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local_only: debug

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hofee 2024-10-29 16:54:42 +00:00
parent 8d7299b482
commit b3a7650d3e
3 changed files with 19 additions and 22 deletions
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30
configs/server/server_train_config.yaml
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@ -3,11 +3,11 @@ runner:
general: general:
seed: 0 seed: 0
device: cuda device: cuda
cuda_visible_devices: "1" cuda_visible_devices: "0"
parallel: False parallel: False
experiment: experiment:
name: debug name: overfit_ab_local_only
root_dir: "experiments" root_dir: "experiments"
use_checkpoint: False use_checkpoint: False
epoch: -1 # -1 stands for last epoch epoch: -1 # -1 stands for last epoch
@ -32,46 +32,46 @@ runner:
dataset: dataset:
OmniObject3d_train: OmniObject3d_train:
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new" root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
model_dir: "../data/scaled_object_meshes" model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt" split_file: "/data/hofee/data/sample.txt"
type: train type: train
cache: True cache: True
ratio: 1 ratio: 1
batch_size: 160 batch_size: 64
num_workers: 16 num_workers: 16
pts_num: 8192 pts_num: 8192
load_from_preprocess: True load_from_preprocess: True
OmniObject3d_test: OmniObject3d_test:
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new" root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
model_dir: "../data/scaled_object_meshes" model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt" split_file: "/data/hofee/data/sample.txt"
type: test type: test
cache: True cache: True
filter_degree: 75 filter_degree: 75
eval_list: eval_list:
- pose_diff - pose_diff
ratio: 0.05 ratio: 1
batch_size: 160 batch_size: 64
num_workers: 12 num_workers: 12
pts_num: 8192 pts_num: 8192
load_from_preprocess: True load_from_preprocess: True
OmniObject3d_val: OmniObject3d_val:
root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new" root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
model_dir: "../data/scaled_object_meshes" model_dir: "../data/scaled_object_meshes"
source: nbv_reconstruction_dataset source: nbv_reconstruction_dataset
split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt" split_file: "/data/hofee/data/sample.txt"
type: test type: test
cache: True cache: True
filter_degree: 75 filter_degree: 75
eval_list: eval_list:
- pose_diff - pose_diff
ratio: 0.005 ratio: 1
batch_size: 160 batch_size: 64
num_workers: 12 num_workers: 12
pts_num: 8192 pts_num: 8192
load_from_preprocess: True load_from_preprocess: True
@ -97,7 +97,7 @@ module:
feature_transform: False feature_transform: False
transformer_seq_encoder: transformer_seq_encoder:
embed_dim: 256 embed_dim: 1280
num_heads: 4 num_heads: 4
ffn_dim: 256 ffn_dim: 256
num_layers: 3 num_layers: 3
@ -106,7 +106,7 @@ module:
gf_view_finder: gf_view_finder:
t_feat_dim: 128 t_feat_dim: 128
pose_feat_dim: 256 pose_feat_dim: 256
main_feat_dim: 3072 main_feat_dim: 2048
regression_head: Rx_Ry_and_T regression_head: Rx_Ry_and_T
pose_mode: rot_matrix pose_mode: rot_matrix
per_point_feature: False per_point_feature: False

5
core/nbv_dataset.py
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@ -34,7 +34,7 @@ class NBVReconstructionDataset(BaseDataset):
#self.model_dir = config["model_dir"] #self.model_dir = config["model_dir"]
self.filter_degree = config["filter_degree"] self.filter_degree = config["filter_degree"]
if self.type == namespace.Mode.TRAIN: if self.type == namespace.Mode.TRAIN:
scale_ratio = 100 scale_ratio = 50
self.datalist = self.datalist*scale_ratio self.datalist = self.datalist*scale_ratio
if self.cache: if self.cache:
expr_root = ConfigManager.get("runner", "experiment", "root_dir") expr_root = ConfigManager.get("runner", "experiment", "root_dir")
@ -198,9 +198,6 @@ class NBVReconstructionDataset(BaseDataset):
collate_data["best_to_world_pose_9d"] = torch.stack( collate_data["best_to_world_pose_9d"] = torch.stack(
[torch.tensor(item["best_to_world_pose_9d"]) for item in batch] [torch.tensor(item["best_to_world_pose_9d"]) for item in batch]
) )
collate_data["scanned_pts_mask"] = torch.stack(
[torch.tensor(item["scanned_pts_mask"]) for item in batch]
)
for key in batch[0].keys(): for key in batch[0].keys():
if key not in [ if key not in [

6
core/pipeline.py
View File

@ -20,8 +20,8 @@ class NBVReconstructionPipeline(nn.Module):
self.pose_encoder = ComponentFactory.create( self.pose_encoder = ComponentFactory.create(
namespace.Stereotype.MODULE, self.module_config["pose_encoder"] namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
) )
self.transformer_seq_encoder = ComponentFactory.create( self.seq_encoder = ComponentFactory.create(
namespace.Stereotype.MODULE, self.module_config["transformer_seq_encoder"] namespace.Stereotype.MODULE, self.module_config["seq_encoder"]
) )
self.view_finder = ComponentFactory.create( self.view_finder = ComponentFactory.create(
namespace.Stereotype.MODULE, self.module_config["view_finder"] namespace.Stereotype.MODULE, self.module_config["view_finder"]
@ -107,7 +107,7 @@ class NBVReconstructionPipeline(nn.Module):
seq_embedding = torch.cat([pose_feat_seq, pts_feat_seq], dim=-1) # Tensor(S x (Dp+Dl)) 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)) 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.seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
main_feat = seq_feat # Tensor(B x Ds) main_feat = seq_feat # Tensor(B x Ds)
if torch.isnan(main_feat).any(): if torch.isnan(main_feat).any():