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local_only: pipeline

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hofee 2024-10-29 12:39:06 +00:00
parent b30e9d535a
commit 234c8bccc3
1 changed files with 1 additions and 6 deletions
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core/pipeline.py
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@ -99,11 +99,6 @@ class NBVReconstructionPipeline(nn.Module):
embedding_list_batch = [] embedding_list_batch = []
combined_scanned_pts_batch = data["combined_scanned_pts"] # Tensor(B x N x 3)
global_scanned_feat = self.pts_encoder.encode_points(
combined_scanned_pts_batch, require_per_point_feat=False
) # global_scanned_feat: Tensor(B x Dg)
for scanned_n_to_world_pose_9d, scanned_pts in zip(scanned_n_to_world_pose_9d_batch, scanned_pts_batch): for scanned_n_to_world_pose_9d, scanned_pts in zip(scanned_n_to_world_pose_9d_batch, scanned_pts_batch):
scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device) # Tensor(S x 9) scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device) # Tensor(S x 9)
scanned_pts = scanned_pts.to(device) # Tensor(S x N x 3) scanned_pts = scanned_pts.to(device) # Tensor(S x N x 3)
@ -113,7 +108,7 @@ class NBVReconstructionPipeline(nn.Module):
embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dl)) embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dl))
seq_feat = self.transformer_seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds) seq_feat = self.transformer_seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg)) main_feat = seq_feat # Tensor(B x Ds)
if torch.isnan(main_feat).any(): if torch.isnan(main_feat).any():
Log.error("nan in main_feat", True) Log.error("nan in main_feat", True)