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global_and_local: pipeline

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hofee 2024-10-29 12:32:42 +00:00
parent ab31ba46a9
commit d8c95b6f0c
2 changed files with 9 additions and 5 deletions
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2
configs/server/server_train_config.yaml
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@ -28,7 +28,7 @@ runner:
#- OmniObject3d_test #- OmniObject3d_test
- OmniObject3d_val - OmniObject3d_val
pipeline: nbv_reconstruction_global_pts_n_num_pipeline pipeline: nbv_reconstruction_pipeline
dataset: dataset:
OmniObject3d_train: OmniObject3d_train:

12
core/pipeline.py
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@ -92,7 +92,9 @@ class NBVReconstructionPipeline(nn.Module):
scanned_n_to_world_pose_9d_batch = data[ scanned_n_to_world_pose_9d_batch = data[
"scanned_n_to_world_pose_9d" "scanned_n_to_world_pose_9d"
] # List(B): Tensor(S x 9) ] # List(B): Tensor(S x 9)
scanned_pts_batch = data[
"scanned_pts"
]
device = next(self.parameters()).device device = next(self.parameters()).device
embedding_list_batch = [] embedding_list_batch = []
@ -102,11 +104,13 @@ class NBVReconstructionPipeline(nn.Module):
combined_scanned_pts_batch, require_per_point_feat=False combined_scanned_pts_batch, require_per_point_feat=False
) # global_scanned_feat: Tensor(B x Dg) ) # global_scanned_feat: Tensor(B x Dg)
for scanned_n_to_world_pose_9d in scanned_n_to_world_pose_9d_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)
pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d) # Tensor(S x Dp) pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d) # Tensor(S x Dp)
seq_embedding = pose_feat_seq pts_feat_seq = self.pts_encoder.encode_points(scanned_pts, require_per_point_feat=False) # Tensor(S x Dl)
embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp)) seq_embedding = torch.cat([pose_feat_seq, pts_feat_seq], dim=-1) # 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 = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))