diff --git a/README.md b/README.md index 30a2410..a44c4f6 100644 --- a/README.md +++ b/README.md @@ -18,38 +18,23 @@ and maps vectors with labels provided by datasets used for pre-training. Use the pretrained TSM model 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*: -- Predict labels (default): ```python -import towhee - -( - towhee.glob('./archery.mp4') - .video_decode.ffmpeg() - .action_classification.tsm( - model_name='tsm_k400_r50_seg8', topk=5) - .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.tsm(model_name='tsm_k400_r50_seg8')) + .output('path', 'labels', 'scores', 'features') ) -``` - - -*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.tsm['frames', ('labels', 'scores', 'features')]( - model_name='tsm_k400_r50_seg8') - .select['path', 'labels', 'scores', 'features']() - .show(formatter={'path': 'video_path'}) -) +DataCollection(p('./archery.mp4')).show() ``` - +
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