Spaces:
Build error
Build error
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import pytest | |
| import torch | |
| from mmcv.utils.parrots_wrapper import _BatchNorm | |
| from mmpose.models.backbones import VGG | |
| 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(): | |
| """Test VGG backbone.""" | |
| with pytest.raises(KeyError): | |
| # VGG depth should be in [11, 13, 16, 19] | |
| VGG(18) | |
| with pytest.raises(AssertionError): | |
| # In VGG: 1 <= num_stages <= 5 | |
| VGG(11, num_stages=0) | |
| with pytest.raises(AssertionError): | |
| # In VGG: 1 <= num_stages <= 5 | |
| VGG(11, num_stages=6) | |
| with pytest.raises(AssertionError): | |
| # len(dilations) == num_stages | |
| VGG(11, dilations=(1, 1), num_stages=3) | |
| with pytest.raises(TypeError): | |
| # pretrained must be a string path | |
| model = VGG(11) | |
| model.init_weights(pretrained=0) | |
| # Test VGG11 norm_eval=True | |
| model = VGG(11, norm_eval=True) | |
| model.init_weights() | |
| model.train() | |
| assert 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) | |
| assert len(feat) == 5 | |
| assert feat[0].shape == (1, 64, 112, 112) | |
| assert feat[1].shape == (1, 128, 56, 56) | |
| assert feat[2].shape == (1, 256, 28, 28) | |
| assert feat[3].shape == (1, 512, 14, 14) | |
| assert 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) | |
| assert len(feat) == 6 | |
| assert feat[0].shape == (1, 64, 112, 112) | |
| assert feat[1].shape == (1, 128, 56, 56) | |
| assert feat[2].shape == (1, 256, 28, 28) | |
| assert feat[3].shape == (1, 512, 14, 14) | |
| assert feat[4].shape == (1, 512, 7, 7) | |
| assert 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) | |
| assert len(feat) == 5 | |
| assert feat[0].shape == (1, 64, 112, 112) | |
| assert feat[1].shape == (1, 128, 56, 56) | |
| assert feat[2].shape == (1, 256, 28, 28) | |
| assert feat[3].shape == (1, 512, 14, 14) | |
| assert 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) | |
| assert len(feat) == 6 | |
| assert feat[0].shape == (1, 64, 112, 112) | |
| assert feat[1].shape == (1, 128, 56, 56) | |
| assert feat[2].shape == (1, 256, 28, 28) | |
| assert feat[3].shape == (1, 512, 14, 14) | |
| assert feat[4].shape == (1, 512, 7, 7) | |
| assert 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) | |
| assert len(feat) == 3 | |
| assert feat[0].shape == (1, 64, 112, 112) | |
| assert feat[1].shape == (1, 128, 56, 56) | |
| assert 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) | |
| assert feat.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) | |
| assert feat.shape == (1, 10) | |