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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import pytest | |
| import torch | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from mmpose.models.backbones import SCNet | |
| from mmpose.models.backbones.scnet import SCBottleneck, SCConv | |
| def is_block(modules): | |
| """Check if is SCNet building block.""" | |
| if isinstance(modules, (SCBottleneck, )): | |
| return True | |
| return False | |
| def is_norm(modules): | |
| """Check if is one of the norms.""" | |
| if isinstance(modules, (_BatchNorm, )): | |
| return True | |
| return False | |
| def all_zeros(modules): | |
| """Check if the weight(and bias) is all zero.""" | |
| weight_zero = torch.equal(modules.weight.data, | |
| torch.zeros_like(modules.weight.data)) | |
| if hasattr(modules, 'bias'): | |
| bias_zero = torch.equal(modules.bias.data, | |
| torch.zeros_like(modules.bias.data)) | |
| else: | |
| bias_zero = True | |
| return weight_zero and bias_zero | |
| 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_scnet_scconv(): | |
| # Test scconv forward | |
| layer = SCConv(64, 64, 1, 4) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = layer(x) | |
| assert x_out.shape == torch.Size([1, 64, 56, 56]) | |
| def test_scnet_bottleneck(): | |
| # Test Bottleneck forward | |
| block = SCBottleneck(64, 64) | |
| x = torch.randn(1, 64, 56, 56) | |
| x_out = block(x) | |
| assert x_out.shape == torch.Size([1, 64, 56, 56]) | |
| def test_scnet_backbone(): | |
| """Test scnet backbone.""" | |
| with pytest.raises(KeyError): | |
| # SCNet depth should be in [50, 101] | |
| SCNet(20) | |
| with pytest.raises(TypeError): | |
| # pretrained must be a string path | |
| model = SCNet(50) | |
| model.init_weights(pretrained=0) | |
| # Test SCNet norm_eval=True | |
| model = SCNet(50, norm_eval=True) | |
| model.init_weights() | |
| model.train() | |
| assert check_norm_state(model.modules(), False) | |
| # Test SCNet50 with first stage frozen | |
| frozen_stages = 1 | |
| model = SCNet(50, frozen_stages=frozen_stages) | |
| model.init_weights() | |
| model.train() | |
| assert model.norm1.training is False | |
| for layer in [model.conv1, model.norm1]: | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| for i in range(1, frozen_stages + 1): | |
| layer = getattr(model, f'layer{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| assert mod.training is False | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| # Test SCNet with BatchNorm forward | |
| model = SCNet(50, out_indices=(0, 1, 2, 3)) | |
| for m in model.modules(): | |
| if is_norm(m): | |
| assert isinstance(m, _BatchNorm) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == torch.Size([2, 256, 56, 56]) | |
| assert feat[1].shape == torch.Size([2, 512, 28, 28]) | |
| assert feat[2].shape == torch.Size([2, 1024, 14, 14]) | |
| assert feat[3].shape == torch.Size([2, 2048, 7, 7]) | |
| # Test SCNet with layers 1, 2, 3 out forward | |
| model = SCNet(50, out_indices=(0, 1, 2)) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 3 | |
| assert feat[0].shape == torch.Size([2, 256, 56, 56]) | |
| assert feat[1].shape == torch.Size([2, 512, 28, 28]) | |
| assert feat[2].shape == torch.Size([2, 1024, 14, 14]) | |
| # Test SEResNet50 with layers 3 (top feature maps) out forward | |
| model = SCNet(50, out_indices=(3, )) | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| assert feat.shape == torch.Size([2, 2048, 7, 7]) | |
| # Test SEResNet50 with checkpoint forward | |
| model = SCNet(50, out_indices=(0, 1, 2, 3), with_cp=True) | |
| for m in model.modules(): | |
| if is_block(m): | |
| assert m.with_cp | |
| model.init_weights() | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == torch.Size([2, 256, 56, 56]) | |
| assert feat[1].shape == torch.Size([2, 512, 28, 28]) | |
| assert feat[2].shape == torch.Size([2, 1024, 14, 14]) | |
| assert feat[3].shape == torch.Size([2, 2048, 7, 7]) | |
| # Test SCNet zero initialization of residual | |
| model = SCNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True) | |
| model.init_weights() | |
| for m in model.modules(): | |
| if isinstance(m, SCBottleneck): | |
| assert all_zeros(m.norm3) | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| assert len(feat) == 4 | |
| assert feat[0].shape == torch.Size([2, 256, 56, 56]) | |
| assert feat[1].shape == torch.Size([2, 512, 28, 28]) | |
| assert feat[2].shape == torch.Size([2, 1024, 14, 14]) | |
| assert feat[3].shape == torch.Size([2, 2048, 7, 7]) | |