diff --git a/configs/local/train_config.yaml b/configs/local/train_config.yaml index 226c302..77e97a9 100644 --- a/configs/local/train_config.yaml +++ b/configs/local/train_config.yaml @@ -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 diff --git a/configs/server/server_train_config.yaml b/configs/server/server_train_config.yaml index 7f15668..2083121 100644 --- a/configs/server/server_train_config.yaml +++ b/configs/server/server_train_config.yaml @@ -7,7 +7,11 @@ runner: parallel: False experiment: +<<<<<<< HEAD name: test_new_pipeline_train_overfit +======= + name: debug +>>>>>>> 63a246c0c87d42f04076a459adcfdc88c954b09c root_dir: "experiments" use_checkpoint: False epoch: -1 # -1 stands for last epoch @@ -32,10 +36,10 @@ runner: dataset: OmniObject3d_train: - root_dir: "/data/hofee/data/packed_preprocessed_data" + 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: "/data/hofee/data/OmniObject3d_train_overfit.txt" + split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt" type: train cache: True ratio: 1 @@ -44,27 +48,27 @@ dataset: pts_num: 4096 load_from_preprocess: True - # OmniObject3d_test: - # root_dir: "/data/hofee/data/packed_preprocessed_data" - # model_dir: "../data/scaled_object_meshes" - # source: nbv_reconstruction_dataset - # split_file: "/data/hofee/data/OmniObject3d_test.txt" - # type: test - # cache: True - # filter_degree: 75 - # eval_list: - # - pose_diff - # ratio: 0.05 - # batch_size: 160 - # num_workers: 12 - # pts_num: 4096 - # load_from_preprocess: True - - OmniObject3d_val: - root_dir: "/data/hofee/data/packed_preprocessed_data" + OmniObject3d_test: + 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: "/data/hofee/data/OmniObject3d_train_overfit.txt" + split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt" + type: test + cache: True + filter_degree: 75 + eval_list: + - pose_diff + ratio: 0.05 + batch_size: 160 + num_workers: 12 + pts_num: 4096 + load_from_preprocess: True + + OmniObject3d_val: + 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/sample.txt" type: test cache: True filter_degree: 75 diff --git a/core/global_pts_n_num_pipeline.py b/core/global_pts_n_num_pipeline.py index b0e44ba..164253c 100644 --- a/core/global_pts_n_num_pipeline.py +++ b/core/global_pts_n_num_pipeline.py @@ -121,19 +121,20 @@ 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.max(partial_perpoint_feat, dim=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.float32).to(device).unsqueeze(-1) # 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)) diff --git a/core/nbv_dataset.py b/core/nbv_dataset.py index 87e7be1..5dafad3 100644 --- a/core/nbv_dataset.py +++ b/core/nbv_dataset.py @@ -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") @@ -174,6 +174,7 @@ class NBVReconstructionDataset(BaseDataset): combined_scanned_views_pts_mask[start_idx:end_idx] = i start_idx = end_idx 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) diff --git a/runners/strategy_generator.py b/runners/strategy_generator.py index 4e9daa7..6c63dc2 100644 --- a/runners/strategy_generator.py +++ b/runners/strategy_generator.py @@ -93,18 +93,8 @@ class StrategyGenerator(Runner): else: nrm = np.load(nrm_path) nrm_list.append(nrm) -<<<<<<< HEAD - pts_list.append(pts) - indices = np.load(idx_path) - -======= - - indices = np.load(idx_path) - pts_list.append(pts) - ->>>>>>> a883a31968b668a26545f2e8766179365308b0e2 scan_points_indices_list.append(indices) if pts.shape[0] > 0: non_zero_cnt += 1