add6

Signed-off-by: xujinling <jinling.xu@zilliz.com>

README.md

@@ -9,14 +9,14 @@ Author: Jinling xu
 An action classification operator generates labels of human activities (with corresponding scores)
 and extracts features for the input video.
 It transforms the video into frames and loads pre-trained models by model names.
-This operator has implemented pre-trained models from [TimeSformer](https://arxiv.org/abs/2102.05095)
+This operator has implemented pre-trained models from [VideoSwinTransformer](https://arxiv.org/abs/2106.13230)
 and maps vectors with labels.
 
 <br />
 
 ## Code Example
 
-Use the pretrained TimeSformer model ('timesformer_k400_8x224')
+Use the pretrained VideoSwinTransformer model ('swin_tiny_patch244_window877_kinetics400_1k')
 to classify and generate a vector for the given video path './archery.mp4' ([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)).
 
  *Write the pipeline in simplified style*:
@@ -25,9 +25,8 @@ to classify and generate a vector for the given video path './archery.mp4' ([dow
 import towhee
 
 (
-    towhee.dc(['./demo_video.mp4']) 
-          .video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 4}) 
-          .runas_op(func=lambda x: [y for y in x]) 
+    towhee.glob('./archery.mp4')
+          .video_decode.ffmpeg() 
           .action_classification.video_swin_transformer(model_name='swin_tiny_patch244_window877_kinetics400_1k')
           .show()
 )
@@ -42,9 +41,8 @@ import towhee
 import towhee
 
 (
-    towhee.dc['path']('./demo_video.mp4')
-          .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 4}) 
-          .runas_op['frames', 'frames'](func=lambda x: [y for y in x]) 
+    towhee.glob['path']('./archery.mp4')
+          .video_decode.ffmpeg['path', 'frames']() 
           .action_classification.video_swin_transformer['frames', ('labels', 'scores', 'features')](model_name='swin_tiny_patch244_window877_kinetics400_1k')
           .select['path', 'labels', 'scores', 'features']()
           .show(formatter={'path': 'video_path'})