- 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++
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) |
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)) |
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) |
@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) |
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)) |
@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) |
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): |
/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++
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/
"vundle
set nocompatible
filetype off
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 |