def histogram_raw(name, min, max, num, sum, sum_squares, bucket_limits,
bucket_counts):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
min: A float or int min value
max: A float or int max value
num: Int number of values
sum: Float or int sum of all values
sum_squares: Float or int sum of squares for all values
bucket_limits: A numeric `Tensor` with upper value per bucket
bucket_counts: A numeric `Tensor` with number of values per bucket
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
hist = HistogramProto(min=min,
max=max,
num=num,
sum=sum,
sum_squares=sum_squares,
bucket_limit=bucket_limits,
bucket=bucket_counts)
return Summary(value=[Summary.Value(tag=name, histo=hist)])
def _add_3d_torch(self,
tag,
data,
step,
logdir=None,
max_outputs=1,
label_to_names=None,
description=None):
walltime = None
if step is None:
raise ValueError("Step is not provided or set.")
mdata = {} if label_to_names is None else {
'label_to_names': label_to_names
}
summary_metadata = metadata.create_summary_metadata(
description=description, metadata=mdata)
writer = self._get_file_writer()
if logdir is None:
logdir = writer.get_logdir()
write_dir = PluginDirectory(logdir, metadata.PLUGIN_NAME)
geometry_metadata_string = _write_geometry_data(write_dir, tag, step, data,
max_outputs)
tensor_proto = TensorProto(dtype='DT_STRING',
string_val=[geometry_metadata_string],
tensor_shape=TensorShapeProto())
writer.add_summary(
Summary(value=[
Summary.Value(
tag=tag, tensor=tensor_proto, metadata=summary_metadata)
]), step, walltime)
def text(tag, text):
plugin_data = SummaryMetadata.PluginData(
plugin_name='text', content=TextPluginData(version=0).SerializeToString())
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(dtype='DT_STRING',
string_val=[text.encode(encoding='utf_8')],
tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=1)]))
return Summary(value=[Summary.Value(tag=tag + '/text_summary', metadata=smd, tensor=tensor)])
def _ImageSummary(tag, height, width, colorspace, encoded_image):
from tensorboard.compat.proto.summary_pb2 import Summary
image = Summary.Image(
height=height,
width=width,
colorspace=colorspace,
encoded_image_string=encoded_image)
return Summary(value=[Summary.Value(tag=tag, image=image)])
def _make_session_start_summary(
hparam_values,
group_name: Optional[str] = None,
start_time_secs: Optional[int] = None,
):
"""Assign values to the hyperparameters in the context of this session.
Args:
hparam_values: a dict of ``hp_name`` -> ``hp_value`` mappings
group_name: optional group name for this session
start_time_secs: optional starting time in seconds
Returns:
"""
if start_time_secs is None:
start_time_secs = int(time.time())
session_start_info = SessionStartInfo(group_name=group_name,
start_time_secs=start_time_secs)
for hp_name, hp_value in hparam_values.items():
# Logging a None would raise an exception when setting session_start_info.hparams[hp_name].number_value = None.
# Logging a float.nan instead would work, but that run would not show at all in the tensorboard hparam plugin.
# The best thing to do here is to skip that value, it will show as a blank cell in the table view of the
# tensorboard plugin. However, that run would not be shown in the parallel coord or in the scatter plot view.
if hp_value is None:
logger.warning(
f"Hyper parameter {hp_name} is `None`: the tensorboard hp plugin "
f"will show this run in table view, but not in parallel coordinates "
f"view or in scatter plot matrix view")
continue
if isinstance(hp_value, (str, list, tuple)):
session_start_info.hparams[hp_name].string_value = str(hp_value)
continue
if isinstance(hp_value, bool):
session_start_info.hparams[hp_name].bool_value = hp_value
continue
if not isinstance(hp_value, (int, float)):
hp_value = make_np(hp_value)[0]
session_start_info.hparams[hp_name].number_value = hp_value
session_start_content = HParamsPluginData(
session_start_info=session_start_info, version=PLUGIN_DATA_VERSION)
session_start_summary_metadata = SummaryMetadata(
plugin_data=SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=session_start_content.SerializeToString()))
session_start_summary = Summary(value=[
Summary.Value(tag=SESSION_START_INFO_TAG,
metadata=session_start_summary_metadata)
])
return session_start_summary
def event(step, values):
s = Summary()
scalar = [
Summary.Value(tag="{}/{}".format(name, field), simple_value=v)
for name, value in zip(names, values)
for field, v in value._asdict().items()
]
hist = [
Summary.Value(tag="{}/inferred_normal_hist".format(name),
histo=inferred_histo(value))
for name, value in zip(names, values)
]
s.value.extend(scalar + hist)
return Event(wall_time=int(step), step=step, summary=s)
def pr_curve_raw(tag, tp, fp, tn, fn, precision, recall, num_thresholds=127, weights=None):
if num_thresholds > 127: # weird, value > 127 breaks protobuf
num_thresholds = 127
data = np.stack((tp, fp, tn, fn, precision, recall))
pr_curve_plugin_data = PrCurvePluginData(
version=0, num_thresholds=num_thresholds).SerializeToString()
plugin_data = SummaryMetadata.PluginData(
plugin_name='pr_curves', content=pr_curve_plugin_data)
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(dtype='DT_FLOAT',
float_val=data.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=data.shape[0]), TensorShapeProto.Dim(size=data.shape[1])]))
return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
def pr_curve(tag, labels, predictions, num_thresholds=127, weights=None):
# weird, value > 127 breaks protobuf
num_thresholds = min(num_thresholds, 127)
data = compute_curve(labels, predictions,
num_thresholds=num_thresholds, weights=weights)
pr_curve_plugin_data = PrCurvePluginData(
version=0, num_thresholds=num_thresholds).SerializeToString()
plugin_data = SummaryMetadata.PluginData(
plugin_name='pr_curves', content=pr_curve_plugin_data)
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(dtype='DT_FLOAT',
float_val=data.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=data.shape[0]), TensorShapeProto.Dim(size=data.shape[1])]))
return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
def _write_logs(self, logs, index):
"""Write log values to the log files
:param logs: holds the loss and metric values computed at the most
recent interval (batch or epoch)
:type logs: dict
:param index: if update_freq='batch', the total number of samples that
have been seen, else if update_freq='epoch', the epoch index
:type index: int
"""
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = Summary(
value=[Summary.Value(tag=name, simple_value=value)])
self.writer.add_summary(summary, index)
self.writer.flush()
def _get_tensor_summary(
name, display_name, description, tensor, content_type, components, json_config
):
"""Creates a tensor summary with summary metadata.
Args:
name: Uniquely identifiable name of the summary op. Could be replaced by
combination of name and type to make it unique even outside of this
summary.
display_name: Will be used as the display name in TensorBoard.
Defaults to `name`.
description: A longform readable description of the summary data. Markdown
is supported.
tensor: Tensor to display in summary.
content_type: Type of content inside the Tensor.
components: Bitmask representing present parts (vertices, colors, etc.) that
belong to the summary.
json_config: A string, JSON-serialized dictionary of ThreeJS classes
configuration.
Returns:
Tensor summary with metadata.
"""
import torch
from tensorboard.plugins.mesh import metadata
tensor = torch.as_tensor(tensor)
tensor_metadata = metadata.create_summary_metadata(
name,
display_name,
content_type,
components,
tensor.shape,
description,
json_config=json_config,
)
tensor = TensorProto(
dtype="DT_FLOAT",
float_val=tensor.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[
TensorShapeProto.Dim(size=tensor.shape[0]),
TensorShapeProto.Dim(size=tensor.shape[1]),
TensorShapeProto.Dim(size=tensor.shape[2]),
]
),
)
tensor_summary = Summary.Value(
tag=metadata.get_instance_name(name, content_type),
tensor=tensor,
metadata=tensor_metadata,
)
return tensor_summary
def scalar(name, scalar, collections=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]`.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
scalar = make_np(scalar)
assert (scalar.squeeze().ndim == 0), 'scalar should be 0D'
scalar = float(scalar)
return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
def scalar(name, tensor, collections=None, new_style=False, double_precision=False):
"""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]`.
new_style: Whether to use new style (tensor field) or old style (simple_value
field). New style could lead to faster data loading.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
tensor = make_np(tensor).squeeze()
assert (
tensor.ndim == 0
), f"Tensor should contain one element (0 dimensions). Was given size: {tensor.size} and {tensor.ndim} dimensions."
# python float is double precision in numpy
scalar = float(tensor)
if new_style:
tensor_proto = TensorProto(float_val=[scalar], dtype="DT_FLOAT")
if double_precision:
tensor_proto = TensorProto(double_val=[scalar], dtype="DT_DOUBLE")
plugin_data = SummaryMetadata.PluginData(plugin_name="scalars")
smd = SummaryMetadata(plugin_data=plugin_data)
return Summary(
value=[
Summary.Value(
tag=name,
tensor=tensor_proto,
metadata=smd,
)
]
)
else:
return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
def _image3_animated_gif(tag: str, image: Union[np.ndarray, torch.Tensor], scale_factor: float = 1.0) -> Summary:
"""Function to actually create the animated gif.
Args:
tag: Data identifier
image: 3D image tensors expected to be in `HWD` format
scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will
scale it to displayable range
"""
assert len(image.shape) == 3, "3D image tensors expected to be in `HWD` format, len(image.shape) != 3"
ims = [(np.asarray((image[:, :, i])) * scale_factor).astype(np.uint8) for i in range(image.shape[2])]
ims = [GifImage.fromarray(im) for im in ims]
img_str = b""
for b_data in PIL.GifImagePlugin.getheader(ims[0])[0]:
img_str += b_data
img_str += b"\x21\xFF\x0B\x4E\x45\x54\x53\x43\x41\x50" b"\x45\x32\x2E\x30\x03\x01\x00\x00\x00"
for i in ims:
for b_data in PIL.GifImagePlugin.getdata(i):
img_str += b_data
img_str += b"\x3B"
summary_image_str = Summary.Image(height=10, width=10, colorspace=1, encoded_image_string=img_str)
image_summary = Summary.Value(tag=tag, image=summary_image_str)
return Summary(value=[image_summary])
def make_video(tensor, fps):
try:
import moviepy # noqa: F401
except ImportError:
print('add_video needs package moviepy')
return
try:
from moviepy import editor as mpy
except ImportError:
print("moviepy is installed, but can't import moviepy.editor.",
"Some packages could be missing [imageio, requests]")
return
import tempfile
t, h, w, c = tensor.shape
# encode sequence of images into gif string
clip = mpy.ImageSequenceClip(list(tensor), fps=fps)
with tempfile.NamedTemporaryFile() as f:
filename = f.name + '.gif'
try:
clip.write_gif(filename, verbose=False, progress_bar=False)
except TypeError:
clip.write_gif(filename, verbose=False)
with open(filename, 'rb') as f:
tensor_string = f.read()
try:
os.remove(filename)
except OSError:
pass
return Summary.Image(height=h,
width=w,
colorspace=c,
encoded_image_string=tensor_string)
def _ScalarSummary(tag, val):
from tensorboard.compat.proto.summary_pb2 import Summary
return Summary(value=[Summary.Value(tag=tag, simple_value=val)])
def _make_experiment_summary(hparam_infos, metric_infos, experiment):
"""Define hyperparameters and metrics.
Args:
hparam_infos: information about all hyperparameters (name, description, type etc.),
list of dicts containing 'name' (required), 'type', 'description', 'display_name',
'domain_discrete', 'domain_interval'
metric_infos: information about all metrics (tag, description etc.),
list of dicts containing 'tag' (required), 'dataset_type', 'description', 'display_name'
experiment: dict containing 'name' (required), 'description', 'time_created_secs', 'user'
Returns:
"""
def make_hparam_info(hparam):
data_types = {
None: DataType.DATA_TYPE_UNSET,
str: DataType.DATA_TYPE_STRING,
list: DataType.DATA_TYPE_STRING,
tuple: DataType.DATA_TYPE_STRING,
bool: DataType.DATA_TYPE_BOOL,
int: DataType.DATA_TYPE_FLOAT64,
float: DataType.DATA_TYPE_FLOAT64,
}
return HParamInfo(
name=hparam["name"],
type=data_types[hparam.get("type")],
description=hparam.get("description"),
display_name=hparam.get("display_name"),
domain_discrete=hparam.get("domain_discrete"),
domain_interval=hparam.get("domain_interval"),
)
def make_metric_info(metric):
return MetricInfo(
name=MetricName(tag=metric["tag"]),
dataset_type=DatasetType.Value(
f'DATASET_{metric.get("dataset_type", "UNKNOWN").upper()}'),
description=metric.get("description"),
display_name=metric.get("display_name"),
)
def make_experiment_info(experiment, metric_infos, hparam_infos):
return Experiment(
name=experiment["name"],
description=experiment.get("description"),
time_created_secs=experiment.get("time_created_secs"),
user=experiment.get("user"),
metric_infos=metric_infos,
hparam_infos=hparam_infos,
)
metric_infos = [make_metric_info(m) for m in metric_infos]
hparam_infos = [make_hparam_info(hp) for hp in hparam_infos]
experiment = make_experiment_info(experiment, metric_infos, hparam_infos)
experiment_content = HParamsPluginData(experiment=experiment,
version=PLUGIN_DATA_VERSION)
experiment_summary_metadata = SummaryMetadata(
plugin_data=SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=experiment_content.SerializeToString()))
experiment_summary = Summary(value=[
Summary.Value(tag=EXPERIMENT_TAG, metadata=experiment_summary_metadata)
])
return experiment_summary
def Summary(tag, **kw):
from tensorboard.compat.proto.summary_pb2 import Summary
return Summary(value=[Summary.Value(tag=tag, **kw)])
def Image(**kw):
from tensorboard.compat.proto.summary_pb2 import Summary
return Summary.Image(**kw)
