Spaces:
Sleeping
Sleeping
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.model_zoo as model_zoo | |
| urls_dic = { | |
| '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', | |
| } | |
| layers_dic = { | |
| '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] | |
| } | |
| def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=dilation, groups=groups, bias=False, dilation=dilation) | |
| def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion: int = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, batch_norm_fn=nn.BatchNorm2d): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = batch_norm_fn(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = batch_norm_fn(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, dilation=1, batch_norm_fn=nn.BatchNorm2d): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = batch_norm_fn(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| padding=dilation, bias=False, dilation=dilation) | |
| self.bn2 = batch_norm_fn(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = batch_norm_fn(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.dilation = dilation | |
| 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, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), batch_norm_fn=nn.BatchNorm2d): | |
| self.batch_norm_fn = batch_norm_fn | |
| self.inplanes = 64 | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=strides[0], padding=3, | |
| bias=False) | |
| self.bn1 = self.batch_norm_fn(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], stride=1, dilation=dilations[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1]) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2]) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3]) | |
| self.inplanes = 1024 | |
| #self.avgpool = nn.AvgPool2d(7, stride=1) | |
| #self.fc = nn.Linear(512 * block.expansion, 1000) | |
| 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), | |
| self.batch_norm_fn(planes * block.expansion), | |
| ) | |
| layers = [block(self.inplanes, planes, stride, downsample, dilation=1, batch_norm_fn=self.batch_norm_fn)] | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, dilation=dilation, batch_norm_fn=self.batch_norm_fn)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| x = self.fc(x) | |
| return x | |