Deep Dive Reverse Video Search raise an error about examples

[jingkl]

import towhee
import time


# Please note the first time run will take time to download model and other files.

start = time.time()

collection = create_milvus_collection('timesformer', 768)

dc = (
    towhee.read_csv('reverse_video_search.csv')
      .runas_op['id', 'id'](func=lambda x: int(x))
      .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 8})
      .action_classification['frames', ('labels', 'scores', 'vec')].timesformer(skip_preprocess=True)
      .tensor_normalize['vec', 'vec']()
      .to_milvus['id', 'vec'](collection=collection, batch=10)
)

end = time.time()

print('Total insert time: %.2fs'%(end-start))
print('Total number of inserted data is {}.'.format(collection.num_entities))

start = time.time()

benchmark = (
    towhee.glob['path']('./test/*/*.mp4')
        .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 8})
        .action_classification['frames', ('labels', 'scores', 'vec')].timesformer(skip_preprocess=True)
        .tensor_normalize['vec', 'vec']()
        .milvus_search['vec', 'result'](collection=collection, limit=10)
        .runas_op['path', 'ground_truth'](func=ground_truth)
        .runas_op['result', 'result'](func=lambda res: [x.id for x in res])
        .with_metrics(['mean_hit_ratio', 'mean_average_precision'])
        .evaluate['ground_truth', 'result']('timesformer')
        .report()
)

end = time.time()

print('Total search time: %.2fs'%(end-start))

[jaelgu]

import towhee
import time


# Please note the first time run will take time to download model and other files.

start = time.time()

collection = create_milvus_collection('timesformer', 768)

dc = (
    towhee.read_csv('reverse_video_search.csv')
      .runas_op['id', 'id'](func=lambda x: int(x))
      .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 8})
      .action_classification['frames', ('labels', 'scores', 'vec')].timesformer(skip_preprocess=True)
      .tensor_normalize['vec', 'vec']()
      .to_milvus['id', 'vec'](collection=collection, batch=10)
)

end = time.time()

print('Total insert time: %.2fs'%(end-start))
print('Total number of inserted data is {}.'.format(collection.num_entities))

start = time.time()

benchmark = (
    towhee.glob['path']('./test/*/*.mp4')
        .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 8})
        .action_classification['frames', ('labels', 'scores', 'vec')].timesformer(skip_preprocess=True)
        .tensor_normalize['vec', 'vec']()
        .milvus_search['vec', 'result'](collection=collection, limit=10)
        .runas_op['path', 'ground_truth'](func=ground_truth)
        .runas_op['result', 'result'](func=lambda res: [x.id for x in res])
        .with_metrics(['mean_hit_ratio', 'mean_average_precision'])
        .evaluate['ground_truth', 'result']('timesformer')
        .report()
)

end = time.time()

print('Total search time: %.2fs'%(end-start))

You need to install the correct towhee version, which at least include https://github.com/towhee-io/towhee/blob/main/towhee/types/video_frame.py (commit 7e8be9ad2)

[jingkl]

Verified, close issue