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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : [email protected] | |
| @File : resnext.py.py | |
| @Time : 8/11/19 8:58 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__ = ['ResNeXt', 'resnext101'] # support resnext 101 | |
| model_urls = { | |
| 'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth', | |
| 'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-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 GroupBottleneck(nn.Module): | |
| expansion = 2 | |
| def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None): | |
| super(GroupBottleneck, 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, groups=groups, bias=False) | |
| self.bn2 = BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False) | |
| self.bn3 = BatchNorm2d(planes * 2) | |
| 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 ResNeXt(nn.Module): | |
| def __init__(self, block, layers, groups=32, num_classes=1000): | |
| self.inplanes = 128 | |
| super(ResNeXt, 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, 128, layers[0], groups=groups) | |
| self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups) | |
| self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups) | |
| self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups) | |
| self.avgpool = nn.AvgPool2d(7, stride=1) | |
| self.fc = nn.Linear(1024 * 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.groups | |
| 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, groups=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, groups, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, groups=groups)) | |
| 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 resnext101(pretrained=False, **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(load_url(model_urls['resnext101']), strict=False) | |
| return model | |