Module keras.distribute.sidecar_evaluator
Python module for evaluation loop.
Expand source code
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Python module for evaluation loop."""
import tensorflow.compat.v2 as tf
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export # pylint: disable=g-direct-tensorflow-import
_PRINT_EVAL_STEP_EVERY_SEC = 60.0
_ITERATIONS_UNINITIALIZED = -1
def list_checkpoint_attributes(ckpt_dir_or_file):
"""Lists all the attributes in a checkpoint.
Checkpoint keys are paths in a checkpoint graph, and attribute is the first
element in the path. e.g. with a checkpoint key
"optimizer/iter/.ATTRIBUTES/VARIABLE_VALUE", optimizer is the attribute. The
attribute is also used to save/restore a variable in a checkpoint,
e.g. tf.train.Checkpoint(optimizer=optimizer, model=model).
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
Returns:
Set of attributes in a checkpoint.
"""
reader = tf.train.load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
return {name.split('/')[0] for name in variable_map.keys()}
@keras_export('keras.experimental.SidecarEvaluator', v1=[])
class SidecarEvaluator(object):
"""A class designed for a dedicated evaluator task.
`SidecarEvaluator` is expected to be run in a process on a separate machine
from the training cluster. It is meant for the purpose of a dedicated
evaluator, evaluating the metric results of a training cluster which has one
or more workers performing the training, and saving checkpoints.
The `SidecarEvaluator` API is compatible with both Custom Training Loop (CTL),
and Keras `Model.fit` to be used in the training cluster. Using the model
(with compiled metrics) provided at `__init__`, `SidecarEvaluator` repeatedly
performs evaluation "epochs" when it finds a checkpoint that has not yet been
used. Depending on the `steps` argument, an eval epoch is evaluation over all
eval data, or up to certain number of steps (batches). See examples below for
how the training program should save the checkpoints in order to be recognized
by `SidecarEvaluator`.
Since under the hood, `SidecarEvaluator` uses `model.evaluate` for evaluation,
it also supports arbitrary Keras callbacks. That is, if one or more callbacks
are provided, their `on_test_batch_begin` and `on_test_batch_end` methods are
called at the start and end of a batch, and their `on_test_begin` and
`on_test_end` are called at the start and end of an evaluation epoch. Note
that `SidecarEvaluator` may skip some checkpoints because it always picks up
the latest checkpoint available, and during an evaluation epoch, multiple
checkpoints can be produced from the training side.
Example:
```python
model = tf.keras.models.Sequential(...)
model.compile(metrics=tf.keras.metrics.SparseCategoricalAccuracy(
name="eval_metrics"))
data = tf.data.Dataset.from_tensor_slices(...)
SidecarEvaluator(
model=model,
data=data,
checkpoint_dir='/tmp/checkpoint_dir', # dir for training-saved checkpoint
steps=None, # Eval until dataset is exhausted
max_evaluations=None, # The evaluation needs to be stopped manually
callbacks=[tf.keras.callbacks.TensorBoard(log_dir='/tmp/log_dir')]
).start()
```
`SidecarEvaluator.start` writes a series of summary
files which can be visualized by tensorboard (which provides a webpage link):
```bash
$ tensorboard --logdir=/tmp/log_dir/validation
...
TensorBoard 2.4.0a0 at http://host:port (Press CTRL+C to quit)
```
If the training cluster uses a CTL, the `checkpoint_dir` should contain
checkpoints that track both `model` and `optimizer`, to fulfill
`SidecarEvaluator`'s expectation. This can be done by a
`tf.train.Checkpoint` and a `tf.train.CheckpointManager`:
```python
checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`.
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
checkpoint_manager = tf.train.CheckpointManager(
checkpoint, checkpoint_dir=..., max_to_keep=...)
checkpoint_manager.save()
```
If the training cluster uses Keras `Model.fit` API, a
`tf.keras.callbacks.ModelCheckpoint` should be used, with
`save_weights_only=True`, and the `filepath` should have 'ckpt-{epoch}'
appended:
```python
checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`.
model_checkpoint = tf.keras.callbacks.ModelCheckpoint(
filepath=os.path.join(checkpoint_dir, 'ckpt-{epoch}'),
save_weights_only=True)
model.fit(dataset, epochs, callbacks=[model_checkpoint])
```
"""
def __init__(self,
model,
data,
checkpoint_dir,
steps=None,
max_evaluations=None,
callbacks=None):
"""Initializes an `SidecarEvaluator` object.
Args:
model: Model to use for evaluation. The model object used here should be a
`tf.keras.Model`, and should be the same as the one that is used in
training, where `tf.keras.Model`s are checkpointed. The model should
have one or more metrics compiled before using `SidecarEvaluator`.
data: The input data for evaluation. `SidecarEvaluator` supports all data
types that Keras `model.evaluate` supports as the input data `x`, such
as a `tf.data.Dataset`.
checkpoint_dir: Directory where checkpoint files are saved.
steps: Number of steps to perform evaluation for, when evaluating a single
checkpoint file. If `None`, evaluation continues until the dataset is
exhausted. For repeated evaluation dataset, user must specify `steps` to
avoid infinite evaluation loop.
max_evaluations: Maximum number of the checkpoint file to be evaluated,
for `SidecarEvaluator` to know when to stop. The evaluator will stop
after it evaluates a checkpoint filepath ending with
'<ckpt_name>-<max_evaluations>'. If using
`tf.train.CheckpointManager.save` for saving checkpoints, the kth saved
checkpoint has the filepath suffix '<ckpt_name>-<k>' (k=1 for the first
saved), and if checkpoints are saved every epoch after training, the
filepath saved at the kth epoch would end with '<ckpt_name>-<k>. Thus,
if training runs for n epochs, and the evaluator should end after the
training finishes, use n for this parameter. Note that this is not
necessarily equal to the number of total evaluations, since some
checkpoints may be skipped if evaluation is slower than checkpoint
creation. If `None`, `SidecarEvaluator` will evaluate indefinitely, and
the user must terminate evaluator program themselves.
callbacks: List of `keras.callbacks.Callback` instances to apply during
evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks).
"""
self.model = model
self.data = data
self.checkpoint_dir = checkpoint_dir
self._iterations = tf.Variable(
name='iterations',
initial_value=_ITERATIONS_UNINITIALIZED,
dtype=tf.int64)
self.max_evaluations = max_evaluations
self.steps = steps
self.callbacks = callbacks or []
def start(self):
"""Starts the evaluation loop."""
optimizer_checkpoint = tf.train.Checkpoint(iter=self._iterations)
checkpoint = tf.train.Checkpoint(
model=self.model, optimizer=optimizer_checkpoint)
for latest_checkpoint in tf.train.checkpoints_iterator(
self.checkpoint_dir):
try:
# `expect_partial` because the checkpoint can have other `Trackable`s
# such as `optimizer`.
checkpoint.restore(latest_checkpoint).expect_partial()
checkpoint_attributes = list_checkpoint_attributes(latest_checkpoint)
# The checkpoint should contain model and optimizer for SidecarEvaluator
# to work. But the model weights saved by ModelCheckpoint callback does
# not contain model as an attribute. To make SidecarEvaluator compatibly
# work in this case, use model.load_weights to load the model's weights,
# while self._iterations is still restored by checkpoint variable.
if 'model' not in checkpoint_attributes:
self.model.load_weights(latest_checkpoint)
# The model checkpoint might not include optimizer in cases, e.g.
# using a custom training loop. Directly assign the iterations
# property to be used in callbacks.
if self.model.optimizer:
self.model.optimizer.iterations.assign(self._iterations)
except (tf.errors.OpError,) as e:
# A couple errors can happen here with the coordinator racing to write
# checkpoint:
# 1) OpError: open failed for <file path>: No such file or directory
# 2) NotFoundError (subclass of OpError): Unsuccessful
# TensorSliceReader constructor.
# TODO(rchao): Remove this except block once b/150954027 is resolved.
logging.info(
'SidecarEvaluator has an error loading '
'checkpoint: %s. Retrying. Error: %s: %s', latest_checkpoint,
e.__class__.__name__, e)
continue
if self._iterations.numpy() == _ITERATIONS_UNINITIALIZED:
raise RuntimeError(
'`iterations` cannot be loaded from the '
'checkpoint file. Please ensure `iterations` is '
'tracked in the `checkpoint` saved by the coordinator.')
logging.info(
'Evaluation starts: Model weights loaded from latest '
'checkpoint file: %s.', latest_checkpoint)
self.model.evaluate(
self.data, steps=self.steps, callbacks=self.callbacks, verbose=2)
return_metrics = {}
for metric in self.model.metrics:
result = metric.result()
if isinstance(result, dict):
return_metrics.update(result)
else:
return_metrics[metric.name] = result
logging.info(
'End of evaluation. Metrics: %s', ' '.join([
'{}={}'.format(name, value.numpy())
for name, value in return_metrics.items()
]))
# TODO(rchao): Make the max evaluation robust in case users save the
# checkpoints with epoch format {epoch:03d}.
if (self.max_evaluations and
latest_checkpoint.endswith('-{}'.format(self.max_evaluations))):
# Exit the loop because we have evaluated the final checkpoint file.
logging.info('Last checkpoint evaluated. SidecarEvaluator stops.')
return
Functions
def list_checkpoint_attributes(ckpt_dir_or_file)
-
Lists all the attributes in a checkpoint.
Checkpoint keys are paths in a checkpoint graph, and attribute is the first element in the path. e.g. with a checkpoint key "optimizer/iter/.ATTRIBUTES/VARIABLE_VALUE", optimizer is the attribute. The attribute is also used to save/restore a variable in a checkpoint, e.g. tf.train.Checkpoint(optimizer=optimizer, model=model).
Args
ckpt_dir_or_file
- Directory with checkpoints file or path to checkpoint.
Returns
Set of attributes in a checkpoint.
Expand source code
def list_checkpoint_attributes(ckpt_dir_or_file): """Lists all the attributes in a checkpoint. Checkpoint keys are paths in a checkpoint graph, and attribute is the first element in the path. e.g. with a checkpoint key "optimizer/iter/.ATTRIBUTES/VARIABLE_VALUE", optimizer is the attribute. The attribute is also used to save/restore a variable in a checkpoint, e.g. tf.train.Checkpoint(optimizer=optimizer, model=model). Args: ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint. Returns: Set of attributes in a checkpoint. """ reader = tf.train.load_checkpoint(ckpt_dir_or_file) variable_map = reader.get_variable_to_shape_map() return {name.split('/')[0] for name in variable_map.keys()}
Classes
class SidecarEvaluator (model, data, checkpoint_dir, steps=None, max_evaluations=None, callbacks=None)
-
A class designed for a dedicated evaluator task.
SidecarEvaluator
is expected to be run in a process on a separate machine from the training cluster. It is meant for the purpose of a dedicated evaluator, evaluating the metric results of a training cluster which has one or more workers performing the training, and saving checkpoints.The
SidecarEvaluator
API is compatible with both Custom Training Loop (CTL), and KerasModel.fit
to be used in the training cluster. Using the model (with compiled metrics) provided at__init__
,SidecarEvaluator
repeatedly performs evaluation "epochs" when it finds a checkpoint that has not yet been used. Depending on thesteps
argument, an eval epoch is evaluation over all eval data, or up to certain number of steps (batches). See examples below for how the training program should save the checkpoints in order to be recognized bySidecarEvaluator
.Since under the hood,
SidecarEvaluator
usesmodel.evaluate
for evaluation, it also supports arbitrary Keras callbacks. That is, if one or more callbacks are provided, theiron_test_batch_begin
andon_test_batch_end
methods are called at the start and end of a batch, and theiron_test_begin
andon_test_end
are called at the start and end of an evaluation epoch. Note thatSidecarEvaluator
may skip some checkpoints because it always picks up the latest checkpoint available, and during an evaluation epoch, multiple checkpoints can be produced from the training side.Example:
model = tf.keras.models.Sequential(...) model.compile(metrics=tf.keras.metrics.SparseCategoricalAccuracy( name="eval_metrics")) data = tf.data.Dataset.from_tensor_slices(...) SidecarEvaluator( model=model, data=data, checkpoint_dir='/tmp/checkpoint_dir', # dir for training-saved checkpoint steps=None, # Eval until dataset is exhausted max_evaluations=None, # The evaluation needs to be stopped manually callbacks=[tf.keras.callbacks.TensorBoard(log_dir='/tmp/log_dir')] ).start()
SidecarEvaluator.start()
writes a series of summary files which can be visualized by tensorboard (which provides a webpage link):$ tensorboard --logdir=/tmp/log_dir/validation ... TensorBoard 2.4.0a0 at http://host:port (Press CTRL+C to quit)
If the training cluster uses a CTL, the
checkpoint_dir
should contain checkpoints that track bothmodel
andoptimizer
, to fulfillSidecarEvaluator
's expectation. This can be done by atf.train.Checkpoint
and atf.train.CheckpointManager
:checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`. checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer) checkpoint_manager = tf.train.CheckpointManager( checkpoint, checkpoint_dir=..., max_to_keep=...) checkpoint_manager.save()
If the training cluster uses Keras
Model.fit
API, atf.keras.callbacks.ModelCheckpoint
should be used, withsave_weights_only=True
, and thefilepath
should have 'ckpt-{epoch}' appended:checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`. model_checkpoint = tf.keras.callbacks.ModelCheckpoint( filepath=os.path.join(checkpoint_dir, 'ckpt-{epoch}'), save_weights_only=True) model.fit(dataset, epochs, callbacks=[model_checkpoint])
Initializes an
SidecarEvaluator
object.Args
model
- Model to use for evaluation. The model object used here should be a
tf.keras.Model
, and should be the same as the one that is used in training, wheretf.keras.Model
s are checkpointed. The model should have one or more metrics compiled before usingSidecarEvaluator
. data
- The input data for evaluation.
SidecarEvaluator
supports all data types that Kerasmodel.evaluate
supports as the input datax
, such as atf.data.Dataset
. checkpoint_dir
- Directory where checkpoint files are saved.
steps
- Number of steps to perform evaluation for, when evaluating a single
checkpoint file. If
None
, evaluation continues until the dataset is exhausted. For repeated evaluation dataset, user must specifysteps
to avoid infinite evaluation loop. max_evaluations
- Maximum number of the checkpoint file to be evaluated,
for
SidecarEvaluator
to know when to stop. The evaluator will stop after it evaluates a checkpoint filepath ending with '- '. If using tf.train.CheckpointManager.save
for saving checkpoints, the kth saved checkpoint has the filepath suffix '- ' (k=1 for the first saved), and if checkpoints are saved every epoch after training, the filepath saved at the kth epoch would end with ' - . Thus, if training runs for n epochs, and the evaluator should end after the training finishes, use n for this parameter. Note that this is not necessarily equal to the number of total evaluations, since some checkpoints may be skipped if evaluation is slower than checkpoint creation. If None
,SidecarEvaluator
will evaluate indefinitely, and the user must terminate evaluator program themselves. callbacks
- List of
Callback
instances to apply during evaluation. See callbacks.
Expand source code
class SidecarEvaluator(object): """A class designed for a dedicated evaluator task. `SidecarEvaluator` is expected to be run in a process on a separate machine from the training cluster. It is meant for the purpose of a dedicated evaluator, evaluating the metric results of a training cluster which has one or more workers performing the training, and saving checkpoints. The `SidecarEvaluator` API is compatible with both Custom Training Loop (CTL), and Keras `Model.fit` to be used in the training cluster. Using the model (with compiled metrics) provided at `__init__`, `SidecarEvaluator` repeatedly performs evaluation "epochs" when it finds a checkpoint that has not yet been used. Depending on the `steps` argument, an eval epoch is evaluation over all eval data, or up to certain number of steps (batches). See examples below for how the training program should save the checkpoints in order to be recognized by `SidecarEvaluator`. Since under the hood, `SidecarEvaluator` uses `model.evaluate` for evaluation, it also supports arbitrary Keras callbacks. That is, if one or more callbacks are provided, their `on_test_batch_begin` and `on_test_batch_end` methods are called at the start and end of a batch, and their `on_test_begin` and `on_test_end` are called at the start and end of an evaluation epoch. Note that `SidecarEvaluator` may skip some checkpoints because it always picks up the latest checkpoint available, and during an evaluation epoch, multiple checkpoints can be produced from the training side. Example: ```python model = tf.keras.models.Sequential(...) model.compile(metrics=tf.keras.metrics.SparseCategoricalAccuracy( name="eval_metrics")) data = tf.data.Dataset.from_tensor_slices(...) SidecarEvaluator( model=model, data=data, checkpoint_dir='/tmp/checkpoint_dir', # dir for training-saved checkpoint steps=None, # Eval until dataset is exhausted max_evaluations=None, # The evaluation needs to be stopped manually callbacks=[tf.keras.callbacks.TensorBoard(log_dir='/tmp/log_dir')] ).start() ``` `SidecarEvaluator.start` writes a series of summary files which can be visualized by tensorboard (which provides a webpage link): ```bash $ tensorboard --logdir=/tmp/log_dir/validation ... TensorBoard 2.4.0a0 at http://host:port (Press CTRL+C to quit) ``` If the training cluster uses a CTL, the `checkpoint_dir` should contain checkpoints that track both `model` and `optimizer`, to fulfill `SidecarEvaluator`'s expectation. This can be done by a `tf.train.Checkpoint` and a `tf.train.CheckpointManager`: ```python checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`. checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer) checkpoint_manager = tf.train.CheckpointManager( checkpoint, checkpoint_dir=..., max_to_keep=...) checkpoint_manager.save() ``` If the training cluster uses Keras `Model.fit` API, a `tf.keras.callbacks.ModelCheckpoint` should be used, with `save_weights_only=True`, and the `filepath` should have 'ckpt-{epoch}' appended: ```python checkpoint_dir = ... # Same `checkpoint_dir` supplied to `SidecarEvaluator`. model_checkpoint = tf.keras.callbacks.ModelCheckpoint( filepath=os.path.join(checkpoint_dir, 'ckpt-{epoch}'), save_weights_only=True) model.fit(dataset, epochs, callbacks=[model_checkpoint]) ``` """ def __init__(self, model, data, checkpoint_dir, steps=None, max_evaluations=None, callbacks=None): """Initializes an `SidecarEvaluator` object. Args: model: Model to use for evaluation. The model object used here should be a `tf.keras.Model`, and should be the same as the one that is used in training, where `tf.keras.Model`s are checkpointed. The model should have one or more metrics compiled before using `SidecarEvaluator`. data: The input data for evaluation. `SidecarEvaluator` supports all data types that Keras `model.evaluate` supports as the input data `x`, such as a `tf.data.Dataset`. checkpoint_dir: Directory where checkpoint files are saved. steps: Number of steps to perform evaluation for, when evaluating a single checkpoint file. If `None`, evaluation continues until the dataset is exhausted. For repeated evaluation dataset, user must specify `steps` to avoid infinite evaluation loop. max_evaluations: Maximum number of the checkpoint file to be evaluated, for `SidecarEvaluator` to know when to stop. The evaluator will stop after it evaluates a checkpoint filepath ending with '<ckpt_name>-<max_evaluations>'. If using `tf.train.CheckpointManager.save` for saving checkpoints, the kth saved checkpoint has the filepath suffix '<ckpt_name>-<k>' (k=1 for the first saved), and if checkpoints are saved every epoch after training, the filepath saved at the kth epoch would end with '<ckpt_name>-<k>. Thus, if training runs for n epochs, and the evaluator should end after the training finishes, use n for this parameter. Note that this is not necessarily equal to the number of total evaluations, since some checkpoints may be skipped if evaluation is slower than checkpoint creation. If `None`, `SidecarEvaluator` will evaluate indefinitely, and the user must terminate evaluator program themselves. callbacks: List of `keras.callbacks.Callback` instances to apply during evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks). """ self.model = model self.data = data self.checkpoint_dir = checkpoint_dir self._iterations = tf.Variable( name='iterations', initial_value=_ITERATIONS_UNINITIALIZED, dtype=tf.int64) self.max_evaluations = max_evaluations self.steps = steps self.callbacks = callbacks or [] def start(self): """Starts the evaluation loop.""" optimizer_checkpoint = tf.train.Checkpoint(iter=self._iterations) checkpoint = tf.train.Checkpoint( model=self.model, optimizer=optimizer_checkpoint) for latest_checkpoint in tf.train.checkpoints_iterator( self.checkpoint_dir): try: # `expect_partial` because the checkpoint can have other `Trackable`s # such as `optimizer`. checkpoint.restore(latest_checkpoint).expect_partial() checkpoint_attributes = list_checkpoint_attributes(latest_checkpoint) # The checkpoint should contain model and optimizer for SidecarEvaluator # to work. But the model weights saved by ModelCheckpoint callback does # not contain model as an attribute. To make SidecarEvaluator compatibly # work in this case, use model.load_weights to load the model's weights, # while self._iterations is still restored by checkpoint variable. if 'model' not in checkpoint_attributes: self.model.load_weights(latest_checkpoint) # The model checkpoint might not include optimizer in cases, e.g. # using a custom training loop. Directly assign the iterations # property to be used in callbacks. if self.model.optimizer: self.model.optimizer.iterations.assign(self._iterations) except (tf.errors.OpError,) as e: # A couple errors can happen here with the coordinator racing to write # checkpoint: # 1) OpError: open failed for <file path>: No such file or directory # 2) NotFoundError (subclass of OpError): Unsuccessful # TensorSliceReader constructor. # TODO(rchao): Remove this except block once b/150954027 is resolved. logging.info( 'SidecarEvaluator has an error loading ' 'checkpoint: %s. Retrying. Error: %s: %s', latest_checkpoint, e.__class__.__name__, e) continue if self._iterations.numpy() == _ITERATIONS_UNINITIALIZED: raise RuntimeError( '`iterations` cannot be loaded from the ' 'checkpoint file. Please ensure `iterations` is ' 'tracked in the `checkpoint` saved by the coordinator.') logging.info( 'Evaluation starts: Model weights loaded from latest ' 'checkpoint file: %s.', latest_checkpoint) self.model.evaluate( self.data, steps=self.steps, callbacks=self.callbacks, verbose=2) return_metrics = {} for metric in self.model.metrics: result = metric.result() if isinstance(result, dict): return_metrics.update(result) else: return_metrics[metric.name] = result logging.info( 'End of evaluation. Metrics: %s', ' '.join([ '{}={}'.format(name, value.numpy()) for name, value in return_metrics.items() ])) # TODO(rchao): Make the max evaluation robust in case users save the # checkpoints with epoch format {epoch:03d}. if (self.max_evaluations and latest_checkpoint.endswith('-{}'.format(self.max_evaluations))): # Exit the loop because we have evaluated the final checkpoint file. logging.info('Last checkpoint evaluated. SidecarEvaluator stops.') return
Methods
def start(self)
-
Starts the evaluation loop.
Expand source code
def start(self): """Starts the evaluation loop.""" optimizer_checkpoint = tf.train.Checkpoint(iter=self._iterations) checkpoint = tf.train.Checkpoint( model=self.model, optimizer=optimizer_checkpoint) for latest_checkpoint in tf.train.checkpoints_iterator( self.checkpoint_dir): try: # `expect_partial` because the checkpoint can have other `Trackable`s # such as `optimizer`. checkpoint.restore(latest_checkpoint).expect_partial() checkpoint_attributes = list_checkpoint_attributes(latest_checkpoint) # The checkpoint should contain model and optimizer for SidecarEvaluator # to work. But the model weights saved by ModelCheckpoint callback does # not contain model as an attribute. To make SidecarEvaluator compatibly # work in this case, use model.load_weights to load the model's weights, # while self._iterations is still restored by checkpoint variable. if 'model' not in checkpoint_attributes: self.model.load_weights(latest_checkpoint) # The model checkpoint might not include optimizer in cases, e.g. # using a custom training loop. Directly assign the iterations # property to be used in callbacks. if self.model.optimizer: self.model.optimizer.iterations.assign(self._iterations) except (tf.errors.OpError,) as e: # A couple errors can happen here with the coordinator racing to write # checkpoint: # 1) OpError: open failed for <file path>: No such file or directory # 2) NotFoundError (subclass of OpError): Unsuccessful # TensorSliceReader constructor. # TODO(rchao): Remove this except block once b/150954027 is resolved. logging.info( 'SidecarEvaluator has an error loading ' 'checkpoint: %s. Retrying. Error: %s: %s', latest_checkpoint, e.__class__.__name__, e) continue if self._iterations.numpy() == _ITERATIONS_UNINITIALIZED: raise RuntimeError( '`iterations` cannot be loaded from the ' 'checkpoint file. Please ensure `iterations` is ' 'tracked in the `checkpoint` saved by the coordinator.') logging.info( 'Evaluation starts: Model weights loaded from latest ' 'checkpoint file: %s.', latest_checkpoint) self.model.evaluate( self.data, steps=self.steps, callbacks=self.callbacks, verbose=2) return_metrics = {} for metric in self.model.metrics: result = metric.result() if isinstance(result, dict): return_metrics.update(result) else: return_metrics[metric.name] = result logging.info( 'End of evaluation. Metrics: %s', ' '.join([ '{}={}'.format(name, value.numpy()) for name, value in return_metrics.items() ])) # TODO(rchao): Make the max evaluation robust in case users save the # checkpoints with epoch format {epoch:03d}. if (self.max_evaluations and latest_checkpoint.endswith('-{}'.format(self.max_evaluations))): # Exit the loop because we have evaluated the final checkpoint file. logging.info('Last checkpoint evaluated. SidecarEvaluator stops.') return