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| #!/usr/bin/env python | |
| # -*- coding:utf-8 -*- | |
| # Author: Donny You(youansheng@gmail.com) | |
| import math | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| from .module_helper import ModuleHelper | |
| model_urls = { | |
| 'resnet18': 'https://download.pytorch.org/backbones/resnet18-5c106cde.pth', | |
| 'resnet34': 'https://download.pytorch.org/backbones/resnet34-333f7ec4.pth', | |
| 'resnet50': 'https://download.pytorch.org/backbones/resnet50-19c8e357.pth', | |
| 'resnet101': 'https://download.pytorch.org/backbones/resnet101-5d3b4d8f.pth', | |
| 'resnet152': 'https://download.pytorch.org/backbones/resnet152-b121ed2d.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, norm_type=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(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, norm_type=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(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, width_multiplier=1.0, num_classes=1000, deep_base=False, norm_type=None): | |
| super(ResNet, self).__init__() | |
| self.inplanes = 128 if deep_base else int(64 * width_multiplier) | |
| self.width_multiplier = width_multiplier | |
| if deep_base: | |
| self.prefix = nn.Sequential(OrderedDict([ | |
| ('conv1', nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)), | |
| ('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)), | |
| ('relu1', nn.ReLU(inplace=False)), | |
| ('conv2', nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)), | |
| ('bn2', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)), | |
| ('relu2', nn.ReLU(inplace=False)), | |
| ('conv3', nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False)), | |
| ('bn3', ModuleHelper.BatchNorm2d(norm_type=norm_type)(self.inplanes)), | |
| ('relu3', nn.ReLU(inplace=False))] | |
| )) | |
| else: | |
| self.prefix = nn.Sequential(OrderedDict([ | |
| ('conv1', nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)), | |
| ('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(self.inplanes)), | |
| ('relu', nn.ReLU(inplace=False))] | |
| )) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False) # change. | |
| self.layer1 = self._make_layer(block, int(64 * width_multiplier), layers[0], norm_type=norm_type) | |
| self.layer2 = self._make_layer(block, int(128 * width_multiplier), layers[1], stride=2, norm_type=norm_type) | |
| self.layer3 = self._make_layer(block, int(256 * width_multiplier), layers[2], stride=2, norm_type=norm_type) | |
| self.layer4 = self._make_layer(block, int(512 * width_multiplier), layers[3], stride=2, norm_type=norm_type) | |
| self.avgpool = nn.AvgPool2d(7, stride=1) | |
| self.fc = nn.Linear(int(512 * block.expansion * width_multiplier), 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, ModuleHelper.BatchNorm2d(norm_type=norm_type, ret_cls=True)): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1, norm_type=None): | |
| 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), | |
| ModuleHelper.BatchNorm2d(norm_type=norm_type)(int(planes * block.expansion * self.width_multiplier)), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, | |
| stride, downsample, norm_type=norm_type)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, norm_type=norm_type)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.prefix(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(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| norm_type (str): choose norm type | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, deep_base=False, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def deepbase_resnet18(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, deep_base=True, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def resnet34(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-34 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def deepbase_resnet34(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-34 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def resnet50(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-50 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type, | |
| width_multiplier=kwargs["width_multiplier"]) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def deepbase_resnet50(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-50 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def resnet101(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def deepbase_resnet101(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def resnet152(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-152 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |
| def deepbase_resnet152(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs): | |
| """Constructs a ResNet-152 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Places | |
| """ | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type) | |
| model = ModuleHelper.load_model(model, pretrained=pretrained) | |
| return model | |