upd inference

configs/local/inference_config.yaml

@@ -15,7 +15,7 @@ runner:
       - OmniObject3d_test
 
   blender_script_path: "C:\\Document\\Local Project\\nbv_rec\\blender\\data_renderer.py"
-  output_dir: "C:\\Document\\Datasets\\inference_scan_pts_overlap_global_full_on_testset"
+  output_dir: "C:\\Document\\Datasets\\debug_output"
   pipeline: nbv_reconstruction_pipeline
   voxel_size: 0.003
   

configs/local/view_generate_config.yaml

@@ -8,11 +8,11 @@ runner:
     root_dir: experiments
   generate:
     port: 5002
-    from: 600
+    from: 0
     to: -1 # -1 means all
-    object_dir: /media/hofee/data/data/object_meshes_part1
+    object_dir: C:\\Document\\Datasets\\ball_meshes
-    table_model_path: "/media/hofee/data/data/others/table.obj"
+    table_model_path: C:\\Document\\Datasets\\table.obj
-    output_dir: /media/hofee/repository/data_part_1
+    output_dir: C:\\Document\\Datasets\\debug_ball_generate_view
     binocular_vision: true
     plane_size: 10
     max_views: 512

runners/inferencer.py

@@ -76,6 +76,8 @@ class Inferencer(Runner):
                 for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100):
                     data = test_set.__getitem__(i)
                     scene_name = data["scene_name"]
+                    if scene_name != "omniobject3d-suitcase_001":
+                        continue
                     inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl")
                     if os.path.exists(inference_result_path):
                         Log.info(f"Inference result already exists for scene: {scene_name}")
@@ -87,7 +89,7 @@ class Inferencer(Runner):
                     
             status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
         
-    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 7):
+    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 5):
         scene_name = data["scene_name"]
         Log.info(f"Processing scene: {scene_name}")
         status_manager.set_status("inference", "inferencer", "scene", scene_name)
@@ -110,10 +112,12 @@ class Inferencer(Runner):
         input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
         input_data["mode"] = namespace.Mode.TEST
         input_pts_N = input_data["combined_scanned_pts"].shape[1]
+        
         root = os.path.dirname(scene_path)
         display_table_info = DataLoadUtil.get_display_table_info(root, scene_name)
         radius = display_table_info["radius"]
         scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
+
         first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
         scanned_view_pts = [first_frame_target_pts]
         history_indices = [first_frame_scan_points_indices]
@@ -124,6 +128,7 @@ class Inferencer(Runner):
         retry = 0
         pred_cr_seq = [last_pred_cr]
         success = 0
+        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], 0.002)
         import time
         while len(pred_cr_seq) < max_iter and retry < max_retry:
             start_time = time.time()
@@ -174,13 +179,9 @@ class Inferencer(Runner):
             end_time = time.time()
             print(f"Time taken for coverage rate computation: {end_time - start_time} seconds")
             print(pred_cr, last_pred_cr, " max: ", data["seq_max_coverage_rate"])
-            print("new added pts num: ", new_added_pts_num)
             if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
                 print("max coverage rate reached!: ", pred_cr)
                 success += 1
-            elif new_added_pts_num < 5:
-                #success += 1
-                print("min added pts num reached!: ", new_added_pts_num)
             if pred_cr <= last_pred_cr + cr_increase_threshold:
                 retry += 1
                 retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
@@ -202,6 +203,12 @@ class Inferencer(Runner):
             if success > 3:
                 break
             last_pred_cr = pred_cr
+            pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
+            if pts_num - last_pts_num < 10:
+                retry += 1
+                print("delta pts num < 10:", pts_num, last_pts_num)
+            last_pts_num = pts_num
+            
 
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
         result = {

runners/view_generator.py

@@ -9,7 +9,7 @@ class ViewGenerator(Runner):
         self.config_path = config_path
         
     def run(self):
-        result = subprocess.run(['/home/hofee/blender-4.0.2-linux-x64/blender', '-b', '-P', '../blender/run_blender.py', '--', self.config_path])
+        result = subprocess.run(['blender', '-b', '-P', '../blender/run_blender.py', '--', self.config_path])
         print()
 
     def create_experiment(self, backup_name=None):