python类rank()的实例源码

vgg_preprocessing.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(2, num_channels, image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(2, channels)
utils.py 文件源码 项目:zhusuan 作者: thu-ml 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def assert_rank_at_least_one(tensor, name):
    """
    Whether the rank of `tensor` is at least one.

    :param tensor: A tensor to be checked.
    :param name: The name of `tensor` for error message.
    :return: The checked tensor.
    """
    return assert_rank_at_least(tensor, 1, name)
y_preprocessing.py 文件源码 项目:spoofnet-tensorflow 作者: yomna-safaa 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
y_vgg_preprocessing.py 文件源码 项目:spoofnet-tensorflow 作者: yomna-safaa 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
vgg_preprocessing.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
vgg_preprocessing.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
tensor_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def normalize_contrast(x):
    """Normalize contrast of an image: forces values to be stricly in [0, 1]

    :param x: image tensor
    :return:
    """
    idx = tf.range(1, tf.rank(x))
    min = tf.reduce_min(x, idx, keep_dims=True)
    max = tf.reduce_max(x, idx, keep_dims=True)
    return (x - min) / (max - min + 1e-5)
tensor_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def intersection_within(bbox, within):
    """Returns the coordinates of the intersection of `bbox` and `within`
    with respect to `within`

    :param bbox:
    :param within:
    :return:
    """
    x1 = tf.maximum(bbox[..., 1], within[..., 1])
    y1 = tf.maximum(bbox[..., 0], within[..., 0])
    x2 = tf.minimum(bbox[..., 1] + bbox[..., 3], within[..., 1] + within[..., 3])
    y2 = tf.minimum(bbox[..., 0] + bbox[..., 2], within[..., 0] + within[..., 2])
    w = x2 - x1
    w = tf.where(tf.less_equal(w, 0), tf.zeros_like(w), w)
    h = y2 - y1
    h = tf.where(tf.less_equal(h, 0), tf.zeros_like(h), h)

    y = y1 - within[..., 0]
    x = x1 - within[..., 1]

    area = h * w
    y = tf.where(tf.greater(area, 0.), y, tf.zeros_like(y))
    x = tf.where(tf.greater(area, 0.), x, tf.zeros_like(x))

    rank = len(bbox.get_shape()) - 1
    return tf.stack((y, x, h, w), rank)
attention_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def extract_glimpse(inpt, attention_params, glimpse_size):
    """Extracts an attention glimpse

    :param inpt: tensor of shape == (batch_size, img_height, img_width)
    :param attention_params: tensor of shape = (batch_size, 6) as
        [uy, sy, dy, ux, sx, dx] with u - mean, s - std, d - stride"
    :param glimpse_size: 2-tuple of ints as (height, width),
        size of the extracted glimpse
    :return: tensor
    """

    ap = attention_params
    shape = inpt.get_shape()
    rank = len(shape)

    assert rank in (3, 4), "Input must be 3 or 4 dimensional tensor"

    inpt_H, inpt_W = shape[1:3]
    if rank == 3:
        inpt = inpt[..., tf.newaxis]
        rank += 1

    Fy = gaussian_mask(ap[..., 0::2], glimpse_size[0], inpt_H)
    Fx = gaussian_mask(ap[..., 1::2], glimpse_size[1], inpt_W)

    gs = []
    for channel in tf.unstack(inpt, axis=rank - 1):
        g = tf.matmul(tf.matmul(Fy, channel, adjoint_a=True), Fx)
        gs.append(g)
    g = tf.stack(gs, axis=rank - 1)

    g.set_shape([shape[0]] + list(glimpse_size))
    return g
attention_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def attention_to_bbox(self, att):
        with tf.variable_scope('attention_to_bbox'):
            yx = att[..., :2] * self.inpt_size[np.newaxis, :2]
            hw = att[..., 2:4] * (self.inpt_size[np.newaxis, :2] - 1)
            bbox = tf.concat(axis=tf.rank(att) - 1, values=(yx, hw))
            bbox.set_shape(att.get_shape()[:-1].concatenate((4,)))
        return bbox
attention_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def bbox_to_attention(self, bbox):
        with tf.variable_scope('ratm_bbox_to_attention'):
            us = bbox[..., :2] / self.inpt_size[np.newaxis, :2]
            ss = 0.5 * bbox[..., 2:] / self.inpt_size[np.newaxis, :2]
            ds = bbox[..., 2:] / (self.inpt_size[np.newaxis, :2] - 1.)

            att = tf.concat(axis=tf.rank(bbox) - 1, values=(us, ss, ds))
        return att
attention_ops.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def _to_attention(self, raw_att, with_bias=True):
        bbox = FixedStdAttention.attention_to_bbox(self, raw_att)
        us = bbox[..., :2]
        if with_bias:
            us += self.offset_bias

        ds = bbox[..., 2:4] / (self.glimpse_size[np.newaxis, :2] - 1)
        ss = self._stride_to_std(ds)

        ap = tf.concat(axis=tf.rank(raw_att) - 1, values=(us, ss, ds), name='attention')
        ap.set_shape(raw_att.get_shape()[:-1].concatenate((6,)))
        return ap
loss.py 文件源码 项目:hart 作者: akosiorek 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def _mask(expr, mask):
    assert mask.dtype == tf.bool, '`mask`.dtype has to be tf.bool'
    mask_rank = tf.rank(mask)
    sample_shape = tf.shape(expr)[mask_rank:]
    flat_shape = tf.concat(([-1], sample_shape), 0)
    flat_expr = tf.reshape(expr, flat_shape)
    flat_mask = tf.reshape(mask, (-1,))

    return tf.boolean_mask(flat_expr, flat_mask)
vgg_preprocessing.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
kernels.py 文件源码 项目:GPflow 作者: GPflow 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def __init__(self, input_dim, output_dim, rank, active_dims=None, name=None):
        """
        A Coregionalization kernel. The inputs to this kernel are _integers_
        (we cast them from floats as needed) which usually specify the
        *outputs* of a Coregionalization model.

        The parameters of this kernel, W, kappa, specify a positive-definite
        matrix B.

          B = W W^T + diag(kappa) .

        The kernel function is then an indexing of this matrix, so

          K(x, y) = B[x, y] .

        We refer to the size of B as "num_outputs x num_outputs", since this is
        the number of outputs in a coregionalization model. We refer to the
        number of columns on W as 'rank': it is the number of degrees of
        correlation between the outputs.

        NB. There is a symmetry between the elements of W, which creates a
        local minimum at W=0. To avoid this, it's recommended to initialize the
        optimization (or MCMC chain) using a random W.
        """
        assert input_dim == 1, "Coregion kernel in 1D only"
        super().__init__(input_dim, active_dims, name=name)

        self.output_dim = output_dim
        self.rank = rank
        self.W = Parameter(np.zeros((self.output_dim, self.rank), dtype=settings.float_type))
        self.kappa = Parameter(np.ones(self.output_dim, dtype=settings.float_type), transform=transforms.positive)
vgg_preprocessing.py 文件源码 项目:Classification_Nets 作者: BobLiu20 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def _crop(image, offset_height, offset_width, crop_height, crop_width):
  """Crops the given image using the provided offsets and sizes.

  Note that the method doesn't assume we know the input image size but it does
  assume we know the input image rank.

  Args:
    image: an image of shape [height, width, channels].
    offset_height: a scalar tensor indicating the height offset.
    offset_width: a scalar tensor indicating the width offset.
    crop_height: the height of the cropped image.
    crop_width: the width of the cropped image.

  Returns:
    the cropped (and resized) image.

  Raises:
    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
      less than the crop size.
  """
  original_shape = tf.shape(image)

  rank_assertion = tf.Assert(
      tf.equal(tf.rank(image), 3),
      ['Rank of image must be equal to 3.'])
  with tf.control_dependencies([rank_assertion]):
    cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])

  size_assertion = tf.Assert(
      tf.logical_and(
          tf.greater_equal(original_shape[0], crop_height),
          tf.greater_equal(original_shape[1], crop_width)),
      ['Crop size greater than the image size.'])

  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))

  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
  # define the crop size.
  with tf.control_dependencies([size_assertion]):
    image = tf.slice(image, offsets, cropped_shape)
  return tf.reshape(image, cropped_shape)
vgg_preprocessing.py 文件源码 项目:Classification_Nets 作者: BobLiu20 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)
vgg_preprocessing.py 文件源码 项目:SSD_tensorflow_VOC 作者: LevinJ 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def _mean_image_subtraction(image, means):
    """Subtracts the given means from each image channel.

    For example:
        means = [123.68, 116.779, 103.939]
        image = _mean_image_subtraction(image, means)

    Note that the rank of `image` must be known.

    Args:
        image: a tensor of size [height, width, C].
        means: a C-vector of values to subtract from each channel.

    Returns:
        the centered image.

    Raises:
        ValueError: If the rank of `image` is unknown, if `image` has a rank other
            than three or if the number of channels in `image` doesn't match the
            number of values in `means`.
    """
    if image.get_shape().ndims != 3:
        raise ValueError('Input must be of size [height, width, C>0]')
    num_channels = image.get_shape().as_list()[-1]
    if len(means) != num_channels:
        raise ValueError('len(means) must match the number of channels')

    channels = tf.split(2, num_channels, image)
    for i in range(num_channels):
        channels[i] -= means[i]
    return tf.concat(channels, axis=2)
decoder.py 文件源码 项目:tefla 作者: openAGI 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def _transpose_batch_time(x):
    """Transpose the batch and time dimensions of a Tensor.

    Retains as much of the static shape information as possible.

    Args:
      x: A tensor of rank 2 or higher.

    Returns:
      x transposed along the first two dimensions.

    Raises:
      ValueError: if `x` is rank 1 or lower.
    """
    x_static_shape = x.get_shape()
    if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
        raise ValueError(
            "Expected input tensor %s to have rank at least 2, but saw shape: %s" %
            (x, x_static_shape))
    x_rank = tf.rank(x)
    x_t = tf.transpose(
        x, tf.concat(
            ([1, 0], tf.range(2, x_rank)), axis=0))
    x_t.set_shape(
        [x_static_shape[1].value, x_static_shape[0].value] + x_static_shape[2:])
    return x_t
gan_metrics.py 文件源码 项目:tefla 作者: openAGI 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def run_inception(images,
                  graph_def=None,
                  default_graph_def_fn=_default_graph_def_fn,
                  image_size=INCEPTION_DEFAULT_IMAGE_SIZE,
                  input_tensor=INCEPTION_INPUT,
                  output_tensor=INCEPTION_OUTPUT):
    """Run images through a pretrained Inception classifier.
    Args:
      images: Input tensors. Must be [batch, height, width, channels]. Input shape
        and values must be in [-1, 1], which can be achieved using
        `preprocess_image`.
      graph_def: A GraphDef proto of a pretrained Inception graph. If `None`,
        call `default_graph_def_fn` to get GraphDef.
      default_graph_def_fn: A function that returns a GraphDef. Used if
        `graph_def` is `None. By default, returns a pretrained InceptionV3 graph.
      image_size: Required image width and height. See unit tests for the default
        values.
      input_tensor: Name of input Tensor.
      output_tensor: Name of output Tensor. This function will compute activations
        at the specified layer. Examples include INCEPTION_V3_OUTPUT and
        INCEPTION_V3_FINAL_POOL which would result in this function computing
        the final logits or the penultimate pooling layer.
    Returns:
      Logits.
    Raises:
      ValueError: If images are not the correct size.
      ValueError: If neither `graph_def` nor `default_graph_def_fn` are provided.
    """
    images = _validate_images(images, image_size)

    if graph_def is None:
        if default_graph_def_fn is None:
            raise ValueError('If `graph_def` is `None`, must provide '
                             '`default_graph_def_fn`.')
        graph_def = default_graph_def_fn()

    activations = run_image_classifier(images, graph_def, input_tensor,
                                       output_tensor)
    if tf.rank(activations) != 2:
        activations = flatten(activations)
    return activations


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