Python v1.to_int64方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def argmax_with_score(logits, axis=None):
  """Argmax along with the value."""
  axis = axis or len(logits.get_shape()) - 1
  predictions = tf.argmax(logits, axis=axis)

  logits_shape = shape_list(logits)
  prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
  prefix_size = 1
  for d in prefix_shape:
    prefix_size *= d

  # Flatten to extract scores
  flat_logits = tf.reshape(logits, [prefix_size, vocab_size])
  flat_predictions = tf.reshape(predictions, [prefix_size])
  flat_indices = tf.stack(
      [tf.range(tf.to_int64(prefix_size)),
       tf.to_int64(flat_predictions)],
      axis=1)
  flat_scores = tf.gather_nd(flat_logits, flat_indices)

  # Unflatten
  scores = tf.reshape(flat_scores, prefix_shape)

  return predictions, scores 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def _get_action_id(extended_indices, action_types, output_vocab_size,
                   model_config):
  """Returns action_id tensor."""
  # This initial value will be broadcast to the length of decode_steps.
  action_ids = tf.constant(0, dtype=tf.int64)
  for action_type_range in _get_action_types_to_range(output_vocab_size,
                                                      model_config):
    is_type = tf.equal(
        tf.constant(action_type_range.action_type, dtype=tf.int64),
        action_types)
    # For each timestep, exactly one of the action_type_ranges will be added,
    # so this sum will populate each entry on exactly one iteration.
    action_ids += (
        tf.to_int64(is_type) *
        (extended_indices - action_type_range.start_index))
  return action_ids 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def tensors_to_item(self, keys_to_tensors):
    """Maps the given dictionary of tensors to a concatenated list of bboxes.

    Args:
      keys_to_tensors: a mapping of TF-Example keys to parsed tensors.

    Returns:
      [time, num_boxes, 4] tensor of bounding box coordinates, in order
          [y_min, x_min, y_max, x_max]. Whether the tensor is a SparseTensor
          or a dense Tensor is determined by the return_dense parameter. Empty
          positions in the sparse tensor are filled with -1.0 values.
    """
    sides = []
    for key in self._full_keys:
      value = keys_to_tensors[key]
      expanded_dims = tf.concat(
          [tf.to_int64(tf.shape(value)),
           tf.constant([1], dtype=tf.int64)], 0)
      side = tf.sparse_reshape(value, expanded_dims)
      sides.append(side)
    bounding_boxes = tf.sparse_concat(2, sides)
    if self._return_dense:
      bounding_boxes = tf.sparse_tensor_to_dense(
          bounding_boxes, default_value=self._default_value)
    return bounding_boxes 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def compute_lengths(symbols_list, eos_symbol, name=None,
                    dtype=tf.int64):
  """Computes sequence lengths given end-of-sequence symbol.

  Args:
    symbols_list: list of [batch_size] tensors of symbols (e.g. integers).
    eos_symbol: end of sequence symbol (e.g. integer).
    name: name for the name scope of this op.
    dtype: type of symbols, default: tf.int64.

  Returns:
    Tensor [batch_size] of lengths of sequences.
  """
  with tf.name_scope(name, 'compute_lengths'):
    max_len = len(symbols_list)
    eos_symbol_ = tf.constant(eos_symbol, dtype=dtype)
    # Array with max_len-time where we have EOS, 0 otherwise. Maximum of this is
    # the first EOS in that example.
    ends = [tf.constant(max_len - i, dtype=tf.int64)
            * tf.to_int64(tf.equal(s, eos_symbol_))
            for i, s in enumerate(symbols_list)]
    # Lengths of sequences, or max_len for sequences that didn't have EOS.
    # Note: examples that don't have EOS will have max value of 0 and value of
    # max_len+1 in lens_.
    lens_ = max_len + 1 - tf.reduce_max(tf.stack(ends, 1), axis=1)
    # For examples that didn't have EOS decrease max_len+1 to max_len as the
    # length.
    lens = tf.subtract(lens_, tf.to_int64(tf.equal(lens_, max_len + 1)))
    return tf.stop_gradient(tf.reshape(lens, [-1])) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def infer(self, features, *args, **kwargs):
    """Produce predictions from the model by running it."""
    del args, kwargs
    if "targets" not in features:
      if "infer_targets" in features:
        targets_shape = common_layers.shape_list(features["infer_targets"])
      elif "inputs" in features:
        targets_shape = common_layers.shape_list(features["inputs"])
        targets_shape[1] = self.hparams.video_num_target_frames
      else:
        raise ValueError("no inputs are given.")
      features["targets"] = tf.zeros(targets_shape, dtype=tf.float32)

    output, _ = self(features)  # pylint: disable=not-callable

    if not isinstance(output, dict):
      output = {"targets": output}

    x = output["targets"]
    if self.is_per_pixel_softmax:
      x_shape = common_layers.shape_list(x)
      x = tf.reshape(x, [-1, x_shape[-1]])
      x = tf.argmax(x, axis=-1)
      x = tf.reshape(x, x_shape[:-1])
    else:
      x = tf.squeeze(x, axis=-1)
      x = tf.to_int64(tf.round(x))
    output["targets"] = x
    if self.hparams.reward_prediction:
      output["target_reward"] = tf.argmax(output["target_reward"], axis=-1)

    # only required for decoding.
    output["outputs"] = output["targets"]
    output["scores"] = output["targets"]
    return output 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def index_last_dim_with_indices(x, indices):
  """Use indices to index into the last axis of x.

  This can be useful for recovering the actual probabilities of a sample from a
  probability distribution.

  Args:
    x: Tensor, n-d.
    indices: Tensor, (n-1)-d, where the dimension sizes match the first (n-1)
      dimensions of x. The values of indices will be used to index into the last
      axis of x.

  Returns:
    Tensor, (n-1)-d.
  """
  assert len(x.shape) == len(indices.shape) + 1

  x_shape = shape_list(x)
  vocab_size = x_shape[-1]

  flat_x = tf.reshape(x, [list_product(x_shape[:-1]), vocab_size])
  flat_indices = tf.reshape(indices, [list_product(x_shape[:-1])])

  idx = tf.stack(
      [
          tf.range(tf.to_int64(shape_list(flat_indices)[0])),
          tf.to_int64(flat_indices)
      ],
      axis=1)
  flat_x_idx = tf.gather_nd(flat_x, idx)

  x_idx = tf.reshape(flat_x_idx, x_shape[:-1])

  return x_idx 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def preprocess_example(self, example, mode, _):
    # Resize from usual size ~1350x60 to 90x4 in this test.
    img = example["inputs"]
    img = tf.to_int64(
        tf.image.resize_images(img, [90, 4], tf.image.ResizeMethod.AREA))
    img = tf.image.per_image_standardization(img)
    example["inputs"] = img
    return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def resize_video_frames(images, size):
  return [tf.to_int64(tf.image.resize_images(
      image, [size, size], tf.image.ResizeMethod.BILINEAR)) for image in images] 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def preprocess_example(self, example, mode, unused_hparams):
    example["inputs"].set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
    example["inputs"] = tf.to_int64(example["inputs"])
    example["inputs"] = tf.reshape(example["inputs"], (-1,))

    del example["targets"]  # Ensure unconditional generation

    return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def resize_by_area(img, size):
  """image resize function used by quite a few image problems."""
  return tf.to_int64(
      tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA)) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def make_multiscale_dilated(image, resolutions, num_channels=3):
  """Returns list of scaled images, one for each resolution.

  Resizes by skipping every nth pixel.

  Args:
    image: Tensor of shape [height, height, num_channels].
    resolutions: List of heights that image's height is resized to. The function
      assumes VALID padding, so the original image's height must be divisible
      by each resolution's height to return the exact resolution size.
    num_channels: Number of channels in image.

  Returns:
    List of Tensors, one for each resolution with shape given by
    [resolutions[i], resolutions[i], num_channels] if resolutions properly
    divide the original image's height; otherwise shape height and width is up
    to valid skips.
  """
  image_height = common_layers.shape_list(image)[0]
  scaled_images = []
  for height in resolutions:
    dilation_rate = image_height // height  # assuming height = width
    scaled_image = image[::dilation_rate, ::dilation_rate]
    scaled_image = tf.to_int64(scaled_image)
    scaled_image.set_shape([None, None, num_channels])
    scaled_images.append(scaled_image)
  return scaled_images 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def preprocess_example(self, example, mode, _):
    # Just resize with area.
    if self._was_reversed:
      example["inputs"] = tf.to_int64(
          tf.image.resize_images(example["inputs"], self.rescale_size,
                                 tf.image.ResizeMethod.AREA))
    else:
      example = imagenet_preprocess_example(example, mode)
      example["inputs"] = tf.to_int64(
          tf.image.resize_images(example["inputs"], self.rescale_size))
    return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def _get_action_type(extended_indices, output_vocab_size, model_config):
  """Returns action_type tensor."""
  action_type = tf.constant(0, dtype=tf.int64)
  for action_type_range in _get_action_types_to_range(output_vocab_size,
                                                      model_config):
    index_in_range = tf.logical_and(
        tf.greater_equal(extended_indices, action_type_range.start_index),
        tf.less(extended_indices, action_type_range.end_index))
    action_type += (
        tf.to_int64(index_in_range) * tf.constant(
            action_type_range.action_type, dtype=tf.int64))
  return action_type 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def get_decode_steps(extended_indices, output_vocab_size, model_config):
  """Convert Tensor of indices in extended vocabulary to DecodeStep."""
  extended_indices = tf.to_int64(extended_indices)
  action_types = _get_action_type(extended_indices, output_vocab_size,
                                  model_config)
  action_ids = _get_action_id(extended_indices, action_types, output_vocab_size,
                              model_config)
  return DecodeSteps(action_types=action_types, action_ids=action_ids) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import to_int64 [as 別名]
def get_extended_indices(decode_steps, output_vocab_size, model_config):
  """Convert DecodeSteps into a tensor of extended action ids."""
  # This initial value will be broadcast to the length of decode_steps.
  extended_action_indices = tf.constant(0, dtype=tf.int64)
  for action_type_range in _get_action_types_to_range(output_vocab_size,
                                                      model_config):
    is_type = tf.equal(
        tf.constant(action_type_range.action_type, dtype=tf.int64),
        decode_steps.action_types)
    # For each timestep, exactly one of the action_type_ranges will be added,
    # so this sum will populate each entry on exactly one iteration.
    extended_action_indices += (
        tf.to_int64(is_type) *
        (decode_steps.action_ids + action_type_range.start_index))
  return extended_action_indices