fcn_resunet_blocks.py 文件源码

def _bn_relu_conv(nb_filter, nb_row, nb_col, subsample=False, upsample=False,
                  batch_norm=True, weight_decay=None):

    def f(input):
        processed = input
        if batch_norm:
            processed = BatchNormalization(mode=0, axis=1)(processed)
        processed = Activation('relu')(processed)
        stride = (1, 1)
        if subsample:
            stride = (2, 2)
        if upsample:
            processed = UpSampling2D(size=(2, 2))(processed)
        return Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col,
                             subsample=stride, init='he_normal',
                             border_mode='same',
                             W_regularizer=_l2(weight_decay))(processed)

    return f


# Adds a shortcut between input and residual block and merges them with 'sum'