import math import torch.nn as nn from torch.hub import load_state_dict_from_url from typing import Type, Any, Callable, Union, List, Optional from torch import Tensor model_urls = { 'resnet50': 'https://github.com/bubbliiiing/pspnet-pytorch/releases/download/v1.0/resnet50s-a75c83cf.pth', 'resnet101': '', } class BasicBlock(nn.Module): expansion: int = 1 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None ) -> 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") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 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: Tensor) -> Tensor: 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 def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1, norm_layer=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = norm_layer(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.bn2 = norm_layer(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = norm_layer(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.dilation = dilation 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, num_classes=1000, dilated=False, deep_base=True, norm_layer=nn.BatchNorm2d): self.inplanes = 128 if deep_base else 64 super(ResNet, self).__init__() if deep_base: self.conv1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False), norm_layer(64), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), norm_layer(64), nn.ReLU(inplace=True), nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False), ) else: self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) 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], norm_layer=norm_layer) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer) if dilated: self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2, norm_layer=norm_layer) self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, norm_layer=norm_layer) else: self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer) self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) 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, norm_layer): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None, multi_grid=False): 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), norm_layer(planes * block.expansion), ) layers = [] multi_dilations = [4, 8, 16] if multi_grid: layers.append(block(self.inplanes, planes, stride, dilation=multi_dilations[0], downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer)) elif dilation == 1 or dilation == 2: layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer)) elif dilation == 4: layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer)) else: raise RuntimeError("=> unknown dilation size: {}".format(dilation)) self.inplanes = planes * block.expansion for i in range(1, blocks): if multi_grid: layers.append(block(self.inplanes, planes, dilation=multi_dilations[i], previous_dilation=dilation, norm_layer=norm_layer)) else: layers.append(block(self.inplanes, planes, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer)) 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 def resnet50(pretrained=False, **kwargs): model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) if pretrained: model.load_state_dict(load_state_dict_from_url(model_urls['resnet50'], "./model_data"), strict=False) return model def resnet101(pretrained=False, **kwargs): model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) # if pretrained: # model.load_state_dict(load_state_dict_from_url(model_urls['resnet101'], "./model_data"), strict=False) return model def resnet18(pretrained: bool = False, progress: bool = True, **kwargs): r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) return model