mmpose-0.29.0/tests/test_backbones/test_seresnet.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import AvgPool2d
from torch.nn.modules.batchnorm import _BatchNorm

from mmpose.models.backbones import SEResNet
from mmpose.models.backbones.resnet import ResLayer
from mmpose.models.backbones.seresnet import SEBottleneck, SELayer


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_selayer():
 # Test selayer forward
 layer = SELayer(64)
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])

 # Test selayer forward with different ratio
 layer = SELayer(64, ratio=8)
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])


def test_bottleneck():

 with pytest.raises(AssertionError):
 # Style must be in ['pytorch', 'caffe']
 SEBottleneck(64, 64, style='tensorflow')

 # Test SEBottleneck with checkpoint forward
 block = SEBottleneck(64, 64, with_cp=True)
 assert block.with_cp
 x = torch.randn(1, 64, 56, 56)
 x_out = block(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])

 # Test Bottleneck style
 block = SEBottleneck(64, 256, stride=2, style='pytorch')
 assert block.conv1.stride == (1, 1)
 assert block.conv2.stride == (2, 2)
 block = SEBottleneck(64, 256, stride=2, style='caffe')
 assert block.conv1.stride == (2, 2)
 assert block.conv2.stride == (1, 1)

 # Test Bottleneck forward
 block = SEBottleneck(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_res_layer():
 # Test ResLayer of 3 Bottleneck w\o downsample
 layer = ResLayer(SEBottleneck, 3, 64, 64, se_ratio=16)
 assert len(layer) == 3
 assert layer[0].conv1.in_channels == 64
 assert layer[0].conv1.out_channels == 16
 for i in range(1, len(layer)):
 assert layer[i].conv1.in_channels == 64
 assert layer[i].conv1.out_channels == 16
 for i in range(len(layer)):
 assert layer[i].downsample is None
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])

 # Test ResLayer of 3 SEBottleneck with downsample
 layer = ResLayer(SEBottleneck, 3, 64, 256, se_ratio=16)
 assert layer[0].downsample[0].out_channels == 256
 for i in range(1, len(layer)):
 assert layer[i].downsample is None
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 256, 56, 56])

 # Test ResLayer of 3 SEBottleneck with stride=2
 layer = ResLayer(SEBottleneck, 3, 64, 256, stride=2, se_ratio=8)
 assert layer[0].downsample[0].out_channels == 256
 assert layer[0].downsample[0].stride == (2, 2)
 for i in range(1, len(layer)):
 assert layer[i].downsample is None
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 256, 28, 28])

 # Test ResLayer of 3 SEBottleneck with stride=2 and average downsample
 layer = ResLayer(
 SEBottleneck, 3, 64, 256, stride=2, avg_down=True, se_ratio=8)
 assert isinstance(layer[0].downsample[0], AvgPool2d)
 assert layer[0].downsample[1].out_channels == 256
 assert layer[0].downsample[1].stride == (1, 1)
 for i in range(1, len(layer)):
 assert layer[i].downsample is None
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 256, 28, 28])


def test_seresnet():
 """Test resnet backbone."""
 with pytest.raises(KeyError):
 # SEResNet depth should be in [50, 101, 152]
 SEResNet(20)

 with pytest.raises(AssertionError):
 # In SEResNet: 1 <= num_stages <= 4
 SEResNet(50, num_stages=0)

 with pytest.raises(AssertionError):
 # In SEResNet: 1 <= num_stages <= 4
 SEResNet(50, num_stages=5)

 with pytest.raises(AssertionError):
 # len(strides) == len(dilations) == num_stages
 SEResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)

 with pytest.raises(TypeError):
 # pretrained must be a string path
 model = SEResNet(50)
 model.init_weights(pretrained=0)

 with pytest.raises(AssertionError):
 # Style must be in ['pytorch', 'caffe']
 SEResNet(50, style='tensorflow')

 # Test SEResNet50 norm_eval=True
 model = SEResNet(50, norm_eval=True)
 model.init_weights()
 model.train()
 assert check_norm_state(model.modules(), False)

 # Test SEResNet50 with torchvision pretrained weight
 model = SEResNet(depth=50, norm_eval=True)
 model.init_weights('torchvision://resnet50')
 model.train()
 assert check_norm_state(model.modules(), False)

 # Test SEResNet50 with first stage frozen
 frozen_stages = 1
 model = SEResNet(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 SEResNet50 with BatchNorm forward
 model = SEResNet(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 == torch.Size([1, 256, 56, 56])
 assert feat[1].shape == torch.Size([1, 512, 28, 28])
 assert feat[2].shape == torch.Size([1, 1024, 14, 14])
 assert feat[3].shape == torch.Size([1, 2048, 7, 7])

 # Test SEResNet50 with layers 1, 2, 3 out forward
 model = SEResNet(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 == torch.Size([1, 256, 56, 56])
 assert feat[1].shape == torch.Size([1, 512, 28, 28])
 assert feat[2].shape == torch.Size([1, 1024, 14, 14])

 # Test SEResNet50 with layers 3 (top feature maps) out forward
 model = SEResNet(50, out_indices=(3, ))
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert feat.shape == torch.Size([1, 2048, 7, 7])

 # Test SEResNet50 with checkpoint forward
 model = SEResNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
 for m in model.modules():
 if isinstance(m, SEBottleneck):
 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 == torch.Size([1, 256, 56, 56])
 assert feat[1].shape == torch.Size([1, 512, 28, 28])
 assert feat[2].shape == torch.Size([1, 1024, 14, 14])
 assert feat[3].shape == torch.Size([1, 2048, 7, 7])

 # Test SEResNet50 zero initialization of residual
 model = SEResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
 model.init_weights()
 for m in model.modules():
 if isinstance(m, SEBottleneck):
 assert all_zeros(m.norm3)
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 4
 assert feat[0].shape == torch.Size([1, 256, 56, 56])
 assert feat[1].shape == torch.Size([1, 512, 28, 28])
 assert feat[2].shape == torch.Size([1, 1024, 14, 14])
 assert feat[3].shape == torch.Size([1, 2048, 7, 7])