fix(evo1): infer batch size after normalizing image dims

`_collect_image_batches` read `batch_size = batch[camera_keys[0]].shape[0]`
before normalizing per-camera tensors to `(B, C, H, W)`. For an unbatched
`(C, H, W)` input (which the function tries to support via the `image.dim() == 3`
branch), this picked up the channel count `C` instead of the real batch size,
making the subsequent per-sample loop iterate `C` times and indexing go
out of bounds.

Normalize each camera tensor up-front, then read `batch_size` from the
normalized batch dim. Adds `test_collect_image_batches_handles_unbatched_chw`
covering the regression.

Reported by Copilot review on huggingface/lerobot#3545.

tests/policies/evo1/test_evo1.py

@@ -180,6 +180,23 @@ def test_evo1_policy_forward_and_inference_use_batched_embedding(monkeypatch):
     assert selected.shape == (2, ACTION_DIM)
 
 
+def test_collect_image_batches_handles_unbatched_chw(monkeypatch):
+    # Regression for an issue where batch_size was read from shape[0] before normalizing
+    # per-camera tensor dims, so an unbatched (C, H, W) input was treated as batch_size=C.
+    monkeypatch.setattr(modeling_evo1, "EVO1", DummyEVO1)
+    policy = modeling_evo1.EVO1Policy(make_config())
+    batch = {
+        OBS_STATE: torch.randn(1, STATE_DIM),
+        f"{OBS_IMAGES}.front": torch.rand(3, 16, 16),
+    }
+
+    image_batches, image_masks = policy._collect_image_batches(batch)
+
+    assert len(image_batches) == 1
+    assert len(image_batches[0]) == policy.config.max_views
+    assert image_masks.tolist() == [[True, False]]
+
+
 def test_evo1_action_mask_accepts_chunk_size_one(monkeypatch):
     monkeypatch.setattr(modeling_evo1, "EVO1", DummyEVO1)
     config = make_config(chunk_size=1, n_action_steps=1)