Python tensorflow.py_func方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def add_noise(ids, sequence_length):
  """Wraps add_noise_python for a batch of tensors."""

  def _add_noise_single(ids, sequence_length):
    noisy_ids = add_noise_python(ids[:sequence_length])
    noisy_sequence_length = len(noisy_ids)
    ids[:noisy_sequence_length] = noisy_ids
    ids[noisy_sequence_length:] = 0
    return ids, np.int32(noisy_sequence_length)

  noisy_ids, noisy_sequence_length = tf.map_fn(
      lambda x: tf.py_func(_add_noise_single, x, [ids.dtype, tf.int32]),
      [ids, sequence_length],
      dtype=[ids.dtype, tf.int32],
      back_prop=False)

  noisy_ids.set_shape(ids.get_shape())
  noisy_sequence_length.set_shape(sequence_length.get_shape())

  return noisy_ids, noisy_sequence_length


# Step 3 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def _py_func_with_gradient(func, inp, Tout, stateful=True, name=None,
                           grad_func=None):
    """
    PyFunc defined as given by Tensorflow
    :param func: Custom Function
    :param inp: Function Inputs
    :param Tout: Ouput Type of out Custom Function
    :param stateful: Calculate Gradients when stateful is True
    :param name: Name of the PyFunction
    :param grad: Custom Gradient Function
    :return:
    """
    # Generate random name in order to avoid conflicts with inbuilt names
    rnd_name = 'PyFuncGrad-' + '%0x' % getrandbits(30 * 4)

    # Register Tensorflow Gradient
    tf.RegisterGradient(rnd_name)(grad_func)

    # Get current graph
    g = tf.get_default_graph()

    # Add gradient override map
    with g.gradient_override_map(
            {"PyFunc": rnd_name, "PyFuncStateless": rnd_name}):
        return tf.py_func(func, inp, Tout, stateful=stateful, name=name) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def simulate(self, action):
    """Step the batch of environments.

    The results of the step can be accessed from the variables defined below.

    Args:
      action: Tensor holding the batch of actions to apply.

    Returns:
      Operation.
    """
    with tf.name_scope('environment/simulate'):
      if action.dtype in (tf.float16, tf.float32, tf.float64):
        action = tf.check_numerics(action, 'action')
      observ_dtype = self._parse_dtype(self._batch_env.observation_space)
      observ, reward, done = tf.py_func(
          lambda a: self._batch_env.step(a)[:3], [action],
          [observ_dtype, tf.float32, tf.bool], name='step')
      observ = tf.check_numerics(observ, 'observ')
      reward = tf.check_numerics(reward, 'reward')
      return tf.group(
          self._observ.assign(observ),
          self._action.assign(action),
          self._reward.assign(reward),
          self._done.assign(done)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def reset(self, indices=None):
    """Reset the batch of environments.

    Args:
      indices: The batch indices of the environments to reset; defaults to all.

    Returns:
      Batch tensor of the new observations.
    """
    if indices is None:
      indices = tf.range(len(self._batch_env))
    observ_dtype = self._parse_dtype(self._batch_env.observation_space)
    observ = tf.py_func(
        self._batch_env.reset, [indices], observ_dtype, name='reset')
    observ = tf.check_numerics(observ, 'observ')
    reward = tf.zeros_like(indices, tf.float32)
    done = tf.zeros_like(indices, tf.bool)
    with tf.control_dependencies([
        tf.scatter_update(self._observ, indices, observ),
        tf.scatter_update(self._reward, indices, reward),
        tf.scatter_update(self._done, indices, done)]):
      return tf.identity(observ) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def simulate(self, action):
    """Step the batch of environments.

    The results of the step can be accessed from the variables defined below.

    Args:
      action: Tensor holding the batch of actions to apply.

    Returns:
      Operation.
    """
    with tf.name_scope('environment/simulate'):
      if action.dtype in (tf.float16, tf.float32, tf.float64):
        action = tf.check_numerics(action, 'action')
      observ_dtype = utils.parse_dtype(self._batch_env.observation_space)
      observ, reward, done = tf.py_func(
          lambda a: self._batch_env.step(a)[:3], [action],
          [observ_dtype, tf.float32, tf.bool], name='step')
      observ = tf.check_numerics(observ, 'observ')
      reward = tf.check_numerics(reward, 'reward')
      reward.set_shape((len(self),))
      done.set_shape((len(self),))
      with tf.control_dependencies([self._observ.assign(observ)]):
        return tf.identity(reward), tf.identity(done) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def _reset_non_empty(self, indices):
    """Reset the batch of environments.

    Args:
      indices: The batch indices of the environments to reset; defaults to all.

    Returns:
      Batch tensor of the new observations.
    """
    observ_dtype = utils.parse_dtype(self._batch_env.observation_space)
    observ = tf.py_func(
        self._batch_env.reset, [indices], observ_dtype, name='reset')
    observ = tf.check_numerics(observ, 'observ')
    with tf.control_dependencies([
        tf.scatter_update(self._observ, indices, observ)]):
      return tf.identity(observ) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def rouge_l_fscore(predictions, labels, **unused_kwargs):
  """ROUGE scores computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge_l_fscore: approx rouge-l f1 score.
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
                               tf.float32)
  return rouge_l_f_score, tf.constant(1.0) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def rouge_2_fscore(predictions, labels, **unused_kwargs):
  """ROUGE-2 F1 score computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge2_fscore: approx rouge-2 f1 score.
  """

  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_2_f_score = tf.py_func(rouge_n, (outputs, labels), tf.float32)
  return rouge_2_f_score, tf.constant(1.0) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def bleu_score(predictions, labels, **unused_kwargs):
  """BLEU score computation between labels and predictions.

  An approximate BLEU scoring method since we do not glue word pieces or
  decode the ids and tokenize the output. By default, we use ngram order of 4
  and use brevity penalty. Also, this does not have beam search.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    bleu: int, approx bleu score
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])

  bleu = tf.py_func(compute_bleu, (labels, outputs), tf.float32)
  return bleu, tf.constant(1.0) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def compute_gradients(self, loss, var_list, **kwargs):
        grads_and_vars = tf.train.AdamOptimizer.compute_gradients(self, loss, var_list, **kwargs)
        grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]
        flat_grad = tf.concat([tf.reshape(g, (-1,)) for g, v in grads_and_vars], axis=0)
        shapes = [v.shape.as_list() for g, v in grads_and_vars]
        sizes = [int(np.prod(s)) for s in shapes]

        num_tasks = self.comm.Get_size()
        buf = np.zeros(sum(sizes), np.float32)

        def _collect_grads(flat_grad):
            self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
            np.divide(buf, float(num_tasks), out=buf)
            return buf

        avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
        avg_flat_grad.set_shape(flat_grad.shape)
        avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
        avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
                    for g, (_, v) in zip(avg_grads, grads_and_vars)]

        return avg_grads_and_vars 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def tf_msssim_np(img1, img2, data_format='NHWC'):
    assert img1.shape.ndims == img2.shape.ndims == 4, 'Expected {}, got {}'.format(data_format, img1.shape, img2.shape)
    assert tf.uint8.is_compatible_with(img1.dtype), 'Expected uint8 intput'
    assert tf.uint8.is_compatible_with(img2.dtype), 'Expected uint8 intput'

    if data_format == 'NCHW':
        def make_NHWC(x):
            return tf.transpose(x, (0, 2, 3, 1), name='make_NHWC')
        return tf_msssim_np(make_NHWC(img1), make_NHWC(img2), data_format='NHWC')

    assert img1.shape[3] == 3, 'Expected 3-channel images, got {}'.format(img1)

    with tf.name_scope('ms-ssim_np'):
        v = tf.py_func(_calc_msssim_orig, [img1, img2], tf.float32, stateful=False, name='MS-SSIM')
        v.set_shape(())
        return v 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def anchor_target_layer(cls_pred, bbox, im_info, scope_name):
    with tf.variable_scope(scope_name) as scope:
        # 'rpn_cls_score', 'gt_boxes', 'im_info'
        rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = \
            tf.py_func(anchor_target_layer_py,
                       [cls_pred, bbox, im_info, [16, ], [16]],
                       [tf.float32, tf.float32, tf.float32, tf.float32])

        rpn_labels = tf.convert_to_tensor(tf.cast(rpn_labels, tf.int32),
                                          name='rpn_labels')
        rpn_bbox_targets = tf.convert_to_tensor(rpn_bbox_targets,
                                                name='rpn_bbox_targets')
        rpn_bbox_inside_weights = tf.convert_to_tensor(rpn_bbox_inside_weights,
                                                       name='rpn_bbox_inside_weights')
        rpn_bbox_outside_weights = tf.convert_to_tensor(rpn_bbox_outside_weights,
                                                        name='rpn_bbox_outside_weights')

        return [rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def rouge_l_fscore(hypothesis, references, **unused_kwargs):
  """ROUGE scores computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions (batch_size, <=max_dec_steps)
    labels: tensor, gold output. (batch_size, max_dec_steps)

  Returns:
    rouge_l_fscore: approx rouge-l f1 score.
  """
  rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (hypothesis, references), [tf.float32])
  #rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (hypothesis, references), tf.float32)
  return rouge_l_f_score 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def rouge_2_fscore(predictions, labels, **unused_kwargs):
  """ROUGE-2 F1 score computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions (batch_size, <=max_dec_steps)
    labels: tensor, gold output. (batch_size, max_dec_steps)

  Returns:
    rouge2_fscore: approx rouge-2 f1 score.
  """

  rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), [tf.float32])
  return rouge_2_f_score, tf.constant(1.0) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import py_func [as 别名]
def generate_gt_graph(gt_quadrilaterals, input_gt_class_ids, image_shape, width_stride, max_gt_num):
    """

    :param gt_quadrilaterals: [mat_gt_num,(x1,y1,x2,y2,x3,y3,x4,y4,tag)] 左上、右上、右下、左下(顺时针)
    :param input_gt_class_ids:
    :param image_shape:
    :param width_stride:
    :param max_gt_num:
    :return:
    """

    gt_quadrilaterals = tf_utils.remove_pad(gt_quadrilaterals)
    input_gt_class_ids = tf_utils.remove_pad(input_gt_class_ids)
    gt_boxes, gt_class_ids = tf.py_func(func=gt_utils.gen_gt_from_quadrilaterals,
                          inp=[gt_quadrilaterals, input_gt_class_ids, image_shape, width_stride],
                          Tout=[tf.float32] * 2)
    return tf_utils.pad_list_to_fixed_size([gt_boxes, gt_class_ids], max_gt_num)