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train_model.py
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train_model.py
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import time
import tensorflow as tf
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.losses import BinaryCrossentropy, MeanAbsoluteError, MeanSquaredError
from tensorflow.keras.metrics import Mean
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers.schedules import PiecewiseConstantDecay
from model_architecture import *
class Model_Training:
def __init__(self, model, loss, learning_rate, checkpoint_dir='./checkpoint/edsr', generator=None, discriminator=None, content_loss='VGG54', model_type = 'None'):
self.now = None
self.loss = loss
self.checkpoint = tf.train.Checkpoint(step=tf.Variable(0), psnr=tf.Variable(-1.0), optimizer=Adam(learning_rate), model=model)
self.checkpoint_manager = tf.train.CheckpointManager(checkpoint=self.checkpoint, directory=checkpoint_dir, max_to_keep=3)
self.restore()
self.model_type = model_type
if self.model_type == 'srgan':
if content_loss == 'VGG22':
self.vgg = srgan.vgg_22()
elif content_loss == 'VGG54':
self.vgg = srgan.vgg_54()
else:
raise ValueError("content_loss must be either 'VGG22' or 'VGG54'")
self.content_loss = content_loss
self.generator = generator
self.discriminator = discriminator
self.generator_optimizer = Adam(learning_rate=learning_rate)
self.discriminator_optimizer = Adam(learning_rate=learning_rate)
self.binary_cross_entropy = BinaryCrossentropy(from_logits=False)
self.mean_squared_error = MeanSquaredError()
@property
def model(self):
return self.checkpoint.model
def train(self, train_dataset, valid_dataset = None, steps=300000, evaluate_every=1000, save_best_only=False):
if self.model_type == 'srgan':
pls_metric = Mean()
dls_metric = Mean()
step = 0
for lr, hr in train_dataset.take(steps):
step += 1
pl, dl = self.train_step(lr, hr)
pls_metric(pl)
dls_metric(dl)
if step % 50 == 0:
print(f'{step}/{steps}, perceptual loss = {pls_metric.result():.4f}, discriminator loss = {dls_metric.result():.4f}')
pls_metric.reset_states()
dls_metric.reset_states()
else:
loss_mean = Mean()
checkpoint_mgr = self.checkpoint_manager
checkpoint = self.checkpoint
self.now = time.perf_counter()
for lr, hr in train_dataset.take(steps - checkpoint.step.numpy()):
checkpoint.step.assign_add(1)
step = checkpoint.step.numpy()
loss = self.train_step(lr, hr)
loss_mean(loss)
if step % evaluate_every == 0:
loss_value = loss_mean.result()
loss_mean.reset_states()
# Compute PSNR on validation dataset
psnr_value = self.evaluate(valid_dataset)
duration = time.perf_counter() - self.now
print(f'{step}/{steps}: loss = {loss_value.numpy():.3f}, PSNR = {psnr_value.numpy():3f} ({duration:.2f}s)')
if save_best_only and psnr_value <= checkpoint.psnr:
self.now = time.perf_counter()
# skip saving checkpoint, no PSNR improvement
continue
checkpoint.psnr = psnr_value
checkpoint_mgr.save()
self.now = time.perf_counter()
@tf.function
def train_step(self, lr, hr):
if self.model_type == 'srgan':
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
lr = tf.cast(lr, tf.float32)
hr = tf.cast(hr, tf.float32)
sr = self.generator(lr, training=True)
hr_output = self.discriminator(hr, training=True)
sr_output = self.discriminator(sr, training=True)
con_loss = self._content_loss(hr, sr)
gen_loss = self._generator_loss(sr_output)
perc_loss = con_loss + 0.001 * gen_loss
disc_loss = self._discriminator_loss(hr_output, sr_output)
gradients_of_generator = gen_tape.gradient(perc_loss, self.generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)
self.generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))
self.discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))
return perc_loss, disc_loss
else:
with tf.GradientTape() as tape:
lr = tf.cast(lr, tf.float32)
hr = tf.cast(hr, tf.float32)
sr = self.checkpoint.model(lr, training=True)
loss_value = self.loss(hr, sr)
gradients = tape.gradient(loss_value, self.checkpoint.model.trainable_variables)
self.checkpoint.optimizer.apply_gradients(zip(gradients, self.checkpoint.model.trainable_variables))
return loss_value
def evaluate(self, dataset):
return evaluate(self.checkpoint.model, dataset)
def restore(self):
if self.checkpoint_manager.latest_checkpoint:
self.checkpoint.restore(self.checkpoint_manager.latest_checkpoint)
print(f'Model restored from checkpoint at step {self.checkpoint.step.numpy()}.')
@tf.function
def _content_loss(self, hr, sr):
sr = preprocess_input(sr)
hr = preprocess_input(hr)
sr_features = self.vgg(sr) / 12.75
hr_features = self.vgg(hr) / 12.75
return self.mean_squared_error(hr_features, sr_features)
def _generator_loss(self, sr_out):
return self.binary_cross_entropy(tf.ones_like(sr_out), sr_out)
def _discriminator_loss(self, hr_out, sr_out):
hr_loss = self.binary_cross_entropy(tf.ones_like(hr_out), hr_out)
sr_loss = self.binary_cross_entropy(tf.zeros_like(sr_out), sr_out)
return hr_loss + sr_loss
class Enhanced_DeepSuper_Resolution_Model_Training(Model_Training):
def __init__(self,model, checkpoint_dir,learning_rate=PiecewiseConstantDecay(boundaries=[200000], values=[1e-4, 5e-5])):
super().__init__(model, loss=MeanAbsoluteError(), learning_rate=learning_rate, checkpoint_dir=checkpoint_dir, model_type='edsr')
def train(self, train_dataset, valid_dataset, steps=300000, evaluate_every=1000, save_best_only=True):
super().train(train_dataset, valid_dataset, steps, evaluate_every, save_best_only)
class Wide_Activation_Super_Resolution_Model_Training(Model_Training):
def __init__(self, model, checkpoint_dir, learning_rate=PiecewiseConstantDecay(boundaries=[200000], values=[1e-3, 5e-4])):
super().__init__(model, loss=MeanAbsoluteError(), learning_rate=learning_rate, checkpoint_dir=checkpoint_dir, model_type='wdsr')
def train(self, train_dataset, valid_dataset, steps=300000, evaluate_every=1000, save_best_only=True):
super().train(train_dataset, valid_dataset, steps, evaluate_every, save_best_only)
class Super_Resolution_GAN_Model_Training(Model_Training):
def __init__(self, model, checkpoint_dir, learning_rate=PiecewiseConstantDecay(boundaries=[100000], values=[1e-4, 1e-5])):
super().__init__(model, loss=MeanSquaredError(), learning_rate=learning_rate, checkpoint_dir=checkpoint_dir, model_type='srgan')
def train(self, train_dataset, valid_dataset, steps=1000000, evaluate_every=1000, save_best_only=True):
super().train(train_dataset, valid_dataset, steps, evaluate_every, save_best_only)