import torch.nn as nn
import torch
import math
import torch.utils.model_zoo as model_zoo
from ...torch_core import Module
__all__ = ['XResNet', 'xresnet18', 'xresnet34_2', 'xresnet50_2', 'xresnet101', 'xresnet152']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__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(Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__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
def conv2d(ni, nf, stride):
return nn.Sequential(nn.Conv2d(ni, nf, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(nf), nn.ReLU(inplace=True))
class XResNet(Module):
def __init__(self, block, layers, c_out=1000):
self.inplanes = 64
super(XResNet, self).__init__()
self.conv1 = conv2d(3, 32, 2)
self.conv2 = conv2d(32, 32, 1)
self.conv3 = conv2d(32, 64, 1)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(512 * block.expansion, c_out)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
for m in self.modules():
if isinstance(m, BasicBlock): m.bn2.weight = nn.Parameter(torch.zeros_like(m.bn2.weight))
if isinstance(m, Bottleneck): m.bn3.weight = nn.Parameter(torch.zeros_like(m.bn3.weight))
if isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride==2: layers.append(nn.AvgPool2d(kernel_size=2, stride=2))
layers += [
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(planes * block.expansion) ]
downsample = nn.Sequential(*layers)
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 forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def xresnet18(pretrained=False, **kwargs):
"""Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet18']))
return model
def xresnet34_2(pretrained=False, **kwargs):
"""Constructs a XResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet34']))
return model
def xresnet50_2(pretrained=False, **kwargs):
"""Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet50']))
return model
def xresnet101(pretrained=False, **kwargs):
"""Constructs a XResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet101']))
return model
def xresnet152(pretrained=False, **kwargs):
"""Constructs a XResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet152']))
return model
