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""" |
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@Author : Peike Li |
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@Contact : peike.li@yahoo.com |
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@File : resnext.py.py |
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@Time : 8/11/19 8:58 PM |
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@Desc : |
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@License : This source code is licensed under the license found in the |
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LICENSE file in the root directory of this source tree. |
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""" |
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import functools |
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import torch.nn as nn |
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import math |
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from torch.utils.model_zoo import load_url |
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from modules import InPlaceABNSync |
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BatchNorm2d = functools.partial(InPlaceABNSync, activation='none') |
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__all__ = ['ResNeXt', 'resnext101'] |
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model_urls = { |
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'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth', |
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'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth' |
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} |
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def conv3x3(in_planes, out_planes, stride=1): |
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"3x3 convolution with padding" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
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padding=1, bias=False) |
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class GroupBottleneck(nn.Module): |
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expansion = 2 |
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def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None): |
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super(GroupBottleneck, self).__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
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padding=1, groups=groups, bias=False) |
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self.bn2 = BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False) |
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self.bn3 = BatchNorm2d(planes * 2) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class ResNeXt(nn.Module): |
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def __init__(self, block, layers, groups=32, num_classes=1000): |
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self.inplanes = 128 |
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super(ResNeXt, self).__init__() |
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self.conv1 = conv3x3(3, 64, stride=2) |
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self.bn1 = BatchNorm2d(64) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(64, 64) |
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self.bn2 = BatchNorm2d(64) |
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self.relu2 = nn.ReLU(inplace=True) |
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self.conv3 = conv3x3(64, 128) |
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self.bn3 = BatchNorm2d(128) |
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self.relu3 = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 128, layers[0], groups=groups) |
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self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups) |
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self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups) |
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self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups) |
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self.avgpool = nn.AvgPool2d(7, stride=1) |
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self.fc = nn.Linear(1024 * block.expansion, num_classes) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups |
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m.weight.data.normal_(0, math.sqrt(2. / n)) |
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elif isinstance(m, BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, stride=1, groups=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d(self.inplanes, planes * block.expansion, |
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kernel_size=1, stride=stride, bias=False), |
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BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, groups, downsample)) |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes, groups=groups)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.relu1(self.bn1(self.conv1(x))) |
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x = self.relu2(self.bn2(self.conv2(x))) |
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x = self.relu3(self.bn3(self.conv3(x))) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.layer4(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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x = self.fc(x) |
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return x |
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def resnext101(pretrained=False, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on Places |
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""" |
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model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls['resnext101']), strict=False) |
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return model |
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