global: inference debug

configs/local/inference_config.yaml

@@ -12,7 +12,7 @@ runner:
 
   test:
     dataset_list:
-      - OmniObject3d_train
+      - OmniObject3d_test
 
   blender_script_path: "/data/hofee/project/nbv_rec/blender/data_renderer.py"
   output_dir: "/data/hofee/data/inference_global_full_on_testset"
@@ -22,7 +22,7 @@ runner:
 dataset:
   OmniObject3d_train:
     root_dir: "/data/hofee/data/new_full_data"
-    model_dir: "/data/hofee/data/object_meshes_part3"
+    model_dir: "/data/hofee/data/scaled_object_meshes"
     source: seq_reconstruction_dataset
     split_file: "/data/hofee/data/sample.txt"
     type: test
@@ -35,9 +35,9 @@ dataset:
 
   OmniObject3d_test:
     root_dir: "/data/hofee/data/new_full_data"
-    model_dir: "/data/hofee/data/object_meshes_part3"
+    model_dir: "/data/hofee/data/scaled_object_meshes"
     source: seq_reconstruction_dataset
-    split_file: "/data/hofee/data/sample.txt"
+    split_file: "/data/hofee/data/new_full_data_list/OmniObject3d_test.txt"
     type: test
     filter_degree: 75
     eval_list:

core/seq_dataset.py

@@ -118,6 +118,7 @@ class SeqReconstructionDataset(BaseDataset):
     def __getitem__(self, index):
         data_item_info = self.datalist[index]
         max_coverage_rate = data_item_info["max_coverage_rate"]
+        best_seq_len = data_item_info["best_seq_len"]
         scene_name = data_item_info["scene_name"]
         (
             scanned_views_pts,
@@ -158,6 +159,7 @@ class SeqReconstructionDataset(BaseDataset):
             "first_scanned_coverage_rate": scanned_coverages_rate, # List(S): Float, range(0, 1)
             "first_scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
             "seq_max_coverage_rate": max_coverage_rate, # Float, range(0, 1)
+            "best_seq_len": best_seq_len, # Int
             "scene_name": scene_name, # String
             "gt_pts": gt_pts, # Ndarray(N x 3)
             "scene_path": os.path.join(self.root_dir, scene_name), # String

runners/inferencer.py

@@ -72,7 +72,7 @@ class Inferencer(Runner):
                     data = test_set.__getitem__(i)
                     status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
                     output = self.predict_sequence(data)
-                    self.save_inference_result(test_set_name, data["scene_name"][0], output)
+                    self.save_inference_result(test_set_name, data["scene_name"], output)
                     
             status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
         
@@ -107,6 +107,7 @@ class Inferencer(Runner):
         retry_no_pts_pose = []
         retry = 0
         pred_cr_seq = [last_pred_cr]
+        success = 0
         while len(pred_cr_seq) < max_iter and retry < max_retry:
             
             output = self.pipeline(input_data)
@@ -132,11 +133,13 @@ class Inferencer(Runner):
                 retry += 1
                 continue
             
+
             pred_cr, new_added_pts_num = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
             print(pred_cr, last_pred_cr, " max: ", data["seq_max_coverage_rate"])
             if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
                 print("max coverage rate reached!: ", pred_cr)
-            if new_added_pts_num < 10:
+                success += 1
+            elif new_added_pts_num < 10:
                 print("min added pts num reached!: ", new_added_pts_num)
             if pred_cr <= last_pred_cr + cr_increase_threshold:
                 retry += 1
@@ -156,32 +159,29 @@ class Inferencer(Runner):
             random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
             input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
 
-            
+            if success > 3:
+                break
             last_pred_cr = pred_cr
 
-        
-        input_data["scanned_pts"] = input_data["scanned_pts"][0].cpu().numpy().tolist()
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
         result = {
             "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
-            "pts_seq": input_data["scanned_pts"],
+            "combined_scanned_pts": input_data["combined_scanned_pts"],
             "target_pts_seq": scanned_view_pts,
             "coverage_rate_seq": pred_cr_seq,
-            "max_coverage_rate": data["max_coverage_rate"][0],
+            "max_coverage_rate": data["seq_max_coverage_rate"],
             "pred_max_coverage_rate": max(pred_cr_seq),
             "scene_name": scene_name,
             "retry_no_pts_pose": retry_no_pts_pose,
             "retry_duplication_pose": retry_duplication_pose,
-            "best_seq_len": data["best_seq_len"][0],
+            "best_seq_len": data["best_seq_len"],
         }
         self.stat_result[scene_name] = {
-            "max_coverage_rate": data["max_coverage_rate"][0],
-            "success_rate": max(pred_cr_seq)/ data["max_coverage_rate"][0],
             "coverage_rate_seq": pred_cr_seq,
             "pred_max_coverage_rate": max(pred_cr_seq),
             "pred_seq_len": len(pred_cr_seq),
         }
-        print('success rate: ', max(pred_cr_seq) / data["max_coverage_rate"][0])
+        print('success rate: ', max(pred_cr_seq))
 
         return result
 

utils/render.py

@@ -71,7 +71,6 @@ class RenderUtil:
             result = subprocess.run([
                 'blender', '-b', '-P', script_path, '--', temp_dir
             ], capture_output=True, text=True)
-            
             if result.returncode != 0:
                 print("Blender script failed:")
                 print(result.stderr)