Python backend.get_session函数代码示例

def set_weights(distribution_strategy, dist_model, weights):
  """Sets the weights of the replicated models.

  The weights of the replicated models are set to the weights of the original
  model. The weights of the replicated model are Mirrored variables and hence
  we need to use the `update` call within a DistributionStrategy scope.

  Args:
    distribution_strategy: DistributionStrategy used to distribute training
        and validation.
    dist_model: The replicated models on the different devices.
    weights: The weights of the original model.
  """
  assign_ops = []
  for layer in dist_model.layers:
    num_param = len(layer.weights)
    layer_weights = weights[:num_param]
    for sw, w in zip(layer.weights, layer_weights):
      if ops.executing_eagerly_outside_functions():
        sw.assign(w)
      else:
        assign_ops.append(distribution_strategy.unwrap(sw.assign(w)))
    weights = weights[num_param:]

  if not ops.executing_eagerly_outside_functions():
    K.get_session(assign_ops).run(assign_ops)

def setup_tpu_session(master):
  """Initializes and returns a Keras/TF session connected the TPU `master`."""
  session = tf_session.Session(
      target=master, config=config_pb2.ConfigProto(isolate_session_state=True))
  K.set_session(session)
  K.get_session().run(tpu.initialize_system())
  return session

def _get_var_for_numpy(distribution_strategy, input_array):
  """Creates a variable and assigns the value of the numpy array to it.

  Args:
    distribution_strategy: The DistributionStrategy used to compile the model.
    input_array: The input numpy array whose value will be assigned to the
      variable we create.

  Returns:
    The variable to which we will copy the value of the input numpy array.

  """
  with ops.device(get_cpu_device(distribution_strategy)):
    # Create and initialize a variable on the CPU device. This is the CPU
    # device of the host in the case of TPUDistributionStrategy.
    input_var = variables.VariableV1(array_ops.zeros(input_array.shape,
                                                     input_array.dtype),
                                     trainable=False, use_resource=True)
  K.get_session().run(input_var.initializer)

  # Create a placeholder for the numpy array input slices. We copy the value
  # of the input numpy array to the variable in slices of size 64 MB to avoid
  # running into memory issues or RPC message limits.
  start_placeholder = array_ops.placeholder(dtypes.int64, ())
  end_placeholder = array_ops.placeholder(dtypes.int64, ())
  slice_placeholder = array_ops.placeholder(input_var.dtype)
  assign_slice_op = input_var[start_placeholder:end_placeholder].assign(
      slice_placeholder)

  # If each batch element is > 64 MB, then we copy each batch element
  # individually. Otherwise, the slices will be < 128 MB. There might be padding
  # which might mean that the slices are 128 MB even if the size of the
  # tensor allocated is less than 128 MB.
  # This formula gives slices with size:
  # ceil(64 MB / byte size per batch element) bytes.
  # Using ceil() guarantees we get a number >= 1.

  # Calculate the size of each batch element.
  byte_size_per_batch_element = np.prod(input_array.shape[1:]) * \
                                input_var.dtype.size

  # Calculate number of elements we want to copy per slice.
  batch_size_per_slice = np.ceil((64 << 20) / byte_size_per_batch_element)

  # Copy slices of the above size starting at 0, except the last slice will be
  # smaller.
  start = 0
  limit = input_array.shape[0]
  while start < limit:
    end = min(start + batch_size_per_slice, limit)
    K.get_session().run(assign_slice_op, feed_dict={
        start_placeholder: start,
        end_placeholder: end,
        slice_placeholder: input_array[start:end]})
    start = end

  return input_var

 def _init_writer(self):
   """Sets file writer."""
   if context.executing_eagerly():
     self.writer = summary_ops_v2.create_file_writer(self.log_dir)
   elif self.write_graph:
     self.writer = tf_summary.FileWriter(self.log_dir, K.get_session().graph)
   else:
     self.writer = tf_summary.FileWriter(self.log_dir)

  def set_model(self, model):
    """Sets Keras model and creates summary ops."""

    self.model = model
    self.sess = K.get_session()
    # only make histogram summary op if it hasn't already been made
    if self.histogram_freq and self.merged is None:
      for layer in self.model.layers:
        for weight in layer.weights:
          mapped_weight_name = weight.name.replace(':', '_')
          tf_summary.histogram(mapped_weight_name, weight)
          if self.write_images:
            w_img = array_ops.squeeze(weight)
            shape = K.int_shape(w_img)
            if len(shape) == 2:  # dense layer kernel case
              if shape[0] > shape[1]:
                w_img = array_ops.transpose(w_img)
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img, [1, shape[0], shape[1], 1])
            elif len(shape) == 3:  # convnet case
              if K.image_data_format() == 'channels_last':
                # switch to channels_first to display
                # every kernel as a separate image
                w_img = array_ops.transpose(w_img, perm=[2, 0, 1])
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img,
                                        [shape[0], shape[1], shape[2], 1])
            elif len(shape) == 1:  # bias case
              w_img = array_ops.reshape(w_img, [1, shape[0], 1, 1])
            else:
              # not possible to handle 3D convnets etc.
              continue

            shape = K.int_shape(w_img)
            assert len(shape) == 4 and shape[-1] in [1, 3, 4]
            tf_summary.image(mapped_weight_name, w_img)

        if self.write_grads:
          for weight in layer.trainable_weights:
            mapped_weight_name = weight.name.replace(':', '_')
            grads = model.optimizer.get_gradients(model.total_loss, weight)

            def is_indexed_slices(grad):
              return type(grad).__name__ == 'IndexedSlices'

            grads = [grad.values if is_indexed_slices(grad) else grad
                     for grad in grads]
            tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads)

        if hasattr(layer, 'output'):
          tf_summary.histogram('{}_out'.format(layer.name), layer.output)
    self.merged = tf_summary.merge_all()

    if self.write_graph:
      self.writer = self._writer_class(self.log_dir, self.sess.graph)
    else:
      self.writer = self._writer_class(self.log_dir)

  def __call__(self, inputs):
    assert isinstance(inputs, list)

    # Strip sample weight from inputs
    if (self.execution_mode == model_fn_lib.ModeKeys.TRAIN or
        self.execution_mode == model_fn_lib.ModeKeys.EVAL):
      input_tensors = self.model._feed_inputs + self.model._feed_targets
      inputs = inputs[:len(input_tensors)]
    else:
      input_tensors = self.model._feed_inputs

    shard_inputs = self._split_tensors(inputs)
    del inputs  # To avoid accident usage.

    # Compute an input specification (used to generate infeed enqueue and
    # dequeue operations).  We use the shape from our input array and the
    # dtype from our model.  A user may pass in a float64 for a float32
    # input: for model compatibility we still must generate a float32 infeed.
    input_specs = []

    # We use the shape and dtype from the first shard to compute the input
    # metadata (`input_specs`); all replicas have the same type and shape.
    for tensor, ary in zip(input_tensors, shard_inputs[0]):
      input_specs.append(
          tensor_spec.TensorSpec(ary.shape, tensor.dtype,
                                 _valid_name(tensor.name)))

    # XLA requires every operation in the graph has a fixed shape.  To
    # handle varying batch sizes we recompile a new sub-graph for each
    # unique input shape.
    shape_key = tuple([tuple(spec.shape.as_list()) for spec in input_specs])

    if shape_key not in self._compilation_cache:
      logging.info('New input shapes; (re-)compiling: mode=%s, %s',
                   self.execution_mode, input_specs)
      new_tpu_model_ops = self._specialize_model(input_specs)
      self._compilation_cache[shape_key] = new_tpu_model_ops
      self._test_model_compiles(new_tpu_model_ops)

    tpu_model_ops = self._compilation_cache[shape_key]

    infeed_dict = {}
    for infeed_tensors, inputs in zip(tpu_model_ops.infeed_tensors,
                                      shard_inputs):
      for tensor, value in zip(infeed_tensors, inputs):
        infeed_dict[tensor] = value

    session = K.get_session()
    _, _, outfeed_outputs = session.run([
        tpu_model_ops.infeed_op, tpu_model_ops.execute_op,
        tpu_model_ops.outfeed_op
    ], infeed_dict)

    # TODO(xiejw): Decide how to reduce outputs, or just discard all but first.
    return outfeed_outputs[:len(outfeed_outputs) // self.num_replicas]

 def tpu_session(self):
   """Yields a TPU session and sets it as the default Keras session."""
   with self._graph.as_default():
     default_session = K.get_session()
     # N.B. We have to call `K.set_session()` AND set our session as the
     # TF default. `K.get_session()` surprisingly does not return the value
     # supplied by K.set_session otherwise.
     K.set_session(self._session)
     with self._session.as_default():
       yield self._session
     K.set_session(default_session)

def setup_tpu_session(tpu_name_or_address):
  """Initializes and returns a Keras/TF session connected the TPU `master`.

  Args:
    tpu_name_or_address: A string that is either the name of the Cloud TPU,
      the grpc address of the Cloud TPU, or (Googlers only) the BNS name of the
      Cloud TPU. If tpu_name_or_address is None, the TPUClusterResolver will
      examine the environment to determine a potential Cloud TPU to use.

  Returns:
    A `tf.Session`.
  """
  cluster_resolver = tpu_cluster_resolver.TPUClusterResolver(
      tpu_name_or_address)
  cluster_spec = cluster_resolver.cluster_spec()
  session = tf_session.Session(
      target=cluster_resolver.master(),
      config=config_pb2.ConfigProto(
          isolate_session_state=True))
  if cluster_spec:
    session.cluster_def.CopyFrom(cluster_spec.as_cluster_def())
  K.set_session(session)
  K.get_session().run(tpu.initialize_system())
  return session

  def _test_model_compiles(self, tpu_model_ops):
    """Verifies that the given TPUModelOp can be compiled via XLA."""
    logging.info('Started compiling')
    start_time = time.clock()

    result = K.get_session().run(tpu_model_ops.compile_op)
    proto = tpu_compilation_result.CompilationResultProto()
    proto.ParseFromString(result)
    if proto.status_error_message:
      raise RuntimeError('Compilation failed: {}'.format(
          proto.status_error_message))

    end_time = time.clock()
    logging.info('Finished compiling. Time elapsed: %s secs',
                 end_time - start_time)

def shutdown_tpu_session(session=None):
  """Shutdown the TPU attached to session.

  This should be called to cleanly shut down the TPU system before the client
  exits.

  Args:
    session: Session to shutdown, or None to use the default session.

  Returns:

  """
  if session is None:
    session = K.get_session()

  session.run(tpu.shutdown_system())

#.........这里部分代码省略.........

  if steps_per_epoch is None:
    raise ValueError('`steps_per_epoch` should be specified when calling '
                     '`fit` on the model.')
  steps_per_run = K.variable(
      value=min(steps_per_epoch, current_strategy.extended.steps_per_run),
      dtype='int32',
      name='steps_per_run')

  ctx = current_strategy.extended.experimental_run_steps_on_iterator(
      step_fn, iterator, iterations=steps_per_run,
      initial_loop_values=initial_loop_values)

  train_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  do_validation = bool(validation_steps)

  if model._compile_distribution:
    distributed_training_utils._copy_weights_to_distributed_model(model, mode)

  callbacks = cbks.configure_callbacks(
      callbacks,
      model,
      do_validation=do_validation,
      epochs=epochs,
      steps_per_epoch=steps_per_epoch,
      verbose=verbose,
      count_mode='steps',
      mode=mode)

  # Calculate the steps each time on the device.
  steps_to_run = [current_strategy.extended.steps_per_run] * (
      steps_per_epoch // current_strategy.extended.steps_per_run)
  if steps_per_epoch % current_strategy.extended.steps_per_run:
    steps_to_run.append(
        steps_per_epoch % current_strategy.extended.steps_per_run)

  callbacks._call_begin_hook(mode)
  for epoch in range(initial_epoch, epochs):
    distributed_training_utils._reset_metrics(model)
    callbacks.on_epoch_begin(epoch)
    epoch_logs = {}
    step_index = 0
    prev_step_count = None
    for step_count in steps_to_run:
      batch_logs = {'batch': step_index, 'size': 1, 'num_steps': step_count}
      callbacks._call_batch_hook(mode, 'begin', step_index, batch_logs)
      if prev_step_count is None or step_count != prev_step_count:
        steps_per_run.load(step_count, K.get_session())
        prev_step_count = step_count
      try:
        _, outputs = K.get_session().run([train_op, output_tensors])
      except errors.OutOfRangeError:
        logging.warning('Your dataset iterator ran out of data; '
                        'interrupting training. Make sure that your dataset '
                        'can generate at least `steps_per_epoch * epochs` '
                        'batches (in this case, %d batches).' %
                        steps_per_epoch * epochs)
        break

      batch_logs.update(outputs)
      callbacks._call_batch_hook(mode, 'end', step_index, batch_logs)
      step_index = step_index + step_count
      if callbacks.model.stop_training:
        break

    if (do_validation and
        training_utils.should_run_validation(validation_freq, epoch)):
      logging.info('Running validation at fit epoch: %s', epoch)

      if model._compile_distribution:
        # Since we create a new clone from the original model we need to copy
        # the weights back to the original model before we can run validation.
        distributed_training_utils._copy_weights_to_original_model(
            model, ModeKeys.TRAIN)

      val_outs = experimental_tpu_test_loop(  # pylint: disable=undefined-variable
          model,
          val_dataset,
          steps=validation_steps,
          verbose=verbose,
          callbacks=callbacks)
      if not isinstance(val_outs, list):
        val_outs = [val_outs]
      # Same labels assumed.
      for label, val_out in zip(out_labels, val_outs):
        epoch_logs['val_' + label] = val_out

    callbacks.on_epoch_end(epoch, epoch_logs)
    if callbacks.model.stop_training:
      break
  callbacks._call_end_hook(mode)

  if model._compile_distribution:
    # Copy the weights back from the replicated model to the original model.
    distributed_training_utils._copy_weights_to_original_model(
        model, ModeKeys.TRAIN)
  scope.__exit__(None, None, None)
  return model.history

def _get_available_devices():
  return [x.name for x in K.get_session().list_devices()]

def _experimental_fit_loop(
    model,
    iterator,
    epochs=100,
    initial_epoch=0,
    steps_per_epoch=None):
  """fit function when using TPU DistributionStrategy for training.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator that returns inputs and targets
      epochs: Number of times to iterate over the data
      initial_epoch: Epoch at which to start training
          (useful for resuming a previous training run)
      steps_per_epoch: Total number of steps (batches of samples)
          before declaring one epoch finished and starting the
          next epoch. Ignored with the default value of `None`.

  Returns:
      Returns `None`.

  Raises:
      ValueError: in case of invalid arguments.
  """
  current_strategy = model._distribution_strategy

  # TODO(priyag): Add validation that shapes are fully defined for TPU case.

  # TODO(priyag, sourabhbajaj): This should be moved into a callback instead.
  K.get_session().run(current_strategy.initialize())

  def _per_device_train_function(model):
    model._make_train_function()
    return (model.train_function.inputs,
            model.train_function.outputs,
            model.train_function.updates_op,
            model.train_function.session_kwargs)

  # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
  K.set_learning_phase(1)

  def step_fn(ctx, inputs, targets):
    """Clones the model and calls make_train_function."""
    # TODO(priyag, sourabhbajaj): Should cache this keyed on input shapes.
    clone_model_on_towers(
        model,
        current_strategy,
        make_callback_model=True,
        inputs=inputs,
        targets=targets)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_tower(
         _per_device_train_function, model._grouped_model)
    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs,
         grouped_updates, grouped_session_args, with_loss_tensor=True)
    combined_fn = K.Function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_train_function',
        **all_session_args)

    # TODO(priyag, sourabhbajaj): Perhaps the aggregation type needs to be
    # something else for different outputs.
    out_labels = model.metrics_names or []
    for label, output in zip(out_labels, combined_fn.outputs):
      ctx.set_last_step_output(label, output,
                               aggregation=distribute_lib.get_loss_reduction())

    # TODO(priyag, sourabhbajaj): Ignoring these things from the combined_fn:
    # feed_dict, session kwargs, run options, run_metadata for now. These should
    # be handled appropriately
    return combined_fn.updates_op

  # Add initial dummy values for loss and other metric tensors.
  initial_loop_values = {}
  initial_loop_values['loss'] = constant_op.constant(1e7)
  for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
    initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)

  with current_strategy.scope():
    # TODO(priyag, sourabhbajaj): Adjust steps_per_run appropriately based on
    # steps_per_epoch and number of epochs.
    ctx = current_strategy.run_steps_on_dataset(
        step_fn, iterator, iterations=current_strategy.steps_per_run,
        initial_loop_values=initial_loop_values)

  train_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  assert steps_per_epoch is not None
#.........这里部分代码省略.........

def _experimental_predict_loop(model, iterator, verbose=0, steps=None):
  """Predict loop for predicting with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring `_predict_loop` finished.
          Ignored with the default value of `None`.

  Returns:
      Array of predictions (if the model has a single output)
      or list of arrays of predictions
      (if the model has multiple outputs).
  """
  current_strategy = model._distribution_strategy
  K.get_session().run(current_strategy.initialize())

  # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
  K.set_learning_phase(0)

  def _per_device_predict_function(model):
    model._make_predict_function()
    return (model.predict_function.inputs,
            model.predict_function.outputs,
            model.predict_function.updates_op,
            model.predict_function.session_kwargs)

  def step_fn(ctx, *inputs):
    """Clones the model and calls make_predict_function."""

    # TODO(priyag, sourabhbajaj): The model gets cloned every time
    # fit/test/predict is called. We should look into caching this keyed on
    # input shapes.
    clone_model_on_replicas(
        model,
        current_strategy,
        make_callback_model=False,
        inputs=inputs,
        mode=_Mode.PREDICT)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_predict_function, args=(model._grouped_model_predict,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_predict_function',
        **all_session_args)

    for label, output in zip(model.output_names, combined_fn.outputs):
      ctx.set_last_step_output(label, output)

    return combined_fn.updates_op

  # Add initial dummy values for outputs.
  initial_loop_values = {}
  batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
  for name, tensor in zip(model.output_names, model.outputs):
    # TODO(priyag): This is a workaround as we do not know the batch dimension
    # of the model's output at this point.
    shape = tensor_shape.TensorShape(tensor.shape.dims)
    shape.dims = [batch_dimension] + shape.dims[1:]
    initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)

  with current_strategy.scope():
    # TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
    ctx = current_strategy.run_steps_on_dataset(
        step_fn, iterator, iterations=1,
        initial_loop_values=initial_loop_values)

  predict_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model_predict)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  assert steps is not None
  # Since we do not know how many samples we will see, we cannot pre-allocate
  # the returned Numpy arrays. Instead, we store one array per batch seen
  # and concatenate them upon returning.
  unconcatenated_outs = [[] for _ in model.outputs]
  for step in range(steps):
    _, batch_outs = K.get_session().run([predict_op, output_tensors])
    # TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
    for i, label in enumerate(model.output_names):
#.........这里部分代码省略.........

def _experimental_test_loop(model, iterator, verbose=0, steps=None,
                            initialize_finalize_strategy=True):
  """Test loop for evaluating with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.
      initialize_finalize_strategy: Should the strategy initialize and finalize
          functions be called.

  Returns:
      Scalar loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the outputs.
  """
  current_strategy = model._distribution_strategy
  if initialize_finalize_strategy:
    K.get_session().run(current_strategy.initialize())

  def _per_device_eval_function(model):
    model._make_eval_function()
    return (model._eval_function.inputs, model._eval_function.outputs,
            model._eval_function.updates_op,
            model._eval_function.session_kwargs)

  # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
  K.set_learning_phase(0)

  def step_fn(ctx, inputs, targets):
    """Clones the model and calls make_eval_function."""
    # TODO(priyag, sourabhbajaj): The model gets cloned every time
    # fit/test/predict is called. We should look into caching this keyed on
    # input shapes.
    clone_model_on_replicas(
        model,
        current_strategy,
        make_callback_model=False,
        inputs=inputs,
        targets=targets,
        mode=_Mode.TEST)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_eval_function, args=(model._grouped_model_test,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_test_function',
        **all_session_args)

    for label, output in zip(model.metrics_names, combined_fn.outputs):
      if label == 'loss':
        aggregation = distribute_lib.get_loss_reduction()
      else:
        # We aggregate all other metrics using mean for now. This is temporary
        # workaround until new metrics are in place.
        aggregation = variable_scope.VariableAggregation.MEAN
      ctx.set_last_step_output(label, output, aggregation)

    return combined_fn.updates_op

  # Add initial dummy values for loss and other metric tensors.
  initial_loop_values = {}
  initial_loop_values['loss'] = constant_op.constant(1e7)
  for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
    initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)

  with current_strategy.scope():
    # TODO(priyag): Use steps_per_run when we use new metrics as they will
    # allow handling metric computation at each step using variables.
    ctx = current_strategy.run_steps_on_dataset(
        step_fn, iterator, iterations=1,
        initial_loop_values=initial_loop_values)

  test_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model_test)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  assert steps is not None
  outs = [0.] * len(model.metrics_names)
#.........这里部分代码省略.........