Analyze running time of process_images() and process_video_frames()

preprocessing/mm_datautils.py

@@ -4,6 +4,7 @@ import torch
 from decord import cpu, VideoReader
 from transformers import BaseImageProcessor
 from typing import List, Union, Tuple
+import time
 
 def expand2square(pil_img, background_color):
     width, height = pil_img.size
@@ -30,7 +31,7 @@ def process_images(
         new_images_aux_list = []
         for i, image in enumerate(images):
             # image.shape: (height, width, channels)
-            print(f'@tcm: In process_images(): frame {i}')
+            # print(f'@tcm: In process_images(): frame {i}')
             if isinstance(image, np.ndarray):
                 image = Image.fromarray(image)
             image_aux_list = []
@@ -44,11 +45,9 @@ 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]
@@ -83,17 +82,21 @@ def process_video_frames(
     image_processor: List[BaseImageProcessor],
     device: str
 ) -> Tuple[List[torch.Tensor], List[Tuple[int, int]]]:
-    print(f'@tcm: In process_video_frames(): begin VideoReader()')
+    init_time = time.time()
     vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
-    print(f'@tcm: In process_video_frames(): end VideoReader()')
+    print(f'@tcm: In process_video_frames(): init_time={time.time()-init_time:4f}')
     fps = float(vr.get_avg_fps())
     frame_indices = np.array([i for i in range(0, len(vr), round(fps),)])
     video = []
+    vectorize_time = time.time()
     for frame_index in frame_indices:
         img = vr[frame_index].asnumpy()
         video.append(img)
     video = np.stack(video) # shape: (num_frames, height, width, channels)
+    print(f'@tcm: In process_video_frames(): vectorize_time={time.time()-vectorize_time:4f}')
     image_sizes = [video[0].shape[:2]]
+    process_time = time.time()
     video = process_images(video, image_processor, device)
+    print(f'@tcm: In process_video_frames(): process_time={time.time()-process_time:4f}')
     video = [item.unsqueeze(0) for item in video]
     return video, image_sizes