def __init__(
self, steps, epochs, config, strategy, is_mixed_precision=False,
):
"""Initialize trainer.
Args:
steps (int): Initial global steps.
epochs (int): Initial global epochs.
config (dict): Config dict loaded from yaml format configuration file.
strategy (tf.distribute): Strategy for distributed training.
is_mixed_precision (bool): Use mixed_precision training or not.
"""
super().__init__(steps, epochs, config)
self._is_mixed_precision = is_mixed_precision
self._strategy = strategy
self._model = None
self._optimizer = None
self._trainable_variables = None
# check if we already apply input_signature for train_step.
self._already_apply_input_signature = False
# create gradient accumulator
self._gradient_accumulator = GradientAccumulator()
self._gradient_accumulator.reset()
def __init__(
self,
steps,
epochs,
config,
strategy,
is_generator_mixed_precision=False,
is_discriminator_mixed_precision=False,
):
"""Initialize trainer.
Args:
steps (int): Initial global steps.
epochs (int): Initial global epochs.
config (dict): Config dict loaded from yaml format configuration file.
"""
super().__init__(steps, epochs, config)
self._is_generator_mixed_precision = is_generator_mixed_precision
self._is_discriminator_mixed_precision = is_discriminator_mixed_precision
self._strategy = strategy
self._already_apply_input_signature = False
self._generator_gradient_accumulator = GradientAccumulator()
self._discriminator_gradient_accumulator = GradientAccumulator()
self._generator_gradient_accumulator.reset()
self._discriminator_gradient_accumulator.reset()
class Seq2SeqBasedTrainer(BasedTrainer, metaclass=abc.ABCMeta):
"""Customized trainer module for Seq2Seq TTS training (Tacotron, FastSpeech)."""
def __init__(
self, steps, epochs, config, strategy, is_mixed_precision=False,
):
"""Initialize trainer.
Args:
steps (int): Initial global steps.
epochs (int): Initial global epochs.
config (dict): Config dict loaded from yaml format configuration file.
strategy (tf.distribute): Strategy for distributed training.
is_mixed_precision (bool): Use mixed_precision training or not.
"""
super().__init__(steps, epochs, config)
self._is_mixed_precision = is_mixed_precision
self._strategy = strategy
self._model = None
self._optimizer = None
self._trainable_variables = None
# check if we already apply input_signature for train_step.
self._already_apply_input_signature = False
# create gradient accumulator
self._gradient_accumulator = GradientAccumulator()
self._gradient_accumulator.reset()
def init_train_eval_metrics(self, list_metrics_name):
with self._strategy.scope():
super().init_train_eval_metrics(list_metrics_name)
def set_model(self, model):
"""Set generator class model (MUST)."""
self._model = model
def get_model(self):
"""Get generator model."""
return self._model
def set_optimizer(self, optimizer):
"""Set optimizer (MUST)."""
self._optimizer = optimizer
if self._is_mixed_precision:
self._optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
self._optimizer, "dynamic"
)
def get_optimizer(self):
"""Get optimizer."""
return self._optimizer
def get_n_gpus(self):
return self._strategy.num_replicas_in_sync
def compile(self, model, optimizer):
self.set_model(model)
self.set_optimizer(optimizer)
self._trainable_variables = self._train_vars()
def _train_vars(self):
if self.config["var_train_expr"]:
list_train_var = self.config["var_train_expr"].split("|")
return [
v
for v in self._model.trainable_variables
if self._check_string_exist(list_train_var, v.name)
]
return self._model.trainable_variables
def _check_string_exist(self, list_string, inp_string):
for string in list_string:
if string in inp_string:
return True
return False
def _get_train_element_signature(self):
return self.train_data_loader.element_spec
def _get_eval_element_signature(self):
return self.eval_data_loader.element_spec
def _train_step(self, batch):
if self._already_apply_input_signature is False:
train_element_signature = self._get_train_element_signature()
eval_element_signature = self._get_eval_element_signature()
self.one_step_forward = tf.function(
self._one_step_forward, input_signature=[train_element_signature]
)
self.one_step_evaluate = tf.function(
self._one_step_evaluate, input_signature=[eval_element_signature]
)
self.one_step_predict = tf.function(
self._one_step_predict, input_signature=[eval_element_signature]
)
self._already_apply_input_signature = True
# run one_step_forward
self.one_step_forward(batch)
# update counts
self.steps += 1
self.tqdm.update(1)
self._check_train_finish()
def _one_step_forward(self, batch):
per_replica_losses = self._strategy.run(
self._one_step_forward_per_replica, args=(batch,)
)
return self._strategy.reduce(
tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None
)
def _calculate_gradient_per_batch(self, batch):
outputs = self._model(**batch, training=True)
per_example_losses, dict_metrics_losses = self.compute_per_example_losses(
batch, outputs
)
per_replica_losses = tf.nn.compute_average_loss(
per_example_losses,
global_batch_size=self.config["batch_size"]
* self.get_n_gpus()
* self.config["gradient_accumulation_steps"],
)
if self._is_mixed_precision:
scaled_per_replica_losses = self._optimizer.get_scaled_loss(
per_replica_losses
)
if self._is_mixed_precision:
scaled_gradients = tf.gradients(
scaled_per_replica_losses, self._trainable_variables
)
gradients = self._optimizer.get_unscaled_gradients(scaled_gradients)
else:
gradients = tf.gradients(per_replica_losses, self._trainable_variables)
# gradient accumulate here
if self.config["gradient_accumulation_steps"] > 1:
self._gradient_accumulator(gradients)
# accumulate loss into metrics
self.update_train_metrics(dict_metrics_losses)
if self.config["gradient_accumulation_steps"] == 1:
return gradients, per_replica_losses
else:
return per_replica_losses
def _one_step_forward_per_replica(self, batch):
if self.config["gradient_accumulation_steps"] == 1:
gradients, per_replica_losses = self._calculate_gradient_per_batch(batch)
self._optimizer.apply_gradients(
zip(gradients, self._trainable_variables), 1.0
)
else:
# gradient acummulation here.
per_replica_losses = 0.0
for i in tf.range(self.config["gradient_accumulation_steps"]):
reduced_batch = {
k: v[
i
* self.config["batch_size"] : (i + 1)
* self.config["batch_size"]
]
for k, v in batch.items()
}
# run 1 step accumulate
reduced_batch_losses = self._calculate_gradient_per_batch(reduced_batch)
# sum per_replica_losses
per_replica_losses += reduced_batch_losses
gradients = self._gradient_accumulator.gradients
self._optimizer.apply_gradients(
zip(gradients, self._trainable_variables), 1.0
)
self._gradient_accumulator.reset()
return per_replica_losses
@abc.abstractmethod
def compute_per_example_losses(self, batch, outputs):
"""Compute per example losses and return dict_metrics_losses
Note that all element of the loss MUST has a shape [batch_size] and
the keys of dict_metrics_losses MUST be in self.list_metrics_name.
Args:
batch: dictionary batch input return from dataloader
outputs: outputs of the model
Returns:
per_example_losses: per example losses for each GPU, shape [B]
dict_metrics_losses: dictionary loss.
"""
per_example_losses = 0.0
dict_metrics_losses = {}
return per_example_losses, dict_metrics_losses
def _eval_epoch(self):
"""Evaluate model one epoch."""
logging.info(f"(Steps: {self.steps}) Start evaluation.")
# calculate loss for each batch
for eval_steps_per_epoch, batch in enumerate(
tqdm(self.eval_data_loader, desc="[eval]"), 1
):
# eval one step
self.one_step_evaluate(batch)
if eval_steps_per_epoch <= self.config["num_save_intermediate_results"]:
# save intermedia
self.generate_and_save_intermediate_result(batch)
logging.info(
f"(Steps: {self.steps}) Finished evaluation "
f"({eval_steps_per_epoch} steps per epoch)."
)
# average loss
for key in self.eval_metrics.keys():
logging.info(
f"(Steps: {self.steps}) eval_{key} = {self.eval_metrics[key].result():.4f}."
)
# record
self._write_to_tensorboard(self.eval_metrics, stage="eval")
# reset
self.reset_states_eval()
def _one_step_evaluate_per_replica(self, batch):
outputs = self._model(**batch, training=False)
_, dict_metrics_losses = self.compute_per_example_losses(batch, outputs)
self.update_eval_metrics(dict_metrics_losses)
def _one_step_evaluate(self, batch):
self._strategy.run(self._one_step_evaluate_per_replica, args=(batch,))
def _one_step_predict_per_replica(self, batch):
outputs = self._model(**batch, training=False)
return outputs
def _one_step_predict(self, batch):
outputs = self._strategy.run(self._one_step_predict_per_replica, args=(batch,))
return outputs
@abc.abstractmethod
def generate_and_save_intermediate_result(self, batch):
return
def create_checkpoint_manager(self, saved_path=None, max_to_keep=10):
"""Create checkpoint management."""
if saved_path is None:
saved_path = self.config["outdir"] + "/checkpoints/"
os.makedirs(saved_path, exist_ok=True)
self.saved_path = saved_path
self.ckpt = tf.train.Checkpoint(
steps=tf.Variable(1), epochs=tf.Variable(1), optimizer=self.get_optimizer()
)
self.ckp_manager = tf.train.CheckpointManager(
self.ckpt, saved_path, max_to_keep=max_to_keep
)
def save_checkpoint(self):
"""Save checkpoint."""
self.ckpt.steps.assign(self.steps)
self.ckpt.epochs.assign(self.epochs)
self.ckp_manager.save(checkpoint_number=self.steps)
self._model.save_weights(self.saved_path + "model-{}.h5".format(self.steps))
def load_checkpoint(self, pretrained_path):
"""Load checkpoint."""
self.ckpt.restore(pretrained_path)
self.steps = self.ckpt.steps.numpy()
self.epochs = self.ckpt.epochs.numpy()
self._optimizer = self.ckpt.optimizer
# re-assign iterations (global steps) for optimizer.
self._optimizer.iterations.assign(tf.cast(self.steps, tf.int64))
# load weights.
self._model.load_weights(self.saved_path + "model-{}.h5".format(self.steps))
def _check_train_finish(self):
"""Check training finished."""
if self.steps >= self.config["train_max_steps"]:
self.finish_train = True
def _check_log_interval(self):
"""Log to tensorboard."""
if self.steps % self.config["log_interval_steps"] == 0:
for metric_name in self.list_metrics_name:
logging.info(
f"(Step: {self.steps}) train_{metric_name} = {self.train_metrics[metric_name].result():.4f}."
)
self._write_to_tensorboard(self.train_metrics, stage="train")
# reset
self.reset_states_train()
def fit(self, train_data_loader, valid_data_loader, saved_path, resume=None):
self.set_train_data_loader(train_data_loader)
self.set_eval_data_loader(valid_data_loader)
self.train_data_loader = self._strategy.experimental_distribute_dataset(
self.train_data_loader
)
self.eval_data_loader = self._strategy.experimental_distribute_dataset(
self.eval_data_loader
)
with self._strategy.scope():
self.create_checkpoint_manager(saved_path=saved_path, max_to_keep=10000)
if len(resume) > 1:
self.load_checkpoint(resume)
logging.info(f"Successfully resumed from {resume}.")
self.run()
class GanBasedTrainer(BasedTrainer):
"""Customized trainer module for GAN TTS training (MelGAN, GAN-TTS, ParallelWaveGAN)."""
def __init__(
self,
steps,
epochs,
config,
strategy,
is_generator_mixed_precision=False,
is_discriminator_mixed_precision=False,
):
"""Initialize trainer.
Args:
steps (int): Initial global steps.
epochs (int): Initial global epochs.
config (dict): Config dict loaded from yaml format configuration file.
"""
super().__init__(steps, epochs, config)
self._is_generator_mixed_precision = is_generator_mixed_precision
self._is_discriminator_mixed_precision = is_discriminator_mixed_precision
self._strategy = strategy
self._already_apply_input_signature = False
self._generator_gradient_accumulator = GradientAccumulator()
self._discriminator_gradient_accumulator = GradientAccumulator()
self._generator_gradient_accumulator.reset()
self._discriminator_gradient_accumulator.reset()
def init_train_eval_metrics(self, list_metrics_name):
with self._strategy.scope():
super().init_train_eval_metrics(list_metrics_name)
def get_n_gpus(self):
return self._strategy.num_replicas_in_sync
def _get_train_element_signature(self):
return self.train_data_loader.element_spec
def _get_eval_element_signature(self):
return self.eval_data_loader.element_spec
def set_gen_model(self, generator_model):
"""Set generator class model (MUST)."""
self._generator = generator_model
def get_gen_model(self):
"""Get generator model."""
return self._generator
def set_dis_model(self, discriminator_model):
"""Set discriminator class model (MUST)."""
self._discriminator = discriminator_model
def get_dis_model(self):
"""Get discriminator model."""
return self._discriminator
def set_gen_optimizer(self, generator_optimizer):
"""Set generator optimizer (MUST)."""
self._gen_optimizer = generator_optimizer
if self._is_generator_mixed_precision:
self._gen_optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
self._gen_optimizer, "dynamic")
def get_gen_optimizer(self):
"""Get generator optimizer."""
return self._gen_optimizer
def set_dis_optimizer(self, discriminator_optimizer):
"""Set discriminator optimizer (MUST)."""
self._dis_optimizer = discriminator_optimizer
if self._is_discriminator_mixed_precision:
self._dis_optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
self._dis_optimizer, "dynamic")
def get_dis_optimizer(self):
"""Get discriminator optimizer."""
return self._dis_optimizer
def compile(self, gen_model, dis_model, gen_optimizer, dis_optimizer):
self.set_gen_model(gen_model)
self.set_dis_model(dis_model)
self.set_gen_optimizer(gen_optimizer)
self.set_dis_optimizer(dis_optimizer)
def _train_step(self, batch):
if self._already_apply_input_signature is False:
train_element_signature = self._get_train_element_signature()
eval_element_signature = self._get_eval_element_signature()
self.one_step_forward = tf.function(
self._one_step_forward,
input_signature=[train_element_signature])
self.one_step_evaluate = tf.function(
self._one_step_evaluate,
input_signature=[eval_element_signature])
self.one_step_predict = tf.function(
self._one_step_predict,
input_signature=[eval_element_signature])
self._already_apply_input_signature = True
# run one_step_forward
self.one_step_forward(batch)
# update counts
self.steps += 1
self.tqdm.update(1)
self._check_train_finish()
def _one_step_forward(self, batch):
per_replica_losses = self._strategy.run(
self._one_step_forward_per_replica, args=(batch, ))
return self._strategy.reduce(tf.distribute.ReduceOp.SUM,
per_replica_losses,
axis=None)
@abc.abstractmethod
def compute_per_example_generator_losses(self, batch, outputs):
"""Compute per example generator losses and return dict_metrics_losses
Note that all element of the loss MUST has a shape [batch_size] and
the keys of dict_metrics_losses MUST be in self.list_metrics_name.
Args:
batch: dictionary batch input return from dataloader
outputs: outputs of the model
Returns:
per_example_losses: per example losses for each GPU, shape [B]
dict_metrics_losses: dictionary loss.
"""
per_example_losses = 0.0
dict_metrics_losses = {}
return per_example_losses, dict_metrics_losses
@abc.abstractmethod
def compute_per_example_discriminator_losses(self, batch, gen_outputs):
"""Compute per example discriminator losses and return dict_metrics_losses
Note that all element of the loss MUST has a shape [batch_size] and
the keys of dict_metrics_losses MUST be in self.list_metrics_name.
Args:
batch: dictionary batch input return from dataloader
outputs: outputs of the model
Returns:
per_example_losses: per example losses for each GPU, shape [B]
dict_metrics_losses: dictionary loss.
"""
per_example_losses = 0.0
dict_metrics_losses = {}
return per_example_losses, dict_metrics_losses
def _calculate_generator_gradient_per_batch(self, batch):
outputs = self._generator(**batch, training=True)
(
per_example_losses,
dict_metrics_losses,
) = self.compute_per_example_generator_losses(batch, outputs)
per_replica_gen_losses = tf.nn.compute_average_loss(
per_example_losses,
global_batch_size=self.config["batch_size"] * self.get_n_gpus() *
self.config["gradient_accumulation_steps"],
)
if self._is_generator_mixed_precision:
scaled_per_replica_gen_losses = self._gen_optimizer.get_scaled_loss(
per_replica_gen_losses)
if self._is_generator_mixed_precision:
scaled_gradients = tf.gradients(
scaled_per_replica_gen_losses,
self._generator.trainable_variables)
gradients = self._gen_optimizer.get_unscaled_gradients(
scaled_gradients)
else:
gradients = tf.gradients(per_replica_gen_losses,
self._generator.trainable_variables)
# gradient accumulate for generator here
if self.config["gradient_accumulation_steps"] > 1:
self._generator_gradient_accumulator(gradients)
# accumulate loss into metrics
self.update_train_metrics(dict_metrics_losses)
if self.config["gradient_accumulation_steps"] == 1:
return gradients, per_replica_gen_losses
else:
return per_replica_gen_losses
def _calculate_discriminator_gradient_per_batch(self, batch):
(
per_example_losses,
dict_metrics_losses,
) = self.compute_per_example_discriminator_losses(
batch, self._generator(**batch, training=True))
per_replica_dis_losses = tf.nn.compute_average_loss(
per_example_losses,
global_batch_size=self.config["batch_size"] * self.get_n_gpus() *
self.config["gradient_accumulation_steps"],
)
if self._is_discriminator_mixed_precision:
scaled_per_replica_dis_losses = self._dis_optimizer.get_scaled_loss(
per_replica_dis_losses)
if self._is_discriminator_mixed_precision:
scaled_gradients = tf.gradients(
scaled_per_replica_dis_losses,
self._discriminator.trainable_variables,
)
gradients = self._dis_optimizer.get_unscaled_gradients(
scaled_gradients)
else:
gradients = tf.gradients(per_replica_dis_losses,
self._discriminator.trainable_variables)
# accumulate loss into metrics
self.update_train_metrics(dict_metrics_losses)
# gradient accumulate for discriminator here
if self.config["gradient_accumulation_steps"] > 1:
self._discriminator_gradient_accumulator(gradients)
if self.config["gradient_accumulation_steps"] == 1:
return gradients, per_replica_dis_losses
else:
return per_replica_dis_losses
def _one_step_forward_per_replica(self, batch):
per_replica_gen_losses = 0.0
per_replica_dis_losses = 0.0
if self.config["gradient_accumulation_steps"] == 1:
(
gradients,
per_replica_gen_losses,
) = self._calculate_generator_gradient_per_batch(batch)
self._gen_optimizer.apply_gradients(
zip(gradients, self._generator.trainable_variables))
else:
# gradient acummulation here.
for i in tf.range(self.config["gradient_accumulation_steps"]):
reduced_batch = {
k: v[i * self.config["batch_size"]:(i + 1) *
self.config["batch_size"]]
for k, v in batch.items()
}
# run 1 step accumulate
reduced_batch_losses = self._calculate_generator_gradient_per_batch(
reduced_batch)
# sum per_replica_losses
per_replica_gen_losses += reduced_batch_losses
gradients = self._generator_gradient_accumulator.gradients
self._gen_optimizer.apply_gradients(
zip(gradients, self._generator.trainable_variables))
self._generator_gradient_accumulator.reset()
# one step discriminator
# recompute y_hat after 1 step generator for discriminator training.
if self.steps >= self.config["discriminator_train_start_steps"]:
if self.config["gradient_accumulation_steps"] == 1:
(
gradients,
per_replica_dis_losses,
) = self._calculate_discriminator_gradient_per_batch(batch)
self._dis_optimizer.apply_gradients(
zip(gradients, self._discriminator.trainable_variables))
else:
# gradient acummulation here.
for i in tf.range(self.config["gradient_accumulation_steps"]):
reduced_batch = {
k: v[i * self.config["batch_size"]:(i + 1) *
self.config["batch_size"]]
for k, v in batch.items()
}
# run 1 step accumulate
reduced_batch_losses = (
self._calculate_discriminator_gradient_per_batch(
reduced_batch))
# sum per_replica_losses
per_replica_dis_losses += reduced_batch_losses
gradients = self._discriminator_gradient_accumulator.gradients
self._dis_optimizer.apply_gradients(
zip(gradients, self._discriminator.trainable_variables))
self._discriminator_gradient_accumulator.reset()
return per_replica_gen_losses + per_replica_dis_losses
def _eval_epoch(self):
"""Evaluate model one epoch."""
logging.info(f"(Steps: {self.steps}) Start evaluation.")
# calculate loss for each batch
for eval_steps_per_epoch, batch in enumerate(
tqdm(self.eval_data_loader, desc="[eval]"), 1):
# eval one step
self.one_step_evaluate(batch)
if eval_steps_per_epoch <= self.config[
"num_save_intermediate_results"]:
# save intermedia
self.generate_and_save_intermediate_result(batch)
logging.info(f"(Steps: {self.steps}) Finished evaluation "
f"({eval_steps_per_epoch} steps per epoch).")
# average loss
for key in self.eval_metrics.keys():
logging.info(
f"(Steps: {self.steps}) eval_{key} = {self.eval_metrics[key].result():.4f}."
)
# record
self._write_to_tensorboard(self.eval_metrics, stage="eval")
# reset
self.reset_states_eval()
def _one_step_evaluate_per_replica(self, batch):
################################################
# one step generator.
outputs = self._generator(**batch, training=False)
_, dict_metrics_losses = self.compute_per_example_generator_losses(
batch, outputs)
# accumulate loss into metrics
self.update_eval_metrics(dict_metrics_losses)
################################################
# one step discriminator
if self.steps >= self.config["discriminator_train_start_steps"]:
_, dict_metrics_losses = self.compute_per_example_discriminator_losses(
batch, outputs)
# accumulate loss into metrics
self.update_eval_metrics(dict_metrics_losses)
################################################
def _one_step_evaluate(self, batch):
self._strategy.run(self._one_step_evaluate_per_replica, args=(batch, ))
def _one_step_predict_per_replica(self, batch):
outputs = self._generator(**batch, training=False)
return outputs
def _one_step_predict(self, batch):
outputs = self._strategy.run(self._one_step_predict_per_replica,
args=(batch, ))
return outputs
@abc.abstractmethod
def generate_and_save_intermediate_result(self, batch):
return
def create_checkpoint_manager(self, saved_path=None, max_to_keep=10):
"""Create checkpoint management."""
if saved_path is None:
saved_path = self.config["outdir"] + "/checkpoints/"
os.makedirs(saved_path, exist_ok=True)
self.saved_path = saved_path
self.ckpt = tf.train.Checkpoint(
steps=tf.Variable(1),
epochs=tf.Variable(1),
gen_optimizer=self.get_gen_optimizer(),
dis_optimizer=self.get_dis_optimizer(),
)
self.ckp_manager = tf.train.CheckpointManager(self.ckpt,
saved_path,
max_to_keep=max_to_keep)
def save_checkpoint(self):
"""Save checkpoint."""
self.ckpt.steps.assign(self.steps)
self.ckpt.epochs.assign(self.epochs)
self.ckp_manager.save(checkpoint_number=self.steps)
utils.save_weights(
self._generator,
self.saved_path + "generator-{}.h5".format(self.steps))
utils.save_weights(
self._discriminator,
self.saved_path + "discriminator-{}.h5".format(self.steps))
def load_checkpoint(self, pretrained_path):
"""Load checkpoint."""
self.ckpt.restore(pretrained_path)
self.steps = self.ckpt.steps.numpy()
self.epochs = self.ckpt.epochs.numpy()
self._gen_optimizer = self.ckpt.gen_optimizer
# re-assign iterations (global steps) for gen_optimizer.
self._gen_optimizer.iterations.assign(tf.cast(self.steps, tf.int64))
# re-assign iterations (global steps) for dis_optimizer.
try:
discriminator_train_start_steps = self.config[
"discriminator_train_start_steps"]
discriminator_train_start_steps = tf.math.maximum(
0, self.steps - discriminator_train_start_steps)
except Exception:
discriminator_train_start_steps = self.steps
self._dis_optimizer = self.ckpt.dis_optimizer
self._dis_optimizer.iterations.assign(
tf.cast(discriminator_train_start_steps, tf.int64))
# load weights.
utils.load_weights(
self._generator,
self.saved_path + "generator-{}.h5".format(self.steps))
utils.load_weights(
self._discriminator,
self.saved_path + "discriminator-{}.h5".format(self.steps))
def _check_train_finish(self):
"""Check training finished."""
if self.steps >= self.config["train_max_steps"]:
self.finish_train = True
if (self.steps != 0 and self.steps
== self.config["discriminator_train_start_steps"]):
self.finish_train = True
logging.info(
f"Finished training only generator at {self.steps}steps, pls resume and continue training."
)
def _check_log_interval(self):
"""Log to tensorboard."""
if self.steps % self.config["log_interval_steps"] == 0:
for metric_name in self.list_metrics_name:
logging.info(
f"(Step: {self.steps}) train_{metric_name} = {self.train_metrics[metric_name].result():.4f}."
)
self._write_to_tensorboard(self.train_metrics, stage="train")
# reset
self.reset_states_train()
def fit(self,
train_data_loader,
valid_data_loader,
saved_path,
resume=None):
self.set_train_data_loader(train_data_loader)
self.set_eval_data_loader(valid_data_loader)
self.train_data_loader = self._strategy.experimental_distribute_dataset(
self.train_data_loader)
self.eval_data_loader = self._strategy.experimental_distribute_dataset(
self.eval_data_loader)
with self._strategy.scope():
self.create_checkpoint_manager(saved_path=saved_path,
max_to_keep=10000)
if len(resume) > 1:
self.load_checkpoint(resume)
logging.info(f"Successfully resumed from {resume}.")
self.run()
