Spaces:
Running
Running
| """ | |
| 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 not "num_batches_tracked" in k1: | |
| if k1 in source_state: | |
| tar_v = source_state[k1] | |
| if v1.shape != tar_v.shape: | |
| # Init the new segmentation channel with zeros | |
| # print(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(2.0 / 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 | |