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158d72bfd2
* add self supervised depth completion. * update. * fix the problem of key inconsistency. * delete args parser. * rename metrics to test_metrics.
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import unittest
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import cv2
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import torch
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from modelscope import get_logger
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.msdatasets import MsDataset
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from modelscope.outputs.outputs import OutputKeys
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import DownloadMode, Tasks
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from modelscope.utils.test_utils import test_level
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os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
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logger = get_logger()
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class SelfSupervisedDepthCompletionTest(unittest.TestCase):
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"""class SelfSupervisedDepthCompletionTest"""
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def setUp(self) -> None:
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self.model_id = 'Damo_XR_Lab/Self_Supervised_Depth_Completion'
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data_dir = MsDataset.load(
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'KITTI_Depth_Dataset',
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namespace='Damo_XR_Lab',
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split='test',
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download_mode=DownloadMode.FORCE_REDOWNLOAD
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).config_kwargs['split_config']['test']
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self.source_dir = os.path.join(data_dir, 'selected_data')
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logger.info(data_dir)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest only')
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def test_run(self):
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"""test running evaluation"""
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snapshot_path = snapshot_download(self.model_id)
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logger.info('snapshot_path: %s', snapshot_path)
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self_supervised_depth_completion = pipeline(
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task=Tasks.self_supervised_depth_completion,
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model=self.model_id
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# ,config_file = os.path.join(modelPath, "configuration.json")
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)
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result = self_supervised_depth_completion(
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dict(model_dir=snapshot_path, source_dir=self.source_dir))
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cv2.imwrite('result.jpg', result[OutputKeys.OUTPUT])
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logger.info(
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'self-supervised-depth-completion_damo.test_run_modelhub done')
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if __name__ == '__main__':
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unittest.main()
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