Python tensorflow.reverse_v2方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def eval(aligned_images, model_path):
    with tf.Graph().as_default():
        sess = tf.Session()
        images_pl = tf.placeholder(tf.float32, shape=[None, 160, 160, 3], name='input_image')
        images = tf.map_fn(lambda frame: tf.reverse_v2(frame, [-1]), images_pl) #BGR TO RGB
        images_norm = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame), images)
        train_mode = tf.placeholder(tf.bool)
        age_logits, gender_logits, _ = inception_resnet_v1.inference(images_norm, keep_probability=0.8,
                                                                     phase_train=train_mode,
                                                                     weight_decay=1e-5)
        gender = tf.argmax(tf.nn.softmax(gender_logits), 1)
        age_ = tf.cast(tf.constant([i for i in range(0, 101)]), tf.float32)
        age = tf.reduce_sum(tf.multiply(tf.nn.softmax(age_logits), age_), axis=1)
        init_op = tf.group(tf.global_variables_initializer(),
                           tf.local_variables_initializer())
        sess.run(init_op)
        saver = tf.train.Saver()
        ckpt = tf.train.get_checkpoint_state(model_path)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
            print("restore and continue training!")
        else:
            pass
        return sess.run([age, gender], feed_dict={images_pl: aligned_images, train_mode: False}) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def get_rotate_preprocess():
  """Returns a function that does 90deg rotations and sets according labels."""

  def _rotate_pp(data):
    data["label"] = tf.constant([0, 1, 2, 3])
    # We use our own instead of tf.image.rot90 because that one broke
    # internally shortly before deadline...
    data["image"] = tf.stack([
        data["image"],
        tf.transpose(tf.reverse_v2(data["image"], [1]), [1, 0, 2]),
        tf.reverse_v2(data["image"], [0, 1]),
        tf.reverse_v2(tf.transpose(data["image"], [1, 0, 2]), [1]),
    ])
    return data

  return _rotate_pp 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def sort_by_field(boxlist, field, order=SortOrder.descend, scope=None):
  """Sort boxes and associated fields according to a scalar field.

  A common use case is reordering the boxes according to descending scores.

  Args:
    boxlist: BoxList holding N boxes.
    field: A BoxList field for sorting and reordering the BoxList.
    order: (Optional) descend or ascend. Default is descend.
    scope: name scope.

  Returns:
    sorted_boxlist: A sorted BoxList with the field in the specified order.

  Raises:
    ValueError: if specified field does not exist
    ValueError: if the order is not either descend or ascend
  """
  with tf.name_scope(scope, 'SortByField'):
    if order != SortOrder.descend and order != SortOrder.ascend:
      raise ValueError('Invalid sort order')

    field_to_sort = boxlist.get_field(field)
    if len(field_to_sort.shape.as_list()) != 1:
      raise ValueError('Field should have rank 1')

    num_boxes = boxlist.num_boxes()
    num_entries = tf.size(field_to_sort)
    length_assert = tf.Assert(
        tf.equal(num_boxes, num_entries),
        ['Incorrect field size: actual vs expected.', num_entries, num_boxes])

    with tf.control_dependencies([length_assert]):
      # TODO: Remove with tf.device when top_k operation runs correctly on GPU.
      with tf.device('/cpu:0'):
        _, sorted_indices = tf.nn.top_k(field_to_sort, num_boxes, sorted=True)

    if order == SortOrder.ascend:
      sorted_indices = tf.reverse_v2(sorted_indices, [0])

    return gather(boxlist, sorted_indices) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def sort_by_field(boxlist, field, order=SortOrder.descend, scope=None):
  """Sort boxes and associated fields according to a scalar field.

  A common use case is reordering the boxes according to descending scores.

  Args:
    boxlist: BoxList holding N boxes.
    field: A BoxList field for sorting and reordering the BoxList.
    order: (Optional) descend or ascend. Default is descend.
    scope: name scope.

  Returns:
    sorted_boxlist: A sorted BoxList with the field in the specified order.

  Raises:
    ValueError: if specified field does not exist
    ValueError: if the order is not either descend or ascend
  """
  with tf.name_scope(scope, 'SortByField'):
    if order != SortOrder.descend and order != SortOrder.ascend:
      raise ValueError('Invalid sort order')

    field_to_sort = boxlist.get_field(field)
    if len(field_to_sort.shape.as_list()) != 1:
      raise ValueError('Field should have rank 1')

    num_boxes = boxlist.num_boxes()
    num_entries = tf.size(field_to_sort)
    length_assert = tf.Assert(
        tf.equal(num_boxes, num_entries),
        ['Incorrect field size: actual vs expected.', num_entries, num_boxes])

    with tf.control_dependencies([length_assert]):
      _, sorted_indices = tf.nn.top_k(field_to_sort, num_boxes, sorted=True)

    if order == SortOrder.ascend:
      sorted_indices = tf.reverse_v2(sorted_indices, [0])

    return gather(boxlist, sorted_indices) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
    """Randomly flips a dimension of the given tensor.

    The decision to randomly flip the `Tensors` is made together. In other words,
    all or none of the images pass in are flipped.

    Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used so
    that we can control for the probability as well as ensure the same decision
    is applied across the images.

    Args:
      tensor_list: A list of `Tensors` with the same number of dimensions.
      prob: The probability of a left-right flip.
      dim: The dimension to flip, 0, 1, ..

    Returns:
      outputs: A list of the possibly flipped `Tensors` as well as an indicator
      `Tensor` at the end whose value is `True` if the inputs were flipped and
      `False` otherwise.

    Raises:
      ValueError: If dim is negative or greater than the dimension of a `Tensor`.
    """
    random_value = tf.random_uniform([])

    def flip():
        flipped = []
        for tensor in tensor_list:
            if dim < 0 or dim >= len(tensor.get_shape().as_list()):
                raise ValueError('dim must represent a valid dimension.')
            flipped.append(tf.reverse_v2(tensor, [dim]))
        return flipped

    is_flipped = tf.less_equal(random_value, prob)
    outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
    if not isinstance(outputs, (list, tuple)):
        outputs = [outputs]
    outputs.append(is_flipped)

    return outputs 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
  """Randomly flips a dimension of the given tensor.

  The decision to randomly flip the `Tensors` is made together. In other words,
  all or none of the images pass in are flipped.

  Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used so
  that we can control for the probability as well as ensure the same decision
  is applied across the images.

  Args:
    tensor_list: A list of `Tensors` with the same number of dimensions.
    prob: The probability of a left-right flip.
    dim: The dimension to flip, 0, 1, ..

  Returns:
    outputs: A list of the possibly flipped `Tensors` as well as an indicator
    `Tensor` at the end whose value is `True` if the inputs were flipped and
    `False` otherwise.

  Raises:
    ValueError: If dim is negative or greater than the dimension of a `Tensor`.
  """
  random_value = tf.random_uniform([])

  def flip():
    flipped = []
    for tensor in tensor_list:
      if dim < 0 or dim >= len(tensor.get_shape().as_list()):
        raise ValueError('dim must represent a valid dimension.')
      flipped.append(tf.reverse_v2(tensor, [dim]))
    return flipped

  is_flipped = tf.less_equal(random_value, prob)
  outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
  if not isinstance(outputs, (list, tuple)):
    outputs = [outputs]
  outputs.append(is_flipped)

  return outputs 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def get_triangle_edge_feature(point_cloud, nn_idx, k=20):
  """Construct edge feature for each point
  Args:
    point_cloud: (batch_size, num_points, 1, num_dims)
    nn_idx: (batch_size, num_points, k)
    k: int

  Returns:
    edge features: (batch_size, num_points, k, num_dims)
  """
  og_batch_size = point_cloud.get_shape().as_list()[0]
  point_cloud = tf.squeeze(point_cloud)
  if og_batch_size == 1:
    point_cloud = tf.expand_dims(point_cloud, 0)

  point_cloud_central = point_cloud

  point_cloud_shape = point_cloud.get_shape()
  batch_size = point_cloud_shape[0].value
  num_points = point_cloud_shape[1].value
  num_dims = point_cloud_shape[2].value

  idx_ = tf.range(batch_size) * num_points
  idx_ = tf.reshape(idx_, [batch_size, 1, 1]) 

  point_cloud_flat = tf.reshape(point_cloud, [-1, num_dims])
  point_cloud_neighbors = tf.gather(point_cloud_flat, nn_idx+idx_)
  point_cloud_central = tf.expand_dims(point_cloud_central, axis=-2)
  point_cloud_central = tf.tile(point_cloud_central, [1, 1, k, 1])
  
  point_cloud_neighbors_reverse = tf.reverse_v2(point_cloud_neighbors, axis =[-2])
  
  edge_feature = tf.concat([point_cloud_central, 
                            point_cloud_neighbors-point_cloud_central, 
                            point_cloud_neighbors_reverse - point_cloud_central], axis=-1)
  return edge_feature 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
            'age': tf.FixedLenFeature([], tf.int64),
            'gender': tf.FixedLenFeature([], tf.int64),
            'file_name': tf.FixedLenFeature([], tf.string)
        })

    # Convert from a scalar string tensor (whose single string has
    # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
    # [mnist.IMAGE_PIXELS].
    # image = tf.image.decode_jpeg(features['image_raw'], channels=3)
    # image = tf.image.resize_images(image, [64, 64])
    # image = tf.cast(image, tf.uint8)
    # image.set_shape([mnist.IMAGE_PIXELS])

    # OPTIONAL: Could reshape into a 28x28 image and apply distortions
    # here.  Since we are not applying any distortions in this
    # example, and the next step expects the image to be flattened
    # into a vector, we don't bother.

    # Convert from [0, 255] -> [-0.5, 0.5] floats.
    # image = image * (1. / 255) - 0.5

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([160 * 160 * 3])
    image = tf.reshape(image, [160, 160, 3])
    image = tf.reverse_v2(image, [-1])
    image = tf.image.per_image_standardization(image)
    # image = tf.cast(image,tf.float32) * (1. / 255) - 0.5

    # Convert label from a scalar uint8 tensor to an int32 scalar.
    age = features['age']
    gender = features['gender']
    file_path = features['file_name']
    return image, age, gender, file_path 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reverse_v2 [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
    """Randomly flips a dimension of the given tensor.

    The decision to randomly flip the `Tensors` is made together.
    In other words, all or none of the images pass in are flipped.

    Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used
     so that we can control for the probability as well as ensure the
     same decision is applied across the images.

    Args:
      tensor_list: A list of `Tensors` with the same number of dimensions.
      prob: The probability of a left-right flip.
      dim: The dimension to flip, 0, 1, ..

    Returns:
      outputs: A list of the possibly flipped `Tensors` as well as an indicator
      `Tensor` at the end whose value is `True` if the inputs were flipped and
      `False` otherwise.

    Raises:
      ValueError: If dim is negative or greater than dimension of a `Tensor`.
    """
    random_value = tf.random_uniform([])

    def flip():
        flipped = []
        for tensor in tensor_list:
            if dim < 0 or dim >= len(tensor.get_shape().as_list()):
                raise ValueError('dim must represent a valid dimension.')
            flipped.append(tf.reverse_v2(tensor, [dim]))
        return flipped

    is_flipped = tf.less_equal(random_value, prob)
    outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
    if not isinstance(outputs, (list, tuple)):
        outputs = [outputs]
    outputs.append(is_flipped)

    return outputs