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| """ |
| @Author : Peike Li |
| @Contact : peike.li@yahoo.com |
| @File : resnet.py |
| @Time : 8/4/19 3:35 PM |
| @Desc : |
| @License : This source code is licensed under the license found in the |
| LICENSE file in the root directory of this source tree. |
| """ |
|
|
| import functools |
| import torch.nn as nn |
| import math |
| from torch.utils.model_zoo import load_url |
|
|
| from modules import InPlaceABNSync |
|
|
| BatchNorm2d = functools.partial(InPlaceABNSync, activation='none') |
|
|
| __all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] |
|
|
| model_urls = { |
| 'resnet18': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth', |
| 'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth', |
| 'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-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 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 |
|
|
|
|
| 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 resnet101(pretrained=False, **kwargs): |
| """Constructs a ResNet-101 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(load_url(model_urls['resnet101']), strict=False) |
| return model |
|
|
