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fix: Fix a spelling typo in the RTC evaluation docstring. (#4253)

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Signed-off-by: nathon-lee <leejianwoo@gmail.com>
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nathon
2026-07-31 19:07:13 +08:00
committed by GitHub
parent 63efc93b0c
commit f2d64506e3
2 changed files with 2 additions and 2 deletions
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examples/rtc/eval_dataset.py
+1 -1
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@@ -417,7 +417,7 @@ class RTCEvaluator:
def run_evaluation(self): def run_evaluation(self):
"""Run evaluation on two random dataset samples using three separate policies. """Run evaluation on two random dataset samples using three separate policies.
Note: Policies are deinitalized after each step to free memory. Large models Note: Policies are deinitialized after each step to free memory. Large models
(e.g., VLA models with billions of parameters) cannot fit three instances in (e.g., VLA models with billions of parameters) cannot fit three instances in
memory simultaneously. By deleting and garbage collecting after each step, memory simultaneously. By deleting and garbage collecting after each step,
we ensure only one policy is loaded at a time. we ensure only one policy is loaded at a time.
src/lerobot/policies/molmoact2/molmoact2_hf_model/modeling_molmoact2.py
+1 -1
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@@ -1801,7 +1801,7 @@ class MolmoAct2VisionBackbone(nn.Module):
valid = pooled_patches_idx >= 0 valid = pooled_patches_idx >= 0
valid_token = torch.any(valid, -1) valid_token = torch.any(valid, -1)
# Use `pooled_patches_idx` to arange the features for image pooling # Use `pooled_patches_idx` to arrange the features for image pooling
batch_idx = torch.arange( batch_idx = torch.arange(
pooled_patches_idx.shape[0], pooled_patches_idx.shape[0],
dtype=torch.long, dtype=torch.long,