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resnet_dida_mixstyle.py
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resnet_dida_mixstyle.py
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import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from .build import BACKBONE_REGISTRY
from .backbone import Backbone
from .dida_module import DIDAModule
from .mixstyle import MixStyle
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False
)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class DIDABlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, shortcut=False):
super().__init__()
# Default channel reduction rate for DIDA Module is m=16
self.conv1 = DIDAModule(3, inplanes, planes, m=16, padding=None, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.shortcut = shortcut
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
if self.shortcut:
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class DIDABottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, shortcut=False):
super().__init__()
# Default channel reduction rate for DIDA Module is m=16
self.conv1 = DIDAModule(3, inplanes, planes, m=16, padding=None, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False
)
self.bn2 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.shortcut = shortcut
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.shortcut:
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(Backbone):
def __init__(self, block, didablock, layers, **kwargs):
self.inplanes = 64
super().__init__()
# backbone network
self.conv1 = nn.Conv2d(
3, 64, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.ms = MixStyle()
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
# Block based on DIDA Module
self.layer4_dida = self._make_dida_layer(didablock, 512, layers[3], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self._out_features = 512 * block.expansion
self._init_params()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False
),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_dida_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False
),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(inplanes, planes))
return nn.Sequential(*layers)
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu'
)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def featuremaps(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.ms1(x)
x = self.layer2(x)
x = self.layer3(x)
x1 = self.layer4(x)
x2 = self.layer4_dida(x)
x = x1 + x2
return x
def forward(self, x):
f = self.featuremaps(x)
v = self.global_avgpool(f)
return v.view(v.size(0), -1)
def init_pretrained_weights(model, model_url):
pretrain_dict = model_zoo.load_url(model_url)
model.load_state_dict(pretrain_dict, strict=False)
"""
Residual network configurations:
--
resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
"""
@BACKBONE_REGISTRY.register()
def resnet18_dida_mixstyle(pretrained=True, **kwargs):
model = ResNet(block=BasicBlock, didablock=DIDABlock, layers=[2, 2, 2, 2])
if pretrained:
init_pretrained_weights(model, model_urls['resnet18'])
return model
@BACKBONE_REGISTRY.register()
def resnet34_dida_mixstyle(pretrained=True, **kwargs):
model = ResNet(block=BasicBlock, didablock=DIDABlock, layers=[3, 4, 6, 3])
if pretrained:
init_pretrained_weights(model, model_urls['resnet34'])
return model
@BACKBONE_REGISTRY.register()
def resnet50_dida_mixstyle(pretrained=True, **kwargs):
model = ResNet(block=Bottleneck, didablock=DIDABottleneck, layers=[3, 4, 6, 3])
if pretrained:
init_pretrained_weights(model, model_urls['resnet50'])
return model
@BACKBONE_REGISTRY.register()
def resnet101_dida_mixstyle(pretrained=True, **kwargs):
model = ResNet(block=Bottleneck, didablock=DIDABottleneck, layers=[3, 4, 23, 3])
if pretrained:
init_pretrained_weights(model, model_urls['resnet101'])
return model
@BACKBONE_REGISTRY.register()
def resnet152_dida_mixstyle(pretrained=True, **kwargs):
model = ResNet(block=Bottleneck, didablock=DIDABottleneck, layers=[3, 8, 36, 3])
if pretrained:
init_pretrained_weights(model, model_urls['resnet152'])
return model