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119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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"""
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isort:skip_file
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"""
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import os
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import unittest
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import numpy as np
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from easycv.predictors.video_classifier import VideoClassificationPredictor, STGCNPredictor
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from tests.ut_config import (PRETRAINED_MODEL_X3D_XS,
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VIDEO_DATA_SMALL_RAW_LOCAL, BASE_LOCAL_PATH)
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class VideoClassificationPredictorTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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def test_single(self):
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checkpoint = PRETRAINED_MODEL_X3D_XS
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config_file = 'configs/video_recognition/x3d/x3d_xs.py'
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predict_op = VideoClassificationPredictor(
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model_path=checkpoint, config_file=config_file)
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img_path = os.path.join(
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VIDEO_DATA_SMALL_RAW_LOCAL,
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'kinetics400/val_256/y5xuvHpDPZQ_000005_000015.mp4')
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input = {'filename': img_path}
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results = predict_op([input])[0]
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self.assertListEqual(results['class'], [55])
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self.assertListEqual(results['class_name'], ['55'])
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self.assertEqual(len(results['class_probs']), 400)
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def test_batch(self):
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checkpoint = PRETRAINED_MODEL_X3D_XS
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config_file = 'configs/video_recognition/x3d/x3d_xs.py'
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predict_op = VideoClassificationPredictor(
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model_path=checkpoint, config_file=config_file)
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img_path = os.path.join(
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VIDEO_DATA_SMALL_RAW_LOCAL,
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'kinetics400/val_256/y5xuvHpDPZQ_000005_000015.mp4')
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input = {'filename': img_path}
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num_imgs = 4
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results = predict_op([input] * num_imgs)
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self.assertEqual(len(results), num_imgs)
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for res in results:
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# self.assertListEqual(res['class'], [55])
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# self.assertListEqual(res['class_name'], ['55'])
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self.assertEqual(len(res['class_probs']), 400)
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class STGCNPredictorTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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def test_single(self):
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checkpoint = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/video/stgcn/stgcn_80e_ntu60_xsub.pth')
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config_file = 'configs/video_recognition/stgcn/stgcn_80e_ntu60_xsub_keypoint.py'
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predict_op = STGCNPredictor(
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model_path=checkpoint, config_file=config_file)
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h, w = 480, 853
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total_frames = 20
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num_person = 2
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inp = dict(
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frame_dir='',
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label=-1,
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img_shape=(h, w),
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original_shape=(h, w),
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start_index=0,
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modality='Pose',
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total_frames=total_frames,
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keypoint=np.random.random((num_person, total_frames, 17, 2)),
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keypoint_score=np.random.random((num_person, total_frames, 17)),
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)
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results = predict_op([inp])[0]
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self.assertIn('class', results)
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self.assertIn('class_name', results)
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self.assertEqual(len(results['class_probs']), 60)
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def test_jit(self):
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checkpoint = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/video/stgcn/stgcn_80e_ntu60_xsub.pth.jit')
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config_file = 'configs/video_recognition/stgcn/stgcn_80e_ntu60_xsub_keypoint.py'
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predict_op = STGCNPredictor(
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model_path=checkpoint, config_file=config_file)
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h, w = 480, 853
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total_frames = 20
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num_person = 2
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inp = dict(
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frame_dir='',
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label=-1,
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img_shape=(h, w),
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original_shape=(h, w),
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start_index=0,
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modality='Pose',
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total_frames=total_frames,
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keypoint=np.random.random((num_person, total_frames, 17, 2)),
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keypoint_score=np.random.random((num_person, total_frames, 17)),
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)
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results = predict_op([inp])[0]
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self.assertIn('class', results)
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self.assertIn('class_name', results)
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self.assertEqual(len(results['class_probs']), 60)
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if __name__ == '__main__':
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unittest.main()
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