206 lines
6.3 KiB
Python
206 lines
6.3 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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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 : resnet.py
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@Time : 8/4/19 3:35 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__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is coming soon!
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model_urls = {
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'resnet18': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth',
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'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
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'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-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 BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = BatchNorm2d(planes)
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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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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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, 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, bias=False)
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self.bn2 = BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = BatchNorm2d(planes * 4)
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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 ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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self.inplanes = 128
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super(ResNet, 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, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AvgPool2d(7, stride=1)
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self.fc = nn.Linear(512 * 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
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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):
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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, 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))
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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 resnet18(pretrained=False, **kwargs):
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"""Constructs a ResNet-18 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
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if pretrained:
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model.load_state_dict(load_url(model_urls['resnet18']))
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return model
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def resnet50(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
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if pretrained:
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model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
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return model
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def resnet101(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 ImageNet
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"""
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
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if pretrained:
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model.load_state_dict(load_url(model_urls['resnet101']), strict=False)
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return model
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