resnext.py 文件源码

def __init__(self, block, depth, cardinality, base_width, num_classes):
    super(CifarResNeXt, self).__init__()

    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
    assert (depth - 2) % 9 == 0, 'depth should be one of 29, 38, 47, 56, 101'
    layer_blocks = (depth - 2) // 9

    self.cardinality = cardinality
    self.base_width = base_width
    self.num_classes = num_classes

    self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
    self.bn_1 = nn.BatchNorm2d(64)

    self.inplanes = 64
    self.stage_1 = self._make_layer(block, 64 , layer_blocks, 1)
    self.stage_2 = self._make_layer(block, 128, layer_blocks, 2)
    self.stage_3 = self._make_layer(block, 256, layer_blocks, 2)
    self.avgpool = nn.AvgPool2d(8)
    self.classifier = nn.Linear(256*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):
        init.kaiming_normal(m.weight)
        m.bias.data.zero_()