remove fps

app_split.py

@@ -5,5 +5,5 @@ from runners.data_spliter import DataSpliter
 class DataSplitApp:
     @staticmethod
     def start():
-        DataSpliter("configs/server/split_dataset_config.yaml").run()
+        DataSpliter("configs/server/server_split_dataset_config.yaml").run()
         

configs/local/strategy_generate_config.yaml

@@ -22,8 +22,6 @@ runner:
   
 datasets:
     OmniObject3d:
-      root_dir: /home/data/hofee/project/nbv_rec_part2_preprocessed
-      from: 960
-      to: 1000 # -1 means end
-
-    
+      root_dir: /data/hofee/nbv_rec_part2_preprocessed
+      from: 155
+      to: 165 # ..-1 means end

configs/server/server_split_dataset_config.yaml

@@ -0,0 +1,22 @@
+
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+  
+  split: # 
+    root_dir: "/data/hofee/data/packed_preprocessed_data"
+    type: "unseen_instance" # "unseen_category"
+    datasets:
+      OmniObject3d_train: 
+        path: "/data/hofee/data/OmniObject3d_train.txt"
+        ratio: 0.9
+
+      OmniObject3d_test: 
+        path: "/data/hofee/data/OmniObject3d_test.txt"
+        ratio: 0.1

configs/server/server_train_config.yaml

@@ -13,7 +13,7 @@ runner:
     epoch: -1 # -1 stands for last epoch
     max_epochs: 5000
     save_checkpoint_interval: 1
-    test_first: True  
+    test_first: False  
 
   train:
     optimizer:
@@ -25,7 +25,7 @@ runner:
   test:
     frequency: 3 # test frequency
     dataset_list:
-      - OmniObject3d_test
+      #- OmniObject3d_test
       - OmniObject3d_val
 
   pipeline: nbv_reconstruction_global_pts_n_num_pipeline
@@ -41,7 +41,7 @@ dataset:
     ratio: 1
     batch_size: 160
     num_workers: 16
-    pts_num: 4096
+    pts_num: 8192
     load_from_preprocess: True
 
   OmniObject3d_test:
@@ -57,7 +57,7 @@ dataset:
     ratio: 0.05
     batch_size: 160
     num_workers: 12
-    pts_num: 4096
+    pts_num: 8192
     load_from_preprocess: True
 
   OmniObject3d_val:
@@ -73,7 +73,7 @@ dataset:
     ratio: 0.005
     batch_size: 160
     num_workers: 12
-    pts_num: 4096
+    pts_num: 8192
     load_from_preprocess: True
 
 

core/global_pts_n_num_pipeline.py

@@ -1,4 +1,5 @@
 import torch
+import time
 from torch import nn
 import PytorchBoot.namespace as namespace
 import PytorchBoot.stereotype as stereotype
@@ -58,7 +59,10 @@ class NBVReconstructionGlobalPointsPipeline(nn.Module):
         return perturbed_x, random_t, target_score, std
 
     def forward_train(self, data):
+        start_time = time.time()
         main_feat = self.get_main_feat(data)
+        end_time = time.time()
+        print("get_main_feat time: ", end_time - start_time)
         """ get std """
         best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
         perturbed_x, random_t, target_score, std = self.pertube_data(

core/nbv_dataset.py

@@ -8,7 +8,7 @@ import torch
 import os
 import sys
 
-sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
+sys.path.append(r"/data/hofee/project/nbv_rec/nbv_reconstruction")
 
 from utils.data_load import DataLoadUtil
 from utils.pose import PoseUtil
@@ -31,7 +31,7 @@ class NBVReconstructionDataset(BaseDataset):
         self.load_from_preprocess = config.get("load_from_preprocess", False)
 
         if self.type == namespace.Mode.TEST:
-            self.model_dir = config["model_dir"]
+            #self.model_dir = config["model_dir"]
             self.filter_degree = config["filter_degree"]
         if self.type == namespace.Mode.TRAIN:
             scale_ratio = 100
@@ -66,6 +66,8 @@ class NBVReconstructionDataset(BaseDataset):
                 if max_coverage_rate > scene_max_coverage_rate:
                     scene_max_coverage_rate = max_coverage_rate
                 max_coverage_rate_list.append(max_coverage_rate)
+                
+            if max_coverage_rate_list:
                 mean_coverage_rate = np.mean(max_coverage_rate_list)
 
             for seq_idx in range(seq_num):
@@ -112,6 +114,10 @@ class NBVReconstructionDataset(BaseDataset):
         except Exception as e:
             Log.error(f"Save cache failed: {e}")
 
+    def voxel_downsample_with_mask(self, pts, voxel_size):
+        pass
+
+
     def __getitem__(self, index):
         data_item_info = self.datalist[index]
         scanned_views = data_item_info["scanned_views"]
@@ -160,9 +166,8 @@ class NBVReconstructionDataset(BaseDataset):
         )
 
         combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
-        fps_downsampled_combined_scanned_pts, fps_idx = PtsUtil.fps_downsample_point_cloud(
-            combined_scanned_views_pts, self.pts_num, require_idx=True
-        )
+        voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002)
+        random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num)
         
         combined_scanned_views_pts_mask = np.zeros(len(combined_scanned_views_pts), dtype=np.uint8)
         
@@ -171,7 +176,6 @@ class NBVReconstructionDataset(BaseDataset):
             end_idx = start_idx + len(scanned_views_pts[i])
             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 = {
@@ -238,10 +242,9 @@ if __name__ == "__main__":
     torch.manual_seed(seed)
     np.random.seed(seed)
     config = {
-        "root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
-        "model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
+        "root_dir": "/data/hofee/data/packed_preprocessed_data",
         "source": "nbv_reconstruction_dataset",
-        "split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_test.txt",
+        "split_file": "/data/hofee/data/OmniObject3d_train.txt",
         "load_from_preprocess": True,
         "ratio": 0.5,
         "batch_size": 2,

runners/strategy_generator.py

@@ -93,10 +93,8 @@ class StrategyGenerator(Runner):
                 else:
                     nrm = np.load(nrm_path)
                 nrm_list.append(nrm)
-                
-            indices = np.load(idx_path) 
             pts_list.append(pts)
-            
+            indices = np.load(idx_path) 
             scan_points_indices_list.append(indices)
             if pts.shape[0] > 0:
                 non_zero_cnt += 1

utils/data_load.py

@@ -53,6 +53,8 @@ class DataLoadUtil:
     @staticmethod
     def get_label_num(root, scene_name):
         label_dir = os.path.join(root, scene_name, "label")
+        if not os.path.exists(label_dir):
+            return 0
         return len(os.listdir(label_dir))
 
     @staticmethod

utils/reconstruction.py

@@ -75,6 +75,7 @@ class ReconstructionUtil:
         cnt_processed_view = 0
         remaining_views.remove(init_view)
         curr_rec_pts_num = combined_point_cloud.shape[0]
+        drop_output_ratio = 0.4
         
         import time
         while remaining_views:
@@ -84,6 +85,8 @@ class ReconstructionUtil:
             best_covered_num = 0
         
             for view_index in remaining_views:
+                if np.random.rand() < drop_output_ratio:
+                    continue
                 if point_cloud_list[view_index].shape[0] == 0:
                     continue
                 if selected_views: