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gen_lr_scheduler = LinearDecay(LEARNING_RATE, EPOCHS * total_batches, DECAY_EPOCHS * total_batches)
dis_lr_scheduler = LinearDecay(LEARNING_RATE, EPOCHS * total_batches, DECAY_EPOCHS * total_batches)
optimizer_gen = tf.keras.optimizers.Adam(gen_lr_scheduler, BETA_1)
optimizer_dis = tf.keras.optimizers.Adam(dis_lr_scheduler, BETA_1)

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def make_generator_model(n_blocks):
    # 6 residual blocks
    # c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3
    # 9 residual blocks
    # c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3
    model = tf.keras.Sequential()

    # Encoding
    model.add(ReflectionPad2d(3, input_shape=(256, 256, 3)))
    model.add(tf.keras.layers.Conv2D(64, (7, 7), strides=(1, 1), padding='valid', use_bias=False))

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def calc_gan_loss(prediction, is_real):
    # Typical GAN loss to set objectives for generator and discriminator
    if is_real:
        return mse_loss(prediction, tf.ones_like(prediction))
    else:
        return mse_loss(prediction, tf.zeros_like(prediction))

def calc_cycle_loss(reconstructed_images, real_images):
    # Cycle loss to make sure reconstructed image looks real
    return mae_loss(reconstructed_images, real_images)

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@tf.function
def train_generator(images_a, images_b):
    real_a = images_a
    real_b = images_b
    with tf.GradientTape() as tape:
        # Use real B to generate B should be identical
        identity_a2b = generator_a2b(real_b, training=True)
        identity_b2a = generator_b2a(real_a, training=True)
        loss_identity_a2b = calc_identity_loss(identity_a2b, real_b)
        loss_identity_b2a = calc_identity_loss(identity_b2a, real_a)

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def make_discriminator_model():
    # C64-C128-C256-C512
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Conv2D(64, (4, 4), strides=(2, 2), padding='same', input_shape=(256, 256, 3)))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))

    model.add(tf.keras.layers.Conv2D(128, (4, 4), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))

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@tf.function
def train_discriminator(images_a, images_b, fake_a2b, fake_b2a):
    real_a = images_a
    real_b = images_b
    with tf.GradientTape() as tape:

        # Discriminator A should classify real_a as A
        loss_gan_dis_a_real = calc_gan_loss(discriminator_a(real_a, training=True), True)
        # Discriminator A should classify generated fake_b2a as not A
        loss_gan_dis_a_fake = calc_gan_loss(discriminator_a(fake_b2a, training=True), False)

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def train_step(images_a, images_b, epoch, step):
    fake_a2b, fake_b2a, gen_loss_dict = train_generator(images_a, images_b)

    fake_b2a_from_pool = fake_pool_b2a.query(fake_b2a)
    fake_a2b_from_pool = fake_pool_a2b.query(fake_a2b)

    dis_loss_dict = train_discriminator(images_a, images_b, fake_a2b_from_pool, fake_b2a_from_pool)

def train(dataset, epochs):
    for epoch in range(checkpoint.epoch+1, epochs+1):

ethanyanjiali

• Install Vim and enable Python feature

sudo git clone https://github.com/vim/vim.git && cd vim
sudo ./configure --with-features=huge --enable-multibyte --enable-pythoninterp=yes --with-python-config-dir=/usr/lib/python2.7/config-x86_64-linux-gnu/ --enable-python3interp=yes --with-python3-config-dir=/usr/lib/python3.5/config-3.5m-x86_64-linux-gnu/ --enable-gui=gtk2 --enable-cscope --prefix=/usr/local/

• Use this as ~/.vimrc

"vundle
set nocompatible
filetype off

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• Install gcc/g++ 7+ Add this line to /etc/apt/sources.list

deb http://ftp.de.debian.org/debian buster main 

And then install gcc/g++ 7

sudo apt-get install gcc-7 g++-7
sudo rm /usr/bin/gcc
sudo rm /usr/bin/g++

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def HourglassModule(inputs, order, filters, num_residual):
    """
    One Hourglass Module. Usually we stacked multiple of them together.
    https://github.com/princeton-vl/pose-hg-train/blob/master/src/models/hg.lua#L3

    inputs:
    order: The remaining order for HG modules to call itself recursively.
    num_residual: Number of residual layers for this HG module.
    """
    # Upper branch