solve merge

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

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

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))
 

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)

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