/
summary_v2.py
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/
summary_v2.py
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Mesh summaries and TensorFlow operations to create them.
V2 versions
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
from tensorboard.compat import tf2 as tf
from tensorboard.compat.proto import summary_pb2
from tensorboard.plugins.mesh import metadata
from tensorboard.plugins.mesh import plugin_data_pb2
from tensorboard.util import tensor_util
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,
)
def _get_json_config(config_dict):
"""Parses and returns JSON string from python dictionary."""
json_config = "{}"
if config_dict is not None:
json_config = json.dumps(config_dict, sort_keys=True)
return json_config
def mesh(
name,
vertices,
faces=None,
colors=None,
config_dict=None,
step=None,
description=None,
):
"""Writes a TensorFlow mesh summary.
Args:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
vertices: Tensor of shape `[dim_1, ..., dim_n, 3]` representing the 3D
coordinates of vertices.
faces: Tensor of shape `[dim_1, ..., dim_n, 3]` containing indices of
vertices within each triangle.
colors: Tensor of shape `[dim_1, ..., dim_n, 3]` containing colors for each
vertex.
config_dict: Dictionary with ThreeJS classes names and 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.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
Returns:
True if all components of the mesh were saved successfully and False
otherwise.
"""
json_config = _get_json_config(config_dict)
# All tensors representing a single mesh will be represented as separate
# summaries internally. Those summaries will be regrouped on the client before
# rendering.
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]
)
summary_scope = (
getattr(tf.summary.experimental, "summary_scope", None)
or tf.summary.summary_scope
)
all_success = True
with summary_scope(name, "mesh_summary", values=tensors):
for tensor in tensors:
all_success = all_success and _write_summary(
name,
description,
tensor.data,
tensor.content_type,
components,
json_config,
step,
)
return all_success
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