Python gfile.DeleteRecursively方法代码示例

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def garbage_collect_exports(export_dir_base, exports_to_keep):
  """Deletes older exports, retaining only a given number of the most recent.

  Export subdirectories are assumed to be named with monotonically increasing
  integers; the most recent are taken to be those with the largest values.

  Args:
    export_dir_base: the base directory under which each export is in a
      versioned subdirectory.
    exports_to_keep: the number of recent exports to retain.
  """
  if exports_to_keep is None:
    return

  keep_filter = gc.largest_export_versions(exports_to_keep)
  delete_filter = gc.negation(keep_filter)
  for p in delete_filter(gc.get_paths(export_dir_base,
                                      parser=_export_version_parser)):
    try:
      gfile.DeleteRecursively(p.path)
    except errors_impl.NotFoundError as e:
      logging.warn('Can not delete %s recursively: %s', p.path, e) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testLoadExistingVariablesDifferentShapeDefaultDoesNotAllowReshape(self):
    model_dir = tempfile.mkdtemp('load_existing_vars_no_reshape')
    if gfile.Exists(model_dir):
      gfile.DeleteRecursively(model_dir)

    init_value0 = [[10.0, 11.0]]
    init_value1 = 20.0
    var_names_to_values = {'v0': init_value0, 'v1': init_value1}

    with self.cached_session() as sess:
      model_path = self.create_checkpoint_from_values(var_names_to_values,
                                                      model_dir)
      var0 = variables_lib2.variable('my_var0', shape=[2, 1])
      var1 = variables_lib2.variable('my_var1', shape=[])

      vars_to_restore = {'v0': var0, 'v1': var1}
      init_fn = variables_lib2.assign_from_checkpoint_fn(
          model_path, vars_to_restore)

      # Initialize the variables.
      sess.run(variables_lib.global_variables_initializer())

      # Perform the assignment.
      with self.assertRaises(errors_impl.InvalidArgumentError):
        init_fn(sess) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def get_summary_writer(tensorboard_dir):
  """Creates a directory for writing summaries and returns a writer."""
  tf.logging.info('TensorBoard directory: %s', tensorboard_dir)
  tf.logging.info('Deleting prior data if exists...')
  try:
    gfile.DeleteRecursively(tensorboard_dir)
  except errors.OpError as err:
    tf.logging.error('Directory did not exist? Error: %s', err)
  tf.logging.info('Deleted! Creating the directory again...')
  gfile.MakeDirs(tensorboard_dir)
  tf.logging.info('Created! Instatiating SummaryWriter...')
  summary_writer = tf.summary.FileWriter(tensorboard_dir)
  return summary_writer 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def tearDownModule():
  gfile.DeleteRecursively(test.get_temp_dir()) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def garbage_collect_exports(export_dir_base, exports_to_keep):
  """Deletes older exports, retaining only a given number of the most recent.

  Export subdirectories are assumed to be named with monotonically increasing
  integers; the most recent are taken to be those with the largest values.

  Args:
    export_dir_base: the base directory under which each export is in a
      versioned subdirectory.
    exports_to_keep: the number of recent exports to retain.
  """
  if exports_to_keep is None:
    return

  keep_filter = gc.largest_export_versions(exports_to_keep)
  delete_filter = gc.negation(keep_filter)

  # Export dir must not end with / or it will break the re match below.
  if export_dir_base.endswith('/'):
    export_dir_base = export_dir_base[:-1]

  # create a simple parser that pulls the export_version from the directory.
  def parser(path):
    match = re.match('^' + export_dir_base + '/(\\d{13})$', path.path)
    if not match:
      return None
    return path._replace(export_version=int(match.group(1)))

  for p in delete_filter(gc.get_paths(export_dir_base, parser=parser)):
    gfile.DeleteRecursively(p.path) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def main():
    trainSetFileNames = os.path.join(FLAGS.data_dir, FLAGS.trainLabels_fn)
    testSetFileNames = os.path.join(FLAGS.data_dir, FLAGS.testLabels_fn)
    
    if not (os.path.exists(trainSetFileNames) & os.path.exists(testSetFileNames)):
        GetLSPData.main()
        
    #if gfile.Exists(FLAGS.train_dir):
        #gfile.DeleteRecursively(FLAGS.train_dir)
    if not gfile.Exists(FLAGS.train_dir):
        gfile.MakeDirs(FLAGS.train_dir)
    
    train(trainSetFileNames, testSetFileNames) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testLoadExistingVariables(self):
    model_dir = tempfile.mkdtemp('load_existing_variables')
    if gfile.Exists(model_dir):
      gfile.DeleteRecursively(model_dir)

    init_value0 = 10.0
    init_value1 = 20.0
    var_names_to_values = {'v0': init_value0, 'v1': init_value1}

    with self.cached_session() as sess:
      model_path = self.create_checkpoint_from_values(var_names_to_values,
                                                      model_dir)
      var0 = variables_lib2.variable('my_var0', shape=[])
      var1 = variables_lib2.variable('my_var1', shape=[])

      vars_to_restore = {'v0': var0, 'v1': var1}
      init_fn = variables_lib2.assign_from_checkpoint_fn(
          model_path, vars_to_restore)

      # Initialize the variables.
      sess.run(variables_lib.global_variables_initializer())

      # Perform the assignment.
      init_fn(sess)

      # Request and test the variable values:
      self.assertEqual(init_value0, var0.eval())
      self.assertEqual(init_value1, var1.eval()) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testLoadExistingVariablesDifferentShapeAllowReshape(self):
    model_dir = tempfile.mkdtemp(
        'load_existing_variables_different_shape_allow_reshape')
    if gfile.Exists(model_dir):
      gfile.DeleteRecursively(model_dir)

    init_value0 = [[10.0, 11.0]]
    init_value1 = 20.0
    var_names_to_values = {'v0': init_value0, 'v1': init_value1}

    with self.cached_session() as sess:
      model_path = self.create_checkpoint_from_values(var_names_to_values,
                                                      model_dir)
      var0 = variables_lib2.variable('my_var0', shape=[2, 1])
      var1 = variables_lib2.variable('my_var1', shape=[])

      vars_to_restore = {'v0': var0, 'v1': var1}
      init_fn = variables_lib2.assign_from_checkpoint_fn(
          model_path, vars_to_restore, reshape_variables=True)

      # Initialize the variables.
      sess.run(variables_lib.global_variables_initializer())

      # Perform the assignment.
      init_fn(sess)

      # Request and test the variable values:
      self.assertAllEqual(np.transpose(np.array(init_value0)), var0.eval())
      self.assertEqual(init_value1, var1.eval()) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testNotFoundError(self):
    model_dir = tempfile.mkdtemp('not_found_error')
    if gfile.Exists(model_dir):
      gfile.DeleteRecursively(model_dir)

    init_value0 = 10.0
    init_value1 = 20.0
    var_names_to_values = {'v0': init_value0, 'v1': init_value1}

    with self.cached_session() as sess:
      model_path = self.create_checkpoint_from_values(var_names_to_values,
                                                      model_dir)
      var0 = variables_lib2.variable('my_var0', shape=[])
      var1 = variables_lib2.variable('my_var1', shape=[])
      var2 = variables_lib2.variable('my_var2', shape=[])

      vars_to_restore = {'v0': var0, 'v1': var1, 'v2': var2}
      init_fn = variables_lib2.assign_from_checkpoint_fn(
          model_path, vars_to_restore)

      # Initialize the variables.
      sess.run(variables_lib.global_variables_initializer())

      # Perform the assignment.
      with self.assertRaises(errors_impl.NotFoundError):
        init_fn(sess) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testMissingVariablesList(self):
    model_dir = tempfile.mkdtemp('missing_variables_list')
    if gfile.Exists(model_dir):
      gfile.DeleteRecursively(model_dir)

    init_value0 = 10.0
    init_value1 = 20.0
    var_names_to_values = {'v0': init_value0, 'v1': init_value1}

    with self.cached_session() as sess:
      model_path = self.create_checkpoint_from_values(var_names_to_values,
                                                      model_dir)
      var0 = variables_lib2.variable('v0', shape=[])
      var1 = variables_lib2.variable('v1', shape=[])
      var2 = variables_lib2.variable('v2', shape=[])

      vars_to_restore = [var0, var1, var2]
      init_fn = variables_lib2.assign_from_checkpoint_fn(
          model_path, vars_to_restore, ignore_missing_vars=True)

      # Initialize the variables.
      sess.run(variables_lib.global_variables_initializer())

      # Perform the assignment.
      init_fn(sess)

      # Request and test the variable values:
      self.assertEqual(init_value0, var0.eval())
      self.assertEqual(init_value1, var1.eval()) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testSummariesAreFlushedToDisk(self):
    checkpoint_dir = tempfile.mkdtemp('summaries_are_flushed')
    logdir = tempfile.mkdtemp('summaries_are_flushed_eval')
    if gfile.Exists(logdir):
      gfile.DeleteRecursively(logdir)

    # Train a Model to completion:
    self._train_model(checkpoint_dir, num_steps=300)

    # Create the model (which can be restored).
    inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
    logistic_classifier(inputs)

    names_to_values = {'bread': 3.4, 'cheese': 4.5, 'tomato': 2.0}

    for k in names_to_values:
      v = names_to_values[k]
      summary_lib.scalar(k, v)

    evaluation.evaluate_repeatedly(
        checkpoint_dir=checkpoint_dir,
        hooks=[
            evaluation.SummaryAtEndHook(log_dir=logdir),
        ],
        max_number_of_evaluations=1)

    self._verify_events(logdir, names_to_values) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testSummaryAtEndHookWithoutSummaries(self):
    logdir = tempfile.mkdtemp('summary_at_end_hook_without_summaires')
    if gfile.Exists(logdir):
      gfile.DeleteRecursively(logdir)

    with ops.Graph().as_default():
      # Purposefully don't add any summaries. The hook will just dump the
      # GraphDef event.
      hook = evaluation.SummaryAtEndHook(log_dir=logdir)
      hook.begin()
      with self.cached_session() as session:
        hook.after_create_session(session, None)
        hook.end(session)
    self._verify_events(logdir, {}) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  if gfile.Exists(FLAGS.eval_dir):
    gfile.DeleteRecursively(FLAGS.eval_dir)
  gfile.MakeDirs(FLAGS.eval_dir)
  evaluate() 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def main(argv=None):  # pylint: disable=unused-argument
  if gfile.Exists(FLAGS.train_dir):
    gfile.DeleteRecursively(FLAGS.train_dir)
  gfile.MakeDirs(FLAGS.train_dir)
  train() 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import DeleteRecursively [as 别名]
def testRecoverSession(self):
    # Create a checkpoint.
    checkpoint_dir = os.path.join(self.get_temp_dir(), "recover_session")
    try:
      gfile.DeleteRecursively(checkpoint_dir)
    except errors.OpError:
      pass                      # Ignore
    gfile.MakeDirs(checkpoint_dir)

    with tf.Graph().as_default():
      v = tf.Variable(1, name="v")
      sm = tf.train.SessionManager(ready_op=tf.report_uninitialized_variables())
      saver = tf.train.Saver({"v": v})
      sess, initialized = sm.recover_session("", saver=saver,
                                             checkpoint_dir=checkpoint_dir)
      self.assertFalse(initialized)
      sess.run(v.initializer)
      self.assertEquals(1, sess.run(v))
      saver.save(sess, os.path.join(checkpoint_dir,
                                    "recover_session_checkpoint"))
    # Create a new Graph and SessionManager and recover.
    with tf.Graph().as_default():
      v = tf.Variable(2, name="v")
      with self.test_session():
        self.assertEqual(False, tf.is_variable_initialized(v).eval())
      sm2 = tf.train.SessionManager(
          ready_op=tf.report_uninitialized_variables())
      saver = tf.train.Saver({"v": v})
      sess, initialized = sm2.recover_session("", saver=saver,
                                              checkpoint_dir=checkpoint_dir)
      self.assertTrue(initialized)
      self.assertEqual(
          True, tf.is_variable_initialized(
              sess.graph.get_tensor_by_name("v:0")).eval(session=sess))
      self.assertEquals(1, sess.run(v))