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refactor(classifier): remove redundant input normalization in predict_reward method

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Khalil Meftah
2026-04-21 14:11:30 +02:00
parent 19697adf7a
commit 402f754209
1 changed files with 0 additions and 4 deletions
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src/lerobot/rewards/classifier/modeling_classifier.py
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@@ -276,10 +276,6 @@ class Classifier(PreTrainedRewardModel):
def predict_reward(self, batch, threshold=0.5): def predict_reward(self, batch, threshold=0.5):
"""Eval method. Returns predicted reward with the decision threshold as argument.""" """Eval method. Returns predicted reward with the decision threshold as argument."""
# Check for both OBS_IMAGE and OBS_IMAGES prefixes
batch = self.normalize_inputs(batch)
batch = self.normalize_targets(batch)
# Extract images from batch dict # Extract images from batch dict
images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)] images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)]