def construct_model(self, crop_size, load_size):
self.A_B_dataset, len_dataset = data.make_zip_dataset(
self.A_img_paths,
self.B_img_paths,
self.batch_size,
load_size,
crop_size,
training=True,
repeat=False,
is_gray_scale=(self.color_depth == 1))
self.len_dataset = len_dataset
self.A2B_pool = data.ItemPool(self.pool_size)
self.B2A_pool = data.ItemPool(self.pool_size)
A_img_paths_test = py.glob(
py.join(self.datasets_dir, self.dataset, 'testA'),
'*.{}'.format(self.image_ext))
B_img_paths_test = py.glob(
py.join(self.datasets_dir, self.dataset, 'testB'),
'*.{}'.format(self.image_ext))
A_B_dataset_test, _ = data.make_zip_dataset(
A_img_paths_test,
B_img_paths_test,
self.batch_size,
load_size,
crop_size,
training=False,
repeat=True,
is_gray_scale=(self.color_depth == 1))
self.test_iter = iter(A_B_dataset_test)
self.G_A2B = module.ResnetGenerator(input_shape=(crop_size, crop_size,
self.color_depth),
output_channels=self.color_depth)
self.G_B2A = module.ResnetGenerator(input_shape=(crop_size, crop_size,
self.color_depth),
output_channels=self.color_depth)
self.D_A = module.ConvDiscriminator(input_shape=(crop_size, crop_size,
self.color_depth))
self.D_B = module.ConvDiscriminator(input_shape=(crop_size, crop_size,
self.color_depth))
self.d_loss_fn, self.g_loss_fn = gan.get_adversarial_losses_fn(
self.adversarial_loss_mode)
self.cycle_loss_fn = tf.losses.MeanAbsoluteError()
self.identity_loss_fn = tf.losses.MeanAbsoluteError()
self.G_lr_scheduler = module.LinearDecay(
self.lr, self.epochs * self.len_dataset,
self.epoch_decay * self.len_dataset)
self.D_lr_scheduler = module.LinearDecay(
self.lr, self.epochs * self.len_dataset,
self.epoch_decay * self.len_dataset)
self.G_optimizer = keras.optimizers.Adam(
learning_rate=self.G_lr_scheduler, beta_1=self.beta_1)
self.D_optimizer = keras.optimizers.Adam(
learning_rate=self.D_lr_scheduler, beta_1=self.beta_1)
d_norm = 'layer_norm'
# networks
# Comment by K.C:
# the following commands set the structure of a G model
G = module.ConvGenerator(input_shape=(1, 1, args.z_dim), output_channels=shape[-1], n_upsamplings=n_G_upsamplings, name='G_%s' % args.dataset)
D = module.ConvDiscriminator(input_shape=shape, n_downsamplings=n_D_downsamplings, norm=d_norm, name='D_%s' % args.dataset)
py.mkdir('%s/summaries' %output_dir)
keras.utils.plot_model(G,'%s/summaries/convGenerator.png' % output_dir, show_shapes=True)
keras.utils.plot_model(D,'%s/summaries/convDiscriminator.png' % output_dir, show_shapes=True)
G.summary()
D.summary()
# adversarial_loss_functions
d_loss_fn, g_loss_fn = gan.get_adversarial_losses_fn(args.adversarial_loss_mode)
G_optimizer = keras.optimizers.Adam(learning_rate=args.lr, beta_1=args.beta_1)
D_optimizer = keras.optimizers.Adam(learning_rate=args.lr, beta_1=args.beta_1)
# ==============================================================================
# = train step =
# ==============================================================================
def take_mean(list):
import numpy as np
tmp = []
for i in range(len(list)):
tmp.append(np.mean(list[0:i+1]))
return {'tmp': tmp}
import functools
import tqdm
import pylib as py
import data
import tensorflow as tf
import tensorflow.keras as keras
import module
import tf2gan as gan
import matplotlib.pyplot as plt
# adversarial_loss_functions
d_loss_fn, g_loss_fn = gan.get_adversarial_losses_fn('wgan')
#img_paths = py.glob('data/faces', '*.jpg')
#dataset, shape, len_dataset = data.make_PETCT_dataset(img_paths, 1)
#n_G_upsamplings = n_D_downsamplings = 4
#img_paths = py.glob('data/faces', '*.jpg')
img_paths = py.glob('data/STS_RGB', '*.png')
dataset, shape, len_dataset = data.make_PETCT_dataset(img_paths, 5)
n_G_upsamplings = n_D_downsamplings = 4
# networks
# Comment by K.C:
# the following commands set the structure of a G model
#G = module.ConvGenerator_1(input_shape=(1, 1, args.z_dim), output_channels=shape[-1], n_upsamplings=n_G_upsamplings, name='G_%s' % args.dataset)
G = module.G_Split_Unet(input_shape=(shape[0], shape[1], 1),
name='G_padding_PETCT',
padding='Same')
def train():
# ===================================== Args =====================================
args = parse_args()
output_dir = os.path.join('output', args.dataset)
os.makedirs(output_dir, exist_ok=True)
settings_path = os.path.join(output_dir, 'settings.json')
pylib.args_to_json(settings_path, args)
# ===================================== Data =====================================
A_img_paths = pylib.glob(
os.path.join(args.datasets_dir, args.dataset, 'trainA'), '*.png')
B_img_paths = pylib.glob(
os.path.join(args.datasets_dir, args.dataset, 'trainB'), '*.png')
print(f'len(A_img_paths) = {len(A_img_paths)}')
print(f'len(B_img_paths) = {len(B_img_paths)}')
load_size = [args.load_size_height, args.load_size_width]
crop_size = [args.crop_size_height, args.crop_size_width]
A_B_dataset, len_dataset = data.make_zip_dataset(A_img_paths,
B_img_paths,
args.batch_size,
load_size,
crop_size,
training=True,
repeat=False)
A2B_pool = data.ItemPool(args.pool_size)
B2A_pool = data.ItemPool(args.pool_size)
A_img_paths_test = pylib.glob(
os.path.join(args.datasets_dir, args.dataset, 'testA'), '*.png')
B_img_paths_test = pylib.glob(
os.path.join(args.datasets_dir, args.dataset, 'testB'), '*.png')
A_B_dataset_test, _ = data.make_zip_dataset(A_img_paths_test,
B_img_paths_test,
args.batch_size,
load_size,
crop_size,
training=False,
repeat=True)
# ===================================== Models =====================================
model_input_shape = crop_size + [
3
] # [args.crop_size_height, args.crop_size_width, 3]
G_A2B = module.ResnetGenerator(input_shape=model_input_shape, n_blocks=6)
G_B2A = module.ResnetGenerator(input_shape=model_input_shape, n_blocks=6)
D_A = module.ConvDiscriminator(input_shape=model_input_shape)
D_B = module.ConvDiscriminator(input_shape=model_input_shape)
d_loss_fn, g_loss_fn = tf2gan.get_adversarial_losses_fn(
args.adversarial_loss_mode)
cycle_loss_fn = tf.losses.MeanAbsoluteError()
identity_loss_fn = tf.losses.MeanAbsoluteError()
G_lr_scheduler = module.LinearDecay(args.lr, args.epochs * len_dataset,
args.epoch_decay * len_dataset)
D_lr_scheduler = module.LinearDecay(args.lr, args.epochs * len_dataset,
args.epoch_decay * len_dataset)
G_optimizer = tf.keras.optimizers.Adam(learning_rate=G_lr_scheduler,
beta_1=args.beta_1)
D_optimizer = tf.keras.optimizers.Adam(learning_rate=D_lr_scheduler,
beta_1=args.beta_1)
# ===================================== Training steps =====================================
@tf.function
def train_generators(A, B):
with tf.GradientTape() as t:
A2B = G_A2B(A, training=True)
B2A = G_B2A(B, training=True)
A2B2A = G_B2A(A2B, training=True)
B2A2B = G_A2B(B2A, training=True)
A2A = G_B2A(A, training=True)
B2B = G_A2B(B, training=True)
A2B_d_logits = D_B(A2B, training=True)
B2A_d_logits = D_A(B2A, training=True)
A2B_g_loss = g_loss_fn(A2B_d_logits)
B2A_g_loss = g_loss_fn(B2A_d_logits)
A2B2A_cycle_loss = cycle_loss_fn(A, A2B2A)
B2A2B_cycle_loss = cycle_loss_fn(B, B2A2B)
A2A_id_loss = identity_loss_fn(A, A2A)
B2B_id_loss = identity_loss_fn(B, B2B)
G_loss = (A2B_g_loss + B2A_g_loss) + (
A2B2A_cycle_loss +
B2A2B_cycle_loss) * args.cycle_loss_weight + (
A2A_id_loss + B2B_id_loss) * args.identity_loss_weight
G_grad = t.gradient(
G_loss, G_A2B.trainable_variables + G_B2A.trainable_variables)
G_optimizer.apply_gradients(
zip(G_grad, G_A2B.trainable_variables + G_B2A.trainable_variables))
return A2B, B2A, {
'A2B_g_loss': A2B_g_loss,
'B2A_g_loss': B2A_g_loss,
'A2B2A_cycle_loss': A2B2A_cycle_loss,
'B2A2B_cycle_loss': B2A2B_cycle_loss,
'A2A_id_loss': A2A_id_loss,
'B2B_id_loss': B2B_id_loss
}
@tf.function
def train_discriminators(A, B, A2B, B2A):
with tf.GradientTape() as t:
A_d_logits = D_A(A, training=True)
B2A_d_logits = D_A(B2A, training=True)
B_d_logits = D_B(B, training=True)
A2B_d_logits = D_B(A2B, training=True)
A_d_loss, B2A_d_loss = d_loss_fn(A_d_logits, B2A_d_logits)
B_d_loss, A2B_d_loss = d_loss_fn(B_d_logits, A2B_d_logits)
D_A_gp = tf2gan.gradient_penalty(functools.partial(D_A,
training=True),
A,
B2A,
mode=args.gradient_penalty_mode)
D_B_gp = tf2gan.gradient_penalty(functools.partial(D_B,
training=True),
B,
A2B,
mode=args.gradient_penalty_mode)
D_loss = (A_d_loss + B2A_d_loss) + (B_d_loss + A2B_d_loss) + (
D_A_gp + D_B_gp) * args.gradient_penalty_weight
D_grad = t.gradient(D_loss,
D_A.trainable_variables + D_B.trainable_variables)
D_optimizer.apply_gradients(
zip(D_grad, D_A.trainable_variables + D_B.trainable_variables))
return {
'A_d_loss': A_d_loss + B2A_d_loss,
'B_d_loss': B_d_loss + A2B_d_loss,
'D_A_gp': D_A_gp,
'D_B_gp': D_B_gp
}
def train_step(A, B):
A2B, B2A, G_loss_dict = train_generators(A, B)
# cannot autograph `A2B_pool`
A2B = A2B_pool(
A2B) # or A2B = A2B_pool(A2B.numpy()), but it is much slower
B2A = B2A_pool(B2A) # because of the communication between CPU and GPU
D_loss_dict = train_discriminators(A, B, A2B, B2A)
return G_loss_dict, D_loss_dict
@tf.function
def sample(A, B):
A2B = G_A2B(A, training=False)
B2A = G_B2A(B, training=False)
A2B2A = G_B2A(A2B, training=False)
B2A2B = G_A2B(B2A, training=False)
return A2B, B2A, A2B2A, B2A2B
# ===================================== Runner code =====================================
# epoch counter
ep_cnt = tf.Variable(initial_value=0, trainable=False, dtype=tf.int64)
# checkpoint
checkpoint = tf2lib.Checkpoint(dict(G_A2B=G_A2B,
G_B2A=G_B2A,
D_A=D_A,
D_B=D_B,
G_optimizer=G_optimizer,
D_optimizer=D_optimizer,
ep_cnt=ep_cnt),
os.path.join(output_dir, 'checkpoints'),
max_to_keep=5)
try: # restore checkpoint including the epoch counter
checkpoint.restore().assert_existing_objects_matched()
except Exception as e:
print(e)
# summary
train_summary_writer = tf.summary.create_file_writer(
os.path.join(output_dir, 'summaries', 'train'))
# sample
test_iter = iter(A_B_dataset_test)
sample_dir = os.path.join(output_dir, 'samples_training')
os.makedirs(sample_dir, exist_ok=True)
# main loop
with train_summary_writer.as_default():
for ep in tqdm.trange(args.epochs, desc='Epoch Loop'):
if ep < ep_cnt:
continue
# update epoch counter
ep_cnt.assign_add(1)
# train for an epoch
for A, B in tqdm.tqdm(A_B_dataset,
desc='Inner Epoch Loop',
total=len_dataset):
G_loss_dict, D_loss_dict = train_step(A, B)
# # summary
tf2lib.summary(G_loss_dict,
step=G_optimizer.iterations,
name='G_losses')
tf2lib.summary(D_loss_dict,
step=G_optimizer.iterations,
name='D_losses')
tf2lib.summary(
{'learning rate': G_lr_scheduler.current_learning_rate},
step=G_optimizer.iterations,
name='learning rate')
# sample
if G_optimizer.iterations.numpy() % 100 == 0:
A, B = next(test_iter)
A2B, B2A, A2B2A, B2A2B = sample(A, B)
img = imlib.immerge(np.concatenate(
[A, A2B, A2B2A, B, B2A, B2A2B], axis=0),
n_rows=6)
imlib.imwrite(
img,
os.path.join(
sample_dir,
'iter-%09d.jpg' % G_optimizer.iterations.numpy()))
# save checkpoint
checkpoint.save(ep)
