def run_latest_checkpoints(self):
"""Evaluation function for evaluating all the existing checkpoints.
This function just runs through all the existing checkpoints.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Load the latest checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config, self.model_name)
ckpt_indices = np.asarray(self.eval_config.ckpt_indices)
if ckpt_indices is not None:
if ckpt_indices[0] == -1:
# Restore the most recent checkpoint
ckpt_idx = num_checkpoints - 1
ckpt_indices = [ckpt_idx]
print(ckpt_idx, num_checkpoints, ckpt_indices)
for ckpt_idx in ckpt_indices:
checkpoint_to_restore = self._saver.last_checkpoints[ckpt_idx]
self.run_checkpoint_once(checkpoint_to_restore)
else:
last_checkpoint_id = -1
number_of_evaluations = 0
# go through all existing checkpoints
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
already_evaluated = ckpt_id in already_evaluated_ckpts
if already_evaluated or ckpt_id <= last_checkpoint_id:
number_of_evaluations = max(
(ckpt_idx + 1, number_of_evaluations))
continue
self.run_checkpoint_once(checkpoint_to_restore)
number_of_evaluations += 1
# Save the id of the latest evaluated checkpoint
last_checkpoint_id = ckpt_id
def repeated_checkpoint_run(self):
"""Periodically evaluates the checkpoints inside the `checkpoint_dir`.
This function evaluates all the existing checkpoints as they are being
generated. If there are none, it sleeps until new checkpoints become
available. Since there is no synchronization guarantee for the trainer
and evaluator, at each iteration it reloads all the checkpoints and
searches for the last checkpoint to continue from. This is meant to be
called in parallel to the trainer to evaluate the models regularly.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Copy kitti native eval code into the predictions folder
if self.do_kitti_native_eval:
evaluator_utils.copy_kitti_native_code(
self.model_config.checkpoint_name)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config, self.full_model)
tf.logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
last_checkpoint_id = -1
number_of_evaluations = 0
while True:
# Load current checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
start = time.time()
if number_of_evaluations >= num_checkpoints:
tf.logging.info(
'No new checkpoints found in %s.'
'Will try again in %d seconds', self.checkpoint_dir,
self.eval_wait_interval)
else:
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = \
self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
already_evaluated = ckpt_id in already_evaluated_ckpts
if already_evaluated or ckpt_id <= last_checkpoint_id:
number_of_evaluations = max(
(ckpt_idx + 1, number_of_evaluations))
continue
self.run_checkpoint_once(checkpoint_to_restore)
number_of_evaluations += 1
# Save the id of the latest evaluated checkpoint
last_checkpoint_id = ckpt_id
time_to_next_eval = start + self.eval_wait_interval - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
def repeated_checkpoint_run(self):
"""Periodically evaluates the checkpoints inside the `checkpoint_dir`.
This function evaluates all the existing checkpoints as they are being
generated. If there are none, it sleeps until new checkpoints become
available. Since there is no synchronization guarantee for the trainer
and evaluator, at each iteration it reloads all the checkpoints and
searches for the last checkpoint to continue from. This is meant to be
called in parallel to the trainer to evaluate the models regularly.
Raises:
ValueError: if model.checkpoint_dir doesn't have at least one
element.
"""
if not os.path.exists(self.checkpoint_dir):
raise ValueError(
'{} must have at least one checkpoint entry.'.format(
self.checkpoint_dir))
# Copy kitti native eval code into the predictions folder
if self.do_kitti_native_eval:
evaluator_utils.copy_kitti_native_code(
self.model_config.checkpoint_name)
if self.skip_evaluated_checkpoints:
already_evaluated_ckpts = self.get_evaluated_ckpts(
self.model_config)
else:
already_evaluated_ckpts = []
tf.logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
last_checkpoint_id = -1
number_of_evaluations = 0
#Dont have to add summary(for model inference at each sample) at repeated evaluation..
#only care avg loss at each ckpt step.
#self.summary_merged = None
evaluated_ckpts = [ckpt for ckpt in already_evaluated_ckpts]
while True:
# Load current checkpoints available
trainer_utils.load_checkpoints(self.checkpoint_dir, self._saver)
num_checkpoints = len(self._saver.last_checkpoints)
no_newckpts = True
evaluated_ckpts.sort()
start = time.time()
for ckpt_idx in range(num_checkpoints):
checkpoint_to_restore = \
self._saver.last_checkpoints[ckpt_idx]
ckpt_id = evaluator_utils.strip_checkpoint_id(
checkpoint_to_restore)
# Check if checkpoint has been evaluated already
if ckpt_id == 0 or ckpt_id in evaluated_ckpts:
continue
else:
no_newckpts = False
print('evaluated ckpts: ', evaluated_ckpts)
print('processing ckpt id: ', ckpt_id)
self.run_checkpoint_once(checkpoint_to_restore)
evaluated_ckpts.append(ckpt_id)
time_to_next_eval = start + self.eval_wait_interval - time.time()
if no_newckpts:
tf.logging.info(
'No new checkpoints found in %s.'
'Will try again in %d seconds', self.checkpoint_dir,
self.eval_wait_interval)
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
