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44 lines
1.4 KiB
44 lines
1.4 KiB
import numpy as np |
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import torch |
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from mmcv.ops import box_iou_rotated |
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from mmcv.utils import collect_env |
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def check_installation(): |
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"""Check whether mmcv-full has been installed successfully.""" |
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np_boxes1 = np.asarray( |
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[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6], |
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[7.0, 7.0, 8.0, 8.0, 0.4]], |
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dtype=np.float32) |
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np_boxes2 = np.asarray( |
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[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5], |
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[5.0, 5.0, 6.0, 7.0, 0.4]], |
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dtype=np.float32) |
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boxes1 = torch.from_numpy(np_boxes1) |
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boxes2 = torch.from_numpy(np_boxes2) |
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# test mmcv-full with CPU ops |
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box_iou_rotated(boxes1, boxes2) |
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print('CPU ops were compiled successfully.') |
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# test mmcv-full with both CPU and CUDA ops |
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if torch.cuda.is_available(): |
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boxes1 = boxes1.cuda() |
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boxes2 = boxes2.cuda() |
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box_iou_rotated(boxes1, boxes2) |
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print('CUDA ops were compiled successfully.') |
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else: |
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print('No CUDA runtime is found, skipping the checking of CUDA ops.') |
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if __name__ == '__main__': |
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print('Start checking the installation of mmcv-full ...') |
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check_installation() |
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print('mmcv-full has been installed successfully.\n') |
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env_info_dict = collect_env() |
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env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) |
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dash_line = '-' * 60 + '\n' |
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print('Environment information:') |
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print(dash_line + env_info + '\n' + dash_line)
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