fix(synthesis): DEM shape is (1, 1, H, W) per encoder probe

Empirically verified against terratorch_terramind_v1_base_generate by
calling model({'DEM': torch.zeros(...)}) with each shape and reading
the encoder error:

(1, 224, 224) -> not enough values to unpack (got 3)
(1, 1, 224, 224) -> OK, LULC out (1, 10, 224, 224)
(1, 1, 1, 224, 224) -> too many values to unpack (got 5)

The encoder unpacks 'B, C, H, W = x.shape' so 4-D is required; DEM
has 1 channel, hence (1, 1, H, W). Local code's comment said this
but the implementation was off by one (.unsqueeze(0) only adds one
dim). Fix the local path too so it doesn't break if anyone runs
synthesis off the AMD path.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/context/terramind_synthesis.py

@@ -290,12 +290,11 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
         try:
             from app import inference as _inf
             if _inf.remote_enabled():
-                # Local code does `torch.from_numpy(dem).unsqueeze(0)` —
-                # i.e. 2-D (H, W) → 3-D (1, H, W). The terramind v1 base
-                # generative encoder adds the batch dim internally; sending
-                # an extra leading dim makes its embedding layer trip on
-                # `B, C, H, W = x.shape` (5-D in, expects 4). Match local.
-                dem_remote = dem[None, :, :].astype("float32")
+                # The terramind v1 base generative encoder embedding
+                # layer unpacks `B, C, H, W = x.shape` (verified against
+                # terratorch_terramind_v1_base_generate). DEM has C=1, so
+                # the on-the-wire shape is (1, 1, H, W) 4-D.
+                dem_remote = dem[None, None, :, :].astype("float32")
                 remote = _inf.terramind("synthesis", None, None, dem_remote,
                                           timeout=timeout_s)
                 if remote.get("ok"):
@@ -343,7 +342,9 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
         torch.manual_seed(DEFAULT_SEED)
 
         model = _ensure_model()
-        dem_t = torch.from_numpy(dem).unsqueeze(0).float()  # (1, 1, H, W)
+        # `dem` is 2-D (H, W) from `_read_dem_patch.src.read(1, ...)`. The
+        # terramind v1 base generative encoder wants (B=1, C=1, H, W) 4-D.
+        dem_t = torch.from_numpy(dem).unsqueeze(0).unsqueeze(0).float()
         if time.time() - t0 > timeout_s:
             return {"ok": False, "skipped": "terramind exceeded budget"}
 

services/riprap-models/main.py

@@ -381,19 +381,17 @@ def _terramind_synthesis_inference(payload: TerramindIn) -> dict[str, Any]:
     import numpy as np
     import torch
     dem_t = torch.from_numpy(dem_np).float()
-    # Match the local-inference shape contract from
-    # app/context/terramind_synthesis.py:_ensure_model — the v1 base
-    # generative encoder wants 3-D (1, H, W) and adds the batch dim
-    # internally. Anything more triggers `B, C, H, W = x.shape` to
-    # unpack 5-D and fail in the embedding layer.
+    # The v1 base generative encoder unpacks `B, C, H, W = x.shape` —
+    # 4-D required. DEM has C=1, so canonical shape is (1, 1, H, W).
+    # Verified empirically against terratorch_terramind_v1_base_generate.
     if dem_t.ndim == 2:
-        dem_t = dem_t.unsqueeze(0)        # (H, W)    -> (1, H, W)
-    elif dem_t.ndim == 4 and dem_t.shape[0] == 1 and dem_t.shape[1] == 1:
-        dem_t = dem_t.squeeze(0)          # (1, 1, H, W) -> (1, H, W)
-    elif dem_t.ndim != 3:
+        dem_t = dem_t.unsqueeze(0).unsqueeze(0)   # (H, W)    -> (1, 1, H, W)
+    elif dem_t.ndim == 3:
+        dem_t = dem_t.unsqueeze(0)                # (1, H, W) -> (1, 1, H, W)
+    elif dem_t.ndim != 4:
         raise HTTPException(status_code=400,
                             detail=f"unexpected DEM shape {tuple(dem_t.shape)}; "
-                                   f"expected (H, W) or (1, H, W)")
+                                   f"expected 4-D (B, C, H, W)")
     dem_t = _to_device(dem_t)
 
     spec = _TERRAMIND_SPECS["synthesis"]