def gif_summary(name, paths, max_outputs, collections=None, family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
channels]` where `channels` is 1 or 3.
max_outputs: Max number of batch elements to generate gifs for.
collections: Optional list of tf.GraphKeys. The collections to add the
summary to. Defaults to [tf.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.
"""
paths = tf.convert_to_tensor(paths)
if summary_op_util.skip_summary():
return tf.constant("")
with summary_op_util.summary_scope(name, family,
values=[paths]) as (tag, scope):
val = tf.py_func(py_gif_summary, [tag, paths, max_outputs],
tf.string,
stateful=False,
name=scope)
summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[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`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
val = _gen_logging_ops.scalar_summary(tags=tag, values=tensor, name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[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`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(name, family,
values=[tensor]) as (tag, scope):
val = _gen_logging_ops.scalar_summary(tags=tag,
values=tensor,
name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(name, family,
values=[tensor]) as (tag, scope):
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
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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
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
def audio(name,
tensor,
sample_rate,
max_outputs=3,
collections=None,
family=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with audio.
The summary has up to `max_outputs` summary values containing audio. The
audio is built from `tensor` which must be 3-D with shape `[batch_size,
frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
assumed to be in the range of `[-1.0, 1.0]` with a sample rate of
`sample_rate`.
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*/audio'.
* If `max_outputs` is greater than 1, the summary value tags are
generated sequentially as '*name*/audio/0', '*name*/audio/1', etc
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]`
or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`.
sample_rate: A Scalar `float32` `Tensor` indicating the sample rate of the
signal in hertz.
max_outputs: Max number of batch elements to generate audio 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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(name, family=family,
values=[tensor]) as (tag, scope):
sample_rate = _ops.convert_to_tensor(sample_rate,
dtype=_dtypes.float32,
name='sample_rate')
val = _gen_logging_ops.audio_summary_v2(tag=tag,
tensor=tensor,
max_outputs=max_outputs,
sample_rate=sample_rate,
name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def tensor_summary(name,
tensor,
summary_description=None,
collections=None,
summary_metadata=None,
family=None,
display_name=None):
"""Outputs a `Summary` protocol buffer with a serialized tensor.proto.
Args:
name: A name for the generated node. If display_name is not set, it will
also serve as the tag name in TensorBoard. (In that case, the tag
name will inherit tf name scopes.)
tensor: A tensor of any type and shape to serialize.
summary_description: A long description of the summary sequence. Markdown
is supported.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
summary_metadata: Optional SummaryMetadata proto (which describes which
plugins may use the summary value).
family: Optional; if provided, used as the prefix of the summary tag,
which controls the name used for display on TensorBoard when
display_name is not set.
display_name: A string used to name this data in TensorBoard. If this is
not set, then the node name will be used instead.
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
if summary_metadata is None:
summary_metadata = _SummaryMetadata()
if summary_description is not None:
summary_metadata.summary_description = summary_description
if display_name is not None:
summary_metadata.display_name = display_name
serialized_summary_metadata = summary_metadata.SerializeToString()
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
val = _gen_logging_ops.tensor_summary_v2(
tensor=tensor,
tag=tag,
name=scope,
serialized_summary_metadata=serialized_summary_metadata)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def gif_summary(name, tensor, max_outputs, fps, collections=None, family=None):
tensor = tf.image.convert_image_dtype(tensor,
dtype=tf.uint8,
saturate=True)
# tensor = tf.convert_to_tensor(tensor)
if summary_op_util.skip_summary():
return tf.constant("")
with summary_op_util.summary_scope(name, family,
values=[tensor]) as (tag, scope):
val = tf.py_func(py_gif_summary, [tag, tensor, max_outputs, fps],
tf.string,
stateful=False,
name=scope)
summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
return val
def gif_summary(name, im_seq, fps, collections=None, family=None):
"""
im_seq: 4D tensor (TxHxWxC) for which GIF is to be generated.
collections: collections to which the summary op is to be added.
"""
if summary_op_util.skip_summary():
return constant_op.constant('')
with summary_op_util.summary_scope(name, family,
values=[im_seq]) as (tag, scope):
gif_summ = tf.py_func(encode_gif, [im_seq, tag, fps],
tf.string,
stateful=False)
summary_op_util.collect(gif_summ, collections,
[tf.GraphKeys.SUMMARIES])
return gif_summ
def audio(name, tensor, sample_rate, max_outputs=3, collections=None,
family=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with audio.
The summary has up to `max_outputs` summary values containing audio. The
audio is built from `tensor` which must be 3-D with shape `[batch_size,
frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
assumed to be in the range of `[-1.0, 1.0]` with a sample rate of
`sample_rate`.
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*/audio'.
* If `max_outputs` is greater than 1, the summary value tags are
generated sequentially as '*name*/audio/0', '*name*/audio/1', etc
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]`
or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`.
sample_rate: A Scalar `float32` `Tensor` indicating the sample rate of the
signal in hertz.
max_outputs: Max number of batch elements to generate audio 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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family=family, values=[tensor]) as (tag, scope):
sample_rate = _ops.convert_to_tensor(
sample_rate, dtype=_dtypes.float32, name='sample_rate')
val = _gen_logging_ops.audio_summary_v2(
tag=tag, tensor=tensor, max_outputs=max_outputs,
sample_rate=sample_rate, name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def merge(inputs, collections=None, name=None):
# pylint: disable=line-too-long
"""Merges summaries.
This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input
summaries.
When the Op is run, it reports an `InvalidArgument` error if multiple values
in the summaries to merge use the same tag.
Args:
inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer resulting from the merging.
Raises:
RuntimeError: If called with eager mode enabled.
@compatibility(eager)
Not compatible with eager execution. To write TensorBoard
summaries under eager execution, use `tf.contrib.summary` instead.
@end_compatbility
"""
# pylint: enable=line-too-long
return # turned to No-op
if _context.executing_eagerly():
raise RuntimeError(
'Merging tf.summary.* ops is not compatible with eager execution. '
'Use tf.contrib.summary instead.')
if _summary_op_util.skip_summary():
return _constant_op.constant('')
name = _summary_op_util.clean_tag(name)
with _ops.name_scope(name, 'Merge', inputs):
val = _gen_logging_ops.merge_summary(inputs=inputs, name=name)
_summary_op_util.collect(val, collections, [])
return val
def merge(inputs, collections=None, name=None):
# pylint: disable=line-too-long
"""Merges summaries.
This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input
summaries.
When the Op is run, it reports an `InvalidArgument` error if multiple values
in the summaries to merge use the same tag.
Args:
inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer resulting from the merging.
Raises:
RuntimeError: If called with eager mode enabled.
@compatibility(eager)
Not compatible with eager execution. To write TensorBoard
summaries under eager execution, use `tf.contrib.summary` instead.
@end_compatibility
"""
# pylint: enable=line-too-long
if _context.executing_eagerly():
raise RuntimeError(
'Merging tf.summary.* ops is not compatible with eager execution. '
'Use tf.contrib.summary instead.')
if _summary_op_util.skip_summary():
return _constant_op.constant('')
name = _summary_op_util.clean_tag(name)
with _ops.name_scope(name, 'Merge', inputs):
val = _gen_logging_ops.merge_summary(inputs=inputs, name=name)
_summary_op_util.collect(val, collections, [])
return val
def gif_summary(name,
tensor,
max_outputs=3,
fps=10,
collections=None,
family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
channels]` where `channels` is 1 or 3.
max_outputs: Max number of batch elements to generate gifs for.
fps: frames per second of the animation
collections: Optional list of tf.GraphKeys. The collections to add the
summary to. Defaults to [tf.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.
Raises:
ValueError: if the given tensor has the wrong shape.
"""
tensor = tf.convert_to_tensor(tensor)
if len(tensor.get_shape()) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but got one "
"of shape: %s" % str(tensor.get_shape()))
tensor = tf.cast(tensor, tf.uint8)
if summary_op_util.skip_summary():
return tf.constant("")
with summary_op_util.summary_scope(name, family,
values=[tensor]) as (tag, scope):
val = tf.py_func(py_gif_summary, [tag, tensor, max_outputs, fps],
tf.string,
stateful=False,
name=scope)
summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
return val
def gif_summary(name, tensor, max_outputs=3, fps=10, collections=None,
family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
channels]` where `channels` is 1 or 3.
max_outputs: Max number of batch elements to generate gifs for.
fps: frames per second of the animation
collections: Optional list of tf.GraphKeys. The collections to add the
summary to. Defaults to [tf.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.
Raises:
ValueError: if the given tensor has the wrong shape.
"""
tensor = tf.convert_to_tensor(tensor)
if len(tensor.get_shape()) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but got one "
"of shape: %s" % str(tensor.get_shape()))
tensor = tf.cast(tensor, tf.uint8)
if summary_op_util.skip_summary():
return tf.constant("")
with summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
val = tf.py_func(
py_gif_summary,
[tag, tensor, max_outputs, fps],
tf.string,
stateful=False,
name=scope)
summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
return val
def histogram(name, values, collections=None, family=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
Adding a histogram summary makes it possible to visualize your data's
distribution in TensorBoard. You can see a detailed explanation of the
TensorBoard histogram dashboard
[here](https://www.tensorflow.org/get_started/tensorboard_histograms).
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `InvalidArgument` error if any value is not finite.
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
values: A real numeric `Tensor`. Any shape. Values to use to
build the histogram.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[values],
default_name='HistogramSummary') as (tag, scope):
val = _gen_logging_ops.histogram_summary(tag=tag,
values=values,
name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def histogram(name, values, collections=None, family=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
Adding a histogram summary makes it possible to visualize your data's
distribution in TensorBoard. You can see a detailed explanation of the
TensorBoard histogram dashboard
[here](https://www.tensorflow.org/get_started/tensorboard_histograms).
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `InvalidArgument` error if any value is not finite.
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
values: A real numeric `Tensor`. Any shape. Values to use to
build the histogram.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[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.
"""
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[values],
default_name='HistogramSummary') as (tag, scope):
val = _gen_logging_ops.histogram_summary(
tag=tag, values=values, name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def gif_summary(name,
tensor,
max_outputs=3,
fps=10,
collections=None,
family=None):
"""Outputs a `Summary` protocol buffer with gif animations.
Args:
name: Name of the summary.
tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
channels]` where `channels` is 1 or 3.
max_outputs: Max number of batch elements to generate gifs for.
fps: frames per second of the animation
collections: Optional list of tf.GraphKeys. The collections to add the
summary to. Defaults to [tf.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.
"""
# print('GIF SUMMARY', tensor.shape, type(tensor))
# rescaled_tensor = np.empty(tensor.shape)
# for i in range(tensor.shape[0]):
# rescaled_tensor[i] = utils.rescale_if_negative(tensor[i])
tensor = tf.convert_to_tensor(tensor)
if summary_op_util.skip_summary():
return tf.constant("")
with summary_op_util.summary_scope(name, family,
values=[tensor]) as (tag, scope):
val = tf.py_func(py_gif_summary, [tag, tensor, max_outputs, fps],
tf.string,
stateful=False,
name=scope)
summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
return val
