global_only: debug

configs/server/server_train_config.yaml

@@ -7,7 +7,7 @@ runner:
     parallel: False
     
   experiment:
-    name: overfit_ab_global_and_local
+    name: overfit_ab_global_only
     root_dir: "experiments"
     use_checkpoint: False
     epoch: -1 # -1 stands for last epoch
@@ -39,7 +39,7 @@ dataset:
     type: train
     cache: True
     ratio: 1
-    batch_size: 32
+    batch_size: 80
     num_workers: 16
     pts_num: 8192
     load_from_preprocess: True
@@ -55,7 +55,7 @@ dataset:
     eval_list:
       - pose_diff
     ratio: 1
-    batch_size: 32
+    batch_size: 80
     num_workers: 12
     pts_num: 8192
     load_from_preprocess: True
@@ -71,7 +71,7 @@ dataset:
     eval_list:
       - pose_diff
     ratio: 1
-    batch_size: 32
+    batch_size: 80
     num_workers: 12
     pts_num: 8192
     load_from_preprocess: True
@@ -92,21 +92,21 @@ module:
 
   pointnet_encoder:
     in_dim: 3
-    out_dim: 512
+    out_dim: 1024
     global_feat: True
     feature_transform: False
 
   transformer_seq_encoder:
-    embed_dim: 768
+    embed_dim: 256
     num_heads: 4
     ffn_dim: 256
     num_layers: 3
-    output_dim: 2048
+    output_dim: 1024
 
   gf_view_finder:
     t_feat_dim: 128
     pose_feat_dim: 256
-    main_feat_dim: 2560
+    main_feat_dim: 2048
     regression_head: Rx_Ry_and_T
     pose_mode: rot_matrix
     per_point_feature: False

core/nbv_dataset.py

@@ -206,11 +206,9 @@ class NBVReconstructionDataset(BaseDataset):
             collate_data["combined_scanned_pts"] = torch.stack(
                 [torch.tensor(item["combined_scanned_pts"]) for item in batch]
             )
-                
             for key in batch[0].keys():
                 if key not in [
                     "scanned_pts",
-                    "scanned_pts_mask",
                     "scanned_n_to_world_pose_9d",
                     "best_to_world_pose_9d",
                     "combined_scanned_pts",

core/pipeline.py

@@ -29,7 +29,6 @@ class NBVReconstructionPipeline(nn.Module):
         
 
         self.eps = float(self.config["eps"])
-        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
 
     def forward(self, data):
         mode = data["mode"]
@@ -92,9 +91,7 @@ class NBVReconstructionPipeline(nn.Module):
         scanned_n_to_world_pose_9d_batch = data[
             "scanned_n_to_world_pose_9d"
         ]  # List(B): Tensor(S x 9)
-        scanned_pts_batch = data[
-            "scanned_pts"
-        ]
+
         device = next(self.parameters()).device
 
         embedding_list_batch = []
@@ -104,13 +101,11 @@ class NBVReconstructionPipeline(nn.Module):
             combined_scanned_pts_batch, require_per_point_feat=False
         )  # global_scanned_feat: Tensor(B x Dg)
 
-        for scanned_n_to_world_pose_9d, scanned_pts in zip(scanned_n_to_world_pose_9d_batch, scanned_pts_batch):
+        for scanned_n_to_world_pose_9d in scanned_n_to_world_pose_9d_batch:
             scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)  # Tensor(S x 9)
-            scanned_pts = scanned_pts.to(device)  # Tensor(S x N x 3)
             pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)  # Tensor(S x Dp) 
-            pts_feat_seq = self.pts_encoder.encode_points(scanned_pts, require_per_point_feat=False)  # Tensor(S x 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))
+            seq_embedding = pose_feat_seq
+            embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
         
         seq_feat = self.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))