ModelScope/tests/pipelines/test_action_recognition.py

# Copyright (c) Alibaba, Inc. and its affiliates.
# !/usr/bin/env python
import os.path as osp
import tempfile
import unittest

from modelscope.fileio import File
from modelscope.pipelines import pipeline
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.test_utils import test_level


class ActionRecognitionTest(unittest.TestCase):

    def setUp(self) -> None:
        self.model_id = 'damo/cv_TAdaConv_action-recognition'

    @unittest.skip('deprecated, download model from model hub instead')
    def test_run_with_direct_file_download(self):
        model_path = 'https://aquila2-online-models.oss-cn-shanghai.aliyuncs.com/maas_test/pytorch_model.pt'
        config_path = 'https://aquila2-online-models.oss-cn-shanghai.aliyuncs.com/maas_test/configuration.json'
        with tempfile.TemporaryDirectory() as tmp_dir:
            model_file = osp.join(tmp_dir, ModelFile.TORCH_MODEL_FILE)
            with open(model_file, 'wb') as ofile1:
                ofile1.write(File.read(model_path))
            config_file = osp.join(tmp_dir, ModelFile.CONFIGURATION)
            with open(config_file, 'wb') as ofile2:
                ofile2.write(File.read(config_path))
            recognition_pipeline = pipeline(
                Tasks.action_recognition, model=tmp_dir)
            result = recognition_pipeline(
                'data/test/videos/action_recognition_test_video.mp4')
            print(f'recognition output: {result}.')

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_modelhub(self):
        recognition_pipeline = pipeline(
            Tasks.action_recognition, model=self.model_id)
        result = recognition_pipeline(
            'data/test/videos/action_recognition_test_video.mp4')

        print(f'recognition output: {result}.')

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_modelhub_default_model(self):
        recognition_pipeline = pipeline(Tasks.action_recognition)
        result = recognition_pipeline(
            'data/test/videos/action_recognition_test_video.mp4')

        print(f'recognition output: {result}.')


if __name__ == '__main__':
    unittest.main()