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# Copyright 2023 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.
"""An example task definition for image classification."""
from typing import Any, List, Optional, Tuple, Sequence, Mapping
import tensorflow as tf, tf_keras
from official.common import dataset_fn
from official.core import base_task
from official.core import task_factory
from official.modeling import tf_utils
from official.vision.dataloaders import input_reader_factory
from official.vision.examples.starter import example_config as exp_cfg
from official.vision.examples.starter import example_input
from official.vision.examples.starter import example_model
@task_factory.register_task_cls(exp_cfg.ExampleTask)
class ExampleTask(base_task.Task):
"""Class of an example task.
A task is a subclass of base_task.Task that defines model, input, loss, metric
and one training and evaluation step, etc.
"""
def build_model(self) -> tf_keras.Model:
"""Builds a model."""
input_specs = tf_keras.layers.InputSpec(shape=[None] +
self.task_config.model.input_size)
model = example_model.build_example_model(
input_specs=input_specs, model_config=self.task_config.model)
return model
def build_inputs(
self,
params: exp_cfg.ExampleDataConfig,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Builds input.
The input from this function is a tf.data.Dataset that has gone through
pre-processing steps, such as augmentation, batching, shuffling, etc.
Args:
params: The experiment config.
input_context: An optional InputContext used by input reader.
Returns:
A tf.data.Dataset object.
"""
num_classes = self.task_config.model.num_classes
input_size = self.task_config.model.input_size
decoder = example_input.Decoder()
parser = example_input.Parser(
output_size=input_size[:2], num_classes=num_classes)
reader = input_reader_factory.input_reader_generator(
params,
dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def build_losses(self,
labels: tf.Tensor,
model_outputs: tf.Tensor,
aux_losses: Optional[Any] = None) -> tf.Tensor:
"""Builds losses for training and validation.
Args:
labels: Input groundt-ruth labels.
model_outputs: Output of the model.
aux_losses: The auxiliarly loss tensors, i.e. `losses` in tf_keras.Model.
Returns:
The total loss tensor.
"""
total_loss = tf_keras.losses.sparse_categorical_crossentropy(
labels, model_outputs, from_logits=True)
total_loss = tf_utils.safe_mean(total_loss)
if aux_losses:
total_loss += tf.add_n(aux_losses)
return total_loss
def build_metrics(self,
training: bool = True) -> Sequence[tf_keras.metrics.Metric]:
"""Gets streaming metrics for training/validation.
This function builds and returns a list of metrics to compute during
training and validation. The list contains objects of subclasses of
tf_keras.metrics.Metric. Training and validation can have different metrics.
Args:
training: Whether the metric is for training or not.
Returns:
A list of tf_keras.metrics.Metric objects.
"""
k = self.task_config.evaluation.top_k
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
tf_keras.metrics.SparseTopKCategoricalAccuracy(
k=k, name='top_{}_accuracy'.format(k))
]
return metrics
def train_step(self,
inputs: Tuple[Any, Any],
model: tf_keras.Model,
optimizer: tf_keras.optimizers.Optimizer,
metrics: Optional[List[Any]] = None) -> Mapping[str, Any]:
"""Does forward and backward.
This example assumes input is a tuple of (features, labels), which follows
the output from data loader, i.e., Parser. The output from Parser is fed
into train_step to perform one step forward and backward pass. Other data
structure, such as dictionary, can also be used, as long as it is consistent
between output from Parser and input used here.
Args:
inputs: A tuple of input tensors of (features, labels).
model: A tf_keras.Model instance.
optimizer: The optimizer for this training step.
metrics: A nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
features, labels = inputs
num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
with tf.GradientTape() as tape:
outputs = model(features, training=True)
# Casting output layer as float32 is necessary when mixed_precision is
# mixed_float16 or mixed_bfloat16 to ensure output is casted as float32.
outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
# Computes per-replica loss.
loss = self.build_losses(
model_outputs=outputs, labels=labels, aux_losses=model.losses)
# Scales loss as the default gradients allreduce performs sum inside the
# optimizer.
scaled_loss = loss / num_replicas
# For mixed_precision policy, when LossScaleOptimizer is used, loss is
# scaled for numerical stability.
if isinstance(optimizer, tf_keras.mixed_precision.LossScaleOptimizer):
scaled_loss = optimizer.get_scaled_loss(scaled_loss)
tvars = model.trainable_variables
grads = tape.gradient(scaled_loss, tvars)
# Scales back gradient before apply_gradients when LossScaleOptimizer is
# used.
if isinstance(optimizer, tf_keras.mixed_precision.LossScaleOptimizer):
grads = optimizer.get_unscaled_gradients(grads)
optimizer.apply_gradients(list(zip(grads, tvars)))
logs = {self.loss: loss}
if metrics:
self.process_metrics(metrics, labels, outputs)
return logs
def validation_step(self,
inputs: Tuple[Any, Any],
model: tf_keras.Model,
metrics: Optional[List[Any]] = None) -> Mapping[str, Any]:
"""Runs validation step.
Args:
inputs: A tuple of input tensors of (features, labels).
model: A tf_keras.Model instance.
metrics: A nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
features, labels = inputs
outputs = self.inference_step(features, model)
outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
loss = self.build_losses(
model_outputs=outputs, labels=labels, aux_losses=model.losses)
logs = {self.loss: loss}
if metrics:
self.process_metrics(metrics, labels, outputs)
return logs
def inference_step(self, inputs: tf.Tensor, model: tf_keras.Model) -> Any:
"""Performs the forward step. It is used in 'validation_step'."""
return model(inputs, training=False)
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