def test_resnet_half_channel(): model = ResNet(50, base_channels=32, out_indices=(0, 1, 2, 3)) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 4 assert feat[0].shape == (1, 128, 56, 56) assert feat[1].shape == (1, 256, 28, 28) assert feat[2].shape == (1, 512, 14, 14) assert feat[3].shape == (1, 1024, 7, 7)
def test_resnet(): """Test resnet backbone.""" with pytest.raises(KeyError): # ResNet depth should be in [18, 34, 50, 101, 152] ResNet(20) with pytest.raises(AssertionError): # In ResNet: 1 <= num_stages <= 4 ResNet(50, num_stages=0) with pytest.raises(AssertionError): # In ResNet: 1 <= num_stages <= 4 ResNet(50, num_stages=5) with pytest.raises(AssertionError): # len(strides) == len(dilations) == num_stages ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3) with pytest.raises(TypeError): # pretrained must be a string path model = ResNet(50) model.init_weights(pretrained=0) with pytest.raises(AssertionError): # Style must be in ['pytorch', 'caffe'] ResNet(50, style='tensorflow') # Test ResNet50 norm_eval=True model = ResNet(50, norm_eval=True) model.init_weights() model.train() assert check_norm_state(model.modules(), False) # Test ResNet50 with torchvision pretrained weight model = ResNet(depth=50, norm_eval=True, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')) model.init_weights() model.train() assert check_norm_state(model.modules(), False) # Test ResNet50 with first stage frozen frozen_stages = 1 model = ResNet(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 ResNet18 forward model = ResNet(18, out_indices=(0, 1, 2, 3)) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 4 assert feat[0].shape == (1, 64, 56, 56) assert feat[1].shape == (1, 128, 28, 28) assert feat[2].shape == (1, 256, 14, 14) assert feat[3].shape == (1, 512, 7, 7) # Test ResNet50 with BatchNorm forward model = ResNet(50, out_indices=(0, 1, 2, 3)) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 4 assert feat[0].shape == (1, 256, 56, 56) assert feat[1].shape == (1, 512, 28, 28) assert feat[2].shape == (1, 1024, 14, 14) assert feat[3].shape == (1, 2048, 7, 7) # Test ResNet50 with DropPath forward model = ResNet(50, out_indices=(0, 1, 2, 3), drop_path_rate=0.5) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 4 assert feat[0].shape == (1, 256, 56, 56) assert feat[1].shape == (1, 512, 28, 28) assert feat[2].shape == (1, 1024, 14, 14) assert feat[3].shape == (1, 2048, 7, 7) # Test ResNet50 with layers 1, 2, 3 out forward model = ResNet(50, 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, 256, 56, 56) assert feat[1].shape == (1, 512, 28, 28) assert feat[2].shape == (1, 1024, 14, 14) # Test ResNet50 with layers 3 (top feature maps) out forward model = ResNet(50, out_indices=(3, )) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 1 assert feat[0].shape == (1, 2048, 7, 7) # Test ResNet50 with checkpoint forward model = ResNet(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(1, 3, 224, 224) feat = model(imgs) assert len(feat) == 4 assert feat[0].shape == (1, 256, 56, 56) assert feat[1].shape == (1, 512, 28, 28) assert feat[2].shape == (1, 1024, 14, 14) assert feat[3].shape == (1, 2048, 7, 7) # zero initialization of residual blocks model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True) model.init_weights() for m in model.modules(): if isinstance(m, Bottleneck): assert all_zeros(m.norm3) elif isinstance(m, BasicBlock): assert all_zeros(m.norm2) # non-zero initialization of residual blocks model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=False) model.init_weights() for m in model.modules(): if isinstance(m, Bottleneck): assert not all_zeros(m.norm3) elif isinstance(m, BasicBlock): assert not all_zeros(m.norm2)