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# 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)
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