caizhongang
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
""" Ref: https://github.com/muelea/shapy/blob/master/regressor/hbw_evaluation/test_submission_format.py """
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import argparse
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import numpy as np
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def test_submission_file_format(
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npz_file: str,
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model_type: str = 'smplx'
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):
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submission = np.load(npz_file)
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# check if keys are named correctly
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keys = [x for x in submission.keys()]
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assert 'image_name' in keys and 'v_shaped' in keys, \
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f"Keys are not correct. Got {keys}, but expected ['image_name', 'v_shaped']"
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image_names = submission['image_name']
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v_shapeds = submission['v_shaped']
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# check if shape and type are correct
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assert type(image_names) == np.ndarray, \
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f"Type of key image_name is not correct. {type(image_names)} given, but np.ndarray expected."
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assert image_names.shape == (1631,), \
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f"Shape of key image_name is not correct. {image_names.shape} given, but (1631,) expected."
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assert type(v_shapeds) == np.ndarray, \
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f"Type of key v_shaped is not correct. {type(image_names)} given, but np.ndarray expected."
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if model_type == 'smplx':
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assert v_shapeds.shape == (1631, 10475, 3), \
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f"Shape of key v_shaped is not correct. {v_shapeds.shape} given, but (1631, 10475, 3) expected."
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else:
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assert v_shapeds.shape == (1631, 6890, 3), \
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f"Shape of key v_shaped is not correct. {v_shapeds.shape} given, but (1631, 6890, 3) expected."
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# check if each image has a prediction
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hbw_images_gt = np.load('../data/SHAPY/hbw_testset_image_names.npy')
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check_prediction_available = np.isin(hbw_images_gt, image_names)
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assert np.all(check_prediction_available), \
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f"Images without predition exist! Missing predictions: \
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\n {hbw_images_gt[~check_prediction_available]}"
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print(f'Your submission file passed the test.')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--input-npz-file',
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dest='input_npz_file', type=str, required=True,
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help='npz containing labels and body shape parameters.')
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parser.add_argument('--model-type', choices=['smpl', 'smplx'], type=str,
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default='smplx',
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help='The model type used for body shape prediction. ')
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args = parser.parse_args()
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test_submission_file_format(
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npz_file=args.input_npz_file,
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model_type=args.model_type
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)
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