model.py 文件源码

def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False, noise=False):
        self.bn = bn
        self.activation = activation
        self.dropout = dropout
        self.noise = noise
        layers = {}
        w = chainer.initializers.Normal(0.02)
        if sample == 'down':
            layers['c'] = L.Convolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        elif sample == 'up':
            layers['c'] = L.Deconvolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        elif sample == 'c7s1':
            layers['c'] = L.Convolution2D(ch0, ch1, 7, 1, 3, initialW=w)
        if bn:
            if self.noise:
                layers['batchnorm'] = L.BatchNormalization(ch1, use_gamma=False)
            else:
                layers['batchnorm'] = L.BatchNormalization(ch1)
        super(CBR, self).__init__(**layers)