model.py 文件源码

def __init__(self, cardinality, depth, nlabels, base_width, widen_factor=4):
        """ Constructor

        Args:
            cardinality: number of convolution groups.
            depth: number of layers.
            nlabels: number of classes
            base_width: base number of channels in each group.
            widen_factor: factor to adjust the channel dimensionality
        """
        super(CifarResNeXt, self).__init__()
        self.cardinality = cardinality
        self.depth = depth
        self.block_depth = (self.depth - 2) // 9
        self.base_width = base_width
        self.widen_factor = widen_factor
        self.nlabels = nlabels
        self.output_size = 64
        self.stages = [64, 64 * self.widen_factor, 128 * self.widen_factor, 256 * self.widen_factor]

        self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
        self.bn_1 = nn.BatchNorm2d(64)
        self.stage_1 = self.block('stage_1', self.stages[0], self.stages[1], 1)
        self.stage_2 = self.block('stage_2', self.stages[1], self.stages[2], 2)
        self.stage_3 = self.block('stage_3', self.stages[2], self.stages[3], 2)
        self.classifier = nn.Linear(self.stages[3], nlabels)
        init.kaiming_normal(self.classifier.weight)

        for key in self.state_dict():
            if key.split('.')[-1] == 'weight':
                if 'conv' in key:
                    init.kaiming_normal(self.state_dict()[key], mode='fan_out')
                if 'bn' in key:
                    self.state_dict()[key][...] = 1
            elif key.split('.')[-1] == 'bias':
                self.state_dict()[key][...] = 0