[Feature] Sparsedemo

demo/README.md

@@ -225,6 +225,7 @@ Optional arguments:
 - `INPUT_STEP`: Input step for sampling frames, which can help to get more spare input. If not specified , it will be set to 1.
 - `DEVICE_TYPE`: Type of device to run the demo. Allowed values are cuda device like `cuda:0` or `cpu`. If not specified, it will be set to `cuda:0`.
 - `THRESHOLD`: Threshold of prediction score for action recognition. Only label with score higher than the threshold will be shown. If not specified, it will be set to 0.01.
+- `STRIDE`: By default, the demo generates a prediction for each single frame, which might cost lots of time. To speed up, you can set the argument `STRIDE` and then the demo will generate a prediction every `STRIDE x sample_length` frames (`sample_length` indicates the size of temporal window from which you sample frames, which equals to `clip_len x frame_interval`). For example, if the sample_length is 64 frames and you set `STRIDE` to 0.5, predictions will be generated every 32 frames. If set as 0, predictions will be generated for each frame. The desired value of `STRIDE` is (0, 1], while it also works for `STRIDE > 1` (the generated predictions will be too sparse). Default: 0.
 
 Examples:
 

demo/long_video_demo.py

@@ -30,7 +30,7 @@ def parse_args():
         description='MMAction2 predict different labels in a long video demo')
     parser.add_argument('config', help='test config file path')
     parser.add_argument('checkpoint', help='checkpoint file/url')
-    parser.add_argument('video', help='video file/url')
+    parser.add_argument('video_path', help='video file/url')
     parser.add_argument('label', help='label file')
     parser.add_argument('out_file', help='output filename')
     parser.add_argument(
@@ -45,12 +45,24 @@ def parse_args():
         type=float,
         default=0.01,
         help='recognition score threshold')
+    parser.add_argument(
+        '--stride',
+        type=float,
+        default=0,
+        help=('the prediction stride equals to stride * sample_length '
+              '(sample_length indicates the size of temporal window from '
+              'which you sample frames, which equals to '
+              'clip_len x frame_interval), if set as 0, the '
+              'prediction stride is 1'))
     args = parser.parse_args()
     return args
 
 
-def show_results():
-    cap = cv2.VideoCapture(video_path)
+def show_results(model, data, label, args):
+    frame_queue = deque(maxlen=args.sample_length)
+    result_queue = deque(maxlen=1)
+
+    cap = cv2.VideoCapture(args.video_path)
     num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
@@ -62,7 +74,7 @@ def show_results():
     frame_size = (frame_width, frame_height)
 
     ind = 0
-    video_writer = cv2.VideoWriter(out_file, fourcc, fps, frame_size)
+    video_writer = cv2.VideoWriter(args.out_file, fourcc, fps, frame_size)
     prog_bar = mmcv.ProgressBar(num_frames)
     backup_frames = []
 
@@ -74,19 +86,19 @@ def show_results():
             # drop it when encounting None
             continue
         backup_frames.append(np.array(frame)[:, :, ::-1])
-        if ind == sample_length:
+        if ind == args.sample_length:
             # provide a quick show at the beginning
             frame_queue.extend(backup_frames)
             backup_frames = []
-        elif ((len(backup_frames) == input_step and ind > sample_length)
-              or ind == num_frames):
+        elif ((len(backup_frames) == args.input_step
+               and ind > args.sample_length) or ind == num_frames):
             # pick a frame from the backup
             # when the backup is full or reach the last frame
             chosen_frame = random.choice(backup_frames)
             backup_frames = []
             frame_queue.append(chosen_frame)
 
-        ret, scores = inference()
+        ret, scores = inference(model, data, args, frame_queue)
 
         if ret:
             num_selected_labels = min(len(label), 5)
@@ -101,7 +113,7 @@ def show_results():
             results = result_queue.popleft()
             for i, result in enumerate(results):
                 selected_label, score = result
-                if score < threshold:
+                if score < args.threshold:
                     break
                 location = (0, 40 + i * 20)
                 text = selected_label + ': ' + str(round(score, 2))
@@ -120,38 +132,40 @@ def show_results():
     cv2.destroyAllWindows()
 
 
-def inference():
-    if len(frame_queue) != sample_length:
+def inference(model, data, args, frame_queue):
+    if len(frame_queue) != args.sample_length:
         # Do no inference when there is no enough frames
         return False, None
 
     cur_windows = list(np.array(frame_queue))
-    img = frame_queue.popleft()
     if data['img_shape'] is None:
-        data['img_shape'] = img.shape[:2]
+        data['img_shape'] = frame_queue[0].shape[:2]
+
     cur_data = data.copy()
     cur_data['imgs'] = cur_windows
-    cur_data = test_pipeline(cur_data)
+    cur_data = args.test_pipeline(cur_data)
     cur_data = collate([cur_data], samples_per_gpu=1)
     if next(model.parameters()).is_cuda:
-        cur_data = scatter(cur_data, [device])[0]
+        cur_data = scatter(cur_data, [args.device])[0]
     with torch.no_grad():
         scores = model(return_loss=False, **cur_data)[0]
+
+    if args.stride > 0:
+        pred_stride = int(args.sample_length * args.stride)
+        for i in range(pred_stride):
+            frame_queue.popleft()
+
+    # for case ``args.stride=0``
+    # deque will automatically popleft one element
+
     return True, scores
 
 
 def main():
-    global frame_queue, threshold, sample_length, data, test_pipeline, model, \
-        out_file, video_path, device, input_step, label, result_queue
-
     args = parse_args()
-    input_step = args.input_step
-    threshold = args.threshold
-    video_path = args.video
-    out_file = args.out_file
 
-    device = torch.device(args.device)
-    model = init_recognizer(args.config, args.checkpoint, device=device)
+    args.device = torch.device(args.device)
+    model = init_recognizer(args.config, args.checkpoint, device=args.device)
     data = dict(img_shape=None, modality='RGB', label=-1)
     with open(args.label, 'r') as f:
         label = [line.strip() for line in f]
@@ -171,10 +185,12 @@ def main():
             # remove step to decode frames
             pipeline_.remove(step)
     test_pipeline = Compose(pipeline_)
+
     assert sample_length > 0
-    frame_queue = deque(maxlen=sample_length)
-    result_queue = deque(maxlen=1)
-    show_results()
+    args.sample_length = sample_length
+    args.test_pipeline = test_pipeline
+
+    show_results(model, data, label, args)
 
 
 if __name__ == '__main__':

docs/changelog.md

@@ -10,6 +10,7 @@
 
 **Improvements**
 
+- Add arg `stride` to long_video_demo.py, to make inference faster ([#468](https://github.com/open-mmlab/mmaction2/pull/468))
 - Support training and testing for Spatio-Temporal Action Detection ([#351](https://github.com/open-mmlab/mmaction2/pull/351))
 - Fix CI due to pip upgrade ([#454](https://github.com/open-mmlab/mmaction2/pull/454))
 - Add markdown lint in pre-commit hook ([#255](https://github.com/open-mmlab/mmaction2/pull/225))