net.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.sample = sample
        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=='none-9':
            layers['c'] = L.Convolution2D(ch0, ch1, 9, 1, 4, initialW=w)
        elif sample=='none-7':
            layers['c'] = L.Convolution2D(ch0, ch1, 7, 1, 3, initialW=w)
        elif sample=='none-5':
            layers['c'] = L.Convolution2D(ch0, ch1, 5, 1, 2, initialW=w)
        else:
            layers['c'] = L.Convolution2D(ch0, ch1, 3, 1, 1, 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)