Debugging learn process_images

preprocessing/entube_dataset.py

@@ -13,7 +13,6 @@ class EnTubeDataset(Dataset):
         image_processor: List[BaseImageProcessor],
         device: str
     ) -> None:
-        print(f'@tcm: In EnTubeDataset.__init__(): start')
         self.videos = []
         self.image_sizes = []
         self.device = device
@@ -23,12 +22,9 @@ class EnTubeDataset(Dataset):
             for file_name in file_names:
                 file_path = os.path.join(folder_path, file_name)
                 print(f'@tcm: In EnTubeDataset.__init__(): file_path={file_path}')
-                torch.cuda.memory._record_memory_history()
                 video, image_size = process_video_frames(file_path, image_processor, device)
-                torch.cuda.memory._dump_snapshot(f"{folder_path.strip('./')}-{file_name}.pickle")
                 self.videos.append(video)
                 self.image_sizes.append(image_size)
-        print(f'@tcm: In EnTubeDataset.__init__(): done')
 
     def __len__(self):
         return len(self.image_sizes)

preprocessing/mm_datautils.py

@@ -24,11 +24,13 @@ def process_images(
     device: str
 ) -> Union[torch.Tensor, List[torch.Tensor]]:
     # images.shape: (4294, 360, 640, 3)
+    print(f'@tcm: In process_images(): images.shape={images.shape}')
     if isinstance(image_processor, list):
         processor_aux_list = image_processor
         new_images_aux_list = []
-        for image in images:
+        for i, image in enumerate(images):
             # image.shape: (height, width, channels)
+            print(f'@tcm: In process_images(): frame {i}')
             if isinstance(image, np.ndarray):
                 image = Image.fromarray(image)
             image_aux_list = []
@@ -42,12 +44,15 @@ def process_images(
                     image_aux = expand2square(
                         image_aux, tuple(int(x * 255) for x in processor_aux.image_mean)
                     ).resize((target_resolution, target_resolution))
+                print(f'@tcm: In process_images(): begin processor_aux.preprocess()')
                 image_aux = processor_aux.preprocess(image_aux, return_tensors="pt")[
                     "pixel_values"
                 ][0]
+                print(f'@tcm: In process_images(): end processor_aux.preprocess()')
                 # image_aux.shape: torch.Size([3, 384, 384])
                 image_aux_list.append(image_aux)
             new_images_aux_list.append(image_aux_list) # torch.Tensor(C, H, W) new_images_aux_list[num_frames][num_processor]
+        print()
         new_images_aux_list = [
             list(batch_image_aux) for batch_image_aux in zip(*new_images_aux_list)
         ] # torch.Tensor(C, H, W) new_images_aux_list[num_processor][num_frames]