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1fe211ffe5
* fix pipeline builder when model is not supported * fix ci & skip --------- Co-authored-by: suluyan.sly@alibaba-inc.com <suluyan.sly@alibaba-inc.com>
68 lines
2.1 KiB
Python
68 lines
2.1 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 torch
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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 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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class NeRFRecon4KTest(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'DAMOXR/cv_nerf-3d-reconstruction-4k-nerf_damo'
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data_dir = MsDataset.load(
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'DAMOXR/nerf_llff_data',
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subset_name='default',
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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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nerf_llff = os.path.join(data_dir, 'nerf_llff_data')
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scene = 'fern'
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data_dir = os.path.join(nerf_llff, scene)
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self.render_dir = 'exp'
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self.data_dic = dict(
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datadir=data_dir,
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dataset_type='llff',
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load_sr=1,
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factor=4,
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ndc=True,
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white_bkgd=False)
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# @unittest.skipUnless(test_level() >= 2, '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_by_direct_model_download(self):
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# snapshot_path = snapshot_download(self.model_id)
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# print('snapshot_path: {}'.format(snapshot_path))
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# nerf_recon_4k = pipeline(
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# Tasks.nerf_recon_4k,
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# model=snapshot_path,
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# data_type='llff',
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# )
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# nerf_recon_4k(
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# dict(data_cfg=self.data_dic, render_dir=self.render_dir))
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@unittest.skip
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@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest only')
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def test_run_modelhub(self):
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nerf_recon_4k = pipeline(
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Tasks.nerf_recon_4k,
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model=self.model_id,
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data_type='llff',
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)
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nerf_recon_4k(dict(data_cfg=self.data_dic, render_dir=self.render_dir))
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print('4k-nerf_damo.test_run_modelhub done')
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if __name__ == '__main__':
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unittest.main()
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