mmpose-0.29.0/tests/test_backbones/test_resnet.py

# 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)