constellation_a_test_trainer_cifar10.py

#!/usr/bin/env python3
"""
GeoLIP Core — Back to Basics
==============================
Conv encoder → sphere → ConstellationCore → classifier.

Two augmented views → InfoNCE + CE + attract + CV + spread.
Anchor push every N batches (self-distillation across time).

Uses constellation.py for all geometric components.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import os, time
from tqdm import tqdm
from torchvision import datasets, transforms
from torch.utils.tensorboard import SummaryWriter

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True


# ══════════════════════════════════════════════════════════════════
# CONV ENCODER
# ══════════════════════════════════════════════════════════════════

class ConvEncoder(nn.Module):
    """6-layer conv backbone → flat vector → project → LayerNorm."""
    def __init__(self, output_dim=192):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1), nn.BatchNorm2d(64), nn.GELU(),
            nn.Conv2d(64, 64, 3, padding=1), nn.BatchNorm2d(64), nn.GELU(),
            nn.MaxPool2d(2),

            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.GELU(),
            nn.Conv2d(128, 128, 3, padding=1), nn.BatchNorm2d(128), nn.GELU(),
            nn.MaxPool2d(2),

            nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256), nn.GELU(),
            nn.Conv2d(256, 256, 3, padding=1), nn.BatchNorm2d(256), nn.GELU(),
            nn.MaxPool2d(2),

            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
        )
        self.proj = nn.Sequential(
            nn.Linear(256, output_dim),
            nn.LayerNorm(output_dim),
        )

    def forward(self, x):
        return self.proj(self.features(x))


# ══════════════════════════════════════════════════════════════════
# GEOLIP CORE — encoder + constellation pipeline
# ══════════════════════════════════════════════════════════════════

class GeoLIPCore(nn.Module):
    """Conv encoder → L2 normalize → ConstellationCore.

    The encoder is the only component that sees pixels.
    Everything after normalization is geometric.
    """
    def __init__(self, num_classes=10, output_dim=192,
                 n_anchors=64, n_comp=8, d_comp=64,
                 anchor_drop=0.15, activation='squared_relu',
                 cv_target=0.22, infonce_temp=0.07):
        super().__init__()
        self.output_dim = output_dim

        self.config = {k: v for k, v in locals().items()
                       if k != 'self' and not k.startswith('_')}

        self.encoder = ConvEncoder(output_dim)
        self.core = ConstellationCore(
            num_classes=num_classes,
            dim=output_dim,
            n_anchors=n_anchors,
            n_comp=n_comp,
            d_comp=d_comp,
            anchor_drop=anchor_drop,
            activation=activation,
            cv_target=cv_target,
            infonce_temp=infonce_temp,
        )

        self._init_encoder_weights()

    def _init_encoder_weights(self):
        for m in self.encoder.modules():
            if isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, (nn.BatchNorm2d, nn.LayerNorm)):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        feat = self.encoder(x)
        emb = F.normalize(feat, dim=-1)
        return self.core(emb)

    def compute_loss(self, output, targets, output_aug=None):
        return self.core.compute_loss(output, targets, output_aug)

    def push_anchors_to_centroids(self, emb_buffer, label_buffer, lr=0.1):
        return self.core.push_anchors_to_centroids(emb_buffer, label_buffer, lr)


# ══════════════════════════════════════════════════════════════════
# DATA
# ══════════════════════════════════════════════════════════════════

CIFAR_MEAN = (0.4914, 0.4822, 0.4465)
CIFAR_STD = (0.2470, 0.2435, 0.2616)


class TwoViewDataset(torch.utils.data.Dataset):
    def __init__(self, base_ds, transform):
        self.base = base_ds
        self.transform = transform
    def __len__(self):
        return len(self.base)
    def __getitem__(self, i):
        img, label = self.base[i]
        return self.transform(img), self.transform(img), label


# ══════════════════════════════════════════════════════════════════
# TRAINING
# ══════════════════════════════════════════════════════════════════

# Config
NUM_CLASSES = 10
OUTPUT_DIM = 256
N_ANCHORS = 64
N_COMP = 8
D_COMP = 64
BATCH = 256
EPOCHS = 100
LR = 3e-4
ACTIVATION = 'squared_relu'

# Anchor push config
PUSH_INTERVAL = 100
PUSH_LR = 0.1
PUSH_BUFFER_SIZE = 5000

print("=" * 60)
print("GeoLIP Core — Conv + ConstellationCore")
print(f"  Encoder: 6-layer conv → {OUTPUT_DIM}-d sphere")
print(f"  Constellation: {N_ANCHORS} anchors, {N_COMP}×{D_COMP} patchwork")
print(f"  Activation: {ACTIVATION}")
print(f"  Loss: CE + InfoNCE + attract + CV(0.22) + spread")
print(f"  Batch: {BATCH}, LR: {LR}, Epochs: {EPOCHS}")
print(f"  Push: every {PUSH_INTERVAL} batches, lr={PUSH_LR}")
print(f"  Device: {DEVICE}")
print("=" * 60)

aug_transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(0.2, 0.2, 0.2, 0.05),
    transforms.ToTensor(),
    transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
])
val_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
])

raw_train = datasets.CIFAR10(root='./data', train=True, download=True)
train_ds = TwoViewDataset(raw_train, aug_transform)
val_ds = datasets.CIFAR10(root='./data', train=False,
                           download=True, transform=val_transform)

train_loader = torch.utils.data.DataLoader(
    train_ds, batch_size=BATCH, shuffle=True,
    num_workers=2, pin_memory=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(
    val_ds, batch_size=BATCH, shuffle=False,
    num_workers=2, pin_memory=True)

print(f"  Train: {len(train_ds):,}  Val: {len(val_ds):,}")

# Build
model = GeoLIPCore(
    num_classes=NUM_CLASSES, output_dim=OUTPUT_DIM,
    n_anchors=N_ANCHORS, n_comp=N_COMP, d_comp=D_COMP,
    activation=ACTIVATION,
).to(DEVICE)

n_params = sum(p.numel() for p in model.parameters())
n_enc = sum(p.numel() for p in model.encoder.parameters())
n_core = sum(p.numel() for p in model.core.parameters())
print(f"  Parameters: {n_params:,} (encoder: {n_enc:,}, core: {n_core:,})")

optimizer = torch.optim.Adam(model.parameters(), lr=LR)
total_steps = len(train_loader) * EPOCHS
warmup_steps = len(train_loader) * 3
scheduler = torch.optim.lr_scheduler.SequentialLR(
    optimizer,
    [torch.optim.lr_scheduler.LinearLR(
        optimizer, start_factor=0.01, total_iters=warmup_steps),
     torch.optim.lr_scheduler.CosineAnnealingLR(
         optimizer, T_max=max(total_steps - warmup_steps, 1), eta_min=1e-6)],
    milestones=[warmup_steps])

scaler = torch.amp.GradScaler("cuda")
os.makedirs("checkpoints", exist_ok=True)
writer = SummaryWriter("runs/geolip_core")
best_acc = 0.0
gs = 0

emb_buffer = None
lbl_buffer = None
push_count = 0

print(f"\n{'='*60}")
print(f"TRAINING — {EPOCHS} epochs")
print(f"{'='*60}")

for epoch in range(EPOCHS):
    model.train()
    t0 = time.time()
    tot_loss, tot_nce_acc, tot_nearest_cos, n = 0, 0, 0, 0
    correct, total = 0, 0

    pbar = tqdm(train_loader, desc=f"E{epoch+1:3d}/{EPOCHS}", unit="b")
    for v1, v2, targets in pbar:
        v1 = v1.to(DEVICE, non_blocking=True)
        v2 = v2.to(DEVICE, non_blocking=True)
        targets = targets.to(DEVICE, non_blocking=True)

        with torch.amp.autocast("cuda", dtype=torch.bfloat16):
            out1 = model(v1)
            out2 = model(v2)
            loss, ld = model.compute_loss(out1, targets, output_aug=out2)

        optimizer.zero_grad(set_to_none=True)
        scaler.scale(loss).backward()
        scaler.unscale_(optimizer)
        nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        scaler.step(optimizer)
        scaler.update()
        scheduler.step()
        gs += 1

        # Accumulate embeddings for anchor push
        with torch.no_grad():
            batch_emb = out1['embedding'].detach().float()
            if emb_buffer is None:
                emb_buffer = batch_emb
                lbl_buffer = targets.detach()
            else:
                emb_buffer = torch.cat([emb_buffer, batch_emb])[-PUSH_BUFFER_SIZE:]
                lbl_buffer = torch.cat([lbl_buffer, targets.detach()])[-PUSH_BUFFER_SIZE:]

        # Periodic anchor push
        if gs % PUSH_INTERVAL == 0 and emb_buffer is not None and emb_buffer.shape[0] > 500:
            moved = model.push_anchors_to_centroids(
                emb_buffer, lbl_buffer, lr=PUSH_LR)
            push_count += 1
            writer.add_scalar("step/anchors_moved", moved, gs)

        preds = out1['logits'].argmax(-1)
        correct += (preds == targets).sum().item()
        total += targets.shape[0]
        tot_loss += loss.item()
        tot_nce_acc += ld.get('nce_acc', 0)
        tot_nearest_cos += ld.get('nearest_cos', 0)
        n += 1

        if n % 10 == 0:
            pbar.set_postfix(
                loss=f"{tot_loss/n:.4f}",
                acc=f"{100*correct/total:.0f}%",
                nce=f"{tot_nce_acc/n:.2f}",
                cos=f"{ld.get('nearest_cos', 0):.3f}",
                push=push_count,
                ordered=True)

    elapsed = time.time() - t0
    train_acc = 100 * correct / total

    # Val
    model.eval()
    vc, vt_n = 0, 0
    all_embs = []
    with torch.no_grad(), torch.amp.autocast("cuda", dtype=torch.bfloat16):
        for imgs, lbls in val_loader:
            imgs = imgs.to(DEVICE)
            lbls = lbls.to(DEVICE)
            out = model(imgs)
            vc += (out['logits'].argmax(-1) == lbls).sum().item()
            vt_n += lbls.shape[0]
            all_embs.append(out['embedding'].float().cpu())

    val_acc = 100 * vc / vt_n

    # CV metric
    embs = torch.cat(all_embs)[:2000].to(DEVICE)
    with torch.no_grad():
        v_cv = GeometricOps.cv_metric(embs, n_samples=200)
        diag = GeometricOps.diagnostics(model.core.constellation, embs)

    writer.add_scalar("epoch/train_acc", train_acc, epoch + 1)
    writer.add_scalar("epoch/val_acc", val_acc, epoch + 1)
    writer.add_scalar("epoch/val_cv", v_cv, epoch + 1)
    writer.add_scalar("epoch/anchors", diag['n_active'], epoch + 1)
    writer.add_scalar("epoch/nearest_cos", tot_nearest_cos / n, epoch + 1)
    writer.add_scalar("epoch/push_count", push_count, epoch + 1)

    mk = ""
    if val_acc > best_acc:
        best_acc = val_acc
        torch.save({
            "state_dict": model.state_dict(),
            "config": model.config,
            "epoch": epoch + 1,
            "val_acc": val_acc,
        }, "checkpoints/geolip_core_best.pt")
        mk = " ★"

    nce_m = tot_nce_acc / n
    cos_m = tot_nearest_cos / n
    cv_band = "✓" if 0.18 <= v_cv <= 0.25 else "✗"
    print(f"  E{epoch+1:3d}: train={train_acc:.1f}% val={val_acc:.1f}% "
          f"loss={tot_loss/n:.4f} nce={nce_m:.2f} cos={cos_m:.3f} "
          f"cv={v_cv:.4f}({cv_band}) anch={diag['n_active']}/{N_ANCHORS} "
          f"push={push_count} ({elapsed:.0f}s){mk}")

writer.close()
print(f"\n  Best val accuracy: {best_acc:.1f}%")
print(f"  Parameters: {n_params:,}")
print(f"\n{'='*60}")
print("DONE")
print(f"{'='*60}")