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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from unittest import TestCase | |
| import torch | |
| from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm | |
| from mmpose.models.backbones import VGG | |
| class TestVGG(TestCase): | |
| def check_norm_state(modules, train_state): | |
| """Check if norm layer is in correct train state.""" | |
| for mod in modules: | |
| if isinstance(mod, _BatchNorm): | |
| if mod.training != train_state: | |
| return False | |
| return True | |
| def test_vgg(self): | |
| """Test VGG backbone.""" | |
| with self.assertRaises(KeyError): | |
| # VGG depth should be in [11, 13, 16, 19] | |
| VGG(18) | |
| with self.assertRaises(AssertionError): | |
| # In VGG: 1 <= num_stages <= 5 | |
| VGG(11, num_stages=0) | |
| with self.assertRaises(AssertionError): | |
| # In VGG: 1 <= num_stages <= 5 | |
| VGG(11, num_stages=6) | |
| with self.assertRaises(AssertionError): | |
| # len(dilations) == num_stages | |
| VGG(11, dilations=(1, 1), num_stages=3) | |
| # Test VGG11 norm_eval=True | |
| model = VGG(11, norm_eval=True) | |
| model.init_weights() | |
| model.train() | |
| self.assertTrue(self.check_norm_state(model.modules(), False)) | |
| # Test VGG11 forward without classifiers | |
| model = VGG(11, out_indices=(0, 1, 2, 3, 4)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 5) | |
| self.assertEqual(feat[0].shape, (1, 64, 112, 112)) | |
| self.assertEqual(feat[1].shape, (1, 128, 56, 56)) | |
| self.assertEqual(feat[2].shape, (1, 256, 28, 28)) | |
| self.assertEqual(feat[3].shape, (1, 512, 14, 14)) | |
| self.assertEqual(feat[4].shape, (1, 512, 7, 7)) | |
| # Test VGG11 forward with classifiers | |
| model = VGG(11, num_classes=10, out_indices=(0, 1, 2, 3, 4, 5)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 6) | |
| self.assertEqual(feat[0].shape, (1, 64, 112, 112)) | |
| self.assertEqual(feat[1].shape, (1, 128, 56, 56)) | |
| self.assertEqual(feat[2].shape, (1, 256, 28, 28)) | |
| self.assertEqual(feat[3].shape, (1, 512, 14, 14)) | |
| self.assertEqual(feat[4].shape, (1, 512, 7, 7)) | |
| self.assertEqual(feat[5].shape, (1, 10)) | |
| # Test VGG11BN forward | |
| model = VGG(11, norm_cfg=dict(type='BN'), out_indices=(0, 1, 2, 3, 4)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 5) | |
| self.assertEqual(feat[0].shape, (1, 64, 112, 112)) | |
| self.assertEqual(feat[1].shape, (1, 128, 56, 56)) | |
| self.assertEqual(feat[2].shape, (1, 256, 28, 28)) | |
| self.assertEqual(feat[3].shape, (1, 512, 14, 14)) | |
| self.assertEqual(feat[4].shape, (1, 512, 7, 7)) | |
| # Test VGG11BN forward with classifiers | |
| model = VGG( | |
| 11, | |
| num_classes=10, | |
| norm_cfg=dict(type='BN'), | |
| out_indices=(0, 1, 2, 3, 4, 5)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 6) | |
| self.assertEqual(feat[0].shape, (1, 64, 112, 112)) | |
| self.assertEqual(feat[1].shape, (1, 128, 56, 56)) | |
| self.assertEqual(feat[2].shape, (1, 256, 28, 28)) | |
| self.assertEqual(feat[3].shape, (1, 512, 14, 14)) | |
| self.assertEqual(feat[4].shape, (1, 512, 7, 7)) | |
| self.assertEqual(feat[5].shape, (1, 10)) | |
| # Test VGG13 with layers 1, 2, 3 out forward | |
| model = VGG(13, out_indices=(0, 1, 2)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 3) | |
| self.assertEqual(feat[0].shape, (1, 64, 112, 112)) | |
| self.assertEqual(feat[1].shape, (1, 128, 56, 56)) | |
| self.assertEqual(feat[2].shape, (1, 256, 28, 28)) | |
| # Test VGG16 with top feature maps out forward | |
| model = VGG(16) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 1) | |
| self.assertEqual(feat[-1].shape, (1, 512, 7, 7)) | |
| # Test VGG19 with classification score out forward | |
| model = VGG(19, num_classes=10) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(1, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertEqual(len(feat), 1) | |
| self.assertEqual(feat[-1].shape, (1, 10)) | |