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debug new training

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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
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2
configs/local/train_config.yaml
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@ -84,7 +84,7 @@ module:
gf_view_finder:
t_feat_dim: 128
pose_feat_dim: 256
main_feat_dim: 2048
main_feat_dim: 3072
regression_head: Rx_Ry_and_T
pose_mode: rot_matrix
per_point_feature: False

32
configs/server/server_train_config.yaml
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@ -7,7 +7,7 @@ runner:
parallel: False
experiment:
name: full_w_global_feat_wo_local_pts_feat
name: debug
root_dir: "experiments"
use_checkpoint: False
epoch: -1 # -1 stands for last epoch
@ -28,14 +28,14 @@ runner:
- OmniObject3d_test
- OmniObject3d_val
pipeline: nbv_reconstruction_global_pts_pipeline
pipeline: nbv_reconstruction_global_pts_n_num_pipeline
dataset:
OmniObject3d_train:
root_dir: "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy"
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt"
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
type: train
cache: True
ratio: 1
@ -45,10 +45,10 @@ dataset:
load_from_preprocess: True
OmniObject3d_test:
root_dir: "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy"
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/OmniObject3d_test.txt"
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
type: test
cache: True
filter_degree: 75
@ -61,10 +61,10 @@ dataset:
load_from_preprocess: True
OmniObject3d_val:
root_dir: "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy"
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt"
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
type: test
cache: True
filter_degree: 75
@ -96,6 +96,15 @@ pipeline:
eps: 1e-5
global_scanned_feat: True
nbv_reconstruction_global_pts_n_num_pipeline:
modules:
pts_encoder: pointnet_encoder
transformer_seq_encoder: transformer_seq_encoder
pose_encoder: pose_encoder
view_finder: gf_view_finder
pts_num_encoder: pts_num_encoder
eps: 1e-5
global_scanned_feat: True
module:
@ -107,7 +116,7 @@ module:
feature_transform: False
transformer_seq_encoder:
embed_dim: 1344
embed_dim: 384
num_heads: 4
ffn_dim: 256
num_layers: 3
@ -116,7 +125,7 @@ module:
gf_view_finder:
t_feat_dim: 128
pose_feat_dim: 256
main_feat_dim: 2048
main_feat_dim: 3072
regression_head: Rx_Ry_and_T
pose_mode: rot_matrix
per_point_feature: False
@ -128,6 +137,9 @@ module:
pose_dim: 9
out_dim: 256
pts_num_encoder:
out_dim: 64
loss_function:
gf_loss:

11
core/global_pts_n_num_pipeline.py
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@ -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
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@ -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)