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 _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 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 custom_scalars(layout):
categories = []
for k, v in layout.items():
charts = []
for chart_name, chart_meatadata in v.items():
tags = chart_meatadata[1]
if chart_meatadata[0] == "Margin":
assert len(tags) == 3
mgcc = layout_pb2.MarginChartContent(series=[
layout_pb2.MarginChartContent.Series(
value=tags[0], lower=tags[1], upper=tags[2])
])
chart = layout_pb2.Chart(title=chart_name, margin=mgcc)
else:
mlcc = layout_pb2.MultilineChartContent(tag=tags)
chart = layout_pb2.Chart(title=chart_name, multiline=mlcc)
charts.append(chart)
categories.append(layout_pb2.Category(title=k, chart=charts))
layout = layout_pb2.Layout(category=categories)
plugin_data = SummaryMetadata.PluginData(plugin_name="custom_scalars")
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(
dtype="DT_STRING",
string_val=[layout.SerializeToString()],
tensor_shape=TensorShapeProto(),
)
return Summary(value=[
Summary.Value(
tag="custom_scalars__config__", tensor=tensor, metadata=smd)
])
def add_3dvolume(self, volume, tag, global_step=None, walltime=None):
filename = tag + "_"
if global_step is None:
filename += datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
else:
filename += str(global_step)
if isinstance(volume, torch.Tensor):
volume = volume.detach().cpu().numpy()
img = ants.from_numpy(volume)
ants.image_write(img, os.path.join(self._log_dir,
filename + ".nii.gz"))
plugin_data = tf.SummaryMetadata.PluginData(
plugin_name="tb_3d_volume_plugin",
content=TextPluginData(version=0).SerializeToString())
metadata = tf.SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(
dtype='DT_STRING',
string_val=[filename.encode(encoding='utf_8')],
tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=1)]))
summary = summary_pb2.Summary(value=[
summary_pb2.Summary.Value(
tag=tag, metadata=metadata, tensor=tensor)
])
self._file_writer.add_summary(summary,
global_step=global_step,
walltime=walltime)
self._file_writer.flush()
def make_tensor_summary(name, nparray):
tensor_pb = TensorProto(
dtype='DT_FLOAT',
float_val=nparray.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=s) for s in nparray.shape]))
return Summary(value=[Summary.Value(tag=name, tensor=tensor_pb)])
def scalar(name, scalar, collections=None, new_style=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.
"""
scalar = make_np(scalar)
assert scalar.squeeze().ndim == 0, "scalar should be 0D"
scalar = float(scalar)
if new_style:
plugin_data = SummaryMetadata.PluginData(plugin_name="scalars")
smd = SummaryMetadata(plugin_data=plugin_data)
return Summary(value=[
Summary.Value(
tag=name,
tensor=TensorProto(float_val=[scalar], dtype="DT_FLOAT"),
metadata=smd,
)
])
else:
return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
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 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 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)])