| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): |
| """3x3 convolution with padding""" |
| return nn.Conv2d(in_planes, |
| out_planes, |
| kernel_size=3, |
| stride=stride, |
| padding=dilation, |
| groups=groups, |
| bias=False, |
| dilation=dilation) |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, |
| inplanes, |
| planes, |
| stride=1, |
| downsample=None, |
| groups=1, |
| base_width=64, |
| dilation=1, |
| norm_layer=None, |
| dcn=None): |
| super(BasicBlock, self).__init__() |
| if norm_layer is None: |
| norm_layer = nn.BatchNorm2d |
| if groups != 1 or base_width != 64: |
| raise ValueError( |
| 'BasicBlock only supports groups=1 and base_width=64') |
| if dilation > 1: |
| raise NotImplementedError( |
| "Dilation > 1 not supported in BasicBlock") |
| |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = norm_layer(planes) |
| self.relu = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = norm_layer(planes) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| identity = 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: |
| identity = self.downsample(x) |
|
|
| out += identity |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, |
| inplanes, |
| planes, |
| stride=1, |
| downsample=None, |
| norm_layer=nn.BatchNorm2d, |
| dcn=None): |
| super(Bottleneck, self).__init__() |
| self.dcn = dcn |
| self.with_dcn = dcn is not None |
|
|
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = norm_layer(planes, momentum=0.1) |
| self.conv2 = nn.Conv2d(planes, |
| planes, |
| kernel_size=3, |
| stride=stride, |
| padding=1, |
| bias=False) |
|
|
| self.bn2 = norm_layer(planes, momentum=0.1) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = norm_layer(planes * 4, momentum=0.1) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| residual = x |
|
|
| out = F.relu(self.bn1(self.conv1(x)), inplace=True) |
| if not self.with_dcn: |
| out = F.relu(self.bn2(self.conv2(out)), inplace=True) |
| elif self.with_modulated_dcn: |
| offset_mask = self.conv2_offset(out) |
| offset = offset_mask[:, :18 * self.deformable_groups, :, :] |
| mask = offset_mask[:, -9 * self.deformable_groups:, :, :] |
| mask = mask.sigmoid() |
| out = F.relu(self.bn2(self.conv2(out, offset, mask))) |
| else: |
| offset = self.conv2_offset(out) |
| out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True) |
|
|
| out = self.conv3(out) |
| out = self.bn3(out) |
|
|
| if self.downsample is not None: |
| residual = self.downsample(x) |
|
|
| out += residual |
| out = F.relu(out) |
|
|
| return out |
|
|
|
|
| class ResNet(nn.Module): |
| """ ResNet """ |
|
|
| def __init__(self, |
| architecture, |
| norm_layer=nn.BatchNorm2d, |
| dcn=None, |
| stage_with_dcn=(False, False, False, False)): |
| super(ResNet, self).__init__() |
| self._norm_layer = norm_layer |
| assert architecture in [ |
| "resnet18", "resnet34", "resnet50", "resnet101", 'resnet152' |
| ] |
| layers = { |
| 'resnet18': [2, 2, 2, 2], |
| 'resnet34': [3, 4, 6, 3], |
| 'resnet50': [3, 4, 6, 3], |
| 'resnet101': [3, 4, 23, 3], |
| 'resnet152': [3, 8, 36, 3], |
| } |
| self.inplanes = 64 |
| if architecture == "resnet18" or architecture == 'resnet34': |
| self.block = BasicBlock |
| else: |
| self.block = Bottleneck |
| self.layers = layers[architecture] |
|
|
| self.conv1 = nn.Conv2d(3, |
| 64, |
| kernel_size=7, |
| stride=2, |
| padding=3, |
| bias=False) |
| self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True) |
| self.relu = nn.ReLU(inplace=True) |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
|
|
| stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn] |
|
|
| self.layer1 = self.make_layer(self.block, |
| 64, |
| self.layers[0], |
| dcn=stage_dcn[0]) |
| self.layer2 = self.make_layer(self.block, |
| 128, |
| self.layers[1], |
| stride=2, |
| dcn=stage_dcn[1]) |
| self.layer3 = self.make_layer(self.block, |
| 256, |
| self.layers[2], |
| stride=2, |
| dcn=stage_dcn[2]) |
|
|
| self.layer4 = self.make_layer(self.block, |
| 512, |
| self.layers[3], |
| stride=2, |
| dcn=stage_dcn[3]) |
|
|
| def forward(self, x): |
| x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) |
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| return x |
|
|
| def stages(self): |
| return [self.layer1, self.layer2, self.layer3, self.layer4] |
|
|
| 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), |
| self._norm_layer(planes * block.expansion), |
| ) |
|
|
| layers = [] |
| layers.append( |
| block(self.inplanes, |
| planes, |
| stride, |
| downsample, |
| norm_layer=self._norm_layer, |
| dcn=dcn)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append( |
| block(self.inplanes, |
| planes, |
| norm_layer=self._norm_layer, |
| dcn=dcn)) |
|
|
| return nn.Sequential(*layers) |
|
|
