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assume always there is state

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mshukor
2025-06-02 20:50:38 +02:00
parent 1fd53b441a
commit c2d645717a
1 changed files with 10 additions and 11 deletions
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21
lerobot/common/policies/smolvla/modeling_smolvla.py
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@@ -616,19 +616,18 @@ class VLAFlowMatching(nn.Module):
num_lang_embs = lang_emb.shape[1] num_lang_embs = lang_emb.shape[1]
att_masks += [0] * num_lang_embs att_masks += [0] * num_lang_embs
if state is not None: state_emb = self.state_proj(state)
state_emb = self.state_proj(state) state_emb = state_emb[:, None, :] if state_emb.ndim == 2 else state_emb
state_emb = state_emb[:, None, :] if state_emb.ndim == 2 else state_emb embs.append(state_emb)
embs.append(state_emb) bsize = state_emb.shape[0]
bsize = state_emb.shape[0] device = state_emb.device
device = state_emb.device
states_seq_len = state_emb.shape[1] states_seq_len = state_emb.shape[1]
state_mask = torch.ones(bsize, states_seq_len, dtype=torch.bool, device=device) state_mask = torch.ones(bsize, states_seq_len, dtype=torch.bool, device=device)
pad_masks.append(state_mask) pad_masks.append(state_mask)
# Set attention masks so that image and language inputs do not attend to state or actions # Set attention masks so that image and language inputs do not attend to state or actions
att_masks += [1] * (states_seq_len) att_masks += [1] * (states_seq_len)
embs = torch.cat(embs, dim=1) embs = torch.cat(embs, dim=1)
pad_masks = torch.cat(pad_masks, dim=1) pad_masks = torch.cat(pad_masks, dim=1)
att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device) att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device)