814 std=0.0183 max=0.9495 min=0.8327\n", " k= 49: mean_dist=0.9374 std=0.0132 max=0.9738 min=0.9056\n", " k= 99: mean_dist=0.9671 std=0.0084 max=0.9896 min=0.9445\n", " anchors with cos > 0.9: mean=0.0 max=0 min=0\n", " anchors with cos > 0.8: mean=0.0 max=0 min=0\n", " anchors with cos > 0.7: mean=0.0 max=1 min=0\n", " anchors with cos > 0.5: mean=0.3 max=2 min=0\n", " anchors with cos > 0.3: mean=2.7 max=8 min=0\n", " anchors with cos > 0.0: mean=227.2 max=284 min=148\n", "\n", "======================================================================\n", "SCAN 6: PER-CLASS ANCHOR AFFINITY\n", "======================================================================\n", " Anchor specialization:\n", " classes per active anchor: mean=12.8 std=9.4\n", " max=40 min=1\n", "\n", " Class spread (anchors per class):\n", " mean=32.2 std=28.8\n", " max=170 (person)\n", " min=2 (bear)\n", "\n", " Top 10 by anchor spread:\n", " person : 170 anchors, 2693 images\n", " chair : 105 anchors, 580 images\n", " car : 98 anchors, 535 images\n", " bottle : 85 anchors, 379 images\n", " dining table : 84 anchors, 501 images\n", " handbag : 81 anchors, 292 images\n", " cup : 80 anchors, 390 images\n", " backpack : 76 anchors, 228 images\n", " truck : 66 anchors, 250 images\n", " book : 65 anchors, 230 images\n", " Bottom 10 by anchor spread:\n", " sheep : 6 anchors, 65 images\n", " tennis racket : 5 anchors, 167 images\n", " giraffe : 5 anchors, 101 images\n", " hair drier : 4 anchors, 9 images\n", " toaster : 4 anchors, 8 images\n", " baseball glove : 4 anchors, 100 images\n", " elephant : 4 anchors, 89 images\n", " skis : 4 anchors, 120 images\n", " zebra : 3 anchors, 85 images\n", " bear : 2 anchors, 49 images\n", "\n", "======================================================================\n", "SCAN 7: INTER-CLASS GEOMETRIC DISTANCES\n", "======================================================================\n", " Inter-class cosine (78 classes with >10 images):\n", " mean=0.0531\n", " max=0.9947 min=-0.4158\n", "\n", " Most similar class pairs:\n", " #1: mouse × keyboard cos=0.9947\n", " #2: baseball bat × baseball glove cos=0.9905\n", " #3: microwave × refrigerator cos=0.9783\n", " #4: spoon × bowl cos=0.9757\n", " #5: skis × snowboard cos=0.9707\n", " #6: microwave × oven cos=0.9615\n", " #7: knife × dining table cos=0.9582\n", " #8: cup × dining table cos=0.9580\n", " #9: laptop × keyboard cos=0.9577\n", " #10: fork × knife cos=0.9565\n", " #11: laptop × mouse cos=0.9531\n", " #12: oven × refrigerator cos=0.9510\n", " #13: knife × spoon cos=0.9489\n", " #14: apple × orange cos=0.9461\n", " #15: broccoli × carrot cos=0.9433\n", "\n", " Most distant class pairs:\n", " #1: person × zebra cos=-0.4158\n", " #2: person × giraffe cos=-0.3890\n", " #3: airplane × bottle cos=-0.3845\n", " #4: snowboard × potted plant cos=-0.3742\n", " #5: snowboard × chair cos=-0.3692\n", " #6: snowboard × vase cos=-0.3677\n", " #7: bench × toothbrush cos=-0.3641\n", " #8: skis × potted plant cos=-0.3635\n", " #9: truck × toothbrush cos=-0.3629\n", " #10: bird × bottle cos=-0.3616\n", " #11: airplane × cup cos=-0.3575\n", " #12: car × toothbrush cos=-0.3572\n", " #13: skis × vase cos=-0.3550\n", " #14: skis × chair cos=-0.3484\n", " #15: horse × bottle cos=-0.3445\n", "\n", " Intra-class spread:\n", " Tightest 10:\n", " giraffe : spread=0.0566 (n=101)\n", " zebra : spread=0.0626 (n=85)\n", " tennis racket : spread=0.0785 (n=167)\n", " baseball glove : spread=0.0871 (n=100)\n", " skis : spread=0.0880 (n=120)\n", " elephant : spread=0.1058 (n=89)\n", " snowboard : spread=0.1145 (n=49)\n", " skateboard : spread=0.1188 (n=127)\n", " surfboard : spread=0.1377 (n=149)\n", " baseball bat : spread=0.1512 (n=97)\n", " Loosest 10:\n", " potted plant : spread=0.5553 (n=172)\n", " umbrella : spread=0.5566 (n=174)\n", " cup : spread=0.5587 (n=390)\n", " car : spread=0.5765 (n=535)\n", " bottle : spread=0.6014 (n=379)\n", " handbag : spread=0.6207 (n=292)\n", " bench : spread=0.6600 (n=235)\n", " chair : spread=0.6621 (n=580)\n", " backpack : spread=0.6764 (n=228)\n", " person : spread=0.8186 (n=2693)\n", "\n", "======================================================================\n", "SCAN 8: LOCAL PENTACHORON CV\n", "======================================================================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "stream", "name": "stdout", "text": [ " Clusters with 10+ members: 102\n", " Local CV: mean=0.4302 std=0.1654\n", " max=1.0834 min=0.1965\n", " Global CV: 0.2199\n", " Ratio (local/global): 1.9564\n", "\n", " Highest local CV (most diverse clusters):\n", " anchor 86: CV=1.0834 n= 103 mean_vol=0.002920\n", " anchor 469: CV=0.8698 n= 89 mean_vol=0.000587\n", " anchor 364: CV=0.8659 n= 194 mean_vol=0.007823\n", " anchor 175: CV=0.8403 n= 88 mean_vol=0.001932\n", " anchor 68: CV=0.8222 n= 161 mean_vol=0.000757\n", " anchor 220: CV=0.7987 n= 114 mean_vol=0.001308\n", " anchor 437: CV=0.7678 n= 129 mean_vol=0.001834\n", " anchor 498: CV=0.7410 n= 78 mean_vol=0.001262\n", " anchor 205: CV=0.6792 n= 48 mean_vol=0.001457\n", " anchor 339: CV=0.6555 n= 11 mean_vol=0.003612\n", " Lowest local CV (most uniform clusters):\n", " anchor 361: CV=0.2631 n= 41 mean_vol=0.012367\n", " anchor 254: CV=0.2615 n= 32 mean_vol=0.019875\n", " anchor 155: CV=0.2565 n= 34 mean_vol=0.017756\n", " anchor 53: CV=0.2527 n= 12 mean_vol=0.025066\n", " anchor 479: CV=0.2503 n= 43 mean_vol=0.021270\n", " anchor 157: CV=0.2354 n= 11 mean_vol=0.031356\n", " anchor 289: CV=0.2137 n= 18 mean_vol=0.018227\n", " anchor 430: CV=0.2126 n= 15 mean_vol=0.041118\n", " anchor 486: CV=0.2002 n= 11 mean_vol=0.016117\n", " anchor 131: CV=0.1965 n= 14 mean_vol=0.030370\n", "\n", "======================================================================\n", "SCAN 9: PROJECTOR ANALYSIS\n", "======================================================================\n", "\n", " clip_l14_openai:\n", " self-sim: mean=0.0538 std=0.1757\n", " eff_dim: 76.3/256\n", " cos→fused: mean=0.9160 std=0.0323\n", "\n", " dinov2_b14:\n", " self-sim: mean=0.1254 std=0.1798\n", " eff_dim: 74.5/256\n", " cos→fused: mean=0.8665 std=0.0459\n", "\n", " siglip_b16_384:\n", " self-sim: mean=0.0332 std=0.1800\n", " eff_dim: 76.3/256\n", " cos→fused: mean=0.9303 std=0.0284\n", "\n", " Cross-expert agreement (projected):\n", " clip_l14_openai × dinov2_b14 : cos=0.6581 std=0.1092\n", " clip_l14_openai × siglip_b16_384 : cos=0.8297 std=0.0749\n", " dinov2_b14 × siglip_b16_384 : cos=0.6966 std=0.1019\n", "\n", " Expert uniqueness (leave-one-out):\n", " Without clip_l14_openai : cos_to_full=0.9756 (uniqueness=0.0244)\n", " Without dinov2_b14 : cos_to_full=0.9652 (uniqueness=0.0348)\n", " Without siglip_b16_384 : cos_to_full=0.9791 (uniqueness=0.0209)\n", "\n", "======================================================================\n", "SCAN 9.5: DUAL-STREAM ANALYSIS\n", "======================================================================\n", "\n", " Shared × Native cosine per expert:\n", " clip_l14_openai : mean=0.3337 std=0.0146 min=0.2898 max=0.4650\n", " dinov2_b14 : mean=0.3576 std=0.0256 min=0.2771 max=0.5046\n", " siglip_b16_384 : mean=0.3503 std=0.0191 min=0.2433 max=0.4444\n", "\n", " Displacement (shared - native):\n", " clip_l14_openai : L2_mean=1.1543 std=0.0127\n", " dinov2_b14 : L2_mean=1.1332 std=0.0228\n", " siglip_b16_384 : L2_mean=1.1398 std=0.0168\n", "\n", " Native effective dimensionality:\n", " clip_l14_openai : eff_dim=71.3/256\n", " dinov2_b14 : eff_dim=62.4/256\n", " siglip_b16_384 : eff_dim=62.8/256\n", "\n", " Cross-expert native agreement:\n", " clip_l14_openai × dinov2_b14 : mean=0.2059 std=0.0037\n", " clip_l14_openai × siglip_b16_384 : mean=0.2078 std=0.0041\n", " dinov2_b14 × siglip_b16_384 : mean=0.7933 std=0.0048\n", "\n", " Shared × Other's Native (cross-stream):\n", " clip_l14_ope_shared × dinov2_b14 _native: mean=0.1277\n", " clip_l14_ope_shared × siglip_b16_3_native: mean=0.2346\n", " dinov2_b14_shared × clip_l14_ope_native: mean=0.0136\n", " dinov2_b14_shared × siglip_b16_3_native: mean=0.2824\n", " siglip_b16_3_shared × clip_l14_ope_native: mean=0.2211\n", " siglip_b16_3_shared × dinov2_b14 _native: mean=0.1836\n", "\n", " Native triangulation divergence from shared:\n", " clip_l14_openai : tri_cos=0.2093 tri_diff=0.0572\n", " dinov2_b14 : tri_cos=0.2464 tri_diff=0.0573\n", " siglip_b16_384 : tri_cos=0.3196 tri_diff=0.0553\n", "\n", " Native pairwise triangulation correlation:\n", " clip_l14_openai × dinov2_b14 : mean=0.2059 std=0.0037\n", " clip_l14_openai × siglip_b16_384 : mean=0.2078 std=0.0041\n", " dinov2_b14 × siglip_b16_384 : mean=0.7933 std=0.0048\n", "\n", " Information content:\n", " Shared tri eff_dim: 71.6\n", " Combined eff_dim: 126.2\n", " Info gain from native: +54.5 dims\n", "\n", "======================================================================\n", "SCAN 10: TRIANGULATION STRUCTURE\n", "======================================================================\n", "\n", " clip_l14_openai triangulation:\n", " mean=0.9993 std=0.0625\n", " min=0.2505 max=1.3152\n", " nearest: mean=0.5721 std=0.0978\n", "\n", " dinov2_b14 triangulation:\n", " mean=0.9993 std=0.0625\n", " min=0.2505 max=1.3152\n", " nearest: mean=0.5721 std=0.0978\n", "\n", " siglip_b16_384 triangulation:\n", " mean=0.9993 std=0.0625\n", " min=0.2505 max=1.3152\n", " nearest: mean=0.5721 std=0.0978\n", "\n", " Expert triangulation correlation:\n", " clip_l14_openai × dinov2_b14 : per_img_cos mean=1.0000 std=0.0000\n", " clip_l14_openai × siglip_b16_384 : per_img_cos mean=1.0000 std=0.0000\n", " dinov2_b14 × siglip_b16_384 : per_img_cos mean=1.0000 std=0.0000\n", "\n", "======================================================================\n", "SUMMARY\n", "======================================================================\n", " Checkpoint: checkpoints/dual_stream_best.pt\n", " mAP: 0.838\n", " Anchors: 512 × 256-d, 201 active (39%)\n", " Embedding eff_dim: 71.6/256\n", " Anchor eff_rank: 256.0/256\n", " Global CV: 0.2199\n", " Anchor CV: 0.0286\n", " Local CV (mean): 0.4302\n", " Utilization entropy: 73.4%\n", " Utilization Gini: 0.8568\n", "\n", "======================================================================\n", "ANALYSIS COMPLETE\n", "======================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "GEOLIP HYPERSPHERE MANIFOLD VISUALIZATION\n", "==========================================\n", "6-panel manifold view + 3-panel expert perspective divergence.\n", "S^255 projected to S^2 via PCA.\n", "\"\"\"\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import math\n", "\n", "DEVICE = \"cpu\"\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOAD + EMBED\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Loading soup...\")\n", "ckpt = torch.load(\"checkpoints/dual_stream_best.pt\", map_location=\"cpu\", weights_only=False)\n", "sd = ckpt[\"state_dict\"]\n", "D_ANCHOR = ckpt[\"config\"][\"d_anchor\"]\n", "N_ANCHORS = ckpt[\"config\"][\"n_anchors\"]\n", "anchors = F.normalize(sd[\"constellation.anchors\"], dim=-1)\n", "\n", "EXPERTS = [\"clip_l14_openai\", \"dinov2_b14\", \"siglip_b16_384\"]\n", "COCO_CLASSES = [\n", " \"person\", \"bicycle\", \"car\", \"motorcycle\", \"airplane\", \"bus\", \"train\",\n", " \"truck\", \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\",\n", " \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\",\n", " \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"backpack\", \"umbrella\",\n", " \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\n", " \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\",\n", " \"knife\", \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\",\n", " \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \"chair\",\n", " \"couch\", \"potted plant\", \"bed\", \"dining table\", \"toilet\", \"tv\",\n", " \"laptop\", \"mouse\", \"remote\", \"keyboard\", \"cell phone\", \"microwave\",\n", " \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"book\", \"clock\", \"vase\",\n", " \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\",\n", "]\n", "\n", "print(\"Loading features...\")\n", "from datasets import load_dataset\n", "\n", "ref = load_dataset(\"AbstractPhil/bulk-coco-features\", EXPERTS[0], split=\"val\")\n", "val_ids = ref[\"image_id\"]; N_val = len(val_ids)\n", "val_id_map = {iid: i for i, iid in enumerate(val_ids)}\n", "val_labels = torch.zeros(N_val, 80)\n", "for i, labs in enumerate(ref[\"labels\"]):\n", " for l in labs:\n", " if l < 80: val_labels[i, l] = 1.0\n", "\n", "val_raw = {}\n", "for name in EXPERTS:\n", " ds = load_dataset(\"AbstractPhil/bulk-coco-features\", name, split=\"val\")\n", " feats = torch.zeros(N_val, 768)\n", " for row in ds:\n", " if row[\"image_id\"] in val_id_map:\n", " feats[val_id_map[row[\"image_id\"]]] = torch.tensor(row[\"features\"], dtype=torch.float32)\n", " val_raw[name] = feats; del ds\n", "\n", "def project_expert(feats, i):\n", " prefix = f\"projectors.{i}.proj_shared\" if f\"projectors.{i}.proj_shared.0.weight\" in sd else f\"projectors.{i}.proj\"\n", " W = sd[f\"{prefix}.0.weight\"]\n", " b = sd[f\"{prefix}.0.bias\"]\n", " lw = sd[f\"{prefix}.1.weight\"]\n", " lb = sd[f\"{prefix}.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", "\n", "print(\"Generating embeddings...\")\n", "with torch.no_grad():\n", " projected = [project_expert(val_raw[name], i) for i, name in enumerate(EXPERTS)]\n", " fused = F.normalize(sum(projected) / 3, dim=-1)\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PCA → 3D\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "emb = fused.numpy()\n", "emb_centered = emb - emb.mean(axis=0, keepdims=True)\n", "U, S, Vt = np.linalg.svd(emb_centered[:5000], full_matrices=False)\n", "pca3 = Vt[:3]\n", "\n", "emb_3d = emb @ pca3.T\n", "anchors_3d = anchors.numpy() @ pca3.T\n", "\n", "var_explained = S[:3]**2 / (S**2).sum()\n", "print(f\"PCA 3D variance: {var_explained.sum()*100:.1f}% \"\n", " f\"({var_explained[0]*100:.1f}%, {var_explained[1]*100:.1f}%, {var_explained[2]*100:.1f}%)\")\n", "\n", "def to_sphere(pts):\n", " norms = np.linalg.norm(pts, axis=-1, keepdims=True)\n", " return pts / (norms + 1e-8)\n", "\n", "emb_s = to_sphere(emb_3d)\n", "anchors_s = to_sphere(anchors_3d)\n", "\n", "# Reference sphere wireframe\n", "phi = np.linspace(0, 2*np.pi, 60)\n", "theta = np.linspace(0, np.pi, 30)\n", "xs = np.outer(np.cos(phi), np.sin(theta))\n", "ys = np.outer(np.sin(phi), np.sin(theta))\n", "zs = np.outer(np.ones_like(phi), np.cos(theta))\n", "\n", "# Primary class per image (most specific)\n", "class_freq = val_labels.sum(0).numpy()\n", "primary_class = np.zeros(N_val, dtype=int)\n", "for i in range(N_val):\n", " present = np.where(val_labels[i].numpy() > 0)[0]\n", " if len(present) > 0:\n", " primary_class[i] = present[class_freq[present].argmin()]\n", "\n", "cmap20 = plt.cm.tab20(np.linspace(0, 1, 20))\n", "class_colors = np.array([cmap20[primary_class[i] % 20] for i in range(N_val)])\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# HELPER\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def setup_ax(ax, title):\n", " ax.set_facecolor('black')\n", " ax.xaxis.pane.fill = False; ax.yaxis.pane.fill = False; ax.zaxis.pane.fill = False\n", " ax.xaxis.pane.set_edgecolor('gray'); ax.yaxis.pane.set_edgecolor('gray')\n", " ax.zaxis.pane.set_edgecolor('gray')\n", " ax.set_xlabel('PC1', color='gray', fontsize=8)\n", " ax.set_ylabel('PC2', color='gray', fontsize=8)\n", " ax.set_zlabel('PC3', color='gray', fontsize=8)\n", " ax.tick_params(colors='gray', labelsize=6)\n", " ax.set_title(title, color='white', fontsize=11, pad=10)\n", " ax.plot_wireframe(xs*0.98, ys*0.98, zs*0.98, alpha=0.03, color='white', linewidth=0.3)\n", " ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_zlim(-1.3, 1.3)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 1: 6-PANEL MANIFOLD VIEW\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 1...\")\n", "fig = plt.figure(figsize=(24, 16), facecolor='black')\n", "fig.suptitle(\n", " 'GeoLIP Hypersphere Manifold — S²⁵⁵ projected to S²\\n'\n", " f'{N_ANCHORS} anchors × {D_ANCHOR}-d × 3 experts | mAP={ckpt[\"mAP\"]:.3f} | eff_dim=76.9',\n", " color='white', fontsize=16, y=0.98)\n", "\n", "# Panel 1: Full manifold\n", "ax1 = fig.add_subplot(231, projection='3d')\n", "setup_ax(ax1, f'Full Manifold — {N_val} embeddings + {N_ANCHORS} anchors')\n", "ax1.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=class_colors, s=1, alpha=0.3)\n", "ax1.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=8, alpha=0.6, marker='^')\n", "\n", "# Panel 2: Class centroids\n", "ax2 = fig.add_subplot(232, projection='3d')\n", "setup_ax(ax2, '80 COCO Class Centroids')\n", "centroids = np.zeros((80, emb.shape[1]))\n", "for c in range(80):\n", " mask = val_labels[:, c].numpy() > 0\n", " if mask.sum() > 0:\n", " centroids[c] = emb[mask].mean(0)\n", "centroids_3d = to_sphere(centroids @ pca3.T)\n", "sizes = val_labels.sum(0).numpy()\n", "sizes_scaled = 20 + 200 * (sizes / sizes.max())\n", "colors80 = plt.cm.hsv(np.linspace(0, 0.95, 80))\n", "ax2.scatter(centroids_3d[:, 0], centroids_3d[:, 1], centroids_3d[:, 2],\n", " c=colors80, s=sizes_scaled, alpha=0.8, edgecolors='white', linewidth=0.3)\n", "for c in [0, 2, 14, 15, 16, 22, 23, 56, 62]:\n", " if sizes[c] > 30:\n", " ax2.text(centroids_3d[c, 0]*1.15, centroids_3d[c, 1]*1.15,\n", " centroids_3d[c, 2]*1.15,\n", " COCO_CLASSES[c], color='white', fontsize=7, ha='center')\n", "\n", "# Panel 3: 50 random with anchor connections\n", "ax3 = fig.add_subplot(233, projection='3d')\n", "setup_ax(ax3, '50 Random — nearest anchor connections')\n", "np.random.seed(42)\n", "idx50 = np.random.choice(N_val, 50, replace=False)\n", "emb_50 = emb_s[idx50]\n", "colors_50 = class_colors[idx50]\n", "with torch.no_grad():\n", " cos_50 = fused[idx50] @ anchors.T\n", " nearest_50 = cos_50.argmax(-1).numpy()\n", "ax3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c='red', s=4, alpha=0.2, marker='^')\n", "ax3.scatter(emb_50[:, 0], emb_50[:, 1], emb_50[:, 2],\n", " c=colors_50, s=40, alpha=0.9, edgecolors='white', linewidth=0.5)\n", "for i in range(50):\n", " a = nearest_50[i]\n", " ax3.plot([emb_50[i, 0], anchors_s[a, 0]],\n", " [emb_50[i, 1], anchors_s[a, 1]],\n", " [emb_50[i, 2], anchors_s[a, 2]],\n", " color='yellow', alpha=0.3, linewidth=0.5)\n", "\n", "# Panel 4: 10 random — triangulation heatmap\n", "ax4 = fig.add_subplot(234, projection='3d')\n", "setup_ax(ax4, '10 Random — anchor affinity heatmap')\n", "idx10 = np.random.choice(N_val, 10, replace=False)\n", "emb_10 = emb_s[idx10]\n", "with torch.no_grad():\n", " cos_10 = (fused[idx10] @ anchors.T).numpy()\n", " mean_cos = cos_10.mean(0)\n", "anchor_heat = (mean_cos - mean_cos.min()) / (mean_cos.max() - mean_cos.min() + 1e-8)\n", "anchor_colors = plt.cm.hot(anchor_heat)\n", "ax4.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=anchor_colors, s=10, alpha=0.6)\n", "ax4.scatter(emb_10[:, 0], emb_10[:, 1], emb_10[:, 2],\n", " c='cyan', s=80, alpha=1.0, edgecolors='white', linewidth=1, zorder=10)\n", "\n", "# Panel 5: Single encoding\n", "ax5 = fig.add_subplot(235, projection='3d')\n", "single_idx = 42\n", "single_class = primary_class[single_idx]\n", "setup_ax(ax5, f'Single Encoding: \"{COCO_CLASSES[single_class]}\" — top 5 anchors')\n", "with torch.no_grad():\n", " cos_single = (fused[single_idx] @ anchors.T).numpy()\n", "single_heat = (cos_single - cos_single.min()) / (cos_single.max() - cos_single.min() + 1e-8)\n", "single_colors = plt.cm.plasma(single_heat)\n", "single_sizes = 2 + 50 * single_heat**3\n", "ax5.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=single_colors, s=single_sizes, alpha=0.7)\n", "single_pt = emb_s[single_idx]\n", "ax5.scatter([single_pt[0]], [single_pt[1]], [single_pt[2]],\n", " c='lime', s=150, alpha=1.0, edgecolors='white', linewidth=2,\n", " zorder=10, marker='*')\n", "top5 = np.argsort(cos_single)[::-1][:5]\n", "for a in top5:\n", " ax5.plot([single_pt[0], anchors_s[a, 0]],\n", " [single_pt[1], anchors_s[a, 1]],\n", " [single_pt[2], anchors_s[a, 2]],\n", " color='lime', alpha=0.6, linewidth=1.5)\n", "\n", "# Panel 6: Radial deviation\n", "ax6 = fig.add_subplot(236, projection='3d')\n", "radii = np.linalg.norm(emb_3d, axis=-1)\n", "setup_ax(ax6, f'PCA Projection Radii — mean={radii.mean():.4f} std={radii.std():.4f}')\n", "radius_dev = radii - radii.mean()\n", "dev_norm = (radius_dev - radius_dev.min()) / (radius_dev.max() - radius_dev.min() + 1e-8)\n", "dev_colors = plt.cm.coolwarm(dev_norm)\n", "scale = 1.0 / radii.max()\n", "ax6.scatter(emb_3d[:, 0]*scale, emb_3d[:, 1]*scale, emb_3d[:, 2]*scale,\n", " c=dev_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"hypersphere_manifold.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: hypersphere_manifold.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 2: EXPERT PERSPECTIVES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 2...\")\n", "fig2 = plt.figure(figsize=(21, 7), facecolor='black')\n", "fig2.suptitle('Expert Perspective Divergence — Same sphere, three lenses',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "has_expert_rot = f\"constellation.expert_rotations.0\" in sd\n", "if has_expert_rot:\n", " expert_R = [sd[f\"constellation.expert_rotations.{i}\"] for i in range(3)]\n", " expert_W = [sd[f\"constellation.expert_whiteners.{i}\"] for i in range(3)]\n", " expert_mu = [sd[f\"constellation.expert_means.{i}\"] for i in range(3)]\n", "else:\n", " expert_R = [torch.eye(D_ANCHOR) for _ in range(3)]\n", " expert_W = [torch.eye(D_ANCHOR) for _ in range(3)]\n", " expert_mu = [torch.zeros(D_ANCHOR) for _ in range(3)]\n", "\n", "with torch.no_grad():\n", " for i, name in enumerate(EXPERTS):\n", " ax = fig2.add_subplot(1, 3, i+1, projection='3d')\n", "\n", " if has_expert_rot:\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " elif f\"projectors.{i}.proj_native.0.weight\" in sd:\n", " W = sd[f\"projectors.{i}.proj_native.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj_native.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj_native.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj_native.1.bias\"]\n", " x = val_raw[name] @ W.T + b\n", " mu_v = x.mean(-1, keepdim=True); var_v = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu_v) / (var_v + 1e-5).sqrt() * lw + lb\n", " rotated = F.normalize(x, dim=-1)\n", " else:\n", " rotated = projected[i]\n", "\n", " rot_np = rotated.numpy()\n", " rot_c = rot_np - rot_np.mean(axis=0, keepdims=True)\n", " _, S_r, Vt_r = np.linalg.svd(rot_c[:5000], full_matrices=False)\n", " rot_3d = to_sphere(rot_np @ Vt_r[:3].T)\n", "\n", " var_exp = S_r[:3]**2 / (S_r**2).sum()\n", " setup_ax(ax, f'{name[:25]}\\nPC variance: {var_exp.sum()*100:.1f}%')\n", " ax.scatter(rot_3d[:, 0], rot_3d[:, 1], rot_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"expert_perspectives.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: expert_perspectives.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 3: ANCHORS ONLY\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 3 — anchors only...\")\n", "\n", "# Anchor visit counts for coloring\n", "with torch.no_grad():\n", " cos_all = fused @ anchors.T\n", " nearest_all = cos_all.argmax(dim=-1)\n", " vc = torch.zeros(N_ANCHORS)\n", " for n in nearest_all:\n", " vc[n] += 1\n", " vc_np = vc.numpy()\n", "\n", "fig3 = plt.figure(figsize=(24, 8), facecolor='black')\n", "fig3.suptitle(f'Constellation — {N_ANCHORS} anchors × {D_ANCHOR}-d on S²⁵⁵',\n", " color='white', fontsize=14, y=1.02)\n", "\n", "# Panel 1: Anchors colored by visit count\n", "ax_a1 = fig3.add_subplot(131, projection='3d')\n", "setup_ax(ax_a1, f'Anchor Utilization — {int((vc_np>0).sum())}/{N_ANCHORS} active')\n", "heat = np.zeros(N_ANCHORS)\n", "active_mask = vc_np > 0\n", "heat[active_mask] = np.log1p(vc_np[active_mask])\n", "heat = heat / (heat.max() + 1e-8)\n", "a_colors = plt.cm.inferno(heat)\n", "a_sizes = 5 + 60 * heat\n", "# Dead anchors in blue\n", "dead_mask = vc_np == 0\n", "ax_a1.scatter(anchors_s[dead_mask, 0], anchors_s[dead_mask, 1], anchors_s[dead_mask, 2],\n", " c='dodgerblue', s=8, alpha=0.4, marker='x', label=f'dead ({int(dead_mask.sum())})')\n", "ax_a1.scatter(anchors_s[active_mask, 0], anchors_s[active_mask, 1], anchors_s[active_mask, 2],\n", " c=a_colors[active_mask], s=a_sizes[active_mask], alpha=0.8)\n", "\n", "# Panel 2: Anchors colored by nearest neighbor distance\n", "ax_a2 = fig3.add_subplot(132, projection='3d')\n", "anchor_sim = (anchors.numpy() @ anchors.numpy().T)\n", "np.fill_diagonal(anchor_sim, -1)\n", "max_neighbor_cos = anchor_sim.max(axis=1)\n", "nn_heat = (max_neighbor_cos - max_neighbor_cos.min()) / (max_neighbor_cos.max() - max_neighbor_cos.min() + 1e-8)\n", "nn_colors = plt.cm.viridis(nn_heat)\n", "setup_ax(ax_a2, f'Anchor Isolation — nearest neighbor cosine\\n'\n", " f'mean={max_neighbor_cos.mean():.3f} max={max_neighbor_cos.max():.3f}')\n", "ax_a2.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=nn_colors, s=15, alpha=0.8)\n", "\n", "# Panel 3: Anchors colored by expert divergence at that anchor\n", "ax_a3 = fig3.add_subplot(133, projection='3d')\n", "with torch.no_grad():\n", " expert_tri_stack = []\n", " if has_expert_rot:\n", " for i in range(3):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " expert_tri_stack.append(1.0 - (rotated @ anchors.T))\n", " elif f\"projectors.0.proj_native.0.weight\" in sd:\n", " def _pn(feats, i):\n", " W = sd[f\"projectors.{i}.proj_native.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj_native.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj_native.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj_native.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", " for i, name in enumerate(EXPERTS):\n", " nat = _pn(val_raw[name], i)\n", " expert_tri_stack.append(1.0 - (nat @ anchors.T))\n", " else:\n", " for p in projected:\n", " expert_tri_stack.append(1.0 - (p @ anchors.T))\n", " tri_stack = torch.stack(expert_tri_stack, dim=-1)\n", " per_anchor_div = tri_stack.std(dim=-1).mean(dim=0).numpy()\n", "\n", "div_heat = (per_anchor_div - per_anchor_div.min()) / (per_anchor_div.max() - per_anchor_div.min() + 1e-8)\n", "div_colors = plt.cm.coolwarm(div_heat)\n", "setup_ax(ax_a3, f'Expert Divergence per Anchor\\n'\n", " f'mean={per_anchor_div.mean():.4f} range=[{per_anchor_div.min():.4f}, {per_anchor_div.max():.4f}]')\n", "ax_a3.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=div_colors, s=15, alpha=0.8)\n", "\n", "# Add connections between closest anchor pairs (top 20)\n", "flat_sim = anchor_sim.copy()\n", "np.fill_diagonal(flat_sim, -999)\n", "for panel_ax in [ax_a1, ax_a2]:\n", " for _ in range(20):\n", " idx_flat = np.argmax(flat_sim)\n", " i_a, j_a = np.unravel_index(idx_flat, flat_sim.shape)\n", " flat_sim[i_a, j_a] = -999; flat_sim[j_a, i_a] = -999\n", " panel_ax.plot([anchors_s[i_a, 0], anchors_s[j_a, 0]],\n", " [anchors_s[i_a, 1], anchors_s[j_a, 1]],\n", " [anchors_s[i_a, 2], anchors_s[j_a, 2]],\n", " color='white', alpha=0.15, linewidth=0.5)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"anchors_only.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: anchors_only.png\")\n", "plt.close()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# FIGURE 4: PAIRWISE EXPERT DIFFERENCES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "print(\"Rendering figure 4 — pairwise expert diffs...\")\n", "\n", "with torch.no_grad():\n", " # Compute per-expert triangulations\n", " # For dual-stream: use native projectors (the actual expert perspectives)\n", " # For fused constellation: use expert rotations\n", " expert_tris = []\n", "\n", " if has_expert_rot:\n", " # Fused constellation: rotate through R/W/mu\n", " for i in range(3):\n", " centered = fused.float() - expert_mu[i]\n", " whitened = centered @ expert_W[i]\n", " rotated = F.normalize(whitened @ expert_R[i].T, dim=-1)\n", " tri = 1.0 - (rotated @ anchors.T)\n", " expert_tris.append(tri)\n", " elif f\"projectors.0.proj_native.0.weight\" in sd:\n", " # Dual-stream: use native projector embeddings\n", " def _proj_native(feats, i):\n", " W = sd[f\"projectors.{i}.proj_native.0.weight\"]\n", " b = sd[f\"projectors.{i}.proj_native.0.bias\"]\n", " lw = sd[f\"projectors.{i}.proj_native.1.weight\"]\n", " lb = sd[f\"projectors.{i}.proj_native.1.bias\"]\n", " x = feats @ W.T + b\n", " mu = x.mean(-1, keepdim=True); var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mu) / (var + 1e-5).sqrt() * lw + lb\n", " return F.normalize(x, dim=-1)\n", " for i, name in enumerate(EXPERTS):\n", " native_emb = _proj_native(val_raw[name], i)\n", " tri = 1.0 - (native_emb @ anchors.T)\n", " expert_tris.append(tri)\n", " else:\n", " # Fallback: use shared projections (will be near-identical)\n", " for p in projected:\n", " tri = 1.0 - (p @ anchors.T)\n", " expert_tris.append(tri)\n", "\n", " # Pairwise diffs\n", " diff_cd = expert_tris[0] - expert_tris[1]\n", " diff_cs = expert_tris[0] - expert_tris[2]\n", " diff_ds = expert_tris[1] - expert_tris[2]\n", " diffs = [diff_cd, diff_cs, diff_ds]\n", " diff_names = [\"CLIP − DINOv2\", \"CLIP − SigLIP\", \"DINOv2 − SigLIP\"]\n", "\n", " abs_tri = expert_tris[0]\n", "\n", " print(f\"\\n Pairwise diff statistics:\")\n", " for name, d in zip(diff_names, diffs):\n", " print(f\" {name:20s}: mean={d.mean():.6f} std={d.std():.6f} \"\n", " f\"min={d.min():.6f} max={d.max():.6f}\")\n", " print(f\" Absolute tri std: {abs_tri.std():.6f}\")\n", " diff_std = diffs[0].std().item()\n", " abs_std = abs_tri.std().item()\n", " print(f\" Ratio (diff/abs): {diff_std / abs_std:.4f}\" if abs_std > 1e-10 else\n", " f\" Ratio (diff/abs): N/A (zero abs std)\")\n", "\n", " # PCA of the diff space\n", " diff_stacked = torch.cat(diffs, dim=-1).numpy()\n", " diff_centered = diff_stacked - diff_stacked.mean(axis=0, keepdims=True)\n", " _, S_diff, Vt_diff = np.linalg.svd(diff_centered[:5000], full_matrices=False)\n", "\n", " # Guard against zero SVDs\n", " s_sum = (S_diff**2).sum()\n", " if s_sum > 1e-20:\n", " diff_3d = to_sphere(diff_centered @ Vt_diff[:3].T)\n", " var_diff = S_diff[:3]**2 / s_sum\n", " eff_dim_diff = float(((S_diff / S_diff.sum())**2).sum()**-1)\n", " else:\n", " diff_3d = np.zeros((len(diff_centered), 3))\n", " var_diff = np.zeros(3)\n", " eff_dim_diff = 0.0\n", " print(f\"\\n Diff space effective dim: {eff_dim_diff:.1f}\")\n", " print(f\" Diff PCA 3D variance: {var_diff.sum()*100:.1f}%\")\n", "\n", " abs_stacked = abs_tri.numpy()\n", " abs_centered = abs_stacked - abs_stacked.mean(axis=0, keepdims=True)\n", " _, S_abs, Vt_abs = np.linalg.svd(abs_centered[:5000], full_matrices=False)\n", " abs_eff = float(((S_abs / S_abs.sum())**2).sum()**-1) if S_abs.sum() > 1e-20 else 0.0\n", " print(f\" Absolute tri effective dim: {abs_eff:.1f}\")\n", "\n", " full_stacked = np.concatenate([abs_stacked, diff_stacked], axis=-1)\n", " full_centered = full_stacked - full_stacked.mean(axis=0, keepdims=True)\n", " _, S_full, Vt_full = np.linalg.svd(full_centered[:5000], full_matrices=False)\n", " full_eff = float(((S_full / S_full.sum())**2).sum()**-1) if S_full.sum() > 1e-20 else 0.0\n", " full_3d = to_sphere(full_centered @ Vt_full[:3].T) if S_full.sum() > 1e-20 else np.zeros((len(full_centered), 3))\n", " print(f\" Full (abs+diffs) effective dim: {full_eff:.1f}\")\n", " print(f\" Information gain from diffs: {full_eff - abs_eff:.1f} dimensions\")\n", "\n", "fig4 = plt.figure(figsize=(28, 14), facecolor='black')\n", "fig4.suptitle(\n", " 'Expert Pairwise Differences — Where the discriminative signal lives\\n'\n", " f'Diff eff_dim={eff_dim_diff:.1f} | Abs eff_dim={abs_eff:.1f} | '\n", " f'Combined eff_dim={full_eff:.1f} | Info gain: +{full_eff-abs_eff:.1f} dims',\n", " color='white', fontsize=14, y=0.98)\n", "\n", "# Row 1: Three pairwise diff distributions on sphere\n", "for col, (name, d) in enumerate(zip(diff_names, diffs)):\n", " ax = fig4.add_subplot(2, 4, col+1, projection='3d')\n", " d_np = d.numpy()\n", "\n", " # Per-image: magnitude of diff vector\n", " diff_mag = np.linalg.norm(d_np, axis=-1)\n", " mag_heat = (diff_mag - diff_mag.min()) / (diff_mag.max() - diff_mag.min() + 1e-8)\n", " mag_colors = plt.cm.magma(mag_heat)\n", "\n", " setup_ax(ax, f'{name}\\nstd={d_np.std():.5f}')\n", " ax.scatter(emb_s[:, 0], emb_s[:, 1], emb_s[:, 2],\n", " c=mag_colors, s=2, alpha=0.5)\n", "\n", "# Panel 4: Diff space PCA\n", "ax_dp = fig4.add_subplot(244, projection='3d')\n", "setup_ax(ax_dp, f'Diff Space PCA\\neff_dim={eff_dim_diff:.1f} var={var_diff.sum()*100:.1f}%')\n", "ax_dp.scatter(diff_3d[:, 0], diff_3d[:, 1], diff_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Row 2: Per-anchor diff analysis\n", "# Per-anchor mean absolute diff (where do experts disagree most?)\n", "with torch.no_grad():\n", " per_anchor_cd = diff_cd.abs().mean(dim=0).numpy()\n", " per_anchor_cs = diff_cs.abs().mean(dim=0).numpy()\n", " per_anchor_ds = diff_ds.abs().mean(dim=0).numpy()\n", " per_anchor_total = (per_anchor_cd + per_anchor_cs + per_anchor_ds) / 3\n", "\n", "# Panel 5: Anchor-level divergence map (total)\n", "ax_a = fig4.add_subplot(245, projection='3d')\n", "total_heat = (per_anchor_total - per_anchor_total.min()) / (per_anchor_total.max() - per_anchor_total.min() + 1e-8)\n", "total_colors = plt.cm.hot(total_heat)\n", "total_sizes = 5 + 40 * total_heat\n", "setup_ax(ax_a, f'Anchor Divergence (all pairs)\\n'\n", " f'range=[{per_anchor_total.min():.5f}, {per_anchor_total.max():.5f}]')\n", "ax_a.scatter(anchors_s[:, 0], anchors_s[:, 1], anchors_s[:, 2],\n", " c=total_colors, s=total_sizes, alpha=0.8)\n", "\n", "# Panel 6: Abs tri PCA vs diff PCA side by side\n", "ax_abs = fig4.add_subplot(246, projection='3d')\n", "abs_3d = to_sphere(abs_centered @ Vt_abs[:3].T)\n", "var_abs_3 = S_abs[:3]**2 / (S_abs**2).sum()\n", "setup_ax(ax_abs, f'Absolute Tri PCA\\neff_dim={abs_eff:.1f} var={var_abs_3.sum()*100:.1f}%')\n", "ax_abs.scatter(abs_3d[:, 0], abs_3d[:, 1], abs_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Panel 7: Combined PCA\n", "ax_full = fig4.add_subplot(247, projection='3d')\n", "var_full_3 = S_full[:3]**2 / (S_full**2).sum()\n", "setup_ax(ax_full, f'Combined (abs+diffs) PCA\\neff_dim={full_eff:.1f} var={var_full_3.sum()*100:.1f}%')\n", "ax_full.scatter(full_3d[:, 0], full_3d[:, 1], full_3d[:, 2],\n", " c=class_colors, s=2, alpha=0.4)\n", "\n", "# Panel 8: Histogram of diff magnitudes\n", "ax_hist = fig4.add_subplot(248)\n", "ax_hist.set_facecolor('black')\n", "for name, d, color in zip(diff_names, diffs,\n", " ['#ff6b6b', '#4ecdc4', '#ffe66d']):\n", " d_np = d.numpy()\n", " per_image_mag = np.linalg.norm(d_np, axis=-1)\n", " ax_hist.hist(per_image_mag, bins=50, alpha=0.6, color=color,\n", " label=name, density=True)\n", "ax_hist.set_xlabel('Diff magnitude (L2)', color='white', fontsize=9)\n", "ax_hist.set_ylabel('Density', color='white', fontsize=9)\n", "ax_hist.set_title('Per-image diff magnitudes', color='white', fontsize=11)\n", "ax_hist.legend(fontsize=8, facecolor='black', edgecolor='gray',\n", " labelcolor='white')\n", "ax_hist.tick_params(colors='gray', labelsize=7)\n", "ax_hist.spines['bottom'].set_color('gray'); ax_hist.spines['left'].set_color('gray')\n", "ax_hist.spines['top'].set_visible(False); ax_hist.spines['right'].set_visible(False)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.savefig(\"pairwise_diffs.png\", dpi=200, facecolor='black',\n", " bbox_inches='tight', pad_inches=0.3)\n", "print(\"Saved: pairwise_diffs.png\")\n", "plt.close()\n", "\n", "print(\"\\nDone.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H3ogFUtcf3AG", "outputId": "d55dfd36-cab8-4664-bfb2-965eccbc8632" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading soup...\n", "Loading features...\n", "Generating embeddings...\n", "PCA 3D variance: 23.6% (9.4%, 7.3%, 6.9%)\n", "Rendering figure 1...\n", "Saved: hypersphere_manifold.png\n", "Rendering figure 2...\n", "Saved: expert_perspectives.png\n", "Rendering figure 3 — anchors only...\n", "Saved: anchors_only.png\n", "Rendering figure 4 — pairwise expert diffs...\n", "\n", " Pairwise diff statistics:\n", " CLIP − DINOv2 : mean=0.005938 std=0.078534 min=-0.352356 max=0.374543\n", " CLIP − SigLIP : mean=0.005545 std=0.078470 min=-0.352054 max=0.351921\n", " DINOv2 − SigLIP : mean=-0.000393 std=0.040184 min=-0.239047 max=0.244375\n", " Absolute tri std: 0.062462\n", " Ratio (diff/abs): 1.2573\n", "\n", " Diff space effective dim: 162.2\n", " Diff PCA 3D variance: 34.6%\n", " Absolute tri effective dim: 71.3\n", " Full (abs+diffs) effective dim: 174.6\n", " Information gain from diffs: 103.3 dimensions\n", "Saved: pairwise_diffs.png\n", "\n", "Done.\n" ] } ] }, { "cell_type": "markdown", "source": [ "# last" ], "metadata": { "id": "gd1nWdsaSXjo" } }, { "cell_type": "code", "source": [ "import torch, json, os\n", "\n", "SAVE_DIR = \".\" # current directory\n", "\n", "config = {\n", " \"expert_dims\": {k: int(v) for k, v in expert_dims.items()},\n", " \"n_anchors\": N_ANCHORS, \"n_comp\": N_COMP, \"d_comp\": D_COMP,\n", " \"n_classes\": N_CLASSES, \"d_shared\": D_SHARED,\n", " \"expert_names\": list(SUBSETS),\n", " \"final_mAP\": 0.732, \"final_cv\": 0.8104,\n", " \"params\": 81764560,\n", "}\n", "\n", "path = \"soup_patchwork.pt\"\n", "torch.save({\"state_dict\": model.state_dict(), \"config\": config}, path)\n", "print(f\"Saved: {path} ({os.path.getsize(path)/1024/1024:.1f} MB)\")\n", "\n", "with open(\"soup_patchwork_config.json\", \"w\") as f:\n", " json.dump(config, f, indent=2)\n", "print(f\"Config saved\")\n", "\n", "# Upload\n", "from huggingface_hub import HfApi\n", "api = HfApi()\n", "repo_id = \"AbstractPhil/geolip-vit-x34\"\n", "api.upload_file(path_or_fileobj=path, path_in_repo=\"soup_patchwork.pt\",\n", " repo_id=repo_id, repo_type=\"model\")\n", "api.upload_file(path_or_fileobj=\"soup_patchwork_config.json\",\n", " path_in_repo=\"config.json\",\n", " repo_id=repo_id, repo_type=\"model\")\n", "print(f\"Uploaded to {repo_id}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "3abb611a705248578d9f3b400dcbf42c", "ae5ddc6303ea45588f74693c83699791", "d2d988399cd244298dcf64a16c2d02e0", "8924fa4d632e4d1398bcd2b8ee6bb70d", "a8a6a01230bc4fb68d2ecbc6120bad72", "44d0db0fbc8249baa1b7390c33428d8d", "c6005371dbbc4f668c544ff12160b088", "dbe24a50cb3340ffbf9e0ac19ed9100d", "d2e71be32a0341c886593af292dc7df3", "5c5a62ed6d3d46e38fc42f77d116ae5c", "f73ecc2daee54e778218a277313a98b0", "a306617446e24e5e84a65d36512c8b58", "ca34b5b0535f4280b3ded721d70d7ab7", "a5f3aba26f2b49d0be4873ca15234d63", "f669b8157ae341df9055fa1c26cc4a8c", "ca6f80bc961845e19188d15b2bb9ee1d", "723c47dfc8a147598b9c79df99843e92", "367e1b26e8ff42b38d8279ffc581a5b3", "b44b604695284a49b6570d87c13be245", "b61e6278ee114c8f9b929a543f116d2f", "0bff01c9619b4f4ab3a61429ac55c323", "b1d9ef32c22d42b7b858ab39a87702c1", "40efd644fa414e49832b5deefa5ef2be", "f1ef6e97a3e846e597e8411ea52410b6", "e0acf4a24abc46c3ad3b5e1d4258e231", "88d9a24766af4530adec0ace85e9a61f", "5e4157eda07745069b743b0241373b6d", "10729969c812479c86623e25b159eec0", "f0f48f206f6f499ebfad9a727dba93ae", "0828325f911043a2aa0e6a1166fac646", "b19db1fbb71244a2a032a117f76276e7", "e0998635e92948d8941f412601c9af22", "c1336333c53e47c082e97c47d6a4932d" ] }, "id": "H8xlf6aHepEA", "outputId": "4f234439-f4c5-404c-c573-64c00dea4541" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saved: soup_patchwork.pt (312.0 MB)\n", "Config saved\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3abb611a705248578d9f3b400dcbf42c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a306617446e24e5e84a65d36512c8b58" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " soup_patchwork.pt : 0%| | 555kB / 327MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "40efd644fa414e49832b5deefa5ef2be" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Uploaded to AbstractPhil/geolip-vit-x34\n" ] } ] } ] }