Использование InMemoryEvaluatorHook с исключением исключений TPU - PullRequest
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Использование InMemoryEvaluatorHook с исключением исключений TPU

0 голосов
/ 09 марта 2019

Я пытался использовать InMemoryEvaluatorHook с TPUEstimator для получения статистики проверки во время обучения моей модели.Использование цикла estimator.train() и estimator.evaluate() было слишком дорогим, поскольку он перестраивал график каждую эпоху, вместо того, чтобы пытаться использовать его повторно (как указано в этом выпуске: https://github.com/tensorflow/tensorflow/issues/13895). Это основной код, который я использую: 

estimator = tf.contrib.tpu.TPUEstimator(
    model_fn=model_fn,
    config=run_config,
    use_tpu=True,
    train_batch_size=self.batch_size,
    eval_batch_size=self.batch_size,
    predict_batch_size=self.batch_size,
    params={})

train_fn = lambda params: input_fn(
    'train', self.data_dir, batch_size=params['batch_size'], train=True)
val_fn = lambda params: input_fn(
    'validation',
    self.data_dir,
    batch_size=params['batch_size'],
    train=False)
train_hook = tf.contrib.estimator.InMemoryEvaluatorHook(
    estimator,
    val_fn,
    steps=self.steps_per_val_epoch,
    every_n_iter=self.steps_per_epoch)
estimator.train(
    input_fn=train_fn,
    steps=self.steps_per_epoch * self.max_num_training_epochs,
    hooks=[
        train_hook,
    ])

Это привело к следующей ошибке: 

Traceback (most recent call last):
  File "dev/google_communicator/worker.py", line 160, in <module>
    main()
  File "dev/google_communicator/worker.py", line 133, in main
    results = evaluator.eval(inputs, outputs)
  File "/darch/deep_architect/contrib/misc/evaluators/tensorflow/tpu_estimator_classification.py", line 278, in eval
    train_hook,
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2409, in train
    rendezvous.raise_errors()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/error_handling.py", line 128, in raise_errors
    six.reraise(typ, value, traceback)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2403, in train
    saving_listeners=saving_listeners
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 354, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1207, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1241, in _train_model_default
    saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1468, in _train_with_estimator_spec
    log_step_count_steps=log_step_count_steps) as mon_sess:
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 504, in MonitoredTrainingSession
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 921, in __init__
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 631, in __init__
    h.begin()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/estimator/python/estimator/hooks.py", line 135, in begin
    self._input_fn, self._hooks, checkpoint_path=None)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1484, in _evaluate_build_graph
    self._call_model_fn_eval(input_fn, self.config))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1520, in _call_model_fn_eval
    features, labels, model_fn_lib.ModeKeys.EVAL, config)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2195, in _call_model_fn
    features, labels, mode, config)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1195, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2631, in _model_fn
    rendezvous=self._rendezvous[mode]),
KeyError: 'eval'

Есть ли лучший способ получить статистику проверки для каждой эпохи с TPU? Если нет, как вы должны выполнять проверку?

Редактировать : я, похоже, обошел эту ошибку, выполнив estimator.train() и estimator.evaluate() за один шаг без крючка, а затем выполнив полное обучение с крючком. К сожалению, послеПри первой оценке возникает ошибка при перезапуске обучения: 

Traceback (most recent call last):
  File "dev/google_communicator/worker.py", line 160, in <module>
    main()
  File "dev/google_communicator/worker.py", line 133, in main
    results = evaluator.eval(inputs, outputs)
  File "/darch/deep_architect/contrib/misc/evaluators/tensorflow/tpu_estimator_classification.py", line 329, in eval
    train_hook,
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2409, in train
    rendezvous.raise_errors()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/error_handling.py", line 128, in raise_errors
    six.reraise(typ, value, traceback)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2403, in train
    saving_listeners=saving_listeners
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 354, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1207, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1241, in _train_model_default
    saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1471, in _train_with_estimator_spec
    _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 671, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1156, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1255, in run
    raise six.reraise(*original_exc_info)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1240, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1312, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1076, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: The TPU system has not been initialized.
         [[{{node TPUReplicate/_compile/_14248540389241865347/_28}} = TPUCompile[NumDynamicShapes=0, Tguaranteed_constants=[], function=cluster_18378946049549366873_f15n_0[], metadata="\n\006\010...6\323\352L", num_computations=1, _device="/job:worker/replica:0/task:0/device:CPU:0"](^cluster/control_before/_0)]]
         [[{{node tpu_compile_succeeded_assert/_1897752282630996029/_29_G679}} = _Recv[client_terminated=false, recv_device="/job:worker/replica:0/task:0/device:TPU:2", send_device="/job:worker/replica:0/task:0/device:CPU:0", send_device_incarnation=2337451129362726278, tensor_name="edge_174_tpu_compile_succeeded_assert/_1897752282630996029/_29", tensor_type=DT_FLOAT, _device="/job:worker/replica:0/task:0/device:TPU:2"]()]]

Чтобы уточнить, перед тем, как выдается ошибка, происходит следующее: два инициализирующих поезда и оценки обращаются к оценщику, обучение для одной эпохи,оценка на проверочном наборе. Когда оценщик пытается перезапустить обучение, выдается это исключение.

Эта открытая проблема может быть актуальной: https://github.com/tensorflow/tensor2tensor/issues/1202

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