# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch import torch.nn as nn from mmcv.cnn import ConvModule from mmcv.utils.parrots_wrapper import _BatchNorm from mmpose.models.backbones import ResNet, ResNetV1d from mmpose.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer, get_expansion) def is_block(modules): """Check if is ResNet building block.""" if isinstance(modules, (BasicBlock, Bottleneck)): 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_get_expansion(): assert get_expansion(Bottleneck, 2) == 2 assert get_expansion(BasicBlock) == 1 assert get_expansion(Bottleneck) == 4 class MyResBlock(nn.Module): expansion = 8 assert get_expansion(MyResBlock) == 8 # expansion must be an integer or None with pytest.raises(TypeError): get_expansion(Bottleneck, '0') # expansion is not specified and cannot be inferred with pytest.raises(TypeError): class SomeModule(nn.Module): pass get_expansion(SomeModule) def test_basic_block(): # expansion must be 1 with pytest.raises(AssertionError): BasicBlock(64, 64, expansion=2) # BasicBlock with stride 1, out_channels == in_channels block = BasicBlock(64, 64) assert block.in_channels == 64 assert block.mid_channels == 64 assert block.out_channels == 64 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 64 assert block.conv1.kernel_size == (3, 3) assert block.conv1.stride == (1, 1) assert block.conv2.in_channels == 64 assert block.conv2.out_channels == 64 assert block.conv2.kernel_size == (3, 3) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == torch.Size([1, 64, 56, 56]) # BasicBlock with stride 1 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1, bias=False), nn.BatchNorm2d(128)) block = BasicBlock(64, 128, downsample=downsample) assert block.in_channels == 64 assert block.mid_channels == 128 assert block.out_channels == 128 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 128 assert block.conv1.kernel_size == (3, 3) assert block.conv1.stride == (1, 1) assert block.conv2.in_channels == 128 assert block.conv2.out_channels == 128 assert block.conv2.kernel_size == (3, 3) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == torch.Size([1, 128, 56, 56]) # BasicBlock with stride 2 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1, stride=2, bias=False), nn.BatchNorm2d(128)) block = BasicBlock(64, 128, stride=2, downsample=downsample) assert block.in_channels == 64 assert block.mid_channels == 128 assert block.out_channels == 128 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 128 assert block.conv1.kernel_size == (3, 3) assert block.conv1.stride == (2, 2) assert block.conv2.in_channels == 128 assert block.conv2.out_channels == 128 assert block.conv2.kernel_size == (3, 3) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == torch.Size([1, 128, 28, 28]) # forward with checkpointing block = BasicBlock(64, 64, with_cp=True) assert block.with_cp x = torch.randn(1, 64, 56, 56, requires_grad=True) x_out = block(x) assert x_out.shape == torch.Size([1, 64, 56, 56]) def test_bottleneck(): # style must be in ['pytorch', 'caffe'] with pytest.raises(AssertionError): Bottleneck(64, 64, style='tensorflow') # expansion must be divisible by out_channels with pytest.raises(AssertionError): Bottleneck(64, 64, expansion=3) # Test Bottleneck style block = Bottleneck(64, 64, stride=2, style='pytorch') assert block.conv1.stride == (1, 1) assert block.conv2.stride == (2, 2) block = Bottleneck(64, 64, stride=2, style='caffe') assert block.conv1.stride == (2, 2) assert block.conv2.stride == (1, 1) # Bottleneck with stride 1 block = Bottleneck(64, 64, style='pytorch') assert block.in_channels == 64 assert block.mid_channels == 16 assert block.out_channels == 64 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 16 assert block.conv1.kernel_size == (1, 1) assert block.conv2.in_channels == 16 assert block.conv2.out_channels == 16 assert block.conv2.kernel_size == (3, 3) assert block.conv3.in_channels == 16 assert block.conv3.out_channels == 64 assert block.conv3.kernel_size == (1, 1) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == (1, 64, 56, 56) # Bottleneck with stride 1 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1), nn.BatchNorm2d(128)) block = Bottleneck(64, 128, style='pytorch', downsample=downsample) assert block.in_channels == 64 assert block.mid_channels == 32 assert block.out_channels == 128 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 32 assert block.conv1.kernel_size == (1, 1) assert block.conv2.in_channels == 32 assert block.conv2.out_channels == 32 assert block.conv2.kernel_size == (3, 3) assert block.conv3.in_channels == 32 assert block.conv3.out_channels == 128 assert block.conv3.kernel_size == (1, 1) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == (1, 128, 56, 56) # Bottleneck with stride 2 and downsample downsample = nn.Sequential( nn.Conv2d(64, 128, kernel_size=1, stride=2), nn.BatchNorm2d(128)) block = Bottleneck( 64, 128, stride=2, style='pytorch', downsample=downsample) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == (1, 128, 28, 28) # Bottleneck with expansion 2 block = Bottleneck(64, 64, style='pytorch', expansion=2) assert block.in_channels == 64 assert block.mid_channels == 32 assert block.out_channels == 64 assert block.conv1.in_channels == 64 assert block.conv1.out_channels == 32 assert block.conv1.kernel_size == (1, 1) assert block.conv2.in_channels == 32 assert block.conv2.out_channels == 32 assert block.conv2.kernel_size == (3, 3) assert block.conv3.in_channels == 32 assert block.conv3.out_channels == 64 assert block.conv3.kernel_size == (1, 1) x = torch.randn(1, 64, 56, 56) x_out = block(x) assert x_out.shape == (1, 64, 56, 56) # Test Bottleneck with checkpointing block = Bottleneck(64, 64, with_cp=True) block.train() assert block.with_cp x = torch.randn(1, 64, 56, 56, requires_grad=True) x_out = block(x) assert x_out.shape == torch.Size([1, 64, 56, 56]) def test_basicblock_reslayer(): # 3 BasicBlock w/o downsample layer = ResLayer(BasicBlock, 3, 32, 32) assert len(layer) == 3 for i in range(3): assert layer[i].in_channels == 32 assert layer[i].out_channels == 32 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 32, 56, 56) # 3 BasicBlock w/ stride 1 and downsample layer = ResLayer(BasicBlock, 3, 32, 64) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].downsample is not None and len(layer[0].downsample) == 2 assert isinstance(layer[0].downsample[0], nn.Conv2d) assert layer[0].downsample[0].stride == (1, 1) for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 56, 56) # 3 BasicBlock w/ stride 2 and downsample layer = ResLayer(BasicBlock, 3, 32, 64, stride=2) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].stride == 2 assert layer[0].downsample is not None and len(layer[0].downsample) == 2 assert isinstance(layer[0].downsample[0], nn.Conv2d) assert layer[0].downsample[0].stride == (2, 2) for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].stride == 1 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 28, 28) # 3 BasicBlock w/ stride 2 and downsample with avg pool layer = ResLayer(BasicBlock, 3, 32, 64, stride=2, avg_down=True) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].stride == 2 assert layer[0].downsample is not None and len(layer[0].downsample) == 3 assert isinstance(layer[0].downsample[0], nn.AvgPool2d) assert layer[0].downsample[0].stride == 2 for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].stride == 1 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 28, 28) def test_bottleneck_reslayer(): # 3 Bottleneck w/o downsample layer = ResLayer(Bottleneck, 3, 32, 32) assert len(layer) == 3 for i in range(3): assert layer[i].in_channels == 32 assert layer[i].out_channels == 32 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 32, 56, 56) # 3 Bottleneck w/ stride 1 and downsample layer = ResLayer(Bottleneck, 3, 32, 64) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].stride == 1 assert layer[0].conv1.out_channels == 16 assert layer[0].downsample is not None and len(layer[0].downsample) == 2 assert isinstance(layer[0].downsample[0], nn.Conv2d) assert layer[0].downsample[0].stride == (1, 1) for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].conv1.out_channels == 16 assert layer[i].stride == 1 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 56, 56) # 3 Bottleneck w/ stride 2 and downsample layer = ResLayer(Bottleneck, 3, 32, 64, stride=2) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].stride == 2 assert layer[0].conv1.out_channels == 16 assert layer[0].downsample is not None and len(layer[0].downsample) == 2 assert isinstance(layer[0].downsample[0], nn.Conv2d) assert layer[0].downsample[0].stride == (2, 2) for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].conv1.out_channels == 16 assert layer[i].stride == 1 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 28, 28) # 3 Bottleneck w/ stride 2 and downsample with avg pool layer = ResLayer(Bottleneck, 3, 32, 64, stride=2, avg_down=True) assert len(layer) == 3 assert layer[0].in_channels == 32 assert layer[0].out_channels == 64 assert layer[0].stride == 2 assert layer[0].conv1.out_channels == 16 assert layer[0].downsample is not None and len(layer[0].downsample) == 3 assert isinstance(layer[0].downsample[0], nn.AvgPool2d) assert layer[0].downsample[0].stride == 2 for i in range(1, 3): assert layer[i].in_channels == 64 assert layer[i].out_channels == 64 assert layer[i].conv1.out_channels == 16 assert layer[i].stride == 1 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 64, 28, 28) # 3 Bottleneck with custom expansion layer = ResLayer(Bottleneck, 3, 32, 32, expansion=2) assert len(layer) == 3 for i in range(3): assert layer[i].in_channels == 32 assert layer[i].out_channels == 32 assert layer[i].stride == 1 assert layer[i].conv1.out_channels == 16 assert layer[i].downsample is None x = torch.randn(1, 32, 56, 56) x_out = layer(x) assert x_out.shape == (1, 32, 56, 56) 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) model.init_weights('torchvision://resnet50') 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 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 feat.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) def test_resnet_v1d(): model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3)) model.init_weights() model.train() assert len(model.stem) == 3 for i in range(3): assert isinstance(model.stem[i], ConvModule) imgs = torch.randn(1, 3, 224, 224) feat = model.stem(imgs) assert feat.shape == (1, 64, 112, 112) 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 ResNet50V1d with first stage frozen frozen_stages = 1 model = ResNetV1d(depth=50, frozen_stages=frozen_stages) assert len(model.stem) == 3 for i in range(3): assert isinstance(model.stem[i], ConvModule) model.init_weights() model.train() check_norm_state(model.stem, False) for param in model.stem.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 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)