Python gen_logging_ops._image_summary方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def build_image_summary(self):
        """
        A simple graph for write image summary
        :return:
        """
        log_image_data = tf.placeholder(tf.uint8, [None, None, 3])
        log_image_name = tf.placeholder(tf.string)
        # import tensorflow.python.ops.gen_logging_ops as logging_ops
        from tensorflow.python.ops import gen_logging_ops
        from tensorflow.python.framework import ops as _ops
        log_image = gen_logging_ops._image_summary(log_image_name, tf.expand_dims(log_image_data, 0), max_images=1)
        _ops.add_to_collection(_ops.GraphKeys.SUMMARIES, log_image)
        # log_image = tf.summary.image(log_image_name, tf.expand_dims(log_image_data, 0), max_outputs=1)
        return log_image, log_image_data, log_image_name 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def build_image_summary(self):
        # A simple graph for write image summary

        log_image_data = tf.placeholder(tf.uint8, [None, None, 3])
        log_image_name = tf.placeholder(tf.string)
        # import tensorflow.python.ops.gen_logging_ops as logging_ops
        from tensorflow.python.ops import gen_logging_ops
        from tensorflow.python.framework import ops as _ops
        log_image = gen_logging_ops._image_summary(log_image_name, tf.expand_dims(log_image_data, 0), max_images=1)
        _ops.add_to_collection(_ops.GraphKeys.SUMMARIES, log_image)
        # log_image = tf.summary.image(log_image_name, tf.expand_dims(log_image_data, 0), max_outputs=1)
        return log_image, log_image_data, log_image_name 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image_summary(tag, tensor, max_images=3, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with images.

  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py)

  The summary has up to `max_images` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
  build the `tag` of the summary values:

  *  If `max_images` is 1, the summary value tag is '*tag*/image'.
  *  If `max_images` is greater than 1, the summary value tags are
     generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.

  Args:
    tag: A scalar `Tensor` of type `string`. Used to build the `tag`
      of the summary values.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_images: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope:
    val = gen_logging_ops._image_summary(
        tag=tag, tensor=tensor, max_images=max_images, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image(name, tensor, max_outputs=3, collections=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_outputs` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` in the outputted Summary.Value protobufs is generated based on the
  name, with a suffix depending on the max_outputs setting:

  *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
  *  If `max_outputs` is greater than 1, the summary value tags are
     generated sequentially as '*name*/image/0', '*name*/image/1', etc.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_outputs: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [_ops.GraphKeys.SUMMARIES]

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  name = _clean_tag(name)
  with _ops.name_scope(name, None, [tensor]) as scope:
    # pylint: disable=protected-access
    val = _gen_logging_ops._image_summary(
        tag=scope.rstrip('/'),
        tensor=tensor,
        max_images=max_outputs,
        name=scope)
    _collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image_summary(tag, tensor, max_images=3, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with images.

  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://www.tensorflow.org/code/tensorflow/contrib/deprecated/__init__.py)

  The summary has up to `max_images` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
  build the `tag` of the summary values:

  *  If `max_images` is 1, the summary value tag is '*tag*/image'.
  *  If `max_images` is greater than 1, the summary value tags are
     generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.

  Args:
    tag: A scalar `Tensor` of type `string`. Used to build the `tag`
      of the summary values.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_images: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope:
    val = gen_logging_ops._image_summary(
        tag=tag, tensor=tensor, max_images=max_images, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image_summary(tag, tensor, max_images=3, collections=None, name=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_images` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
  build the `tag` of the summary values:

  *  If `max_images` is 1, the summary value tag is '*tag*/image'.
  *  If `max_images` is greater than 1, the summary value tags are
     generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.

  Args:
    tag: A scalar `Tensor` of type `string`. Used to build the `tag`
      of the summary values.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_images: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope:
    val = gen_logging_ops._image_summary(
        tag=tag, tensor=tensor, max_images=max_images, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image(name, tensor, max_outputs=3, collections=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_images` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` in the outputted Summary.Value protobufs is generated based on the
  name, with a suffix depending on the max_outputs setting:

  *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
  *  If `max_outputs` is greater than 1, the summary value tags are
     generated sequentially as '*name*/image/0', '*name*/image/1', etc.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_outputs: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [_ops.GraphKeys.SUMMARIES]

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  name = _clean_tag(name)
  with _ops.name_scope(name, None, [tensor]) as scope:
    # pylint: disable=protected-access
    val = _gen_logging_ops._image_summary(
        tag=scope.rstrip('/'),
        tensor=tensor,
        max_images=max_outputs,
        name=scope)
    _collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _image_summary [as 别名]
def image(name, tensor, max_outputs=3, collections=None, family=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_outputs` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` in the outputted Summary.Value protobufs is generated based on the
  name, with a suffix depending on the max_outputs setting:

  *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
  *  If `max_outputs` is greater than 1, the summary value tags are
     generated sequentially as '*name*/image/0', '*name*/image/1', etc.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_outputs: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [_ops.GraphKeys.SUMMARIES]
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with _summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    # pylint: disable=protected-access
    val = _gen_logging_ops._image_summary(
        tag=tag, tensor=tensor, max_images=max_outputs, name=scope)
    _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val