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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.
This file is deprecated. See `summary_v2.py` instead.
"""
import json
import tensorflow as tf
from tensorboard.plugins.mesh import metadata
from tensorboard.plugins.mesh import plugin_data_pb2
from tensorboard.plugins.mesh import summary_v2
# Export V2 versions.
mesh = summary_v2.mesh
mesh_pb = summary_v2.mesh_pb
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 _get_display_name(name, display_name):
"""Returns display_name from display_name and name."""
if display_name is None:
return name
return display_name
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 op(
name,
vertices,
faces=None,
colors=None,
display_name=None,
description=None,
collections=None,
config_dict=None,
):
"""Creates a TensorFlow summary op for mesh rendering.
DEPRECATED: see `summary_v2.py` instead.
Args:
name: A name for this summary operation.
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.
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.
collections: Which TensorFlow graph collections to add the summary op to.
Defaults to `['summaries']`. Can usually be ignored.
config_dict: Dictionary with ThreeJS classes names and configuration.
Returns:
Merged summary for mesh/point cloud representation.
"""
display_name = _get_display_name(name, display_name)
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.
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:
summaries.append(
_get_tensor_summary(
name,
display_name,
description,
tensor.data,
tensor.content_type,
components,
json_config,
collections,
)
)
all_summaries = tf.compat.v1.summary.merge(
summaries, collections=collections, name=name
)
return all_summaries
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.
DEPRECATED: see `summary_v2.py` instead.
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 = [
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 = tf.compat.v1.make_tensor_proto(
tensor.data, dtype=tensor.data_type
)
summary_metadata = metadata.create_summary_metadata(
name,
display_name,
tensor.content_type,
components,
shape,
description,
json_config=json_config,
)
tag = metadata.get_instance_name(name, tensor.content_type)
summaries.append((tag, summary_metadata, tensor_proto))
summary = tf.compat.v1.Summary()
for tag, summary_metadata, tensor_proto in summaries:
tf_summary_metadata = tf.compat.v1.SummaryMetadata.FromString(
summary_metadata.SerializeToString()
)
summary.value.add(
tag=tag, metadata=tf_summary_metadata, tensor=tensor_proto
)
return summary
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