experiments/STT
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Remove dead code

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This commit is contained in:
Reuben Morais 2021-08-26 10:57:03 +02:00
parent 07ed417627
commit b62fa678e6
1 changed files with 21 additions and 63 deletions
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84
training/coqui_stt_training/train.py
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@ -240,45 +240,28 @@ def average_gradients(tower_gradients):
return average_grads
# Logging
# =======
def early_training_checks():
# Check for proper scorer early
if Config.scorer_path:
scorer = Scorer(
Config.lm_alpha, Config.lm_beta, Config.scorer_path, Config.alphabet
)
del scorer
def log_variable(variable, gradient=None):
r"""
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient.
"""
name = variable.name.replace(":", "_")
mean = tf.reduce_mean(input_tensor=variable)
tfv1.summary.scalar(name="%s/mean" % name, tensor=mean)
tfv1.summary.scalar(
name="%s/sttdev" % name,
tensor=tf.sqrt(tf.reduce_mean(input_tensor=tf.square(variable - mean))),
)
tfv1.summary.scalar(
name="%s/max" % name, tensor=tf.reduce_max(input_tensor=variable)
)
tfv1.summary.scalar(
name="%s/min" % name, tensor=tf.reduce_min(input_tensor=variable)
)
tfv1.summary.histogram(name=name, values=variable)
if gradient is not None:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
tfv1.summary.histogram(name="%s/gradients" % name, values=grad_values)
def log_grads_and_vars(grads_and_vars):
r"""
Let's also introduce a helper function for logging collections of gradient/variable tuples.
"""
for gradient, variable in grads_and_vars:
log_variable(variable, gradient=gradient)
if (
Config.train_files
and Config.test_files
and Config.load_checkpoint_dir != Config.save_checkpoint_dir
):
log_warn(
"WARNING: You specified different values for --load_checkpoint_dir "
"and --save_checkpoint_dir, but you are running training and testing "
"in a single invocation. The testing step will respect --load_checkpoint_dir, "
"and thus WILL NOT TEST THE CHECKPOINT CREATED BY THE TRAINING STEP. "
"Train and test in two separate invocations, specifying the correct "
"--load_checkpoint_dir in both cases, or use the same location "
"for loading and saving."
)
def train():
@ -387,7 +370,6 @@ def train():
# Average tower gradients across GPUs
avg_tower_gradients = average_gradients(gradients)
log_grads_and_vars(avg_tower_gradients)
# global_step is automagically incremented by the optimizer
global_step = tfv1.train.get_or_create_global_step()
@ -665,30 +647,6 @@ def train():
log_debug("Session closed.")
def early_training_checks():
# Check for proper scorer early
if Config.scorer_path:
scorer = Scorer(
Config.lm_alpha, Config.lm_beta, Config.scorer_path, Config.alphabet
)
del scorer
if (
Config.train_files
and Config.test_files
and Config.load_checkpoint_dir != Config.save_checkpoint_dir
):
log_warn(
"WARNING: You specified different values for --load_checkpoint_dir "
"and --save_checkpoint_dir, but you are running training and testing "
"in a single invocation. The testing step will respect --load_checkpoint_dir, "
"and thus WILL NOT TEST THE CHECKPOINT CREATED BY THE TRAINING STEP. "
"Train and test in two separate invocations, specifying the correct "
"--load_checkpoint_dir in both cases, or use the same location "
"for loading and saving."
)
def main():
initialize_globals_from_cli()