@ -19,36 +19,22 @@ and maps vectors with labels.
Use the pretrained VideoSwinTransformer model ('swin_t_k400_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* :
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
(
towhee.glob('./archery.mp4')
.video_decode.ffmpeg()
.action_classification.video_swin_transformer(model_name='swin_t_k400_1k')
.show()
from towhee.dc2 import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frames', ops.video_decode.ffmpeg())
.map('frames', ('labels', 'scores', 'features'),
ops.action_classification.video_swin_transformer(model_name='swin_t_k400_1k'))
.output('path', 'labels', 'scores', 'features')
)
```
< img src = "./result1.png" width = "800px" / >
< br / >
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
(
towhee.glob['path']('./archery.mp4')
.video_decode.ffmpeg['path', 'frames']()
.action_classification.video_swin_transformer['frames', ('labels', 'scores', 'features')](model_name='swin_t_k400_1k')
.select['path', 'labels', 'scores', 'features']()
.show(formatter={'path': 'video_path'})
)
DataCollection(p('./archery.mp4')).show()
```
< img src = "./result2 .png" width = "800px" / >
< img src = "./result.png" width = "800px" / >
< br / >
