| """ |
| resnet.py - A modified ResNet structure |
| We append extra channels to the first conv by some network surgery |
| """ |
|
|
| from collections import OrderedDict |
| import math |
|
|
| import torch |
| import torch.nn as nn |
| from torch.utils import model_zoo |
|
|
|
|
| def load_weights_add_extra_dim(target, source_state, extra_dim=1): |
| new_dict = OrderedDict() |
|
|
| for k1, v1 in target.state_dict().items(): |
| if 'num_batches_tracked' not in k1: |
| if k1 in source_state: |
| tar_v = source_state[k1] |
|
|
| if v1.shape != tar_v.shape: |
| |
| |
| c, _, w, h = v1.shape |
| pads = torch.zeros((c, extra_dim, w, h), device=tar_v.device) |
| nn.init.orthogonal_(pads) |
| tar_v = torch.cat([tar_v, pads], 1) |
|
|
| new_dict[k1] = tar_v |
|
|
| target.load_state_dict(new_dict) |
|
|
|
|
| model_urls = { |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
| } |
|
|
|
|
| def conv3x3(in_planes, out_planes, stride=1, dilation=1): |
| return nn.Conv2d(in_planes, |
| out_planes, |
| kernel_size=3, |
| stride=stride, |
| padding=dilation, |
| dilation=dilation, |
| bias=False) |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.relu = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation) |
| self.bn2 = nn.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) |
| 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, dilation=1): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.conv2 = nn.Conv2d(planes, |
| planes, |
| kernel_size=3, |
| stride=stride, |
| dilation=dilation, |
| padding=dilation, |
| bias=False) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(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=(3, 4, 23, 3), extra_dim=0): |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| self.conv1 = nn.Conv2d(3 + extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False) |
| self.bn1 = nn.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) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
|
|
| 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, nn.BatchNorm2d): |
| m.weight.data.fill_(1) |
| m.bias.data.zero_() |
|
|
| def _make_layer(self, block, planes, blocks, stride=1, dilation=1): |
| 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), |
| nn.BatchNorm2d(planes * block.expansion), |
| ) |
|
|
| layers = [block(self.inplanes, planes, stride, downsample)] |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes, dilation=dilation)) |
|
|
| return nn.Sequential(*layers) |
|
|
|
|
| def resnet18(pretrained=True, extra_dim=0): |
| model = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim) |
| if pretrained: |
| load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim) |
| return model |
|
|
|
|
| def resnet50(pretrained=True, extra_dim=0): |
| model = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim) |
| if pretrained: |
| load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim) |
| return model |
|
|
