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https://github.com/open-mmlab/mmrazor.git
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1c03a07350
* tmp * add new mmdet models * add docstring * pass test and pre-commit * rm razor tracer * update fx tracer, now it can automatically wrap methods and functions. * update tracer passed models * add warning for torch <1.12.0 fix bug for python3.6 update placeholder to support placeholder.XXX * fix bug * update docs * fix lint * fix parse_cfg in configs * restore mutablechannel * test ite prune algorithm when using dist * add get_model_from_path to MMModelLibrrary * add mm models to DefaultModelLibrary * add uts * fix bug * fix bug * add uts * add uts * add uts * add uts * fix bug * restore ite_prune_algorithm * update doc * PruneTracer -> ChannelAnalyzer * prune_tracer -> channel_analyzer * add test for fxtracer * fix bug * fix bug * PruneTracer -> ChannelAnalyzer refine * CustomFxTracer -> MMFxTracer * fix bug when test with torch<1.12 * update print log * fix lint * rm unuseful code Co-authored-by: liukai <liukai@pjlab.org.cn> Co-authored-by: jacky <jacky@xx.com> Co-authored-by: Your Name <you@example.com> Co-authored-by: liukai <your_email@abc.example>
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import unittest
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import torch
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from .data.model_library import (DefaultModelLibrary, MMClsModelLibrary,
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MMDetModelLibrary, MMModelLibrary,
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MMSegModelLibrary, ModelGenerator,
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TorchModelLibrary)
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from .data.models import SingleLineModel
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from .data.tracer_passed_models import (BackwardPassedModelManager,
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FxPassedModelManager)
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TEST_DATA = os.getenv('TEST_DATA') == 'true'
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class TestModelLibrary(unittest.TestCase):
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def test_mmcls(self):
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if not TEST_DATA:
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self.skipTest('not test data to save time.')
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library = MMClsModelLibrary(exclude=['cutmax', 'cifar'])
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self.assertTrue(library.is_default_includes_cover_all_models())
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def test_defaul_library(self):
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if not TEST_DATA:
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self.skipTest('not test data to save time.')
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library = DefaultModelLibrary()
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self.assertTrue(library.is_default_includes_cover_all_models())
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def test_torchlibrary(self):
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if not TEST_DATA:
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self.skipTest('not test data to save time.')
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library = TorchModelLibrary()
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self.assertTrue(library.is_default_includes_cover_all_models())
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def test_mmdet(self):
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if not TEST_DATA:
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self.skipTest('not test data to save time.')
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library = MMDetModelLibrary()
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self.assertTrue(library.is_default_includes_cover_all_models())
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def test_mmseg(self):
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if not TEST_DATA:
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self.skipTest('not test data to save time.')
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library = MMSegModelLibrary()
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self.assertTrue(library.is_default_includes_cover_all_models())
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def test_get_model_by_config(self):
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config = 'mmcls::resnet/resnet34_8xb32_in1k.py'
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Model = MMModelLibrary.get_model_from_path(config)
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_ = Model()
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def test_passed_models(self):
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try:
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print(FxPassedModelManager().include_models())
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print(BackwardPassedModelManager().include_models())
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except Exception:
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self.fail()
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class TestModels(unittest.TestCase):
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def _test_a_model(self, Model):
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model = Model()
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x = torch.rand(2, 3, 224, 224)
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y = model(x)
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self.assertSequenceEqual(y.shape, [2, 1000])
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def test_models(self):
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library = DefaultModelLibrary()
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for Model in library.include_models():
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with self.subTest(model=Model):
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self._test_a_model(Model)
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def test_generator(self):
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Model = ModelGenerator('model', SingleLineModel)
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model = Model()
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model.eval()
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self.assertEqual(model.training, False)
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model.train()
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self.assertEqual(model.training, True)
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