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ba4db97507
* add vq_compression model * add vq_compression model * check pre-commit for lint test * fix by flake8 * update * update * update * the last update * the laast update * update test_level>=0 --------- Co-authored-by: 剑匣 <zackary.sz@alibaba-inc.com>
91 lines
3.4 KiB
Python
91 lines
3.4 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 NeRFReconVQCompressionBlender(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'DAMOXR/cv_nerf_3d-reconstruction_vector-quantize-compression'
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pretrained_model = 'ficus_demo.pt'
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data_dir = MsDataset.load(
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'nerf_recon_dataset', namespace='damo',
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split='train').config_kwargs['split_config']['train']
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nerf_synthetic_dataset = os.path.join(data_dir, 'nerf_synthetic')
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self.blender_scene = 'ficus'
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data_dir = os.path.join(nerf_synthetic_dataset, self.blender_scene)
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self.pipeline = pipeline(
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Tasks.nerf_recon_vq_compression,
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model=self.model_id,
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dataset_name='blender',
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data_dir=data_dir,
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downsample=1,
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ndc_ray=False,
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ckpt_path=pretrained_model)
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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_evalutaion(self):
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render_dir = f'./exp/{self.blender_scene}'
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self.pipeline(
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dict(test_mode='evaluation_test', render_dir=render_dir, N_vis=5))
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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_render_path(self):
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render_dir = f'./exp/{self.blender_scene}'
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self.pipeline(
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dict(test_mode='render_path', render_dir=render_dir, N_vis=30))
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class NeRFReconVQCompressionLLFF(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'DAMOXR/cv_nerf_3d-reconstruction_vector-quantize-compression'
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pretrained_model = 'fern_demo.pt'
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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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).config_kwargs['split_config']['test']
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nerf_llff = os.path.join(data_dir, 'nerf_llff_data')
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self.llff_scene = 'fern'
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data_dir = os.path.join(nerf_llff, self.llff_scene)
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self.pipeline = pipeline(
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Tasks.nerf_recon_vq_compression,
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model=self.model_id,
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dataset_name='llff',
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data_dir=data_dir,
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downsample=4,
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ndc_ray=True,
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ckpt_path=pretrained_model)
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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_evalutaion(self):
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render_dir = f'./exp/{self.llff_scene}'
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self.pipeline(
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dict(test_mode='evaluation_test', render_dir=render_dir, N_vis=5))
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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_render_path(self):
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render_dir = f'./exp/{self.llff_scene}'
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self.pipeline(
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dict(test_mode='render_path', render_dir=render_dir, N_vis=10))
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if __name__ == '__main__':
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unittest.main()
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