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# 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])
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