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#引入需要的包
from keras.models import Model
from keras.layers import Input, Dense, Flatten, Reshape
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from .PixelShuffler import PixelShuffler
# 这个PixelShuffler 层，完成图片高分辨率输出，用于将低分辨率的图片转化成高分辨率的图片，通俗的讲就是去马赛克。
# 相关理论和论文可以搜pixel shuffle自行寻找，这里用的是人写好了keras的PixelShuffler layer 代码在gist也有

optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999) #优化器
IMAGE_SHAPE = (64, 64, 3)
ENCODER_DIM = 1024

#卷积操作封装，一个2D的卷积后面跟一个LeakyReLU
def conv(filters):
 def block(x):
        x = Conv2D(filters, kernel_size=5, strides=2, padding='same')(x)
        x = LeakyReLU(0.1)(x)
 return x
 return block

# 反卷积，所以用到了 PixelShuffler
def upscale(filters):
 def block(x):
        x = Conv2D(filters * 4, kernel_size=3, padding='same')(x)
        x = LeakyReLU(0.1)(x)
        x = PixelShuffler()(x)
 return x
 return block
# 因为用了keras，所以每层之间的关系看起来就十分简单了，这里就不详述了
# 最后的操作肯定也是要压扁后送到全连接层在输出模型
# 这里不需要pooling和dropout，因为我们做的不是分类，所以不能丢失信息
# 编码器
def Encoder():
    input_ = Input(shape=IMAGE_SHAPE)
    x = input_
    x = conv(128)(x)
    x = conv(256)(x)
    x = conv(512)(x)
    x = conv(1024)(x)
    x = Dense(ENCODER_DIM)(Flatten()(x))
    x = Dense(4 * 4 * 1024)(x)
    x = Reshape((4, 4, 1024))(x)
    x = upscale(512)(x)
 return Model(input_, x)

# 解码器
def Decoder():
    input_ = Input(shape=(8, 8, 512))
    x = input_
    x = upscale(256)(x)
    x = upscale(128)(x)
    x = upscale(64)(x)
    x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
 return Model(input_, x)

encoder = Encoder()
decoder_A = Decoder()
decoder_B = Decoder()
x = Input(shape=IMAGE_SHAPE)

#A和B是两个模型，分别是原型和替换
autoencoder_A = Model(x, decoder_A(encoder(x)))
autoencoder_B = Model(x, decoder_B(encoder(x)))
autoencoder_A.compile(optimizer=optimizer, loss='mean_absolute_error')
autoencoder_B.compile(optimizer=optimizer, loss='mean_absolute_error')