archs.py 文件源码

def rnet1(input_shapes, n_classes):
    def conv(size, x):
        x = Convolution2D(size, 3, 3, border_mode='same', init='he_normal', bias=False)(x)
        x = BatchNormalization(axis=1, mode=0)(x)
        x = PReLU(shared_axes=[2, 3])(x)
        return x

    def unet_block(sizes, inp):
        x = inp

        skips = []

        for sz in sizes[:-1]:
            x = conv(sz, x)
            skips.append(x)
            x = MaxPooling2D((2, 2))(x)

        x = conv(sizes[-1], x)

        for sz in reversed(sizes[:-1]):
            x = conv(sz, merge([UpSampling2D((2, 2))(x), skips.pop()], mode='concat', concat_axis=1))

        return x

    def fcn_block(sizes, inp):
        x = inp

        for sz in sizes:
            x = conv(sz, x)

        return Dropout(0.2)(x)

    # Build piramid of inputs
    inp0 = Input(input_shapes['in'], name='in')
    inp1 = AveragePooling2D((2, 2))(inp0)
    inp2 = AveragePooling2D((2, 2))(inp1)

    # Build outputs in resnet fashion
    out2 = unet_block([32, 48], inp2)
    #out2 = merge([unet_block([32, 48, 32], merge([inp2, out2], mode='concat', concat_axis=1)), out2], mode='sum')

    out1 = UpSampling2D((2, 2))(out2)
    #out1 = merge([unet_block([32, 32, 48], merge([inp1, out1], mode='concat', concat_axis=1)), out1], mode='sum')
    out1 = merge([unet_block([32, 48, 64], merge([inp1, out1], mode='concat', concat_axis=1)), out1], mode='sum')

    out0 = UpSampling2D((2, 2))(out1)
    out0 = merge([unet_block([32, 48, 64], merge([inp0, out0], mode='concat', concat_axis=1)), out0], mode='sum')
    out0 = merge([unet_block([32, 48, 64], merge([inp0, out0], mode='concat', concat_axis=1)), out0], mode='sum')

    # Final convolution
    out = Convolution2D(n_classes, 1, 1, activation='sigmoid')(out0)

    return Model(input=inp0, output=out)