densenet_fc.py 文件源码

def __transition_up_block(ip, nb_filters, type='upsampling', output_shape=None, weight_decay=1E-4):
    ''' SubpixelConvolutional Upscaling (factor = 2)
    Args:
        ip: keras tensor
        nb_filters: number of layers
        type: can be 'upsampling', 'subpixel', 'deconv', or 'atrous'. Determines type of upsampling performed
        output_shape: required if type = 'deconv'. Output shape of tensor
        weight_decay: weight decay factor
    Returns: keras tensor, after applying upsampling operation.
    '''

    if type == 'upsampling':
        x = UpSampling2D()(ip)
    elif type == 'subpixel':
        x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
                          bias=False, init='he_uniform')(ip)
        x = SubPixelUpscaling(scale_factor=2)(x)
        x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
                          bias=False, init='he_uniform')(x)
    elif type == 'atrous':
        # waiting on https://github.com/fchollet/keras/issues/4018
        x = AtrousConvolution2D(nb_filters, 3, 3, activation="relu", W_regularizer=l2(weight_decay),
                                bias=False, atrous_rate=(2, 2), init='he_uniform')(ip)
    else:
        x = Deconvolution2D(nb_filters, 3, 3, output_shape, activation='relu', border_mode='same',
                            subsample=(2, 2), init='he_uniform')(ip)

    return x