update TensorFlow2x test method

TensorFlow2x/ComputeVision/Classification/models-master/official/core/base_task.py

+# Copyright 2021 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.
+
+"""Defines the base task abstraction."""
+import abc
+from typing import Optional
+
+from absl import logging
+import tensorflow as tf
+
+from official.core import config_definitions
+from official.modeling import optimization
+from official.modeling import performance
+
+OptimizationConfig = optimization.OptimizationConfig
+RuntimeConfig = config_definitions.RuntimeConfig
+
+
+class Task(tf.Module, metaclass=abc.ABCMeta):
+  """A single-replica view of training procedure.
+
+  Tasks provide artifacts for training/validation procedures, including
+  loading/iterating over Datasets, training/validation steps, calculating the
+  loss and customized metrics with reduction.
+  """
+
+  # Special keys in train/validate step returned logs.
+  loss = "loss"
+
+  def __init__(self,
+               params,
+               logging_dir: Optional[str] = None,
+               name: Optional[str] = None):
+    """Task initialization.
+
+    Args:
+      params: the task configuration instance, which can be any of dataclass,
+        ConfigDict, namedtuple, etc.
+      logging_dir: a string pointing to where the model, summaries etc. will be
+        saved. You can also write additional stuff in this directory.
+      name: the task name.
+    """
+    super().__init__(name=name)
+    self._task_config = params
+    self._logging_dir = logging_dir
+
+  @property
+  def task_config(self):
+    return self._task_config
+
+  @property
+  def logging_dir(self) -> str:
+    return self._logging_dir
+
+  @classmethod
+  def create_optimizer(cls, optimizer_config: OptimizationConfig,
+                       runtime_config: Optional[RuntimeConfig] = None):
+    """Creates an TF optimizer from configurations.
+
+    Args:
+      optimizer_config: the parameters of the Optimization settings.
+      runtime_config: the parameters of the runtime.
+
+    Returns:
+      A tf.optimizers.Optimizer object.
+    """
+    opt_factory = optimization.OptimizerFactory(optimizer_config)
+    optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
+    # Configuring optimizer when loss_scale is set in runtime config. This helps
+    # avoiding overflow/underflow for float16 computations.
+    if runtime_config:
+      optimizer = performance.configure_optimizer(
+          optimizer,
+          use_float16=runtime_config.mixed_precision_dtype == "float16",
+          loss_scale=runtime_config.loss_scale)
+
+    return optimizer
+
+  def initialize(self, model: tf.keras.Model):
+    """[Optional] A callback function used as CheckpointManager's init_fn.
+
+    This function will be called when no checkpoint is found for the model.
+    If there is a checkpoint, the checkpoint will be loaded and this function
+    will not be called. You can use this callback function to load a pretrained
+    checkpoint, saved under a directory other than the model_dir.
+
+    Args:
+      model: The keras.Model built or used by this task.
+    """
+    ckpt_dir_or_file = self.task_config.init_checkpoint
+    logging.info("Trying to load pretrained checkpoint from %s",
+                 ckpt_dir_or_file)
+    if tf.io.gfile.isdir(ckpt_dir_or_file):
+      ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
+    if not ckpt_dir_or_file:
+      return
+
+    if hasattr(model, "checkpoint_items"):
+      checkpoint_items = model.checkpoint_items
+    else:
+      checkpoint_items = dict(model=model)
+    ckpt = tf.train.Checkpoint(**checkpoint_items)
+    status = ckpt.read(ckpt_dir_or_file)
+    status.expect_partial().assert_existing_objects_matched()
+    logging.info("Finished loading pretrained checkpoint from %s",
+                 ckpt_dir_or_file)
+
+  def build_model(self) -> tf.keras.Model:
+    """[Optional] Creates model architecture.
+
+    Returns:
+      A model instance.
+    """  # pytype: disable=bad-return-type  # typed-keras
+
+  @abc.abstractmethod
+  def build_inputs(self,
+                   params,
+                   input_context: Optional[tf.distribute.InputContext] = None):
+    """Returns a dataset or a nested structure of dataset functions.
+
+    Dataset functions define per-host datasets with the per-replica batch size.
+    With distributed training, this method runs on remote hosts.
+
+    Args:
+      params: hyperparams to create input pipelines, which can be any of
+        dataclass, ConfigDict, namedtuple, etc.
+      input_context: optional distribution input pipeline context.
+
+    Returns:
+      A nested structure of per-replica input functions.
+    """
+
+  def build_losses(self, labels, model_outputs, aux_losses=None) -> tf.Tensor:
+    """Standard interface to compute losses.
+
+    Args:
+      labels: optional label tensors.
+      model_outputs: a nested structure of output tensors.
+      aux_losses: auxiliary loss tensors, i.e. `losses` in keras.Model.
+
+    Returns:
+      The total loss tensor.
+    """
+    del model_outputs, labels
+
+    if aux_losses is None:
+      losses = [tf.constant(0.0, dtype=tf.float32)]
+    else:
+      losses = aux_losses
+    total_loss = tf.add_n(losses)
+    return total_loss
+
+  def build_metrics(self, training: bool = True):
+    """Gets streaming metrics for training/validation."""
+    del training
+    return []
+
+  def process_metrics(self, metrics, labels, model_outputs, **kwargs):
+    """Process and update metrics.
+
+    Called when using custom training loop API.
+
+    Args:
+      metrics: a nested structure of metrics objects. The return of function
+        self.build_metrics.
+      labels: a tensor or a nested structure of tensors.
+      model_outputs: a tensor or a nested structure of tensors. For example,
+        output of the keras model built by self.build_model.
+      **kwargs: other args.
+    """
+    for metric in metrics:
+      metric.update_state(labels, model_outputs)
+
+  def process_compiled_metrics(self, compiled_metrics, labels, model_outputs):
+    """Process and update compiled_metrics.
+
+    call when using compile/fit API.
+
+    Args:
+      compiled_metrics: the compiled metrics (model.compiled_metrics).
+      labels: a tensor or a nested structure of tensors.
+      model_outputs: a tensor or a nested structure of tensors. For example,
+        output of the keras model built by self.build_model.
+    """
+    compiled_metrics.update_state(labels, model_outputs)
+
+  def train_step(self,
+                 inputs,
+                 model: tf.keras.Model,
+                 optimizer: tf.keras.optimizers.Optimizer,
+                 metrics=None):
+    """Does forward and backward.
+
+    With distribution strategies, this method runs on devices.
+
+    Args:
+      inputs: a dictionary of input tensors.
+      model: the model, forward pass definition.
+      optimizer: the optimizer for this training step.
+      metrics: a nested structure of metrics objects.
+
+    Returns:
+      A dictionary of logs.
+    """
+    if isinstance(inputs, tuple) and len(inputs) == 2:
+      features, labels = inputs
+    else:
+      features, labels = inputs, inputs
+    with tf.GradientTape() as tape:
+      outputs = model(features, training=True)
+      # Computes per-replica loss.
+      if model.compiled_loss:
+        loss = model.compiled_loss(
+            labels, outputs, regularization_losses=model.losses)
+        loss += self.build_losses(
+            labels=labels, model_outputs=outputs, aux_losses=None)
+      else:
+        loss = self.build_losses(
+            labels=labels, model_outputs=outputs, aux_losses=model.losses)
+      # Scales loss as the default gradients allreduce performs sum inside the
+      # optimizer.
+      scaled_loss = loss / tf.distribute.get_strategy().num_replicas_in_sync
+
+      # For mixed precision, when a LossScaleOptimizer is used, the loss is
+      # scaled to avoid numeric underflow.
+      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)
+
+    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)
+    if model.compiled_metrics:
+      self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
+      logs.update({m.name: m.result() for m in metrics or []})
+      logs.update({m.name: m.result() for m in model.metrics})
+    return logs
+
+  def validation_step(self, inputs, model: tf.keras.Model, metrics=None):
+    """Validation step.
+
+    With distribution strategies, this method runs on devices.
+
+    Args:
+      inputs: a dictionary of input tensors.
+      model: the keras.Model.
+      metrics: a nested structure of metrics objects.
+
+    Returns:
+      A dictionary of logs.
+    """
+    if isinstance(inputs, tuple) and len(inputs) == 2:
+      features, labels = inputs
+    else:
+      features, labels = inputs, inputs
+    outputs = self.inference_step(features, model)
+    loss = self.build_losses(
+        labels=labels, model_outputs=outputs, aux_losses=model.losses)
+    logs = {self.loss: loss}
+    if metrics:
+      self.process_metrics(metrics, labels, outputs)
+    if model.compiled_metrics:
+      self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
+      logs.update({m.name: m.result() for m in metrics or []})
+      logs.update({m.name: m.result() for m in model.metrics})
+    return logs
+
+  def inference_step(self, inputs, model: tf.keras.Model):
+    """Performs the forward step.
+
+    With distribution strategies, this method runs on devices.
+
+    Args:
+      inputs: a dictionary of input tensors.
+      model: the keras.Model.
+
+    Returns:
+      Model outputs.
+    """
+    return model(inputs, training=False)
+
+  def aggregate_logs(self, state, step_logs):
+    """Optional aggregation over logs returned from a validation step.
+
+    Given step_logs from a validation step, this function aggregates the logs
+    after each eval_step() (see eval_reduce() function in
+    official/core/base_trainer.py). It runs on CPU and can be used to aggregate
+    metrics during validation, when there are too many metrics that cannot fit
+    into TPU memory. Note that this may increase latency due to data transfer
+    between TPU and CPU. Also, the step output from a validation step may be a
+    tuple with elements from replicas, and a concatenation of the elements is
+    needed in such case.
+
+    Args:
+      state: The current state of training, for example, it can be a sequence of
+        metrics.
+      step_logs: Logs from a validation step. Can be a dictionary.
+    """
+    pass
+
+  def reduce_aggregated_logs(self,
+                             aggregated_logs,
+                             global_step: Optional[tf.Tensor] = None):
+    """Optional reduce of aggregated logs over validation steps.
+
+    This function reduces aggregated logs at the end of validation, and can be
+    used to compute the final metrics. It runs on CPU and in each eval_end() in
+    base trainer (see eval_end() function in official/core/base_trainer.py).
+
+    Args:
+      aggregated_logs: Aggregated logs over multiple validation steps.
+      global_step: An optional variable of global step.
+
+    Returns:
+      A dictionary of reduced results.
+    """
+    return {}

TensorFlow2x/ComputeVision/Classification/models-master/official/core/base_trainer.py

+# Copyright 2021 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.
+
+"""Standard Trainer implementation.
+
+The base trainer implements the Orbit `StandardTrainable` and
+`StandardEvaluable` interfaces. Trainers inside this project should be
+interchangable and independent on model architectures and tasks.
+"""
+import functools
+from typing import Union, Optional
+from absl import logging
+import gin
+import orbit
+import tensorflow as tf
+
+from official.core import base_task
+from official.core import config_definitions
+from official.modeling import optimization
+
+ExperimentConfig = config_definitions.ExperimentConfig
+TrainerConfig = config_definitions.TrainerConfig
+
+
+class Recovery:
+  """Built-in model blowup recovery module.
+
+  Checks the loss value by the given threshold. If applicable, recover the
+  model by reading the checkpoint on disk.
+  """
+
+  def __init__(self,
+               loss_upper_bound: float,
+               checkpoint_manager: tf.train.CheckpointManager,
+               recovery_begin_steps: int = 0,
+               recovery_max_trials: int = 3):
+    self.recover_counter = 0
+    self.recovery_begin_steps = recovery_begin_steps
+    self.recovery_max_trials = recovery_max_trials
+    self.loss_upper_bound = loss_upper_bound
+    self.checkpoint_manager = checkpoint_manager
+
+  def should_recover(self, loss_value, global_step):
+    if tf.math.is_nan(loss_value):
+      return True
+    if (global_step >= self.recovery_begin_steps and
+        loss_value > self.loss_upper_bound):
+      return True
+    return False
+
+  def maybe_recover(self, loss_value, global_step):
+    """Conditionally recovers the training by triggering checkpoint restoration.
+
+    Args:
+      loss_value: the loss value as a float.
+      global_step: the number of global training steps.
+
+    Raises:
+      RuntimeError: when recovery happens more than the max number of trials,
+      the job should crash.
+    """
+    if not self.should_recover(loss_value, global_step):
+      return
+    self.recover_counter += 1
+    if self.recover_counter > self.recovery_max_trials:
+      raise RuntimeError(
+          "The loss value is NaN or out of range after training loop and "
+          f"this happens {self.recover_counter} times.")
+    # Loads the previous good checkpoint.
+    checkpoint_path = self.checkpoint_manager.restore_or_initialize()
+    logging.warning(
+        "Recovering the model from checkpoint: %s. The loss value becomes "
+        "%f at step %d.", checkpoint_path, loss_value, global_step)
+
+
+class _AsyncTrainer(orbit.StandardTrainer, orbit.StandardEvaluator):
+  """Trainer class for both sync and async Strategy."""
+
+  def init_async(self):
+    """Initializes the Async Trainer base class."""
+    assert isinstance(self._strategy, tf.distribute.Strategy)
+    self._is_async = isinstance(
+        self._strategy, tf.distribute.experimental.ParameterServerStrategy)
+    self._coordinator = None
+    if self._is_async:
+      self._coordinator = (
+          tf.distribute.experimental.coordinator.ClusterCoordinator(
+              self._strategy))
+
+  def join(self):
+    """Join all async steps. Only useful in aysnc training."""
+    if getattr(self, "_is_async", False):
+      self._coordinator.join()
+
+  def create_train_loop_fn(self):
+    """Creates a eval loop from the given step function and options."""
+    train_loop_fn = super().create_train_loop_fn()
+    if getattr(self, "_is_async", False):
+
+      def _async_loop_fn(iterator, num_steps):
+        self._coordinator.schedule(train_loop_fn, args=(iterator, num_steps))
+
+      return _async_loop_fn
+    else:
+      return train_loop_fn
+
+  def create_eval_loop_fn(self, has_state: bool):
+    """Creates a training loop from the given step function and options."""
+    eval_loop_fn = super().create_eval_loop_fn(has_state)
+
+    if getattr(self, "_is_async", False):
+      if has_state:
+        raise ValueError(
+            "Stateful eval loop is not supported in async training.")
+
+      def _async_loop_fn(iterator, num_steps, state=None, reduce_fn=None):
+        assert state is None
+        assert reduce_fn is None
+        self._coordinator.schedule(eval_loop_fn, args=(iterator, num_steps))
+
+      return _async_loop_fn
+    else:
+      return eval_loop_fn
+
+  def distribute_dataset(self, dataset_or_fn, *args, **kwargs):
+    """A utility function to help create a `tf.distribute.DistributedDataset`.
+
+    Args:
+      dataset_or_fn: A instance of `tf.data.Dataset`, or a "dataset function"
+        returning a `tf.data.Dataset`. If it is a function, it may optionally
+        have an argument named `input_context` which will be passed a
+        `tf.distribute.InputContext` instance.
+      *args: Any positional arguments to pass through to `dataset_or_fn`.
+      **kwargs: Any keyword arguments to pass through to `dataset_or_fn`.
+
+    Returns:
+      A distributed Dataset.
+    """
+    if getattr(self, "_is_async", False):
+      per_worker_dataset_fn = functools.partial(
+          orbit.utils.make_distributed_dataset, self._strategy, dataset_or_fn,
+          *args, **kwargs)
+      per_worker_dataset_fn = tf.function(per_worker_dataset_fn)
+
+      return self._coordinator.create_per_worker_dataset(per_worker_dataset_fn)
+    else:
+      return orbit.utils.make_distributed_dataset(self._strategy, dataset_or_fn,
+                                                  *args, **kwargs)
+
+
+def get_runtime_options(config: ExperimentConfig):
+  """Get tf.distribute.RunOptions from config."""
+  xla_options = {}
+  if config.runtime.tpu_enable_xla_dynamic_padder is not None:
+    xla_options["enable_xla_dynamic_padder"] = (
+        config.runtime.tpu_enable_xla_dynamic_padder)
+  return tf.distribute.RunOptions(
+      experimental_xla_options=tf.tpu.XLAOptions(**xla_options))
+
+
+@gin.configurable
+class Trainer(_AsyncTrainer):
+  """Implements the common trainer shared for TensorFlow models."""
+
+  # pylint: disable=super-init-not-called
+  def __init__(
+      self,
+      config: ExperimentConfig,
+      task: base_task.Task,
+      model: tf.keras.Model,
+      optimizer: tf.optimizers.Optimizer,
+      train: bool = True,
+      evaluate: bool = True,
+      train_dataset: Optional[Union[tf.data.Dataset,
+                                    tf.distribute.DistributedDataset]] = None,
+      validation_dataset: Optional[Union[
+          tf.data.Dataset, tf.distribute.DistributedDataset]] = None,
+      checkpoint_exporter=None):
+    """Initialize common trainer for TensorFlow models.
+
+    Args:
+      config: An `ExperimentConfig` instance specifying experiment config.
+      task: A base_task.Task instance.
+      model: The model instance, e.g. a tf.keras.Model instance.
+      optimizer: tf.optimizers.Optimizer instance.
+      train: bool, whether or not this trainer will be used for training.
+        default to True.
+      evaluate: bool, whether or not this trainer will be used for evaluation.
+        default to True.
+      train_dataset: a dataset object created for training. With tf.distribute,
+        it needs to be a `DistributedDataset`.
+      validation_dataset: a dataset object created for evaluation. With
+        tf.distribute, it needs to be a `DistributedDataset`. The evaluator will
+        create a dataset iterator for each eval round, so the dataset does not
+        need to repeat.
+      checkpoint_exporter: an object that has the `maybe_export_checkpoint`
+        interface.
+    """
+    # Gets the current distribution strategy. If not inside any strategy scope,
+    # it gets a single-replica no-op strategy.
+    self._strategy = tf.distribute.get_strategy()
+    self._validate_params(
+        config,
+        check_train_data=train_dataset is None,
+        check_validation_data=validation_dataset is None)
+    self._config = config
+    self._task = task
+    self._model = model
+    self._optimizer = optimizer
+    self._checkpoint_exporter = checkpoint_exporter
+    self._recovery = None
+    # Runtime options are only applied to train_step.
+    # We use default for eval_step.
+    self._runtime_options = get_runtime_options(config)
+
+    # Creates a shadow copy of the weights to store weights moving average.
+    if isinstance(self._optimizer, optimization.ExponentialMovingAverage
+                 ) and not self._optimizer.has_shadow_copy:
+      self._optimizer.shadow_copy(self._model)
+
+    # global_step increases by 1 after each training iteration.
+    # We should have global_step.numpy() == self.optimizer.iterations.numpy()
+    # when there is only 1 optimizer.
+    self._global_step = orbit.utils.create_global_step()
+    if hasattr(self.model, "checkpoint_items"):
+      checkpoint_items = self.model.checkpoint_items
+    else:
+      checkpoint_items = {}
+    self._checkpoint = tf.train.Checkpoint(
+        global_step=self.global_step,
+        model=self.model,
+        optimizer=self.optimizer,
+        **checkpoint_items)
+
+    self._train_loss = tf.keras.metrics.Mean("training_loss", dtype=tf.float32)
+    self._validation_loss = tf.keras.metrics.Mean(
+        "validation_loss", dtype=tf.float32)
+    model_metrics = model.metrics if hasattr(model, "metrics") else []
+    self._train_metrics = self.task.build_metrics(
+        training=True) + model_metrics
+    self._validation_metrics = self.task.build_metrics(
+        training=False) + model_metrics
+
+    self.init_async()
+
+    if train:
+      train_dataset = train_dataset or self.distribute_dataset(
+          self.task.build_inputs, self.config.task.train_data)
+      orbit.StandardTrainer.__init__(
+          self,
+          train_dataset,
+          options=orbit.StandardTrainerOptions(
+              use_tf_while_loop=config.trainer.train_tf_while_loop,
+              use_tf_function=config.trainer.train_tf_function,
+              use_tpu_summary_optimization=config.trainer.allow_tpu_summary))
+
+    if evaluate:
+      validation_dataset = validation_dataset or self.distribute_dataset(
+          self.task.build_inputs, self.config.task.validation_data)
+      orbit.StandardEvaluator.__init__(
+          self,
+          validation_dataset,
+          options=orbit.StandardEvaluatorOptions(
+              use_tf_function=config.trainer.eval_tf_function,
+              use_tf_while_loop=config.trainer.eval_tf_while_loop))
+
+  def _validate_params(self,
+                       config,
+                       check_train_data=True,
+                       check_validation_data=True):
+    r"""Validates if the configuration object passed to the Trainer.
+
+    The experiment configuration should be structured as:
+    \trainer
+    \task
+      \train_data
+      \validation_data
+
+    Args:
+      config: a namedtuple, dataclass, ConfigDict, etc.
+      check_train_data: whether to check task.train_data field.
+      check_validation_data: whether to check task.validation_data field.
+    """
+    if not hasattr(config, "trainer"):
+      raise AttributeError("The trainer requires the configuration contains an"
+                           " attribute `trainer`.")
+
+    if not hasattr(config, "task"):
+      raise AttributeError("The trainer requires the configuration contains an"
+                           " attribute `task`.")
+
+    if check_train_data and not hasattr(config.task, "train_data"):
+      raise AttributeError("The trainer requires the configuration contains an"
+                           " attribute `task.train_data`.")
+
+    if check_validation_data and not hasattr(config.task, "validation_data"):
+      raise AttributeError("The trainer requires the configuration contains an"
+                           " attribute `task.validation_data`.")
+
+  @property
+  def strategy(self):
+    return self._strategy
+
+  @property
+  def config(self):
+    return self._config
+
+  @property
+  def task(self):
+    return self._task
+
+  @property
+  def model(self):
+    return self._model
+
+  @property
+  def optimizer(self):
+    if hasattr(self, "_optimizer"):
+      return self._optimizer
+    else:
+      return None
+
+  @property
+  def global_step(self):
+    return self._global_step
+
+  @property
+  def train_loss(self):
+    """Accesses the training loss metric object."""
+    return self._train_loss
+
+  @property
+  def validation_loss(self):
+    """Accesses the validation loss metric object."""
+    return self._validation_loss
+
+  @property
+  def train_metrics(self):
+    """Accesses all training metric objects."""
+    return self._train_metrics
+
+  @property
+  def validation_metrics(self):
+    """Accesses all validation metric metric objects."""
+    return self._validation_metrics
+
+  def initialize(self):
+    """A callback function.
+
+    This function will be called when no checkpoint found for the model.
+    If there is a checkpoint, the checkpoint will be loaded and this function
+    will not be called. Tasks may use this callback function to load a
+    pretrained checkpoint, saved under a directory other than the model_dir.
+    """
+    self.task.initialize(self.model)
+
+  @property
+  def checkpoint(self):
+    """Accesses the training checkpoint."""
+    return self._checkpoint
+
+  # TODO(yejiayu): Remove this once all deps are fixed.
+  def add_recovery(self, params: TrainerConfig,
+                   checkpoint_manager: tf.train.CheckpointManager):
+    if params.recovery_max_trials >= 0:
+      self._recovery = Recovery(
+          loss_upper_bound=params.loss_upper_bound,
+          recovery_begin_steps=params.recovery_begin_steps,
+          recovery_max_trials=params.recovery_max_trials,
+          checkpoint_manager=checkpoint_manager)
+
+  def train_loop_end(self):
+    """See base class."""
+    self.join()
+    logs = {}
+    for metric in self.train_metrics + [self.train_loss]:
+      logs[metric.name] = metric.result()
+      metric.reset_states()
+    if callable(self.optimizer.learning_rate):
+      # Maybe a self-implemented optimizer does not have `optimizer.iterations`.
+      # So just to be safe here.
+      if hasattr(self.optimizer, "iterations"):
+        logs["learning_rate"] = self.optimizer.learning_rate(
+            self.optimizer.iterations)
+      else:
+        logs["learning_rate"] = self.optimizer.learning_rate(self.global_step)
+    else:
+      logs["learning_rate"] = self.optimizer.learning_rate
+    return logs
+
+  def train_step(self, iterator):
+    """See base class."""
+
+    def step_fn(inputs):
+      if self.config.runtime.enable_xla and (self.config.runtime.num_gpus > 0):
+        task_train_step = tf.function(self.task.train_step, jit_compile=True)
+      else:
+        task_train_step = self.task.train_step
+      logs = task_train_step(
+          inputs,
+          model=self.model,
+          optimizer=self.optimizer,
+          metrics=self.train_metrics)
+      self._train_loss.update_state(logs[self.task.loss])
+      self.global_step.assign_add(1)
+
+    self.strategy.run(
+        step_fn, args=(next(iterator),), options=self._runtime_options)
+
+  def eval_begin(self):
+    """Sets up metrics."""
+    for metric in self.validation_metrics + [self.validation_loss]:
+      metric.reset_states()
+    # Swaps weights to test on weights moving average.
+    if self.optimizer and isinstance(self.optimizer,
+                                     optimization.ExponentialMovingAverage):
+      self.optimizer.swap_weights()
+
+  def eval_step(self, iterator):
+    """See base class."""
+
+    def step_fn(inputs):
+      logs = self.task.validation_step(
+          inputs, model=self.model, metrics=self.validation_metrics)
+      if self.task.loss in logs:
+        self._validation_loss.update_state(logs[self.task.loss])
+      return logs
+
+    distributed_outputs = self.strategy.run(step_fn, args=(next(iterator),))
+    return tf.nest.map_structure(self.strategy.experimental_local_results,
+                                 distributed_outputs)
+
+  def eval_end(self, aggregated_logs=None):
+    """Processes evaluation results."""
+    self.join()
+    logs = {}
+    for metric in self.validation_metrics:
+      logs[metric.name] = metric.result()
+    if self.validation_loss.count.numpy() != 0:
+      logs[self.validation_loss.name] = self.validation_loss.result()
+    else:
+      # `self.validation_loss` metric was not updated, because the validation
+      # loss was not returned from the task's `validation_step` method.
+      logging.info("The task did not report validation loss.")
+    if aggregated_logs:
+      metrics = self.task.reduce_aggregated_logs(
+          aggregated_logs, global_step=self.global_step)
+      logs.update(metrics)
+
+    if self._checkpoint_exporter:
+      self._checkpoint_exporter.maybe_export_checkpoint(
+          self.checkpoint, logs, self.global_step.numpy())
+      metric_name = self.config.trainer.best_checkpoint_eval_metric
+      logs["best_" +
+           metric_name] = self._checkpoint_exporter.best_ckpt_logs[metric_name]
+
+    # Swaps back weights after testing when EMA is used.
+    # This happens after best checkpoint export so that average weights used for
+    # eval are exported instead of regular weights.
+    if self.optimizer and isinstance(self.optimizer,
+                                     optimization.ExponentialMovingAverage):
+      self.optimizer.swap_weights()
+    return logs
+
+  def eval_reduce(self, state=None, step_outputs=None):
+    return self.task.aggregate_logs(state, step_outputs)

TensorFlow2x/ComputeVision/Classification/models-master/official/core/base_trainer_test.py

+# Copyright 2021 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.
+
+"""Tests for tensorflow_models.core.trainers.trainer."""
+# pylint: disable=g-direct-tensorflow-import
+import gc
+import multiprocessing
+import os
+import sys
+
+from absl.testing import parameterized
+import orbit
+import portpicker
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import strategy_combinations
+from official.core import base_trainer as trainer_lib
+from official.core import config_definitions as cfg
+from official.core import train_lib
+from official.utils.testing import mock_task
+
+TPU_TEST = 'test_tpu' in sys.argv[0]
+GPU_TEST = 'test_gpu' in sys.argv[0]
+
+
+def all_strategy_combinations():
+  return combinations.combine(
+      distribution=[
+          strategy_combinations.default_strategy,
+          strategy_combinations.cloud_tpu_strategy,
+          strategy_combinations.one_device_strategy_gpu,
+      ],)
+
+
+def create_in_process_cluster(num_workers, num_ps):
+  """Creates and starts local servers and returns the cluster_resolver."""
+  worker_ports = [portpicker.pick_unused_port() for _ in range(num_workers)]
+  ps_ports = [portpicker.pick_unused_port() for _ in range(num_ps)]
+
+  cluster_dict = {}
+  cluster_dict['worker'] = ['localhost:%s' % port for port in worker_ports]
+  if num_ps > 0:
+    cluster_dict['ps'] = ['localhost:%s' % port for port in ps_ports]
+
+  cluster_spec = tf.train.ClusterSpec(cluster_dict)
+
+  # Workers need some inter_ops threads to work properly.
+  worker_config = tf.compat.v1.ConfigProto()
+  if multiprocessing.cpu_count() < num_workers + 1:
+    worker_config.inter_op_parallelism_threads = num_workers + 1
+
+  for i in range(num_workers):
+    tf.distribute.Server(
+        cluster_spec,
+        job_name='worker',
+        task_index=i,
+        config=worker_config,
+        protocol='grpc')
+
+  for i in range(num_ps):
+    tf.distribute.Server(
+        cluster_spec, job_name='ps', task_index=i, protocol='grpc')
+
+  cluster_resolver = tf.distribute.cluster_resolver.SimpleClusterResolver(
+      cluster_spec, rpc_layer='grpc')
+  return cluster_resolver
+
+
+def dataset_fn(input_context=None):
+  del input_context
+
+  def dummy_data(_):
+    return tf.zeros((1, 1), dtype=tf.float32)
+
+  dataset = tf.data.Dataset.range(1)
+  dataset = dataset.repeat()
+  dataset = dataset.map(
+      dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+  return dataset
+
+
+class MockAsyncTrainer(trainer_lib._AsyncTrainer):
+  """Mock AsyncTrainer to test the _AsyncTrainer class."""
+
+  def __init__(self):
+    self._strategy = tf.distribute.get_strategy()
+    self.init_async()
+
+    self.global_step = tf.Variable(
+        0,
+        dtype=tf.int64,
+        name='global_step',
+        trainable=False,
+        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
+    self.eval_global_step = tf.Variable(
+        0,
+        dtype=tf.int64,
+        name='eval_global_step',
+        trainable=False,
+        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
+
+    train_dataset = self.distribute_dataset(dataset_fn)
+    orbit.StandardTrainer.__init__(
+        self, train_dataset, options=orbit.StandardTrainerOptions())
+
+    validation_dataset = self.distribute_dataset(dataset_fn)
+    orbit.StandardEvaluator.__init__(
+        self,
+        validation_dataset,
+        options=orbit.StandardEvaluatorOptions(use_tf_while_loop=True))
+
+  def train_loop_begin(self):
+    self.global_step.assign(0)
+
+  def train_step(self, iterator):
+
+    def replica_step(_):
+      self.global_step.assign_add(1)
+
+    self._strategy.run(replica_step, args=(next(iterator),))
+
+  def train_loop_end(self):
+    self.join()
+    return self.global_step.numpy()
+
+  def eval_begin(self):
+    self.eval_global_step.assign(0)
+
+  def eval_step(self, iterator):
+
+    def replica_step(_):
+      self.eval_global_step.assign_add(1)
+
+    self._strategy.run(replica_step, args=(next(iterator),))
+
+  def eval_end(self):
+    self.join()
+    return self.eval_global_step.numpy()
+
+
+class RecoveryTest(tf.test.TestCase):
+
+  def test_recovery_module(self):
+    ckpt = tf.train.Checkpoint(v=tf.Variable(1, dtype=tf.int32))
+    model_dir = self.get_temp_dir()
+    manager = tf.train.CheckpointManager(ckpt, model_dir, max_to_keep=1)
+    recovery_module = trainer_lib.Recovery(
+        loss_upper_bound=1.0,
+        checkpoint_manager=manager,
+        recovery_begin_steps=1,
+        recovery_max_trials=1)
+    self.assertFalse(recovery_module.should_recover(1.1, 0))
+    self.assertFalse(recovery_module.should_recover(0.1, 1))
+    self.assertTrue(recovery_module.should_recover(1.1, 2))
+
+    # First triggers the recovery once.
+    recovery_module.maybe_recover(1.1, 10)
+
+    # Second time, it raises.
+    with self.assertRaisesRegex(
+        RuntimeError, 'The loss value is NaN .*'):
+      recovery_module.maybe_recover(1.1, 10)
+
+
+class TrainerTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self._config = cfg.ExperimentConfig(
+        trainer=cfg.TrainerConfig(
+            optimizer_config=cfg.OptimizationConfig({
+                'optimizer': {
+                    'type': 'sgd'
+                },
+                'learning_rate': {
+                    'type': 'constant'
+                }
+            })))
+
+  def tearDown(self):
+    gc.collect()
+    # This will only contain uncollectable garbage, i.e. reference cycles
+    # involving objects with __del__ defined.
+    self.assertEmpty(gc.garbage)
+    super().tearDown()
+
+  def create_test_trainer(self, config, model_dir=None, task=None):
+    task = task or mock_task.MockTask(config.task, logging_dir=model_dir)
+    ckpt_exporter = train_lib.maybe_create_best_ckpt_exporter(config, model_dir)
+    trainer = trainer_lib.Trainer(
+        config,
+        task,
+        model=task.build_model(),
+        optimizer=task.create_optimizer(config.trainer.optimizer_config,
+                                        config.runtime),
+        checkpoint_exporter=ckpt_exporter)
+    return trainer
+
+  @combinations.generate(all_strategy_combinations())
+  def test_trainer_train(self, distribution):
+    with distribution.scope():
+      trainer = self.create_test_trainer(self._config)
+      logs = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
+      self.assertIn('training_loss', logs)
+      self.assertIn('learning_rate', logs)
+
+  @combinations.generate(all_strategy_combinations())
+  def test_trainer_passing_datasets(self, distribution):
+    with distribution.scope():
+      task = mock_task.MockTask(self._config)
+      train_dataset = orbit.utils.make_distributed_dataset(
+          distribution, task.build_inputs, self._config.task.train_data)
+      validation_dataset = orbit.utils.make_distributed_dataset(
+          distribution, task.build_inputs, self._config.task.validation_data)
+      self._config.task.train_data = None
+      self._config.task.validation_data = None
+      trainer = trainer_lib.Trainer(
+          self._config,
+          task,
+          model=task.build_model(),
+          optimizer=task.create_optimizer(self._config.trainer.optimizer_config,
+                                          self._config.runtime),
+          train_dataset=train_dataset,
+          validation_dataset=validation_dataset)
+    logs = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
+    self.assertIn('training_loss', logs)
+    self.assertIn('learning_rate', logs)
+    logs = trainer.evaluate(tf.convert_to_tensor(5, dtype=tf.int32))
+    self.assertIn('validation_loss', logs)
+
+  def test_base_async_trainer(self):
+    if TPU_TEST or GPU_TEST:
+      self.skipTest('Aysnc training is not available on GPU/GPU.')
+    num_workers = 3
+    num_ps = 2
+    cluster_resolver = create_in_process_cluster(num_workers, num_ps)
+    distribution = tf.distribute.experimental.ParameterServerStrategy(
+        cluster_resolver)
+    with distribution.scope():
+      trainer = MockAsyncTrainer()
+      trainer.init_async()
+      self.assertIsInstance(
+          trainer._coordinator,
+          tf.distribute.experimental.coordinator.ClusterCoordinator)
+      self.assertEqual(trainer.train(tf.constant(10)), 10)
+      self.assertEqual(trainer.evaluate(tf.constant(11)), 11)
+
+  def test_async_trainer_train(self):
+    if TPU_TEST or GPU_TEST:
+      self.skipTest('Aysnc training is not available on GPU/TPU.')
+    num_workers = 3
+    num_ps = 2
+    cluster_resolver = create_in_process_cluster(num_workers, num_ps)
+    distribution = tf.distribute.experimental.ParameterServerStrategy(
+        cluster_resolver)
+    with distribution.scope():
+      config = cfg.ExperimentConfig(**self._config.as_dict())
+      config.trainer.eval_tf_while_loop = True
+      trainer = self.create_test_trainer(config)
+      logs = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
+      self.assertIn('training_loss', logs)
+      self.assertIn('learning_rate', logs)
+
+  def test_async_trainer_validate(self):
+    if TPU_TEST or GPU_TEST:
+      self.skipTest('Aysnc training is not available on GPU/GPU.')
+    num_workers = 3
+    num_ps = 2
+    cluster_resolver = create_in_process_cluster(num_workers, num_ps)
+    distribution = tf.distribute.experimental.ParameterServerStrategy(
+        cluster_resolver)
+    with distribution.scope():
+      config = cfg.ExperimentConfig(**self._config.as_dict())
+      config.trainer.eval_tf_while_loop = True
+      trainer = self.create_test_trainer(config)
+      logs = trainer.evaluate(tf.convert_to_tensor(5, dtype=tf.int32))
+      self.assertIn('acc', logs)
+      self.assertIn('validation_loss', logs)
+
+  @combinations.generate(all_strategy_combinations())
+  def test_trainer_validate(self, distribution):
+    with distribution.scope():
+      trainer = self.create_test_trainer(self._config)
+      logs = trainer.evaluate(tf.convert_to_tensor(5, dtype=tf.int32))
+      self.assertEqual(logs['counter'], 5. * distribution.num_replicas_in_sync)
+      self.assertIn('validation_loss', logs)
+
+  @combinations.generate(all_strategy_combinations())
+  def test_trainer_validate_without_loss(self, distribution):
+
+    class MockTaskWithoutValidationLoss(mock_task.MockTask):
+
+      def validation_step(self, inputs, model, metrics=None):
+        # Disable validation loss.
+        logs = super().validation_step(inputs, model)
+        del logs[self.loss]
+        return logs
+
+    with distribution.scope():
+      task = MockTaskWithoutValidationLoss()
+      trainer = self.create_test_trainer(self._config, task=task)
+      logs = trainer.evaluate(tf.convert_to_tensor(5, dtype=tf.int32))
+      self.assertEqual(logs['counter'], 5. * distribution.num_replicas_in_sync)
+      self.assertNotIn('validation_loss', logs)
+
+  @combinations.generate(
+      combinations.combine(
+          mixed_precision_dtype=['float32', 'bfloat16', 'float16'],
+          loss_scale=[None, 'dynamic', 128, 256],
+      ))
+  def test_configure_optimizer(self, mixed_precision_dtype, loss_scale):
+    config = cfg.ExperimentConfig(
+        runtime=cfg.RuntimeConfig(
+            mixed_precision_dtype=mixed_precision_dtype, loss_scale=loss_scale),
+        trainer=cfg.TrainerConfig(
+            optimizer_config=cfg.OptimizationConfig({
+                'optimizer': {
+                    'type': 'sgd'
+                },
+                'learning_rate': {
+                    'type': 'constant'
+                },
+            })))
+    trainer = self.create_test_trainer(config)
+    if mixed_precision_dtype == 'float16':
+      self.assertIsInstance(trainer.optimizer,
+                            tf.keras.mixed_precision.LossScaleOptimizer)
+      if loss_scale in (None, 'dynamic'):
+        self.assertTrue(trainer.optimizer.dynamic)
+      else:
+        self.assertFalse(trainer.optimizer.dynamic)
+        self.assertEqual(trainer.optimizer.initial_scale, loss_scale)
+    else:
+      self.assertIsInstance(trainer.optimizer, tf.keras.optimizers.SGD)
+
+    metrics = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
+    self.assertIn('training_loss', metrics)
+
+  def test_export_best_ckpt(self):
+    config = cfg.ExperimentConfig(
+        trainer=cfg.TrainerConfig(
+            best_checkpoint_export_subdir='best_ckpt',
+            best_checkpoint_eval_metric='acc',
+            optimizer_config=cfg.OptimizationConfig({
+                'optimizer': {
+                    'type': 'sgd'
+                },
+                'learning_rate': {
+                    'type': 'constant'
+                }
+            })))
+    model_dir = self.get_temp_dir()
+    trainer = self.create_test_trainer(config, model_dir=model_dir)
+    trainer.train(tf.convert_to_tensor(1, dtype=tf.int32))
+    trainer.evaluate(tf.convert_to_tensor(1, dtype=tf.int32))
+    self.assertTrue(
+        tf.io.gfile.exists(os.path.join(model_dir, 'best_ckpt', 'info.json')))
+
+  def test_model_with_compiled_loss(self):
+    task = mock_task.MockTask()
+    model = task.build_model()
+    model.compile(loss=tf.keras.losses.CategoricalCrossentropy())
+    trainer = trainer_lib.Trainer(
+        self._config,
+        task,
+        model=model,
+        optimizer=task.create_optimizer(self._config.trainer.optimizer_config))
+    logs = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
+    self.assertIn('training_loss', logs)
+
+
+if __name__ == '__main__':
+  tf.test.main()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/config_definitions.py

+# Copyright 2021 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.
+
+"""Common configuration settings."""
+
+import dataclasses
+from typing import Optional, Sequence, Union
+
+from official.modeling.hyperparams import base_config
+from official.modeling.optimization.configs import optimization_config
+
+OptimizationConfig = optimization_config.OptimizationConfig
+
+
+@dataclasses.dataclass
+class DataConfig(base_config.Config):
+  """The base configuration for building datasets.
+
+  Attributes:
+    input_path: The path to the input. It can be either (1) a str indicating a
+      file path/pattern, or (2) a str indicating multiple file paths/patterns
+      separated by comma (e.g "a, b, c" or no spaces "a,b,c"), or (3) a list of
+      str, each of which is a file path/pattern or multiple file paths/patterns
+      separated by comma, or (4) a dictionary of the previous three approaches
+      for more advanced data mixing using named access. It should not be
+      specified when the following `tfds_name` is specified.
+    tfds_name: The name of the tensorflow dataset (TFDS). It should not be
+      specified when the above `input_path` is specified.
+    tfds_split: A str indicating which split of the data to load from TFDS. It
+      is required when above `tfds_name` is specified.
+    global_batch_size: The global batch size across all replicas.
+    is_training: Whether this data is used for training or not. This flag is
+      useful for consumers of this object to determine whether the data should
+      be repeated or shuffled.
+    drop_remainder: Whether the last batch should be dropped in the case it has
+      fewer than `global_batch_size` elements.
+    shuffle_buffer_size: The buffer size used for shuffling training data.
+    cache: Whether to cache dataset examples. If `True`, we will cache the
+      dataset after applying the decode_fn and parse_fn. It can be used to avoid
+      re-reading from disk, re-decoding and re-parsing the example on the second
+      epoch, but it requires significant memory overhead.
+    cycle_length: The number of files that will be processed concurrently when
+      interleaving files.
+    block_length: The number of consecutive elements to produce from each input
+      element before cycling to another input element when interleaving files.
+    deterministic: A boolean controlling whether determinism should be enforced.
+    sharding: Whether sharding is used in the input pipeline.
+    enable_tf_data_service: A boolean indicating whether to enable tf.data
+      service for the input pipeline.
+    tf_data_service_address: The URI of a tf.data service to offload
+      preprocessing onto during training. The URI should be in the format
+      "protocol://address", e.g. "grpc://tf-data-service:5050". It can be
+        overridden by `FLAGS.tf_data_service` flag in the binary.
+    tf_data_service_job_name: The name of the tf.data service job. This argument
+      makes it possible for multiple datasets to share the same job. The default
+      behavior is that the dataset creates anonymous, exclusively owned jobs.
+    tfds_data_dir: A str specifying the directory to read/write TFDS data.
+    tfds_as_supervised: A bool. When loading dataset from TFDS, if True, the
+      returned tf.data.Dataset will have a 2-tuple structure (input, label)
+      according to builder.info.supervised_keys; if False, the default, the
+      returned tf.data.Dataset will have a dictionary with all the features.
+    tfds_skip_decoding_feature: A str to indicate which features are skipped for
+      decoding when loading dataset from TFDS. Use comma to separate multiple
+      features. The main use case is to skip the image/video decoding for better
+      performance.
+    seed: An optional seed to use for deterministic shuffling/preprocessing.
+  """
+  input_path: Union[Sequence[str], str, base_config.Config] = ""
+  tfds_name: str = ""
+  tfds_split: str = ""
+  global_batch_size: int = 0
+  is_training: bool = None
+  drop_remainder: bool = True
+  shuffle_buffer_size: int = 100
+  cache: bool = False
+  cycle_length: Optional[int] = None
+  block_length: int = 1
+  deterministic: Optional[bool] = None
+  sharding: bool = True
+  enable_tf_data_service: bool = False
+  tf_data_service_address: Optional[str] = None
+  tf_data_service_job_name: Optional[str] = None
+  tfds_data_dir: str = ""
+  tfds_as_supervised: bool = False
+  tfds_skip_decoding_feature: str = ""
+  seed: Optional[int] = None
+
+
+@dataclasses.dataclass
+class RuntimeConfig(base_config.Config):
+  """High-level configurations for Runtime.
+
+  These include parameters that are not directly related to the experiment,
+  e.g. directories, accelerator type, etc.
+
+  Attributes:
+    distribution_strategy: e.g. 'mirrored', 'tpu', etc.
+    enable_xla: Whether or not to enable XLA.
+    per_gpu_thread_count: thread count per GPU.
+    gpu_thread_mode: Whether and how the GPU device uses its own threadpool.
+    dataset_num_private_threads: Number of threads for a private threadpool
+      created for all datasets computation.
+    tpu: The address of the TPU to use, if any.
+    num_gpus: The number of GPUs to use, if any.
+    worker_hosts: comma-separated list of worker ip:port pairs for running
+      multi-worker models with DistributionStrategy.
+    task_index: If multi-worker training, the task index of this worker.
+    all_reduce_alg: Defines the algorithm for performing all-reduce.
+    num_packs: Sets `num_packs` in the cross device ops used in
+      MirroredStrategy.  For details, see tf.distribute.NcclAllReduce.
+    mixed_precision_dtype: dtype of mixed precision policy. It can be 'float32',
+      'float16', or 'bfloat16'.
+    loss_scale: The type of loss scale, or 'float' value. This is used when
+      setting the mixed precision policy.
+    run_eagerly: Whether or not to run the experiment eagerly.
+    batchnorm_spatial_persistent: Whether or not to enable the spatial
+      persistent mode for CuDNN batch norm kernel for improved GPU performance.
+  """
+  distribution_strategy: str = "mirrored"
+  enable_xla: bool = False
+  gpu_thread_mode: Optional[str] = None
+  dataset_num_private_threads: Optional[int] = None
+  per_gpu_thread_count: int = 0
+  tpu: Optional[str] = None
+  num_gpus: int = 0
+  worker_hosts: Optional[str] = None
+  task_index: int = -1
+  all_reduce_alg: Optional[str] = None
+  num_packs: int = 1
+  mixed_precision_dtype: Optional[str] = None
+  loss_scale: Optional[Union[str, float]] = None
+  run_eagerly: bool = False
+  batchnorm_spatial_persistent: bool = False
+
+  # XLA runtime params.
+  # XLA params are only applied to the train_step.
+  # These augments can improve training speed. They can also improve eval, but
+  # may reduce usability and users would need to make changes to code.
+
+  # Whether to enable XLA dynamic padder
+  # infrastructure to handle dynamic shapes inputs inside XLA. True by
+  # default. Disabling this may cause correctness issues with dynamic shapes
+  # inputs, as XLA will just assume the inputs are with padded shapes. However
+  # users can optionally set it to False to improve device time if masking is
+  # already handled in the user side.
+  # If None, will respect XLA default.
+  tpu_enable_xla_dynamic_padder: Optional[bool] = None
+
+  # Global model parallelism configurations.
+  num_cores_per_replica: int = 1
+  default_shard_dim: int = -1
+
+  def model_parallelism(self):
+    return dict(
+        num_cores_per_replica=self.num_cores_per_replica,
+        default_shard_dim=self.default_shard_dim)
+
+
+@dataclasses.dataclass
+class TrainerConfig(base_config.Config):
+  """Configuration for trainer.
+
+  Attributes:
+    optimizer_config: optimizer config, it includes optimizer, learning rate,
+      and warmup schedule configs.
+    train_tf_while_loop: whether or not to use tf while loop.
+    train_tf_function: whether or not to use tf_function for training loop.
+    eval_tf_function: whether or not to use tf_function for eval.
+    allow_tpu_summary: Whether to allow summary happen inside the XLA program
+      runs on TPU through automatic outside compilation.
+    steps_per_loop: number of steps per loop to report training metrics. This
+      can also be used to reduce host worker communication in a TPU setup.
+    summary_interval: number of steps between each summary.
+    checkpoint_interval: number of steps between checkpoints.
+    max_to_keep: max checkpoints to keep.
+    continuous_eval_timeout: maximum number of seconds to wait between
+      checkpoints, if set to None, continuous eval will wait indefinitely. This
+      is only used continuous_train_and_eval and continuous_eval modes. Default
+      value is 1 hrs.
+    train_steps: number of train steps.
+    validation_steps: number of eval steps. If `None`, the entire eval dataset
+      is used.
+    validation_interval: number of training steps to run between evaluations.
+    best_checkpoint_export_subdir: if set, the trainer will keep track of the
+      best evaluation metric, and export the corresponding best checkpoint under
+      `model_dir/best_checkpoint_export_subdir`. Note that this only works if
+      mode contains eval (such as `train_and_eval`, `continuous_eval`, and
+      `continuous_train_and_eval`).
+    best_checkpoint_eval_metric: for exporting the best checkpoint, which
+      evaluation metric the trainer should monitor. This can be any evaluation
+      metric appears on tensorboard.
+    best_checkpoint_metric_comp: for exporting the best checkpoint, how the
+      trainer should compare the evaluation metrics. This can be either `higher`
+      (higher the better) or `lower` (lower the better).
+    validation_summary_subdir: A 'str', sub directory for saving eval summary.
+  """
+  optimizer_config: OptimizationConfig = OptimizationConfig()
+  # Orbit settings.
+  train_tf_while_loop: bool = True
+  train_tf_function: bool = True
+  eval_tf_function: bool = True
+  eval_tf_while_loop: bool = False
+  allow_tpu_summary: bool = False
+  # Trainer intervals.
+  steps_per_loop: int = 1000
+  summary_interval: int = 1000
+  checkpoint_interval: int = 1000
+  # Checkpoint manager.
+  max_to_keep: int = 5
+  continuous_eval_timeout: int = 60 * 60
+  # Train/Eval routines.
+  train_steps: int = 0
+  # Sets validation steps to be -1 to evaluate the entire dataset.
+  validation_steps: int = -1
+  validation_interval: int = 1000
+  # Best checkpoint export.
+  best_checkpoint_export_subdir: str = ""
+  best_checkpoint_eval_metric: str = ""
+  best_checkpoint_metric_comp: str = "higher"
+  # Blowup recovery.
+  loss_upper_bound: float = 1e6
+  recovery_begin_steps: int = 0  # Enforcing the loss bound after these steps.
+  # When max trials < 0, no recovery module; max trials = 0, we will check
+  # the condition and fail the job if the condition happens; max trials > 0,
+  # we will retore the model states.
+  recovery_max_trials: int = 0
+  validation_summary_subdir: str = "validation"
+
+
+@dataclasses.dataclass
+class TaskConfig(base_config.Config):
+  init_checkpoint: str = ""
+  model: Optional[base_config.Config] = None
+  train_data: DataConfig = DataConfig()
+  validation_data: DataConfig = DataConfig()
+  name: Optional[str] = None
+
+
+@dataclasses.dataclass
+class ExperimentConfig(base_config.Config):
+  """Top-level configuration."""
+  task: TaskConfig = TaskConfig()
+  trainer: TrainerConfig = TrainerConfig()
+  runtime: RuntimeConfig = RuntimeConfig()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/exp_factory.py

+# Copyright 2021 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.
+
+"""Experiment factory methods."""
+
+from official.core import config_definitions as cfg
+from official.core import registry
+
+
+_REGISTERED_CONFIGS = {}
+
+
+def register_config_factory(name):
+  """Register ExperimentConfig factory method."""
+  return registry.register(_REGISTERED_CONFIGS, name)
+
+
+def get_exp_config(exp_name: str) -> cfg.ExperimentConfig:
+  """Looks up the `ExperimentConfig` according to the `exp_name`."""
+  exp_creater = registry.lookup(_REGISTERED_CONFIGS, exp_name)
+  return exp_creater()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/export_base.py

+# Copyright 2021 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.
+
+"""Base class for model export."""
+
+import abc
+import functools
+from typing import Any, Callable, Dict, Mapping, List, Optional, Text, Union
+
+import tensorflow as tf
+from tensorflow.python.saved_model.model_utils import export_utils
+
+
+class ExportModule(tf.Module, metaclass=abc.ABCMeta):
+  """Base Export Module."""
+
+  def __init__(self,
+               params,
+               model: Union[tf.Module, tf.keras.Model],
+               inference_step: Optional[Callable[..., Any]] = None,
+               *,
+               preprocessor: Optional[Callable[..., Any]] = None,
+               postprocessor: Optional[Callable[..., Any]] = None):
+    """Instantiates an ExportModel.
+
+    Examples:
+
+    `inference_step` must be a function that has `model` as an kwarg or the
+    second positional argument.
+    ```
+    def _inference_step(inputs, model=None):
+      return model(inputs, training=False)
+
+    module = ExportModule(params, model, inference_step=_inference_step)
+    ```
+
+    `preprocessor` and `postprocessor` could be either functions or `tf.Module`.
+    The usages of preprocessor and postprocessor are managed by the
+    implementation of `serve()` method.
+
+    Args:
+      params: A dataclass for parameters to the module.
+      model: A model instance which contains weights and forward computation.
+      inference_step: An optional callable to forward-pass the model. If not
+        specified, it creates a parital function with `model` as an required
+        kwarg.
+      preprocessor: An optional callable to preprocess the inputs.
+      postprocessor: An optional callable to postprocess the model outputs.
+    """
+    super().__init__(name=None)
+    self.model = model
+    self.params = params
+
+    if inference_step is not None:
+      self.inference_step = functools.partial(inference_step, model=self.model)
+    else:
+      self.inference_step = functools.partial(
+          self.model.__call__, training=False)
+    self.preprocessor = preprocessor
+    self.postprocessor = postprocessor
+
+  @abc.abstractmethod
+  def serve(self) -> Mapping[Text, tf.Tensor]:
+    """The bare inference function which should run on all devices.
+
+    Expecting tensors are passed in through keyword arguments. Returns a
+    dictionary of tensors, when the keys will be used inside the SignatureDef.
+    """
+
+  @abc.abstractmethod
+  def get_inference_signatures(
+      self, function_keys: Dict[Text, Text]) -> Mapping[Text, Any]:
+    """Get defined function signatures."""
+
+
+def export(export_module: ExportModule,
+           function_keys: Union[List[Text], Dict[Text, Text]],
+           export_savedmodel_dir: Text,
+           checkpoint_path: Optional[Text] = None,
+           timestamped: bool = True,
+           save_options: Optional[tf.saved_model.SaveOptions] = None) -> Text:
+  """Exports to SavedModel format.
+
+  Args:
+    export_module: a ExportModule with the keras Model and serving tf.functions.
+    function_keys: a list of string keys to retrieve pre-defined serving
+      signatures. The signaute keys will be set with defaults. If a dictionary
+      is provided, the values will be used as signature keys.
+    export_savedmodel_dir: Output saved model directory.
+    checkpoint_path: Object-based checkpoint path or directory.
+    timestamped: Whether to export the savedmodel to a timestamped directory.
+    save_options: `SaveOptions` for `tf.saved_model.save`.
+
+  Returns:
+    The savedmodel directory path.
+  """
+  ckpt_dir_or_file = checkpoint_path
+  if ckpt_dir_or_file is not None and tf.io.gfile.isdir(ckpt_dir_or_file):
+    ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
+  if ckpt_dir_or_file:
+    checkpoint = tf.train.Checkpoint(model=export_module.model)
+    checkpoint.read(
+        ckpt_dir_or_file).assert_existing_objects_matched().expect_partial()
+  if isinstance(function_keys, list):
+    if len(function_keys) == 1:
+      function_keys = {
+          function_keys[0]: tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+      }
+    else:
+      raise ValueError(
+          "If the function_keys is a list, it must contain a single element. %s"
+          % function_keys)
+
+  signatures = export_module.get_inference_signatures(function_keys)
+  if timestamped:
+    export_dir = export_utils.get_timestamped_export_dir(
+        export_savedmodel_dir).decode("utf-8")
+  else:
+    export_dir = export_savedmodel_dir
+  tf.saved_model.save(
+      export_module, export_dir, signatures=signatures, options=save_options)
+  return export_dir

TensorFlow2x/ComputeVision/Classification/models-master/official/core/export_base_test.py

+# Copyright 2021 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.
+
+"""Tests for official.core.export_base."""
+import os
+from typing import Any, Dict, Mapping, Text
+
+import tensorflow as tf
+
+from official.core import export_base
+
+
+class TestModule(export_base.ExportModule):
+
+  @tf.function
+  def serve(self, inputs: tf.Tensor) -> Mapping[Text, tf.Tensor]:
+    x = inputs if self.preprocessor is None else self.preprocessor(
+        inputs=inputs)
+    x = self.inference_step(x)
+    x = self.postprocessor(x) if self.postprocessor else x
+    return {'outputs': x}
+
+  def get_inference_signatures(
+      self, function_keys: Dict[Text, Text]) -> Mapping[Text, Any]:
+    input_signature = tf.TensorSpec(shape=[None, None], dtype=tf.float32)
+    return {'foo': self.serve.get_concrete_function(input_signature)}
+
+
+class ExportBaseTest(tf.test.TestCase):
+
+  def test_export_module(self):
+    tmp_dir = self.get_temp_dir()
+    model = tf.keras.layers.Dense(2)
+    inputs = tf.ones([2, 4], tf.float32)
+    expected_output = model(inputs, training=False)
+    module = TestModule(params=None, model=model)
+    ckpt_path = tf.train.Checkpoint(model=model).save(
+        os.path.join(tmp_dir, 'ckpt'))
+    export_dir = export_base.export(
+        module, ['foo'],
+        export_savedmodel_dir=tmp_dir,
+        checkpoint_path=ckpt_path,
+        timestamped=True)
+    self.assertTrue(os.path.exists(os.path.join(export_dir, 'saved_model.pb')))
+    self.assertTrue(
+        os.path.exists(
+            os.path.join(export_dir, 'variables', 'variables.index')))
+    self.assertTrue(
+        os.path.exists(
+            os.path.join(export_dir, 'variables',
+                         'variables.data-00000-of-00001')))
+
+    imported = tf.saved_model.load(export_dir)
+    output = imported.signatures['foo'](inputs)
+    self.assertAllClose(output['outputs'].numpy(), expected_output.numpy())
+
+  def test_custom_inference_step(self):
+    tmp_dir = self.get_temp_dir()
+    model = tf.keras.layers.Dense(2)
+    inputs = tf.ones([2, 4], tf.float32)
+
+    def _inference_step(inputs, model):
+      return tf.nn.softmax(model(inputs, training=False))
+
+    module = TestModule(
+        params=None, model=model, inference_step=_inference_step)
+    expected_output = _inference_step(inputs, model)
+    ckpt_path = tf.train.Checkpoint(model=model).save(
+        os.path.join(tmp_dir, 'ckpt'))
+    export_dir = export_base.export(
+        module, ['foo'],
+        export_savedmodel_dir=tmp_dir,
+        checkpoint_path=ckpt_path,
+        timestamped=False)
+    imported = tf.saved_model.load(export_dir)
+    output = imported.signatures['foo'](inputs)
+    self.assertAllClose(output['outputs'].numpy(), expected_output.numpy())
+
+  def test_processors(self):
+    model = tf.Module()
+    inputs = tf.zeros((), tf.float32)
+
+    def _inference_step(inputs, model):
+      del model
+      return inputs + 1.0
+
+    def _preprocessor(inputs):
+      print(inputs)
+      return inputs + 0.1
+
+    module = TestModule(
+        params=None,
+        model=model,
+        inference_step=_inference_step,
+        preprocessor=_preprocessor)
+    output = module.serve(inputs)
+    self.assertAllClose(output['outputs'].numpy(), 1.1)
+
+    class _PostProcessor(tf.Module):
+
+      def __call__(self, inputs):
+        return inputs + 0.01
+
+    module = TestModule(
+        params=None,
+        model=model,
+        inference_step=_inference_step,
+        preprocessor=_preprocessor,
+        postprocessor=_PostProcessor())
+    output = module.serve(inputs)
+    self.assertAllClose(output['outputs'].numpy(), 1.11)
+
+
+if __name__ == '__main__':
+  tf.test.main()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/input_reader.py

+# Copyright 2021 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.
+
+"""A common dataset reader."""
+import random
+from typing import Any, Callable, Dict, List, Optional, Sequence, Text, Union
+
+from absl import logging
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from official.core import config_definitions as cfg
+
+
+def _get_random_integer():
+  return random.randint(0, (1 << 31) - 1)
+
+
+def _maybe_map_fn(dataset: tf.data.Dataset,
+                  fn: Optional[Callable[..., Any]] = None) -> tf.data.Dataset:
+  """Calls dataset.map if a valid function is passed in."""
+  return dataset if fn is None else dataset.map(
+      fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+
+
+def match_files(input_path: Union[Sequence[str], str]) -> List[str]:
+  """Matches files from an input_path."""
+  matched_files = []
+  # Read dataset from files.
+  usage = ('`input_path` should be either (1) a str indicating a file '
+           'path/pattern, or (2) a str indicating multiple file '
+           'paths/patterns separated by comma (e.g "a, b, c" or no spaces '
+           '"a,b,c", or (3) a list of str, each of which is a file '
+           'path/pattern or multiple file paths/patterns separated by '
+           'comma, but got: %s')
+  if isinstance(input_path, str):
+    input_path_list = [input_path]
+  elif isinstance(input_path, (list, tuple)):
+    if any(not isinstance(x, str) for x in input_path):
+      raise ValueError(usage % input_path)
+    input_path_list = input_path
+  else:
+    raise ValueError(usage % input_path)
+
+  for input_path in input_path_list:
+    input_patterns = input_path.strip().split(',')
+    for input_pattern in input_patterns:
+      input_pattern = input_pattern.strip()
+      if not input_pattern:
+        continue
+      if '*' in input_pattern or '?' in input_pattern:
+        tmp_matched_files = tf.io.gfile.glob(input_pattern)
+        if not tmp_matched_files:
+          raise ValueError('%s does not match any files.' % input_pattern)
+        matched_files.extend(tmp_matched_files)
+      else:
+        matched_files.append(input_pattern)
+
+  if not matched_files:
+    raise ValueError('%s does not match any files.' % input_path)
+
+  return matched_files
+
+
+def _read_files_then_shard(matched_files: List[str],
+                           dataset_fn,
+                           input_context: Optional[
+                               tf.distribute.InputContext] = None,
+                           sharding: bool = False,
+                           repeat: bool = False) -> tf.data.Dataset:
+  """Sends all data files to every worker and then shard by data."""
+  dataset = dataset_fn(matched_files)
+
+  # When `input_file` is a path to a single file or the number of files is
+  # less than the number of input pipelines, disable auto sharding
+  # so that same input file is sent to all workers.
+  options = tf.data.Options()
+  options.experimental_distribute.auto_shard_policy = (
+      tf.data.experimental.AutoShardPolicy.OFF)
+  dataset = dataset.with_options(options)
+  # Do not enable sharding if tf.data service is enabled, as sharding will be
+  # handled inside tf.data service.
+  if sharding and input_context and (input_context.num_input_pipelines > 1):
+    dataset = dataset.shard(input_context.num_input_pipelines,
+                            input_context.input_pipeline_id)
+
+  if repeat:
+    dataset = dataset.repeat()
+  return dataset
+
+
+def _shard_files_then_read(matched_files: List[str],
+                           dataset_fn,
+                           input_context: Optional[
+                               tf.distribute.InputContext] = None,
+                           seed: Optional[Union[int, tf.Tensor]] = None,
+                           is_training: bool = False,
+                           sharding: bool = False,
+                           cache: bool = False,
+                           cycle_length: Optional[int] = None,
+                           block_length: Optional[int] = None,
+                           deterministic: bool = False) -> tf.data.Dataset:
+  """Shards the data files and then sent a split to every worker to read."""
+  dataset = tf.data.Dataset.from_tensor_slices(matched_files)
+
+  # Shuffle and repeat at file level.
+  # If cache is enabled, `reshuffle_each_iteration` is set to False,
+  # because we will read the same cached data in every iteration anyway.
+  if is_training:
+    # We need a seed to shuffle the files so that when each TPU workers gets
+    # its own shard the files do not overlap.
+    if sharding and seed is None:
+      seed = _get_random_integer()
+    dataset = dataset.shuffle(
+        len(matched_files),
+        seed=seed,
+        reshuffle_each_iteration=True if not cache else False)
+
+  # Do not enable sharding if tf.data service is enabled, as sharding will be
+  # handled inside tf.data service.
+  if sharding and input_context and (input_context.num_input_pipelines > 1):
+    dataset = dataset.shard(input_context.num_input_pipelines,
+                            input_context.input_pipeline_id)
+
+  # If cache is enabled, we will call `repeat()` later after `cache()`.
+  if is_training and not cache:
+    dataset = dataset.repeat()
+
+  dataset = dataset.interleave(
+      map_func=dataset_fn,
+      cycle_length=cycle_length,
+      block_length=block_length,
+      num_parallel_calls=(cycle_length
+                          if cycle_length else tf.data.experimental.AUTOTUNE),
+      deterministic=deterministic)
+  return dataset
+
+
+def _read_tfds(tfds_builder: tfds.core.DatasetBuilder,
+               tfds_split: Text,
+               tfds_skip_decoding_feature: Text,
+               tfds_as_supervised: bool,
+               input_context: Optional[tf.distribute.InputContext] = None,
+               seed: Optional[Union[int, tf.Tensor]] = None,
+               is_training: bool = False,
+               cache: bool = False,
+               cycle_length: Optional[int] = None,
+               block_length: Optional[int] = None) -> tf.data.Dataset:
+  """Reads a dataset from tfds."""
+  # No op if exist.
+  tfds_builder.download_and_prepare()
+
+  read_config = tfds.ReadConfig(
+      interleave_cycle_length=cycle_length,
+      interleave_block_length=block_length,
+      input_context=input_context,
+      shuffle_seed=seed)
+  decoders = {}
+  if tfds_skip_decoding_feature:
+    for skip_feature in tfds_skip_decoding_feature.split(','):
+      decoders[skip_feature.strip()] = tfds.decode.SkipDecoding()
+  dataset = tfds_builder.as_dataset(
+      split=tfds_split,
+      shuffle_files=is_training,
+      as_supervised=tfds_as_supervised,
+      decoders=decoders,
+      read_config=read_config)
+
+  if is_training and not cache:
+    dataset = dataset.repeat()
+  return dataset
+
+
+class InputReader:
+  """Input reader that returns a tf.data.Dataset instance."""
+
+  # A static random number which is the same across different InputReader
+  # instances.
+  static_randnum = _get_random_integer()
+
+  def __init__(self,
+               params: cfg.DataConfig,
+               dataset_fn=tf.data.TFRecordDataset,
+               decoder_fn: Optional[Callable[..., Any]] = None,
+               combine_fn: Optional[Callable[..., Any]] = None,
+               sample_fn: Optional[Callable[..., Any]] = None,
+               parser_fn: Optional[Callable[..., Any]] = None,
+               transform_and_batch_fn: Optional[Callable[
+                   [tf.data.Dataset, Optional[tf.distribute.InputContext]],
+                   tf.data.Dataset]] = None,
+               postprocess_fn: Optional[Callable[..., Any]] = None):
+    """Initializes an InputReader instance.
+
+    Args:
+      params: A config_definitions.DataConfig object.
+      dataset_fn: A `tf.data.Dataset` that consumes the input files. For
+        example, it can be `tf.data.TFRecordDataset`.
+      decoder_fn: An optional `callable` that takes the serialized data string
+        and decodes them into the raw tensor dictionary.
+      combine_fn: An optional `callable` that takes a dictionarty of
+        `tf.data.Dataset` objects as input and outputs a combined dataset. It
+        will be executed after the decoder_fn and before the sample_fn.
+      sample_fn: An optional `callable` that takes a `tf.data.Dataset` object as
+        input and outputs the transformed dataset. It performs sampling on the
+        decoded raw tensors dict before the parser_fn.
+      parser_fn: An optional `callable` that takes the decoded raw tensors dict
+        and parse them into a dictionary of tensors that can be consumed by the
+        model. It will be executed after decoder_fn.
+      transform_and_batch_fn: An optional `callable` that takes a
+        `tf.data.Dataset` object and an optional `tf.distribute.InputContext` as
+        input, and returns a `tf.data.Dataset` object. It will be executed after
+        `parser_fn` to transform and batch the dataset; if None, after
+        `parser_fn` is executed, the dataset will be batched into per-replica
+        batch size.
+      postprocess_fn: A optional `callable` that processes batched tensors. It
+        will be executed after batching.
+    """
+    if params.input_path and params.tfds_name:
+      raise ValueError('At most one of `input_path` and `tfds_name` can be '
+                       'specified, but got %s and %s.' %
+                       (params.input_path, params.tfds_name))
+
+    if isinstance(params.input_path,
+                  cfg.base_config.Config) and combine_fn is None:
+      raise ValueError(
+          'A `combine_fn` is required if the `input_path` is a dictionary.')
+
+    self._tfds_builder = None
+    self._matched_files = None
+    if not params.input_path:
+      # Read dataset from TFDS.
+      if not params.tfds_split:
+        raise ValueError(
+            '`tfds_name` is %s, but `tfds_split` is not specified.' %
+            params.tfds_name)
+      self._tfds_builder = tfds.builder(
+          params.tfds_name, data_dir=params.tfds_data_dir)
+    else:
+      self._matched_files = self.get_files(params.input_path)
+
+    self._global_batch_size = params.global_batch_size
+    self._is_training = params.is_training
+    self._drop_remainder = params.drop_remainder
+    self._shuffle_buffer_size = params.shuffle_buffer_size
+    self._cache = params.cache
+    self._cycle_length = params.cycle_length
+    self._block_length = params.block_length
+    self._deterministic = params.deterministic
+    self._sharding = params.sharding
+    self._tfds_split = params.tfds_split
+    self._tfds_as_supervised = params.tfds_as_supervised
+    self._tfds_skip_decoding_feature = params.tfds_skip_decoding_feature
+
+    self._dataset_fn = dataset_fn
+    self._decoder_fn = decoder_fn
+    self._combine_fn = combine_fn
+    self._sample_fn = sample_fn
+    self._parser_fn = parser_fn
+    self._transform_and_batch_fn = transform_and_batch_fn
+    self._postprocess_fn = postprocess_fn
+    self._seed = params.seed
+
+    # When tf.data service is enabled, each data service worker should get
+    # different random seeds. Thus, we set `seed` to None.
+    # Sharding should also be disabled because tf data service handles how
+    # each worker shard data with `processing_mode` in distribute method.
+    if params.enable_tf_data_service:
+      self._seed = None
+      self._sharding = False
+
+    self._enable_tf_data_service = (
+        params.enable_tf_data_service and params.tf_data_service_address)
+    self._tf_data_service_address = params.tf_data_service_address
+    if self._enable_tf_data_service:
+      # Add a random seed as the tf.data service job name suffix, so tf.data
+      # service doesn't reuse the previous state if TPU worker gets preempted.
+      self._tf_data_service_job_name = (
+          params.tf_data_service_job_name + str(self.static_randnum))
+      self._enable_round_robin_tf_data_service = params.get(
+          'enable_round_robin_tf_data_service', False)
+
+  @property
+  def tfds_info(self) -> tfds.core.DatasetInfo:
+    """Returns TFDS dataset info, if available."""
+    if self._tfds_builder:
+      return self._tfds_builder.info
+    else:
+      raise ValueError('tfds_info is not available, because the dataset '
+                       'is not loaded from tfds.')
+
+  def get_files(self, input_path):
+    """Gets matched files. Can be overridden by subclasses."""
+    if not input_path:
+      return None
+    # we want to combine / mix datasets
+    if isinstance(input_path, cfg.base_config.Config):
+      matched_files = {}
+      for k, v in input_path.as_dict().items():
+        matched_files[k] = match_files(v)
+    # single dataset
+    else:
+      matched_files = match_files(input_path)
+    return matched_files
+
+  def _read_data_source(
+      self,
+      matched_files: Union[Dict[str, List[str]], List[str]],
+      dataset_fn,
+      input_context: Optional[tf.distribute.InputContext] = None,
+      tfds_builder: Optional[tfds.core.DatasetBuilder] = None):
+    """Reads the data source (files/tfds) to a dataset."""
+
+    def _files_to_dataset(files: List[str]) -> tf.data.Dataset:
+      if len(files) > 1:
+        if input_context and (len(files) < input_context.num_input_pipelines):
+          logging.warn(
+              'The number of files %d is less than the number of input pipelines '
+              '%d. We will send all input files to every worker. '
+              'Please consider sharding your data into more files.', len(files),
+              input_context.num_input_pipelines)
+          return _read_files_then_shard(
+              files,
+              dataset_fn,
+              input_context,
+              sharding=self._sharding,
+              repeat=self._is_training and not self._cache)
+        else:
+          return _shard_files_then_read(
+              files,
+              dataset_fn,
+              input_context,
+              seed=self._seed,
+              is_training=self._is_training,
+              sharding=self._sharding,
+              cache=self._cache,
+              cycle_length=self._cycle_length,
+              block_length=self._block_length,
+              deterministic=self._deterministic)
+      elif len(files) == 1:
+        return _read_files_then_shard(
+            files,
+            dataset_fn,
+            input_context,
+            sharding=self._sharding,
+            repeat=self._is_training and not self._cache)
+      else:
+        raise ValueError('It is unexpected that `tfds_builder` is None and '
+                         'there is also no `files`.')
+
+    if tfds_builder:
+      dataset = _read_tfds(
+          tfds_builder=self._tfds_builder,
+          tfds_split=self._tfds_split,
+          tfds_skip_decoding_feature=self._tfds_skip_decoding_feature,
+          tfds_as_supervised=self._tfds_as_supervised,
+          input_context=input_context,
+          seed=self._seed,
+          is_training=self._is_training,
+          cache=self._cache,
+          cycle_length=self._cycle_length,
+          block_length=self._block_length)
+    elif isinstance(matched_files, (list, tuple)):
+      dataset = _files_to_dataset(matched_files)
+    elif isinstance(matched_files, dict):
+      dataset = {}
+      for k, fs in matched_files.items():
+        dataset[k] = _files_to_dataset(fs)
+    else:
+      raise ValueError('`matched_files` should be a list or dict.')
+
+    return dataset
+
+  def _decode_and_parse_dataset(
+      self,
+      dataset: Union[tf.data.Dataset, Dict[Text, tf.data.Dataset]],
+      batch_size: int,
+      input_context: Optional[tf.distribute.InputContext] = None
+  ) -> tf.data.Dataset:
+    """Returns a tf.data.Dataset object after shuffling, decoding, and parsing."""
+
+    def _shuffle_and_decode(ds):
+      # If cache is enabled, we will call `shuffle()` later after `cache()`.
+      if self._is_training and not self._cache:
+        ds = ds.shuffle(self._shuffle_buffer_size, seed=self._seed)
+      # Decode
+      ds = _maybe_map_fn(ds, self._decoder_fn)
+      return ds
+
+    dataset = tf.nest.map_structure(_shuffle_and_decode, dataset)
+    if tf.nest.is_nested(dataset):
+      dataset = self._combine_fn(dataset)
+
+    if self._sample_fn is not None:
+      dataset = dataset.apply(self._sample_fn)
+    dataset = _maybe_map_fn(dataset, self._parser_fn)
+
+    if self._cache:
+      dataset = dataset.cache()
+      if self._is_training:
+        dataset = dataset.repeat()
+        dataset = dataset.shuffle(self._shuffle_buffer_size, seed=self._seed)
+
+    if self._transform_and_batch_fn is not None:
+      dataset = self._transform_and_batch_fn(dataset, input_context)
+    else:
+      per_replica_batch_size = input_context.get_per_replica_batch_size(
+          batch_size) if input_context else batch_size
+      dataset = dataset.batch(
+          per_replica_batch_size, drop_remainder=self._drop_remainder)
+
+    return dataset
+
+  def _maybe_apply_data_service(
+      self,
+      dataset: tf.data.Dataset,
+      input_context: Optional[tf.distribute.InputContext] = None
+  ) -> tf.data.Dataset:
+    """Potentially distributes a dataset."""
+    if self._enable_tf_data_service and input_context:
+      if self._enable_round_robin_tf_data_service:
+        replicas_per_input_pipeline = input_context.num_replicas_in_sync // (
+            input_context.num_input_pipelines)
+        base_consumer_index = input_context.input_pipeline_id * (
+            replicas_per_input_pipeline)
+        num_consumers = input_context.num_input_pipelines * (
+            replicas_per_input_pipeline)
+        range_dataset = tf.data.Dataset.range(replicas_per_input_pipeline)
+        dataset = range_dataset.map(lambda i: dataset.apply(  # pylint: disable=g-long-lambda
+            tf.data.experimental.service.distribute(
+                processing_mode='parallel_epochs',
+                service=self._tf_data_service_address,
+                job_name=self._tf_data_service_job_name,
+                consumer_index=base_consumer_index + i,
+                num_consumers=num_consumers)))
+        # Use parallel interleave to read multiple batches from a tf.data
+        # service worker in parallel.
+        dataset = dataset.interleave(
+            lambda x: x,
+            cycle_length=replicas_per_input_pipeline,
+            num_parallel_calls=replicas_per_input_pipeline,
+            deterministic=True)
+      else:
+        dataset = dataset.apply(
+            tf.data.experimental.service.distribute(
+                processing_mode='parallel_epochs',
+                service=self._tf_data_service_address,
+                job_name=self._tf_data_service_job_name))
+    return dataset
+
+  def read(self,
+           input_context: Optional[tf.distribute.InputContext] = None,
+           dataset: Optional[tf.data.Dataset] = None) -> tf.data.Dataset:
+    """Generates a tf.data.Dataset object."""
+    if dataset is None:
+      dataset = self._read_data_source(
+          self._matched_files, self._dataset_fn, input_context,
+          self._tfds_builder)
+    dataset = self._decode_and_parse_dataset(dataset, self._global_batch_size,
+                                             input_context)
+    dataset = _maybe_map_fn(dataset, self._postprocess_fn)
+    dataset = self._maybe_apply_data_service(dataset, input_context)
+
+    if self._deterministic is not None:
+      options = tf.data.Options()
+      options.experimental_deterministic = self._deterministic
+      dataset = dataset.with_options(options)
+    return dataset.prefetch(tf.data.experimental.AUTOTUNE)

TensorFlow2x/ComputeVision/Classification/models-master/official/core/registry.py

+# Copyright 2021 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.
+
+"""Registry utility."""
+
+
+def register(registered_collection, reg_key):
+  """Register decorated function or class to collection.
+
+  Register decorated function or class into registered_collection, in a
+  hierarchical order. For example, when reg_key="my_model/my_exp/my_config_0"
+  the decorated function or class is stored under
+  registered_collection["my_model"]["my_exp"]["my_config_0"].
+  This decorator is supposed to be used together with the lookup() function in
+  this file.
+
+  Args:
+    registered_collection: a dictionary. The decorated function or class will be
+      put into this collection.
+    reg_key: The key for retrieving the registered function or class. If reg_key
+      is a string, it can be hierarchical like my_model/my_exp/my_config_0
+  Returns:
+    A decorator function
+  Raises:
+    KeyError: when function or class to register already exists.
+  """
+  def decorator(fn_or_cls):
+    """Put fn_or_cls in the dictionary."""
+    if isinstance(reg_key, str):
+      hierarchy = reg_key.split("/")
+      collection = registered_collection
+      for h_idx, entry_name in enumerate(hierarchy[:-1]):
+        if entry_name not in collection:
+          collection[entry_name] = {}
+        collection = collection[entry_name]
+        if not isinstance(collection, dict):
+          raise KeyError(
+              "Collection path {} at position {} already registered as "
+              "a function or class.".format(entry_name, h_idx))
+      leaf_reg_key = hierarchy[-1]
+    else:
+      collection = registered_collection
+      leaf_reg_key = reg_key
+
+    if leaf_reg_key in collection:
+      raise KeyError("Function or class {} registered multiple times.".format(
+          leaf_reg_key))
+
+    collection[leaf_reg_key] = fn_or_cls
+    return fn_or_cls
+  return decorator
+
+
+def lookup(registered_collection, reg_key):
+  """Lookup and return decorated function or class in the collection.
+
+  Lookup decorated function or class in registered_collection, in a
+  hierarchical order. For example, when
+  reg_key="my_model/my_exp/my_config_0",
+  this function will return
+  registered_collection["my_model"]["my_exp"]["my_config_0"].
+
+  Args:
+    registered_collection: a dictionary. The decorated function or class will be
+      retrieved from this collection.
+    reg_key: The key for retrieving the registered function or class. If reg_key
+      is a string, it can be hierarchical like my_model/my_exp/my_config_0
+  Returns:
+    The registered function or class.
+  Raises:
+    LookupError: when reg_key cannot be found.
+  """
+  if isinstance(reg_key, str):
+    hierarchy = reg_key.split("/")
+    collection = registered_collection
+    for h_idx, entry_name in enumerate(hierarchy):
+      if entry_name not in collection:
+        raise LookupError(
+            f"collection path {entry_name} at position {h_idx} is never "
+            f"registered. Please make sure the {entry_name} and its library is "
+            "imported and linked to the trainer binary.")
+      collection = collection[entry_name]
+    return collection
+  else:
+    if reg_key not in registered_collection:
+      raise LookupError(
+          f"registration key {reg_key} is never "
+          f"registered. Please make sure the {reg_key} and its library is "
+          "imported and linked to the trainer binary.")
+    return registered_collection[reg_key]

TensorFlow2x/ComputeVision/Classification/models-master/official/core/registry_test.py

+# Copyright 2021 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.
+
+"""Tests for registry."""
+
+import tensorflow as tf
+from official.core import registry
+
+
+class RegistryTest(tf.test.TestCase):
+
+  def test_register(self):
+    collection = {}
+
+    @registry.register(collection, 'functions/func_0')
+    def func_test():
+      pass
+
+    self.assertEqual(registry.lookup(collection, 'functions/func_0'), func_test)
+
+    @registry.register(collection, 'classes/cls_0')
+    class ClassRegistryKey:
+      pass
+
+    self.assertEqual(
+        registry.lookup(collection, 'classes/cls_0'), ClassRegistryKey)
+
+    @registry.register(collection, ClassRegistryKey)
+    class ClassRegistryValue:
+      pass
+
+    self.assertEqual(
+        registry.lookup(collection, ClassRegistryKey), ClassRegistryValue)
+
+  def test_register_hierarchy(self):
+    collection = {}
+
+    @registry.register(collection, 'functions/func_0')
+    def func_test0():
+      pass
+
+    @registry.register(collection, 'func_1')
+    def func_test1():
+      pass
+
+    @registry.register(collection, func_test1)
+    def func_test2():
+      pass
+
+    expected_collection = {
+        'functions': {
+            'func_0': func_test0,
+        },
+        'func_1': func_test1,
+        func_test1: func_test2,
+    }
+    self.assertEqual(collection, expected_collection)
+
+  def test_register_error(self):
+    collection = {}
+
+    @registry.register(collection, 'functions/func_0')
+    def func_test0():  # pylint: disable=unused-variable
+      pass
+
+    with self.assertRaises(KeyError):
+
+      @registry.register(collection, 'functions/func_0/sub_func')
+      def func_test1():  # pylint: disable=unused-variable
+        pass
+
+    with self.assertRaises(LookupError):
+      registry.lookup(collection, 'non-exist')
+
+
+if __name__ == '__main__':
+  tf.test.main()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/task_factory.py

+# Copyright 2021 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.
+
+"""A global factory to register and access all registered tasks."""
+
+from official.core import registry
+
+_REGISTERED_TASK_CLS = {}
+
+
+# TODO(b/158741360): Add type annotations once pytype checks across modules.
+def register_task_cls(task_config_cls):
+  """Decorates a factory of Tasks for lookup by a subclass of TaskConfig.
+
+  This decorator supports registration of tasks as follows:
+
+  ```
+  @dataclasses.dataclass
+  class MyTaskConfig(TaskConfig):
+    # Add fields here.
+    pass
+
+  @register_task_cls(MyTaskConfig)
+  class MyTask(Task):
+    # Inherits def __init__(self, task_config).
+    pass
+
+  my_task_config = MyTaskConfig()
+  my_task = get_task(my_task_config)  # Returns MyTask(my_task_config).
+  ```
+
+  Besisdes a class itself, other callables that create a Task from a TaskConfig
+  can be decorated by the result of this function, as long as there is at most
+  one registration for each config class.
+
+  Args:
+    task_config_cls: a subclass of TaskConfig (*not* an instance of TaskConfig).
+      Each task_config_cls can only be used for a single registration.
+
+  Returns:
+    A callable for use as class decorator that registers the decorated class
+    for creation from an instance of task_config_cls.
+  """
+  return registry.register(_REGISTERED_TASK_CLS, task_config_cls)
+
+
+def get_task(task_config, **kwargs):
+  """Creates a Task (of suitable subclass type) from task_config."""
+  # TODO(hongkuny): deprecate the task factory to use config.BUILDER.
+  if task_config.BUILDER is not None:
+    return task_config.BUILDER(task_config, **kwargs)
+  return get_task_cls(task_config.__class__)(task_config, **kwargs)
+
+
+# The user-visible get_task() is defined after classes have been registered.
+# TODO(b/158741360): Add type annotations once pytype checks across modules.
+def get_task_cls(task_config_cls):
+  task_cls = registry.lookup(_REGISTERED_TASK_CLS, task_config_cls)
+  return task_cls

TensorFlow2x/ComputeVision/Classification/models-master/official/core/test_utils.py

+# Copyright 2021 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.
+
+"""Utils for testing."""
+
+import tensorflow as tf
+
+
+class FakeKerasModel(tf.keras.Model):
+  """Fake keras model for testing."""
+
+  def __init__(self):
+    super().__init__()
+    self.dense = tf.keras.layers.Dense(4, activation=tf.nn.relu)
+    self.dense2 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
+
+  def call(self, inputs):
+    return self.dense2(self.dense(inputs))
+
+
+class _Dense(tf.Module):
+  """A dense layer."""
+
+  def __init__(self, input_dim, output_size, name=None):
+    super().__init__(name=name)
+    with self.name_scope:
+      self.w = tf.Variable(
+          tf.random.normal([input_dim, output_size]), name='w')
+      self.b = tf.Variable(tf.zeros([output_size]), name='b')
+
+  @tf.Module.with_name_scope
+  def __call__(self, x):
+    y = tf.matmul(x, self.w) + self.b
+    return tf.nn.relu(y)
+
+
+class FakeModule(tf.Module):
+  """Fake model using tf.Module for testing."""
+
+  def __init__(self, input_size, name=None):
+    super().__init__(name=name)
+    with self.name_scope:
+      self.dense = _Dense(input_size, 4, name='dense')
+      self.dense2 = _Dense(4, 4, name='dense_1')
+
+  @tf.Module.with_name_scope
+  def __call__(self, x):
+    return self.dense2(self.dense(x))

TensorFlow2x/ComputeVision/Classification/models-master/official/core/train_lib.py

+# Copyright 2021 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.
+
+"""TFM common training driver library."""
+# pytype: disable=attribute-error
+import os
+from typing import Any, Mapping, Optional, Tuple
+
+# Import libraries
+
+from absl import logging
+import orbit
+import tensorflow as tf
+
+from official.core import actions
+from official.core import base_task
+from official.core import base_trainer
+from official.core import config_definitions
+from official.core import train_utils
+
+maybe_create_best_ckpt_exporter = train_utils.maybe_create_best_ckpt_exporter
+
+
+def run_experiment(
+    distribution_strategy: tf.distribute.Strategy,
+    task: base_task.Task,
+    mode: str,
+    params: config_definitions.ExperimentConfig,
+    model_dir: str,
+    run_post_eval: bool = False,
+    save_summary: bool = True,
+    trainer: Optional[base_trainer.Trainer] = None,
+    controller_cls=orbit.Controller
+) -> Tuple[tf.keras.Model, Mapping[str, Any]]:
+  """Runs train/eval configured by the experiment params.
+
+  Args:
+    distribution_strategy: A distribution distribution_strategy.
+    task: A Task instance.
+    mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
+      or 'continuous_eval'.
+    params: ExperimentConfig instance.
+    model_dir: A 'str', a path to store model checkpoints and summaries.
+    run_post_eval: Whether to run post eval once after training, metrics logs
+      are returned.
+    save_summary: Whether to save train and validation summary.
+    trainer: the base_trainer.Trainer instance. It should be created within the
+      strategy.scope().
+    controller_cls: The controller class to manage the train and eval process.
+      Must be a orbit.Controller subclass.
+
+  Returns:
+    A 2-tuple of (model, eval_logs).
+      model: `tf.keras.Model` instance.
+      eval_logs: returns eval metrics logs when run_post_eval is set to True,
+        otherwise, returns {}.
+  """
+
+  with distribution_strategy.scope():
+    if not trainer:
+      trainer = train_utils.create_trainer(
+          params,
+          task,
+          train='train' in mode,
+          evaluate=('eval' in mode) or run_post_eval,
+          checkpoint_exporter=maybe_create_best_ckpt_exporter(
+              params, model_dir))
+
+  if trainer.checkpoint:
+    if model_dir is None:
+      raise ValueError('model_dir must be specified, but got None')
+    checkpoint_manager = tf.train.CheckpointManager(
+        trainer.checkpoint,
+        directory=model_dir,
+        max_to_keep=params.trainer.max_to_keep,
+        step_counter=trainer.global_step,
+        checkpoint_interval=params.trainer.checkpoint_interval,
+        init_fn=trainer.initialize)
+  else:
+    checkpoint_manager = None
+
+  controller = controller_cls(
+      strategy=distribution_strategy,
+      trainer=trainer if 'train' in mode else None,
+      evaluator=trainer,
+      global_step=trainer.global_step,
+      steps_per_loop=params.trainer.steps_per_loop,
+      checkpoint_manager=checkpoint_manager,
+      summary_dir=os.path.join(model_dir, 'train') if (save_summary) else None,
+      eval_summary_dir=os.path.join(model_dir,
+                                    params.trainer.validation_summary_subdir) if
+      (save_summary) else None,
+      summary_interval=params.trainer.summary_interval if
+      (save_summary) else None,
+      train_actions=actions.get_train_actions(
+          params, trainer, model_dir, checkpoint_manager=checkpoint_manager),
+      eval_actions=actions.get_eval_actions(params, trainer, model_dir))
+
+  logging.info('Starts to execute mode: %s', mode)
+  with distribution_strategy.scope():
+    if mode == 'train':
+      controller.train(steps=params.trainer.train_steps)
+    elif mode == 'train_and_eval':
+      controller.train_and_evaluate(
+          train_steps=params.trainer.train_steps,
+          eval_steps=params.trainer.validation_steps,
+          eval_interval=params.trainer.validation_interval)
+    elif mode == 'eval':
+      controller.evaluate(steps=params.trainer.validation_steps)
+    elif mode == 'continuous_eval':
+
+      def timeout_fn():
+        if trainer.global_step.numpy() >= params.trainer.train_steps:
+          return True
+        return False
+
+      controller.evaluate_continuously(
+          steps=params.trainer.validation_steps,
+          timeout=params.trainer.continuous_eval_timeout,
+          timeout_fn=timeout_fn)
+    else:
+      raise NotImplementedError('The mode is not implemented: %s' % mode)
+
+  num_params = train_utils.try_count_params(trainer.model)
+  if num_params is not None:
+    logging.info('Number of trainable params in model: %f Millions.',
+                 num_params / 10.**6)
+
+  flops = train_utils.try_count_flops(trainer.model)
+  if flops is not None:
+    logging.info('FLOPs (multi-adds) in model: %f Billions.',
+                 flops / 10.**9 / 2)
+
+  if run_post_eval:
+    with distribution_strategy.scope():
+      return trainer.model, trainer.evaluate(
+          tf.convert_to_tensor(params.trainer.validation_steps))
+  else:
+    return trainer.model, {}

TensorFlow2x/ComputeVision/Classification/models-master/official/core/train_lib_test.py

+# Copyright 2021 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.
+
+"""Tests for train_ctl_lib."""
+import json
+import os
+
+from absl import flags
+from absl.testing import flagsaver
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import strategy_combinations
+from official.common import flags as tfm_flags
+# pylint: disable=unused-import
+from official.common import registry_imports
+# pylint: enable=unused-import
+from official.core import task_factory
+from official.core import train_lib
+from official.core import train_utils
+from official.utils.testing import mock_task
+
+FLAGS = flags.FLAGS
+
+tfm_flags.define_flags()
+
+
+class TrainTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super(TrainTest, self).setUp()
+    self._test_config = {
+        'trainer': {
+            'checkpoint_interval': 10,
+            'steps_per_loop': 10,
+            'summary_interval': 10,
+            'train_steps': 10,
+            'validation_steps': 5,
+            'validation_interval': 10,
+            'continuous_eval_timeout': 1,
+            'validation_summary_subdir': 'validation',
+            'optimizer_config': {
+                'optimizer': {
+                    'type': 'sgd',
+                },
+                'learning_rate': {
+                    'type': 'constant'
+                }
+            }
+        },
+    }
+
+  @combinations.generate(
+      combinations.combine(
+          distribution_strategy=[
+              strategy_combinations.default_strategy,
+              strategy_combinations.cloud_tpu_strategy,
+              strategy_combinations.one_device_strategy_gpu,
+          ],
+          flag_mode=['train', 'eval', 'train_and_eval'],
+          run_post_eval=[True, False]))
+  def test_end_to_end(self, distribution_strategy, flag_mode, run_post_eval):
+    model_dir = self.get_temp_dir()
+    flags_dict = dict(
+        experiment='mock',
+        mode=flag_mode,
+        model_dir=model_dir,
+        params_override=json.dumps(self._test_config))
+    with flagsaver.flagsaver(**flags_dict):
+      params = train_utils.parse_configuration(flags.FLAGS)
+      train_utils.serialize_config(params, model_dir)
+      with distribution_strategy.scope():
+        task = task_factory.get_task(params.task, logging_dir=model_dir)
+
+      _, logs = train_lib.run_experiment(
+          distribution_strategy=distribution_strategy,
+          task=task,
+          mode=flag_mode,
+          params=params,
+          model_dir=model_dir,
+          run_post_eval=run_post_eval)
+
+    if 'eval' in flag_mode:
+      self.assertTrue(
+          tf.io.gfile.exists(
+              os.path.join(model_dir,
+                           params.trainer.validation_summary_subdir)))
+    if run_post_eval:
+      self.assertNotEmpty(logs)
+    else:
+      self.assertEmpty(logs)
+    self.assertNotEmpty(
+        tf.io.gfile.glob(os.path.join(model_dir, 'params.yaml')))
+    if flag_mode == 'eval':
+      return
+    self.assertNotEmpty(
+        tf.io.gfile.glob(os.path.join(model_dir, 'checkpoint')))
+    # Tests continuous evaluation.
+    _, logs = train_lib.run_experiment(
+        distribution_strategy=distribution_strategy,
+        task=task,
+        mode='continuous_eval',
+        params=params,
+        model_dir=model_dir,
+        run_post_eval=run_post_eval)
+
+  @combinations.generate(
+      combinations.combine(
+          distribution_strategy=[
+              strategy_combinations.default_strategy,
+              strategy_combinations.cloud_tpu_strategy,
+              strategy_combinations.one_device_strategy_gpu,
+          ],
+          flag_mode=['train', 'train_and_eval'],
+      ))
+  def test_recovery_nan_error(self, distribution_strategy, flag_mode):
+    model_dir = self.get_temp_dir()
+    flags_dict = dict(
+        experiment='mock',
+        mode=flag_mode,
+        model_dir=model_dir,
+        params_override=json.dumps(self._test_config))
+    with flagsaver.flagsaver(**flags_dict):
+      params = train_utils.parse_configuration(flags.FLAGS)
+      train_utils.serialize_config(params, model_dir)
+      with distribution_strategy.scope():
+        # task = task_factory.get_task(params.task, logging_dir=model_dir)
+        task = mock_task.MockTask(params.task, logging_dir=model_dir)
+
+        # Set the loss to NaN to trigger RunTimeError.
+        def build_losses(labels, model_outputs, aux_losses=None):
+          del labels, model_outputs
+          return tf.constant([np.nan], tf.float32) + aux_losses
+
+        task.build_losses = build_losses
+
+      with self.assertRaises(RuntimeError):
+        train_lib.run_experiment(
+            distribution_strategy=distribution_strategy,
+            task=task,
+            mode=flag_mode,
+            params=params,
+            model_dir=model_dir)
+
+  @combinations.generate(
+      combinations.combine(
+          distribution_strategy=[
+              strategy_combinations.default_strategy,
+              strategy_combinations.cloud_tpu_strategy,
+              strategy_combinations.one_device_strategy_gpu,
+          ],
+          flag_mode=['train'],
+      ))
+  def test_recovery(self, distribution_strategy, flag_mode):
+    loss_threshold = 1.0
+    model_dir = self.get_temp_dir()
+    flags_dict = dict(
+        experiment='mock',
+        mode=flag_mode,
+        model_dir=model_dir,
+        params_override=json.dumps(self._test_config))
+    with flagsaver.flagsaver(**flags_dict):
+      params = train_utils.parse_configuration(flags.FLAGS)
+      params.trainer.loss_upper_bound = loss_threshold
+      params.trainer.recovery_max_trials = 1
+      train_utils.serialize_config(params, model_dir)
+      with distribution_strategy.scope():
+        task = task_factory.get_task(params.task, logging_dir=model_dir)
+
+      # Saves a checkpoint for reference.
+      model = task.build_model()
+      checkpoint = tf.train.Checkpoint(model=model)
+      checkpoint_manager = tf.train.CheckpointManager(
+          checkpoint, self.get_temp_dir(), max_to_keep=2)
+      checkpoint_manager.save()
+      before_weights = model.get_weights()
+
+      def build_losses(labels, model_outputs, aux_losses=None):
+        del labels, model_outputs
+        return tf.constant([loss_threshold], tf.float32) + aux_losses
+
+      task.build_losses = build_losses
+
+      model, _ = train_lib.run_experiment(
+          distribution_strategy=distribution_strategy,
+          task=task,
+          mode=flag_mode,
+          params=params,
+          model_dir=model_dir)
+      after_weights = model.get_weights()
+      for left, right in zip(before_weights, after_weights):
+        self.assertAllEqual(left, right)
+
+  def test_parse_configuration(self):
+    model_dir = self.get_temp_dir()
+    flags_dict = dict(
+        experiment='mock',
+        mode='train',
+        model_dir=model_dir,
+        params_override=json.dumps(self._test_config))
+    with flagsaver.flagsaver(**flags_dict):
+      params = train_utils.parse_configuration(flags.FLAGS, lock_return=True)
+      with self.assertRaises(ValueError):
+        params.override({'task': {'init_checkpoint': 'Foo'}})
+
+      params = train_utils.parse_configuration(flags.FLAGS, lock_return=False)
+      params.override({'task': {'init_checkpoint': 'Bar'}})
+      self.assertEqual(params.task.init_checkpoint, 'Bar')
+
+
+if __name__ == '__main__':
+  tf.test.main()

TensorFlow2x/ComputeVision/Classification/models-master/official/core/train_utils.py

+# Copyright 2021 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.
+
+"""Training utils."""
+import copy
+import json
+import os
+import pprint
+from typing import Any, Callable, Dict, List, Optional, Union
+
+from absl import logging
+import dataclasses
+import gin
+import orbit
+import tensorflow as tf
+
+# pylint: disable=g-direct-tensorflow-import
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
+# pylint: enable=g-direct-tensorflow-import
+from official.core import base_task
+from official.core import base_trainer
+from official.core import config_definitions
+from official.core import exp_factory
+from official.modeling import hyperparams
+
+
+def get_leaf_nested_dict(d: Dict[str, Any], keys: List[str]) -> Dict[str, Any]:
+  """Get leaf from a dictionary with arbitrary depth with a list of keys.
+
+  Args:
+    d: The dictionary to extract value from.
+    keys: The list of keys to extract values recursively.
+
+  Returns:
+    The value of the leaf.
+
+  Raises:
+    KeyError: If the value of keys extracted is a dictionary.
+  """
+  leaf = d
+  for k in keys:
+    if not isinstance(leaf, dict) or k not in leaf:
+      raise KeyError(
+          'Path not exist while traversing the dictionary: d with keys'
+          ': %s.' % keys)
+    leaf = leaf[k]
+
+  if isinstance(leaf, dict):
+    raise KeyError('The value extracted with keys: %s is not a leaf of the '
+                   'dictionary: %s.' % (keys, d))
+  return leaf
+
+
+def cast_leaf_nested_dict(d: Dict[str, Any],
+                          cast_fn: Callable[[Any], Any]) -> Dict[str, Any]:
+  """Cast the leaves of a dictionary with arbitrary depth in place.
+
+  Args:
+    d: The dictionary to extract value from.
+    cast_fn: The casting function.
+
+  Returns:
+    A dictionray with the same structure as d.
+  """
+  for key, value in d.items():
+    if isinstance(value, dict):
+      d[key] = cast_leaf_nested_dict(value, cast_fn)
+    else:
+      d[key] = cast_fn(value)
+  return d
+
+
+def maybe_create_best_ckpt_exporter(params: config_definitions.ExperimentConfig,
+                                    data_dir: str) -> Any:
+  """Maybe create a BestCheckpointExporter object, according to the config."""
+  export_subdir = params.trainer.best_checkpoint_export_subdir
+  metric_name = params.trainer.best_checkpoint_eval_metric
+  metric_comp = params.trainer.best_checkpoint_metric_comp
+  if data_dir and export_subdir and metric_name:
+    best_ckpt_dir = os.path.join(data_dir, export_subdir)
+    best_ckpt_exporter = BestCheckpointExporter(best_ckpt_dir, metric_name,
+                                                metric_comp)
+    logging.info(
+        'Created the best checkpoint exporter. '
+        'data_dir: %s, export_subdir: %s, metric_name: %s', data_dir,
+        export_subdir, metric_name)
+  else:
+    best_ckpt_exporter = None
+
+  return best_ckpt_exporter
+
+
+# TODO(b/180147589): Add tests for this module.
+class BestCheckpointExporter:
+  """Keeps track of the best result, and saves its checkpoint.
+
+  Orbit will support an API for checkpoint exporter. This class will be used
+  together with orbit once this functionality is ready.
+  """
+
+  def __init__(self, export_dir: str, metric_name: str, metric_comp: str):
+    """Initialization.
+
+    Args:
+      export_dir: The directory that will contain exported checkpoints.
+      metric_name: Indicates which metric to look at, when determining which
+        result is better. If eval_logs being passed to maybe_export_checkpoint
+        is a nested dictionary, use `|` as a seperator for different layers.
+      metric_comp: Indicates how to compare results. Either `lower` or `higher`.
+    """
+    self._export_dir = export_dir
+    self._metric_name = metric_name.split('|')
+    self._metric_comp = metric_comp
+    if self._metric_comp not in ('lower', 'higher'):
+      raise ValueError('best checkpoint metric comp must be one of '
+                       'higher, lower. Got: {}'.format(self._metric_comp))
+    tf.io.gfile.makedirs(os.path.dirname(self.best_ckpt_logs_path))
+    self._best_ckpt_logs = self._maybe_load_best_eval_metric()
+    self._checkpoint_manager = None
+
+  def _get_checkpoint_manager(self, checkpoint):
+    """Gets an existing checkpoint manager or creates a new one."""
+    if self._checkpoint_manager is None or (self._checkpoint_manager.checkpoint
+                                            != checkpoint):
+      logging.info('Creates a new checkpoint manager.')
+      self._checkpoint_manager = tf.train.CheckpointManager(
+          checkpoint,
+          directory=self._export_dir,
+          max_to_keep=1,
+          checkpoint_name='best_ckpt')
+
+    return self._checkpoint_manager
+
+  def maybe_export_checkpoint(
+      self, checkpoint, eval_logs, global_step, write_logs=True) -> bool:
+    """Compare eval_logs with past eval_logs and export checkpoint if better."""
+    logging.info('[BestCheckpointExporter] received eval_logs: %s, at step: %d',
+                 eval_logs, global_step)
+    if self._best_ckpt_logs is None or self._new_metric_is_better(
+        self._best_ckpt_logs, eval_logs):
+      self._best_ckpt_logs = eval_logs
+      if write_logs:
+        self.export_best_eval_metric(self._best_ckpt_logs, global_step)
+      self._get_checkpoint_manager(checkpoint).save()
+      return True
+    return False
+
+  def _maybe_load_best_eval_metric(self):
+    if not tf.io.gfile.exists(self.best_ckpt_logs_path):
+      return None
+    with tf.io.gfile.GFile(self.best_ckpt_logs_path, 'r') as reader:
+      return json.loads(reader.read())
+
+  def _new_metric_is_better(self, old_logs, new_logs):
+    """Check if the metric in new_logs is better than the metric in old_logs."""
+    old_value = float(
+        orbit.utils.get_value(
+            get_leaf_nested_dict(old_logs, self._metric_name)))
+    new_value = float(
+        orbit.utils.get_value(
+            get_leaf_nested_dict(new_logs, self._metric_name)))
+
+    logging.info('[BestCheckpointExporter] comparing results. old: %f, new: %f',
+                 old_value, new_value)
+    if self._metric_comp == 'higher':
+      if new_value > old_value:
+        logging.info('[BestCheckpointExporter] '
+                     'the new number is better since it is higher.')
+        return True
+    else:  # self._metric_comp == 'lower':
+      if new_value < old_value:
+        logging.info('[BestCheckpointExporter] '
+                     'the new number is better since it is lower.')
+        return True
+    return False
+
+  def export_best_eval_metric(self, eval_logs, global_step):
+    """Export evaluation results of the best checkpoint into a json file."""
+    eval_logs_ext = copy.copy(eval_logs)
+    eval_logs_ext['best_ckpt_global_step'] = global_step
+    eval_logs_ext = cast_leaf_nested_dict(
+        eval_logs_ext, lambda x: float(orbit.utils.get_value(x)))
+    # Saving json file is very fast.
+    with tf.io.gfile.GFile(self.best_ckpt_logs_path, 'w') as writer:
+      writer.write(json.dumps(eval_logs_ext, indent=4) + '\n')
+
+  @property
+  def best_ckpt_logs(self):
+    return self._best_ckpt_logs
+
+  @property
+  def best_ckpt_logs_path(self):
+    return os.path.join(self._export_dir, 'info.json')
+
+  @property
+  def best_ckpt_path(self):
+    """Returns the best ckpt path or None if there is no ckpt yet."""
+    return tf.train.latest_checkpoint(self._export_dir)
+
+
+@gin.configurable
+def create_trainer(params: config_definitions.ExperimentConfig,
+                   task: base_task.Task,
+                   train: bool,
+                   evaluate: bool,
+                   checkpoint_exporter: Optional[BestCheckpointExporter] = None,
+                   trainer_cls=base_trainer.Trainer) -> base_trainer.Trainer:
+  """Create trainer."""
+  logging.info('Running default trainer.')
+  model = task.build_model()
+  optimizer = task.create_optimizer(params.trainer.optimizer_config,
+                                    params.runtime)
+  return trainer_cls(
+      params,
+      task,
+      model=model,
+      optimizer=optimizer,
+      train=train,
+      evaluate=evaluate,
+      checkpoint_exporter=checkpoint_exporter)
+
+
+@dataclasses.dataclass
+class ParseConfigOptions:
+  """Use this dataclass instead of FLAGS to customize parse_configuration()."""
+  experiment: str
+  config_file: List[str]
+  tpu: str = ''
+  tf_data_service: str = ''
+  params_override: str = ''
+
+  def __contains__(self, name):
+    return name in dataclasses.asdict(self)
+
+
+def parse_configuration(flags_obj, lock_return=True, print_return=True):
+  """Parses ExperimentConfig from flags."""
+
+  if flags_obj.experiment is None:
+    raise ValueError('The flag --experiment must be specified.')
+
+  # 1. Get the default config from the registered experiment.
+  params = exp_factory.get_exp_config(flags_obj.experiment)
+
+  # 2. Get the first level of override from `--config_file`.
+  #    `--config_file` is typically used as a template that specifies the common
+  #    override for a particular experiment.
+  for config_file in flags_obj.config_file or []:
+    params = hyperparams.override_params_dict(
+        params, config_file, is_strict=True)
+
+  # 3. Override the TPU address and tf.data service address.
+  params.override({
+      'runtime': {
+          'tpu': flags_obj.tpu,
+      },
+  })
+  if ('tf_data_service' in flags_obj and flags_obj.tf_data_service and
+      isinstance(params.task, config_definitions.TaskConfig)):
+    params.override({
+        'task': {
+            'train_data': {
+                'tf_data_service_address': flags_obj.tf_data_service,
+            },
+            'validation_data': {
+                'tf_data_service_address': flags_obj.tf_data_service,
+            }
+        }
+    })
+
+  # 4. Get the second level of override from `--params_override`.
+  #    `--params_override` is typically used as a further override over the
+  #    template. For example, one may define a particular template for training
+  #    ResNet50 on ImageNet in a config file and pass it via `--config_file`,
+  #    then define different learning rates and pass it via `--params_override`.
+  if flags_obj.params_override:
+    params = hyperparams.override_params_dict(
+        params, flags_obj.params_override, is_strict=True)
+
+  params.validate()
+  if lock_return:
+    params.lock()
+
+  if print_return:
+    pp = pprint.PrettyPrinter()
+    logging.info('Final experiment parameters:\n%s',
+                 pp.pformat(params.as_dict()))
+
+  return params
+
+
+def serialize_config(params: config_definitions.ExperimentConfig,
+                     model_dir: str):
+  """Serializes and saves the experiment config."""
+  if model_dir is None:
+    raise ValueError('model_dir must be specified, but got None')
+  params_save_path = os.path.join(model_dir, 'params.yaml')
+  logging.info('Saving experiment configuration to %s', params_save_path)
+  tf.io.gfile.makedirs(model_dir)
+  hyperparams.save_params_dict_to_yaml(params, params_save_path)
+
+
+def save_gin_config(filename_suffix: str, model_dir: str):
+  """Serializes and saves the experiment config."""
+  gin_save_path = os.path.join(
+      model_dir, 'operative_config.{}.gin'.format(filename_suffix))
+  logging.info('Saving gin configurations to %s', gin_save_path)
+  tf.io.gfile.makedirs(model_dir)
+  with tf.io.gfile.GFile(gin_save_path, 'w') as f:
+    f.write(gin.operative_config_str())
+
+
+def read_global_step_from_checkpoint(ckpt_file_path):
+  """Read global step from checkpoint, or get global step from its filename."""
+  global_step = tf.Variable(-1, dtype=tf.int64)
+  ckpt = tf.train.Checkpoint(global_step=global_step)
+  try:
+    ckpt.restore(ckpt_file_path).expect_partial()
+    global_step_maybe_restored = global_step.numpy()
+  except tf.errors.InvalidArgumentError:
+    global_step_maybe_restored = -1
+
+  if global_step_maybe_restored == -1:
+    raise ValueError('global_step not found in checkpoint {}. '
+                     'If you want to run finetune eval jobs, you need to '
+                     'make sure that your pretrain model writes '
+                     'global_step in its checkpoints.'.format(ckpt_file_path))
+  global_step_restored = global_step.numpy()
+  logging.info('get global_step %d from checkpoint %s', global_step_restored,
+               ckpt_file_path)
+  return global_step_restored
+
+
+def write_json_summary(log_dir, global_step, eval_metrics):
+  """Dump evaluation metrics to json file."""
+  serializable_dict = {}
+  for name, value in eval_metrics.items():
+    if hasattr(value, 'numpy'):
+      serializable_dict[name] = str(value.numpy())
+    else:
+      serializable_dict[name] = str(value)
+  output_json = os.path.join(log_dir, 'metrics-{}.json'.format(global_step))
+  logging.info('Evaluation results at pretrain step %d: %s', global_step,
+               serializable_dict)
+  with tf.io.gfile.GFile(output_json, 'w') as writer:
+    writer.write(json.dumps(serializable_dict, indent=4) + '\n')
+
+
+def write_summary(summary_writer, global_step, eval_metrics):
+  """Write evaluation metrics to TF summary."""
+  numeric_dict = {}
+  for name, value in eval_metrics.items():
+    numeric_dict[name] = float(orbit.utils.get_value(value))
+  with summary_writer.as_default():
+    for name, value in numeric_dict.items():
+      tf.summary.scalar(name, value, step=global_step)
+    summary_writer.flush()
+
+
+def remove_ckpts(model_dir):
+  """Remove model checkpoints, so we can restart."""
+  ckpts = os.path.join(model_dir, 'ckpt-*')
+  logging.info('removing checkpoint files %s', ckpts)
+  for file_to_remove in tf.io.gfile.glob(ckpts):
+    tf.io.gfile.rmtree(file_to_remove)
+
+  file_to_remove = os.path.join(model_dir, 'checkpoint')
+  if tf.io.gfile.exists(file_to_remove):
+    tf.io.gfile.remove(file_to_remove)
+
+
+def write_model_params(model: Union[tf.Module, tf.keras.Model],
+                       output_path: str) -> None:
+  """Writes the model parameters and shapes to a file.
+
+  Args:
+    model: A model instance.
+    output_path: Output file path.
+  """
+  with tf.io.gfile.GFile(output_path, 'w') as f:
+    total_params = 0
+    for var in model.variables:
+      shape = tf.shape(var)
+      total_params += tf.math.reduce_prod(shape).numpy()
+      f.write(f'{var.name} {shape.numpy().tolist()}\n')
+    f.write(f'\nTotal params: {total_params}\n')
+
+
+def try_count_params(
+    model: Union[tf.Module, tf.keras.Model],
+    trainable_only: bool = False):
+  """Count the number of parameters if model is possible.
+
+  Args:
+    model: Try to count the number of params in this model.
+    trainable_only: Whether to calculate trainable params only. This flag is
+      not used when the model has `count_params` attribute.
+
+  Returns:
+    The number of parameters or None.
+  """
+  if hasattr(model, 'count_params'):
+    try:
+      return model.count_params()
+    except ValueError:
+      logging.info('Number of trainable params unknown, because the build() '
+                   'methods in keras layers were not called. This is probably '
+                   'because the model was not feed any input, e.g., the max '
+                   'train step already reached before this run.')
+      return None
+  else:
+    total_params = 0
+    variables = model.trainable_variables if trainable_only else model.variables
+    for var in variables:
+      shape = tf.shape(var)
+      total_params += tf.math.reduce_prod(shape).numpy()
+  return total_params
+
+
+def try_count_flops(model: Union[tf.Module, tf.keras.Model],
+                    inputs_kwargs: Optional[Dict[str, Any]] = None,
+                    output_path: Optional[str] = None):
+  """Counts and returns model FLOPs.
+
+  Args:
+    model: A model instance.
+    inputs_kwargs: An optional dictionary of argument pairs specifying inputs'
+      shape specifications to getting corresponding concrete function.
+    output_path: A file path to write the profiling results to.
+
+  Returns:
+    The model's FLOPs.
+  """
+  if hasattr(model, 'inputs'):
+    try:
+      # Get input shape and set batch size to 1.
+      if model.inputs:
+        inputs = [
+            tf.TensorSpec([1] + input.shape[1:], input.dtype)
+            for input in model.inputs
+        ]
+        concrete_func = tf.function(model).get_concrete_function(inputs)
+      # If model.inputs is invalid, try to use the input to get concrete
+      # function for model.call (subclass model).
+      else:
+        concrete_func = tf.function(model.call).get_concrete_function(
+            **inputs_kwargs)
+      frozen_func, _ = convert_variables_to_constants_v2_as_graph(concrete_func)
+
+      # Calculate FLOPs.
+      run_meta = tf.compat.v1.RunMetadata()
+      opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()
+      if output_path is not None:
+        opts['output'] = f'file:outfile={output_path}'
+      else:
+        opts['output'] = 'none'
+      flops = tf.compat.v1.profiler.profile(
+          graph=frozen_func.graph, run_meta=run_meta, options=opts)
+      return flops.total_float_ops
+    except Exception as e:  # pylint: disable=broad-except
+      logging.info(
+          'Failed to count model FLOPs with error %s, because the build() '
+          'methods in keras layers were not called. This is probably because '
+          'the model was not feed any input, e.g., the max train step already '
+          'reached before this run.', e)
+      return None
+  return None

TensorFlow2x/ComputeVision/Classification/models-master/official/core/train_utils_test.py

+# Copyright 2021 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.
+
+"""Tests for official.core.train_utils."""
+
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from official.core import test_utils
+from official.core import train_utils
+
+
+class TrainUtilsTest(tf.test.TestCase):
+
+  def test_get_leaf_nested_dict(self):
+    d = {'a': {'i': {'x': 5}}}
+    self.assertEqual(train_utils.get_leaf_nested_dict(d, ['a', 'i', 'x']), 5)
+
+  def test_get_leaf_nested_dict_not_leaf(self):
+    with self.assertRaisesRegex(KeyError, 'The value extracted with keys.*'):
+      d = {'a': {'i': {'x': 5}}}
+      train_utils.get_leaf_nested_dict(d, ['a', 'i'])
+
+  def test_get_leaf_nested_dict_path_not_exist_missing_key(self):
+    with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
+      d = {'a': {'i': {'x': 5}}}
+      train_utils.get_leaf_nested_dict(d, ['a', 'i', 'y'])
+
+  def test_get_leaf_nested_dict_path_not_exist_out_of_range(self):
+    with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
+      d = {'a': {'i': {'x': 5}}}
+      train_utils.get_leaf_nested_dict(d, ['a', 'i', 'z'])
+
+  def test_get_leaf_nested_dict_path_not_exist_meets_leaf(self):
+    with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
+      d = {'a': {'i': 5}}
+      train_utils.get_leaf_nested_dict(d, ['a', 'i', 'z'])
+
+  def test_cast_leaf_nested_dict(self):
+    d = {'a': {'i': {'x': '123'}}, 'b': 456.5}
+    d = train_utils.cast_leaf_nested_dict(d, int)
+    self.assertEqual(d['a']['i']['x'], 123)
+    self.assertEqual(d['b'], 456)
+
+  def test_write_model_params_keras_model(self):
+    inputs = np.zeros([2, 3])
+    model = test_utils.FakeKerasModel()
+    model(inputs)  # Must do forward pass to build the model.
+
+    filepath = os.path.join(self.create_tempdir(), 'model_params.txt')
+    train_utils.write_model_params(model, filepath)
+    actual = tf.io.gfile.GFile(filepath, 'r').read().splitlines()
+
+    expected = [
+        'fake_keras_model/dense/kernel:0 [3, 4]',
+        'fake_keras_model/dense/bias:0 [4]',
+        'fake_keras_model/dense_1/kernel:0 [4, 4]',
+        'fake_keras_model/dense_1/bias:0 [4]',
+        '',
+        'Total params: 36',
+    ]
+    self.assertEqual(actual, expected)
+
+  def test_write_model_params_module(self):
+    inputs = np.zeros([2, 3], dtype=np.float32)
+    model = test_utils.FakeModule(3, name='fake_module')
+    model(inputs)  # Must do forward pass to build the model.
+
+    filepath = os.path.join(self.create_tempdir(), 'model_params.txt')
+    train_utils.write_model_params(model, filepath)
+    actual = tf.io.gfile.GFile(filepath, 'r').read().splitlines()
+
+    expected = [
+        'fake_module/dense/b:0 [4]',
+        'fake_module/dense/w:0 [3, 4]',
+        'fake_module/dense_1/b:0 [4]',
+        'fake_module/dense_1/w:0 [4, 4]',
+        '',
+        'Total params: 36',
+    ]
+    self.assertEqual(actual, expected)
+
+
+if __name__ == '__main__':
+  tf.test.main()

TensorFlow2x/ComputeVision/Classification/models-master/official/modeling/__init__.py

+# Copyright 2021 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.
+

TensorFlow2x/ComputeVision/Classification/models-master/official/modeling/activations/__init__.py

+# Copyright 2021 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.
+
+"""Activations package definition."""
+from official.modeling.activations.gelu import gelu
+from official.modeling.activations.relu import relu6
+from official.modeling.activations.sigmoid import hard_sigmoid
+from official.modeling.activations.swish import hard_swish
+from official.modeling.activations.swish import identity
+from official.modeling.activations.swish import simple_swish

TensorFlow2x/ComputeVision/Classification/models-master/official/modeling/activations/gelu.py

+# Copyright 2021 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.
+
+"""Gaussian error linear unit."""
+
+import tensorflow as tf
+
+
+@tf.keras.utils.register_keras_serializable(package='Text')
+def gelu(x):
+  """Gaussian Error Linear Unit.
+
+  This is a smoother version of the RELU.
+  Original paper: https://arxiv.org/abs/1606.08415
+  Args:
+    x: float Tensor to perform activation.
+
+  Returns:
+    `x` with the GELU activation applied.
+  """
+  return tf.keras.activations.gelu(x, approximate=True)

TensorFlow2x/ComputeVision/Classification/models-master/official/modeling/activations/gelu_test.py

+# Copyright 2021 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.
+
+"""Tests for the Gaussian error linear unit."""
+
+import tensorflow as tf
+
+from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import
+from official.modeling import activations
+
+
+@keras_parameterized.run_all_keras_modes
+class GeluTest(keras_parameterized.TestCase):
+
+  def test_gelu(self):
+    expected_data = [[0.14967535, 0., -0.10032465],
+                     [-0.15880796, -0.04540223, 2.9963627]]
+    gelu_data = activations.gelu([[.25, 0, -.25], [-1, -2, 3]])
+    self.assertAllClose(expected_data, gelu_data)
+
+
+if __name__ == '__main__':
+  tf.test.main()