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)
