preresnet.py 文件源码

def __init__(self, block, depth, num_classes):
    """ Constructor
    Args:
      depth: number of layers.
      num_classes: number of classes
      base_width: base width
    """
    super(CifarPreResNet, self).__init__()

    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
    assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
    layer_blocks = (depth - 2) // 6
    print ('CifarPreResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks))

    self.num_classes = num_classes

    self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)

    self.inplanes = 16
    self.stage_1 = self._make_layer(block, 16, layer_blocks, 1)
    self.stage_2 = self._make_layer(block, 32, layer_blocks, 2)
    self.stage_3 = self._make_layer(block, 64, layer_blocks, 2)
    self.lastact = nn.Sequential(nn.BatchNorm2d(64*block.expansion), nn.ReLU(inplace=True))
    self.avgpool = nn.AvgPool2d(8)
    self.classifier = nn.Linear(64*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))
        #m.bias.data.zero_()
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
      elif isinstance(m, nn.Linear):
        init.kaiming_normal(m.weight)
        m.bias.data.zero_()