def mesh_pb(tag,
vertices,
faces=None,
colors=None,
config_dict=None,
description=None):
"""Create a mesh summary to save in pb format.
Args:
tag: String tag for the summary.
vertices: numpy array of shape `[dim_1, ..., dim_n, 3]` representing the 3D
coordinates of vertices.
faces: numpy array of shape `[dim_1, ..., dim_n, 3]` containing indices of
vertices within each triangle.
colors: numpy array of shape `[dim_1, ..., dim_n, 3]` containing colors for
each vertex.
config_dict: Dictionary with ThreeJS classes names and configuration.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
Returns:
Instance of tf.Summary class.
"""
json_config = _get_json_config(config_dict)
summaries = []
tensors = [
metadata.MeshTensor(vertices, plugin_data_pb2.MeshPluginData.VERTEX,
tf.float32),
metadata.MeshTensor(faces, plugin_data_pb2.MeshPluginData.FACE,
tf.int32),
metadata.MeshTensor(colors, plugin_data_pb2.MeshPluginData.COLOR,
tf.uint8),
]
tensors = [tensor for tensor in tensors if tensor.data is not None]
components = metadata.get_components_bitmask(
[tensor.content_type for tensor in tensors])
for tensor in tensors:
shape = tensor.data.shape
shape = [dim if dim is not None else -1 for dim in shape]
tensor_proto = tensor_util.make_tensor_proto(tensor.data,
dtype=tensor.data_type)
summary_metadata = metadata.create_summary_metadata(
tag,
None, # display_name
tensor.content_type,
components,
shape,
description,
json_config=json_config,
)
instance_tag = metadata.get_instance_name(tag, tensor.content_type)
summaries.append((instance_tag, summary_metadata, tensor_proto))
summary = summary_pb2.Summary()
for instance_tag, summary_metadata, tensor_proto in summaries:
summary.value.add(tag=instance_tag,
metadata=summary_metadata,
tensor=tensor_proto)
return summary
def test_get_instance_name(self):
"""Tests proper creation of instance name based on display_name."""
display_name = 'my_mesh'
instance_name = metadata.get_instance_name(
display_name,
plugin_data_pb2.MeshPluginData.ContentType.Value('VERTEX'))
self.assertEqual('%s_VERTEX' % display_name, instance_name)
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 pb(name,
vertices,
faces=None,
colors=None,
display_name=None,
description=None,
config_dict=None):
"""Create a mesh summary to save in pb format.
Args:
name: A name for this summary operation.
vertices: numpy array of shape `[dim_1, ..., dim_n, 3]` representing the 3D
coordinates of vertices.
faces: numpy array of shape `[dim_1, ..., dim_n, 3]` containing indices of
vertices within each triangle.
colors: numpy array of shape `[dim_1, ..., dim_n, 3]` containing colors for
each vertex.
display_name: If set, will be used as the display name in TensorBoard.
Defaults to `name`.
description: A longform readable description of the summary data. Markdown
is supported.
config_dict: Dictionary with ThreeJS classes names and configuration.
Returns:
Instance of tf.Summary class.
"""
display_name = _get_display_name(name, display_name)
json_config = _get_json_config(config_dict)
summaries = []
tensors = [(vertices, plugin_data_pb2.MeshPluginData.VERTEX, tf.float32),
(faces, plugin_data_pb2.MeshPluginData.FACE, tf.int32),
(colors, plugin_data_pb2.MeshPluginData.COLOR, tf.uint8)]
for tensor, content_type, data_type in tensors:
if tensor is None:
continue
tensor_shape = tensor.shape
tensor = tf.compat.v1.make_tensor_proto(tensor, dtype=data_type)
summary_metadata = metadata.create_summary_metadata(
name,
display_name,
content_type,
tensor_shape,
description,
json_config=json_config)
tag = metadata.get_instance_name(name, content_type)
summaries.append((tag, summary_metadata, tensor))
summary = tf.Summary()
for tag, summary_metadata, tensor in summaries:
tf_summary_metadata = tf.SummaryMetadata.FromString(
summary_metadata.SerializeToString())
summary.value.add(tag=tag, metadata=tf_summary_metadata, tensor=tensor)
return summary
def _get_tensor_summary(
name,
display_name,
description,
tensor,
content_type,
components,
json_config,
collections,
):
"""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 `tag`.
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.
collections: List of collections to add this summary to.
Returns:
Tensor summary with metadata.
"""
tensor = tf.convert_to_tensor(value=tensor)
shape = tensor.shape.as_list()
shape = [dim if dim is not None else -1 for dim in shape]
tensor_metadata = metadata.create_summary_metadata(
name,
display_name,
content_type,
components,
shape,
description,
json_config=json_config,
)
tensor_summary = tf.compat.v1.summary.tensor_summary(
metadata.get_instance_name(name, content_type),
tensor,
summary_metadata=tensor_metadata,
collections=collections,
)
return tensor_summary
def _write_summary(name, description, tensor, content_type, components,
json_config, step):
"""Creates a tensor summary with summary metadata.
Args:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
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.
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf.summary.experimental.get_step()`, which must
not be None.
Returns:
A boolean indicating if summary was saved successfully or not.
"""
tensor = tf.convert_to_tensor(value=tensor)
shape = tensor.shape.as_list()
shape = [dim if dim is not None else -1 for dim in shape]
tensor_metadata = metadata.create_summary_metadata(
name,
None, # display_name
content_type,
components,
shape,
description,
json_config=json_config,
)
return tf.summary.write(
tag=metadata.get_instance_name(name, content_type),
tensor=tensor,
step=step,
metadata=tensor_metadata,
)
