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"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" | |
import math | |
import torch.nn as nn | |
from torch.nn import BatchNorm2d | |
from .utils import load_url | |
__all__ = ['ResNet', 'resnet50'] | |
model_urls = { | |
'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.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(BasicBlock, self).__init__() | |
self.conv1 = conv3x3(inplanes, planes, stride) | |
self.bn1 = BatchNorm2d(planes) | |
self.relu = nn.ReLU(inplace=True) | |
self.conv2 = conv3x3(planes, planes) | |
self.bn2 = 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(Bottleneck, self).__init__() | |
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
self.bn1 = BatchNorm2d(planes) | |
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
padding=1, bias=False) | |
self.bn2 = BatchNorm2d(planes) | |
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
self.bn3 = BatchNorm2d(planes * 4) | |
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 ResNet(nn.Module): | |
def __init__(self, block, layers, num_classes=1000): | |
self.inplanes = 128 | |
super(ResNet, self).__init__() | |
self.conv1 = conv3x3(3, 64, stride=2) | |
self.bn1 = BatchNorm2d(64) | |
self.relu1 = nn.ReLU(inplace=True) | |
self.conv2 = conv3x3(64, 64) | |
self.bn2 = BatchNorm2d(64) | |
self.relu2 = nn.ReLU(inplace=True) | |
self.conv3 = conv3x3(64, 128) | |
self.bn3 = BatchNorm2d(128) | |
self.relu3 = 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.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.AvgPool2d(7, stride=1) | |
self.fc = nn.Linear(512 * block.expansion, num_classes) | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
m.weight.data.normal_(0, math.sqrt(2. / n)) | |
elif isinstance(m, BatchNorm2d): | |
m.weight.data.fill_(1) | |
m.bias.data.zero_() | |
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), | |
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 forward(self, x): | |
x = self.relu1(self.bn1(self.conv1(x))) | |
x = self.relu2(self.bn2(self.conv2(x))) | |
x = self.relu3(self.bn3(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 resnet50(pretrained=False, **kwargs): | |
"""Constructs a ResNet-50 model. | |
Args: | |
pretrained (bool): If True, returns a model pre-trained on ImageNet | |
""" | |
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
if pretrained: | |
model.load_state_dict(load_url(model_urls['resnet50']), strict=False) | |
return model | |
def resnet18(pretrained=False, **kwargs): | |
"""Constructs a ResNet-18 model. | |
Args: | |
pretrained (bool): If True, returns a model pre-trained on ImageNet | |
""" | |
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
if pretrained: | |
model.load_state_dict(load_url(model_urls['resnet18'])) | |
return model |