fix(prithvi): patch v2 datamodule transforms for IBM inference.py compat

prithvi_nyc_phase14.yaml uses GenericNonGeoSegmentationDataModule which
omits test_transform (leaves it None) and sets aug to terratorch Normalize
(only handles 4D/5D tensors). IBM inference.py:run_model() calls both on a
3D image dict, producing TypeError then ValueError for all neighborhood
queries locally.

Patch inside _ensure_model() when test_transform is None:
- test_transform → albumentations.Compose([ToTensorV2()]) matching IBM base
- aug → kornia.AugmentationSequential with v2-specific means/stds read from
the existing terratorch Normalize, preserving NYC Pluvial normalization

Full transform chain smoke-tested: (H,W,C)→CHW→BCHW dtype=float32.
Blast radius was all neighborhood intent queries, not Hollis specifically.
See /tmp/PRITHVI-HOLLIS-DIAGNOSIS.md for full analysis.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/flood_layers/prithvi_live.py

@@ -167,6 +167,25 @@ def _ensure_model():
                     log.info("prithvi_live: building v2 model from "
                              "yaml=%s ckpt=%s", v2_yaml, v2_ckpt)
                     m = LightningInferenceModel.from_config(v2_yaml, v2_ckpt)
+                    # prithvi_nyc_phase14.yaml uses GenericNonGeoSegmentationDataModule
+                    # which omits test_transform (→ None) and uses terratorch Normalize
+                    # for aug (only handles 4D/5D). IBM inference.py:run_model() calls
+                    # both on a 3D dict. Patch both to match the IBM base contract:
+                    # ToTensorV2 for test_transform; Kornia AugmentationSequential
+                    # (accepts dict input, adds batch dim) for aug.
+                    if getattr(getattr(m, 'datamodule', None),
+                               'test_transform', None) is None:
+                        import albumentations as A
+                        import kornia.augmentation as _Ka
+                        from albumentations.pytorch import ToTensorV2
+                        m.datamodule.test_transform = A.Compose([ToTensorV2()])
+                        _old = m.datamodule.aug
+                        m.datamodule.aug = _Ka.AugmentationSequential(
+                            _Ka.Normalize(_old.means.view(-1).tolist(),
+                                          _old.stds.view(-1).tolist()),
+                            data_keys=None)
+                        log.info("prithvi_live: patched v2 datamodule transforms "
+                                 "for IBM inference.py compat")
                 else:
                     log.warning("prithvi_live: v2 yaml/ckpt not "
                                 "discoverable in %s; falling back to base",