import torch.nn as nn import math __all__ = [ 'preact_resnet18', 'preact_resnet34', 'preact_resnet50', 'preact_resnet101', 'preact_resnet152' ] 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 PreactBasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, preactivate=True): super(PreactBasicBlock, self).__init__() self.pre_bn = self.pre_relu = None if preactivate: self.pre_bn = nn.BatchNorm2d(inplanes) self.pre_relu = nn.ReLU(inplace=True) self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.downsample = downsample self.stride = stride self.preactivate = preactivate def forward(self, x): if self.preactivate: preact = self.pre_bn(x) preact = self.pre_relu(preact) else: preact = x out = self.conv1(preact) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) if self.downsample is not None: residual = self.downsample(preact) else: residual = x out += residual return out class PreactBottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, preactivate=True): super(PreactBottleneck, self).__init__() self.pre_bn = self.pre_relu = None if preactivate: self.pre_bn = nn.BatchNorm2d(inplanes) self.pre_relu = nn.ReLU(inplace=True) 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, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.relu1 = nn.ReLU(inplace=True) self.relu2 = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride self.preactivate = preactivate def forward(self, x): if self.preactivate: preact = self.pre_bn(x) preact = self.pre_relu(preact) else: preact = x out = self.conv1(preact) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out = self.conv3(out) if self.downsample is not None: residual = self.downsample(preact) else: residual = x out += residual return out class PreactResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, deep_stem=False, avg_down=False, bypass_last_bn=False, bn=None): super(PreactResNet, self).__init__() global bypass_bn_weight_list bypass_bn_weight_list = [] self.inplanes = 64 self.deep_stem = deep_stem self.avg_down = avg_down if self.deep_stem: self.conv1 = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.Conv2d(32, 64, 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 = 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) self.final_bn = nn.BatchNorm2d(512 * block.expansion) self.final_relu = nn.ReLU(inplace=True) 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, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): n = m.weight.size(1) m.weight.data.normal_(0, 1.0 / float(n)) m.bias.data.zero_() if bypass_last_bn: for param in bypass_bn_weight_list: param.data.zero_() def _make_layer(self, block, planes, blocks, stride=1, avg_down=False): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: if self.avg_down: downsample = nn.Sequential( nn.AvgPool2d(stride, stride=stride, ceil_mode=True, count_include_pad=False), nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False), # BN(planes * block.expansion), ) else: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), # BN(planes * block.expansion), ) # On the first residual block in the first residual layer we don't pre-activate, # because we take care of that (+ maxpool) after the initial conv layer preactivate_first = stride != 1 layers = [] layers.append( block(self.inplanes, planes, stride, downsample, preactivate_first)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) 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.final_bn(x) x = self.final_relu(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x def preact_resnet18(**kwargs): model = PreactResNet(PreactBasicBlock, [2, 2, 2, 2], **kwargs) return model def preact_resnet34(**kwargs): model = PreactResNet(PreactBasicBlock, [3, 4, 6, 3], **kwargs) return model def preact_resnet50(**kwargs): model = PreactResNet(PreactBottleneck, [3, 4, 6, 3], **kwargs) return model def preact_resnet101(**kwargs): model = PreactResNet(PreactBottleneck, [3, 4, 23, 3], **kwargs) return model def preact_resnet152(**kwargs): model = PreactResNet(PreactBottleneck, [3, 8, 36, 3], **kwargs) return model