From 26c3cb4c7a15bc7759bd089525253ce312f907e0 Mon Sep 17 00:00:00 2001
From: hofee <lexhofee@gmail.com>
Date: Tue, 29 Oct 2024 17:12:24 +0000
Subject: [PATCH] global_and_local: debug

---
 configs/server/server_train_config.yaml | 2 +-
 core/nbv_dataset.py                     | 5 +----
 core/pipeline.py                        | 6 +++---
 3 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/configs/server/server_train_config.yaml b/configs/server/server_train_config.yaml
index 2b69702..77a922f 100644
--- a/configs/server/server_train_config.yaml
+++ b/configs/server/server_train_config.yaml
@@ -106,7 +106,7 @@ module:
   gf_view_finder:
     t_feat_dim: 128
     pose_feat_dim: 256
-    main_feat_dim: 2048
+    main_feat_dim: 2560
     regression_head: Rx_Ry_and_T
     pose_mode: rot_matrix
     per_point_feature: False
diff --git a/core/nbv_dataset.py b/core/nbv_dataset.py
index d47975a..f17e68b 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 = 100
+            scale_ratio = 50
             self.datalist = self.datalist*scale_ratio
         if self.cache:
             expr_root = ConfigManager.get("runner", "experiment", "root_dir")
@@ -206,9 +206,6 @@ class NBVReconstructionDataset(BaseDataset):
             collate_data["combined_scanned_pts"] = torch.stack(
                 [torch.tensor(item["combined_scanned_pts"]) 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():
                 if key not in [
diff --git a/core/pipeline.py b/core/pipeline.py
index ec05e32..afd391b 100644
--- a/core/pipeline.py
+++ b/core/pipeline.py
@@ -20,8 +20,8 @@ class NBVReconstructionPipeline(nn.Module):
         self.pose_encoder = ComponentFactory.create(
             namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
         )
-        self.transformer_seq_encoder = ComponentFactory.create(
-            namespace.Stereotype.MODULE, self.module_config["transformer_seq_encoder"]
+        self.seq_encoder = ComponentFactory.create(
+            namespace.Stereotype.MODULE, self.module_config["seq_encoder"]
         )
         self.view_finder = ComponentFactory.create(
             namespace.Stereotype.MODULE, self.module_config["view_finder"]
@@ -112,7 +112,7 @@ class NBVReconstructionPipeline(nn.Module):
             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_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 = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))
 
         if torch.isnan(main_feat).any():