Python v1.convert_to_tensor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 convert_to_tensor [as 別名]
def __init__(self):
    similarity_calc = region_similarity_calculator.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(
        matched_threshold=ssd_constants.MATCH_THRESHOLD,
        unmatched_threshold=ssd_constants.MATCH_THRESHOLD,
        negatives_lower_than_unmatched=True,
        force_match_for_each_row=True)

    box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=ssd_constants.BOX_CODER_SCALES)

    self.default_boxes = DefaultBoxes()('ltrb')
    self.default_boxes = box_list.BoxList(
        tf.convert_to_tensor(self.default_boxes))
    self.assigner = target_assigner.TargetAssigner(
        similarity_calc, matcher, box_coder) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def global_pool(input_tensor, pool_op=tf.nn.avg_pool):
  """Applies avg pool to produce 1x1 output.

  NOTE: This function is funcitonally equivalenet to reduce_mean, but it has
  baked in average pool which has better support across hardware.

  Args:
    input_tensor: input tensor
    pool_op: pooling op (avg pool is default)
  Returns:
    a tensor batch_size x 1 x 1 x depth.
  """
  shape = input_tensor.get_shape().as_list()
  if shape[1] is None or shape[2] is None:
    kernel_size = tf.convert_to_tensor(
        [1, tf.shape(input_tensor)[1],
         tf.shape(input_tensor)[2], 1])
  else:
    kernel_size = [1, shape[1], shape[2], 1]
  output = pool_op(
      input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID')
  # Recover output shape, for unknown shape.
  output.set_shape([None, 1, 1, None])
  return output 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def get_synthetic_inputs(self, input_name, nclass):
    inputs = tf.random_uniform(self.get_input_shapes('train')[0],
                               dtype=self.get_input_data_types('train')[0])
    inputs = variables.VariableV1(inputs, trainable=False,
                                  collections=[tf.GraphKeys.LOCAL_VARIABLES],
                                  name=input_name)
    labels = tf.convert_to_tensor(
        np.random.randint(28, size=[self.batch_size, self.max_label_length]))
    input_lengths = tf.convert_to_tensor(
        [self.max_time_steps] * self.batch_size)
    label_lengths = tf.convert_to_tensor(
        [self.max_label_length] * self.batch_size)
    return [inputs, labels, input_lengths, label_lengths]

  # TODO(laigd): support fp16.
  # TODO(laigd): support multiple gpus. 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def test_temperature_normal(self, temperature):
    with tf.Graph().as_default():
      rng = np.random.RandomState(0)
      # in numpy, so that multiple calls don't trigger different random numbers.
      loc_t = tf.convert_to_tensor(rng.randn(5, 5))
      scale_t = tf.convert_to_tensor(rng.rand(5, 5))
      tempered_normal = glow_ops.TemperedNormal(
          loc=loc_t, scale=scale_t, temperature=temperature)
      # smoke test for a single sample.
      smoke_sample = tempered_normal.sample()
      samples = tempered_normal.sample((10000,), seed=0)

      with tf.Session() as sess:
        ops = [samples, loc_t, scale_t, smoke_sample]
        samples_np, loc_exp, scale_exp, _ = sess.run(ops)
        scale_exp *= temperature
        loc_act = np.mean(samples_np, axis=0)
        scale_act = np.std(samples_np, axis=0)
        self.assertTrue(np.allclose(loc_exp, loc_act, atol=1e-2))
        self.assertTrue(np.allclose(scale_exp, scale_act, atol=1e-2)) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def linear_interpolate_rank(self):
    with tf.Graph().as_default():
      # Since rank is 1, the first channel should remain 1.0.
      # and the second channel should be interpolated between 1.0 and 6.0
      z1 = np.ones(shape=(4, 4, 2))
      z2 = np.copy(z1)
      z2[:, :, 0] += 0.01
      z2[:, :, 1] += 5.0
      coeffs = np.linspace(0.0, 1.0, 11)
      z1 = np.expand_dims(z1, axis=0)
      z2 = np.expand_dims(z2, axis=0)
      tensor1 = tf.convert_to_tensor(z1, dtype=tf.float32)
      tensor2 = tf.convert_to_tensor(z2, dtype=tf.float32)
      lin_interp_max = glow_ops.linear_interpolate_rank(
          tensor1, tensor2, coeffs)
      with tf.Session() as sess:
        lin_interp_np_max = sess.run(lin_interp_max)
        for lin_interp_np, coeff in zip(lin_interp_np_max, coeffs):
          exp_val = 1.0 + coeff * (6.0 - 1.0)
          self.assertTrue(np.allclose(lin_interp_np[:, :, 0], 1.0))
          self.assertTrue(np.allclose(lin_interp_np[:, :, 1], exp_val)) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def shape_list(x):
  """Return list of dims, statically where possible."""
  x = tf.convert_to_tensor(x)

  # If unknown rank, return dynamic shape
  if x.get_shape().dims is None:
    return tf.shape(x)

  static = x.get_shape().as_list()
  shape = tf.shape(x)

  ret = []
  for i, dim in enumerate(static):
    if dim is None:
      dim = shape[i]
    ret.append(dim)
  return ret 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def cast_like(x, y):
  """Cast x to y's dtype, if necessary."""
  x = tf.convert_to_tensor(x)
  y = tf.convert_to_tensor(y)

  if x.dtype.base_dtype == y.dtype.base_dtype:
    return x

  cast_x = tf.cast(x, y.dtype)
  if cast_x.device != x.device:
    x_name = "(eager Tensor)"
    try:
      x_name = x.name
    except AttributeError:
      pass
    tf.logging.warning("Cast for %s may induce copy from '%s' to '%s'", x_name,
                       x.device, cast_x.device)
  return cast_x 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def testRightShiftBlockwiseND(self):
    tensor = tf.convert_to_tensor(np.array([[
        [[1], [2], [3], [4]],
        [[5], [6], [7], [8]],
        [[9], [10], [11], [12]],
        [[13], [14], [15], [16]],
    ]], dtype=np.float32))
    val = common_attention.right_shift_blockwise_nd(tensor, (2, 2))
    res = self.evaluate(val)
    expected_val = np.array([[
        [[0], [1], [6], [3]],
        [[2], [5], [4], [7]],
        [[8], [9], [14], [11]],
        [[10], [13], [12], [15]],
    ]], dtype=np.float32)
    self.assertAllClose(expected_val, res) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def _aspect_preserving_resize(image, smallest_side):
  """Resize images preserving the original aspect ratio.

  Args:
    image: A 3-D image `Tensor`.
    smallest_side: A python integer or scalar `Tensor` indicating the size of
    the smallest side after resize.

  Returns:
    resized_image: A 3-D tensor containing the resized image.
  """
  smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)

  shape = tf.shape(image)
  height = shape[0]
  width = shape[1]
  new_height, new_width = _smallest_size_at_least(height, width, smallest_side)
  image = tf.expand_dims(image, 0)
  resized_image = tf.image.resize_images(
      image, size=[new_height, new_width], method=tf.image.ResizeMethod.BICUBIC)

  resized_image = tf.squeeze(resized_image)
  resized_image.set_shape([None, None, 3])
  return resized_image 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def testShapesAndReconstructions(self, transform_name, target_channels):
    # Transform the data, test shape
    transform = self.transform_pairs[transform_name][0]
    target_shape = tuple([self.batch_size]
                         + self.spec_shape + [target_channels])
    with self.cached_session() as sess:
      spectra_np = sess.run(transform(self.audio))
    self.assertEqual(spectra_np.shape, target_shape)

    # Reconstruct the audio, test shape
    inv_transform = self.transform_pairs[transform_name][1]
    with self.cached_session() as sess:
      recon_np = sess.run(inv_transform(tf.convert_to_tensor(spectra_np)))
    self.assertEqual(recon_np.shape, (self.batch_size, self.audio_length, 1))

    # Test reconstruction error
    # Mel compression adds differences so skip
    if transform_name != 'melspecgrams':
      # Edges have known differences due to windowing
      edge = self.spec_shape[1] * 2
      diff = np.abs(self.audio_np[:, edge:-edge] - recon_np[:, edge:-edge])
      rms = np.mean(diff**2.0)**0.5
      print(transform_name, 'RMS:', rms)
      self.assertLessEqual(rms, 1e-5) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def _call_sampler(sample_n_fn, sample_shape, name=None):
  """Reshapes vector of samples."""
  with tf.name_scope(name, "call_sampler", values=[sample_shape]):
    sample_shape = tf.convert_to_tensor(
        sample_shape, dtype=tf.int32, name="sample_shape")
    # Ensure sample_shape is a vector (vs just a scalar).
    pad = tf.cast(tf.equal(tf.rank(sample_shape), 0), tf.int32)
    sample_shape = tf.reshape(
        sample_shape,
        tf.pad(tf.shape(sample_shape),
               paddings=[[pad, 0]],
               constant_values=1))
    samples = sample_n_fn(tf.reduce_prod(sample_shape))
    batch_event_shape = tf.shape(samples)[1:]
    final_shape = tf.concat([sample_shape, batch_event_shape], 0)
    return tf.reshape(samples, final_shape) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def categorical_sample(logits, dtype=tf.int32,
                       sample_shape=(), seed=None):
  """Samples from categorical distribution."""
  logits = tf.convert_to_tensor(logits, name="logits")
  event_size = tf.shape(logits)[-1]
  batch_shape_tensor = tf.shape(logits)[:-1]
  def _sample_n(n):
    """Sample vector of categoricals."""
    if logits.shape.ndims == 2:
      logits_2d = logits
    else:
      logits_2d = tf.reshape(logits, [-1, event_size])
    sample_dtype = tf.int64 if logits.dtype.size > 4 else tf.int32
    draws = tf.multinomial(
        logits_2d, n, seed=seed, output_dtype=sample_dtype)
    draws = tf.reshape(
        tf.transpose(draws),
        tf.concat([[n], batch_shape_tensor], 0))
    return tf.cast(draws, dtype)
  return _call_sampler(_sample_n, sample_shape) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def __init__(self, sample_fn, sample_shape, sample_dtype,
               start_inputs, end_fn, next_inputs_fn=None):
    """Initializer.

    Args:
      sample_fn: A callable that takes `outputs` and emits tensor `sample_ids`.
      sample_shape: Either a list of integers, or a 1-D Tensor of type `int32`,
        the shape of the each sample in the batch returned by `sample_fn`.
      sample_dtype: the dtype of the sample returned by `sample_fn`.
      start_inputs: The initial batch of inputs.
      end_fn: A callable that takes `sample_ids` and emits a `bool` vector
        shaped `[batch_size]` indicating whether each sample is an end token.
      next_inputs_fn: (Optional) A callable that takes `sample_ids` and returns
        the next batch of inputs. If not provided, `sample_ids` is used as the
        next batch of inputs.
    """
    self._sample_fn = sample_fn
    self._end_fn = end_fn
    self._sample_shape = tf.TensorShape(sample_shape)
    self._sample_dtype = sample_dtype
    self._next_inputs_fn = next_inputs_fn
    self._batch_size = tf.shape(start_inputs)[0]
    self._start_inputs = tf.convert_to_tensor(
        start_inputs, name="start_inputs") 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def test_pad_image_tensor_to_spec_shape(self):
    varlen_spec = utils.ExtendedTensorSpec(
        shape=(3, 2, 2, 1),
        dtype=tf.uint8,
        name='varlen',
        data_format='png',
        varlen_default_value=3.0)
    test_data = [[
        [[[1]] * 2] * 2,
        [[[2]] * 2] * 2,
    ]]
    prepadded_tensor = tf.convert_to_tensor(test_data, dtype=varlen_spec.dtype)
    tensor = utils.pad_or_clip_tensor_to_spec_shape(prepadded_tensor,
                                                    varlen_spec)
    with self.session() as sess:
      np_tensor = sess.run(tensor)
      self.assertAllEqual(
          np_tensor,
          np.array([[
              [[[1]] * 2] * 2,
              [[[2]] * 2] * 2,
              [[[3]] * 2] * 2,
          ]])) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import convert_to_tensor [as 別名]
def maybe_ignore_batch(spec_or_tensors, ignore_batch = False):
  """Optionally strips the batch dimension and returns new spec.

  Args:
    spec_or_tensors: A dict, (named)tuple, list or a hierarchy thereof filled by
      TensorSpecs(subclasses) or Tensors.
    ignore_batch: If True, the spec_or_batch's batch dimensions are ignored for
      shape comparison.

  Returns:
    spec_or_tensors: If ignore_batch=True we return a spec structure with the
      stripped batch_dimension otherwise we return spec_or_tensors.
  """
  if ignore_batch:
    def map_fn(spec):
      if isinstance(spec, np.ndarray):
        spec = tf.convert_to_tensor(spec)
      if isinstance(spec, tf.Tensor):
        return ExtendedTensorSpec.from_tensor(spec[0])
      else:
        return ExtendedTensorSpec.from_spec(spec, shape=spec.shape[1:])
    return nest.map_structure(
        map_fn,
        spec_or_tensors)
  return spec_or_tensors