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""" |
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resnet.py - A modified ResNet structure |
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We append extra channels to the first conv by some network surgery |
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""" |
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from collections import OrderedDict |
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import math |
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import torch |
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import torch.nn as nn |
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from torch.utils import model_zoo |
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def load_weights_add_extra_dim(target, source_state, extra_dim=1): |
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new_dict = OrderedDict() |
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for k1, v1 in target.state_dict().items(): |
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if 'num_batches_tracked' not in k1: |
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if k1 in source_state: |
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tar_v = source_state[k1] |
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if v1.shape != tar_v.shape: |
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c, _, w, h = v1.shape |
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pads = torch.zeros((c, extra_dim, w, h), device=tar_v.device) |
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nn.init.orthogonal_(pads) |
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tar_v = torch.cat([tar_v, pads], 1) |
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new_dict[k1] = tar_v |
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target.load_state_dict(new_dict) |
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model_urls = { |
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
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} |
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def conv3x3(in_planes, out_planes, stride=1, dilation=1): |
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return nn.Conv2d(in_planes, |
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out_planes, |
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kernel_size=3, |
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stride=stride, |
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padding=dilation, |
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dilation=dilation, |
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bias=False) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): |
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super(Bottleneck, self).__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, |
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planes, |
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kernel_size=3, |
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stride=stride, |
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dilation=dilation, |
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padding=dilation, |
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bias=False) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * 4) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0): |
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self.inplanes = 64 |
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super(ResNet, self).__init__() |
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self.conv1 = nn.Conv2d(3 + extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2. / n)) |
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elif isinstance(m, nn.BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, stride=1, dilation=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d(self.inplanes, |
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planes * block.expansion, |
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kernel_size=1, |
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stride=stride, |
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bias=False), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [block(self.inplanes, planes, stride, downsample)] |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes, dilation=dilation)) |
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return nn.Sequential(*layers) |
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def resnet18(pretrained=True, extra_dim=0): |
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model = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim) |
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if pretrained: |
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load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim) |
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return model |
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def resnet50(pretrained=True, extra_dim=0): |
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model = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim) |
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if pretrained: |
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load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim) |
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return model |
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