Python array_ops.reverse函数代码示例

def rot90(image, k=1):
  """Rotate an image counter-clockwise by 90 degrees.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    k: Number of times the image is rotated by 90 degrees.

  Returns:
    A rotated 3-D tensor of the same type and shape as `image`.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  k %= 4
  if k == 0:
    return image
  elif k == 1:
    return array_ops.transpose(
        array_ops.reverse(image, [False, True, False]),
        [1, 0, 2], name='rot90')
  elif k == 2:
    return array_ops.reverse(image, [True, True, False], name='rot90')
  elif k == 3:
    return array_ops.reverse(
        array_ops.transpose(image, [1, 0, 2], name='rot90'),
        [False, True, False])

  def testReverse1DimAuto(self):
    x_np = [1, 4, 9]

    for use_gpu in [False, True]:
      with self.test_session(use_gpu=use_gpu):
        x_tf = array_ops.reverse(x_np, [True]).eval()
        self.assertAllEqual(x_tf, np.asarray(x_np)[::-1])

def random_flip_up_down(image, seed=None):
  """Randomly flips an image vertically (upside down).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the first
  dimension, which is `height`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror_cond = math_ops.less(uniform_random, .5)
  result = control_flow_ops.cond(mirror_cond,
                                 lambda: array_ops.reverse(image, [0]),
                                 lambda: image)
  return fix_image_flip_shape(image, result)

  def _reverse1DimAuto(self, np_dtype):
    x_np = np.array([1, 2, 3, 4, 5], dtype=np_dtype)

    for use_gpu in [False, True]:
      with self.test_session(use_gpu=use_gpu):
        x_tf = array_ops.reverse(x_np, [True]).eval()
        self.assertAllEqual(x_tf, np.asarray(x_np)[::-1])

 def _reverse(input_, seq_lengths, seq_dim, batch_dim):
   if seq_lengths is not None:
     return array_ops.reverse_sequence(
         input=input_, seq_lengths=seq_lengths,
         seq_dim=seq_dim, batch_dim=batch_dim)
   else:
     return array_ops.reverse(input_, axis=[seq_dim])

  def testReverseWithConstDims(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dims = constant_op.constant([3, 1], name='DimsConst')
      reverse = array_ops.reverse(conv, dims)
      output = array_ops.identity(reverse)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if node.name.startswith('LayoutOptimizerTranspose'):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
      self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-ReverseV2-0-0', nodes)
      self.assertIn('LayoutOptimizer-ReverseV2-DimsConst', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

def calculate_sequence_by_mask(mask, time_major):
  """Calculate the sequence length tensor (1-D) based on the masking tensor.

  The masking tensor is a 2D boolean tensor with shape [batch, timestep]. For
  any timestep that should be masked, the corresponding field will be False.
  Consider the following example:
    a = [[True, True, False, False],
         [True, False, True, False]]
  It is a (2, 4) tensor, and the corresponding sequence length result should be
  1D tensor with value [2, 3]. Note that for the second example, we need to find
  the index of the last True value, which is 2 and sequence length is 3.

  Args:
    mask: Boolean tensor with shape [batch, timestep] or [timestep, batch] if
      time_major=True.
    time_major: Boolean, which indicates whether the mask is time major or batch
      major.
  Returns:
    sequence_length: 1D int32 tensor.
  """
  timestep_index = 0 if time_major else 1
  max_seq_length = array_ops.shape(mask)[timestep_index]
  reversed_mask = math_ops.cast(array_ops.reverse(mask, axis=[timestep_index]),
                                dtypes.int32)
  # Use the argmax to find the index of leading 1 in the reversed mask, which is
  # the index of the last True value in the original mask.
  reversed_index = math_ops.argmax(reversed_mask, axis=timestep_index,
                                   output_type=dtypes.int32)
  return max_seq_length - reversed_index

def random_flip_left_right(image, seed=None):
  """Randomly flip an image horizontally (left to right).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the
  second dimension, which is `width`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      @{tf.set_random_seed}
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror_cond = math_ops.less(uniform_random, .5)
  result = control_flow_ops.cond(mirror_cond,
                                 lambda: array_ops.reverse(image, [1]),
                                 lambda: image)
  return fix_image_flip_shape(image, result)

  def testReverseWithNonConstDims(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dims = array_ops.placeholder(dtype='int32')
      reverse = array_ops.reverse(conv, dims)
      output = array_ops.identity(reverse)

      dims_val = [2, 3]
      with session.Session() as sess:
        output_val_ref = sess.run(output, feed_dict={dims: dims_val})

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(
            output, run_metadata=metadata, feed_dict={
                dims: dims_val
            })

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('ReverseV2-0-0', nodes)
      self._assert_map_nhwc_to_nchw('ReverseV2-1', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

 def _reverse(input_, seq_lengths, seq_axis, batch_axis):
   if seq_lengths is not None:
     return array_ops.reverse_sequence(
         input=input_,
         seq_lengths=seq_lengths,
         seq_axis=seq_axis,
         batch_axis=batch_axis)
   else:
     return array_ops.reverse(input_, axis=[seq_axis])

 def _reverse(input_, seq_lengths, seq_dim, batch_dim):
   if seq_lengths is not None:
     return array_ops.reverse_sequence(
         input=input_, seq_lengths=seq_lengths,
         seq_dim=seq_dim, batch_dim=batch_dim)
   else:
     # See b/69305369.
     assert not use_tpu, (
         'Bidirectional with variable sequence lengths unsupported on TPU')
     return array_ops.reverse(input_, axis=[seq_dim])

def erosion2d(value, kernel, strides, rates, padding, name=None):
  """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors.

  The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
  `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e.,
  each input channel is processed independently of the others with its own
  structuring function. The `output` tensor has shape
  `[batch, out_height, out_width, depth]`. The spatial dimensions of the
  output tensor depend on the `padding` algorithm. We currently only support the
  default "NHWC" `data_format`.

  In detail, the grayscale morphological 2-D erosion is given by:

      output[b, y, x, c] =
         min_{dy, dx} value[b,
                            strides[1] * y - rates[1] * dy,
                            strides[2] * x - rates[2] * dx,
                            c] -
                      kernel[dy, dx, c]

  Duality: The erosion of `value` by the `kernel` is equal to the negation of
  the dilation of `-value` by the reflected `kernel`.

  Args:
    value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
    kernel: A `Tensor`. Must have the same type as `value`.
      3-D with shape `[kernel_height, kernel_width, depth]`.
    strides: A list of `ints` that has length `>= 4`.
      1-D of length 4. The stride of the sliding window for each dimension of
      the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
    rates: A list of `ints` that has length `>= 4`.
      1-D of length 4. The input stride for atrous morphological dilation.
      Must be: `[1, rate_height, rate_width, 1]`.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    name: A name for the operation (optional). If not specified "erosion2d"
      is used.

  Returns:
    A `Tensor`. Has the same type as `value`.
    4-D with shape `[batch, out_height, out_width, depth]`.

  Raises:
    ValueError: If the `value` depth does not match `kernel`' shape, or if
      padding is other than `'VALID'` or `'SAME'`.
  """
  with ops.op_scope([value, kernel], name, "erosion2d") as name:
    # Reduce erosion to dilation by duality.
    return math_ops.neg(gen_nn_ops.dilation2d(input=math_ops.neg(value),
                                              filter=array_ops.reverse(
                                                  kernel, [True, True, False]),
                                              strides=strides,
                                              rates=rates,
                                              padding=padding,
                                              name=name))

  def event_shape(self, name='event_shape'):
    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.

    Args:
      name: name to give to the op

    Returns:
      `Tensor` `event_shape`
    """
    with ops.name_scope(self.name):
      with ops.op_scope([self._alpha], name):
        return array_ops.reverse(array_ops.shape(self._alpha), [True])[0]

def _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins):
  """Convert histograms to auc.

  Args:
    hist_true_acc:  `Tensor` holding accumulated histogram of scores for records
      that were `True`.
    hist_false_acc:  `Tensor` holding accumulated histogram of scores for
      records that were `False`.
    nbins:  Integer number of bins in the histograms.

  Returns:
    Scalar `Tensor` estimating AUC.
  """
  # Note that this follows the "Approximating AUC" section in:
  # Efficient AUC learning curve calculation, R. R. Bouckaert,
  # AI'06 Proceedings of the 19th Australian joint conference on Artificial
  # Intelligence: advances in Artificial Intelligence
  # Pages 181-191.
  # Note that the above paper has an error, and we need to re-order our bins to
  # go from high to low score.

  # Normalize histogram so we get fraction in each bin.
  normed_hist_true = math_ops.truediv(hist_true_acc,
                                      math_ops.reduce_sum(hist_true_acc))
  normed_hist_false = math_ops.truediv(hist_false_acc,
                                       math_ops.reduce_sum(hist_false_acc))

  # These become delta x, delta y from the paper.
  delta_y_t = array_ops.reverse(normed_hist_true, [True], name='delta_y_t')
  delta_x_t = array_ops.reverse(normed_hist_false, [True], name='delta_x_t')

  # strict_1d_cumsum requires float32 args.
  delta_y_t = math_ops.cast(delta_y_t, dtypes.float32)
  delta_x_t = math_ops.cast(delta_x_t, dtypes.float32)

  # Trapezoidal integration, \int_0^1 0.5 * (y_t + y_{t-1}) dx_t
  y_t = _strict_1d_cumsum(delta_y_t, nbins)
  first_trap = delta_x_t[0] * y_t[0] / 2.0
  other_traps = delta_x_t[1:] * (y_t[1:] + y_t[:nbins - 1]) / 2.0
  return math_ops.add(first_trap, math_ops.reduce_sum(other_traps), name='auc')

def _PostProcessOutput(extended_acc_state, extended_final_state, func_cell,
                       total_time, inputs_lengths, is_reversed):
  """Post-process output of recurrent.

  This function takes the accumulated extended state and extracts the requested
  state and output.

  When `inputs_lengths` has been set, it extracts the output from the
  accumulated state. It also sets outputs past.

  When `is_reversed` is true, the output will be reversed in this function.

  It also sets the static shape information.

  Args:
    extended_acc_state: A structure containing the accumulated state at each
      time. It may contain the output at each time as well.
    extended_final_state: A structure containing the final state. It may
      contain the output at the final time.
    func_cell: The functional wrapper around the cell.
    total_time: A scalar integer tensor.
    inputs_lengths: An integer tensor with one entry per input.
    is_reversed: A boolean to indicate if the sequence is reversed.

  Returns:
    A tuple with the outputs at each time, and the final state.
  """
  if inputs_lengths is None or is_reversed:
    flat_final_state = func_cell.MaybeRemoveOutputFromState(
        nest.flatten(extended_final_state))
    tf_state = nest.pack_sequence_as(func_cell.state_template, flat_final_state)
  else:
    # The accumulated state is over the entire sequence, so we pick it
    # out from the acc_state sequence.
    flat_acc_state = func_cell.MaybeRemoveOutputFromState(
        nest.flatten(extended_acc_state))
    acc_state = nest.pack_sequence_as(
        func_cell.state_template, flat_acc_state)
    tf_state = _PickFinalStateFromHistory(acc_state, inputs_lengths)

  output_from_state = func_cell.GetOutputFromState(extended_acc_state)
  if is_reversed:
    output_from_state = array_ops.reverse(output_from_state, [0])
  tf_output = array_ops.transpose(output_from_state, [1, 0, 2])
  tf_output.set_shape(
      [func_cell.output_shape[0], total_time, func_cell.output_shape[1]])
  if inputs_lengths is not None:
    # Need set the outputs to zero.
    tf_output = _ApplyLengthsToBatch(inputs_lengths, tf_output)
  _SetShapeFromTemplate(tf_state, func_cell.state_template)
  return tf_output, tf_state