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Bug: Fix VQ-Bet not working when n_action_pred_token=1 (#420) 

Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
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Joe Clinton 2024-09-09 10:41:13 +02:00 committed by GitHub
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1 changed files with 11 additions and 6 deletions
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lerobot/common/policies/vqbet/modeling_vqbet.py
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@ -350,17 +350,22 @@ class VQBeTModel(nn.Module):
# get action features (pass through GPT) # get action features (pass through GPT)
features = self.policy(input_tokens) features = self.policy(input_tokens)
# len(self.config.input_shapes) is the number of different observation modes. this line gets the index of action prompt tokens. # len(self.config.input_shapes) is the number of different observation modes.
# this line gets the index of action prompt tokens.
historical_act_pred_index = np.arange(0, n_obs_steps) * (len(self.config.input_shapes) + 1) + len( historical_act_pred_index = np.arange(0, n_obs_steps) * (len(self.config.input_shapes) + 1) + len(
self.config.input_shapes self.config.input_shapes
) )
# only extract the output tokens at the position of action query: # only extract the output tokens at the position of action query:
# Behavior Transformer (BeT), and VQ-BeT are both sequence-to-sequence prediction models, mapping sequential observation to sequential action (please refer to section 2.2 in BeT paper https://arxiv.org/pdf/2206.11251). # Behavior Transformer (BeT), and VQ-BeT are both sequence-to-sequence prediction models,
# Thus, it predict historical action sequence, in addition to current and future actions (predicting future actions : optional). # mapping sequential observation to sequential action (please refer to section 2.2 in BeT paper https://arxiv.org/pdf/2206.11251).
# Thus, it predicts a historical action sequence, in addition to current and future actions (predicting future actions : optional).
if len_additional_action_token > 0:
features = torch.cat( features = torch.cat(
[features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1 [features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1
) )
else:
features = features[:, historical_act_pred_index]
# pass through action head # pass through action head
action_head_output = self.action_head(features) action_head_output = self.action_head(features)
# if rollout, VQ-BeT don't calculate loss # if rollout, VQ-BeT don't calculate loss