def __init__(self, block, layers, num_classes=10): self.inplanes = 16 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, 16, layers[0]) self.layer2 = self._make_layer(block, 32, layers[1], stride=2) self.layer3 = self._make_layer(block, 64, layers[2], stride=2, last_phase=True) self.avgpool = nn.AvgPool2d(8, stride=1) self.fc = modified_linear.CosineLinear(64 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0)
def __init__(self, block, layers, num_classes=1000): self.inplanes = 64 super(ResNetMtl, self).__init__() self.conv1 = Conv2dMtl(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, last_phase=True) self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = modified_linear.CosineLinear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, Conv2dMtl): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0)