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| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import math |
| from collections import OrderedDict |
| import torch.nn as nn |
|
|
| from lib.models.tools.module_helper import ModuleHelper |
| from lib.models.backbones.resnet.wide_resnet_models import WiderResNetA2 |
|
|
| model_urls = { |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
| 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
| 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
| 'resnet152': 'https://download.pytorch.org/models/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, bn_type=None): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes) |
| self.relu = nn.ReLU(inplace=False) |
| self.relu_in = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_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 = out + residual |
| out = self.relu_in(out) |
|
|
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, bn_type=None): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes) |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| padding=1, bias=False) |
| self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * 4) |
| self.relu = nn.ReLU(inplace=False) |
| self.relu_in = 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 = out + residual |
| out = self.relu_in(out) |
|
|
| return out |
|
|
|
|
| class ResNet(nn.Module): |
|
|
| def __init__(self, block, layers, num_classes=1000, deep_base=False, bn_type=None): |
| super(ResNet, self).__init__() |
| self.inplanes = 128 if deep_base else 64 |
| if deep_base: |
| self.resinit = nn.Sequential(OrderedDict([ |
| ('conv1', nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)), |
| ('bn1', ModuleHelper.BatchNorm2d(bn_type=bn_type)(64)), |
| ('relu1', nn.ReLU(inplace=False)), |
| ('conv2', nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)), |
| ('bn2', ModuleHelper.BatchNorm2d(bn_type=bn_type)(64)), |
| ('relu2', nn.ReLU(inplace=False)), |
| ('conv3', nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False)), |
| ('bn3', ModuleHelper.BatchNorm2d(bn_type=bn_type)(self.inplanes)), |
| ('relu3', nn.ReLU(inplace=False))] |
| )) |
| else: |
| self.resinit = nn.Sequential(OrderedDict([ |
| ('conv1', nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)), |
| ('bn1', ModuleHelper.BatchNorm2d(bn_type=bn_type)(self.inplanes)), |
| ('relu1', nn.ReLU(inplace=False))] |
| )) |
|
|
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) |
|
|
| self.layer1 = self._make_layer(block, 64, layers[0], bn_type=bn_type) |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2, bn_type=bn_type) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2, bn_type=bn_type) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2, bn_type=bn_type) |
| 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, ModuleHelper.BatchNorm2d(bn_type=bn_type, ret_cls=True)): |
| m.weight.data.fill_(1) |
| m.bias.data.zero_() |
|
|
| def _make_layer(self, block, planes, blocks, stride=1, bn_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(bn_type=bn_type)(planes * block.expansion), |
| ) |
|
|
| layers = [] |
| layers.append(block(self.inplanes, planes, stride, downsample, bn_type=bn_type)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes, bn_type=bn_type)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def forward(self, x): |
| x = self.resinit(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 |
|
|
|
|
| class ResNetModels(object): |
|
|
| def __init__(self, configer): |
| self.configer = configer |
|
|
| def resnet18(self, **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], deep_base=False, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def deepbase_resnet18(self, **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], deep_base=True, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def resnet34(self, **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], deep_base=False, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def deepbase_resnet34(self, **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], deep_base=True, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def resnet50(self, **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], deep_base=False, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def deepbase_resnet50(self, **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], deep_base=True, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def resnet101(self, **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], deep_base=False, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def deepbase_resnet101(self, **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], deep_base=True, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained')) |
| return model |
|
|
| def resnet152(self, **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], deep_base=False, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, all_match=False, pretrained=self.configer.get('network', 'pretrained'), network="resnet152") |
| return model |
|
|
| def deepbase_resnet152(self, **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], deep_base=True, |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, all_match=False, pretrained=self.configer.get('network', 'pretrained'), network="resnet152") |
| return model |
|
|
| def wide_resnet16(self, **kwargs): |
| """Constructs a WideResNet-16 model. |
| """ |
| model = WiderResNetA2([1, 1, 1, 1, 1, 1], |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, network="wide_resnet") |
| return model |
|
|
| def wide_resnet20(self, **kwargs): |
| """Constructs a WideResNet-20 model. |
| """ |
| model = WiderResNetA2([1, 1, 1, 3, 1, 1], |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, network="wide_resnet") |
| return model |
|
|
| def wide_resnet38(self, **kwargs): |
| """Constructs a WideResNet-38 model. |
| """ |
| model = WiderResNetA2([3, 3, 6, 3, 1, 1], |
| bn_type=self.configer.get('network', 'bn_type'), **kwargs) |
| model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, network="wide_resnet") |
| return model |
|
|
