(self, dequeue_fn)
return eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks
Converts user provided model_fn` as a single eval step on TPU.

    Similar to training, the user provided `model_fn` takes input tuple
    (features, labels) and produces the TPUEstimatorSpec with eval_metrics for
    eval `mode`. This usually represents a single evaluation computation on CPU.

    For TPU evaluation, a eval (computation) step is first wrapped in a
    tf.while_loop control flow to repeat for many times and then replicated to
    all TPU shards. Besides the input and output are slightly different. Input,
    features and labels, should be taken from TPU infeed rather than input
    pipeline (input_fn) directly. Output is managed in two stages.  First, the
    model outputs as the result of evaluation computation, usually model logits,
    should be transferred from TPU system to CPU. Then, all model outputs are
    concatenated first on CPU and sent to the metric_fn for metrics computation.
    To fit TPU evaluation pattern, the original eval computation should be
    reformed, which is the returned `eval_step`.

    Args:
      dequeue_fn: The function to retrieve inputs, features and labels, from TPU
        infeed dequeue channel.

    Returns:
      A tuple of eval_fn, host_calls, and captured scaffold_fn. The eval_fn
      representing the eval step for TPU.
Converts user provided model_fn` as a single eval step on TPU.
Converts user provided model_fn as a single eval step on TPU .
def convert_to_single_tpu_eval_step(self, dequeue_fn):
    """Converts user provided model_fn` as a single eval step on TPU.

    Similar to training, the user provided `model_fn` takes input tuple
    (features, labels) and produces the TPUEstimatorSpec with eval_metrics for
    eval `mode`. This usually represents a single evaluation computation on CPU.

    For TPU evaluation, a eval (computation) step is first wrapped in a
    tf.while_loop control flow to repeat for many times and then replicated to
    all TPU shards. Besides the input and output are slightly different. Input,
    features and labels, should be taken from TPU infeed rather than input
    pipeline (input_fn) directly. Output is managed in two stages.  First, the
    model outputs as the result of evaluation computation, usually model logits,
    should be transferred from TPU system to CPU. Then, all model outputs are
    concatenated first on CPU and sent to the metric_fn for metrics computation.
    To fit TPU evaluation pattern, the original eval computation should be
    reformed, which is the returned `eval_step`.

    Args:
      dequeue_fn: The function to retrieve inputs, features and labels, from TPU
        infeed dequeue channel.

    Returns:
      A tuple of eval_fn, host_calls, and captured scaffold_fn. The eval_fn
      representing the eval step for TPU.
    """
    host_calls = _OutfeedHostCall(self._ctx)
    captured_scaffold_fn = _CapturedObject()
    captured_eval_hooks = _CapturedObject()

    def eval_step(total_loss, *cache):
      """Evaluation step function for use inside a while loop."""
      inputs = dequeue_fn()
      features, labels = inputs.features_and_labels()

      # Consume the current cache
      tpu_estimator_spec = self._call_model_fn(features, labels, cache=cache)
      if not isinstance(tpu_estimator_spec, model_fn_lib._TPUEstimatorSpec):  # pylint: disable=protected-access
        raise RuntimeError(
            'estimator_spec used by TPU evaluation must have type'
            '`TPUEstimatorSpec`. Got {}'.format(type(tpu_estimator_spec)))

      # Retrieve the new returned cache
      cache = tpu_estimator_spec.cache
      loss = tpu_estimator_spec.loss

      captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn)
      captured_eval_hooks.capture(tpu_estimator_spec.evaluation_hooks)

      to_record = {}
      if tpu_estimator_spec.eval_metrics:
        to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics
      if tpu_estimator_spec.host_call is not None:
        # We assume that evaluate won't update global step, so we don't wrap
        # this host_call.
        to_record['host_call'] = tpu_estimator_spec.host_call
      host_calls.record(to_record)

      with ops.control_dependencies(host_calls.create_enqueue_op()):
        return [math_ops.add(total_loss, loss)] + cache

    return eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks
def convert_to_single_tpu_eval_step ( self , dequeue_fn ) : host_calls = _OutfeedHostCall ( self . _ctx ) captured_scaffold_fn = _CapturedObject ( ) captured_eval_hooks = _CapturedObject ( ) def eval_step ( total_loss , * cache ) : """Evaluation step function for use inside a while loop.""" inputs = dequeue_fn ( ) features , labels = inputs . features_and_labels ( ) # Consume the current cache tpu_estimator_spec = self . _call_model_fn ( features , labels , cache = cache ) if not isinstance ( tpu_estimator_spec , model_fn_lib . _TPUEstimatorSpec ) : # pylint: disable=protected-access raise RuntimeError ( 'estimator_spec used by TPU evaluation must have type' '`TPUEstimatorSpec`. Got {}' . format ( type ( tpu_estimator_spec ) ) ) # Retrieve the new returned cache cache = tpu_estimator_spec . cache loss = tpu_estimator_spec . loss captured_scaffold_fn . capture ( tpu_estimator_spec . scaffold_fn ) captured_eval_hooks . capture ( tpu_estimator_spec . evaluation_hooks ) to_record = { } if tpu_estimator_spec . eval_metrics : to_record [ 'eval_metrics' ] = tpu_estimator_spec . eval_metrics if tpu_estimator_spec . host_call is not None : # We assume that evaluate won't update global step, so we don't wrap # this host_call. to_record [ 'host_call' ] = tpu_estimator_spec . host_call host_calls . record ( to_record ) with ops . control_dependencies ( host_calls . create_enqueue_op ( ) ) : return [ math_ops . add ( total_loss , loss ) ] + cache return eval_step , host_calls , captured_scaffold_fn , captured_eval_hooks