hofee/nbv_reconstruction
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

debug new training

Browse Source
This commit is contained in:
hofee
2024-10-28 19:15:48 +00:00
parent a883a31968
commit 63a246c0c8
4 changed files with 31 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
core/global_pts_n_num_pipeline.py
View File

@@ -117,22 +117,21 @@ class NBVReconstructionGlobalPointsPipeline(nn.Module):
for seq_idx in range(seq_len):
partial_idx_in_combined_pts = scanned_mask == seq_idx # Ndarray(V), N->V idx mask
partial_perpoint_feat = perpoint_scanned_feat[partial_idx_in_combined_pts] # Ndarray(V x Dl)
partial_feat = torch.mean(partial_perpoint_feat, dim=0)[0] # Tensor(Dl)
partial_feat = torch.mean(partial_perpoint_feat, dim=0) # Tensor(Dl)
partial_feat_seq.append(partial_feat)
scanned_target_pts_num.append(partial_perpoint_feat.shape[0])
scanned_target_pts_num = torch.tensor(scanned_target_pts_num, dtype=torch.int32).to(device) # Tensor(S)
scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device) # Tensor(S x 9)
scanned_target_pts_num = torch.tensor(scanned_target_pts_num, dtype=torch.float32).unsqueeze(-1).to(device) # Tensor(S x 1)
scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device) # Tensor(S x 9)
pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d) # Tensor(S x Dp)
pts_num_feat_seq = self.pts_num_encoder.encode_pts_num(scanned_target_pts_num) # Tensor(S x Dn)
partial_feat_seq = torch.stack(partial_feat_seq) # Tensor(S x Dl)
seq_embedding = torch.cat([pose_feat_seq, pts_num_feat_seq, partial_feat_seq], dim=-1) # Tensor(S x (Dp+Dn+Dl))
embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dn+Dl))
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))
if torch.isnan(main_feat).any():

9
core/nbv_dataset.py
View File

@@ -34,7 +34,7 @@ class NBVReconstructionDataset(BaseDataset):
self.model_dir = config["model_dir"]
self.filter_degree = config["filter_degree"]
if self.type == namespace.Mode.TRAIN:
scale_ratio = 1
scale_ratio = 100
self.datalist = self.datalist*scale_ratio
if self.cache:
expr_root = ConfigManager.get("runner", "experiment", "root_dir")
@@ -122,7 +122,7 @@ class NBVReconstructionDataset(BaseDataset):
scanned_views_pts,
scanned_coverages_rate,
scanned_n_to_world_pose,
) = ([], [], [], [])
) = ([], [], [])
for view in scanned_views:
frame_idx = view[0]
coverage_rate = view[1]
@@ -164,7 +164,7 @@ class NBVReconstructionDataset(BaseDataset):
combined_scanned_views_pts, self.pts_num, require_idx=True
)
combined_scanned_views_pts_mask = np.zeros(len(scanned_views_pts), dtype=np.uint8)
combined_scanned_views_pts_mask = np.zeros(len(combined_scanned_views_pts), dtype=np.uint8)
start_idx = 0
for i in range(len(scanned_views_pts)):
@@ -174,9 +174,6 @@ class NBVReconstructionDataset(BaseDataset):
fps_downsampled_combined_scanned_pts_mask = combined_scanned_views_pts_mask[fps_idx]
data_item = {
"scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
"scanned_pts_mask": np.asarray(fps_downsampled_combined_scanned_pts_mask,dtype=np.uint8), # Ndarray(N), range(0, S)