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| import torch.nn as nn | |
| import math | |
| import torch.utils.model_zoo as model_zoo | |
| BatchNorm2d = nn.BatchNorm2d | |
| __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', | |
| 'resnet152'] | |
| 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 constant_init(module, constant, bias=0): | |
| nn.init.constant_(module.weight, constant) | |
| if hasattr(module, 'bias'): | |
| nn.init.constant_(module.bias, bias) | |
| 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, dcn=None): | |
| super(BasicBlock, self).__init__() | |
| self.with_dcn = dcn is not None | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.with_modulated_dcn = False | |
| if self.with_dcn: | |
| fallback_on_stride = dcn.get('fallback_on_stride', False) | |
| self.with_modulated_dcn = dcn.get('modulated', False) | |
| # self.conv2 = conv3x3(planes, planes) | |
| if not self.with_dcn or fallback_on_stride: | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, | |
| padding=1, bias=False) | |
| else: | |
| deformable_groups = dcn.get('deformable_groups', 1) | |
| if not self.with_modulated_dcn: | |
| from network.backbone.assets.dcn import DeformConv | |
| conv_op = DeformConv | |
| offset_channels = 18 | |
| else: | |
| from network.backbone.assets.dcn import ModulatedDeformConv | |
| conv_op = ModulatedDeformConv | |
| offset_channels = 27 | |
| self.conv2_offset = nn.Conv2d( | |
| planes, | |
| deformable_groups * offset_channels, | |
| kernel_size=3, | |
| padding=1) | |
| self.conv2 = conv_op( | |
| planes, | |
| planes, | |
| kernel_size=3, | |
| padding=1, | |
| deformable_groups=deformable_groups, | |
| bias=False) | |
| self.bn2 = BatchNorm2d(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) | |
| if not self.with_dcn: | |
| out = self.conv2(out) | |
| elif self.with_modulated_dcn: | |
| offset_mask = self.conv2_offset(out) | |
| offset = offset_mask[:, :18, :, :] | |
| mask = offset_mask[:, -9:, :, :].sigmoid() | |
| out = self.conv2(out, offset, mask) | |
| else: | |
| offset = self.conv2_offset(out) | |
| out = self.conv2(out, offset) | |
| 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, dcn=None): | |
| super(Bottleneck, self).__init__() | |
| self.with_dcn = dcn is not None | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = BatchNorm2d(planes) | |
| fallback_on_stride = False | |
| self.with_modulated_dcn = False | |
| if self.with_dcn: | |
| fallback_on_stride = dcn.get('fallback_on_stride', False) | |
| self.with_modulated_dcn = dcn.get('modulated', False) | |
| if not self.with_dcn or fallback_on_stride: | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, | |
| stride=stride, padding=1, bias=False) | |
| else: | |
| deformable_groups = dcn.get('deformable_groups', 1) | |
| if not self.with_modulated_dcn: | |
| from network.backbone.assets.dcn import DeformConv | |
| conv_op = DeformConv | |
| offset_channels = 18 | |
| else: | |
| from network.backbone.assets.dcn import ModulatedDeformConv | |
| conv_op = ModulatedDeformConv | |
| offset_channels = 27 | |
| self.conv2_offset = nn.Conv2d( | |
| planes, deformable_groups * offset_channels, | |
| kernel_size=3, | |
| padding=1) | |
| self.conv2 = conv_op( | |
| planes, planes, kernel_size=3, padding=1, stride=stride, | |
| deformable_groups=deformable_groups, bias=False) | |
| self.bn2 = BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = BatchNorm2d(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.dcn = dcn | |
| self.with_dcn = dcn is not None | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| # out = self.conv2(out) | |
| if not self.with_dcn: | |
| out = self.conv2(out) | |
| elif self.with_modulated_dcn: | |
| offset_mask = self.conv2_offset(out) | |
| offset = offset_mask[:, :18, :, :] | |
| mask = offset_mask[:, -9:, :, :].sigmoid() | |
| out = self.conv2(out, offset, mask) | |
| else: | |
| offset = self.conv2_offset(out) | |
| out = self.conv2(out, offset) | |
| 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, num_classes=1000, | |
| dcn=None, stage_with_dcn=(False, False, False, False)): | |
| self.dcn = dcn | |
| self.stage_with_dcn = stage_with_dcn | |
| self.inplanes = 64 | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer( | |
| block, 128, layers[1], stride=2, dcn=dcn) | |
| self.layer3 = self._make_layer( | |
| block, 256, layers[2], stride=2, dcn=dcn) | |
| self.layer4 = self._make_layer( | |
| block, 512, layers[3], stride=2, dcn=dcn) | |
| self.avgpool = nn.AvgPool2d(7, stride=1) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) | |
| self.smooth = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=1) | |
| 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, BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| if self.dcn is not None: | |
| for m in self.modules(): | |
| if isinstance(m, Bottleneck) or isinstance(m, BasicBlock): | |
| if hasattr(m, 'conv2_offset'): | |
| constant_init(m.conv2_offset, 0) | |
| def _make_layer(self, block, planes, blocks, stride=1, dcn=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), | |
| BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, | |
| stride, downsample, dcn=dcn)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, dcn=dcn)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x1 = self.maxpool(x) | |
| x2 = self.layer1(x1) | |
| x3 = self.layer2(x2) | |
| x4 = self.layer3(x3) | |
| x5 = self.layer4(x4) | |
| return x1, x2, x3, x4, x5 | |
| def resnet18(pretrained=True, **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet18']), strict=False) | |
| return model | |
| def deformable_resnet18(pretrained=True, **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], | |
| dcn=dict(modulated=True, | |
| deformable_groups=1, | |
| fallback_on_stride=False), | |
| stage_with_dcn=[False, True, True, True], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet18']), strict=False) | |
| return model | |
| def resnet34(pretrained=True, **kwargs): | |
| """Constructs a ResNet-34 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet34']), strict=False) | |
| return model | |
| def resnet50(pretrained=True, **kwargs): | |
| """Constructs a ResNet-50 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet50']), strict=False) | |
| return model | |
| def deformable_resnet50(pretrained=True, **kwargs): | |
| """Constructs a ResNet-50 model with deformable conv. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], | |
| dcn=dict(modulated=True, | |
| deformable_groups=1, | |
| fallback_on_stride=False), | |
| stage_with_dcn=[False, True, True, True], | |
| **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet50']), strict=False) | |
| return model | |
| def resnet101(pretrained=True, **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet101']), strict=False) | |
| return model | |
| def resnet152(pretrained=True, **kwargs): | |
| """Constructs a ResNet-152 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url( | |
| model_urls['resnet152']), strict=False) | |
| return model | |