orbit/examples/single_task/single_task_evaluator.py
# Copyright 2024 The Orbit 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
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"""An evaluator object that can evaluate models with a single output."""
import orbit
import tensorflow as tf, tf_keras
class SingleTaskEvaluator(orbit.StandardEvaluator):
"""Evaluates a single-output model on a given dataset.
This evaluator will handle running a model with one output on a single
dataset, and will apply the output of that model to one or more
`tf_keras.metrics.Metric` objects.
"""
def __init__(self,
eval_dataset,
label_key,
model,
metrics,
evaluator_options=None):
"""Initializes a `SingleTaskEvaluator` instance.
If the `SingleTaskEvaluator` should run its model under a distribution
strategy, it should be created within that strategy's scope.
Arguments:
eval_dataset: A `tf.data.Dataset` or `DistributedDataset` that contains a
string-keyed dict of `Tensor`s.
label_key: The key corresponding to the label value in feature
dictionaries dequeued from `eval_dataset`. This key will be removed from
the dictionary before it is passed to the model.
model: A `tf.Module` or Keras `Model` object to evaluate.
metrics: A single `tf_keras.metrics.Metric` object, or a list of
`tf_keras.metrics.Metric` objects.
evaluator_options: An optional `orbit.StandardEvaluatorOptions` object.
"""
self.label_key = label_key
self.model = model
self.metrics = metrics if isinstance(metrics, list) else [metrics]
# Capture the strategy from the containing scope.
self.strategy = tf.distribute.get_strategy()
super(SingleTaskEvaluator, self).__init__(
eval_dataset=eval_dataset, options=evaluator_options)
def eval_begin(self):
"""Actions to take once before every eval loop."""
for metric in self.metrics:
metric.reset_states()
def eval_step(self, iterator):
"""One eval step. Called multiple times per eval loop by the superclass."""
def step_fn(inputs):
# Extract the target value and delete it from the input dict, so that
# the model never sees it.
target = inputs.pop(self.label_key)
output = self.model(inputs)
for metric in self.metrics:
metric.update_state(target, output)
# This is needed to handle distributed computation.
self.strategy.run(step_fn, args=(next(iterator),))
def eval_end(self):
"""Actions to take once after an eval loop."""
with self.strategy.scope():
# Export the metrics.
metrics = {metric.name: metric.result() for metric in self.metrics}
return metrics