Spaces:
Running
Running
File size: 7,547 Bytes
cf2a15a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# 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
"""
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
|