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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 |
| |
|
