|
| 1 | +import functools |
| 2 | +import torch.nn as nn |
| 3 | +import torch.nn.functional as F |
| 4 | + |
| 5 | +from utils.pyt_utils import load_model |
| 6 | + |
| 7 | + |
| 8 | +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', |
| 9 | + 'resnet152'] |
| 10 | + |
| 11 | + |
| 12 | +def conv3x3(in_planes, out_planes, stride=1): |
| 13 | + """3x3 convolution with padding""" |
| 14 | + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
| 15 | + padding=1, bias=False) |
| 16 | + |
| 17 | + |
| 18 | +class BasicBlock(nn.Module): |
| 19 | + expansion = 1 |
| 20 | + |
| 21 | + def __init__(self, inplanes, planes, stride=1, norm_layer=None, |
| 22 | + bn_eps=1e-5, bn_momentum=0.1, downsample=None, inplace=True, |
| 23 | + has_relu=True): |
| 24 | + super(BasicBlock, self).__init__() |
| 25 | + self.conv1 = conv3x3(inplanes, planes, stride) |
| 26 | + self.bn1 = norm_layer(planes, eps=bn_eps, momentum=bn_momentum) |
| 27 | + self.relu = nn.ReLU(inplace=inplace) |
| 28 | + self.relu_inplace = nn.ReLU(inplace=True) |
| 29 | + self.conv2 = conv3x3(planes, planes) |
| 30 | + self.bn2 = norm_layer(planes, eps=bn_eps, momentum=bn_momentum) |
| 31 | + self.downsample = downsample |
| 32 | + if downsample is None and inplace != planes: |
| 33 | + self.downsample = nn.Sequential( |
| 34 | + nn.Conv2d(inplanes, planes, |
| 35 | + kernel_size=1, stride=stride, bias=False), |
| 36 | + norm_layer(planes, eps=bn_eps,momentum=bn_momentum)) |
| 37 | + self.stride = stride |
| 38 | + self.inplace = inplace |
| 39 | + self.has_relu = has_relu |
| 40 | + |
| 41 | + def forward(self, x): |
| 42 | + residual = x |
| 43 | + |
| 44 | + out = self.conv1(x) |
| 45 | + out = self.bn1(out) |
| 46 | + out = self.relu(out) |
| 47 | + |
| 48 | + out = self.conv2(out) |
| 49 | + out = self.bn2(out) |
| 50 | + |
| 51 | + if self.downsample is not None: |
| 52 | + residual = self.downsample(x) |
| 53 | + |
| 54 | + if self.inplace: |
| 55 | + out += residual |
| 56 | + else: |
| 57 | + out = out + residual |
| 58 | + |
| 59 | + if self.has_relu: |
| 60 | + out = self.relu_inplace(out) |
| 61 | + |
| 62 | + return out |
| 63 | + |
| 64 | +class ResBlock(nn.Module): |
| 65 | + def __init__(self, in_channels, out_channels, stride=1, |
| 66 | + expansion=2, norm_layer=None, bn_eps=1e-5, |
| 67 | + bn_momentum=0.1, has_relu=True, has_bias=False): |
| 68 | + super(ResBlock, self).__init__() |
| 69 | + self.has_relu = has_relu |
| 70 | + mid_channels = out_channels // expansion |
| 71 | + self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=has_bias) |
| 72 | + self.bn1 = norm_layer(mid_channels, eps=bn_eps, momentum=bn_momentum) |
| 73 | + self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, |
| 74 | + padding=1, bias=has_bias) |
| 75 | + self.bn2 = norm_layer(mid_channels, eps=bn_eps, momentum=bn_momentum) |
| 76 | + self.conv3 = nn.Conv2d(mid_channels, out_channels, kernel_size=1, bias=has_bias) |
| 77 | + self.bn3 = norm_layer(out_channels, eps=bn_eps, momentum=bn_momentum) |
| 78 | + if in_channels != out_channels: |
| 79 | + self.down_sampler = nn.Sequential( |
| 80 | + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), |
| 81 | + norm_layer(out_channels, eps=bn_eps,momentum=bn_momentum)) |
| 82 | + else: |
| 83 | + self.down_sampler = None |
| 84 | + |
| 85 | + def forward(self, x): |
| 86 | + residual = x |
| 87 | + |
| 88 | + out = self.conv1(x) |
| 89 | + out = self.bn1(out) |
| 90 | + out = F.relu(out) |
| 91 | + |
| 92 | + out = self.conv2(out) |
| 93 | + out = self.bn2(out) |
| 94 | + out = F.relu(out) |
| 95 | + |
| 96 | + out = self.conv3(out) |
| 97 | + out = self.bn3(out) |
| 98 | + |
| 99 | + if self.down_sampler is not None: |
| 100 | + residual = self.down_sampler(x) |
| 101 | + |
| 102 | + out += residual |
| 103 | + if self.has_relu: |
| 104 | + out = F.relu(out, inplace=True) |
| 105 | + |
| 106 | + return out |
| 107 | + |
| 108 | +class Bottleneck(nn.Module): |
| 109 | + expansion = 4 |
| 110 | + |
| 111 | + def __init__(self, inplanes, planes, stride=1, |
| 112 | + norm_layer=None, bn_eps=1e-5, bn_momentum=0.1, |
| 113 | + downsample=None, inplace=True, has_relu=True): |
| 114 | + super(Bottleneck, self).__init__() |
| 115 | + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| 116 | + self.bn1 = norm_layer(planes, eps=bn_eps, momentum=bn_momentum) |
| 117 | + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| 118 | + padding=1, bias=False) |
| 119 | + self.bn2 = norm_layer(planes, eps=bn_eps, momentum=bn_momentum) |
| 120 | + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, |
| 121 | + bias=False) |
| 122 | + self.bn3 = norm_layer(planes * self.expansion, eps=bn_eps, |
| 123 | + momentum=bn_momentum) |
| 124 | + self.has_relu = has_relu |
| 125 | + self.relu = nn.ReLU(inplace=inplace) |
| 126 | + self.relu_inplace = nn.ReLU(inplace=True) |
| 127 | + self.downsample = downsample |
| 128 | + self.stride = stride |
| 129 | + self.inplace = inplace |
| 130 | + |
| 131 | + def forward(self, x): |
| 132 | + residual = x |
| 133 | + |
| 134 | + out = self.conv1(x) |
| 135 | + out = self.bn1(out) |
| 136 | + out = self.relu(out) |
| 137 | + |
| 138 | + out = self.conv2(out) |
| 139 | + out = self.bn2(out) |
| 140 | + out = self.relu(out) |
| 141 | + |
| 142 | + out = self.conv3(out) |
| 143 | + out = self.bn3(out) |
| 144 | + |
| 145 | + if self.downsample is not None: |
| 146 | + residual = self.downsample(x) |
| 147 | + |
| 148 | + if self.inplace: |
| 149 | + out += residual |
| 150 | + else: |
| 151 | + out = out + residual |
| 152 | + if self.has_relu: |
| 153 | + out = self.relu_inplace(out) |
| 154 | + |
| 155 | + return out |
| 156 | + |
| 157 | + |
| 158 | +class ResNet(nn.Module): |
| 159 | + |
| 160 | + def __init__(self, block, layers, norm_layer=nn.BatchNorm2d, bn_eps=1e-5, |
| 161 | + bn_momentum=0.1, deep_stem=False, stem_width=32, inplace=True): |
| 162 | + self.inplanes = stem_width * 2 if deep_stem else 64 |
| 163 | + super(ResNet, self).__init__() |
| 164 | + if deep_stem: |
| 165 | + self.conv1 = nn.Sequential( |
| 166 | + nn.Conv2d(3, stem_width, kernel_size=3, stride=2, padding=1, |
| 167 | + bias=False), |
| 168 | + norm_layer(stem_width, eps=bn_eps, momentum=bn_momentum), |
| 169 | + nn.ReLU(inplace=inplace), |
| 170 | + nn.Conv2d(stem_width, stem_width, kernel_size=3, stride=1, |
| 171 | + padding=1, |
| 172 | + bias=False), |
| 173 | + norm_layer(stem_width, eps=bn_eps, momentum=bn_momentum), |
| 174 | + nn.ReLU(inplace=inplace), |
| 175 | + nn.Conv2d(stem_width, stem_width * 2, kernel_size=3, stride=1, |
| 176 | + padding=1, |
| 177 | + bias=False), |
| 178 | + ) |
| 179 | + else: |
| 180 | + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| 181 | + bias=False) |
| 182 | + |
| 183 | + self.bn1 = norm_layer(stem_width * 2 if deep_stem else 64, eps=bn_eps, |
| 184 | + momentum=bn_momentum) |
| 185 | + self.relu = nn.ReLU(inplace=inplace) |
| 186 | + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 187 | + self.layer1 = self._make_layer(block, norm_layer, 64, layers[0], |
| 188 | + inplace, |
| 189 | + bn_eps=bn_eps, bn_momentum=bn_momentum) |
| 190 | + self.layer2 = self._make_layer(block, norm_layer, 128, layers[1], |
| 191 | + inplace, stride=2, |
| 192 | + bn_eps=bn_eps, bn_momentum=bn_momentum) |
| 193 | + self.layer3 = self._make_layer(block, norm_layer, 256, layers[2], |
| 194 | + inplace, stride=2, |
| 195 | + bn_eps=bn_eps, bn_momentum=bn_momentum) |
| 196 | + self.layer4 = self._make_layer(block, norm_layer, 512, layers[3], |
| 197 | + inplace, stride=2, |
| 198 | + bn_eps=bn_eps, bn_momentum=bn_momentum) |
| 199 | + self.layer_channel_nums = (256, 512, 1024, 2048) |
| 200 | + |
| 201 | + def _make_layer(self, block, norm_layer, planes, blocks, inplace=True, |
| 202 | + stride=1, bn_eps=1e-5, bn_momentum=0.1): |
| 203 | + downsample = None |
| 204 | + if stride != 1 or self.inplanes != planes * block.expansion: |
| 205 | + downsample = nn.Sequential( |
| 206 | + nn.Conv2d(self.inplanes, planes * block.expansion, |
| 207 | + kernel_size=1, stride=stride, bias=False), |
| 208 | + norm_layer(planes * block.expansion, eps=bn_eps, |
| 209 | + momentum=bn_momentum), |
| 210 | + ) |
| 211 | + |
| 212 | + layers = [] |
| 213 | + layers.append(block(self.inplanes, planes, stride, norm_layer, bn_eps, |
| 214 | + bn_momentum, downsample, inplace)) |
| 215 | + self.inplanes = planes * block.expansion |
| 216 | + for i in range(1, blocks): |
| 217 | + layers.append(block(self.inplanes, planes, |
| 218 | + norm_layer=norm_layer, bn_eps=bn_eps, |
| 219 | + bn_momentum=bn_momentum, inplace=inplace)) |
| 220 | + |
| 221 | + return nn.Sequential(*layers) |
| 222 | + |
| 223 | + def forward(self, x): |
| 224 | + x = self.conv1(x) |
| 225 | + x = self.bn1(x) |
| 226 | + x = self.relu(x) |
| 227 | + x = self.maxpool(x) |
| 228 | + |
| 229 | + blocks = [] |
| 230 | + x = self.layer1(x) |
| 231 | + blocks.append(x) |
| 232 | + x = self.layer2(x) |
| 233 | + blocks.append(x) |
| 234 | + x = self.layer3(x) |
| 235 | + blocks.append(x) |
| 236 | + x = self.layer4(x) |
| 237 | + blocks.append(x) |
| 238 | + |
| 239 | + return blocks |
| 240 | + |
| 241 | + |
| 242 | +def resnet18(pretrained_model=None, **kwargs): |
| 243 | + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
| 244 | + |
| 245 | + if pretrained_model is not None: |
| 246 | + model = load_model(model, pretrained_model) |
| 247 | + return model |
| 248 | + |
| 249 | + |
| 250 | +def resnet34(pretrained_model=None, **kwargs): |
| 251 | + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
| 252 | + |
| 253 | + if pretrained_model is not None: |
| 254 | + model = load_model(model, pretrained_model) |
| 255 | + return model |
| 256 | + |
| 257 | + |
| 258 | +def resnet50(pretrained_model=None, **kwargs): |
| 259 | + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
| 260 | + |
| 261 | + if pretrained_model is not None: |
| 262 | + model = load_model(model, pretrained_model) |
| 263 | + return model |
| 264 | + |
| 265 | + |
| 266 | +def resnet101(pretrained_model=None, **kwargs): |
| 267 | + model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
| 268 | + |
| 269 | + if pretrained_model is not None: |
| 270 | + model = load_model(model, pretrained_model) |
| 271 | + return model |
| 272 | + |
| 273 | + |
| 274 | +def resnet152(pretrained_model=None, **kwargs): |
| 275 | + model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
| 276 | + |
| 277 | + if pretrained_model is not None: |
| 278 | + model = load_model(model, pretrained_model) |
| 279 | + return model |
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