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solver.py
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from tqdm import tqdm
from pathlib import Path
import tensorflow as tf
from tensorflow.keras import optimizers, metrics
from src import losses
from src.loader import DataLoader
from src.gene import Generator
from src.disc import Discriminator
from src.losses import (
generator_loss,
discriminator_loss,
cycle_loss,
identity_loss
)
class Solver:
def __init__(self, config):
self.mode = config["mode"]
self.data_config = config["dataset"]
self.aug_config = config["augmentation"]
self.get_dataset()
training_config = config["training"]
self.epochs = training_config["epochs"]
self.print_iter = training_config["print_iter"]
self.save_epoch = training_config["save_epoch"]
self.gene_AB = Generator()
self.gene_BA = Generator()
self.disc_A = Discriminator()
self.disc_B = Discriminator()
self.gene_AB_opt = optimizers.Adam(**training_config["g_optimizer"])
self.gene_BA_opt = optimizers.Adam(**training_config["g_optimizer"])
self.disc_A_opt = optimizers.Adam(**training_config["d_optimizer"])
self.disc_B_opt = optimizers.Adam(**training_config["d_optimizer"])
self.gene_AB_losses = metrics.Mean()
self.gene_BA_losses = metrics.Mean()
self.disc_A_losses = metrics.Mean()
self.disc_B_losses = metrics.Mean()
self.test_gene_AB_losses = metrics.Mean()
self.test_gene_BA_losses = metrics.Mean()
self.test_disc_A_losses = metrics.Mean()
self.test_disc_B_losses = metrics.Mean()
self.get_ckpt_manager(training_config["save_path"])
def train(self):
n_iters = len(self.dataset)
valid_loss = 1e6
for epoch in range(self.epochs):
self.resets()
for (i, (ldct, ndct)) in enumerate(self.dataset):
self.train_batch(ldct, ndct)
if (i+1) % self.print_iter == 0:
print(f"[{epoch+1}/{self.epochs}] Epoch, [{i+1}/{n_iters}] Iter")
print(f"Generator(LDCT->NDCT) Loss: {self.gene_AB_losses.result():.5f}", end=" ")
print(f"Generator(NDCT->LDCT) Loss: {self.gene_BA_losses.result():.5f}")
print(f"Discriminator(LDCT) Loss: {self.disc_A_losses.result():.5f}", end=" ")
print(f"Discriminator(NDCT) Loss: {self.disc_B_losses.result():.5f}")
self.test_steps(True)
print(f"===== Validation [{epoch+1}/{self.epochs}] Epoch")
print(f"===== Generator(LDCT->NDCT) Loss: {self.test_gene_AB_losses.result():.5f}", end=" ")
print(f"Generator(NDCT->LDCT) Loss: {self.test_gene_BA_losses.result():.5f}")
print(f"===== Discriminator(LDCT) Loss: {self.test_disc_A_losses.result():.5f}", end=" ")
print(f"Discriminator(NDCT) Loss: {self.test_disc_B_losses.result():.5f}")
valid_total = self.test_gene_AB_losses.result().numpy()
if valid_total < valid_loss:
print(f"Validation loss reduced from {valid_loss:.5f} to {valid_total:.5f}")
valid_loss = valid_total
ckpt_save_path = self.ckpt_manager.save()
if (epoch+1) % self.save_epoch == 0:
f = self.config["training"]["save_path"] / Path(f"epoch_{epoch+1}.h5")
self.model.save_weights(str(f))
print("save ", str(f))
def test(self, ep=None):
self.load_weight()
self.test_steps(False)
raise NotImplementedError
def get_dataset(self):
if self.mode == "train":
self.dataset = DataLoader(
self.mode,
**self.data_config,
train_pair=False
)
self.dataset.set_params(**self.aug_config)
self.valid_dataset = DataLoader(
"valid",
**self.data_config,
train_pair=True
)
if self.mode == "test":
self.dataset = DataLoader(
self.mode,
**self.data_config,
train_pair=True
)
def get_ckpt_manager(self, ckpt_path, keep=5):
self.ckpt = tf.train.Checkpoint(
gene_AB=self.gene_AB, gene_BA=self.gene_BA,
disc_A=self.disc_A, disc_B=self.disc_B,
gene_AB_opt=self.gene_AB_opt, gene_BA_opt=self.gene_BA_opt,
disc_A_opt=self.disc_A_opt, disc_B_opt=self.disc_B_opt
)
self.ckpt_manager = tf.train.CheckpointManager(self.ckpt, ckpt_path, max_to_keep=keep)
def resets(self):
self.gene_AB_losses.reset_states()
self.gene_BA_losses.reset_states()
self.disc_A_losses.reset_states()
self.disc_B_losses.reset_states()
self.test_gene_AB_losses.reset_states()
self.test_gene_BA_losses.reset_states()
self.test_disc_A_losses.reset_states()
self.test_disc_B_losses.reset_states()
def load_weight(self):
f = str(self.config["training"]["save_path"])
self.ckpt.restore(tf.train.latest_checkpoint(f)).expect_partial()
@tf.function
def train_batch(self, A, B):
with tf.GradientTape(persistent=True) as tape:
fake_B = self.gene_AB(A)
fake_A = self.gene_BA(B)
cycle_A = self.gene_BA(fake_B)
cycle_B = self.gene_AB(fake_A)
iden_A = self.gene_BA(A)
iden_B = self.gene_AB(B)
real_A_logit = self.disc_A(A)
fake_A_logit = self.disc_A(fake_A)
real_B_logit = self.disc_B(B)
fake_B_logit = self.disc_B(fake_B)
AB_loss = generator_loss(fake_B_logit)
BA_loss = generator_loss(fake_A_logit)
iden_A_loss = identity_loss(A, iden_A)
iden_B_loss = identity_loss(B, iden_B)
c_loss = cycle_loss(A, cycle_A) + cycle_loss(B, cycle_B)
gene_AB_loss = AB_loss + iden_A_loss + c_loss
gene_BA_loss = BA_loss + iden_B_loss + c_loss
disc_A_loss = discriminator_loss(real_A_logit, fake_A_logit)
disc_B_loss = discriminator_loss(real_B_logit, fake_B_logit)
gene_AB_grads = tape.gradient(gene_AB_loss, self.gene_AB.trainable_variables)
gene_BA_grads = tape.gradient(gene_BA_loss, self.gene_BA.trainable_variables)
disc_A_grads = tape.gradient(disc_A_loss, self.disc_A.trainable_variables)
disc_B_grads = tape.gradient(disc_B_loss, self.disc_B.trainable_variables)
self.gene_AB_opt.apply_gradients(zip(gene_AB_grads, self.gene_AB.trainable_variables))
self.gene_BA_opt.apply_gradients(zip(gene_BA_grads, self.gene_BA.trainable_variables))
self.disc_A_opt.apply_gradients(zip(disc_A_grads, self.disc_A.trainable_variables))
self.disc_B_opt.apply_gradients(zip(disc_B_grads, self.disc_B.trainable_variables))
self.gene_AB_losses(gene_AB_loss)
self.gene_BA_losses(gene_BA_loss)
self.disc_A_losses(disc_A_loss)
self.disc_B_losses(disc_B_loss)
@tf.function
def test_batch(self, A, B):
fake_B = self.gene_AB(A)
fake_A = self.gene_BA(B)
cycle_A = self.gene_BA(fake_B)
cycle_B = self.gene_AB(fake_A)
iden_A = self.gene_BA(A)
iden_B = self.gene_AB(B)
real_A_logit = self.disc_A(A)
fake_A_logit = self.disc_A(fake_A)
real_B_logit = self.disc_B(B)
fake_B_logit = self.disc_B(fake_B)
AB_loss = generator_loss(fake_B_logit)
BA_loss = generator_loss(fake_A_logit)
iden_A_loss = identity_loss(A, iden_A)
iden_B_loss = identity_loss(B, iden_B)
c_loss = cycle_loss(A, cycle_A) + cycle_loss(B, cycle_B)
gene_AB_loss = AB_loss + iden_A_loss + c_loss
gene_BA_loss = BA_loss + iden_B_loss + c_loss
disc_A_loss = discriminator_loss(real_A_logit, fake_A_logit)
disc_B_loss = discriminator_loss(real_B_logit, fake_B_logit)
self.test_gene_AB_losses(gene_AB_loss)
self.test_gene_BA_losses(gene_BA_loss)
self.test_disc_A_losses(disc_A_loss)
self.test_disc_B_losses(disc_B_loss)
def test_steps(self, valid=True):
if valid:
datasets = self.valid_dataset
else:
datasets = tqdm(self.dataset)
for ldct, ndct in datasets:
self.test_batch(ldct, ndct)