TF2.0 support for the mesh plugin

tensorboard/plugins/mesh/BUILD

@@ -102,7 +102,23 @@ py_library(
     ],
     deps = [
         ":metadata",
+        ":summary_v2",
         ":protos_all_py_pb2",
+        "//tensorboard:expect_tensorflow_installed",
+    ],
+)
+
+py_library(
+    name = "summary_v2",
+    srcs = ["summary_v2.py"],
+    srcs_version = "PY2AND3",
+    visibility = [
+        "//visibility:public",
+    ],
+    deps = [
+        ":metadata",
+        "//tensorboard/util:tensor_util",
+        "//tensorboard/compat/proto:protos_all_py_pb2",
         "//tensorboard/compat:tensorflow",
     ],
 )
@@ -120,6 +136,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "summary_v2_test",
+    size = "small",
+    srcs = ["summary_v2_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":summary",
+        ":test_utils",
+        ":metadata",
+        ":protos_all_py_pb2",
+        "//tensorboard:expect_tensorflow_installed",
+        "//tensorboard/util:test_util",
+    ],
+)
+
 tb_proto_library(
     name = "protos_all",
     srcs = ["plugin_data.proto"],
@@ -163,7 +194,27 @@ py_binary(
     deps = [
         ":demo_utils",
         ":summary",
-        "//tensorboard/compat:tensorflow",
+        "//tensorboard:expect_absl_app_installed",
+        "//tensorboard:expect_absl_flags_installed",
+        "//tensorboard:expect_tensorflow_installed",
+        "//tensorboard:expect_numpy_installed",
+    ],
+)
+
+py_binary(
+    name = "mesh_demo_v2",
+    srcs = ["mesh_demo_v2.py"],
+    srcs_version = "PY2AND3",
+    visibility = [
+        "//visibility:public",
+    ],
+    deps = [
+        ":demo_utils",
+        ":summary_v2",
+        "//tensorboard:expect_absl_app_installed",
+        "//tensorboard:expect_absl_flags_installed",
+        "//tensorboard:expect_tensorflow_installed",
+        "//tensorboard:expect_numpy_installed",
     ],
 )
 

tensorboard/plugins/mesh/mesh_demo_v2.py

@@ -0,0 +1,90 @@
+# 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.
+# ==============================================================================
+"""Simple demo which displays constant 3D mesh."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from absl import app
+from absl import flags
+import numpy as np
+import tensorflow.compat.v2 as tf
+
+from tensorboard.plugins.mesh import summary_v2 as mesh_summary
+from tensorboard.plugins.mesh import demo_utils
+
+
+flags.DEFINE_string('logdir', '/tmp/mesh_demo',
+                    'Directory to write event logs to.')
+flags.DEFINE_string('mesh_path', None, 'Path to PLY file to visualize.')
+
+FLAGS = flags.FLAGS
+
+tf.enable_v2_behavior()
+
+# Max number of steps to run training with.
+_MAX_STEPS = 10
+
+
+def train_step(vertices, faces, colors, config_dict, step):
+  """Executes summary as a train step."""
+  # Change colors over time.
+  t = float(step) / _MAX_STEPS
+  transformed_colors = t * (255 - colors) + (1 - t) * colors
+  mesh_summary.mesh(
+      'mesh_color_tensor', vertices=vertices, faces=faces,
+      colors=transformed_colors, config_dict=config_dict, step=step)
+
+
+def run():
+  """Runs training steps with a mesh summary."""
+  # Mesh summaries only work on TensorFlow 2.x.
+  if int(tf.__version__.split('.')[0]) < 1:
+    raise ImportError('TensorFlow 2.x is required to run this demo.')
+  # Flag mesh_path is required.
+  if FLAGS.mesh_path is None:
+    raise ValueError(
+      'Flag --mesh_path is required and must contain path to PLY file.')
+  # Camera and scene configuration.
+  config_dict = {
+      'camera': {'cls': 'PerspectiveCamera', 'fov': 75}
+  }
+
+  # Read sample PLY file.
+  vertices, colors, faces = demo_utils.read_ascii_ply(FLAGS.mesh_path)
+
+  # Add batch dimension.
+  vertices = np.expand_dims(vertices, 0)
+  faces = np.expand_dims(faces, 0)
+  colors = np.expand_dims(colors, 0)
+
+  # Create summary writer.
+  writer = tf.summary.create_file_writer(FLAGS.logdir)
+
+  with writer.as_default():
+    for step in range(_MAX_STEPS):
+      train_step(vertices, faces, colors, config_dict, step)
+
+
+def main(unused_argv):
+  print('Saving output to %s.' % FLAGS.logdir)
+  run()
+  print('Done. Output saved to %s.' % FLAGS.logdir)
+
+
+if __name__ == '__main__':
+  app.run(main)

tensorboard/plugins/mesh/summary.py

@@ -22,8 +22,11 @@
 
 from tensorboard.plugins.mesh import metadata
 from tensorboard.plugins.mesh import plugin_data_pb2
+from tensorboard.plugins.mesh import summary_v2
 
-PLUGIN_NAME = 'mesh'
+# Export V2 versions.
+mesh = summary_v2.mesh
+mesh_pb = summary_v2.mesh_pb
 
 
 def _get_tensor_summary(

tensorboard/plugins/mesh/summary_v2.py

@@ -0,0 +1,184 @@
+# 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