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susnato

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tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/mesh/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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 module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.geometry.representation.mesh import normals from tensorflow_graphics.geometry.representation.mesh import sampler from tensorflow_graphics.geometry.representation.mesh import utils from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.geometry. __all__ = _export_api.get_modules()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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 module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.geometry.representation.mesh import normals from tensorflow_graphics.geometry.representation.mesh import sampler from tensorflow_graphics.geometry.representation.mesh import utils from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.geometry. __all__ = _export_api.get_modules()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/neural_voxel_renderer/layers.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Utility functions for keras layers.""" import tensorflow as tf layers = tf.keras.layers initializer = tf.keras.initializers.glorot_normal() rate = 0.7 def norm_layer(tensor, normalization): if normalization.lower() == 'batchnorm': tensor = layers.BatchNormalization()(tensor) return tensor def upconv(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """Upconvolution as upsampling and convolution.""" tensor = layers.UpSampling2D()(tensor) tensor = layers.Conv2D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_block_3d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None', relu=True): """3D convolution block with normalization and leaky relu.""" tensor = layers.Conv3D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) if relu: tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_t_block_3d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None', relu=True): """2D transpose convolution block with normalization and leaky relu.""" tensor = layers.Conv3DTranspose(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) if relu: tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_block_2d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """2D convolution block with normalization and leaky relu.""" tensor = layers.Conv2D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_t_block_2d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """2D transpose convolution block with normalization and leaky relu.""" tensor = layers.Conv2DTranspose(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def residual_block_2d(x, nfilters, strides=(1, 1), normalization='None'): """2D residual block.""" shortcut = x x = layers.Conv2D(nfilters, kernel_size=(3, 3), strides=strides, padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) x = layers.LeakyReLU()(x) x = layers.Conv2D(nfilters, kernel_size=(3, 3), strides=(1, 1), padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) if strides != (1, 1): shortcut = layers.Conv2D(nfilters, kernel_size=(1, 1), strides=strides, padding='same')(shortcut) x = norm_layer(x, normalization) x = layers.add([shortcut, x]) x = layers.LeakyReLU()(x) return x def residual_block_3d(x, nfilters, strides=(1, 1, 1), normalization='None'): """3D residual block.""" shortcut = x x = layers.Conv3D(nfilters, kernel_size=(3, 3, 3), strides=strides, padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) x = layers.LeakyReLU()(x) x = layers.Conv3D(nfilters, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) if strides != (1, 1, 1): shortcut = layers.Conv3D(nfilters, kernel_size=(1, 1, 1), strides=strides, padding='same')(shortcut) x = norm_layer(x, normalization) x = layers.add([shortcut, x]) x = layers.LeakyReLU()(x) return x

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Utility functions for keras layers.""" import tensorflow as tf layers = tf.keras.layers initializer = tf.keras.initializers.glorot_normal() rate = 0.7 def norm_layer(tensor, normalization): if normalization.lower() == 'batchnorm': tensor = layers.BatchNormalization()(tensor) return tensor def upconv(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """Upconvolution as upsampling and convolution.""" tensor = layers.UpSampling2D()(tensor) tensor = layers.Conv2D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_block_3d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None', relu=True): """3D convolution block with normalization and leaky relu.""" tensor = layers.Conv3D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) if relu: tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_t_block_3d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None', relu=True): """2D transpose convolution block with normalization and leaky relu.""" tensor = layers.Conv3DTranspose(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) if relu: tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_block_2d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """2D convolution block with normalization and leaky relu.""" tensor = layers.Conv2D(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def conv_t_block_2d(tensor, nfilters, size, strides, alpha_lrelu=0.2, normalization='None'): """2D transpose convolution block with normalization and leaky relu.""" tensor = layers.Conv2DTranspose(nfilters, size, strides=strides, padding='same', kernel_initializer=initializer, use_bias=False)(tensor) tensor = norm_layer(tensor, normalization) tensor = layers.LeakyReLU(alpha=alpha_lrelu)(tensor) if normalization.lower() == 'dropout': tensor = layers.Dropout(rate)(tensor) return tensor def residual_block_2d(x, nfilters, strides=(1, 1), normalization='None'): """2D residual block.""" shortcut = x x = layers.Conv2D(nfilters, kernel_size=(3, 3), strides=strides, padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) x = layers.LeakyReLU()(x) x = layers.Conv2D(nfilters, kernel_size=(3, 3), strides=(1, 1), padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) if strides != (1, 1): shortcut = layers.Conv2D(nfilters, kernel_size=(1, 1), strides=strides, padding='same')(shortcut) x = norm_layer(x, normalization) x = layers.add([shortcut, x]) x = layers.LeakyReLU()(x) return x def residual_block_3d(x, nfilters, strides=(1, 1, 1), normalization='None'): """3D residual block.""" shortcut = x x = layers.Conv3D(nfilters, kernel_size=(3, 3, 3), strides=strides, padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) x = layers.LeakyReLU()(x) x = layers.Conv3D(nfilters, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same', kernel_initializer=initializer)(x) x = norm_layer(x, normalization) if strides != (1, 1, 1): shortcut = layers.Conv3D(nfilters, kernel_size=(1, 1, 1), strides=strides, padding='same')(shortcut) x = norm_layer(x, normalization) x = layers.add([shortcut, x]) x = layers.LeakyReLU()(x) return x

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/reflectance/lambertian.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the Lambertian reflectance.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def brdf(direction_incoming_light, direction_outgoing_light, surface_normal, albedo, name=None): """Evaluates the brdf of a Lambertian surface. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Note: The gradient of this function is not smooth when the dot product of the normal with any light is 0.0. Args: direction_incoming_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized incoming light vector. direction_outgoing_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized outgoing light vector. surface_normal: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized surface normal. albedo: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents albedo with values in [0,1]. name: A name for this op. Defaults to "lambertian_brdf". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the amount of reflected light in any outgoing direction. Raises: ValueError: if the shape of `direction_incoming_light`, `direction_outgoing_light`, `surface_normal`, `shininess` or `albedo` is not supported. InvalidArgumentError: if at least one element of `albedo` is outside of [0,1]. """ with tf.compat.v1.name_scope(name, "lambertian_brdf", [ direction_incoming_light, direction_outgoing_light, surface_normal, albedo ]): direction_incoming_light = tf.convert_to_tensor( value=direction_incoming_light) direction_outgoing_light = tf.convert_to_tensor( value=direction_outgoing_light) surface_normal = tf.convert_to_tensor(value=surface_normal) albedo = tf.convert_to_tensor(value=albedo) shape.check_static( tensor=direction_incoming_light, tensor_name="direction_incoming_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=direction_outgoing_light, tensor_name="direction_outgoing_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_normal, tensor_name="surface_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=albedo, tensor_name="albedo", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(direction_incoming_light, direction_outgoing_light, surface_normal, albedo), tensor_names=("direction_incoming_light", "direction_outgoing_light", "surface_normal", "albedo"), last_axes=-2, broadcast_compatible=True) direction_incoming_light = asserts.assert_normalized( direction_incoming_light) direction_outgoing_light = asserts.assert_normalized( direction_outgoing_light) surface_normal = asserts.assert_normalized(surface_normal) albedo = asserts.assert_all_in_range(albedo, 0.0, 1.0, open_bounds=False) # Checks whether the incoming or outgoing light point behind the surface. dot_incoming_light_surface_normal = vector.dot(-direction_incoming_light, surface_normal) dot_outgoing_light_surface_normal = vector.dot(direction_outgoing_light, surface_normal) min_dot = tf.minimum(dot_incoming_light_surface_normal, dot_outgoing_light_surface_normal) common_shape = shape.get_broadcasted_shape(min_dot.shape, albedo.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) albedo = tf.broadcast_to(albedo, common_shape) return tf.compat.v1.where(condition, albedo / math.pi, tf.zeros_like(albedo)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the Lambertian reflectance.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def brdf(direction_incoming_light, direction_outgoing_light, surface_normal, albedo, name=None): """Evaluates the brdf of a Lambertian surface. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Note: The gradient of this function is not smooth when the dot product of the normal with any light is 0.0. Args: direction_incoming_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized incoming light vector. direction_outgoing_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized outgoing light vector. surface_normal: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized surface normal. albedo: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents albedo with values in [0,1]. name: A name for this op. Defaults to "lambertian_brdf". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the amount of reflected light in any outgoing direction. Raises: ValueError: if the shape of `direction_incoming_light`, `direction_outgoing_light`, `surface_normal`, `shininess` or `albedo` is not supported. InvalidArgumentError: if at least one element of `albedo` is outside of [0,1]. """ with tf.compat.v1.name_scope(name, "lambertian_brdf", [ direction_incoming_light, direction_outgoing_light, surface_normal, albedo ]): direction_incoming_light = tf.convert_to_tensor( value=direction_incoming_light) direction_outgoing_light = tf.convert_to_tensor( value=direction_outgoing_light) surface_normal = tf.convert_to_tensor(value=surface_normal) albedo = tf.convert_to_tensor(value=albedo) shape.check_static( tensor=direction_incoming_light, tensor_name="direction_incoming_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=direction_outgoing_light, tensor_name="direction_outgoing_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_normal, tensor_name="surface_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=albedo, tensor_name="albedo", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(direction_incoming_light, direction_outgoing_light, surface_normal, albedo), tensor_names=("direction_incoming_light", "direction_outgoing_light", "surface_normal", "albedo"), last_axes=-2, broadcast_compatible=True) direction_incoming_light = asserts.assert_normalized( direction_incoming_light) direction_outgoing_light = asserts.assert_normalized( direction_outgoing_light) surface_normal = asserts.assert_normalized(surface_normal) albedo = asserts.assert_all_in_range(albedo, 0.0, 1.0, open_bounds=False) # Checks whether the incoming or outgoing light point behind the surface. dot_incoming_light_surface_normal = vector.dot(-direction_incoming_light, surface_normal) dot_outgoing_light_surface_normal = vector.dot(direction_outgoing_light, surface_normal) min_dot = tf.minimum(dot_incoming_light_surface_normal, dot_outgoing_light_surface_normal) common_shape = shape.get_broadcasted_shape(min_dot.shape, albedo.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) albedo = tf.broadcast_to(albedo, common_shape) return tf.compat.v1.where(condition, albedo / math.pi, tf.zeros_like(albedo)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/loss/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/layer/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/layer/tests/pointnet_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for pointnet layers.""" # pylint: disable=invalid-name from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.nn.layer.pointnet import ClassificationHead from tensorflow_graphics.nn.layer.pointnet import PointNetConv2Layer from tensorflow_graphics.nn.layer.pointnet import PointNetDenseLayer from tensorflow_graphics.nn.layer.pointnet import PointNetVanillaClassifier from tensorflow_graphics.nn.layer.pointnet import VanillaEncoder from tensorflow_graphics.util import test_case class RandomForwardExecutionTest(test_case.TestCase): @parameterized.parameters( ((32, 2048, 1, 3), (32), (.5), True), ((32, 2048, 1, 3), (32), (.5), False), ((32, 2048, 1, 2), (16), (.99), True), ) def test_conv2(self, input_shape, channels, momentum, training): B, N, X, _ = input_shape inputs = tf.random.uniform(input_shape) layer = PointNetConv2Layer(channels, momentum) outputs = layer(inputs, training=training) assert outputs.shape == (B, N, X, channels) @parameterized.parameters( ((32, 1024), (40), (.5), True), ((32, 2048), (20), (.5), False), ((32, 512), (10), (.99), True), ) def test_dense(self, input_shape, channels, momentum, training): B, _ = input_shape inputs = tf.random.uniform(input_shape) layer = PointNetDenseLayer(channels, momentum) outputs = layer(inputs, training=training) assert outputs.shape == (B, channels) @parameterized.parameters( ((32, 2048, 3), (.9), True), ((32, 2048, 2), (.5), False), ((32, 2048, 3), (.99), True), ) def test_vanilla_encoder(self, input_shape, momentum, training): B = input_shape[0] inputs = tf.random.uniform(input_shape) encoder = VanillaEncoder(momentum) outputs = encoder(inputs, training=training) assert outputs.shape == (B, 1024) @parameterized.parameters( ((16, 1024), (20), (.9), True), ((8, 2048), (40), (.5), False), ((32, 512), (10), (.99), True), ) def test_classification_head(self, input_shape, num_classes, momentum, training): B = input_shape[0] inputs = tf.random.uniform(input_shape) head = ClassificationHead(num_classes, momentum) outputs = head(inputs, training=training) assert outputs.shape == (B, num_classes) @parameterized.parameters( ((32, 1024, 3), 40, True), ((32, 1024, 2), 40, False), ((16, 2048, 3), 20, True), ((16, 2048, 2), 20, False), ) def test_vanilla_classifier(self, input_shape, num_classes, training): B = input_shape[0] C = num_classes inputs = tf.random.uniform(input_shape) model = PointNetVanillaClassifier(num_classes, momentum=.5) logits = model(inputs, training) assert logits.shape == (B, C) labels = tf.random.uniform((B,), minval=0, maxval=C, dtype=tf.int64) PointNetVanillaClassifier.loss(labels, logits) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for pointnet layers.""" # pylint: disable=invalid-name from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.nn.layer.pointnet import ClassificationHead from tensorflow_graphics.nn.layer.pointnet import PointNetConv2Layer from tensorflow_graphics.nn.layer.pointnet import PointNetDenseLayer from tensorflow_graphics.nn.layer.pointnet import PointNetVanillaClassifier from tensorflow_graphics.nn.layer.pointnet import VanillaEncoder from tensorflow_graphics.util import test_case class RandomForwardExecutionTest(test_case.TestCase): @parameterized.parameters( ((32, 2048, 1, 3), (32), (.5), True), ((32, 2048, 1, 3), (32), (.5), False), ((32, 2048, 1, 2), (16), (.99), True), ) def test_conv2(self, input_shape, channels, momentum, training): B, N, X, _ = input_shape inputs = tf.random.uniform(input_shape) layer = PointNetConv2Layer(channels, momentum) outputs = layer(inputs, training=training) assert outputs.shape == (B, N, X, channels) @parameterized.parameters( ((32, 1024), (40), (.5), True), ((32, 2048), (20), (.5), False), ((32, 512), (10), (.99), True), ) def test_dense(self, input_shape, channels, momentum, training): B, _ = input_shape inputs = tf.random.uniform(input_shape) layer = PointNetDenseLayer(channels, momentum) outputs = layer(inputs, training=training) assert outputs.shape == (B, channels) @parameterized.parameters( ((32, 2048, 3), (.9), True), ((32, 2048, 2), (.5), False), ((32, 2048, 3), (.99), True), ) def test_vanilla_encoder(self, input_shape, momentum, training): B = input_shape[0] inputs = tf.random.uniform(input_shape) encoder = VanillaEncoder(momentum) outputs = encoder(inputs, training=training) assert outputs.shape == (B, 1024) @parameterized.parameters( ((16, 1024), (20), (.9), True), ((8, 2048), (40), (.5), False), ((32, 512), (10), (.99), True), ) def test_classification_head(self, input_shape, num_classes, momentum, training): B = input_shape[0] inputs = tf.random.uniform(input_shape) head = ClassificationHead(num_classes, momentum) outputs = head(inputs, training=training) assert outputs.shape == (B, num_classes) @parameterized.parameters( ((32, 1024, 3), 40, True), ((32, 1024, 2), 40, False), ((16, 2048, 3), 20, True), ((16, 2048, 2), 20, False), ) def test_vanilla_classifier(self, input_shape, num_classes, training): B = input_shape[0] C = num_classes inputs = tf.random.uniform(input_shape) model = PointNetVanillaClassifier(num_classes, momentum=.5) logits = model(inputs, training) assert logits.shape == (B, C) labels = tf.random.uniform((B,), minval=0, maxval=C, dtype=tf.int64) PointNetVanillaClassifier.loss(labels, logits) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/tests/fscore_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the fscore metric.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.metric import fscore from tensorflow_graphics.nn.metric import precision from tensorflow_graphics.nn.metric import recall from tensorflow_graphics.util import test_case def random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def random_tensor_shape(): tensor_size = np.random.randint(5) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() def binary_precision_function(ground_truth, predictions): return precision.evaluate(ground_truth, predictions, classes=[1]) def binary_recall_function(ground_truth, predictions): return recall.evaluate(ground_truth, predictions, classes=[1]) class FscoreTest(test_case.TestCase): @parameterized.parameters( # Precision = 0.5, Recall = 0.25. ((0, 1, 1, 1, 1), (1, 1, 0, 0, 0), 2 * (0.5 * 0.25) / (0.5 + 0.25)), # Precision = 1, Recall = 1. ((0, 0, 0, 1, 1, 1, 0, 1), (0, 0, 0, 1, 1, 1, 0, 1), 1), # Precision = 0, Recall = 0. ((0, 1, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0), 0)) def test_evaluate_preset(self, ground_truth, predictions, expected_fscore): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_fscore, tensor_shape) result = fscore.evaluate( ground_truth_labels, predicted_labels, precision_function=binary_precision_function, recall_function=binary_recall_function) self.assertAllClose(expected, result) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 5, 3), (4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 4), (2, 4, 5)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(fscore.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 3), (2, 5, 1)), ((None, 2, 6), (4, 2, None)), ((3, 1, 1, 2), (3, 5, 8, 2)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(fscore.evaluate, shapes) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the fscore metric.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.metric import fscore from tensorflow_graphics.nn.metric import precision from tensorflow_graphics.nn.metric import recall from tensorflow_graphics.util import test_case def random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def random_tensor_shape(): tensor_size = np.random.randint(5) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() def binary_precision_function(ground_truth, predictions): return precision.evaluate(ground_truth, predictions, classes=[1]) def binary_recall_function(ground_truth, predictions): return recall.evaluate(ground_truth, predictions, classes=[1]) class FscoreTest(test_case.TestCase): @parameterized.parameters( # Precision = 0.5, Recall = 0.25. ((0, 1, 1, 1, 1), (1, 1, 0, 0, 0), 2 * (0.5 * 0.25) / (0.5 + 0.25)), # Precision = 1, Recall = 1. ((0, 0, 0, 1, 1, 1, 0, 1), (0, 0, 0, 1, 1, 1, 0, 1), 1), # Precision = 0, Recall = 0. ((0, 1, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0), 0)) def test_evaluate_preset(self, ground_truth, predictions, expected_fscore): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_fscore, tensor_shape) result = fscore.evaluate( ground_truth_labels, predicted_labels, precision_function=binary_precision_function, recall_function=binary_recall_function) self.assertAllClose(expected, result) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 5, 3), (4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 4), (2, 4, 5)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(fscore.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 3), (2, 5, 1)), ((None, 2, 6), (4, 2, None)), ((3, 1, 1, 2), (3, 5, 8, 2)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(fscore.evaluate, shapes) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/interpolation/tests/bspline_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for bspline.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.math.interpolation import bspline from tensorflow_graphics.util import test_case class BSplineTest(test_case.TestCase): @parameterized.parameters((0.0, (1.0,)), (1.0, (1.0,))) def test_constant_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 0 return expected values.""" self.assertAllClose(bspline._constant(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (1.0, 0.0)), (1.0, (0.0, 1.0))) def test_linear_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 1 return expected values.""" self.assertAllClose(bspline._linear(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (0.5, 0.5, 0.0)), (1.0, (0.0, 0.5, 0.5))) def test_quadratic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 2 return expected values.""" self.assertAllClose(bspline._quadratic(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0, 0.0)), (1.0, (0.0, 1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0))) def test_cubic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 3 return expected values.""" self.assertAllClose(bspline._cubic(position), weights) # pylint: disable=protected-access @parameterized.parameters( (0.0, (1.0 / 24.0, 11.0 / 24.0, 11.0 / 24.0, 1.0 / 24.0, 0.0)), (1.0, (0.0, 1.0 / 24.0, 11.0 / 24.0, 11.0 / 24.0, 1.0 / 24.0))) def test_quartic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 4 return expected values.""" self.assertAllClose(bspline._quartic(position), weights) # pylint: disable=protected-access @parameterized.parameters( (((0.5,), (1.5,), (2.5,)), (((0.5, 0.5),), ((0.5, 0.5),), ((0.5, 0.5),)), (((0,), (1,), (2,))), 1, True), ((0.0, 1.0), ((0.5, 0.5, 0.0), (0.0, 0.5, 0.5)), (0, 0), 2, False), ) def test_knot_weights_sparse_mode_preset(self, positions, gt_weights, gt_shifts, degree, cyclical): """Tests that sparse mode returns correct results.""" weights, shifts = bspline.knot_weights( positions, num_knots=3, degree=degree, cyclical=cyclical, sparse_mode=True) self.assertAllClose(weights, gt_weights) self.assertAllClose(shifts, gt_shifts) @parameterized.parameters( (((0.5,),), (((0.5, 0.5, 0.0),),), 1), (((1.5,),), (((0.0, 0.5, 0.5),),), 1), (((2.5,),), (((0.5, 0.0, 0.5),),), 1), (((0.5,), (1.5,), (2.5,)), (((1.0 / 8.0, 0.75, 1.0 / 8.0),), ((1.0 / 8.0, 1.0 / 8.0, 0.75),), ((0.75, 1.0 / 8.0, 1.0 / 8.0),)), 2), ) def test_knot_weights_preset(self, position, weights, degree): """Tests that knot weights are correct when degree < num_knots - 1.""" self.assertAllClose( bspline.knot_weights( position, num_knots=3, degree=degree, cyclical=True), weights) @parameterized.parameters((((0.0,), (0.25,), (0.5,), (0.75,)),)) def test_full_degree_non_cyclical_knot_weights(self, positions): """Tests that noncyclical weights are correct when using max degree.""" cyclical_weights = bspline.knot_weights( positions=positions, num_knots=3, degree=2, cyclical=True) noncyclical_weights = bspline.knot_weights( positions=positions, num_knots=3, degree=2, cyclical=False) self.assertAllClose(cyclical_weights, noncyclical_weights) @parameterized.parameters( ("must have the same number of dimensions", ((None, 2), (None, 3, 3))), ("must have the same number of dimensions", ((2,), (3,))), ) def test_interpolate_with_weights_exception_is_raised(self, error_msg, shapes): """Tests that exception is raised when wrong number of knots is given.""" self.assert_exception_is_raised( bspline.interpolate_with_weights, error_msg, shapes=shapes) @parameterized.parameters( (((0.5,), (0.0,), (0.9,)), (((0.5, 1.5), (1.5, 1.5), (2.5, 3.5)),))) def test_interpolate_with_weights_preset(self, positions, knots): """Tests that interpolate_with_weights works correctly.""" degree = 1 cyclical = False interp1 = bspline.interpolate(knots, positions, degree, cyclical) weights = bspline.knot_weights(positions, 2, degree, cyclical) interp2 = bspline.interpolate_with_weights(knots, weights) self.assertAllClose(interp1, interp2) @parameterized.parameters( (1, 2), (1, None), (2, 2), (2, None), (3, 2), (3, None), (4, 2), (4, None), ) def test_knot_weights_exception_is_not_raised(self, positions_rank, dims): shapes = ([dims] * positions_rank,) self.assert_exception_is_not_raised( bspline.knot_weights, shapes=shapes, num_knots=3, degree=2, cyclical=True) @parameterized.parameters( ("Degree should be between 0 and 4.", 6, -1), ("Degree should be between 0 and 4.", 6, 5), ("Degree cannot be >= number of knots.", 2, 2), ("Degree cannot be >= number of knots.", 2, 3), ) def test_knot_weights_exception_is_raised(self, error_msg, num_knots, degree): self.assert_exception_is_raised( bspline.knot_weights, error_msg, shapes=((10, 1),), num_knots=num_knots, degree=degree, cyclical=True) @parameterized.parameters( (1, 0, True), (1, 0, False), (2, 1, True), (2, 1, False), (3, 1, True), (3, 1, False), (3, 2, True), (3, 2, False), (4, 1, True), (4, 1, False), (4, 3, True), (4, 3, False), (5, 1, True), (5, 1, False), (5, 4, True), (5, 4, False), ) def test_knot_weights_jacobian_is_correct(self, num_knots, degree, cyclical): """Tests that Jacobian is correct.""" positions_init = np.random.random_sample((10, 1)) scale = num_knots if cyclical else num_knots - degree positions_init *= scale def dense_mode_fn(positions): return bspline.knot_weights( positions=positions, num_knots=num_knots, degree=degree, cyclical=cyclical, sparse_mode=False) def sparse_mode_fn(positions): return bspline.knot_weights( positions=positions, num_knots=num_knots, degree=degree, cyclical=cyclical, sparse_mode=True)[0] with self.subTest(name="dense_mode"): self.assert_jacobian_is_correct_fn(dense_mode_fn, [positions_init]) with self.subTest(name="sparse_mode"): self.assert_jacobian_is_correct_fn(sparse_mode_fn, [positions_init]) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for bspline.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.math.interpolation import bspline from tensorflow_graphics.util import test_case class BSplineTest(test_case.TestCase): @parameterized.parameters((0.0, (1.0,)), (1.0, (1.0,))) def test_constant_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 0 return expected values.""" self.assertAllClose(bspline._constant(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (1.0, 0.0)), (1.0, (0.0, 1.0))) def test_linear_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 1 return expected values.""" self.assertAllClose(bspline._linear(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (0.5, 0.5, 0.0)), (1.0, (0.0, 0.5, 0.5))) def test_quadratic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 2 return expected values.""" self.assertAllClose(bspline._quadratic(position), weights) # pylint: disable=protected-access @parameterized.parameters((0.0, (1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0, 0.0)), (1.0, (0.0, 1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0))) def test_cubic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 3 return expected values.""" self.assertAllClose(bspline._cubic(position), weights) # pylint: disable=protected-access @parameterized.parameters( (0.0, (1.0 / 24.0, 11.0 / 24.0, 11.0 / 24.0, 1.0 / 24.0, 0.0)), (1.0, (0.0, 1.0 / 24.0, 11.0 / 24.0, 11.0 / 24.0, 1.0 / 24.0))) def test_quartic_basis_boundary_values(self, position, weights): """Tests that basis functions of degree 4 return expected values.""" self.assertAllClose(bspline._quartic(position), weights) # pylint: disable=protected-access @parameterized.parameters( (((0.5,), (1.5,), (2.5,)), (((0.5, 0.5),), ((0.5, 0.5),), ((0.5, 0.5),)), (((0,), (1,), (2,))), 1, True), ((0.0, 1.0), ((0.5, 0.5, 0.0), (0.0, 0.5, 0.5)), (0, 0), 2, False), ) def test_knot_weights_sparse_mode_preset(self, positions, gt_weights, gt_shifts, degree, cyclical): """Tests that sparse mode returns correct results.""" weights, shifts = bspline.knot_weights( positions, num_knots=3, degree=degree, cyclical=cyclical, sparse_mode=True) self.assertAllClose(weights, gt_weights) self.assertAllClose(shifts, gt_shifts) @parameterized.parameters( (((0.5,),), (((0.5, 0.5, 0.0),),), 1), (((1.5,),), (((0.0, 0.5, 0.5),),), 1), (((2.5,),), (((0.5, 0.0, 0.5),),), 1), (((0.5,), (1.5,), (2.5,)), (((1.0 / 8.0, 0.75, 1.0 / 8.0),), ((1.0 / 8.0, 1.0 / 8.0, 0.75),), ((0.75, 1.0 / 8.0, 1.0 / 8.0),)), 2), ) def test_knot_weights_preset(self, position, weights, degree): """Tests that knot weights are correct when degree < num_knots - 1.""" self.assertAllClose( bspline.knot_weights( position, num_knots=3, degree=degree, cyclical=True), weights) @parameterized.parameters((((0.0,), (0.25,), (0.5,), (0.75,)),)) def test_full_degree_non_cyclical_knot_weights(self, positions): """Tests that noncyclical weights are correct when using max degree.""" cyclical_weights = bspline.knot_weights( positions=positions, num_knots=3, degree=2, cyclical=True) noncyclical_weights = bspline.knot_weights( positions=positions, num_knots=3, degree=2, cyclical=False) self.assertAllClose(cyclical_weights, noncyclical_weights) @parameterized.parameters( ("must have the same number of dimensions", ((None, 2), (None, 3, 3))), ("must have the same number of dimensions", ((2,), (3,))), ) def test_interpolate_with_weights_exception_is_raised(self, error_msg, shapes): """Tests that exception is raised when wrong number of knots is given.""" self.assert_exception_is_raised( bspline.interpolate_with_weights, error_msg, shapes=shapes) @parameterized.parameters( (((0.5,), (0.0,), (0.9,)), (((0.5, 1.5), (1.5, 1.5), (2.5, 3.5)),))) def test_interpolate_with_weights_preset(self, positions, knots): """Tests that interpolate_with_weights works correctly.""" degree = 1 cyclical = False interp1 = bspline.interpolate(knots, positions, degree, cyclical) weights = bspline.knot_weights(positions, 2, degree, cyclical) interp2 = bspline.interpolate_with_weights(knots, weights) self.assertAllClose(interp1, interp2) @parameterized.parameters( (1, 2), (1, None), (2, 2), (2, None), (3, 2), (3, None), (4, 2), (4, None), ) def test_knot_weights_exception_is_not_raised(self, positions_rank, dims): shapes = ([dims] * positions_rank,) self.assert_exception_is_not_raised( bspline.knot_weights, shapes=shapes, num_knots=3, degree=2, cyclical=True) @parameterized.parameters( ("Degree should be between 0 and 4.", 6, -1), ("Degree should be between 0 and 4.", 6, 5), ("Degree cannot be >= number of knots.", 2, 2), ("Degree cannot be >= number of knots.", 2, 3), ) def test_knot_weights_exception_is_raised(self, error_msg, num_knots, degree): self.assert_exception_is_raised( bspline.knot_weights, error_msg, shapes=((10, 1),), num_knots=num_knots, degree=degree, cyclical=True) @parameterized.parameters( (1, 0, True), (1, 0, False), (2, 1, True), (2, 1, False), (3, 1, True), (3, 1, False), (3, 2, True), (3, 2, False), (4, 1, True), (4, 1, False), (4, 3, True), (4, 3, False), (5, 1, True), (5, 1, False), (5, 4, True), (5, 4, False), ) def test_knot_weights_jacobian_is_correct(self, num_knots, degree, cyclical): """Tests that Jacobian is correct.""" positions_init = np.random.random_sample((10, 1)) scale = num_knots if cyclical else num_knots - degree positions_init *= scale def dense_mode_fn(positions): return bspline.knot_weights( positions=positions, num_knots=num_knots, degree=degree, cyclical=cyclical, sparse_mode=False) def sparse_mode_fn(positions): return bspline.knot_weights( positions=positions, num_knots=num_knots, degree=degree, cyclical=cyclical, sparse_mode=True)[0] with self.subTest(name="dense_mode"): self.assert_jacobian_is_correct_fn(dense_mode_fn, [positions_init]) with self.subTest(name="sparse_mode"): self.assert_jacobian_is_correct_fn(sparse_mode_fn, [positions_init]) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/deformation_energy/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/deformation_energy/as_conformal_as_possible.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements TensorFlow As Rigid As Possible utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.geometry.transformation import quaternion from tensorflow_graphics.math import vector from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def energy(vertices_rest_pose, vertices_deformed_pose, quaternions, edges, vertex_weight=None, edge_weight=None, conformal_energy=True, aggregate_loss=True, name=None): """Estimates an As Conformal As Possible (ACAP) fitting energy. For a given mesh in rest pose, this function evaluates a variant of the ACAP [1] fitting energy for a batch of deformed meshes. The vertex weights and edge weights are defined on the rest pose. The method implemented here is similar to [2], but with an added free variable capturing a scale factor per vertex. [1]: Yusuke Yoshiyasu, Wan-Chun Ma, Eiichi Yoshida, and Fumio Kanehiro. "As-Conformal-As-Possible Surface Registration." Computer Graphics Forum. Vol. 33. No. 5. 2014.</br> [2]: Olga Sorkine, and Marc Alexa. "As-rigid-as-possible surface modeling". Symposium on Geometry Processing. Vol. 4. 2007. Note: In the description of the arguments, V corresponds to the number of vertices in the mesh, and E to the number of edges in this mesh. Note: In the following, A1 to An are optional batch dimensions. Args: vertices_rest_pose: A tensor of shape `[V, 3]` containing the position of all the vertices of the mesh in rest pose. vertices_deformed_pose: A tensor of shape `[A1, ..., An, V, 3]` containing the position of all the vertices of the mesh in deformed pose. quaternions: A tensor of shape `[A1, ..., An, V, 4]` defining a rigid transformation to apply to each vertex of the rest pose. See Section 2 from [1] for further details. edges: A tensor of shape `[E, 2]` defining indices of vertices that are connected by an edge. vertex_weight: An optional tensor of shape `[V]` defining the weight associated with each vertex. Defaults to a tensor of ones. edge_weight: A tensor of shape `[E]` defining the weight of edges. Common choices for these weights include uniform weighting, and cotangent weights. Defaults to a tensor of ones. conformal_energy: A `bool` indicating whether each vertex is associated with a scale factor or not. If this parameter is True, scaling information must be encoded in the norm of `quaternions`. If this parameter is False, this function implements the energy described in [2]. aggregate_loss: A `bool` defining whether the returned loss should be an aggregate measure. When True, the mean squared error is returned. When False, returns two losses for every edge of the mesh. name: A name for this op. Defaults to "as_conformal_as_possible_energy". Returns: When aggregate_loss is `True`, returns a tensor of shape `[A1, ..., An]` containing the ACAP energies. When aggregate_loss is `False`, returns a tensor of shape `[A1, ..., An, 2*E]` containing each term of the summation described in the equation 7 of [2]. Raises: ValueError: if the shape of `vertices_rest_pose`, `vertices_deformed_pose`, `quaternions`, `edges`, `vertex_weight`, or `edge_weight` is not supported. """ with tf.compat.v1.name_scope(name, "as_conformal_as_possible_energy", [ vertices_rest_pose, vertices_deformed_pose, quaternions, edges, conformal_energy, vertex_weight, edge_weight ]): vertices_rest_pose = tf.convert_to_tensor(value=vertices_rest_pose) vertices_deformed_pose = tf.convert_to_tensor(value=vertices_deformed_pose) quaternions = tf.convert_to_tensor(value=quaternions) edges = tf.convert_to_tensor(value=edges) if vertex_weight is not None: vertex_weight = tf.convert_to_tensor(value=vertex_weight) if edge_weight is not None: edge_weight = tf.convert_to_tensor(value=edge_weight) shape.check_static( tensor=vertices_rest_pose, tensor_name="vertices_rest_pose", has_rank=2, has_dim_equals=(-1, 3)) shape.check_static( tensor=vertices_deformed_pose, tensor_name="vertices_deformed_pose", has_rank_greater_than=1, has_dim_equals=(-1, 3)) shape.check_static( tensor=quaternions, tensor_name="quaternions", has_rank_greater_than=1, has_dim_equals=(-1, 4)) shape.compare_batch_dimensions( tensors=(vertices_deformed_pose, quaternions), last_axes=(-3, -3), broadcast_compatible=False) shape.check_static( tensor=edges, tensor_name="edges", has_rank=2, has_dim_equals=(-1, 2)) tensors_with_vertices = [vertices_rest_pose, vertices_deformed_pose, quaternions] names_with_vertices = ["vertices_rest_pose", "vertices_deformed_pose", "quaternions"] axes_with_vertices = [-2, -2, -2] if vertex_weight is not None: shape.check_static( tensor=vertex_weight, tensor_name="vertex_weight", has_rank=1) tensors_with_vertices.append(vertex_weight) names_with_vertices.append("vertex_weight") axes_with_vertices.append(0) shape.compare_dimensions( tensors=tensors_with_vertices, axes=axes_with_vertices, tensor_names=names_with_vertices) if edge_weight is not None: shape.check_static( tensor=edge_weight, tensor_name="edge_weight", has_rank=1) shape.compare_dimensions( tensors=(edges, edge_weight), axes=(0, 0), tensor_names=("edges", "edge_weight")) if not conformal_energy: quaternions = quaternion.normalize(quaternions) # Extracts the indices of vertices. indices_i, indices_j = tf.unstack(edges, axis=-1) # Extracts the vertices we need per term. vertices_i_rest = tf.gather(vertices_rest_pose, indices_i, axis=-2) vertices_j_rest = tf.gather(vertices_rest_pose, indices_j, axis=-2) vertices_i_deformed = tf.gather(vertices_deformed_pose, indices_i, axis=-2) vertices_j_deformed = tf.gather(vertices_deformed_pose, indices_j, axis=-2) # Extracts the weights we need per term. weights_shape = vertices_i_rest.shape.as_list()[-2] if vertex_weight is not None: weight_i = tf.gather(vertex_weight, indices_i) weight_j = tf.gather(vertex_weight, indices_j) else: weight_i = weight_j = tf.ones( weights_shape, dtype=vertices_rest_pose.dtype) weight_i = tf.expand_dims(weight_i, axis=-1) weight_j = tf.expand_dims(weight_j, axis=-1) if edge_weight is not None: weight_ij = edge_weight else: weight_ij = tf.ones(weights_shape, dtype=vertices_rest_pose.dtype) weight_ij = tf.expand_dims(weight_ij, axis=-1) # Extracts the rotation we need per term. quaternion_i = tf.gather(quaternions, indices_i, axis=-2) quaternion_j = tf.gather(quaternions, indices_j, axis=-2) # Computes the energy. deformed_ij = vertices_i_deformed - vertices_j_deformed rotated_rest_ij = quaternion.rotate((vertices_i_rest - vertices_j_rest), quaternion_i) energy_ij = weight_i * weight_ij * (deformed_ij - rotated_rest_ij) deformed_ji = vertices_j_deformed - vertices_i_deformed rotated_rest_ji = quaternion.rotate((vertices_j_rest - vertices_i_rest), quaternion_j) energy_ji = weight_j * weight_ij * (deformed_ji - rotated_rest_ji) energy_ij_squared = vector.dot(energy_ij, energy_ij, keepdims=False) energy_ji_squared = vector.dot(energy_ji, energy_ji, keepdims=False) if aggregate_loss: average_energy_ij = tf.reduce_mean( input_tensor=energy_ij_squared, axis=-1) average_energy_ji = tf.reduce_mean( input_tensor=energy_ji_squared, axis=-1) return (average_energy_ij + average_energy_ji) / 2.0 return tf.concat((energy_ij_squared, energy_ji_squared), axis=-1) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements TensorFlow As Rigid As Possible utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.geometry.transformation import quaternion from tensorflow_graphics.math import vector from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def energy(vertices_rest_pose, vertices_deformed_pose, quaternions, edges, vertex_weight=None, edge_weight=None, conformal_energy=True, aggregate_loss=True, name=None): """Estimates an As Conformal As Possible (ACAP) fitting energy. For a given mesh in rest pose, this function evaluates a variant of the ACAP [1] fitting energy for a batch of deformed meshes. The vertex weights and edge weights are defined on the rest pose. The method implemented here is similar to [2], but with an added free variable capturing a scale factor per vertex. [1]: Yusuke Yoshiyasu, Wan-Chun Ma, Eiichi Yoshida, and Fumio Kanehiro. "As-Conformal-As-Possible Surface Registration." Computer Graphics Forum. Vol. 33. No. 5. 2014.</br> [2]: Olga Sorkine, and Marc Alexa. "As-rigid-as-possible surface modeling". Symposium on Geometry Processing. Vol. 4. 2007. Note: In the description of the arguments, V corresponds to the number of vertices in the mesh, and E to the number of edges in this mesh. Note: In the following, A1 to An are optional batch dimensions. Args: vertices_rest_pose: A tensor of shape `[V, 3]` containing the position of all the vertices of the mesh in rest pose. vertices_deformed_pose: A tensor of shape `[A1, ..., An, V, 3]` containing the position of all the vertices of the mesh in deformed pose. quaternions: A tensor of shape `[A1, ..., An, V, 4]` defining a rigid transformation to apply to each vertex of the rest pose. See Section 2 from [1] for further details. edges: A tensor of shape `[E, 2]` defining indices of vertices that are connected by an edge. vertex_weight: An optional tensor of shape `[V]` defining the weight associated with each vertex. Defaults to a tensor of ones. edge_weight: A tensor of shape `[E]` defining the weight of edges. Common choices for these weights include uniform weighting, and cotangent weights. Defaults to a tensor of ones. conformal_energy: A `bool` indicating whether each vertex is associated with a scale factor or not. If this parameter is True, scaling information must be encoded in the norm of `quaternions`. If this parameter is False, this function implements the energy described in [2]. aggregate_loss: A `bool` defining whether the returned loss should be an aggregate measure. When True, the mean squared error is returned. When False, returns two losses for every edge of the mesh. name: A name for this op. Defaults to "as_conformal_as_possible_energy". Returns: When aggregate_loss is `True`, returns a tensor of shape `[A1, ..., An]` containing the ACAP energies. When aggregate_loss is `False`, returns a tensor of shape `[A1, ..., An, 2*E]` containing each term of the summation described in the equation 7 of [2]. Raises: ValueError: if the shape of `vertices_rest_pose`, `vertices_deformed_pose`, `quaternions`, `edges`, `vertex_weight`, or `edge_weight` is not supported. """ with tf.compat.v1.name_scope(name, "as_conformal_as_possible_energy", [ vertices_rest_pose, vertices_deformed_pose, quaternions, edges, conformal_energy, vertex_weight, edge_weight ]): vertices_rest_pose = tf.convert_to_tensor(value=vertices_rest_pose) vertices_deformed_pose = tf.convert_to_tensor(value=vertices_deformed_pose) quaternions = tf.convert_to_tensor(value=quaternions) edges = tf.convert_to_tensor(value=edges) if vertex_weight is not None: vertex_weight = tf.convert_to_tensor(value=vertex_weight) if edge_weight is not None: edge_weight = tf.convert_to_tensor(value=edge_weight) shape.check_static( tensor=vertices_rest_pose, tensor_name="vertices_rest_pose", has_rank=2, has_dim_equals=(-1, 3)) shape.check_static( tensor=vertices_deformed_pose, tensor_name="vertices_deformed_pose", has_rank_greater_than=1, has_dim_equals=(-1, 3)) shape.check_static( tensor=quaternions, tensor_name="quaternions", has_rank_greater_than=1, has_dim_equals=(-1, 4)) shape.compare_batch_dimensions( tensors=(vertices_deformed_pose, quaternions), last_axes=(-3, -3), broadcast_compatible=False) shape.check_static( tensor=edges, tensor_name="edges", has_rank=2, has_dim_equals=(-1, 2)) tensors_with_vertices = [vertices_rest_pose, vertices_deformed_pose, quaternions] names_with_vertices = ["vertices_rest_pose", "vertices_deformed_pose", "quaternions"] axes_with_vertices = [-2, -2, -2] if vertex_weight is not None: shape.check_static( tensor=vertex_weight, tensor_name="vertex_weight", has_rank=1) tensors_with_vertices.append(vertex_weight) names_with_vertices.append("vertex_weight") axes_with_vertices.append(0) shape.compare_dimensions( tensors=tensors_with_vertices, axes=axes_with_vertices, tensor_names=names_with_vertices) if edge_weight is not None: shape.check_static( tensor=edge_weight, tensor_name="edge_weight", has_rank=1) shape.compare_dimensions( tensors=(edges, edge_weight), axes=(0, 0), tensor_names=("edges", "edge_weight")) if not conformal_energy: quaternions = quaternion.normalize(quaternions) # Extracts the indices of vertices. indices_i, indices_j = tf.unstack(edges, axis=-1) # Extracts the vertices we need per term. vertices_i_rest = tf.gather(vertices_rest_pose, indices_i, axis=-2) vertices_j_rest = tf.gather(vertices_rest_pose, indices_j, axis=-2) vertices_i_deformed = tf.gather(vertices_deformed_pose, indices_i, axis=-2) vertices_j_deformed = tf.gather(vertices_deformed_pose, indices_j, axis=-2) # Extracts the weights we need per term. weights_shape = vertices_i_rest.shape.as_list()[-2] if vertex_weight is not None: weight_i = tf.gather(vertex_weight, indices_i) weight_j = tf.gather(vertex_weight, indices_j) else: weight_i = weight_j = tf.ones( weights_shape, dtype=vertices_rest_pose.dtype) weight_i = tf.expand_dims(weight_i, axis=-1) weight_j = tf.expand_dims(weight_j, axis=-1) if edge_weight is not None: weight_ij = edge_weight else: weight_ij = tf.ones(weights_shape, dtype=vertices_rest_pose.dtype) weight_ij = tf.expand_dims(weight_ij, axis=-1) # Extracts the rotation we need per term. quaternion_i = tf.gather(quaternions, indices_i, axis=-2) quaternion_j = tf.gather(quaternions, indices_j, axis=-2) # Computes the energy. deformed_ij = vertices_i_deformed - vertices_j_deformed rotated_rest_ij = quaternion.rotate((vertices_i_rest - vertices_j_rest), quaternion_i) energy_ij = weight_i * weight_ij * (deformed_ij - rotated_rest_ij) deformed_ji = vertices_j_deformed - vertices_i_deformed rotated_rest_ji = quaternion.rotate((vertices_j_rest - vertices_i_rest), quaternion_j) energy_ji = weight_j * weight_ij * (deformed_ji - rotated_rest_ji) energy_ij_squared = vector.dot(energy_ij, energy_ij, keepdims=False) energy_ji_squared = vector.dot(energy_ji, energy_ji, keepdims=False) if aggregate_loss: average_energy_ij = tf.reduce_mean( input_tensor=energy_ij_squared, axis=-1) average_energy_ji = tf.reduce_mean( input_tensor=energy_ji_squared, axis=-1) return (average_energy_ij + average_energy_ji) / 2.0 return tf.concat((energy_ij_squared, energy_ji_squared), axis=-1) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/recall.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the recall metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _cast_to_int(prediction): return tf.cast(x=prediction, dtype=tf.int32) def evaluate(ground_truth, prediction, classes=None, reduce_average=True, prediction_to_category_function=_cast_to_int, name=None): """Computes the recall metric for the given ground truth and predictions. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth labels. Will be cast to int32. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predictions (which can be continuous). classes: An integer or a list/tuple of integers representing the classes for which the recall will be evaluated. In case 'classes' is 'None', the number of classes will be inferred from the given values and the recall will be calculated for each of the classes. Defaults to 'None'. reduce_average: Whether to calculate the average of the recall for each class and return a single recall value. Defaults to true. prediction_to_category_function: A function to associate a `prediction` to a category. Defaults to rounding down the value of the prediction to the nearest integer value. name: A name for this op. Defaults to "recall_evaluate". Returns: A tensor of shape `[A1, ..., An, C]`, where the last axis represents the recall calculated for each of the requested classes. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "recall_evaluate", [ground_truth, prediction]): ground_truth = tf.cast( x=tf.convert_to_tensor(value=ground_truth), dtype=tf.int32) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) prediction = prediction_to_category_function(prediction) if classes is None: num_classes = tf.math.maximum( tf.math.reduce_max(input_tensor=ground_truth), tf.math.reduce_max(input_tensor=prediction)) + 1 classes = tf.range(num_classes) else: classes = tf.convert_to_tensor(value=classes) # Make sure classes is a tensor of rank 1. classes = tf.reshape(classes, [1]) if tf.rank(classes) == 0 else classes # Create a confusion matrix for each of the classes (with dimensions # [A1, ..., An, C, N]). classes = tf.expand_dims(classes, -1) ground_truth_per_class = tf.equal(tf.expand_dims(ground_truth, -2), classes) prediction_per_class = tf.equal(tf.expand_dims(prediction, -2), classes) # Caluclate the recall for each of the classes. true_positives = tf.math.reduce_sum( input_tensor=tf.cast( x=tf.math.logical_and(ground_truth_per_class, prediction_per_class), dtype=tf.float32), axis=-1) total_ground_truth_positives = tf.math.reduce_sum( input_tensor=tf.cast(x=ground_truth_per_class, dtype=tf.float32), axis=-1) recall_per_class = safe_ops.safe_signed_div(true_positives, total_ground_truth_positives) if reduce_average: return tf.math.reduce_mean(input_tensor=recall_per_class, axis=-1) else: return recall_per_class # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the recall metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _cast_to_int(prediction): return tf.cast(x=prediction, dtype=tf.int32) def evaluate(ground_truth, prediction, classes=None, reduce_average=True, prediction_to_category_function=_cast_to_int, name=None): """Computes the recall metric for the given ground truth and predictions. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth labels. Will be cast to int32. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predictions (which can be continuous). classes: An integer or a list/tuple of integers representing the classes for which the recall will be evaluated. In case 'classes' is 'None', the number of classes will be inferred from the given values and the recall will be calculated for each of the classes. Defaults to 'None'. reduce_average: Whether to calculate the average of the recall for each class and return a single recall value. Defaults to true. prediction_to_category_function: A function to associate a `prediction` to a category. Defaults to rounding down the value of the prediction to the nearest integer value. name: A name for this op. Defaults to "recall_evaluate". Returns: A tensor of shape `[A1, ..., An, C]`, where the last axis represents the recall calculated for each of the requested classes. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "recall_evaluate", [ground_truth, prediction]): ground_truth = tf.cast( x=tf.convert_to_tensor(value=ground_truth), dtype=tf.int32) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) prediction = prediction_to_category_function(prediction) if classes is None: num_classes = tf.math.maximum( tf.math.reduce_max(input_tensor=ground_truth), tf.math.reduce_max(input_tensor=prediction)) + 1 classes = tf.range(num_classes) else: classes = tf.convert_to_tensor(value=classes) # Make sure classes is a tensor of rank 1. classes = tf.reshape(classes, [1]) if tf.rank(classes) == 0 else classes # Create a confusion matrix for each of the classes (with dimensions # [A1, ..., An, C, N]). classes = tf.expand_dims(classes, -1) ground_truth_per_class = tf.equal(tf.expand_dims(ground_truth, -2), classes) prediction_per_class = tf.equal(tf.expand_dims(prediction, -2), classes) # Caluclate the recall for each of the classes. true_positives = tf.math.reduce_sum( input_tensor=tf.cast( x=tf.math.logical_and(ground_truth_per_class, prediction_per_class), dtype=tf.float32), axis=-1) total_ground_truth_positives = tf.math.reduce_sum( input_tensor=tf.cast(x=ground_truth_per_class, dtype=tf.float32), axis=-1) recall_per_class = safe_ops.safe_signed_div(true_positives, total_ground_truth_positives) if reduce_average: return tf.math.reduce_mean(input_tensor=recall_per_class, axis=-1) else: return recall_per_class # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/cvxnet/lib/utils.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections from os import path import numpy as np import scipy as sp from skimage import measure import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib.libmise import mise import trimesh Stats = collections.namedtuple("Stats", ["iou", "chamfer", "fscore"]) SYSNET_CLASSES = { "02691156": "airplane", "02933112": "cabinet", "03001627": "chair", "03636649": "lamp", "04090263": "rifle", "04379243": "table", "04530566": "watercraft", "02828884": "bench", "02958343": "car", "03211117": "display", "03691459": "speaker", "04256520": "sofa", "04401088": "telephone", "all": "all", } def define_flags(): """Define command line flags.""" flags = tf.app.flags # Model flags flags.DEFINE_enum("model", "multiconvex", list(k for k in models.model_dict.keys()), "Name of the model.") flags.DEFINE_float("sharpness", 75., "Sharpness term.") flags.DEFINE_integer("n_parts", 50, "Number of convexes uesd.") flags.DEFINE_integer("n_half_planes", 25, "Number of half spaces used.") flags.DEFINE_integer("latent_size", 256, "The size of latent code.") flags.DEFINE_integer("dims", 3, "The dimension of query points.") flags.DEFINE_bool("image_input", False, "Use color images as input if True.") flags.DEFINE_float("vis_scale", 1.3, "Scale of bbox used when extracting meshes.") flags.DEFINE_float("level_set", 0.5, "Level set used for extracting surfaces.") # Dataset flags flags.DEFINE_enum("dataset", "shapenet", list(k for k in datasets.dataset_dict.keys()), "Name of the dataset.") flags.DEFINE_integer("image_h", 137, "The height of the color images.") flags.DEFINE_integer("image_w", 137, "The width of the color images.") flags.DEFINE_integer("image_d", 3, "The channels of color images.") flags.DEFINE_integer("depth_h", 224, "The height of depth images.") flags.DEFINE_integer("depth_w", 224, "The width of depth images.") flags.DEFINE_integer("depth_d", 20, "The number of depth views.") flags.DEFINE_integer("n_views", 24, "The number of color images views.") flags.DEFINE_string("data_dir", None, "The base directory to load data from.") flags.mark_flag_as_required("data_dir") flags.DEFINE_string("obj_class", "*", "Object class used from dataset.") # Training flags flags.DEFINE_float("lr", 1e-4, "Start learning rate.") flags.DEFINE_string( "train_dir", None, "The base directory to save training info and" "checkpoints.") flags.DEFINE_integer("save_every", 20000, "The number of steps to save checkpoint.") flags.DEFINE_integer("max_steps", 800000, "The number of steps of training.") flags.DEFINE_integer("batch_size", 32, "Batch size.") flags.DEFINE_integer("sample_bbx", 1024, "The number of bounding box sample points.") flags.DEFINE_integer("sample_surf", 1024, "The number of surface sample points.") flags.DEFINE_float("weight_overlap", 0.1, "Weight of overlap_loss") flags.DEFINE_float("weight_balance", 0.01, "Weight of balance_loss") flags.DEFINE_float("weight_center", 0.001, "Weight of center_loss") flags.mark_flag_as_required("train_dir") # Eval flags flags.DEFINE_bool("extract_mesh", False, "Extract meshes and set to disk if True.") flags.DEFINE_bool("surface_metrics", False, "Measure surface metrics and save to csv if True.") flags.DEFINE_string("mesh_dir", None, "Path to load ground truth meshes.") flags.DEFINE_string("trans_dir", None, "Path to load pred-to-target transformations.") flags.DEFINE_bool("eval_once", False, "Evaluate the model only once if True.") def mesh_name_helper(name): name = name[0].decode("utf-8") split = name.find("-") cls_name = name[:split] obj_name = name[split + 1:] return cls_name, obj_name def extract_mesh(input_val, params, indicators, input_holder, params_holder, points_holder, sess, args): """Extracting meshes from an indicator function. Args: input_val: np.array, [1, height, width, channel], input image. params: tf.Operation, hyperplane parameter hook. indicators: tf.Operation, indicator hook. input_holder: tf.Placeholder, input image placeholder. params_holder: tf.Placeholder, hyperplane parameter placeholder. points_holder: tf.Placeholder, query point placeholder. sess: tf.Session, running sess. args: tf.app.flags.FLAGS, configurations. Returns: mesh: trimesh.Trimesh, the extracted mesh. """ mesh_extractor = mise.MISE(64, 1, args.level_set) points = mesh_extractor.query() params_val = sess.run(params, {input_holder: input_val}) while points.shape[0] != 0: orig_points = points points = points.astype(np.float32) points = ( (np.expand_dims(points, axis=0) / mesh_extractor.resolution - 0.5) * args.vis_scale) n_points = points.shape[1] values = [] for i in range(0, n_points, 100000): # Add this to prevent OOM. value = sess.run(indicators, { params_holder: params_val, points_holder: points[:, i:i + 100000] }) values.append(value) values = np.concatenate(values, axis=1) values = values[0, :, 0].astype(np.float64) mesh_extractor.update(orig_points, values) points = mesh_extractor.query() value_grid = mesh_extractor.to_dense() value_grid = np.pad(value_grid, 1, "constant", constant_values=-1e6) verts, faces, normals, unused_var = measure.marching_cubes_lewiner( value_grid, min(args.level_set, value_grid.max() * 0.75)) del normals verts -= 1 verts /= np.array([ value_grid.shape[0] - 3, value_grid.shape[1] - 3, value_grid.shape[2] - 3 ], dtype=np.float32) verts = args.vis_scale * (verts - 0.5) faces = np.stack([faces[..., 1], faces[..., 0], faces[..., 2]], axis=-1) return trimesh.Trimesh(vertices=verts, faces=faces) def transform_mesh(mesh, name, trans_dir): """Transform mesh back to the same coordinate of ground truth. Args: mesh: trimesh.Trimesh, predicted mesh before transformation. name: Tensor, hash name of the mesh as recorded in the dataset. trans_dir: string, path to the directory for loading transformations. Returns: mesh: trimesh.Trimesh, the transformed mesh. """ if trans_dir is None: raise ValueError("Need to specify args.trans_dir for loading pred-to-target" "transformations.") cls_name, obj_name = mesh_name_helper(name) with tf.io.gfile.GFile( path.join(trans_dir, "test", cls_name, obj_name, "occnet_to_gaps.txt"), "r") as fin: tx = np.loadtxt(fin).reshape([4, 4]) mesh.apply_transform(np.linalg.inv(tx)) return mesh def save_mesh(mesh, name, eval_dir): """Save a mesh to disk. Args: mesh: trimesh.Trimesh, the mesh to save. name: Tensor, hash name of the mesh as recorded in the dataset. eval_dir: string, path to the directory to save the mesh. """ cls_name, obj_name = mesh_name_helper(name) cls_dir = path.join(eval_dir, "meshes", cls_name) if not tf.io.gfile.isdir(cls_dir): tf.io.gfile.makedirs(cls_dir) with tf.io.gfile.GFile(path.join(cls_dir, obj_name + ".obj"), "w") as fout: mesh.export(fout, file_type="obj") def distance_field_helper(source, target): target_kdtree = sp.spatial.cKDTree(target) distances, unused_var = target_kdtree.query(source, n_jobs=-1) return distances def compute_surface_metrics(mesh, name, mesh_dir): """Compute surface metrics (chamfer distance and f-score) for one example. Args: mesh: trimesh.Trimesh, the mesh to evaluate. name: Tensor, hash name of the mesh as recorded in the dataset. mesh_dir: string, path to the directory for loading ground truth meshes. Returns: chamfer: float, chamfer distance. fscore: float, f-score. """ if mesh_dir is None: raise ValueError("Need to specify args.mesh_dir for loading ground truth.") cls_name, obj_name = mesh_name_helper(name) with tf.io.gfile.GFile( path.join(mesh_dir, "test", cls_name, obj_name, "model_occnet.ply"), "rb", ) as fin: mesh_gt = trimesh.Trimesh(**trimesh.exchange.ply.load_ply(fin)) # Chamfer eval_points = 100000 point_gt = mesh_gt.sample(eval_points) point_gt = point_gt.astype(np.float32) point_pred = mesh.sample(eval_points) point_pred = point_pred.astype(np.float32) pred_to_gt = distance_field_helper(point_pred, point_gt) gt_to_pred = distance_field_helper(point_gt, point_pred) chamfer = np.mean(pred_to_gt**2) + np.mean(gt_to_pred**2) # Fscore tau = 1e-4 eps = 1e-9 pred_to_gt = (pred_to_gt**2) gt_to_pred = (gt_to_pred**2) prec_tau = (pred_to_gt <= tau).astype(np.float32).mean() * 100. recall_tau = (gt_to_pred <= tau).astype(np.float32).mean() * 100. fscore = (2 * prec_tau * recall_tau) / max(prec_tau + recall_tau, eps) # Following the tradition to scale chamfer distance up by 10. return chamfer * 100., fscore def init_stats(): """Initialize evaluation stats.""" stats = {} for k in SYSNET_CLASSES: stats[k] = { "cnt": 0, "iou": 0., "chamfer": 0., "fscore": 0., } return stats def update_stats(example_stats, name, shapenet_stats): """Update evaluation statistics. Args: example_stats: Stats, the stats of one example. name: Tensor, hash name of the example as recorded in the dataset. shapenet_stats: dict, the current stats of the whole dataset. """ cls_name, unused_var = mesh_name_helper(name) shapenet_stats[cls_name]["cnt"] += 1 shapenet_stats[cls_name]["iou"] += example_stats.iou shapenet_stats[cls_name]["chamfer"] += example_stats.chamfer shapenet_stats[cls_name]["fscore"] += example_stats.fscore shapenet_stats["all"]["cnt"] += 1 shapenet_stats["all"]["iou"] += example_stats.iou shapenet_stats["all"]["chamfer"] += example_stats.chamfer shapenet_stats["all"]["fscore"] += example_stats.fscore def average_stats(shapenet_stats): """Average the accumulated stats of the whole dataset.""" for k, v in shapenet_stats.items(): cnt = max(v["cnt"], 1) shapenet_stats[k] = { "iou": v["iou"] / cnt, "chamfer": v["chamfer"] / cnt, "fscore": v["fscore"] / cnt, } def write_stats(stats, eval_dir, step): """Write stats of the dataset to disk. Args: stats: dict, statistics to save. eval_dir: string, path to the directory to save the statistics. step: int, the global step of the checkpoint. """ if not tf.io.gfile.isdir(eval_dir): tf.io.gfile.makedirs(eval_dir) with tf.io.gfile.GFile(path.join(eval_dir, "stats_{}.csv".format(step)), "w") as fout: fout.write("class,iou,chamfer,fscore\n") for k in sorted(stats.keys()): if k == "all": continue fout.write("{0},{1},{2},{3}\n".format( SYSNET_CLASSES[k], stats[k]["iou"], stats[k]["chamfer"], stats[k]["fscore"], )) fout.write("all,{0},{1},{2}".format( stats["all"]["iou"], stats["all"]["chamfer"], stats["all"]["fscore"], ))

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections from os import path import numpy as np import scipy as sp from skimage import measure import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib.libmise import mise import trimesh Stats = collections.namedtuple("Stats", ["iou", "chamfer", "fscore"]) SYSNET_CLASSES = { "02691156": "airplane", "02933112": "cabinet", "03001627": "chair", "03636649": "lamp", "04090263": "rifle", "04379243": "table", "04530566": "watercraft", "02828884": "bench", "02958343": "car", "03211117": "display", "03691459": "speaker", "04256520": "sofa", "04401088": "telephone", "all": "all", } def define_flags(): """Define command line flags.""" flags = tf.app.flags # Model flags flags.DEFINE_enum("model", "multiconvex", list(k for k in models.model_dict.keys()), "Name of the model.") flags.DEFINE_float("sharpness", 75., "Sharpness term.") flags.DEFINE_integer("n_parts", 50, "Number of convexes uesd.") flags.DEFINE_integer("n_half_planes", 25, "Number of half spaces used.") flags.DEFINE_integer("latent_size", 256, "The size of latent code.") flags.DEFINE_integer("dims", 3, "The dimension of query points.") flags.DEFINE_bool("image_input", False, "Use color images as input if True.") flags.DEFINE_float("vis_scale", 1.3, "Scale of bbox used when extracting meshes.") flags.DEFINE_float("level_set", 0.5, "Level set used for extracting surfaces.") # Dataset flags flags.DEFINE_enum("dataset", "shapenet", list(k for k in datasets.dataset_dict.keys()), "Name of the dataset.") flags.DEFINE_integer("image_h", 137, "The height of the color images.") flags.DEFINE_integer("image_w", 137, "The width of the color images.") flags.DEFINE_integer("image_d", 3, "The channels of color images.") flags.DEFINE_integer("depth_h", 224, "The height of depth images.") flags.DEFINE_integer("depth_w", 224, "The width of depth images.") flags.DEFINE_integer("depth_d", 20, "The number of depth views.") flags.DEFINE_integer("n_views", 24, "The number of color images views.") flags.DEFINE_string("data_dir", None, "The base directory to load data from.") flags.mark_flag_as_required("data_dir") flags.DEFINE_string("obj_class", "*", "Object class used from dataset.") # Training flags flags.DEFINE_float("lr", 1e-4, "Start learning rate.") flags.DEFINE_string( "train_dir", None, "The base directory to save training info and" "checkpoints.") flags.DEFINE_integer("save_every", 20000, "The number of steps to save checkpoint.") flags.DEFINE_integer("max_steps", 800000, "The number of steps of training.") flags.DEFINE_integer("batch_size", 32, "Batch size.") flags.DEFINE_integer("sample_bbx", 1024, "The number of bounding box sample points.") flags.DEFINE_integer("sample_surf", 1024, "The number of surface sample points.") flags.DEFINE_float("weight_overlap", 0.1, "Weight of overlap_loss") flags.DEFINE_float("weight_balance", 0.01, "Weight of balance_loss") flags.DEFINE_float("weight_center", 0.001, "Weight of center_loss") flags.mark_flag_as_required("train_dir") # Eval flags flags.DEFINE_bool("extract_mesh", False, "Extract meshes and set to disk if True.") flags.DEFINE_bool("surface_metrics", False, "Measure surface metrics and save to csv if True.") flags.DEFINE_string("mesh_dir", None, "Path to load ground truth meshes.") flags.DEFINE_string("trans_dir", None, "Path to load pred-to-target transformations.") flags.DEFINE_bool("eval_once", False, "Evaluate the model only once if True.") def mesh_name_helper(name): name = name[0].decode("utf-8") split = name.find("-") cls_name = name[:split] obj_name = name[split + 1:] return cls_name, obj_name def extract_mesh(input_val, params, indicators, input_holder, params_holder, points_holder, sess, args): """Extracting meshes from an indicator function. Args: input_val: np.array, [1, height, width, channel], input image. params: tf.Operation, hyperplane parameter hook. indicators: tf.Operation, indicator hook. input_holder: tf.Placeholder, input image placeholder. params_holder: tf.Placeholder, hyperplane parameter placeholder. points_holder: tf.Placeholder, query point placeholder. sess: tf.Session, running sess. args: tf.app.flags.FLAGS, configurations. Returns: mesh: trimesh.Trimesh, the extracted mesh. """ mesh_extractor = mise.MISE(64, 1, args.level_set) points = mesh_extractor.query() params_val = sess.run(params, {input_holder: input_val}) while points.shape[0] != 0: orig_points = points points = points.astype(np.float32) points = ( (np.expand_dims(points, axis=0) / mesh_extractor.resolution - 0.5) * args.vis_scale) n_points = points.shape[1] values = [] for i in range(0, n_points, 100000): # Add this to prevent OOM. value = sess.run(indicators, { params_holder: params_val, points_holder: points[:, i:i + 100000] }) values.append(value) values = np.concatenate(values, axis=1) values = values[0, :, 0].astype(np.float64) mesh_extractor.update(orig_points, values) points = mesh_extractor.query() value_grid = mesh_extractor.to_dense() value_grid = np.pad(value_grid, 1, "constant", constant_values=-1e6) verts, faces, normals, unused_var = measure.marching_cubes_lewiner( value_grid, min(args.level_set, value_grid.max() * 0.75)) del normals verts -= 1 verts /= np.array([ value_grid.shape[0] - 3, value_grid.shape[1] - 3, value_grid.shape[2] - 3 ], dtype=np.float32) verts = args.vis_scale * (verts - 0.5) faces = np.stack([faces[..., 1], faces[..., 0], faces[..., 2]], axis=-1) return trimesh.Trimesh(vertices=verts, faces=faces) def transform_mesh(mesh, name, trans_dir): """Transform mesh back to the same coordinate of ground truth. Args: mesh: trimesh.Trimesh, predicted mesh before transformation. name: Tensor, hash name of the mesh as recorded in the dataset. trans_dir: string, path to the directory for loading transformations. Returns: mesh: trimesh.Trimesh, the transformed mesh. """ if trans_dir is None: raise ValueError("Need to specify args.trans_dir for loading pred-to-target" "transformations.") cls_name, obj_name = mesh_name_helper(name) with tf.io.gfile.GFile( path.join(trans_dir, "test", cls_name, obj_name, "occnet_to_gaps.txt"), "r") as fin: tx = np.loadtxt(fin).reshape([4, 4]) mesh.apply_transform(np.linalg.inv(tx)) return mesh def save_mesh(mesh, name, eval_dir): """Save a mesh to disk. Args: mesh: trimesh.Trimesh, the mesh to save. name: Tensor, hash name of the mesh as recorded in the dataset. eval_dir: string, path to the directory to save the mesh. """ cls_name, obj_name = mesh_name_helper(name) cls_dir = path.join(eval_dir, "meshes", cls_name) if not tf.io.gfile.isdir(cls_dir): tf.io.gfile.makedirs(cls_dir) with tf.io.gfile.GFile(path.join(cls_dir, obj_name + ".obj"), "w") as fout: mesh.export(fout, file_type="obj") def distance_field_helper(source, target): target_kdtree = sp.spatial.cKDTree(target) distances, unused_var = target_kdtree.query(source, n_jobs=-1) return distances def compute_surface_metrics(mesh, name, mesh_dir): """Compute surface metrics (chamfer distance and f-score) for one example. Args: mesh: trimesh.Trimesh, the mesh to evaluate. name: Tensor, hash name of the mesh as recorded in the dataset. mesh_dir: string, path to the directory for loading ground truth meshes. Returns: chamfer: float, chamfer distance. fscore: float, f-score. """ if mesh_dir is None: raise ValueError("Need to specify args.mesh_dir for loading ground truth.") cls_name, obj_name = mesh_name_helper(name) with tf.io.gfile.GFile( path.join(mesh_dir, "test", cls_name, obj_name, "model_occnet.ply"), "rb", ) as fin: mesh_gt = trimesh.Trimesh(**trimesh.exchange.ply.load_ply(fin)) # Chamfer eval_points = 100000 point_gt = mesh_gt.sample(eval_points) point_gt = point_gt.astype(np.float32) point_pred = mesh.sample(eval_points) point_pred = point_pred.astype(np.float32) pred_to_gt = distance_field_helper(point_pred, point_gt) gt_to_pred = distance_field_helper(point_gt, point_pred) chamfer = np.mean(pred_to_gt**2) + np.mean(gt_to_pred**2) # Fscore tau = 1e-4 eps = 1e-9 pred_to_gt = (pred_to_gt**2) gt_to_pred = (gt_to_pred**2) prec_tau = (pred_to_gt <= tau).astype(np.float32).mean() * 100. recall_tau = (gt_to_pred <= tau).astype(np.float32).mean() * 100. fscore = (2 * prec_tau * recall_tau) / max(prec_tau + recall_tau, eps) # Following the tradition to scale chamfer distance up by 10. return chamfer * 100., fscore def init_stats(): """Initialize evaluation stats.""" stats = {} for k in SYSNET_CLASSES: stats[k] = { "cnt": 0, "iou": 0., "chamfer": 0., "fscore": 0., } return stats def update_stats(example_stats, name, shapenet_stats): """Update evaluation statistics. Args: example_stats: Stats, the stats of one example. name: Tensor, hash name of the example as recorded in the dataset. shapenet_stats: dict, the current stats of the whole dataset. """ cls_name, unused_var = mesh_name_helper(name) shapenet_stats[cls_name]["cnt"] += 1 shapenet_stats[cls_name]["iou"] += example_stats.iou shapenet_stats[cls_name]["chamfer"] += example_stats.chamfer shapenet_stats[cls_name]["fscore"] += example_stats.fscore shapenet_stats["all"]["cnt"] += 1 shapenet_stats["all"]["iou"] += example_stats.iou shapenet_stats["all"]["chamfer"] += example_stats.chamfer shapenet_stats["all"]["fscore"] += example_stats.fscore def average_stats(shapenet_stats): """Average the accumulated stats of the whole dataset.""" for k, v in shapenet_stats.items(): cnt = max(v["cnt"], 1) shapenet_stats[k] = { "iou": v["iou"] / cnt, "chamfer": v["chamfer"] / cnt, "fscore": v["fscore"] / cnt, } def write_stats(stats, eval_dir, step): """Write stats of the dataset to disk. Args: stats: dict, statistics to save. eval_dir: string, path to the directory to save the statistics. step: int, the global step of the checkpoint. """ if not tf.io.gfile.isdir(eval_dir): tf.io.gfile.makedirs(eval_dir) with tf.io.gfile.GFile(path.join(eval_dir, "stats_{}.csv".format(step)), "w") as fout: fout.write("class,iou,chamfer,fscore\n") for k in sorted(stats.keys()): if k == "all": continue fout.write("{0},{1},{2},{3}\n".format( SYSNET_CLASSES[k], stats[k]["iou"], stats[k]["chamfer"], stats[k]["fscore"], )) fout.write("all,{0},{1},{2}".format( stats["all"]["iou"], stats["all"]["chamfer"], stats["all"]["fscore"], ))

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/transformation/tests/rotation_matrix_3d_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for 3d rotation matrix.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation import axis_angle from tensorflow_graphics.geometry.transformation import quaternion from tensorflow_graphics.geometry.transformation import rotation_matrix_3d from tensorflow_graphics.geometry.transformation.tests import test_data as td from tensorflow_graphics.geometry.transformation.tests import test_helpers from tensorflow_graphics.util import test_case class RotationMatrix3dTest(test_case.TestCase): @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_rotation_matrix_normalized_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" angles = test_helpers.generate_preset_test_euler_angles() matrix_input = rotation_matrix_3d.from_euler(angles) matrix_output = rotation_matrix_3d.assert_rotation_matrix_normalized( matrix_input) self.assertTrue(matrix_input is matrix_output) # pylint: disable=g-generic-assert @parameterized.parameters((np.float32), (np.float64)) def test_assert_rotation_matrix_normalized_preset(self, dtype): """Checks that assert_normalized function works as expected.""" angles = test_helpers.generate_preset_test_euler_angles().astype(dtype) matrix = rotation_matrix_3d.from_euler(angles) matrix_rescaled = matrix * 1.01 matrix_normalized = rotation_matrix_3d.assert_rotation_matrix_normalized( matrix) self.evaluate(matrix_normalized) with self.assertRaises(tf.errors.InvalidArgumentError): # pylint: disable=g-error-prone-assert-raises self.evaluate(rotation_matrix_3d.assert_rotation_matrix_normalized( matrix_rescaled)) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ) def test_assert_rotation_matrix_normalized_exception_not_raised( self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised( rotation_matrix_3d.assert_rotation_matrix_normalized, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_assert_rotation_matrix_normalized_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised( rotation_matrix_3d.assert_rotation_matrix_normalized, error_msg, shapes) @parameterized.parameters( ((3,), (1,)), ((None, 3), (None, 1)), ((1, 3), (1, 1)), ((2, 3), (2, 1)), ((1, 3), (1,)), ((3,), (1, 1)), ) def test_from_axis_angle_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_axis_angle, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (None,)), ) def test_from_axis_angle_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_axis_angle, error_msg, shapes) def test_from_axis_angle_normalized_preset(self): """Tests that axis-angles can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() axis, angle = axis_angle.from_euler(euler_angles) matrix_axis_angle = rotation_matrix_3d.from_axis_angle(axis, angle) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_axis_angle_normalized_random(self): """Tests that axis-angles can be converted to rotation matrices.""" tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() random_axis = np.random.normal(size=tensor_shape + [3]) random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) random_angle = np.random.normal(size=tensor_shape + [1]) matrix_axis_angle = rotation_matrix_3d.from_axis_angle( random_axis, random_angle) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(tensor_shape + [1])) @parameterized.parameters( ((td.AXIS_3D_X, td.ANGLE_45), (td.MAT_3D_X_45,)), ((td.AXIS_3D_Y, td.ANGLE_45), (td.MAT_3D_Y_45,)), ((td.AXIS_3D_Z, td.ANGLE_45), (td.MAT_3D_Z_45,)), ((td.AXIS_3D_X, td.ANGLE_90), (td.MAT_3D_X_90,)), ((td.AXIS_3D_Y, td.ANGLE_90), (td.MAT_3D_Y_90,)), ((td.AXIS_3D_Z, td.ANGLE_90), (td.MAT_3D_Z_90,)), ((td.AXIS_3D_X, td.ANGLE_180), (td.MAT_3D_X_180,)), ((td.AXIS_3D_Y, td.ANGLE_180), (td.MAT_3D_Y_180,)), ((td.AXIS_3D_Z, td.ANGLE_180), (td.MAT_3D_Z_180,)), ) def test_from_axis_angle_preset(self, test_inputs, test_outputs): """Tests that an axis-angle maps to correct matrix.""" self.assert_output_is_correct(rotation_matrix_3d.from_axis_angle, test_inputs, test_outputs) def test_from_axis_angle_random(self): """Tests conversion to matrix.""" tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() random_axis = np.random.normal(size=tensor_shape + [3]) random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) random_angle = np.random.normal(size=tensor_shape + [1]) matrix_axis_angle = rotation_matrix_3d.from_axis_angle( random_axis, random_angle) random_quaternion = quaternion.from_axis_angle(random_axis, random_angle) matrix_quaternion = rotation_matrix_3d.from_quaternion(random_quaternion) self.assertAllClose(matrix_axis_angle, matrix_quaternion, rtol=1e-3) # Checks that resulting rotation matrices are normalized. self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(tensor_shape + [1])) @parameterized.parameters( ((td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_X, -td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, -td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, -td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_Z,)), ) def test_from_axis_angle_rotate_vector_preset(self, test_inputs, test_outputs): """Tests the directionality of axis-angle rotations.""" def func(axis, angle, point): matrix = rotation_matrix_3d.from_axis_angle(axis, angle) return rotation_matrix_3d.rotate(point, matrix) self.assert_output_is_correct(func, test_inputs, test_outputs) @parameterized.parameters( ((3,),), ((None, 3),), ((2, 3),), ) def test_from_euler_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_euler, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,)),) def test_from_euler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_euler, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_jacobian_preset(self): """Test the Jacobian of the from_euler function.""" x_init = test_helpers.generate_preset_test_euler_angles() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_euler, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_jacobian_random(self): """Test the Jacobian of the from_euler function.""" x_init = test_helpers.generate_random_test_euler_angles() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_euler, [x_init]) def test_from_euler_normalized_preset(self): """Tests that euler angles can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() matrix = rotation_matrix_3d.from_euler(euler_angles) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_euler_normalized_random(self): """Tests that euler angles can be converted to rotation matrices.""" random_euler_angles = test_helpers.generate_random_test_euler_angles() matrix = rotation_matrix_3d.from_euler(random_euler_angles) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix), np.ones(random_euler_angles.shape[0:-1] + (1,))) @parameterized.parameters( ((td.AXIS_3D_0,), (td.MAT_3D_ID,)), ((td.ANGLE_45 * td.AXIS_3D_X,), (td.MAT_3D_X_45,)), ((td.ANGLE_45 * td.AXIS_3D_Y,), (td.MAT_3D_Y_45,)), ((td.ANGLE_45 * td.AXIS_3D_Z,), (td.MAT_3D_Z_45,)), ((td.ANGLE_90 * td.AXIS_3D_X,), (td.MAT_3D_X_90,)), ((td.ANGLE_90 * td.AXIS_3D_Y,), (td.MAT_3D_Y_90,)), ((td.ANGLE_90 * td.AXIS_3D_Z,), (td.MAT_3D_Z_90,)), ((td.ANGLE_180 * td.AXIS_3D_X,), (td.MAT_3D_X_180,)), ((td.ANGLE_180 * td.AXIS_3D_Y,), (td.MAT_3D_Y_180,)), ((td.ANGLE_180 * td.AXIS_3D_Z,), (td.MAT_3D_Z_180,)), ) def test_from_euler_preset(self, test_inputs, test_outputs): """Tests that Euler angles create the expected matrix.""" self.assert_output_is_correct(rotation_matrix_3d.from_euler, test_inputs, test_outputs) def test_from_euler_random(self): """Tests that Euler angles produce the same result as axis-angle.""" angles = test_helpers.generate_random_test_euler_angles() matrix = rotation_matrix_3d.from_euler(angles) tensor_tile = angles.shape[:-1] x_axis = np.tile(td.AXIS_3D_X, tensor_tile + (1,)) y_axis = np.tile(td.AXIS_3D_Y, tensor_tile + (1,)) z_axis = np.tile(td.AXIS_3D_Z, tensor_tile + (1,)) x_angle = np.expand_dims(angles[..., 0], axis=-1) y_angle = np.expand_dims(angles[..., 1], axis=-1) z_angle = np.expand_dims(angles[..., 2], axis=-1) x_rotation = rotation_matrix_3d.from_axis_angle(x_axis, x_angle) y_rotation = rotation_matrix_3d.from_axis_angle(y_axis, y_angle) z_rotation = rotation_matrix_3d.from_axis_angle(z_axis, z_angle) expected_matrix = tf.matmul(z_rotation, tf.matmul(y_rotation, x_rotation)) self.assertAllClose(expected_matrix, matrix, rtol=1e-3) @parameterized.parameters( ((3,),), ((None, 3),), ) def test_from_euler_with_small_angles_approximation_exception_not_raised( self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised( rotation_matrix_3d.from_euler_with_small_angles_approximation, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,)),) def test_from_euler_with_small_angles_approximation_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( rotation_matrix_3d.from_euler_with_small_angles_approximation, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_with_small_angles_approximation_jacobian_random(self): """Test the Jacobian of from_euler_with_small_angles_approximation.""" x_init = test_helpers.generate_random_test_euler_angles( min_angle=-0.17, max_angle=0.17) self.assert_jacobian_is_correct_fn( rotation_matrix_3d.from_euler_with_small_angles_approximation, [x_init]) def test_from_euler_with_small_angles_approximation_random(self): """Tests small_angles approximation by comparing to exact calculation.""" # Only generate small angles. For a test tolerance of 1e-3, 0.16 was found # empirically to be the range where the small angle approximation works. random_euler_angles = test_helpers.generate_random_test_euler_angles( min_angle=-0.16, max_angle=0.16) exact_matrix = rotation_matrix_3d.from_euler(random_euler_angles) approximate_matrix = ( rotation_matrix_3d.from_euler_with_small_angles_approximation( random_euler_angles)) self.assertAllClose(exact_matrix, approximate_matrix, atol=1e-3) @parameterized.parameters( ((4,),), ((None, 4),), ) def test_from_quaternion_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_quaternion, shapes) @parameterized.parameters( ("must have exactly 4 dimensions in axis -1", (None,)),) def test_from_quaternion_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_quaternion, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_quaternion_jacobian_preset(self): """Test the Jacobian of the from_quaternion function.""" x_init = test_helpers.generate_preset_test_quaternions() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_quaternion, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_quaternion_jacobian_random(self): """Test the Jacobian of the from_quaternion function.""" x_init = test_helpers.generate_random_test_quaternions() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_quaternion, [x_init]) def test_from_quaternion_normalized_preset(self): """Tests that quaternions can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() quat = quaternion.from_euler(euler_angles) matrix_quat = rotation_matrix_3d.from_quaternion(quat) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_quat), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_quaternion_normalized_random(self): """Tests that random quaternions can be converted to rotation matrices.""" random_quaternion = test_helpers.generate_random_test_quaternions() tensor_shape = random_quaternion.shape[:-1] random_matrix = rotation_matrix_3d.from_quaternion(random_quaternion) self.assertAllEqual( rotation_matrix_3d.is_valid(random_matrix), np.ones(tensor_shape + (1,))) def test_from_quaternion_preset(self): """Tests that a quaternion maps to correct matrix.""" preset_quaternions = test_helpers.generate_preset_test_quaternions() preset_matrices = test_helpers.generate_preset_test_rotation_matrices_3d() self.assertAllClose(preset_matrices, rotation_matrix_3d.from_quaternion(preset_quaternions)) def test_from_quaternion_random(self): """Tests conversion to matrix.""" random_euler_angles = test_helpers.generate_random_test_euler_angles() random_quaternions = quaternion.from_euler(random_euler_angles) random_rotation_matrices = rotation_matrix_3d.from_euler( random_euler_angles) self.assertAllClose(random_rotation_matrices, rotation_matrix_3d.from_quaternion(random_quaternions)) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ((2, 3, 3),), ) def test_inverse_exception_not_raised(self, *shapes): """Checks the inputs of the rotate function.""" self.assert_exception_is_not_raised(rotation_matrix_3d.inverse, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_inverse_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.inverse, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_inverse_jacobian_preset(self): """Test the Jacobian of the inverse function.""" x_init = test_helpers.generate_preset_test_rotation_matrices_3d() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.inverse, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_inverse_jacobian_random(self): """Test the Jacobian of the inverse function.""" x_init = test_helpers.generate_random_test_rotation_matrix_3d() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.inverse, [x_init]) def test_inverse_normalized_random(self): """Checks that inverted rotation matrices are valid rotations.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) predicted_invert_random_matrix = rotation_matrix_3d.inverse(random_matrix) self.assertAllEqual( rotation_matrix_3d.is_valid(predicted_invert_random_matrix), np.ones(tensor_tile + (1,))) def test_inverse_random(self): """Checks that inverting rotated points results in no transformation.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) random_point = np.random.normal(size=tensor_tile + (3,)) rotated_random_points = rotation_matrix_3d.rotate(random_point, random_matrix) predicted_invert_random_matrix = rotation_matrix_3d.inverse(random_matrix) predicted_invert_rotated_random_points = rotation_matrix_3d.rotate( rotated_random_points, predicted_invert_random_matrix) self.assertAllClose( random_point, predicted_invert_rotated_random_points, rtol=1e-6) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ((2, 3, 3),), ) def test_is_valid_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.is_valid, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_is_valid_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(rotation_matrix_3d.is_valid, error_msg, shape) def test_is_valid_random(self): """Tests that is_valid works as intended.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] rotation_matrix = rotation_matrix_3d.from_euler(random_euler_angle) pred_normalized = rotation_matrix_3d.is_valid(rotation_matrix) with self.subTest(name="all_normalized"): self.assertAllEqual(pred_normalized, np.ones(shape=tensor_tile + (1,), dtype=bool)) with self.subTest(name="non_orthonormal"): test_matrix = np.array([[2., 0., 0.], [0., 0.5, 0], [0., 0., 1.]]) pred_normalized = rotation_matrix_3d.is_valid(test_matrix) self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) with self.subTest(name="negative_orthonormal"): test_matrix = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) pred_normalized = rotation_matrix_3d.is_valid(test_matrix) self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) @parameterized.parameters( ((3,), (3, 3)), ((None, 3), (None, 3, 3)), ((1, 3), (1, 3, 3)), ((2, 3), (2, 3, 3)), ((3,), (1, 3, 3)), ((1, 3), (3, 3)), ) def test_rotate_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.rotate, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,), (3, 3)), ("must have a rank greater than 1", (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3, None)), ("must have exactly 3 dimensions in axis -2", (3,), (None, 3)), ) def test_rotate_exception_raised(self, error_msg, *shapes): """Checks the inputs of the rotate function.""" self.assert_exception_is_raised(rotation_matrix_3d.rotate, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_rotate_jacobian_preset(self): """Test the Jacobian of the rotate function.""" x_matrix_init = test_helpers.generate_preset_test_rotation_matrices_3d() tensor_shape = x_matrix_init.shape[:-1] x_point_init = np.random.uniform(size=tensor_shape) self.assert_jacobian_is_correct_fn(rotation_matrix_3d.rotate, [x_point_init, x_matrix_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_rotate_jacobian_random(self): """Test the Jacobian of the rotate function.""" x_matrix_init = test_helpers.generate_random_test_rotation_matrix_3d() tensor_shape = x_matrix_init.shape[:-1] x_point_init = np.random.uniform(size=tensor_shape) self.assert_jacobian_is_correct_fn(rotation_matrix_3d.rotate, [x_point_init, x_matrix_init]) @parameterized.parameters( ((td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((-td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((-td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y,)), ((td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((-td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Z), (td.AXIS_3D_Z,)), ) def test_rotate_vector_preset(self, test_inputs, test_outputs): """Tests that the rotate function produces the expected results.""" def func(angles, point): matrix = rotation_matrix_3d.from_euler(angles) return rotation_matrix_3d.rotate(point, matrix) self.assert_output_is_correct(func, test_inputs, test_outputs) def test_rotate_vs_rotate_quaternion_random(self): """Tests that the rotate provide the same results as quaternion.rotate.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) random_quaternion = quaternion.from_rotation_matrix(random_matrix) random_point = np.random.normal(size=tensor_tile + (3,)) ground_truth = quaternion.rotate(random_point, random_quaternion) prediction = rotation_matrix_3d.rotate(random_point, random_matrix) self.assertAllClose(ground_truth, prediction, rtol=1e-6) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for 3d rotation matrix.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation import axis_angle from tensorflow_graphics.geometry.transformation import quaternion from tensorflow_graphics.geometry.transformation import rotation_matrix_3d from tensorflow_graphics.geometry.transformation.tests import test_data as td from tensorflow_graphics.geometry.transformation.tests import test_helpers from tensorflow_graphics.util import test_case class RotationMatrix3dTest(test_case.TestCase): @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_rotation_matrix_normalized_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" angles = test_helpers.generate_preset_test_euler_angles() matrix_input = rotation_matrix_3d.from_euler(angles) matrix_output = rotation_matrix_3d.assert_rotation_matrix_normalized( matrix_input) self.assertTrue(matrix_input is matrix_output) # pylint: disable=g-generic-assert @parameterized.parameters((np.float32), (np.float64)) def test_assert_rotation_matrix_normalized_preset(self, dtype): """Checks that assert_normalized function works as expected.""" angles = test_helpers.generate_preset_test_euler_angles().astype(dtype) matrix = rotation_matrix_3d.from_euler(angles) matrix_rescaled = matrix * 1.01 matrix_normalized = rotation_matrix_3d.assert_rotation_matrix_normalized( matrix) self.evaluate(matrix_normalized) with self.assertRaises(tf.errors.InvalidArgumentError): # pylint: disable=g-error-prone-assert-raises self.evaluate(rotation_matrix_3d.assert_rotation_matrix_normalized( matrix_rescaled)) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ) def test_assert_rotation_matrix_normalized_exception_not_raised( self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised( rotation_matrix_3d.assert_rotation_matrix_normalized, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_assert_rotation_matrix_normalized_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised( rotation_matrix_3d.assert_rotation_matrix_normalized, error_msg, shapes) @parameterized.parameters( ((3,), (1,)), ((None, 3), (None, 1)), ((1, 3), (1, 1)), ((2, 3), (2, 1)), ((1, 3), (1,)), ((3,), (1, 1)), ) def test_from_axis_angle_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_axis_angle, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (None,)), ) def test_from_axis_angle_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_axis_angle, error_msg, shapes) def test_from_axis_angle_normalized_preset(self): """Tests that axis-angles can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() axis, angle = axis_angle.from_euler(euler_angles) matrix_axis_angle = rotation_matrix_3d.from_axis_angle(axis, angle) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_axis_angle_normalized_random(self): """Tests that axis-angles can be converted to rotation matrices.""" tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() random_axis = np.random.normal(size=tensor_shape + [3]) random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) random_angle = np.random.normal(size=tensor_shape + [1]) matrix_axis_angle = rotation_matrix_3d.from_axis_angle( random_axis, random_angle) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(tensor_shape + [1])) @parameterized.parameters( ((td.AXIS_3D_X, td.ANGLE_45), (td.MAT_3D_X_45,)), ((td.AXIS_3D_Y, td.ANGLE_45), (td.MAT_3D_Y_45,)), ((td.AXIS_3D_Z, td.ANGLE_45), (td.MAT_3D_Z_45,)), ((td.AXIS_3D_X, td.ANGLE_90), (td.MAT_3D_X_90,)), ((td.AXIS_3D_Y, td.ANGLE_90), (td.MAT_3D_Y_90,)), ((td.AXIS_3D_Z, td.ANGLE_90), (td.MAT_3D_Z_90,)), ((td.AXIS_3D_X, td.ANGLE_180), (td.MAT_3D_X_180,)), ((td.AXIS_3D_Y, td.ANGLE_180), (td.MAT_3D_Y_180,)), ((td.AXIS_3D_Z, td.ANGLE_180), (td.MAT_3D_Z_180,)), ) def test_from_axis_angle_preset(self, test_inputs, test_outputs): """Tests that an axis-angle maps to correct matrix.""" self.assert_output_is_correct(rotation_matrix_3d.from_axis_angle, test_inputs, test_outputs) def test_from_axis_angle_random(self): """Tests conversion to matrix.""" tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() random_axis = np.random.normal(size=tensor_shape + [3]) random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) random_angle = np.random.normal(size=tensor_shape + [1]) matrix_axis_angle = rotation_matrix_3d.from_axis_angle( random_axis, random_angle) random_quaternion = quaternion.from_axis_angle(random_axis, random_angle) matrix_quaternion = rotation_matrix_3d.from_quaternion(random_quaternion) self.assertAllClose(matrix_axis_angle, matrix_quaternion, rtol=1e-3) # Checks that resulting rotation matrices are normalized. self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_axis_angle), np.ones(tensor_shape + [1])) @parameterized.parameters( ((td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_X, -td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, -td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, -td.ANGLE_90, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_Z), (td.AXIS_3D_Z,)), ) def test_from_axis_angle_rotate_vector_preset(self, test_inputs, test_outputs): """Tests the directionality of axis-angle rotations.""" def func(axis, angle, point): matrix = rotation_matrix_3d.from_axis_angle(axis, angle) return rotation_matrix_3d.rotate(point, matrix) self.assert_output_is_correct(func, test_inputs, test_outputs) @parameterized.parameters( ((3,),), ((None, 3),), ((2, 3),), ) def test_from_euler_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_euler, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,)),) def test_from_euler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_euler, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_jacobian_preset(self): """Test the Jacobian of the from_euler function.""" x_init = test_helpers.generate_preset_test_euler_angles() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_euler, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_jacobian_random(self): """Test the Jacobian of the from_euler function.""" x_init = test_helpers.generate_random_test_euler_angles() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_euler, [x_init]) def test_from_euler_normalized_preset(self): """Tests that euler angles can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() matrix = rotation_matrix_3d.from_euler(euler_angles) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_euler_normalized_random(self): """Tests that euler angles can be converted to rotation matrices.""" random_euler_angles = test_helpers.generate_random_test_euler_angles() matrix = rotation_matrix_3d.from_euler(random_euler_angles) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix), np.ones(random_euler_angles.shape[0:-1] + (1,))) @parameterized.parameters( ((td.AXIS_3D_0,), (td.MAT_3D_ID,)), ((td.ANGLE_45 * td.AXIS_3D_X,), (td.MAT_3D_X_45,)), ((td.ANGLE_45 * td.AXIS_3D_Y,), (td.MAT_3D_Y_45,)), ((td.ANGLE_45 * td.AXIS_3D_Z,), (td.MAT_3D_Z_45,)), ((td.ANGLE_90 * td.AXIS_3D_X,), (td.MAT_3D_X_90,)), ((td.ANGLE_90 * td.AXIS_3D_Y,), (td.MAT_3D_Y_90,)), ((td.ANGLE_90 * td.AXIS_3D_Z,), (td.MAT_3D_Z_90,)), ((td.ANGLE_180 * td.AXIS_3D_X,), (td.MAT_3D_X_180,)), ((td.ANGLE_180 * td.AXIS_3D_Y,), (td.MAT_3D_Y_180,)), ((td.ANGLE_180 * td.AXIS_3D_Z,), (td.MAT_3D_Z_180,)), ) def test_from_euler_preset(self, test_inputs, test_outputs): """Tests that Euler angles create the expected matrix.""" self.assert_output_is_correct(rotation_matrix_3d.from_euler, test_inputs, test_outputs) def test_from_euler_random(self): """Tests that Euler angles produce the same result as axis-angle.""" angles = test_helpers.generate_random_test_euler_angles() matrix = rotation_matrix_3d.from_euler(angles) tensor_tile = angles.shape[:-1] x_axis = np.tile(td.AXIS_3D_X, tensor_tile + (1,)) y_axis = np.tile(td.AXIS_3D_Y, tensor_tile + (1,)) z_axis = np.tile(td.AXIS_3D_Z, tensor_tile + (1,)) x_angle = np.expand_dims(angles[..., 0], axis=-1) y_angle = np.expand_dims(angles[..., 1], axis=-1) z_angle = np.expand_dims(angles[..., 2], axis=-1) x_rotation = rotation_matrix_3d.from_axis_angle(x_axis, x_angle) y_rotation = rotation_matrix_3d.from_axis_angle(y_axis, y_angle) z_rotation = rotation_matrix_3d.from_axis_angle(z_axis, z_angle) expected_matrix = tf.matmul(z_rotation, tf.matmul(y_rotation, x_rotation)) self.assertAllClose(expected_matrix, matrix, rtol=1e-3) @parameterized.parameters( ((3,),), ((None, 3),), ) def test_from_euler_with_small_angles_approximation_exception_not_raised( self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised( rotation_matrix_3d.from_euler_with_small_angles_approximation, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,)),) def test_from_euler_with_small_angles_approximation_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( rotation_matrix_3d.from_euler_with_small_angles_approximation, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_euler_with_small_angles_approximation_jacobian_random(self): """Test the Jacobian of from_euler_with_small_angles_approximation.""" x_init = test_helpers.generate_random_test_euler_angles( min_angle=-0.17, max_angle=0.17) self.assert_jacobian_is_correct_fn( rotation_matrix_3d.from_euler_with_small_angles_approximation, [x_init]) def test_from_euler_with_small_angles_approximation_random(self): """Tests small_angles approximation by comparing to exact calculation.""" # Only generate small angles. For a test tolerance of 1e-3, 0.16 was found # empirically to be the range where the small angle approximation works. random_euler_angles = test_helpers.generate_random_test_euler_angles( min_angle=-0.16, max_angle=0.16) exact_matrix = rotation_matrix_3d.from_euler(random_euler_angles) approximate_matrix = ( rotation_matrix_3d.from_euler_with_small_angles_approximation( random_euler_angles)) self.assertAllClose(exact_matrix, approximate_matrix, atol=1e-3) @parameterized.parameters( ((4,),), ((None, 4),), ) def test_from_quaternion_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.from_quaternion, shapes) @parameterized.parameters( ("must have exactly 4 dimensions in axis -1", (None,)),) def test_from_quaternion_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.from_quaternion, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_quaternion_jacobian_preset(self): """Test the Jacobian of the from_quaternion function.""" x_init = test_helpers.generate_preset_test_quaternions() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_quaternion, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_quaternion_jacobian_random(self): """Test the Jacobian of the from_quaternion function.""" x_init = test_helpers.generate_random_test_quaternions() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.from_quaternion, [x_init]) def test_from_quaternion_normalized_preset(self): """Tests that quaternions can be converted to rotation matrices.""" euler_angles = test_helpers.generate_preset_test_euler_angles() quat = quaternion.from_euler(euler_angles) matrix_quat = rotation_matrix_3d.from_quaternion(quat) self.assertAllEqual( rotation_matrix_3d.is_valid(matrix_quat), np.ones(euler_angles.shape[0:-1] + (1,))) def test_from_quaternion_normalized_random(self): """Tests that random quaternions can be converted to rotation matrices.""" random_quaternion = test_helpers.generate_random_test_quaternions() tensor_shape = random_quaternion.shape[:-1] random_matrix = rotation_matrix_3d.from_quaternion(random_quaternion) self.assertAllEqual( rotation_matrix_3d.is_valid(random_matrix), np.ones(tensor_shape + (1,))) def test_from_quaternion_preset(self): """Tests that a quaternion maps to correct matrix.""" preset_quaternions = test_helpers.generate_preset_test_quaternions() preset_matrices = test_helpers.generate_preset_test_rotation_matrices_3d() self.assertAllClose(preset_matrices, rotation_matrix_3d.from_quaternion(preset_quaternions)) def test_from_quaternion_random(self): """Tests conversion to matrix.""" random_euler_angles = test_helpers.generate_random_test_euler_angles() random_quaternions = quaternion.from_euler(random_euler_angles) random_rotation_matrices = rotation_matrix_3d.from_euler( random_euler_angles) self.assertAllClose(random_rotation_matrices, rotation_matrix_3d.from_quaternion(random_quaternions)) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ((2, 3, 3),), ) def test_inverse_exception_not_raised(self, *shapes): """Checks the inputs of the rotate function.""" self.assert_exception_is_not_raised(rotation_matrix_3d.inverse, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_inverse_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(rotation_matrix_3d.inverse, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_inverse_jacobian_preset(self): """Test the Jacobian of the inverse function.""" x_init = test_helpers.generate_preset_test_rotation_matrices_3d() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.inverse, [x_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_inverse_jacobian_random(self): """Test the Jacobian of the inverse function.""" x_init = test_helpers.generate_random_test_rotation_matrix_3d() self.assert_jacobian_is_correct_fn(rotation_matrix_3d.inverse, [x_init]) def test_inverse_normalized_random(self): """Checks that inverted rotation matrices are valid rotations.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) predicted_invert_random_matrix = rotation_matrix_3d.inverse(random_matrix) self.assertAllEqual( rotation_matrix_3d.is_valid(predicted_invert_random_matrix), np.ones(tensor_tile + (1,))) def test_inverse_random(self): """Checks that inverting rotated points results in no transformation.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) random_point = np.random.normal(size=tensor_tile + (3,)) rotated_random_points = rotation_matrix_3d.rotate(random_point, random_matrix) predicted_invert_random_matrix = rotation_matrix_3d.inverse(random_matrix) predicted_invert_rotated_random_points = rotation_matrix_3d.rotate( rotated_random_points, predicted_invert_random_matrix) self.assertAllClose( random_point, predicted_invert_rotated_random_points, rtol=1e-6) @parameterized.parameters( ((3, 3),), ((None, 3, 3),), ((2, 3, 3),), ) def test_is_valid_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.is_valid, shapes) @parameterized.parameters( ("must have a rank greater than 1", (3,)), ("must have exactly 3 dimensions in axis -1", (3, None)), ("must have exactly 3 dimensions in axis -2", (None, 3)), ) def test_is_valid_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(rotation_matrix_3d.is_valid, error_msg, shape) def test_is_valid_random(self): """Tests that is_valid works as intended.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] rotation_matrix = rotation_matrix_3d.from_euler(random_euler_angle) pred_normalized = rotation_matrix_3d.is_valid(rotation_matrix) with self.subTest(name="all_normalized"): self.assertAllEqual(pred_normalized, np.ones(shape=tensor_tile + (1,), dtype=bool)) with self.subTest(name="non_orthonormal"): test_matrix = np.array([[2., 0., 0.], [0., 0.5, 0], [0., 0., 1.]]) pred_normalized = rotation_matrix_3d.is_valid(test_matrix) self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) with self.subTest(name="negative_orthonormal"): test_matrix = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) pred_normalized = rotation_matrix_3d.is_valid(test_matrix) self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) @parameterized.parameters( ((3,), (3, 3)), ((None, 3), (None, 3, 3)), ((1, 3), (1, 3, 3)), ((2, 3), (2, 3, 3)), ((3,), (1, 3, 3)), ((1, 3), (3, 3)), ) def test_rotate_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(rotation_matrix_3d.rotate, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (None,), (3, 3)), ("must have a rank greater than 1", (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3, None)), ("must have exactly 3 dimensions in axis -2", (3,), (None, 3)), ) def test_rotate_exception_raised(self, error_msg, *shapes): """Checks the inputs of the rotate function.""" self.assert_exception_is_raised(rotation_matrix_3d.rotate, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_rotate_jacobian_preset(self): """Test the Jacobian of the rotate function.""" x_matrix_init = test_helpers.generate_preset_test_rotation_matrices_3d() tensor_shape = x_matrix_init.shape[:-1] x_point_init = np.random.uniform(size=tensor_shape) self.assert_jacobian_is_correct_fn(rotation_matrix_3d.rotate, [x_point_init, x_matrix_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_rotate_jacobian_random(self): """Test the Jacobian of the rotate function.""" x_matrix_init = test_helpers.generate_random_test_rotation_matrix_3d() tensor_shape = x_matrix_init.shape[:-1] x_point_init = np.random.uniform(size=tensor_shape) self.assert_jacobian_is_correct_fn(rotation_matrix_3d.rotate, [x_point_init, x_matrix_init]) @parameterized.parameters( ((td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((-td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((-td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y,)), ((td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((-td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Z), (td.AXIS_3D_Z,)), ) def test_rotate_vector_preset(self, test_inputs, test_outputs): """Tests that the rotate function produces the expected results.""" def func(angles, point): matrix = rotation_matrix_3d.from_euler(angles) return rotation_matrix_3d.rotate(point, matrix) self.assert_output_is_correct(func, test_inputs, test_outputs) def test_rotate_vs_rotate_quaternion_random(self): """Tests that the rotate provide the same results as quaternion.rotate.""" random_euler_angle = test_helpers.generate_random_test_euler_angles() tensor_tile = random_euler_angle.shape[:-1] random_matrix = rotation_matrix_3d.from_euler(random_euler_angle) random_quaternion = quaternion.from_rotation_matrix(random_matrix) random_point = np.random.normal(size=tensor_tile + (3,)) ground_truth = quaternion.rotate(random_point, random_quaternion) prediction = rotation_matrix_3d.rotate(random_point, random_matrix) self.assertAllClose(ground_truth, prediction, rtol=1e-6) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/tests/math_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for OpenGL math routines.""" import math from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.rendering.opengl import math as glm from tensorflow_graphics.util import test_case class MathTest(test_case.TestCase): def test_model_to_eye_preset(self): """Tests that model_to_eye generates expected results..""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) camera_position = ((0.0, 0.0, 0.0), (0.1, 0.2, 0.3)) look_at_point = ((0.0, 0.0, 1.0), (0.4, 0.5, 0.6)) up_vector = ((0.0, 1.0, 0.0), (0.7, 0.8, 0.9)) pred = glm.model_to_eye(point, camera_position, look_at_point, up_vector) gt = ((-2.0, 3.0, -4.0), (2.08616257e-07, 1.27279234, -6.58179379)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (3,), (3,), (3,)), ((None, 3), (None, 3), (None, 3), (None, 3)), ((100, 3), (3,), (3,), (3,)), ((None, 1, 3), (None, 2, 3), (None, 2, 3), (None, 2, 3)), ) def test_model_to_eye_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_eye, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (2,)), ("Not all batch dimensions are identical", (3,), (2, 3), (3, 3), (3, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3), (3, 3), (3, 3), (3, 3)), ) def test_model_to_eye_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.model_to_eye, error_msg, shapes) def test_model_to_eye_jacobian_preset(self): """Tests the Jacobian of model_to_eye.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.1, 0.2, 0.3))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.4, 0.5, 0.6))) up_vector_init = np.array(((0.0, 1.0, 0.0), (0.7, 0.8, 0.9))) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_model_to_eye_jacobian_random(self): """Tests the Jacobian of model_to_eye.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) up_vector_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_eye_to_clip_preset(self): """Tests that eye_to_clip generates expected results.""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) vertical_field_of_view = ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,)) aspect_ratio = ((1.5,), (1.6,)) near_plane = ((1.0,), (2.0,)) far_plane = ((10.0,), (11.0,)) pred = glm.eye_to_clip(point, vertical_field_of_view, aspect_ratio, near_plane, far_plane) gt = ((2.30940104, 5.19615173, -7.11111116, -4.0), (4.02095032, 8.57802773, -12.11111069, -5.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (1,), (1,), (1,), (1,)), ((None, 3), (None, 1), (None, 1), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 1), (None, 5, 1), (None, 5, 1), (None, 5, 1)), ) def test_eye_to_clip_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.eye_to_clip, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (1,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (1,), (2,)), ("Not all batch dimensions are broadcast-compatible", (3, 3), (2, 1), (1,), (1,), (1,)), ) def test_eye_to_clip_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.eye_to_clip, error_msg, shapes) def test_eye_to_clip_jacobian_preset(self): """Tests the Jacobian of eye_to_clip.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,))) aspect_ratio_init = np.array(((1.5,), (1.6,))) near_init = np.array(((1.0,), (2.0,))) far_init = np.array(((10.0,), (11.0,))) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=1e-5) def test_eye_to_clip_jacobian_random(self): """Tests the Jacobian of eye_to_clip.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) eps = np.finfo(np.float64).eps vertical_field_of_view_init = np.random.uniform( eps, math.pi - eps, size=tensor_shape + [1]) aspect_ratio_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) near_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(eps, 10.0, size=tensor_shape + [1]) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=1e-03) def test_clip_to_ndc_preset(self): """Tests that clip_to_ndc generates expected results.""" point = ((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0)) pred = glm.clip_to_ndc(point) gt = ((2.0, 4.0, 8.0), (4.0, 8.0, 16.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((4,)), ((None, 4),), ((None, 5, 4),), ) def test_clip_to_ndc_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.clip_to_ndc, shapes) def test_clip_to_ndc_exception_raised(self): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( glm.clip_to_ndc, "must have exactly 4 dimensions in axis -1", ((2,),)) def test_clip_to_ndc_jacobian_preset(self): """Tests the Jacobian of clip_to_ndc.""" point_init = np.array(((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0))) self.assert_jacobian_is_correct_fn(glm.clip_to_ndc, [point_init]) def test_clip_to_ndc_jacobian_random(self): """Tests the Jacobian of clip_to_ndc.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [4]) self.assert_jacobian_is_correct_fn( glm.clip_to_ndc, [point_init], atol=1e-04) def test_ndc_to_screen_preset(self): """Tests that ndc_to_screen generates expected results.""" point = ((1.1, 2.2, 3.3), (5.1, 5.2, 5.3)) lower_left_corner = ((6.4, 4.8), (0.0, 0.0)) screen_dimensions = ((640.0, 480.0), (300.0, 400.0)) near = ((1.0,), (11.0,)) far = ((10.0,), (100.0,)) pred = glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far) gt = ((678.40002441, 772.79998779, 20.34999847), (915.0, 1240.0, 291.3500061)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (2,), (2,), (1,), (1,)), ((None, 3), (None, 2), (None, 2), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 2), (None, 5, 2), (None, 5, 1), (None, 5, 1)), ) def test_ndc_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.ndc_to_screen, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (2,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (2,), (3,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (1,), (3,)), ("Not all batch dimensions are identical", (3,), (2, 2), (3, 2), (3, 1), (3, 1)), ("Not all batch dimensions are broadcast-compatible", (4, 3), (3, 2), (3, 2), (3, 1), (3, 1)), ) def test_ndc_to_screen_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.ndc_to_screen, error_msg, shapes) def test_ndc_to_screen_exception_near_raised(self): """Tests that an exception is raised when `near` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(-1.0, 0.0, size=(1,)) far = np.random.uniform(1.0, 2.0, size=(1,)) with self.subTest("negative_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, np.array((0.0,)), far)) def test_ndc_to_screen_exception_far_raised(self): """Tests that an exception is raised if `far` is not greater than `near`.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(-1.0, 0.0, size=(1,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) def test_ndc_to_screen_exception_screen_dimensions_raised(self): """Tests that an exception is raised when `screen_dimensions` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(-1.0, 0.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(0.1, 1.0, size=(1,)) with self.subTest("negative_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, np.array((0.0, 0.0)), near, far)) def test_ndc_to_screen_jacobian_preset(self): """Tests the Jacobian of ndc_to_screen.""" point_init = np.array(((1.1, 2.2, 3.3), (5.1, 5.2, 5.3))) lower_left_corner_init = np.array(((6.4, 4.8), (0.0, 0.0))) screen_dimensions_init = np.array(((640.0, 480.0), (300.0, 400.0))) near_init = np.array(((1.0,), (11.0,))) far_init = np.array(((10.0,), (100.0,))) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_ndc_to_screen_jacobian_random(self): """Tests the Jacobian of ndc_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 1.0, 1000.0, size=tensor_shape + [2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=(1,)) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_model_to_screen_preset(self): """Tests that model_to_screen generates expected results.""" point_world_space = np.array(((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1))) camera_position = np.array(((0.0, 0.0, 0.0), (0.4, -0.8, 0.1))) camera_up = np.array(((0.0, 1.0, 0.0), (0.0, 0.0, 1.0))) look_at_point = np.array(((0.0, 0.0, 1.0), (0.0, 1.0, 0.0))) vertical_field_of_view = np.array( ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,))) lower_left_corner = np.array(((0.0, 0.0), (10.0, 20.0))) screen_dimensions = np.array(((501.0, 501.0), (400.0, 600.0))) near = np.array(((0.01,), (1.0,))) far = np.array(((4.0,), (3.0,))) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position, look_at_point, camera_up) perspective_matrix = perspective.right_handed( vertical_field_of_view, screen_dimensions[..., 0:1] / screen_dimensions[..., 1:2], near, far) pred_screen, pred_w = glm.model_to_screen(point_world_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) gt_screen = ((-13.23016357, 599.30444336, 4.00215721), (98.07017517, -95.40383911, 3.1234405)) gt_w = ((5.1,), (3.42247,)) self.assertAllClose(pred_screen, gt_screen, atol=1e-5, rtol=1e-5) self.assertAllClose(pred_w, gt_w) @parameterized.parameters( ((3,), (4, 4), (4, 4), (2,), (2,)), ((640, 480, 3), (4, 4), (4, 4), (2,), (2,)), ((None, 3), (None, 4, 4), (None, 4, 4), (None, 2), (None, 2)), ((3,), (None, 1, 4, 4), (None, 1, 4, 4), (None, 1, 2), (None, 1, 2)), ) def test_model_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_screen, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (2,), (4, 4), (4, 4)), ("must have exactly 4 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (3,), (4, 3), (4, 4)), ("must have exactly 4 dimensions in axis -2", (9.0, 12.0), (0.0, 0.0), (3,), (3, 4), (4, 4)), ("must have exactly 4 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (3,), (4, 4), (4, 3)), ("must have exactly 4 dimensions in axis -2", (9.0, 12.0), (0.0, 0.0), (3,), (4, 4), (3, 4)), ("Not all batch dimensions are broadcast-compatible", (9.0, 12.0), (0.0, 0.0), (2, 3), (3, 4, 4), (3, 4, 4)), ) def test_model_to_screen_exception_raised(self, error_msg, screen_dimensions, lower_left_corner, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( func=glm.model_to_screen, error_msg=error_msg, shapes=shapes, screen_dimensions=screen_dimensions, lower_left_corner=lower_left_corner) def test_model_to_screen_jacobian_preset(self): """Tests the Jacobian of model_to_screen.""" point_world_space_init = np.array(((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.4, -0.8, 0.1))) camera_up_init = np.array(((0.0, 1.0, 0.0), (0.0, 0.0, 1.0))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.0, 1.0, 0.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,))) lower_left_corner_init = np.array(((0.0, 0.0), (10.0, 20.0))) screen_dimensions_init = np.array(((501.0, 501.0), (400.0, 600.0))) near_init = np.array(((0.01,), (1.0,))) far_init = np.array(((4.0,), (3.0,))) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position_init, look_at_init, camera_up_init) perspective_matrix = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) args = [ point_world_space_init, model_to_eye_matrix, perspective_matrix, screen_dimensions_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args, atol=1e-4) # TODO(julienvalentin): will be fixed before submission # with self.subTest(name="jacobian_w"): # self.assert_jacobian_is_correct_fn( # lambda *args: glm.model_to_screen(*args)[1], args) def test_model_to_screen_jacobian_random(self): """Tests the Jacobian of model_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_world_space_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) camera_up_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [2]) near_init = np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position_init, look_at_init, camera_up_init) perspective_matrix = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) args = [ point_world_space_init, model_to_eye_matrix, perspective_matrix, screen_dimensions_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args, atol=1e-4) # TODO(julienvalentin): will be fixed before submission # with self.subTest(name="jacobian_w"): # self.assert_jacobian_is_correct_fn( # lambda *args: glm.model_to_screen(*args)[1], args) def test_perspective_correct_interpolation_preset(self): """Tests that perspective_correct_interpolation generates expected results.""" camera_origin = np.array((0.0, 0.0, 0.0)) camera_up = np.array((0.0, 1.0, 0.0)) look_at_point = np.array((0.0, 0.0, 1.0)) fov = np.array((90.0 * np.math.pi / 180.0,)) bottom_left = np.array((0.0, 0.0)) image_size = np.array((501.0, 501.0)) near_plane = np.array((0.01,)) far_plane = np.array((10.0,)) batch_size = np.random.randint(1, 5) triangle_x_y = np.random.uniform(-10.0, 10.0, (batch_size, 3, 2)) triangle_z = np.random.uniform(2.0, 10.0, (batch_size, 3, 1)) triangles = np.concatenate((triangle_x_y, triangle_z), axis=-1) # Builds barycentric weights. barycentric_weights = np.random.uniform(size=(batch_size, 3)) barycentric_weights = barycentric_weights / np.sum( barycentric_weights, axis=-1, keepdims=True) # Barycentric interpolation of vertex positions. convex_combination = np.einsum("ba, bac -> bc", barycentric_weights, triangles) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (image_size[0:1] / image_size[1:2]), near_plane, far_plane) # Computes where those points project in screen coordinates. pixel_position, _ = glm.model_to_screen(convex_combination, model_to_eye_matrix, perspective_matrix, image_size, bottom_left) # Builds attributes. num_pixels = pixel_position.shape[0] attribute_size = np.random.randint(10) attributes = np.random.uniform(size=(num_pixels, 3, attribute_size)) prediction = glm.perspective_correct_interpolation(triangles, attributes, pixel_position[..., 0:2], model_to_eye_matrix, perspective_matrix, image_size, bottom_left) groundtruth = np.einsum("ba, bac -> bc", barycentric_weights, attributes) self.assertAllClose(prediction, groundtruth) def test_perspective_correct_interpolation_jacobian_preset(self): """Tests the Jacobian of perspective_correct_interpolation.""" vertices_init = np.tile( ((-0.2857143, 0.2857143, 5.0), (0.2857143, 0.2857143, 0.5), (0.0, -0.2857143, 1.0)), (2, 1, 1)) attributes_init = np.tile( (((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))), (2, 1, 1)) pixel_position_init = np.array(((125.5, 375.5), (250.5, 250.5))) camera_position_init = np.tile((0.0, 0.0, 0.0), (2, 3, 1)) look_at_init = np.tile((0.0, 0.0, 1.0), (2, 3, 1)) up_vector_init = np.tile((0.0, 1.0, 0.0), (2, 3, 1)) vertical_field_of_view_init = np.tile((1.0471975511965976,), (2, 3, 1)) screen_dimensions_init = np.tile((501.0, 501.0), (2, 3, 1)) near_init = np.tile((0.01,), (2, 3, 1)) far_init = np.tile((10.0,), (2, 3, 1)) lower_left_corner_init = np.tile((0.0, 0.0), (2, 3, 1)) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn(glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_perspective_correct_interpolation_jacobian_random(self): """Tests the Jacobian of perspective_correct_interpolation.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() vertices_init = np.random.uniform(size=tensor_shape + [3, 3]) num_attributes = np.random.randint(1, 10) attributes_init = np.random.uniform(size=tensor_shape + [3, num_attributes]) pixel_position_init = np.random.uniform(size=tensor_shape + [2]) camera_position_init = np.random.uniform(size=tensor_shape + [3, 3]) look_at_init = np.random.uniform(size=tensor_shape + [3, 3]) up_vector_init = np.random.uniform(size=tensor_shape + [3, 3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) screen_dimensions_init = np.random.uniform( 1.0, 10.0, size=tensor_shape + [3, 2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [3, 1]) far_init = near_init + np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [3, 2]) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn( glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ], atol=1e-4) @parameterized.parameters( ((3, 3), (2,), (4, 4), (4, 4), (2,)), ((3, 3), (7, 2), (4, 4), (4, 4), (2,)), ((3, 3), (None, 2), (4, 4), (4, 4), (2,)), ((7, 3, 3), (2,), (4, 4), (4, 4), (2,)), ((None, 3, 3), (2,), (4, 4), (4, 4), (2,)), ) def test_perspective_correct_barycentrics_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.perspective_correct_barycentrics, shapes) @parameterized.parameters( ("must have exactly 2 dimensions in axis -1", (3, 3), (2,), (4, 4), (4, 4), (3,)), ("must have exactly 3 dimensions in axis -1", (3, 4), (2,), (4, 4), (4, 4), (3,)), ("must have exactly 3 dimensions in axis -2", (4, 3), (2,), (4, 4), (4, 4), (3,)), ) def test_perspective_correct_barycentrics_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.perspective_correct_barycentrics, error_msg, shapes) def test_perspective_correct_barycentrics_preset(self): """Tests that perspective_correct_barycentrics generates expected results.""" camera_origin = np.array((0.0, 0.0, 0.0)) camera_up = np.array((0.0, 1.0, 0.0)) look_at_point = np.array((0.0, 0.0, 1.0)) fov = np.array((90.0 * np.math.pi / 180.0,)) bottom_left = np.array((0.0, 0.0)) image_size = np.array((501.0, 501.0)) near_plane = np.array((0.01,)) far_plane = np.array((10.0,)) batch_size = np.random.randint(1, 5) triangle_x_y = np.random.uniform(-10.0, 10.0, (batch_size, 3, 2)) triangle_z = np.random.uniform(2.0, 10.0, (batch_size, 3, 1)) triangles = np.concatenate((triangle_x_y, triangle_z), axis=-1) # Builds barycentric weights. barycentric_weights = np.random.uniform(size=(batch_size, 3)) barycentric_weights = barycentric_weights / np.sum( barycentric_weights, axis=-1, keepdims=True) # Barycentric interpolation of vertex positions. convex_combination = np.einsum("ba, bac -> bc", barycentric_weights, triangles) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (image_size[0:1] / image_size[1:2]), near_plane, far_plane) # Computes where those points project in screen coordinates. pixel_position, _ = glm.model_to_screen(convex_combination, model_to_eye_matrix, perspective_matrix, image_size, bottom_left) prediction = glm.perspective_correct_barycentrics(triangles, pixel_position[..., 0:2], model_to_eye_matrix, perspective_matrix, image_size, bottom_left) self.assertAllClose(prediction, barycentric_weights) def test_perspective_correct_barycentrics_jacobian_random(self): """Tests the Jacobian of perspective_correct_barycentrics.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() vertices_init = np.random.uniform(size=tensor_shape + [3, 3]) pixel_position_init = np.random.uniform(size=tensor_shape + [2]) camera_position_init = np.random.uniform(size=tensor_shape + [3, 3]) look_at_init = np.random.uniform(size=tensor_shape + [3, 3]) up_vector_init = np.random.uniform(size=tensor_shape + [3, 3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) screen_dimensions_init = np.random.uniform( 1.0, 10.0, size=tensor_shape + [3, 2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [3, 1]) far_init = near_init + np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [3, 2]) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn( glm.perspective_correct_barycentrics, [ vertices_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ], atol=1e-4) @parameterized.parameters( ((3, 7), (3,)), ((2, 3, 7), (2, 3)), ((None, 3, 7), (None, 3)), ) def test_interpolate_attributes_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.interpolate_attributes, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -2", (2, 7), (3,)), ("must have exactly 3 dimensions in axis -1", (3, 7), (2,)), ("Not all batch dimensions are broadcast-compatible", (5, 3, 7), (4, 3)), ) def test_interpolate_attributes_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.interpolate_attributes, error_msg, shapes) def test_interpolate_attributes_random(self): """Checks the output of interpolate_attributes.""" attributes = np.random.uniform(-1.0, 1.0, size=(3,)) barycentric = np.random.uniform(0.0, 1.0, size=(3,)) barycentric = barycentric / np.linalg.norm( barycentric, axis=-1, ord=1, keepdims=True) groundtruth = np.sum(attributes * barycentric, keepdims=True) attributes = np.reshape(attributes, (3, 1)) prediction = glm.interpolate_attributes(attributes, barycentric) self.assertAllClose(groundtruth, prediction) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_interpolate_attributes_jacobian_random(self): """Tests the jacobian of interpolate_attributes.""" batch_size = np.random.randint(1, 5) attributes = np.random.uniform(-1.0, 1.0, size=(batch_size, 3, 1)) barycentric = np.random.uniform( 0.0, 1.0, size=( batch_size, 3, )) barycentric = barycentric / np.linalg.norm( barycentric, axis=-1, ord=1, keepdims=True) self.assert_jacobian_is_correct_fn(glm.interpolate_attributes, [attributes, barycentric]) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for OpenGL math routines.""" import math from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.rendering.opengl import math as glm from tensorflow_graphics.util import test_case class MathTest(test_case.TestCase): def test_model_to_eye_preset(self): """Tests that model_to_eye generates expected results..""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) camera_position = ((0.0, 0.0, 0.0), (0.1, 0.2, 0.3)) look_at_point = ((0.0, 0.0, 1.0), (0.4, 0.5, 0.6)) up_vector = ((0.0, 1.0, 0.0), (0.7, 0.8, 0.9)) pred = glm.model_to_eye(point, camera_position, look_at_point, up_vector) gt = ((-2.0, 3.0, -4.0), (2.08616257e-07, 1.27279234, -6.58179379)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (3,), (3,), (3,)), ((None, 3), (None, 3), (None, 3), (None, 3)), ((100, 3), (3,), (3,), (3,)), ((None, 1, 3), (None, 2, 3), (None, 2, 3), (None, 2, 3)), ) def test_model_to_eye_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_eye, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (2,)), ("Not all batch dimensions are identical", (3,), (2, 3), (3, 3), (3, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3), (3, 3), (3, 3), (3, 3)), ) def test_model_to_eye_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.model_to_eye, error_msg, shapes) def test_model_to_eye_jacobian_preset(self): """Tests the Jacobian of model_to_eye.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.1, 0.2, 0.3))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.4, 0.5, 0.6))) up_vector_init = np.array(((0.0, 1.0, 0.0), (0.7, 0.8, 0.9))) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_model_to_eye_jacobian_random(self): """Tests the Jacobian of model_to_eye.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) up_vector_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn( glm.model_to_eye, [point_init, camera_position_init, look_at_init, up_vector_init]) def test_eye_to_clip_preset(self): """Tests that eye_to_clip generates expected results.""" point = ((2.0, 3.0, 4.0), (3.0, 4.0, 5.0)) vertical_field_of_view = ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,)) aspect_ratio = ((1.5,), (1.6,)) near_plane = ((1.0,), (2.0,)) far_plane = ((10.0,), (11.0,)) pred = glm.eye_to_clip(point, vertical_field_of_view, aspect_ratio, near_plane, far_plane) gt = ((2.30940104, 5.19615173, -7.11111116, -4.0), (4.02095032, 8.57802773, -12.11111069, -5.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (1,), (1,), (1,), (1,)), ((None, 3), (None, 1), (None, 1), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 1), (None, 5, 1), (None, 5, 1), (None, 5, 1)), ) def test_eye_to_clip_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.eye_to_clip, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (1,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (1,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (1,), (1,), (1,), (2,)), ("Not all batch dimensions are broadcast-compatible", (3, 3), (2, 1), (1,), (1,), (1,)), ) def test_eye_to_clip_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.eye_to_clip, error_msg, shapes) def test_eye_to_clip_jacobian_preset(self): """Tests the Jacobian of eye_to_clip.""" point_init = np.array(((2.0, 3.0, 4.0), (3.0, 4.0, 5.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (50.0 * math.pi / 180.0,))) aspect_ratio_init = np.array(((1.5,), (1.6,))) near_init = np.array(((1.0,), (2.0,))) far_init = np.array(((10.0,), (11.0,))) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=1e-5) def test_eye_to_clip_jacobian_random(self): """Tests the Jacobian of eye_to_clip.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) eps = np.finfo(np.float64).eps vertical_field_of_view_init = np.random.uniform( eps, math.pi - eps, size=tensor_shape + [1]) aspect_ratio_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) near_init = np.random.uniform(eps, 100.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(eps, 10.0, size=tensor_shape + [1]) self.assert_jacobian_is_correct_fn( glm.eye_to_clip, [ point_init, vertical_field_of_view_init, aspect_ratio_init, near_init, far_init ], atol=1e-03) def test_clip_to_ndc_preset(self): """Tests that clip_to_ndc generates expected results.""" point = ((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0)) pred = glm.clip_to_ndc(point) gt = ((2.0, 4.0, 8.0), (4.0, 8.0, 16.0)) self.assertAllClose(pred, gt) @parameterized.parameters( ((4,)), ((None, 4),), ((None, 5, 4),), ) def test_clip_to_ndc_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.clip_to_ndc, shapes) def test_clip_to_ndc_exception_raised(self): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( glm.clip_to_ndc, "must have exactly 4 dimensions in axis -1", ((2,),)) def test_clip_to_ndc_jacobian_preset(self): """Tests the Jacobian of clip_to_ndc.""" point_init = np.array(((4.0, 8.0, 16.0, 2.0), (4.0, 8.0, 16.0, 1.0))) self.assert_jacobian_is_correct_fn(glm.clip_to_ndc, [point_init]) def test_clip_to_ndc_jacobian_random(self): """Tests the Jacobian of clip_to_ndc.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [4]) self.assert_jacobian_is_correct_fn( glm.clip_to_ndc, [point_init], atol=1e-04) def test_ndc_to_screen_preset(self): """Tests that ndc_to_screen generates expected results.""" point = ((1.1, 2.2, 3.3), (5.1, 5.2, 5.3)) lower_left_corner = ((6.4, 4.8), (0.0, 0.0)) screen_dimensions = ((640.0, 480.0), (300.0, 400.0)) near = ((1.0,), (11.0,)) far = ((10.0,), (100.0,)) pred = glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far) gt = ((678.40002441, 772.79998779, 20.34999847), (915.0, 1240.0, 291.3500061)) self.assertAllClose(pred, gt) @parameterized.parameters( ((3,), (2,), (2,), (1,), (1,)), ((None, 3), (None, 2), (None, 2), (None, 1), (None, 1)), ((None, 5, 3), (None, 5, 2), (None, 5, 2), (None, 5, 1), (None, 5, 1)), ) def test_ndc_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.ndc_to_screen, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (2,), (2,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (1,), (2,), (1,), (1,)), ("must have exactly 2 dimensions in axis -1", (3,), (2,), (3,), (1,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (2,), (1,)), ("must have exactly 1 dimensions in axis -1", (3,), (2,), (2,), (1,), (3,)), ("Not all batch dimensions are identical", (3,), (2, 2), (3, 2), (3, 1), (3, 1)), ("Not all batch dimensions are broadcast-compatible", (4, 3), (3, 2), (3, 2), (3, 1), (3, 1)), ) def test_ndc_to_screen_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.ndc_to_screen, error_msg, shapes) def test_ndc_to_screen_exception_near_raised(self): """Tests that an exception is raised when `near` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(-1.0, 0.0, size=(1,)) far = np.random.uniform(1.0, 2.0, size=(1,)) with self.subTest("negative_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_near"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, np.array((0.0,)), far)) def test_ndc_to_screen_exception_far_raised(self): """Tests that an exception is raised if `far` is not greater than `near`.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(1.0, 2.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(-1.0, 0.0, size=(1,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) def test_ndc_to_screen_exception_screen_dimensions_raised(self): """Tests that an exception is raised when `screen_dimensions` is not strictly positive.""" point = np.random.uniform(size=(3,)) lower_left_corner = np.random.uniform(size=(2,)) screen_dimensions = np.random.uniform(-1.0, 0.0, size=(2,)) near = np.random.uniform(1.0, 10.0, size=(1,)) far = near + np.random.uniform(0.1, 1.0, size=(1,)) with self.subTest("negative_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, screen_dimensions, near, far)) with self.subTest("zero_screen_dimensions"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( glm.ndc_to_screen(point, lower_left_corner, np.array((0.0, 0.0)), near, far)) def test_ndc_to_screen_jacobian_preset(self): """Tests the Jacobian of ndc_to_screen.""" point_init = np.array(((1.1, 2.2, 3.3), (5.1, 5.2, 5.3))) lower_left_corner_init = np.array(((6.4, 4.8), (0.0, 0.0))) screen_dimensions_init = np.array(((640.0, 480.0), (300.0, 400.0))) near_init = np.array(((1.0,), (11.0,))) far_init = np.array(((10.0,), (100.0,))) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_ndc_to_screen_jacobian_random(self): """Tests the Jacobian of ndc_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_init = np.random.uniform(size=tensor_shape + [3]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 1.0, 1000.0, size=tensor_shape + [2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=(1,)) self.assert_jacobian_is_correct_fn(glm.ndc_to_screen, [ point_init, lower_left_corner_init, screen_dimensions_init, near_init, far_init ]) def test_model_to_screen_preset(self): """Tests that model_to_screen generates expected results.""" point_world_space = np.array(((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1))) camera_position = np.array(((0.0, 0.0, 0.0), (0.4, -0.8, 0.1))) camera_up = np.array(((0.0, 1.0, 0.0), (0.0, 0.0, 1.0))) look_at_point = np.array(((0.0, 0.0, 1.0), (0.0, 1.0, 0.0))) vertical_field_of_view = np.array( ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,))) lower_left_corner = np.array(((0.0, 0.0), (10.0, 20.0))) screen_dimensions = np.array(((501.0, 501.0), (400.0, 600.0))) near = np.array(((0.01,), (1.0,))) far = np.array(((4.0,), (3.0,))) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position, look_at_point, camera_up) perspective_matrix = perspective.right_handed( vertical_field_of_view, screen_dimensions[..., 0:1] / screen_dimensions[..., 1:2], near, far) pred_screen, pred_w = glm.model_to_screen(point_world_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) gt_screen = ((-13.23016357, 599.30444336, 4.00215721), (98.07017517, -95.40383911, 3.1234405)) gt_w = ((5.1,), (3.42247,)) self.assertAllClose(pred_screen, gt_screen, atol=1e-5, rtol=1e-5) self.assertAllClose(pred_w, gt_w) @parameterized.parameters( ((3,), (4, 4), (4, 4), (2,), (2,)), ((640, 480, 3), (4, 4), (4, 4), (2,), (2,)), ((None, 3), (None, 4, 4), (None, 4, 4), (None, 2), (None, 2)), ((3,), (None, 1, 4, 4), (None, 1, 4, 4), (None, 1, 2), (None, 1, 2)), ) def test_model_to_screen_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.model_to_screen, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (2,), (4, 4), (4, 4)), ("must have exactly 4 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (3,), (4, 3), (4, 4)), ("must have exactly 4 dimensions in axis -2", (9.0, 12.0), (0.0, 0.0), (3,), (3, 4), (4, 4)), ("must have exactly 4 dimensions in axis -1", (9.0, 12.0), (0.0, 0.0), (3,), (4, 4), (4, 3)), ("must have exactly 4 dimensions in axis -2", (9.0, 12.0), (0.0, 0.0), (3,), (4, 4), (3, 4)), ("Not all batch dimensions are broadcast-compatible", (9.0, 12.0), (0.0, 0.0), (2, 3), (3, 4, 4), (3, 4, 4)), ) def test_model_to_screen_exception_raised(self, error_msg, screen_dimensions, lower_left_corner, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised( func=glm.model_to_screen, error_msg=error_msg, shapes=shapes, screen_dimensions=screen_dimensions, lower_left_corner=lower_left_corner) def test_model_to_screen_jacobian_preset(self): """Tests the Jacobian of model_to_screen.""" point_world_space_init = np.array(((3.1, 4.1, 5.1), (-1.1, 2.2, -3.1))) camera_position_init = np.array(((0.0, 0.0, 0.0), (0.4, -0.8, 0.1))) camera_up_init = np.array(((0.0, 1.0, 0.0), (0.0, 0.0, 1.0))) look_at_init = np.array(((0.0, 0.0, 1.0), (0.0, 1.0, 0.0))) vertical_field_of_view_init = np.array( ((60.0 * math.pi / 180.0,), (65 * math.pi / 180,))) lower_left_corner_init = np.array(((0.0, 0.0), (10.0, 20.0))) screen_dimensions_init = np.array(((501.0, 501.0), (400.0, 600.0))) near_init = np.array(((0.01,), (1.0,))) far_init = np.array(((4.0,), (3.0,))) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position_init, look_at_init, camera_up_init) perspective_matrix = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) args = [ point_world_space_init, model_to_eye_matrix, perspective_matrix, screen_dimensions_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args, atol=1e-4) # TODO(julienvalentin): will be fixed before submission # with self.subTest(name="jacobian_w"): # self.assert_jacobian_is_correct_fn( # lambda *args: glm.model_to_screen(*args)[1], args) def test_model_to_screen_jacobian_random(self): """Tests the Jacobian of model_to_screen.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() point_world_space_init = np.random.uniform(size=tensor_shape + [3]) camera_position_init = np.random.uniform(size=tensor_shape + [3]) camera_up_init = np.random.uniform(size=tensor_shape + [3]) look_at_init = np.random.uniform(size=tensor_shape + [3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [2]) screen_dimensions_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [2]) near_init = np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) far_init = near_init + np.random.uniform(0.1, 1.0, size=tensor_shape + [1]) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_position_init, look_at_init, camera_up_init) perspective_matrix = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) args = [ point_world_space_init, model_to_eye_matrix, perspective_matrix, screen_dimensions_init, lower_left_corner_init ] with self.subTest(name="jacobian_y_projection"): self.assert_jacobian_is_correct_fn( lambda *args: glm.model_to_screen(*args)[0], args, atol=1e-4) # TODO(julienvalentin): will be fixed before submission # with self.subTest(name="jacobian_w"): # self.assert_jacobian_is_correct_fn( # lambda *args: glm.model_to_screen(*args)[1], args) def test_perspective_correct_interpolation_preset(self): """Tests that perspective_correct_interpolation generates expected results.""" camera_origin = np.array((0.0, 0.0, 0.0)) camera_up = np.array((0.0, 1.0, 0.0)) look_at_point = np.array((0.0, 0.0, 1.0)) fov = np.array((90.0 * np.math.pi / 180.0,)) bottom_left = np.array((0.0, 0.0)) image_size = np.array((501.0, 501.0)) near_plane = np.array((0.01,)) far_plane = np.array((10.0,)) batch_size = np.random.randint(1, 5) triangle_x_y = np.random.uniform(-10.0, 10.0, (batch_size, 3, 2)) triangle_z = np.random.uniform(2.0, 10.0, (batch_size, 3, 1)) triangles = np.concatenate((triangle_x_y, triangle_z), axis=-1) # Builds barycentric weights. barycentric_weights = np.random.uniform(size=(batch_size, 3)) barycentric_weights = barycentric_weights / np.sum( barycentric_weights, axis=-1, keepdims=True) # Barycentric interpolation of vertex positions. convex_combination = np.einsum("ba, bac -> bc", barycentric_weights, triangles) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (image_size[0:1] / image_size[1:2]), near_plane, far_plane) # Computes where those points project in screen coordinates. pixel_position, _ = glm.model_to_screen(convex_combination, model_to_eye_matrix, perspective_matrix, image_size, bottom_left) # Builds attributes. num_pixels = pixel_position.shape[0] attribute_size = np.random.randint(10) attributes = np.random.uniform(size=(num_pixels, 3, attribute_size)) prediction = glm.perspective_correct_interpolation(triangles, attributes, pixel_position[..., 0:2], model_to_eye_matrix, perspective_matrix, image_size, bottom_left) groundtruth = np.einsum("ba, bac -> bc", barycentric_weights, attributes) self.assertAllClose(prediction, groundtruth) def test_perspective_correct_interpolation_jacobian_preset(self): """Tests the Jacobian of perspective_correct_interpolation.""" vertices_init = np.tile( ((-0.2857143, 0.2857143, 5.0), (0.2857143, 0.2857143, 0.5), (0.0, -0.2857143, 1.0)), (2, 1, 1)) attributes_init = np.tile( (((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))), (2, 1, 1)) pixel_position_init = np.array(((125.5, 375.5), (250.5, 250.5))) camera_position_init = np.tile((0.0, 0.0, 0.0), (2, 3, 1)) look_at_init = np.tile((0.0, 0.0, 1.0), (2, 3, 1)) up_vector_init = np.tile((0.0, 1.0, 0.0), (2, 3, 1)) vertical_field_of_view_init = np.tile((1.0471975511965976,), (2, 3, 1)) screen_dimensions_init = np.tile((501.0, 501.0), (2, 3, 1)) near_init = np.tile((0.01,), (2, 3, 1)) far_init = np.tile((10.0,), (2, 3, 1)) lower_left_corner_init = np.tile((0.0, 0.0), (2, 3, 1)) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn(glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_perspective_correct_interpolation_jacobian_random(self): """Tests the Jacobian of perspective_correct_interpolation.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() vertices_init = np.random.uniform(size=tensor_shape + [3, 3]) num_attributes = np.random.randint(1, 10) attributes_init = np.random.uniform(size=tensor_shape + [3, num_attributes]) pixel_position_init = np.random.uniform(size=tensor_shape + [2]) camera_position_init = np.random.uniform(size=tensor_shape + [3, 3]) look_at_init = np.random.uniform(size=tensor_shape + [3, 3]) up_vector_init = np.random.uniform(size=tensor_shape + [3, 3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) screen_dimensions_init = np.random.uniform( 1.0, 10.0, size=tensor_shape + [3, 2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [3, 1]) far_init = near_init + np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [3, 2]) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn( glm.perspective_correct_interpolation, [ vertices_init, attributes_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ], atol=1e-4) @parameterized.parameters( ((3, 3), (2,), (4, 4), (4, 4), (2,)), ((3, 3), (7, 2), (4, 4), (4, 4), (2,)), ((3, 3), (None, 2), (4, 4), (4, 4), (2,)), ((7, 3, 3), (2,), (4, 4), (4, 4), (2,)), ((None, 3, 3), (2,), (4, 4), (4, 4), (2,)), ) def test_perspective_correct_barycentrics_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.perspective_correct_barycentrics, shapes) @parameterized.parameters( ("must have exactly 2 dimensions in axis -1", (3, 3), (2,), (4, 4), (4, 4), (3,)), ("must have exactly 3 dimensions in axis -1", (3, 4), (2,), (4, 4), (4, 4), (3,)), ("must have exactly 3 dimensions in axis -2", (4, 3), (2,), (4, 4), (4, 4), (3,)), ) def test_perspective_correct_barycentrics_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.perspective_correct_barycentrics, error_msg, shapes) def test_perspective_correct_barycentrics_preset(self): """Tests that perspective_correct_barycentrics generates expected results.""" camera_origin = np.array((0.0, 0.0, 0.0)) camera_up = np.array((0.0, 1.0, 0.0)) look_at_point = np.array((0.0, 0.0, 1.0)) fov = np.array((90.0 * np.math.pi / 180.0,)) bottom_left = np.array((0.0, 0.0)) image_size = np.array((501.0, 501.0)) near_plane = np.array((0.01,)) far_plane = np.array((10.0,)) batch_size = np.random.randint(1, 5) triangle_x_y = np.random.uniform(-10.0, 10.0, (batch_size, 3, 2)) triangle_z = np.random.uniform(2.0, 10.0, (batch_size, 3, 1)) triangles = np.concatenate((triangle_x_y, triangle_z), axis=-1) # Builds barycentric weights. barycentric_weights = np.random.uniform(size=(batch_size, 3)) barycentric_weights = barycentric_weights / np.sum( barycentric_weights, axis=-1, keepdims=True) # Barycentric interpolation of vertex positions. convex_combination = np.einsum("ba, bac -> bc", barycentric_weights, triangles) # Build matrices. model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (image_size[0:1] / image_size[1:2]), near_plane, far_plane) # Computes where those points project in screen coordinates. pixel_position, _ = glm.model_to_screen(convex_combination, model_to_eye_matrix, perspective_matrix, image_size, bottom_left) prediction = glm.perspective_correct_barycentrics(triangles, pixel_position[..., 0:2], model_to_eye_matrix, perspective_matrix, image_size, bottom_left) self.assertAllClose(prediction, barycentric_weights) def test_perspective_correct_barycentrics_jacobian_random(self): """Tests the Jacobian of perspective_correct_barycentrics.""" tensor_size = np.random.randint(1, 3) tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist() vertices_init = np.random.uniform(size=tensor_shape + [3, 3]) pixel_position_init = np.random.uniform(size=tensor_shape + [2]) camera_position_init = np.random.uniform(size=tensor_shape + [3, 3]) look_at_init = np.random.uniform(size=tensor_shape + [3, 3]) up_vector_init = np.random.uniform(size=tensor_shape + [3, 3]) vertical_field_of_view_init = np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) screen_dimensions_init = np.random.uniform( 1.0, 10.0, size=tensor_shape + [3, 2]) near_init = np.random.uniform(1.0, 10.0, size=tensor_shape + [3, 1]) far_init = near_init + np.random.uniform( 0.1, 1.0, size=tensor_shape + [3, 1]) lower_left_corner_init = np.random.uniform(size=tensor_shape + [3, 2]) # Build matrices. model_to_eye_matrix_init = look_at.right_handed(camera_position_init, look_at_init, up_vector_init) perspective_matrix_init = perspective.right_handed( vertical_field_of_view_init, screen_dimensions_init[..., 0:1] / screen_dimensions_init[..., 1:2], near_init, far_init) self.assert_jacobian_is_correct_fn( glm.perspective_correct_barycentrics, [ vertices_init, pixel_position_init, model_to_eye_matrix_init, perspective_matrix_init, screen_dimensions_init, lower_left_corner_init ], atol=1e-4) @parameterized.parameters( ((3, 7), (3,)), ((2, 3, 7), (2, 3)), ((None, 3, 7), (None, 3)), ) def test_interpolate_attributes_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(glm.interpolate_attributes, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis -2", (2, 7), (3,)), ("must have exactly 3 dimensions in axis -1", (3, 7), (2,)), ("Not all batch dimensions are broadcast-compatible", (5, 3, 7), (4, 3)), ) def test_interpolate_attributes_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(glm.interpolate_attributes, error_msg, shapes) def test_interpolate_attributes_random(self): """Checks the output of interpolate_attributes.""" attributes = np.random.uniform(-1.0, 1.0, size=(3,)) barycentric = np.random.uniform(0.0, 1.0, size=(3,)) barycentric = barycentric / np.linalg.norm( barycentric, axis=-1, ord=1, keepdims=True) groundtruth = np.sum(attributes * barycentric, keepdims=True) attributes = np.reshape(attributes, (3, 1)) prediction = glm.interpolate_attributes(attributes, barycentric) self.assertAllClose(groundtruth, prediction) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_interpolate_attributes_jacobian_random(self): """Tests the jacobian of interpolate_attributes.""" batch_size = np.random.randint(1, 5) attributes = np.random.uniform(-1.0, 1.0, size=(batch_size, 3, 1)) barycentric = np.random.uniform( 0.0, 1.0, size=( batch_size, 3, )) barycentric = barycentric / np.linalg.norm( barycentric, axis=-1, ord=1, keepdims=True) self.assert_jacobian_is_correct_fn(glm.interpolate_attributes, [attributes, barycentric]) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/reflectance/tests/blinn_phong_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for Blinn-Phong reflectance.""" import math import sys from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.rendering.reflectance import blinn_phong from tensorflow_graphics.util import test_case class BlinnPhongTest(test_case.TestCase): @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_brdf_jacobian_random(self): """Tests the Jacobian of brdf.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() direction_incoming_light_init = np.random.uniform( -1.0, 1.0, size=tensor_shape + [3]) direction_outgoing_light_init = np.random.uniform( -1.0, 1.0, size=tensor_shape + [3]) surface_normal_init = np.random.uniform(-1.0, 1.0, size=tensor_shape + [3]) shininess_init = np.random.uniform(size=tensor_shape + [1]) albedo_init = np.random.random(tensor_shape + [3]) self.assert_jacobian_is_correct_fn(blinn_phong.brdf, [ direction_incoming_light_init, direction_outgoing_light_init, surface_normal_init, shininess_init, albedo_init ]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_brdf_jacobian_preset(self): delta = 1e-5 direction_incoming_light_init = np.array((delta, -1.0, 0.0)) direction_outgoing_light_init = np.array((delta, 1.0, 0.0)) surface_normal_init = np.array((1.0, 0.0, 0.0)) shininess_init = np.array((1.0,)) albedo_init = np.array((1.0, 1.0, 1.0)) self.assert_jacobian_is_correct_fn( blinn_phong.brdf, [ direction_incoming_light_init, direction_outgoing_light_init, surface_normal_init, shininess_init, albedo_init ], delta=delta / 10.0) @parameterized.parameters( (-1.0, 1.0, 1.0 / math.pi), (1.0, 1.0, 0.0), (-1.0, -1.0, 0.0), (1.0, -1.0, 0.0), ) def test_brdf_random(self, incoming_yz, outgoing_yz, ratio): tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() shininess = np.zeros(shape=tensor_shape + [1]) albedo = np.random.uniform(low=0.0, high=1.0, size=tensor_shape + [3]) direction_incoming_light = np.random.uniform( low=-1.0, high=1.0, size=tensor_shape + [3]) direction_outgoing_light = np.random.uniform( low=-1.0, high=1.0, size=tensor_shape + [3]) surface_normal = np.array((0.0, 1.0, 1.0)) direction_incoming_light[..., 1:3] = incoming_yz direction_outgoing_light[..., 1:3] = outgoing_yz direction_incoming_light = direction_incoming_light / np.linalg.norm( direction_incoming_light, axis=-1, keepdims=True) direction_outgoing_light = direction_outgoing_light / np.linalg.norm( direction_outgoing_light, axis=-1, keepdims=True) surface_normal = surface_normal / np.linalg.norm( surface_normal, axis=-1, keepdims=True) gt = albedo * ratio pred = blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo) self.assertAllClose(gt, pred) def test_brdf_exceptions_raised(self): """Tests that the exceptions are raised correctly.""" direction_incoming_light = np.random.uniform(-1.0, 1.0, size=(3,)) direction_outgoing_light = np.random.uniform(-1.0, 1.0, size=(3,)) surface_normal = np.random.uniform(-1.0, 1.0, size=(3,)) shininess = np.random.uniform(0.0, 1.0, size=(1,)) albedo = np.random.uniform(0.0, 1.0, (3,)) with self.subTest(name="assert_on_direction_incoming_light_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) direction_incoming_light /= np.linalg.norm( direction_incoming_light, axis=-1) with self.subTest(name="assert_on_direction_outgoing_light_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) direction_outgoing_light /= np.linalg.norm( direction_outgoing_light, axis=-1) with self.subTest(name="assert_on_surface_normal_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) surface_normal /= np.linalg.norm(surface_normal, axis=-1) with self.subTest(name="assert_on_albedo_not_normalized"): albedo = np.random.uniform(-10.0, -sys.float_info.epsilon, (3,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) albedo = np.random.uniform(sys.float_info.epsilon, 10.0, (3,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) @parameterized.parameters( ((3,), (3,), (3,), (1,), (3,)), ((None, 3), (None, 3), (None, 3), (None, 1), (None, 3)), ((1, 3), (1, 3), (1, 3), (1, 1), (1, 3)), ((2, 3), (2, 3), (2, 3), (2, 1), (2, 3)), ((1, 3), (1, 2, 3), (1, 2, 1, 3), (1, 2, 1), (1, 3)), ((3,), (1, 3), (1, 2, 3), (1, 2, 2, 1), (1, 2, 2, 2, 3)), ((1, 2, 2, 2, 3), (1, 2, 2, 3), (1, 2, 3), (1, 1), (3,)), ) def test_brdf_shape_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(blinn_phong.brdf, shape) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (1,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (4,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (4,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (1,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (2,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (4,), (1,), (3,)), ("must have exactly 1 dimensions in axis -1", (3,), (3,), (3,), (2,), (3,)), ("must have exactly 1 dimensions in axis -1", (3,), (3,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (4,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (2,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (1,)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3), (3,), (1,), (3,)), ) def test_brdf_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(blinn_phong.brdf, error_msg, shape) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for Blinn-Phong reflectance.""" import math import sys from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.rendering.reflectance import blinn_phong from tensorflow_graphics.util import test_case class BlinnPhongTest(test_case.TestCase): @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_brdf_jacobian_random(self): """Tests the Jacobian of brdf.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() direction_incoming_light_init = np.random.uniform( -1.0, 1.0, size=tensor_shape + [3]) direction_outgoing_light_init = np.random.uniform( -1.0, 1.0, size=tensor_shape + [3]) surface_normal_init = np.random.uniform(-1.0, 1.0, size=tensor_shape + [3]) shininess_init = np.random.uniform(size=tensor_shape + [1]) albedo_init = np.random.random(tensor_shape + [3]) self.assert_jacobian_is_correct_fn(blinn_phong.brdf, [ direction_incoming_light_init, direction_outgoing_light_init, surface_normal_init, shininess_init, albedo_init ]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_brdf_jacobian_preset(self): delta = 1e-5 direction_incoming_light_init = np.array((delta, -1.0, 0.0)) direction_outgoing_light_init = np.array((delta, 1.0, 0.0)) surface_normal_init = np.array((1.0, 0.0, 0.0)) shininess_init = np.array((1.0,)) albedo_init = np.array((1.0, 1.0, 1.0)) self.assert_jacobian_is_correct_fn( blinn_phong.brdf, [ direction_incoming_light_init, direction_outgoing_light_init, surface_normal_init, shininess_init, albedo_init ], delta=delta / 10.0) @parameterized.parameters( (-1.0, 1.0, 1.0 / math.pi), (1.0, 1.0, 0.0), (-1.0, -1.0, 0.0), (1.0, -1.0, 0.0), ) def test_brdf_random(self, incoming_yz, outgoing_yz, ratio): tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() shininess = np.zeros(shape=tensor_shape + [1]) albedo = np.random.uniform(low=0.0, high=1.0, size=tensor_shape + [3]) direction_incoming_light = np.random.uniform( low=-1.0, high=1.0, size=tensor_shape + [3]) direction_outgoing_light = np.random.uniform( low=-1.0, high=1.0, size=tensor_shape + [3]) surface_normal = np.array((0.0, 1.0, 1.0)) direction_incoming_light[..., 1:3] = incoming_yz direction_outgoing_light[..., 1:3] = outgoing_yz direction_incoming_light = direction_incoming_light / np.linalg.norm( direction_incoming_light, axis=-1, keepdims=True) direction_outgoing_light = direction_outgoing_light / np.linalg.norm( direction_outgoing_light, axis=-1, keepdims=True) surface_normal = surface_normal / np.linalg.norm( surface_normal, axis=-1, keepdims=True) gt = albedo * ratio pred = blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo) self.assertAllClose(gt, pred) def test_brdf_exceptions_raised(self): """Tests that the exceptions are raised correctly.""" direction_incoming_light = np.random.uniform(-1.0, 1.0, size=(3,)) direction_outgoing_light = np.random.uniform(-1.0, 1.0, size=(3,)) surface_normal = np.random.uniform(-1.0, 1.0, size=(3,)) shininess = np.random.uniform(0.0, 1.0, size=(1,)) albedo = np.random.uniform(0.0, 1.0, (3,)) with self.subTest(name="assert_on_direction_incoming_light_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) direction_incoming_light /= np.linalg.norm( direction_incoming_light, axis=-1) with self.subTest(name="assert_on_direction_outgoing_light_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) direction_outgoing_light /= np.linalg.norm( direction_outgoing_light, axis=-1) with self.subTest(name="assert_on_surface_normal_not_normalized"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) surface_normal /= np.linalg.norm(surface_normal, axis=-1) with self.subTest(name="assert_on_albedo_not_normalized"): albedo = np.random.uniform(-10.0, -sys.float_info.epsilon, (3,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) albedo = np.random.uniform(sys.float_info.epsilon, 10.0, (3,)) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( blinn_phong.brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo)) @parameterized.parameters( ((3,), (3,), (3,), (1,), (3,)), ((None, 3), (None, 3), (None, 3), (None, 1), (None, 3)), ((1, 3), (1, 3), (1, 3), (1, 1), (1, 3)), ((2, 3), (2, 3), (2, 3), (2, 1), (2, 3)), ((1, 3), (1, 2, 3), (1, 2, 1, 3), (1, 2, 1), (1, 3)), ((3,), (1, 3), (1, 2, 3), (1, 2, 2, 1), (1, 2, 2, 2, 3)), ((1, 2, 2, 2, 3), (1, 2, 2, 3), (1, 2, 3), (1, 1), (3,)), ) def test_brdf_shape_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(blinn_phong.brdf, shape) @parameterized.parameters( ("must have exactly 3 dimensions in axis -1", (1,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (4,), (3,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (4,), (3,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (1,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (2,), (1,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (4,), (1,), (3,)), ("must have exactly 1 dimensions in axis -1", (3,), (3,), (3,), (2,), (3,)), ("must have exactly 1 dimensions in axis -1", (3,), (3,), (3,), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (4,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (2,)), ("must have exactly 3 dimensions in axis -1", (3,), (3,), (3,), (1,), (1,)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3), (3,), (1,), (3,)), ) def test_brdf_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(blinn_phong.brdf, error_msg, shape) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/optimizer/levenberg_marquardt.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""This module implements a Levenberg-Marquardt optimizer. Minimizes \\(\min_{\mathbf{x}} \sum_i \|\mathbf{r}_i(\mathbf{x})\|^2_2\\) where \\(\mathbf{r}_i(\mathbf{x})\\) are the residuals. This function implements Levenberg-Marquardt, an iterative process that linearizes the residuals and iteratively finds a displacement \\(\Delta \mathbf{x}\\) such that at iteration \\(t\\) an update \\(\mathbf{x}_{t+1} = \mathbf{x}_{t} + \Delta \mathbf{x}\\) improving the loss can be computed. The displacement is computed by solving an optimization problem \\(\min_{\Delta \mathbf{x}} \sum_i \|\mathbf{J}_i(\mathbf{x}_{t})\Delta\mathbf{x} + \mathbf{r}_i(\mathbf{x}_t)\|^2_2 + \lambda\|\Delta \mathbf{x} \|_2^2\\) where \\(\mathbf{J}_i(\mathbf{x}_{t})\\) is the Jacobian of \\(\mathbf{r}_i\\) computed at \\(\mathbf{x}_t\\), and \\(\lambda\\) is a scalar weight. More details on Levenberg-Marquardt can be found on [this page.] (https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api def _values_and_jacobian(residuals, variables): """Computes the residual values and the Jacobian matrix. Args: residuals: A list of residuals. variables: A list of variables. Returns: The residual values and the Jacobian matrix. """ def _compute_residual_values(residuals, variables): """Computes the residual values.""" return tf.concat([ tf.reshape(residual(*variables), shape=(-1,)) for residual in residuals ], axis=-1) def _compute_jacobian(values, variables, tape): """Computes the Jacobian matrix.""" jacobians = tape.jacobian( values, variables, unconnected_gradients=tf.UnconnectedGradients.ZERO) return tf.concat([ tf.reshape(jacobian, shape=(tf.shape(input=jacobian)[0], -1)) for jacobian in jacobians ], axis=-1) with tf.GradientTape(watch_accessed_variables=False, persistent=True) as tape: for variable in variables: tape.watch(variable) values = _compute_residual_values(residuals, variables) jacobian = _compute_jacobian(values, variables, tape) del tape values = tf.expand_dims(values, axis=-1) return values, jacobian def minimize(residuals, variables, max_iterations, regularizer=1e-20, regularizer_multiplier=10.0, callback=None, name=None): r"""Minimizes a set of residuals in the least-squares sense. Args: residuals: A residual or a list/tuple of residuals. A residual is a Python `callable`. variables: A variable or a list or tuple of variables defining the starting point of the minimization. max_iterations: The maximum number of iterations. regularizer: The regularizer is used to damped the stepsize when the iterations are becoming unstable. The bigger the regularizer is the smaller the stepsize becomes. regularizer_multiplier: If an iteration does not decrease the objective a new regularizer is computed by scaling it by this multiplier. callback: A callback function that will be called at each iteration. In graph mode the callback should return an op or list of ops that will execute the callback logic. The callback needs to be of the form f(iteration, objective_value, variables). A callback is a Python `callable`. The callback could be used for logging, for example if one wants to print the objective value at each iteration. name: A name for this op. Defaults to "levenberg_marquardt_minimize". Returns: The value of the objective function and variables attained at the final iteration of the minimization procedure. Raises: ValueError: If max_iterations is not at least 1. InvalidArgumentError: This exception is only raised in graph mode if the Cholesky decomposition is not successful. One likely fix is to increase the regularizer. In eager mode this exception is catched and the regularizer is increased automatically. Examples: ```python x = tf.constant(np.random.random_sample(size=(1,2)), dtype=tf.float32) y = tf.constant(np.random.random_sample(size=(3,1)), dtype=tf.float32) def f1(x, y): return x + y def f2(x, y): return x * y def callback(iteration, objective_value, variables): def print_output(iteration, objective_value, *variables): print("Iteration:", iteration, "Objective Value:", objective_value) for variable in variables: print(variable) inp = [iteration, objective_value] + variables return tf.py_function(print_output, inp, []) minimize_op = minimize(residuals=(f1, f2), variables=(x, y), max_iterations=10, callback=callback) if not tf.executing_eagerly(): with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(minimize_op) ``` """ if not isinstance(variables, (tuple, list)): variables = [variables] with tf.compat.v1.name_scope(name, 'levenberg_marquardt_minimize', variables): if not isinstance(residuals, (tuple, list)): residuals = [residuals] if isinstance(residuals, tuple): residuals = list(residuals) if isinstance(variables, tuple): variables = list(variables) variables = [tf.convert_to_tensor(value=variable) for variable in variables] multiplier = tf.constant(regularizer_multiplier, dtype=variables[0].dtype) if max_iterations <= 0: raise ValueError("'max_iterations' needs to be at least 1.") def _cond(iteration, regularizer, objective_value, variables): """Returns whether any iteration still needs to be performed.""" del regularizer, objective_value, variables return iteration < max_iterations def _body(iteration, regularizer, objective_value, variables): """Main optimization loop.""" iteration += tf.constant(1, dtype=tf.int32) values, jacobian = _values_and_jacobian(residuals, variables) # Solves the normal equation. try: updates = tf.linalg.lstsq(jacobian, values, l2_regularizer=regularizer) shapes = [tf.shape(input=variable) for variable in variables] splits = [tf.reduce_prod(input_tensor=shape) for shape in shapes] updates = tf.split(tf.squeeze(updates, axis=-1), splits) new_variables = [ variable - tf.reshape(update, shape) for variable, update, shape in zip(variables, updates, shapes) ] new_objective_value = tf.reduce_sum(input_tensor=[ tf.nn.l2_loss(residual(*new_variables)) for residual in residuals ]) # If the new estimated solution does not decrease the objective value, # no updates are performed, but a new regularizer is computed. cond = tf.less(new_objective_value, objective_value) regularizer = tf.compat.v1.where( cond, x=regularizer, y=regularizer * multiplier) objective_value = tf.compat.v1.where( cond, x=new_objective_value, y=objective_value) variables = [ tf.compat.v1.where(cond, x=new_variable, y=variable) for variable, new_variable in zip(variables, new_variables) ] # Note that catching InvalidArgumentError will only work in eager mode. except tf.errors.InvalidArgumentError: regularizer *= multiplier if callback is not None: callback_ops = callback(iteration, objective_value, variables) if callback_ops is not None: if not isinstance(callback_ops, (tuple, list)): callback_ops = [callback_ops] with tf.control_dependencies(callback_ops): iteration = tf.identity(iteration) objective_value = tf.identity(objective_value) variables = [tf.identity(v) for v in variables] return iteration, regularizer, objective_value, variables starting_value = tf.reduce_sum(input_tensor=[ tf.nn.l2_loss(residual(*variables)) for residual in residuals ]) dtype = variables[0].dtype initial = ( tf.constant(0, dtype=tf.int32), # Initial iteration number. tf.constant(regularizer, dtype=dtype), # Initial regularizer. starting_value, # Initial objective value. variables, # Initial variables. ) _, _, final_objective_value, final_variables = tf.while_loop( cond=_cond, body=_body, loop_vars=initial, parallel_iterations=1) return final_objective_value, final_variables # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""This module implements a Levenberg-Marquardt optimizer. Minimizes \\(\min_{\mathbf{x}} \sum_i \|\mathbf{r}_i(\mathbf{x})\|^2_2\\) where \\(\mathbf{r}_i(\mathbf{x})\\) are the residuals. This function implements Levenberg-Marquardt, an iterative process that linearizes the residuals and iteratively finds a displacement \\(\Delta \mathbf{x}\\) such that at iteration \\(t\\) an update \\(\mathbf{x}_{t+1} = \mathbf{x}_{t} + \Delta \mathbf{x}\\) improving the loss can be computed. The displacement is computed by solving an optimization problem \\(\min_{\Delta \mathbf{x}} \sum_i \|\mathbf{J}_i(\mathbf{x}_{t})\Delta\mathbf{x} + \mathbf{r}_i(\mathbf{x}_t)\|^2_2 + \lambda\|\Delta \mathbf{x} \|_2^2\\) where \\(\mathbf{J}_i(\mathbf{x}_{t})\\) is the Jacobian of \\(\mathbf{r}_i\\) computed at \\(\mathbf{x}_t\\), and \\(\lambda\\) is a scalar weight. More details on Levenberg-Marquardt can be found on [this page.] (https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api def _values_and_jacobian(residuals, variables): """Computes the residual values and the Jacobian matrix. Args: residuals: A list of residuals. variables: A list of variables. Returns: The residual values and the Jacobian matrix. """ def _compute_residual_values(residuals, variables): """Computes the residual values.""" return tf.concat([ tf.reshape(residual(*variables), shape=(-1,)) for residual in residuals ], axis=-1) def _compute_jacobian(values, variables, tape): """Computes the Jacobian matrix.""" jacobians = tape.jacobian( values, variables, unconnected_gradients=tf.UnconnectedGradients.ZERO) return tf.concat([ tf.reshape(jacobian, shape=(tf.shape(input=jacobian)[0], -1)) for jacobian in jacobians ], axis=-1) with tf.GradientTape(watch_accessed_variables=False, persistent=True) as tape: for variable in variables: tape.watch(variable) values = _compute_residual_values(residuals, variables) jacobian = _compute_jacobian(values, variables, tape) del tape values = tf.expand_dims(values, axis=-1) return values, jacobian def minimize(residuals, variables, max_iterations, regularizer=1e-20, regularizer_multiplier=10.0, callback=None, name=None): r"""Minimizes a set of residuals in the least-squares sense. Args: residuals: A residual or a list/tuple of residuals. A residual is a Python `callable`. variables: A variable or a list or tuple of variables defining the starting point of the minimization. max_iterations: The maximum number of iterations. regularizer: The regularizer is used to damped the stepsize when the iterations are becoming unstable. The bigger the regularizer is the smaller the stepsize becomes. regularizer_multiplier: If an iteration does not decrease the objective a new regularizer is computed by scaling it by this multiplier. callback: A callback function that will be called at each iteration. In graph mode the callback should return an op or list of ops that will execute the callback logic. The callback needs to be of the form f(iteration, objective_value, variables). A callback is a Python `callable`. The callback could be used for logging, for example if one wants to print the objective value at each iteration. name: A name for this op. Defaults to "levenberg_marquardt_minimize". Returns: The value of the objective function and variables attained at the final iteration of the minimization procedure. Raises: ValueError: If max_iterations is not at least 1. InvalidArgumentError: This exception is only raised in graph mode if the Cholesky decomposition is not successful. One likely fix is to increase the regularizer. In eager mode this exception is catched and the regularizer is increased automatically. Examples: ```python x = tf.constant(np.random.random_sample(size=(1,2)), dtype=tf.float32) y = tf.constant(np.random.random_sample(size=(3,1)), dtype=tf.float32) def f1(x, y): return x + y def f2(x, y): return x * y def callback(iteration, objective_value, variables): def print_output(iteration, objective_value, *variables): print("Iteration:", iteration, "Objective Value:", objective_value) for variable in variables: print(variable) inp = [iteration, objective_value] + variables return tf.py_function(print_output, inp, []) minimize_op = minimize(residuals=(f1, f2), variables=(x, y), max_iterations=10, callback=callback) if not tf.executing_eagerly(): with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(minimize_op) ``` """ if not isinstance(variables, (tuple, list)): variables = [variables] with tf.compat.v1.name_scope(name, 'levenberg_marquardt_minimize', variables): if not isinstance(residuals, (tuple, list)): residuals = [residuals] if isinstance(residuals, tuple): residuals = list(residuals) if isinstance(variables, tuple): variables = list(variables) variables = [tf.convert_to_tensor(value=variable) for variable in variables] multiplier = tf.constant(regularizer_multiplier, dtype=variables[0].dtype) if max_iterations <= 0: raise ValueError("'max_iterations' needs to be at least 1.") def _cond(iteration, regularizer, objective_value, variables): """Returns whether any iteration still needs to be performed.""" del regularizer, objective_value, variables return iteration < max_iterations def _body(iteration, regularizer, objective_value, variables): """Main optimization loop.""" iteration += tf.constant(1, dtype=tf.int32) values, jacobian = _values_and_jacobian(residuals, variables) # Solves the normal equation. try: updates = tf.linalg.lstsq(jacobian, values, l2_regularizer=regularizer) shapes = [tf.shape(input=variable) for variable in variables] splits = [tf.reduce_prod(input_tensor=shape) for shape in shapes] updates = tf.split(tf.squeeze(updates, axis=-1), splits) new_variables = [ variable - tf.reshape(update, shape) for variable, update, shape in zip(variables, updates, shapes) ] new_objective_value = tf.reduce_sum(input_tensor=[ tf.nn.l2_loss(residual(*new_variables)) for residual in residuals ]) # If the new estimated solution does not decrease the objective value, # no updates are performed, but a new regularizer is computed. cond = tf.less(new_objective_value, objective_value) regularizer = tf.compat.v1.where( cond, x=regularizer, y=regularizer * multiplier) objective_value = tf.compat.v1.where( cond, x=new_objective_value, y=objective_value) variables = [ tf.compat.v1.where(cond, x=new_variable, y=variable) for variable, new_variable in zip(variables, new_variables) ] # Note that catching InvalidArgumentError will only work in eager mode. except tf.errors.InvalidArgumentError: regularizer *= multiplier if callback is not None: callback_ops = callback(iteration, objective_value, variables) if callback_ops is not None: if not isinstance(callback_ops, (tuple, list)): callback_ops = [callback_ops] with tf.control_dependencies(callback_ops): iteration = tf.identity(iteration) objective_value = tf.identity(objective_value) variables = [tf.identity(v) for v in variables] return iteration, regularizer, objective_value, variables starting_value = tf.reduce_sum(input_tensor=[ tf.nn.l2_loss(residual(*variables)) for residual in residuals ]) dtype = variables[0].dtype initial = ( tf.constant(0, dtype=tf.int32), # Initial iteration number. tf.constant(regularizer, dtype=dtype), # Initial regularizer. starting_value, # Initial objective value. variables, # Initial variables. ) _, _, final_objective_value, final_variables = tf.while_loop( cond=_cond, body=_body, loop_vars=initial, parallel_iterations=1) return final_objective_value, final_variables # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/type_alias.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Type aliases for Python 3 typing.""" from typing import Union, Sequence import numpy as np import tensorflow as tf Integer = Union[int, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64] Float = Union[float, np.float16, np.float32, np.float64] TensorLike = Union[Integer, Float, Sequence, np.ndarray, tf.Tensor, tf.Variable]

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Type aliases for Python 3 typing.""" from typing import Union, Sequence import numpy as np import tensorflow as tf Integer = Union[int, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64] Float = Union[float, np.float16, np.float32, np.float64] TensorLike = Union[Integer, Float, Sequence, np.ndarray, tf.Tensor, tf.Variable]

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/voxels/tests/visual_hull_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for visual hull voxel rendering.""" from absl.testing import flagsaver from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.rendering.voxels import visual_hull from tensorflow_graphics.rendering.voxels.tests import test_helpers from tensorflow_graphics.util import test_case class VisualHullTest(test_case.TestCase): @parameterized.parameters( (0, (8, 16, 6, 1)), (1, (12, 8, 16, 6, 3)), ) def test_render_shape_exception_not_raised(self, axis, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(visual_hull.render, shape, axis=axis) @parameterized.parameters( ("must have a rank greater than 3", 2, (3,)), ("must have a rank greater than 3", 2, (16, 6, 3)), ("'axis' needs to be 0, 1 or 2", 5, (8, 16, 6, 1)), ) def test_render_shape_exception_raised(self, error_msg, axis, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(visual_hull.render, error_msg, shape, axis=axis) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_render_jacobian_random(self): """Tests the Jacobian of render.""" voxels_init = test_helpers.generate_random_test_voxels_render() self.assert_jacobian_is_correct_fn(visual_hull.render, [voxels_init]) def test_render_preset(self): """Checks that render returns the expected value.""" x_voxels_init, y_images_init = test_helpers.generate_preset_test_voxels_visual_hull_render( ) voxels = tf.convert_to_tensor(value=x_voxels_init) y_images = tf.convert_to_tensor(value=y_images_init) y = visual_hull.render(voxels) self.assertAllClose(y_images, y) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for visual hull voxel rendering.""" from absl.testing import flagsaver from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.rendering.voxels import visual_hull from tensorflow_graphics.rendering.voxels.tests import test_helpers from tensorflow_graphics.util import test_case class VisualHullTest(test_case.TestCase): @parameterized.parameters( (0, (8, 16, 6, 1)), (1, (12, 8, 16, 6, 3)), ) def test_render_shape_exception_not_raised(self, axis, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(visual_hull.render, shape, axis=axis) @parameterized.parameters( ("must have a rank greater than 3", 2, (3,)), ("must have a rank greater than 3", 2, (16, 6, 3)), ("'axis' needs to be 0, 1 or 2", 5, (8, 16, 6, 1)), ) def test_render_shape_exception_raised(self, error_msg, axis, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(visual_hull.render, error_msg, shape, axis=axis) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_render_jacobian_random(self): """Tests the Jacobian of render.""" voxels_init = test_helpers.generate_random_test_voxels_render() self.assert_jacobian_is_correct_fn(visual_hull.render, [voxels_init]) def test_render_preset(self): """Checks that render returns the expected value.""" x_voxels_init, y_images_init = test_helpers.generate_preset_test_voxels_visual_hull_render( ) voxels = tf.convert_to_tensor(value=x_voxels_init) y_images = tf.convert_to_tensor(value=y_images_init) y = visual_hull.render(voxels) self.assertAllClose(y_images, y) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/io/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """`tensorflow_graphics.io` module.""" # pylint: disable=g-import-not-at-top from tensorflow_graphics.util.doc import _import_tfg_docs if _import_tfg_docs(): from tensorflow_graphics.io import triangle_mesh from tensorflow_graphics.io import exr from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering.reflectance. __all__ = _export_api.get_modules() # pylint: enable=g-import-not-at-top

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """`tensorflow_graphics.io` module.""" # pylint: disable=g-import-not-at-top from tensorflow_graphics.util.doc import _import_tfg_docs if _import_tfg_docs(): from tensorflow_graphics.io import triangle_mesh from tensorflow_graphics.io import exr from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering.reflectance. __all__ = _export_api.get_modules() # pylint: enable=g-import-not-at-top

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/transformation/tests/linear_blend_skinning_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for linear blend skinning.""" # pylint: disable=line-too-long from absl.testing import flagsaver from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.geometry.transformation import linear_blend_skinning from tensorflow_graphics.geometry.transformation.tests import test_helpers from tensorflow_graphics.util import test_case class LinearBlendSkinningTest(test_case.TestCase): # pyformat: disable @parameterized.parameters( ((3,), (7,), (7, 3, 3), (7, 3)), ((None, 3), (None, 9), (None, 9, 3, 3), (None, 9, 3)), ((7, 1, 3), (1, 4, 11), (5, 11, 3, 3), (1, 11, 3)), ((7, 4, 3), (4, 11), (11, 3, 3), (11, 3)), ((3,), (5, 4, 11), (11, 3, 3), (11, 3)), ) # pyformat: enable def test_blend_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(linear_blend_skinning.blend, shapes) # pyformat: disable @parameterized.parameters( ("points must have exactly 3 dimensions in axis -1", (None,), (7,), (7, 3, 3), (7, 3)), ("bone_rotations must have a rank greater than 2", (3,), (7,), (3, 3), (3,)), ("bone_rotations must have exactly 3 dimensions in axis -1", (3,), (7,), (7, 3, None), (7, 3)), ("bone_rotations must have exactly 3 dimensions in axis -2", (3,), (7,), (7, None, 3), (7, 3)), ("bone_translations must have a rank greater than 1", (3,), (7,), (7, 3, 3), (3,)), ("bone_translations must have exactly 3 dimensions in axis -1", (3,), (7,), (7, 3, 3), (7, None)), (r"Tensors \[\'skinning_weights\', \'bone_rotations\'\] must have the same number of dimensions in axes", (3,), (9,), (7, 3, 3), (9, 3)), (r"Tensors \[\'skinning_weights\', \'bone_translations\'\] must have the same number of dimensions in axes", (3,), (9,), (9, 3, 3), (7, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3, 3), (3, 1, 7), (7, 3, 3), (7, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3, 3), (2, 1, 7), (3, 7, 3, 3), (2, 7, 3)), ) # pyformat: enable def test_blend_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(linear_blend_skinning.blend, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_blend_jacobian_random(self): """Test the Jacobian of the blend function.""" (x_points_init, x_weights_init, x_rotations_init, x_translations_init) = test_helpers.generate_random_test_lbs_blend() self.assert_jacobian_is_correct_fn( linear_blend_skinning.blend, [x_points_init, x_weights_init, x_rotations_init, x_translations_init]) def test_blend_preset(self): """Checks that blend returns the expected value.""" (x_points_init, x_weights_init, x_rotations_init, x_translations_init, y_blended_points_init) = test_helpers.generate_preset_test_lbs_blend() x_points = tf.convert_to_tensor(value=x_points_init) x_weights = tf.convert_to_tensor(value=x_weights_init) x_rotations = tf.convert_to_tensor(value=x_rotations_init) x_translations = tf.convert_to_tensor(value=x_translations_init) y_blended_points = tf.convert_to_tensor(value=y_blended_points_init) y = linear_blend_skinning.blend(x_points, x_weights, x_rotations, x_translations) self.assertAllClose(y_blended_points, y) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for linear blend skinning.""" # pylint: disable=line-too-long from absl.testing import flagsaver from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.geometry.transformation import linear_blend_skinning from tensorflow_graphics.geometry.transformation.tests import test_helpers from tensorflow_graphics.util import test_case class LinearBlendSkinningTest(test_case.TestCase): # pyformat: disable @parameterized.parameters( ((3,), (7,), (7, 3, 3), (7, 3)), ((None, 3), (None, 9), (None, 9, 3, 3), (None, 9, 3)), ((7, 1, 3), (1, 4, 11), (5, 11, 3, 3), (1, 11, 3)), ((7, 4, 3), (4, 11), (11, 3, 3), (11, 3)), ((3,), (5, 4, 11), (11, 3, 3), (11, 3)), ) # pyformat: enable def test_blend_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(linear_blend_skinning.blend, shapes) # pyformat: disable @parameterized.parameters( ("points must have exactly 3 dimensions in axis -1", (None,), (7,), (7, 3, 3), (7, 3)), ("bone_rotations must have a rank greater than 2", (3,), (7,), (3, 3), (3,)), ("bone_rotations must have exactly 3 dimensions in axis -1", (3,), (7,), (7, 3, None), (7, 3)), ("bone_rotations must have exactly 3 dimensions in axis -2", (3,), (7,), (7, None, 3), (7, 3)), ("bone_translations must have a rank greater than 1", (3,), (7,), (7, 3, 3), (3,)), ("bone_translations must have exactly 3 dimensions in axis -1", (3,), (7,), (7, 3, 3), (7, None)), (r"Tensors \[\'skinning_weights\', \'bone_rotations\'\] must have the same number of dimensions in axes", (3,), (9,), (7, 3, 3), (9, 3)), (r"Tensors \[\'skinning_weights\', \'bone_translations\'\] must have the same number of dimensions in axes", (3,), (9,), (9, 3, 3), (7, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3, 3), (3, 1, 7), (7, 3, 3), (7, 3)), ("Not all batch dimensions are broadcast-compatible", (2, 3, 3), (2, 1, 7), (3, 7, 3, 3), (2, 7, 3)), ) # pyformat: enable def test_blend_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(linear_blend_skinning.blend, error_msg, shapes) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_blend_jacobian_random(self): """Test the Jacobian of the blend function.""" (x_points_init, x_weights_init, x_rotations_init, x_translations_init) = test_helpers.generate_random_test_lbs_blend() self.assert_jacobian_is_correct_fn( linear_blend_skinning.blend, [x_points_init, x_weights_init, x_rotations_init, x_translations_init]) def test_blend_preset(self): """Checks that blend returns the expected value.""" (x_points_init, x_weights_init, x_rotations_init, x_translations_init, y_blended_points_init) = test_helpers.generate_preset_test_lbs_blend() x_points = tf.convert_to_tensor(value=x_points_init) x_weights = tf.convert_to_tensor(value=x_weights_init) x_rotations = tf.convert_to_tensor(value=x_rotations_init) x_translations = tf.convert_to_tensor(value=x_translations_init) y_blended_points = tf.convert_to_tensor(value=y_blended_points_init) y = linear_blend_skinning.blend(x_points, x_weights, x_rotations, x_translations) self.assertAllClose(y_blended_points, y) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/transformation/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/image/tests/transformer_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for image transformation functionalities.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_addons import image as tfa_image from tensorflow_graphics.image import transformer from tensorflow_graphics.util import test_case class TransformerTest(test_case.TestCase, parameterized.TestCase): @parameterized.parameters( ((None, 1, 2, None), (None, 3, 3)), ((1, 2, 3, 4), (1, 3, 3)), ) def test_perspective_transform_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(transformer.perspective_transform, shape) @parameterized.parameters( ("must have a rank of 4.", (1, 1, 1), (1, 3, 3)), ("must have a rank of 3.", (1, 1, 1, 1), (3, 3)), ("Not all batch dimensions are identical.", (1, 1, 1, 1), (2, 3, 3)), ) def test_perspective_transform_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(transformer.perspective_transform, error_msg, shape) @parameterized.parameters( (tf.float32, "NEAREST"), (tf.float64, "NEAREST"), (tf.float32, "BILINEAR"), (tf.float64, "BILINEAR"), ) def test_perspective_transform_half_integer_centers_preset( self, dtype, interpolation): """Tests that we can reproduce the results of tf.image.resize.""" image = tf.constant( ((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)), dtype=dtype) scale = 3 transformation = tf.constant( ((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)), dtype=dtype) image_shape = tf.shape(image) image_resized_shape = image_shape * scale image = image[tf.newaxis, ..., tf.newaxis] transformation = transformation[tf.newaxis, ...] image_resized = tf.image.resize( image, size=image_resized_shape, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR if interpolation == "NEAREST" else tf.image.ResizeMethod.BILINEAR) image_transformed = transformer.perspective_transform( image, transformation, resampling_type=transformer.ResamplingType.NEAREST if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR, border_type=transformer.BorderType.DUPLICATE, output_shape=image_resized_shape) self.assertAllClose(image_resized, image_transformed) @parameterized.parameters( (tf.float32, "NEAREST"), (tf.float64, "NEAREST"), (tf.float32, "BILINEAR"), (tf.float64, "BILINEAR"), ) def test_perspective_transform_integer_centers_preset(self, dtype, interpolation): """Tests that we can reproduce the results of tfa_image.transform.""" image = tf.constant( ((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)), dtype=dtype) scale = 3 transformation = tf.constant( ((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)), dtype=dtype) image_shape = tf.shape(image) image_resized_shape = image_shape * scale image = image[tf.newaxis, ..., tf.newaxis] transformation = transformation[tf.newaxis, ...] image_resized = tfa_image.transform( tf.cast(image, tf.float32), tf.cast( tfa_image.transform_ops.matrices_to_flat_transforms(transformation), tf.float32), interpolation=interpolation, output_shape=image_resized_shape) image_transformed = transformer.perspective_transform( image, transformation, resampling_type=transformer.ResamplingType.NEAREST if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR, pixel_type=transformer.PixelType.INTEGER, output_shape=image_resized_shape) self.assertAllClose(image_resized, image_transformed) def test_perspective_transform_jacobian_random(self): """Tests the Jacobian of the transform function.""" tensor_shape = np.random.randint(2, 4, size=4) image_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist()) transformation_init = np.random.uniform( 0.0, 1.0, size=(tensor_shape[0], 3, 3)) self.assert_jacobian_is_correct_fn( lambda x: transformer.perspective_transform(x, transformation_init), [image_init]) self.assert_jacobian_is_correct_fn( lambda x: transformer.perspective_transform(image_init, x), [transformation_init]) @parameterized.parameters( ((None, 1, 2, None), (None, 2)), ((1, 3, 2, 4), (1, 2)), ) def test_sample_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(transformer.sample, shape) @parameterized.parameters( ("must have a rank of 4.", (1, 1, 1), (1, 2)), ("must have a rank greater than 1", (1, 1, 1, 1), (2,)), ("Not all batch dimensions are identical.", (1, 1, 1, 1), (2, 2)), ) def test_sample_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(transformer.sample, error_msg, shape) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for image transformation functionalities.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_addons import image as tfa_image from tensorflow_graphics.image import transformer from tensorflow_graphics.util import test_case class TransformerTest(test_case.TestCase, parameterized.TestCase): @parameterized.parameters( ((None, 1, 2, None), (None, 3, 3)), ((1, 2, 3, 4), (1, 3, 3)), ) def test_perspective_transform_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(transformer.perspective_transform, shape) @parameterized.parameters( ("must have a rank of 4.", (1, 1, 1), (1, 3, 3)), ("must have a rank of 3.", (1, 1, 1, 1), (3, 3)), ("Not all batch dimensions are identical.", (1, 1, 1, 1), (2, 3, 3)), ) def test_perspective_transform_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(transformer.perspective_transform, error_msg, shape) @parameterized.parameters( (tf.float32, "NEAREST"), (tf.float64, "NEAREST"), (tf.float32, "BILINEAR"), (tf.float64, "BILINEAR"), ) def test_perspective_transform_half_integer_centers_preset( self, dtype, interpolation): """Tests that we can reproduce the results of tf.image.resize.""" image = tf.constant( ((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)), dtype=dtype) scale = 3 transformation = tf.constant( ((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)), dtype=dtype) image_shape = tf.shape(image) image_resized_shape = image_shape * scale image = image[tf.newaxis, ..., tf.newaxis] transformation = transformation[tf.newaxis, ...] image_resized = tf.image.resize( image, size=image_resized_shape, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR if interpolation == "NEAREST" else tf.image.ResizeMethod.BILINEAR) image_transformed = transformer.perspective_transform( image, transformation, resampling_type=transformer.ResamplingType.NEAREST if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR, border_type=transformer.BorderType.DUPLICATE, output_shape=image_resized_shape) self.assertAllClose(image_resized, image_transformed) @parameterized.parameters( (tf.float32, "NEAREST"), (tf.float64, "NEAREST"), (tf.float32, "BILINEAR"), (tf.float64, "BILINEAR"), ) def test_perspective_transform_integer_centers_preset(self, dtype, interpolation): """Tests that we can reproduce the results of tfa_image.transform.""" image = tf.constant( ((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)), dtype=dtype) scale = 3 transformation = tf.constant( ((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)), dtype=dtype) image_shape = tf.shape(image) image_resized_shape = image_shape * scale image = image[tf.newaxis, ..., tf.newaxis] transformation = transformation[tf.newaxis, ...] image_resized = tfa_image.transform( tf.cast(image, tf.float32), tf.cast( tfa_image.transform_ops.matrices_to_flat_transforms(transformation), tf.float32), interpolation=interpolation, output_shape=image_resized_shape) image_transformed = transformer.perspective_transform( image, transformation, resampling_type=transformer.ResamplingType.NEAREST if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR, pixel_type=transformer.PixelType.INTEGER, output_shape=image_resized_shape) self.assertAllClose(image_resized, image_transformed) def test_perspective_transform_jacobian_random(self): """Tests the Jacobian of the transform function.""" tensor_shape = np.random.randint(2, 4, size=4) image_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist()) transformation_init = np.random.uniform( 0.0, 1.0, size=(tensor_shape[0], 3, 3)) self.assert_jacobian_is_correct_fn( lambda x: transformer.perspective_transform(x, transformation_init), [image_init]) self.assert_jacobian_is_correct_fn( lambda x: transformer.perspective_transform(image_init, x), [transformation_init]) @parameterized.parameters( ((None, 1, 2, None), (None, 2)), ((1, 3, 2, 4), (1, 2)), ) def test_sample_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(transformer.sample, shape) @parameterized.parameters( ("must have a rank of 4.", (1, 1, 1), (1, 2)), ("must have a rank greater than 1", (1, 1, 1, 1), (2,)), ("Not all batch dimensions are identical.", (1, 1, 1, 1), (2, 2)), ) def test_sample_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(transformer.sample, error_msg, shape) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/triangle_rasterizer.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements a differentiable rasterizer of triangular meshes. The resulting rendering contains perspective-correct interpolation of attributes defined at the vertices of the rasterized meshes. This rasterizer does not provide gradients through visibility, but it does through visible geometry and attributes. """ import tensorflow as tf from tensorflow_graphics.rendering import rasterization_backend from tensorflow_graphics.rendering.opengl import math as glm from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def _perspective_correct_barycentrics(vertices_per_pixel, model_to_eye_matrix, perspective_matrix, image_size_float): """Creates the pixels grid and computes barycentrics.""" # Construct the pixel grid with half-integer pixel centers. width = image_size_float[1] height = image_size_float[0] px = tf.linspace(0.5, width - 0.5, num=int(width)) py = tf.linspace(0.5, height - 0.5, num=int(height)) xv, yv = tf.meshgrid(px, py) pixel_position = tf.stack((xv, yv), axis=-1) return glm.perspective_correct_barycentrics(vertices_per_pixel, pixel_position, model_to_eye_matrix, perspective_matrix, (width, height)) def _perspective_correct_attributes(attribute, barycentrics, triangles, triangle_index, len_batch_shape): attribute = tf.gather(attribute, triangles, axis=-2) attribute_per_pixel = tf.gather( attribute, triangle_index, axis=-3, batch_dims=len_batch_shape) return glm.interpolate_attributes(attribute_per_pixel, barycentrics) def _dim_value(dim): return 1 if dim is None else tf.compat.v1.dimension_value(dim) def rasterize(vertices, triangles, attributes, model_to_eye_matrix, perspective_matrix, image_size, backend=rasterization_backend.RasterizationBackends.OPENGL, name=None): """Rasterizes the scene. Note: In the following, A1 to An are optional batch dimensions. Args: vertices: A tensor of shape `[A1, ..., An, V, 3]` containing batches of `V` vertices, each defined by a 3D point. triangles: A tensor of shape `[T, 3]` containing `T` triangles, each associated with 3 vertices from `vertices`. attributes: A dictionary of tensors, each of shape `[A1, ..., An, V, K_a]` containing batches of `V` vertices, each associated with K-dimensional attributes. K_a may vary by attribute. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]` containing batches of matrices used to transform vertices from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]` containing batches of matrices used to project vertices from eye to clip coordinates. image_size: A tuple (height, width) containing the dimensions in pixels of the rasterized image. backend: A rasterization_backend.RasterizationBackends enum containing the backend method to use for rasterization. name: A name for this op. Defaults to 'triangle_rasterizer_rasterize'. Returns: A dictionary. The key "mask" is of shape `[A1, ..., An, height, width, 1]` and stores a value of `0` of the pixel is assciated with the background, and `1` with the foreground. The key "barycentrics" is of shape `[A1, ..., An, height, width, 3]` and stores barycentric weights. Finally, the dictionary contains perspective correct interpolated attributes of shape `[A1, ..., An, height, width, K]` per entry in the `attributes` dictionary. """ with tf.compat.v1.name_scope(name, "triangle_rasterizer_rasterize", (vertices, triangles, attributes, model_to_eye_matrix, perspective_matrix)): vertices = tf.convert_to_tensor(value=vertices) triangles = tf.convert_to_tensor(value=triangles) model_to_eye_matrix = tf.convert_to_tensor(value=model_to_eye_matrix) perspective_matrix = tf.convert_to_tensor(value=perspective_matrix) shape.check_static( tensor=vertices, tensor_name="vertices", has_rank_greater_than=1, has_dim_equals=((-1, 3))) shape.check_static( tensor=triangles, tensor_name="triangles", has_rank=2, has_dim_equals=((-1, 3))) shape.check_static( tensor=model_to_eye_matrix, tensor_name="model_to_eye_matrix", has_dim_equals=(((-2, 4), (-1, 4)))) shape.check_static( tensor=perspective_matrix, tensor_name="perspective_matrix", has_dim_equals=(((-2, 4), (-1, 4)))) image_size_float = (float(image_size[0]), float(image_size[1])) image_size_backend = (int(image_size[1]), int(image_size[0])) view_projection_matrix = tf.linalg.matmul(perspective_matrix, model_to_eye_matrix) rasterized = rasterization_backend.rasterize(vertices, triangles, view_projection_matrix, image_size_backend, backend) outputs = { "mask": rasterized.foreground_mask, "triangle_indices": rasterized.triangle_id } # Extract batch shape in order to make sure it is preserved after `gather` # operation. batch_shape = rasterized.triangle_id.shape[:-3] batch_shape = [_dim_value(dim) for dim in batch_shape] vertices_per_pixel = tf.gather( vertices, rasterized.vertex_ids, batch_dims=len(batch_shape)) barycentrics = _perspective_correct_barycentrics(vertices_per_pixel, model_to_eye_matrix, perspective_matrix, image_size_float) mask_float = tf.cast(rasterized.foreground_mask, vertices.dtype) outputs["barycentrics"] = mask_float * barycentrics for key, attribute in attributes.items(): attribute = tf.convert_to_tensor(value=attribute) outputs[key] = mask_float * _perspective_correct_attributes( attribute, barycentrics, triangles, rasterized.triangle_id[..., 0], len(batch_shape)) return outputs # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements a differentiable rasterizer of triangular meshes. The resulting rendering contains perspective-correct interpolation of attributes defined at the vertices of the rasterized meshes. This rasterizer does not provide gradients through visibility, but it does through visible geometry and attributes. """ import tensorflow as tf from tensorflow_graphics.rendering import rasterization_backend from tensorflow_graphics.rendering.opengl import math as glm from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def _perspective_correct_barycentrics(vertices_per_pixel, model_to_eye_matrix, perspective_matrix, image_size_float): """Creates the pixels grid and computes barycentrics.""" # Construct the pixel grid with half-integer pixel centers. width = image_size_float[1] height = image_size_float[0] px = tf.linspace(0.5, width - 0.5, num=int(width)) py = tf.linspace(0.5, height - 0.5, num=int(height)) xv, yv = tf.meshgrid(px, py) pixel_position = tf.stack((xv, yv), axis=-1) return glm.perspective_correct_barycentrics(vertices_per_pixel, pixel_position, model_to_eye_matrix, perspective_matrix, (width, height)) def _perspective_correct_attributes(attribute, barycentrics, triangles, triangle_index, len_batch_shape): attribute = tf.gather(attribute, triangles, axis=-2) attribute_per_pixel = tf.gather( attribute, triangle_index, axis=-3, batch_dims=len_batch_shape) return glm.interpolate_attributes(attribute_per_pixel, barycentrics) def _dim_value(dim): return 1 if dim is None else tf.compat.v1.dimension_value(dim) def rasterize(vertices, triangles, attributes, model_to_eye_matrix, perspective_matrix, image_size, backend=rasterization_backend.RasterizationBackends.OPENGL, name=None): """Rasterizes the scene. Note: In the following, A1 to An are optional batch dimensions. Args: vertices: A tensor of shape `[A1, ..., An, V, 3]` containing batches of `V` vertices, each defined by a 3D point. triangles: A tensor of shape `[T, 3]` containing `T` triangles, each associated with 3 vertices from `vertices`. attributes: A dictionary of tensors, each of shape `[A1, ..., An, V, K_a]` containing batches of `V` vertices, each associated with K-dimensional attributes. K_a may vary by attribute. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]` containing batches of matrices used to transform vertices from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]` containing batches of matrices used to project vertices from eye to clip coordinates. image_size: A tuple (height, width) containing the dimensions in pixels of the rasterized image. backend: A rasterization_backend.RasterizationBackends enum containing the backend method to use for rasterization. name: A name for this op. Defaults to 'triangle_rasterizer_rasterize'. Returns: A dictionary. The key "mask" is of shape `[A1, ..., An, height, width, 1]` and stores a value of `0` of the pixel is assciated with the background, and `1` with the foreground. The key "barycentrics" is of shape `[A1, ..., An, height, width, 3]` and stores barycentric weights. Finally, the dictionary contains perspective correct interpolated attributes of shape `[A1, ..., An, height, width, K]` per entry in the `attributes` dictionary. """ with tf.compat.v1.name_scope(name, "triangle_rasterizer_rasterize", (vertices, triangles, attributes, model_to_eye_matrix, perspective_matrix)): vertices = tf.convert_to_tensor(value=vertices) triangles = tf.convert_to_tensor(value=triangles) model_to_eye_matrix = tf.convert_to_tensor(value=model_to_eye_matrix) perspective_matrix = tf.convert_to_tensor(value=perspective_matrix) shape.check_static( tensor=vertices, tensor_name="vertices", has_rank_greater_than=1, has_dim_equals=((-1, 3))) shape.check_static( tensor=triangles, tensor_name="triangles", has_rank=2, has_dim_equals=((-1, 3))) shape.check_static( tensor=model_to_eye_matrix, tensor_name="model_to_eye_matrix", has_dim_equals=(((-2, 4), (-1, 4)))) shape.check_static( tensor=perspective_matrix, tensor_name="perspective_matrix", has_dim_equals=(((-2, 4), (-1, 4)))) image_size_float = (float(image_size[0]), float(image_size[1])) image_size_backend = (int(image_size[1]), int(image_size[0])) view_projection_matrix = tf.linalg.matmul(perspective_matrix, model_to_eye_matrix) rasterized = rasterization_backend.rasterize(vertices, triangles, view_projection_matrix, image_size_backend, backend) outputs = { "mask": rasterized.foreground_mask, "triangle_indices": rasterized.triangle_id } # Extract batch shape in order to make sure it is preserved after `gather` # operation. batch_shape = rasterized.triangle_id.shape[:-3] batch_shape = [_dim_value(dim) for dim in batch_shape] vertices_per_pixel = tf.gather( vertices, rasterized.vertex_ids, batch_dims=len(batch_shape)) barycentrics = _perspective_correct_barycentrics(vertices_per_pixel, model_to_eye_matrix, perspective_matrix, image_size_float) mask_float = tf.cast(rasterized.foreground_mask, vertices.dtype) outputs["barycentrics"] = mask_float * barycentrics for key, attribute in attributes.items(): attribute = tf.convert_to_tensor(value=attribute) outputs[key] = mask_float * _perspective_correct_attributes( attribute, barycentrics, triangles, rasterized.triangle_id[..., 0], len(batch_shape)) return outputs # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/tests/grid_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for grid.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation import grid from tensorflow_graphics.util import test_case class GridTest(test_case.TestCase): @parameterized.parameters( (((1,), (1,), (1,)), (tf.float32, tf.float32, tf.int32)), (((1, 1), (1, 1), (1,)), (tf.float32, tf.float32, tf.int32)), ) def test_generate_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(grid.generate, shapes, dtypes) @parameterized.parameters( ("starts must have a rank greater than 0", (), (None,), (None,)), ("stops must have a rank greater than 0", (None,), (), (None,)), ("nums must have a rank of 1", (None,), (None,), ()), ("Not all batch dimensions are identical.", (1,), (0,), (1,)), ("Not all batch dimensions are identical.", (0,), (1,), (1,)), ("must have the same number of dimensions", (1,), (1,), (0,)), ) def test_generate_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_raised(grid.generate, error_msg, shapes) @parameterized.parameters( (((-1.,), (1.,), (3,)), (((-1.,), (0.,), (1.,)),)), ((((-1.,), (-1.,)), ((1.,), (1.,)), (1,)), ((((-1.,),), ((-1.,),)),)), ) def test_generate_preset(self, test_inputs, test_outputs): """Test the uniform grid generation using fix test cases.""" self.assert_output_is_correct( grid.generate, test_inputs, test_outputs, tile=False) def test_generate_random(self): """Test the uniform grid generation.""" starts = np.array((0., 0.), dtype=np.float32) stops = np.random.randint(1, 10, size=(2)) nums = stops + 1 stops = stops.astype(np.float32) g = grid.generate(starts, stops, nums) shape = nums.tolist() + [2] xv, yv = np.meshgrid(range(shape[0]), range(shape[1]), indexing="ij") gt = np.stack((xv, yv), axis=-1).astype(np.float32) self.assertAllClose(g, gt) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for grid.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation import grid from tensorflow_graphics.util import test_case class GridTest(test_case.TestCase): @parameterized.parameters( (((1,), (1,), (1,)), (tf.float32, tf.float32, tf.int32)), (((1, 1), (1, 1), (1,)), (tf.float32, tf.float32, tf.int32)), ) def test_generate_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(grid.generate, shapes, dtypes) @parameterized.parameters( ("starts must have a rank greater than 0", (), (None,), (None,)), ("stops must have a rank greater than 0", (None,), (), (None,)), ("nums must have a rank of 1", (None,), (None,), ()), ("Not all batch dimensions are identical.", (1,), (0,), (1,)), ("Not all batch dimensions are identical.", (0,), (1,), (1,)), ("must have the same number of dimensions", (1,), (1,), (0,)), ) def test_generate_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_raised(grid.generate, error_msg, shapes) @parameterized.parameters( (((-1.,), (1.,), (3,)), (((-1.,), (0.,), (1.,)),)), ((((-1.,), (-1.,)), ((1.,), (1.,)), (1,)), ((((-1.,),), ((-1.,),)),)), ) def test_generate_preset(self, test_inputs, test_outputs): """Test the uniform grid generation using fix test cases.""" self.assert_output_is_correct( grid.generate, test_inputs, test_outputs, tile=False) def test_generate_random(self): """Test the uniform grid generation.""" starts = np.array((0., 0.), dtype=np.float32) stops = np.random.randint(1, 10, size=(2)) nums = stops + 1 stops = stops.astype(np.float32) g = grid.generate(starts, stops, nums) shape = nums.tolist() + [2] xv, yv = np.meshgrid(range(shape[0]), range(shape[1]), indexing="ij") gt = np.stack((xv, yv), axis=-1).astype(np.float32) self.assertAllClose(g, gt) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/light/point_light.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the rendering equation for a point light.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def estimate_radiance(point_light_radiance, point_light_position, surface_point_position, surface_point_normal, observation_point, brdf, name=None, reflected_light_fall_off=False): """Estimates the spectral radiance of a point light reflected from the surface point towards the observation point. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. B1 to Bm are optional batch dimensions for the lights, which must be broadcast compatible. Note: In case the light or the observation point are located behind the surface the function will return 0. Note: The gradient of this function is not smooth when the dot product of the normal with the light-to-surface or surface-to-observation vectors is 0. Args: point_light_radiance: A tensor of shape '[B1, ..., Bm, K]', where the last axis represents the radiance of the point light at a specific wave length. point_light_position: A tensor of shape `[B1, ..., Bm, 3]`, where the last axis represents the position of the point light. surface_point_position: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the position of the surface point. surface_point_normal: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the normalized surface normal at the given surface point. observation_point: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the observation point. brdf: The BRDF of the surface as a function of: incoming_light_direction - The incoming light direction as the last axis of a tensor with shape `[A1, ..., An, 3]`. outgoing_light_direction - The outgoing light direction as the last axis of a tensor with shape `[A1, ..., An, 3]`. surface_point_normal - The surface normal as the last axis of a tensor with shape `[A1, ..., An, 3]`. Note - The BRDF should return a tensor of size '[A1, ..., An, K]' where the last axis represents the amount of reflected light in each wave length. name: A name for this op. Defaults to "estimate_radiance". reflected_light_fall_off: A boolean specifying whether or not to include the fall off of the light reflected from the surface towards the observation point in the calculation. Defaults to False. Returns: A tensor of shape `[A1, ..., An, B1, ..., Bm, K]`, where the last axis represents the amount of light received at the observation point after being reflected from the given surface point. Raises: ValueError: if the shape of `point_light_position`, `surface_point_position`, `surface_point_normal`, or `observation_point` is not supported. InvalidArgumentError: if 'surface_point_normal' is not normalized. """ with tf.compat.v1.name_scope(name, "estimate_radiance", [ point_light_radiance, point_light_position, surface_point_position, surface_point_normal, observation_point, brdf ]): point_light_radiance = tf.convert_to_tensor(value=point_light_radiance) point_light_position = tf.convert_to_tensor(value=point_light_position) surface_point_position = tf.convert_to_tensor(value=surface_point_position) surface_point_normal = tf.convert_to_tensor(value=surface_point_normal) observation_point = tf.convert_to_tensor(value=observation_point) shape.check_static( tensor=point_light_position, tensor_name="point_light_position", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_point_position, tensor_name="surface_point_position", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_point_normal, tensor_name="surface_point_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=observation_point, tensor_name="observation_point", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(surface_point_position, surface_point_normal, observation_point), tensor_names=("surface_point_position", "surface_point_normal", "observation_point"), last_axes=-2, broadcast_compatible=True) shape.compare_batch_dimensions( tensors=(point_light_radiance, point_light_position), tensor_names=("point_light_radiance", "point_light_position"), last_axes=-2, broadcast_compatible=True) surface_point_normal = asserts.assert_normalized(surface_point_normal) # Get the number of lights dimensions (B1,...,Bm). lights_num_dimensions = max( len(point_light_radiance.shape), len(point_light_position.shape)) - 1 # Reshape the other parameters so they can be broadcasted to the output of # shape [A1,...,An, B1,...,Bm, K]. surface_point_position = tf.reshape( surface_point_position, surface_point_position.shape[:-1] + (1,) * lights_num_dimensions + (3,)) surface_point_normal = tf.reshape( surface_point_normal, surface_point_normal.shape[:-1] + (1,) * lights_num_dimensions + (3,)) observation_point = tf.reshape( observation_point, observation_point.shape[:-1] + (1,) * lights_num_dimensions + (3,)) light_to_surface_point = surface_point_position - point_light_position distance_light_surface_point = tf.norm( tensor=light_to_surface_point, axis=-1, keepdims=True) incoming_light_direction = tf.math.l2_normalize( light_to_surface_point, axis=-1) surface_to_observation_point = observation_point - surface_point_position outgoing_light_direction = tf.math.l2_normalize( surface_to_observation_point, axis=-1) brdf_value = brdf(incoming_light_direction, outgoing_light_direction, surface_point_normal) incoming_light_dot_surface_normal = vector.dot(-incoming_light_direction, surface_point_normal) outgoing_light_dot_surface_normal = vector.dot(outgoing_light_direction, surface_point_normal) estimated_radiance = (point_light_radiance * \ brdf_value * incoming_light_dot_surface_normal) / \ (4. * math.pi * tf.math.square(distance_light_surface_point)) if reflected_light_fall_off: distance_surface_observation_point = tf.norm( tensor=surface_to_observation_point, axis=-1, keepdims=True) estimated_radiance = estimated_radiance / \ tf.math.square(distance_surface_observation_point) # Create a condition for checking whether the light or observation point are # behind the surface. min_dot = tf.minimum(incoming_light_dot_surface_normal, outgoing_light_dot_surface_normal) common_shape = shape.get_broadcasted_shape(min_dot.shape, estimated_radiance.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) return tf.compat.v1.where(condition, estimated_radiance, tf.zeros_like(estimated_radiance)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the rendering equation for a point light.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def estimate_radiance(point_light_radiance, point_light_position, surface_point_position, surface_point_normal, observation_point, brdf, name=None, reflected_light_fall_off=False): """Estimates the spectral radiance of a point light reflected from the surface point towards the observation point. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. B1 to Bm are optional batch dimensions for the lights, which must be broadcast compatible. Note: In case the light or the observation point are located behind the surface the function will return 0. Note: The gradient of this function is not smooth when the dot product of the normal with the light-to-surface or surface-to-observation vectors is 0. Args: point_light_radiance: A tensor of shape '[B1, ..., Bm, K]', where the last axis represents the radiance of the point light at a specific wave length. point_light_position: A tensor of shape `[B1, ..., Bm, 3]`, where the last axis represents the position of the point light. surface_point_position: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the position of the surface point. surface_point_normal: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the normalized surface normal at the given surface point. observation_point: A tensor of shape `[A1, ..., An, 3]`, where the last axis represents the observation point. brdf: The BRDF of the surface as a function of: incoming_light_direction - The incoming light direction as the last axis of a tensor with shape `[A1, ..., An, 3]`. outgoing_light_direction - The outgoing light direction as the last axis of a tensor with shape `[A1, ..., An, 3]`. surface_point_normal - The surface normal as the last axis of a tensor with shape `[A1, ..., An, 3]`. Note - The BRDF should return a tensor of size '[A1, ..., An, K]' where the last axis represents the amount of reflected light in each wave length. name: A name for this op. Defaults to "estimate_radiance". reflected_light_fall_off: A boolean specifying whether or not to include the fall off of the light reflected from the surface towards the observation point in the calculation. Defaults to False. Returns: A tensor of shape `[A1, ..., An, B1, ..., Bm, K]`, where the last axis represents the amount of light received at the observation point after being reflected from the given surface point. Raises: ValueError: if the shape of `point_light_position`, `surface_point_position`, `surface_point_normal`, or `observation_point` is not supported. InvalidArgumentError: if 'surface_point_normal' is not normalized. """ with tf.compat.v1.name_scope(name, "estimate_radiance", [ point_light_radiance, point_light_position, surface_point_position, surface_point_normal, observation_point, brdf ]): point_light_radiance = tf.convert_to_tensor(value=point_light_radiance) point_light_position = tf.convert_to_tensor(value=point_light_position) surface_point_position = tf.convert_to_tensor(value=surface_point_position) surface_point_normal = tf.convert_to_tensor(value=surface_point_normal) observation_point = tf.convert_to_tensor(value=observation_point) shape.check_static( tensor=point_light_position, tensor_name="point_light_position", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_point_position, tensor_name="surface_point_position", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_point_normal, tensor_name="surface_point_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=observation_point, tensor_name="observation_point", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(surface_point_position, surface_point_normal, observation_point), tensor_names=("surface_point_position", "surface_point_normal", "observation_point"), last_axes=-2, broadcast_compatible=True) shape.compare_batch_dimensions( tensors=(point_light_radiance, point_light_position), tensor_names=("point_light_radiance", "point_light_position"), last_axes=-2, broadcast_compatible=True) surface_point_normal = asserts.assert_normalized(surface_point_normal) # Get the number of lights dimensions (B1,...,Bm). lights_num_dimensions = max( len(point_light_radiance.shape), len(point_light_position.shape)) - 1 # Reshape the other parameters so they can be broadcasted to the output of # shape [A1,...,An, B1,...,Bm, K]. surface_point_position = tf.reshape( surface_point_position, surface_point_position.shape[:-1] + (1,) * lights_num_dimensions + (3,)) surface_point_normal = tf.reshape( surface_point_normal, surface_point_normal.shape[:-1] + (1,) * lights_num_dimensions + (3,)) observation_point = tf.reshape( observation_point, observation_point.shape[:-1] + (1,) * lights_num_dimensions + (3,)) light_to_surface_point = surface_point_position - point_light_position distance_light_surface_point = tf.norm( tensor=light_to_surface_point, axis=-1, keepdims=True) incoming_light_direction = tf.math.l2_normalize( light_to_surface_point, axis=-1) surface_to_observation_point = observation_point - surface_point_position outgoing_light_direction = tf.math.l2_normalize( surface_to_observation_point, axis=-1) brdf_value = brdf(incoming_light_direction, outgoing_light_direction, surface_point_normal) incoming_light_dot_surface_normal = vector.dot(-incoming_light_direction, surface_point_normal) outgoing_light_dot_surface_normal = vector.dot(outgoing_light_direction, surface_point_normal) estimated_radiance = (point_light_radiance * \ brdf_value * incoming_light_dot_surface_normal) / \ (4. * math.pi * tf.math.square(distance_light_surface_point)) if reflected_light_fall_off: distance_surface_observation_point = tf.norm( tensor=surface_to_observation_point, axis=-1, keepdims=True) estimated_radiance = estimated_radiance / \ tf.math.square(distance_surface_observation_point) # Create a condition for checking whether the light or observation point are # behind the surface. min_dot = tf.minimum(incoming_light_dot_surface_normal, outgoing_light_dot_surface_normal) common_shape = shape.get_broadcasted_shape(min_dot.shape, estimated_radiance.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) return tf.compat.v1.where(condition, estimated_radiance, tf.zeros_like(estimated_radiance)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/interpolation/slerp.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tensorflow.graphics slerp interpolation module. Spherical linear interpolation (slerp) is defined for both quaternions and for regular M-D vectors, and act slightly differently because of inherent ambiguity of quaternions. This module has two functions returning the interpolation weights for quaternions (quaternion_weights) and for vectors (vector_weights), which can then be used in a weighted sum to calculate the final interpolated quaternions and vectors. A helper interpolate function is also provided. The main differences between two methods are: vector_weights: can get any M-D tensor as input, does not expect normalized vectors as input, returns unnormalized outputs (in general) for unnormalized inputs. quaternion_weights: expects M-D tensors with a last dimension of 4, assumes normalized input, checks for ambiguity by looking at the angle between quaternions, returns normalized quaternions naturally. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import enum import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape class InterpolationType(enum.Enum): """Defines interpolation methods for slerp module.""" VECTOR = 0 QUATERNION = 1 def _safe_dot(vector1, vector2, eps): """Calculates dot product while ensuring it is in the range [-1, 1].""" dot_product = vector.dot(vector1, vector2) # Safely shrink to make sure machine precision does not cause the dot # product to be outside the [-1.0, 1.0] range. return safe_ops.safe_shrink( vector=dot_product, minval=-1.0, maxval=1.0, open_bounds=False, eps=eps) def interpolate(vector1, vector2, percent, method=InterpolationType.QUATERNION, eps=None, name=None): """Applies slerp to vectors or quaternions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. percent: A `float` or a tensor with shape broadcastable to the shape of input vectors. method: An enumerated constant from the class InterpolationType, which is either InterpolationType.QUATERNION (default) if the input vectors are 4-D quaternions, or InterpolationType.VECTOR if they are regular M-D vectors. eps: A small float for operation safety. If left None, its value is automatically selected using dtype of input vectors. name: A name for this op. Defaults to "vector_weights" or "quaternion_weights" depending on the method. Returns: A tensor of shape [A1, ... , An, M]` which stores the result of the interpolation. Raises: ValueError: if method is not amongst enumerated constants defined in InterpolationType. """ if method == InterpolationType.QUATERNION: weight1, weight2 = quaternion_weights( vector1, vector2, percent, eps=eps, name=name) elif method == InterpolationType.VECTOR: weight1, weight2 = vector_weights( vector1, vector2, percent, eps=eps, name=name) else: raise ValueError("Unknown interpolation type supplied.") return interpolate_with_weights(vector1, vector2, weight1, weight2) def interpolate_with_weights(vector1, vector2, weight1, weight2, name=None): """Interpolates vectors by taking their weighted sum. Interpolation for all variants of slerp is a simple weighted sum over inputs. Therefore this function simply returns weight1 * vector1 + weight2 * vector2. Note: In the following, A1 to An are optional batch dimensions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. weight1: A `float` or a tensor describing weights for the `vector1` and with a shape broadcastable to the shape of the input vectors. weight2: A `float` or a tensor describing weights for the `vector2` and with a shape broadcastable to the shape of the input vectors. name: A name for this op. Defaults to "interpolate_with_weights". Returns: A tensor of shape `[A1, ... , An, M]` containing the result of the interpolation. """ with tf.compat.v1.name_scope(name, "interpolate_with_weights", [vector1, vector2, weight1, weight2]): return weight1 * vector1 + weight2 * vector2 def quaternion_weights(quaternion1, quaternion2, percent, eps=None, name=None): """Calculates slerp weights for two normalized quaternions. Given a percent and two normalized quaternions, this function returns the slerp weights. It can also produce extrapolation weights when percent is outside of the [0, 1] range. It reduces to lerp when input quaternions are almost parallel or anti-parallel. Input quaternions are assumed to be normalized. The tf.graphics debug flag TFG_ADD_ASSERTS_TO_GRAPH defined in tfg_flags.py can be set to add assertions to the graph that check whether the inputs are normalized, and whether Inf or Nan values are produced. Note: In the following, A1 to An are optional batch dimensions. Args: quaternion1: A tensor of shape `[A1, ... , An, 4]` storing normalized quaternions in its last dimension. quaternion2: A tensor of shape `[A1, ... , An, 4]` storing normalized quaternions in its last dimension. percent: A `float` or a tensor with a shape broadcastable to the shape `[A1, ... , An]`. eps: A `float` used to make operations safe. When left as None, the function automatically picks the best epsilon based on the dtype and the operation. name: A name for this op. Defaults to "quaternion_weights". Raises: ValueError: If the shapes of quaternions do not match, if the last dimensions of quaternions are not 4, or if percent is neither a float, nor a tensor with last dimension 1. Returns: Two tensors of shape `[A1, ... , An, 1]` each, which are the two slerp weights for each quaternion. """ with tf.compat.v1.name_scope(name, "quaternion_weights", [quaternion1, quaternion2, percent]): quaternion1 = tf.convert_to_tensor(value=quaternion1) quaternion2 = tf.convert_to_tensor(value=quaternion2) percent = tf.convert_to_tensor(value=percent, dtype=quaternion1.dtype) if percent.shape.ndims == 0: percent = tf.expand_dims(percent, axis=0) shape.check_static( tensor=quaternion1, tensor_name="quaternion1", has_dim_equals=(-1, 4)) shape.check_static( tensor=quaternion2, tensor_name="quaternion2", has_dim_equals=(-1, 4)) shape.compare_batch_dimensions( tensors=(quaternion1, quaternion2, percent), last_axes=(-2, -2, -1), broadcast_compatible=True, tensor_names=("quaternion1", "quaternion2", "percent")) quaternion1 = asserts.assert_normalized(quaternion1) quaternion2 = asserts.assert_normalized(quaternion2) dot_product = _safe_dot(quaternion1, quaternion2, eps) # Take the shorter path theta = tf.acos(tf.abs(dot_product)) # safe_sinpx_div_sinx returns p for very small x, which means slerp reduces # to lerp automatically. scale1 = safe_ops.safe_sinpx_div_sinx(theta, 1.0 - percent, eps) scale2 = safe_ops.safe_sinpx_div_sinx(theta, percent, eps) # Flip the sign of scale1 if quaternions are in different hemispheres. # tf.sign can make scale1 zero if quaternions are orthogonal. scale1 *= safe_ops.nonzero_sign(dot_product) return scale1, scale2 def vector_weights(vector1, vector2, percent, eps=None, name=None): """Spherical linear interpolation (slerp) between two unnormalized vectors. This function applies geometric slerp to unnormalized vectors by first normalizing them to return the interpolation weights. It reduces to lerp when input vectors are exactly anti-parallel. Note: In the following, A1 to An are optional batch dimensions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. percent: A `float` or tensor with shape broadcastable to the shape of input vectors. eps: A small float for operation safety. If left None, its value is automatically selected using dtype of input vectors. name: A name for this op. Defaults to "vector_weights". Raises: ValueError: if the shape of `vector1`, `vector2`, or `percent` is not supported. Returns: Two tensors of shape `[A1, ... , An, 1]`, representing interpolation weights for each input vector. """ with tf.compat.v1.name_scope(name, "vector_weights", [vector1, vector2, percent]): vector1 = tf.convert_to_tensor(value=vector1) vector2 = tf.convert_to_tensor(value=vector2) percent = tf.convert_to_tensor(value=percent, dtype=vector1.dtype) if percent.shape.ndims == 0: percent = tf.expand_dims(percent, axis=0) shape.compare_dimensions( tensors=(vector1, vector2), axes=-1, tensor_names=("vector1", "vector2")) shape.compare_batch_dimensions( tensors=(vector1, vector2, percent), last_axes=(-2, -2, -1), broadcast_compatible=True, tensor_names=("vector1", "vector2", "percent")) normalized1 = tf.nn.l2_normalize(vector1, axis=-1) normalized2 = tf.nn.l2_normalize(vector2, axis=-1) dot_product = _safe_dot(normalized1, normalized2, eps) theta = tf.acos(dot_product) scale1 = safe_ops.safe_sinpx_div_sinx(theta, 1.0 - percent, eps) scale2 = safe_ops.safe_sinpx_div_sinx(theta, percent, eps) return scale1, scale2 # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tensorflow.graphics slerp interpolation module. Spherical linear interpolation (slerp) is defined for both quaternions and for regular M-D vectors, and act slightly differently because of inherent ambiguity of quaternions. This module has two functions returning the interpolation weights for quaternions (quaternion_weights) and for vectors (vector_weights), which can then be used in a weighted sum to calculate the final interpolated quaternions and vectors. A helper interpolate function is also provided. The main differences between two methods are: vector_weights: can get any M-D tensor as input, does not expect normalized vectors as input, returns unnormalized outputs (in general) for unnormalized inputs. quaternion_weights: expects M-D tensors with a last dimension of 4, assumes normalized input, checks for ambiguity by looking at the angle between quaternions, returns normalized quaternions naturally. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import enum import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape class InterpolationType(enum.Enum): """Defines interpolation methods for slerp module.""" VECTOR = 0 QUATERNION = 1 def _safe_dot(vector1, vector2, eps): """Calculates dot product while ensuring it is in the range [-1, 1].""" dot_product = vector.dot(vector1, vector2) # Safely shrink to make sure machine precision does not cause the dot # product to be outside the [-1.0, 1.0] range. return safe_ops.safe_shrink( vector=dot_product, minval=-1.0, maxval=1.0, open_bounds=False, eps=eps) def interpolate(vector1, vector2, percent, method=InterpolationType.QUATERNION, eps=None, name=None): """Applies slerp to vectors or quaternions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. percent: A `float` or a tensor with shape broadcastable to the shape of input vectors. method: An enumerated constant from the class InterpolationType, which is either InterpolationType.QUATERNION (default) if the input vectors are 4-D quaternions, or InterpolationType.VECTOR if they are regular M-D vectors. eps: A small float for operation safety. If left None, its value is automatically selected using dtype of input vectors. name: A name for this op. Defaults to "vector_weights" or "quaternion_weights" depending on the method. Returns: A tensor of shape [A1, ... , An, M]` which stores the result of the interpolation. Raises: ValueError: if method is not amongst enumerated constants defined in InterpolationType. """ if method == InterpolationType.QUATERNION: weight1, weight2 = quaternion_weights( vector1, vector2, percent, eps=eps, name=name) elif method == InterpolationType.VECTOR: weight1, weight2 = vector_weights( vector1, vector2, percent, eps=eps, name=name) else: raise ValueError("Unknown interpolation type supplied.") return interpolate_with_weights(vector1, vector2, weight1, weight2) def interpolate_with_weights(vector1, vector2, weight1, weight2, name=None): """Interpolates vectors by taking their weighted sum. Interpolation for all variants of slerp is a simple weighted sum over inputs. Therefore this function simply returns weight1 * vector1 + weight2 * vector2. Note: In the following, A1 to An are optional batch dimensions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. weight1: A `float` or a tensor describing weights for the `vector1` and with a shape broadcastable to the shape of the input vectors. weight2: A `float` or a tensor describing weights for the `vector2` and with a shape broadcastable to the shape of the input vectors. name: A name for this op. Defaults to "interpolate_with_weights". Returns: A tensor of shape `[A1, ... , An, M]` containing the result of the interpolation. """ with tf.compat.v1.name_scope(name, "interpolate_with_weights", [vector1, vector2, weight1, weight2]): return weight1 * vector1 + weight2 * vector2 def quaternion_weights(quaternion1, quaternion2, percent, eps=None, name=None): """Calculates slerp weights for two normalized quaternions. Given a percent and two normalized quaternions, this function returns the slerp weights. It can also produce extrapolation weights when percent is outside of the [0, 1] range. It reduces to lerp when input quaternions are almost parallel or anti-parallel. Input quaternions are assumed to be normalized. The tf.graphics debug flag TFG_ADD_ASSERTS_TO_GRAPH defined in tfg_flags.py can be set to add assertions to the graph that check whether the inputs are normalized, and whether Inf or Nan values are produced. Note: In the following, A1 to An are optional batch dimensions. Args: quaternion1: A tensor of shape `[A1, ... , An, 4]` storing normalized quaternions in its last dimension. quaternion2: A tensor of shape `[A1, ... , An, 4]` storing normalized quaternions in its last dimension. percent: A `float` or a tensor with a shape broadcastable to the shape `[A1, ... , An]`. eps: A `float` used to make operations safe. When left as None, the function automatically picks the best epsilon based on the dtype and the operation. name: A name for this op. Defaults to "quaternion_weights". Raises: ValueError: If the shapes of quaternions do not match, if the last dimensions of quaternions are not 4, or if percent is neither a float, nor a tensor with last dimension 1. Returns: Two tensors of shape `[A1, ... , An, 1]` each, which are the two slerp weights for each quaternion. """ with tf.compat.v1.name_scope(name, "quaternion_weights", [quaternion1, quaternion2, percent]): quaternion1 = tf.convert_to_tensor(value=quaternion1) quaternion2 = tf.convert_to_tensor(value=quaternion2) percent = tf.convert_to_tensor(value=percent, dtype=quaternion1.dtype) if percent.shape.ndims == 0: percent = tf.expand_dims(percent, axis=0) shape.check_static( tensor=quaternion1, tensor_name="quaternion1", has_dim_equals=(-1, 4)) shape.check_static( tensor=quaternion2, tensor_name="quaternion2", has_dim_equals=(-1, 4)) shape.compare_batch_dimensions( tensors=(quaternion1, quaternion2, percent), last_axes=(-2, -2, -1), broadcast_compatible=True, tensor_names=("quaternion1", "quaternion2", "percent")) quaternion1 = asserts.assert_normalized(quaternion1) quaternion2 = asserts.assert_normalized(quaternion2) dot_product = _safe_dot(quaternion1, quaternion2, eps) # Take the shorter path theta = tf.acos(tf.abs(dot_product)) # safe_sinpx_div_sinx returns p for very small x, which means slerp reduces # to lerp automatically. scale1 = safe_ops.safe_sinpx_div_sinx(theta, 1.0 - percent, eps) scale2 = safe_ops.safe_sinpx_div_sinx(theta, percent, eps) # Flip the sign of scale1 if quaternions are in different hemispheres. # tf.sign can make scale1 zero if quaternions are orthogonal. scale1 *= safe_ops.nonzero_sign(dot_product) return scale1, scale2 def vector_weights(vector1, vector2, percent, eps=None, name=None): """Spherical linear interpolation (slerp) between two unnormalized vectors. This function applies geometric slerp to unnormalized vectors by first normalizing them to return the interpolation weights. It reduces to lerp when input vectors are exactly anti-parallel. Note: In the following, A1 to An are optional batch dimensions. Args: vector1: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. vector2: A tensor of shape `[A1, ... , An, M]`, which stores a normalized vector in its last dimension. percent: A `float` or tensor with shape broadcastable to the shape of input vectors. eps: A small float for operation safety. If left None, its value is automatically selected using dtype of input vectors. name: A name for this op. Defaults to "vector_weights". Raises: ValueError: if the shape of `vector1`, `vector2`, or `percent` is not supported. Returns: Two tensors of shape `[A1, ... , An, 1]`, representing interpolation weights for each input vector. """ with tf.compat.v1.name_scope(name, "vector_weights", [vector1, vector2, percent]): vector1 = tf.convert_to_tensor(value=vector1) vector2 = tf.convert_to_tensor(value=vector2) percent = tf.convert_to_tensor(value=percent, dtype=vector1.dtype) if percent.shape.ndims == 0: percent = tf.expand_dims(percent, axis=0) shape.compare_dimensions( tensors=(vector1, vector2), axes=-1, tensor_names=("vector1", "vector2")) shape.compare_batch_dimensions( tensors=(vector1, vector2, percent), last_axes=(-2, -2, -1), broadcast_compatible=True, tensor_names=("vector1", "vector2", "percent")) normalized1 = tf.nn.l2_normalize(vector1, axis=-1) normalized2 = tf.nn.l2_normalize(vector2, axis=-1) dot_product = _safe_dot(normalized1, normalized2, eps) theta = tf.acos(dot_product) scale1 = safe_ops.safe_sinpx_div_sinx(theta, 1.0 - percent, eps) scale2 = safe_ops.safe_sinpx_div_sinx(theta, percent, eps) return scale1, scale2 # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Projects module."""

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Projects module."""

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/shapenet/shapenet.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Shapenet Core dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import csv import json import os import textwrap import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds from tensorflow_datasets import features as tfds_features from tensorflow_graphics.datasets import features as tfg_features _CITATION = """ @techreport{shapenet2015, title = {{ShapeNet: An Information-Rich 3D Model Repository}}, author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher}, number = {arXiv:1512.03012 [cs.GR]}, institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago}, year = {2015} } """ _DESCRIPTION = """ ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore is linked to an appropriate synset in WordNet (version 3.0). The synsets will be extracted from the taxonomy.json file in the ShapeNetCore.v2.zip archive and the splits from http://shapenet.cs.stanford.edu/shapenet/obj-zip/SHREC16/all.csv """ _TAXONOMY_FILE_NAME = 'taxonomy.json' _SPLIT_FILE_URL = \ 'http://shapenet.cs.stanford.edu/shapenet/obj-zip/SHREC16/all.csv' class ShapenetConfig(tfds.core.BuilderConfig): """Base class for Shapenet BuilderConfigs. The Shapenet database builder delegates the implementation of info, split_generators and generate_examples to the specified ShapenetConfig. This is done to allow multiple versions of the dataset. """ def info(self, dataset_builder): """Delegated Shapenet._info.""" raise NotImplementedError('Abstract method') def split_generators(self, dl_manager, dataset_builder): """Delegated Shapenet._split_generators.""" raise NotImplementedError('Abstract method') def generate_examples(self, **kwargs): """Delegated Shapenet._generate_examples.""" raise NotImplementedError('Abstract method') class MeshConfig(ShapenetConfig): """A Shapenet config for loading the original .obj files.""" _MODEL_SUBPATH = os.path.join('models', 'model_normalized.obj') def __init__(self, model_subpath=_MODEL_SUBPATH): super(MeshConfig, self).__init__( name='shapenet_trimesh', description=_DESCRIPTION, version=tfds.core.Version('1.0.0')) self.model_subpath = model_subpath def info(self, dataset_builder): return tfds.core.DatasetInfo( builder=dataset_builder, description=_DESCRIPTION, features=tfds_features.FeaturesDict({ 'trimesh': tfg_features.TriangleMesh(), 'label': tfds_features.ClassLabel(num_classes=353), 'model_id': tfds_features.Text(), }), supervised_keys=('trimesh', 'label'), # Homepage of the dataset for documentation homepage='https://shapenet.org/', citation=_CITATION, ) def split_generators(self, dl_manager, dataset_builder): # Extract the synset ids from the taxonomy file and update the ClassLabel # feature. with tf.io.gfile.GFile( os.path.join(dl_manager.manual_dir, _TAXONOMY_FILE_NAME)) as taxonomy_file: labels = [x['synsetId'] for x in json.loads(taxonomy_file.read())] # Remove duplicate labels (the json file contains two identical entries # for synset '04591713'). labels = list(collections.OrderedDict.fromkeys(labels)) dataset_builder.info.features['label'].names = labels split_file = dl_manager.download(_SPLIT_FILE_URL) fieldnames = ['id', 'synset', 'sub_synset', 'model_id', 'split'] model_items = collections.defaultdict(list) with tf.io.gfile.GFile(split_file) as csvfile: for row in csv.DictReader(csvfile, fieldnames): model_items[row['split']].append(row) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['train'] }, ), tfds.core.SplitGenerator( name=tfds.Split.TEST, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['test'] }, ), tfds.core.SplitGenerator( name=tfds.Split.VALIDATION, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['val'] }, ), ] def generate_examples(self, base_dir, models): """Yields examples. The structure of the examples: { 'trimesh': tensorflow_graphics.datasets.features.TriangleMesh 'label': tensorflow_datasets.features.ClassLabel 'model_id': tensorflow_datasets.features.Text } Args: base_dir: The base directory of shapenet. models: The list of models in the split. """ for model in models: synset = model['synset'] model_id = model['model_id'] model_filepath = os.path.join(base_dir, synset, model_id, self.model_subpath) # If the model doesn't exist, skip it. if not tf.io.gfile.exists(model_filepath): continue yield model_id, { 'trimesh': model_filepath, 'label': synset, 'model_id': model_id, } class Shapenet(tfds.core.GeneratorBasedBuilder): """ShapeNetCore V2. Example usage of the dataset: import tensorflow_datasets as tfds from tensorflow_graphics.datasets.shapenet import Shapenet data_set = Shapenet.load( split='train', download_and_prepare_kwargs={ 'download_config': tfds.download.DownloadConfig(manual_dir='~/shapenet_base') }) for example in data_set.take(1): trimesh, label, model_id = example['trimesh'], example['label'], example['model_id'] """ BUILDER_CONFIGS = [MeshConfig()] VERSION = tfds.core.Version('1.0.0') @staticmethod def load(*args, **kwargs): return tfds.load('shapenet', *args, **kwargs) # pytype: disable=wrong-arg-count MANUAL_DOWNLOAD_INSTRUCTIONS = textwrap.dedent("""\ manual_dir should contain the extracted ShapeNetCore.v2.zip archive. You need to register on https://shapenet.org/download/shapenetcore in order to get the link to download the dataset. """) def _info(self): return self.builder_config.info(self) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" return self.builder_config.split_generators(dl_manager, self) def _generate_examples(self, **kwargs): """Yields examples.""" return self.builder_config.generate_examples(**kwargs)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Shapenet Core dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import csv import json import os import textwrap import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds from tensorflow_datasets import features as tfds_features from tensorflow_graphics.datasets import features as tfg_features _CITATION = """ @techreport{shapenet2015, title = {{ShapeNet: An Information-Rich 3D Model Repository}}, author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher}, number = {arXiv:1512.03012 [cs.GR]}, institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago}, year = {2015} } """ _DESCRIPTION = """ ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore is linked to an appropriate synset in WordNet (version 3.0). The synsets will be extracted from the taxonomy.json file in the ShapeNetCore.v2.zip archive and the splits from http://shapenet.cs.stanford.edu/shapenet/obj-zip/SHREC16/all.csv """ _TAXONOMY_FILE_NAME = 'taxonomy.json' _SPLIT_FILE_URL = \ 'http://shapenet.cs.stanford.edu/shapenet/obj-zip/SHREC16/all.csv' class ShapenetConfig(tfds.core.BuilderConfig): """Base class for Shapenet BuilderConfigs. The Shapenet database builder delegates the implementation of info, split_generators and generate_examples to the specified ShapenetConfig. This is done to allow multiple versions of the dataset. """ def info(self, dataset_builder): """Delegated Shapenet._info.""" raise NotImplementedError('Abstract method') def split_generators(self, dl_manager, dataset_builder): """Delegated Shapenet._split_generators.""" raise NotImplementedError('Abstract method') def generate_examples(self, **kwargs): """Delegated Shapenet._generate_examples.""" raise NotImplementedError('Abstract method') class MeshConfig(ShapenetConfig): """A Shapenet config for loading the original .obj files.""" _MODEL_SUBPATH = os.path.join('models', 'model_normalized.obj') def __init__(self, model_subpath=_MODEL_SUBPATH): super(MeshConfig, self).__init__( name='shapenet_trimesh', description=_DESCRIPTION, version=tfds.core.Version('1.0.0')) self.model_subpath = model_subpath def info(self, dataset_builder): return tfds.core.DatasetInfo( builder=dataset_builder, description=_DESCRIPTION, features=tfds_features.FeaturesDict({ 'trimesh': tfg_features.TriangleMesh(), 'label': tfds_features.ClassLabel(num_classes=353), 'model_id': tfds_features.Text(), }), supervised_keys=('trimesh', 'label'), # Homepage of the dataset for documentation homepage='https://shapenet.org/', citation=_CITATION, ) def split_generators(self, dl_manager, dataset_builder): # Extract the synset ids from the taxonomy file and update the ClassLabel # feature. with tf.io.gfile.GFile( os.path.join(dl_manager.manual_dir, _TAXONOMY_FILE_NAME)) as taxonomy_file: labels = [x['synsetId'] for x in json.loads(taxonomy_file.read())] # Remove duplicate labels (the json file contains two identical entries # for synset '04591713'). labels = list(collections.OrderedDict.fromkeys(labels)) dataset_builder.info.features['label'].names = labels split_file = dl_manager.download(_SPLIT_FILE_URL) fieldnames = ['id', 'synset', 'sub_synset', 'model_id', 'split'] model_items = collections.defaultdict(list) with tf.io.gfile.GFile(split_file) as csvfile: for row in csv.DictReader(csvfile, fieldnames): model_items[row['split']].append(row) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['train'] }, ), tfds.core.SplitGenerator( name=tfds.Split.TEST, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['test'] }, ), tfds.core.SplitGenerator( name=tfds.Split.VALIDATION, gen_kwargs={ 'base_dir': dl_manager.manual_dir, 'models': model_items['val'] }, ), ] def generate_examples(self, base_dir, models): """Yields examples. The structure of the examples: { 'trimesh': tensorflow_graphics.datasets.features.TriangleMesh 'label': tensorflow_datasets.features.ClassLabel 'model_id': tensorflow_datasets.features.Text } Args: base_dir: The base directory of shapenet. models: The list of models in the split. """ for model in models: synset = model['synset'] model_id = model['model_id'] model_filepath = os.path.join(base_dir, synset, model_id, self.model_subpath) # If the model doesn't exist, skip it. if not tf.io.gfile.exists(model_filepath): continue yield model_id, { 'trimesh': model_filepath, 'label': synset, 'model_id': model_id, } class Shapenet(tfds.core.GeneratorBasedBuilder): """ShapeNetCore V2. Example usage of the dataset: import tensorflow_datasets as tfds from tensorflow_graphics.datasets.shapenet import Shapenet data_set = Shapenet.load( split='train', download_and_prepare_kwargs={ 'download_config': tfds.download.DownloadConfig(manual_dir='~/shapenet_base') }) for example in data_set.take(1): trimesh, label, model_id = example['trimesh'], example['label'], example['model_id'] """ BUILDER_CONFIGS = [MeshConfig()] VERSION = tfds.core.Version('1.0.0') @staticmethod def load(*args, **kwargs): return tfds.load('shapenet', *args, **kwargs) # pytype: disable=wrong-arg-count MANUAL_DOWNLOAD_INSTRUCTIONS = textwrap.dedent("""\ manual_dir should contain the extracted ShapeNetCore.v2.zip archive. You need to register on https://shapenet.org/download/shapenetcore in order to get the link to download the dataset. """) def _info(self): return self.builder_config.info(self) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" return self.builder_config.split_generators(dl_manager, self) def _generate_examples(self, **kwargs): """Yields examples.""" return self.builder_config.generate_examples(**kwargs)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/cvxnet/train.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Training Loop.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def main(unused_argv): tf.set_random_seed(2191997) np.random.seed(6281996) logging.info("=> Starting ...") # Select dataset. logging.info("=> Preparing datasets ...") data = datasets.get_dataset(FLAGS.dataset, "train", FLAGS) batch = tf.data.make_one_shot_iterator(data).get_next() # Select model. logging.info("=> Creating {} model".format(FLAGS.model)) model = models.get_model(FLAGS.model, FLAGS) optimizer = tf.train.AdamOptimizer(FLAGS.lr) # Set up the graph train_loss, train_op, global_step = model.compute_loss( batch, training=True, optimizer=optimizer) # Training hooks stop_hook = tf.train.StopAtStepHook(last_step=FLAGS.max_steps) summary_writer = tf.summary.FileWriter(FLAGS.train_dir) ops = tf.get_collection(tf.GraphKeys.SUMMARIES) summary_hook = tf.train.SummarySaverHook( save_steps=100, summary_writer=summary_writer, summary_op=ops) step_counter_hook = tf.train.StepCounterHook(summary_writer=summary_writer) hooks = [stop_hook, step_counter_hook, summary_hook] logging.info("=> Start training loop ...") with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.train_dir, hooks=hooks, scaffold=None, save_checkpoint_steps=FLAGS.save_every, save_checkpoint_secs=None, save_summaries_steps=None, save_summaries_secs=None, log_step_count_steps=None, max_wait_secs=3600) as mon_sess: while not mon_sess.should_stop(): mon_sess.run([batch, train_loss, global_step, train_op]) if __name__ == "__main__": tf.app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Training Loop.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def main(unused_argv): tf.set_random_seed(2191997) np.random.seed(6281996) logging.info("=> Starting ...") # Select dataset. logging.info("=> Preparing datasets ...") data = datasets.get_dataset(FLAGS.dataset, "train", FLAGS) batch = tf.data.make_one_shot_iterator(data).get_next() # Select model. logging.info("=> Creating {} model".format(FLAGS.model)) model = models.get_model(FLAGS.model, FLAGS) optimizer = tf.train.AdamOptimizer(FLAGS.lr) # Set up the graph train_loss, train_op, global_step = model.compute_loss( batch, training=True, optimizer=optimizer) # Training hooks stop_hook = tf.train.StopAtStepHook(last_step=FLAGS.max_steps) summary_writer = tf.summary.FileWriter(FLAGS.train_dir) ops = tf.get_collection(tf.GraphKeys.SUMMARIES) summary_hook = tf.train.SummarySaverHook( save_steps=100, summary_writer=summary_writer, summary_op=ops) step_counter_hook = tf.train.StepCounterHook(summary_writer=summary_writer) hooks = [stop_hook, step_counter_hook, summary_hook] logging.info("=> Start training loop ...") with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.train_dir, hooks=hooks, scaffold=None, save_checkpoint_steps=FLAGS.save_every, save_checkpoint_secs=None, save_summaries_steps=None, save_summaries_secs=None, log_step_count_steps=None, max_wait_secs=3600) as mon_sess: while not mon_sess.should_stop(): mon_sess.run([batch, train_loss, global_step, train_op]) if __name__ == "__main__": tf.app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/g3doc/build_docs.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Script to generate external api_docs for tf-graphics.""" # flake8: noqa from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import app from absl import flags from tensorflow_docs.api_generator import generate_lib os.environ["TFG_DOC_IMPORTS"] = "1" import tensorflow_graphics as tfg # pylint: disable=g-import-not-at-top FLAGS = flags.FLAGS flags.DEFINE_string("output_dir", "/tmp/graphics_api", "Where to output the docs") flags.DEFINE_string( "code_url_prefix", "https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics", "The url prefix for links to code.") flags.DEFINE_bool("search_hints", True, "Include metadata search hints in the generated files") flags.DEFINE_string("site_path", "graphics/api_docs/python", "Path prefix in the _toc.yaml") def main(_): doc_generator = generate_lib.DocGenerator( root_title="Tensorflow Graphics", py_modules=[("tfg", tfg)], base_dir=os.path.dirname(tfg.__file__), search_hints=FLAGS.search_hints, code_url_prefix=FLAGS.code_url_prefix, site_path=FLAGS.site_path) doc_generator.build(output_dir=FLAGS.output_dir) if __name__ == "__main__": app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Script to generate external api_docs for tf-graphics.""" # flake8: noqa from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import app from absl import flags from tensorflow_docs.api_generator import generate_lib os.environ["TFG_DOC_IMPORTS"] = "1" import tensorflow_graphics as tfg # pylint: disable=g-import-not-at-top FLAGS = flags.FLAGS flags.DEFINE_string("output_dir", "/tmp/graphics_api", "Where to output the docs") flags.DEFINE_string( "code_url_prefix", "https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics", "The url prefix for links to code.") flags.DEFINE_bool("search_hints", True, "Include metadata search hints in the generated files") flags.DEFINE_string("site_path", "graphics/api_docs/python", "Path prefix in the _toc.yaml") def main(_): doc_generator = generate_lib.DocGenerator( root_title="Tensorflow Graphics", py_modules=[("tfg", tfg)], base_dir=os.path.dirname(tfg.__file__), search_hints=FLAGS.search_hints, code_url_prefix=FLAGS.code_url_prefix, site_path=FLAGS.site_path) doc_generator.build(output_dir=FLAGS.output_dir) if __name__ == "__main__": app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/tfg_flags.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Global flags to be used by various modules.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import flags FLAGS = flags.FLAGS TFG_ADD_ASSERTS_TO_GRAPH = 'tfg_add_asserts_to_graph' flags.DEFINE_boolean( TFG_ADD_ASSERTS_TO_GRAPH, False, 'If True, calling tensorflow_graphics functions may add assert ' 'nodes to the graph where necessary.', short_name='tfg_debug') # The util functions or classes are not exported. __all__ = []

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Global flags to be used by various modules.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import flags FLAGS = flags.FLAGS TFG_ADD_ASSERTS_TO_GRAPH = 'tfg_add_asserts_to_graph' flags.DEFINE_boolean( TFG_ADD_ASSERTS_TO_GRAPH, False, 'If True, calling tensorflow_graphics functions may add assert ' 'nodes to the graph where necessary.', short_name='tfg_debug') # The util functions or classes are not exported. __all__ = []

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/image/color_space/constants.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Constant parameters for color space conversion.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Conversion constants following the naming convention from the 'theory of the # transformation' section at https://en.wikipedia.org/wiki/SRGB. srgb_gamma = {"A": 0.055, "PHI": 12.92, "K0": 0.04045, "GAMMA": 2.4}

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Constant parameters for color space conversion.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Conversion constants following the naming convention from the 'theory of the # transformation' section at https://en.wikipedia.org/wiki/SRGB. srgb_gamma = {"A": 0.055, "PHI": 12.92, "K0": 0.04045, "GAMMA": 2.4}

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/reflectance/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Reflectance module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.rendering.reflectance import blinn_phong from tensorflow_graphics.rendering.reflectance import lambertian from tensorflow_graphics.rendering.reflectance import phong from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering.reflectance. __all__ = _export_api.get_modules()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Reflectance module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.rendering.reflectance import blinn_phong from tensorflow_graphics.rendering.reflectance import lambertian from tensorflow_graphics.rendering.reflectance import phong from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering.reflectance. __all__ = _export_api.get_modules()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Rendering module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=g-import-not-at-top from tensorflow_graphics.util.doc import _import_tfg_docs if _import_tfg_docs(): from tensorflow_graphics.rendering import camera from tensorflow_graphics.rendering import opengl from tensorflow_graphics.rendering import reflectance from tensorflow_graphics.rendering import voxels from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering. __all__ = _export_api.get_modules() # pylint: enable=g-import-not-at-top

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Rendering module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=g-import-not-at-top from tensorflow_graphics.util.doc import _import_tfg_docs if _import_tfg_docs(): from tensorflow_graphics.rendering import camera from tensorflow_graphics.rendering import opengl from tensorflow_graphics.rendering import reflectance from tensorflow_graphics.rendering import voxels from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.rendering. __all__ = _export_api.get_modules() # pylint: enable=g-import-not-at-top

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/interpolation/tests/weighted_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for google3.third_party.py.tensorflow_graphics.interpolation.weighted.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.util import test_case class WeightedTest(test_case.TestCase): def _get_tensors_from_shapes(self, num_points, dim_points, num_outputs, num_pts_to_interpolate): points = np.random.uniform(size=(num_points, dim_points)) weights = np.random.uniform(size=(num_outputs, num_pts_to_interpolate)) indices = np.asarray([ np.random.permutation(num_points)[:num_pts_to_interpolate].tolist() for _ in range(num_outputs) ]) indices = np.expand_dims(indices, axis=-1) return points, weights, indices @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_exception_not_raised(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether exceptions are not raised for compatible shapes.""" points, weights, indices = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) self.assert_exception_is_not_raised( weighted.interpolate, shapes=[], points=points, weights=weights, indices=indices, normalize=True) @parameterized.parameters( ("must have a rank greater than 1", ((3,), (None, 2), (None, 2, 0))), ("must have a rank greater than 1", ((None, 3), (None, 2), (1,))), ("must have exactly 1 dimensions in axis -1", ((None, 3), (None, 2), (None, 2, 2))), ("must have the same number of dimensions", ((None, 3), (None, 2), (None, 3, 1))), ("Not all batch dimensions are broadcast-compatible.", ((None, 3), (None, 5, 2), (None, 4, 2, 1))), ) def test_interpolate_exception_raised(self, error_msg, shapes): """Tests whether exceptions are raised for incompatible shapes.""" self.assert_exception_is_raised( weighted.interpolate, error_msg, shapes=shapes, normalize=False) @parameterized.parameters( (((-1.0, 1.0), (1.0, 1.0), (3.0, 1.0), (-1.0, -1.0), (1.0, -1.0), (3.0, -1.0)), ((0.25, 0.25, 0.25, 0.25), (0.5, 0.5, 0.0, 0.0)), (((0,), (1,), (3,), (4,)), ((1,), (2,), (4,), (5,))), False, ((0.0, 0.0), (2.0, 1.0))),) def test_interpolate_preset(self, points, weights, indices, _, out): """Tests whether interpolation results are correct.""" weights = tf.convert_to_tensor(value=weights) result_unnormalized = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=False) result_normalized = weighted.interpolate( points=points, weights=2.0 * weights, indices=indices, normalize=True) estimated_unnormalized = self.evaluate(result_unnormalized) estimated_normalized = self.evaluate(result_normalized) self.assertAllClose(estimated_unnormalized, out) self.assertAllClose(estimated_normalized, out) @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_negative_weights_raised(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether exception is raised when weights are negative.""" points, weights, indices = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) weights *= -1.0 with self.assertRaises(tf.errors.InvalidArgumentError): result = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=True) self.evaluate(result) @parameterized.parameters( (((-1.0, 1.0), (1.0, 1.0), (3.0, 1.0), (-1.0, -1.0), (1.0, -1.0), (3.0, -1.0)), ((1.0, -1.0, 1.0, -1.0), (0.0, 0.0, 0.0, 0.0)), (((0,), (1,), (3,), (4,)), ((1,), (2,), (4,), (5,))), ((0.0, 0.0), (0.0, 0.0)))) def test_interp_unnormalizable_raised_(self, points, weights, indices, _): """Tests whether exception is raised when weights are unnormalizable.""" with self.assertRaises(tf.errors.InvalidArgumentError): result = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=True, allow_negative_weights=True) self.evaluate(result) @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_jacobian_random(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether jacobian is correct.""" points_np, weights_np, indices_np = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) def interpolate_fn(points, weights): return weighted.interpolate( points=points, weights=weights, indices=indices_np, normalize=True) self.assert_jacobian_is_correct_fn(interpolate_fn, [points_np, weights_np]) @parameterized.parameters( ((3, 2), (2, 2)), ((None, 3, 2), (None, 1, 2)), ((10, 5, 3, 2), (10, 5, 2, 2)), ) def test_get_barycentric_coordinates_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(weighted.get_barycentric_coordinates, shapes) @parameterized.parameters( ("triangle_vertices must have exactly 2 dimensions in axis -1", (3, 1), (1, 2)), ("triangle_vertices must have exactly 3 dimensions in axis -2", (2, 2), (1, 2)), ("pixels must have exactly 2 dimensions in axis -1", (3, 2), (1, 3)), ("Not all batch dimensions are broadcast-compatible", (5, 3, 2), (2, 10, 2)), ) def test_get_barycentric_coordinates_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(weighted.get_barycentric_coordinates, error_msg, shape) def test_get_barycentric_coordinates_jacobian_random(self): """Tests the Jacobian of get_barycentric_coordinates.""" tensor_size = np.random.randint(2) tensor_shape = np.random.randint(1, 2, size=(tensor_size)).tolist() triangle_vertices_init = 0.4 * np.random.random( tensor_shape + [3, 2]).astype(np.float64) - 0.2 triangle_vertices_init += np.array( ((0.25, 0.25), (0.5, 0.75), (0.75, 0.25))) pixels_init = np.random.random(tensor_shape + [3, 2]).astype(np.float64) barycentric_fn = weighted.get_barycentric_coordinates self.assert_jacobian_is_correct_fn( lambda vertices, pixels: barycentric_fn(vertices, pixels)[0], [triangle_vertices_init, pixels_init]) def test_get_barycentric_coordinates_normalized(self): """Tests whether the barycentric coordinates are normalized.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() num_pixels = np.random.randint(1, 10) pixels_shape = tensor_shape + [num_pixels] triangle_vertices = np.random.random(tensor_shape + [3, 2]) pixels = np.random.random(pixels_shape + [2]) barycentric_coordinates, _ = weighted.get_barycentric_coordinates( triangle_vertices, pixels) barycentric_coordinates_sum = tf.reduce_sum( input_tensor=barycentric_coordinates, axis=-1) self.assertAllClose(barycentric_coordinates_sum, np.full(pixels_shape, 1.0)) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for google3.third_party.py.tensorflow_graphics.interpolation.weighted.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.util import test_case class WeightedTest(test_case.TestCase): def _get_tensors_from_shapes(self, num_points, dim_points, num_outputs, num_pts_to_interpolate): points = np.random.uniform(size=(num_points, dim_points)) weights = np.random.uniform(size=(num_outputs, num_pts_to_interpolate)) indices = np.asarray([ np.random.permutation(num_points)[:num_pts_to_interpolate].tolist() for _ in range(num_outputs) ]) indices = np.expand_dims(indices, axis=-1) return points, weights, indices @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_exception_not_raised(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether exceptions are not raised for compatible shapes.""" points, weights, indices = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) self.assert_exception_is_not_raised( weighted.interpolate, shapes=[], points=points, weights=weights, indices=indices, normalize=True) @parameterized.parameters( ("must have a rank greater than 1", ((3,), (None, 2), (None, 2, 0))), ("must have a rank greater than 1", ((None, 3), (None, 2), (1,))), ("must have exactly 1 dimensions in axis -1", ((None, 3), (None, 2), (None, 2, 2))), ("must have the same number of dimensions", ((None, 3), (None, 2), (None, 3, 1))), ("Not all batch dimensions are broadcast-compatible.", ((None, 3), (None, 5, 2), (None, 4, 2, 1))), ) def test_interpolate_exception_raised(self, error_msg, shapes): """Tests whether exceptions are raised for incompatible shapes.""" self.assert_exception_is_raised( weighted.interpolate, error_msg, shapes=shapes, normalize=False) @parameterized.parameters( (((-1.0, 1.0), (1.0, 1.0), (3.0, 1.0), (-1.0, -1.0), (1.0, -1.0), (3.0, -1.0)), ((0.25, 0.25, 0.25, 0.25), (0.5, 0.5, 0.0, 0.0)), (((0,), (1,), (3,), (4,)), ((1,), (2,), (4,), (5,))), False, ((0.0, 0.0), (2.0, 1.0))),) def test_interpolate_preset(self, points, weights, indices, _, out): """Tests whether interpolation results are correct.""" weights = tf.convert_to_tensor(value=weights) result_unnormalized = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=False) result_normalized = weighted.interpolate( points=points, weights=2.0 * weights, indices=indices, normalize=True) estimated_unnormalized = self.evaluate(result_unnormalized) estimated_normalized = self.evaluate(result_normalized) self.assertAllClose(estimated_unnormalized, out) self.assertAllClose(estimated_normalized, out) @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_negative_weights_raised(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether exception is raised when weights are negative.""" points, weights, indices = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) weights *= -1.0 with self.assertRaises(tf.errors.InvalidArgumentError): result = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=True) self.evaluate(result) @parameterized.parameters( (((-1.0, 1.0), (1.0, 1.0), (3.0, 1.0), (-1.0, -1.0), (1.0, -1.0), (3.0, -1.0)), ((1.0, -1.0, 1.0, -1.0), (0.0, 0.0, 0.0, 0.0)), (((0,), (1,), (3,), (4,)), ((1,), (2,), (4,), (5,))), ((0.0, 0.0), (0.0, 0.0)))) def test_interp_unnormalizable_raised_(self, points, weights, indices, _): """Tests whether exception is raised when weights are unnormalizable.""" with self.assertRaises(tf.errors.InvalidArgumentError): result = weighted.interpolate( points=points, weights=weights, indices=indices, normalize=True, allow_negative_weights=True) self.evaluate(result) @parameterized.parameters( (3, 4, 2, 3), (5, 4, 5, 3), (5, 6, 5, 5), (2, 6, 5, 1), ) def test_interpolate_jacobian_random(self, dim_points, num_points, num_outputs, num_pts_to_interpolate): """Tests whether jacobian is correct.""" points_np, weights_np, indices_np = self._get_tensors_from_shapes( num_points, dim_points, num_outputs, num_pts_to_interpolate) def interpolate_fn(points, weights): return weighted.interpolate( points=points, weights=weights, indices=indices_np, normalize=True) self.assert_jacobian_is_correct_fn(interpolate_fn, [points_np, weights_np]) @parameterized.parameters( ((3, 2), (2, 2)), ((None, 3, 2), (None, 1, 2)), ((10, 5, 3, 2), (10, 5, 2, 2)), ) def test_get_barycentric_coordinates_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(weighted.get_barycentric_coordinates, shapes) @parameterized.parameters( ("triangle_vertices must have exactly 2 dimensions in axis -1", (3, 1), (1, 2)), ("triangle_vertices must have exactly 3 dimensions in axis -2", (2, 2), (1, 2)), ("pixels must have exactly 2 dimensions in axis -1", (3, 2), (1, 3)), ("Not all batch dimensions are broadcast-compatible", (5, 3, 2), (2, 10, 2)), ) def test_get_barycentric_coordinates_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(weighted.get_barycentric_coordinates, error_msg, shape) def test_get_barycentric_coordinates_jacobian_random(self): """Tests the Jacobian of get_barycentric_coordinates.""" tensor_size = np.random.randint(2) tensor_shape = np.random.randint(1, 2, size=(tensor_size)).tolist() triangle_vertices_init = 0.4 * np.random.random( tensor_shape + [3, 2]).astype(np.float64) - 0.2 triangle_vertices_init += np.array( ((0.25, 0.25), (0.5, 0.75), (0.75, 0.25))) pixels_init = np.random.random(tensor_shape + [3, 2]).astype(np.float64) barycentric_fn = weighted.get_barycentric_coordinates self.assert_jacobian_is_correct_fn( lambda vertices, pixels: barycentric_fn(vertices, pixels)[0], [triangle_vertices_init, pixels_init]) def test_get_barycentric_coordinates_normalized(self): """Tests whether the barycentric coordinates are normalized.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() num_pixels = np.random.randint(1, 10) pixels_shape = tensor_shape + [num_pixels] triangle_vertices = np.random.random(tensor_shape + [3, 2]) pixels = np.random.random(pixels_shape + [2]) barycentric_coordinates, _ = weighted.get_barycentric_coordinates( triangle_vertices, pixels) barycentric_coordinates_sum = tf.reduce_sum( input_tensor=barycentric_coordinates, axis=-1) self.assertAllClose(barycentric_coordinates_sum, np.full(pixels_shape, 1.0)) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/cvxnet/eval.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Evaluation.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from os import path import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def main(unused_argv): tf.set_random_seed(2191997) np.random.seed(6281996) logging.info('=> Starting ...') eval_dir = path.join(FLAGS.train_dir, 'eval') # Select dataset. logging.info('=> Preparing datasets ...') data = datasets.get_dataset(FLAGS.dataset, 'test', FLAGS) batch = tf.data.make_one_shot_iterator(data).get_next() # Select model. logging.info('=> Creating {} model'.format(FLAGS.model)) model = models.get_model(FLAGS.model, FLAGS) # Set up the graph global_step = tf.train.get_or_create_global_step() test_loss, test_iou = model.compute_loss(batch, training=False) if FLAGS.extract_mesh or FLAGS.surface_metrics: img_ch = 3 if FLAGS.image_input else FLAGS.depth_d input_holder = tf.placeholder(tf.float32, [None, 224, 224, img_ch]) params = model.encode(input_holder, training=False) params_holder = tf.placeholder(tf.float32, [None, model.n_params]) points_holder = tf.placeholder(tf.float32, [None, None, FLAGS.dims]) indicators, unused_var = model.decode( params_holder, points_holder, training=False) if (not FLAGS.extract_mesh) or (not FLAGS.surface_metrics): summary_writer = tf.summary.FileWriter(eval_dir) iou_holder = tf.placeholder(tf.float32) iou_summary = tf.summary.scalar('test_iou', iou_holder) logging.info('=> Evaluating ...') last_step = -1 while True: shapenet_stats = utils.init_stats() with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.train_dir, hooks=[], save_checkpoint_steps=None, save_checkpoint_secs=None, save_summaries_steps=None, save_summaries_secs=None, log_step_count_steps=None, max_wait_secs=3600) as mon_sess: step_val = mon_sess.run(global_step) if step_val <= last_step: continue else: last_step = step_val while not mon_sess.should_stop(): batch_val, unused_var, test_iou_val = mon_sess.run( [batch, test_loss, test_iou]) if FLAGS.extract_mesh or FLAGS.surface_metrics: if FLAGS.image_input: input_val = batch_val['image'] else: input_val = batch_val['depth'] mesh = utils.extract_mesh( input_val, params, indicators, input_holder, params_holder, points_holder, mon_sess, FLAGS, ) if FLAGS.trans_dir is not None: utils.transform_mesh(mesh, batch_val['name'], FLAGS.trans_dir) if FLAGS.extract_mesh: utils.save_mesh(mesh, batch_val['name'], eval_dir) if FLAGS.surface_metrics: chamfer, fscore = utils.compute_surface_metrics( mesh, batch_val['name'], FLAGS.mesh_dir) else: chamfer = fscore = 0. example_stats = utils.Stats( iou=test_iou_val[0], chamfer=chamfer, fscore=fscore) utils.update_stats(example_stats, batch_val['name'], shapenet_stats) utils.average_stats(shapenet_stats) if (not FLAGS.extract_mesh) and (not FLAGS.surface_metrics): with tf.Session() as sess: iou_summary_val = sess.run( iou_summary, feed_dict={iou_holder: shapenet_stats['all']['iou']}) summary_writer.add_summary(iou_summary_val, step_val) summary_writer.flush() if FLAGS.surface_metrics: utils.write_stats( shapenet_stats, eval_dir, step_val, ) if FLAGS.eval_once or step_val >= FLAGS.max_steps: break if __name__ == '__main__': tf.app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Evaluation.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from os import path import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.cvxnet.lib import datasets from tensorflow_graphics.projects.cvxnet.lib import models from tensorflow_graphics.projects.cvxnet.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def main(unused_argv): tf.set_random_seed(2191997) np.random.seed(6281996) logging.info('=> Starting ...') eval_dir = path.join(FLAGS.train_dir, 'eval') # Select dataset. logging.info('=> Preparing datasets ...') data = datasets.get_dataset(FLAGS.dataset, 'test', FLAGS) batch = tf.data.make_one_shot_iterator(data).get_next() # Select model. logging.info('=> Creating {} model'.format(FLAGS.model)) model = models.get_model(FLAGS.model, FLAGS) # Set up the graph global_step = tf.train.get_or_create_global_step() test_loss, test_iou = model.compute_loss(batch, training=False) if FLAGS.extract_mesh or FLAGS.surface_metrics: img_ch = 3 if FLAGS.image_input else FLAGS.depth_d input_holder = tf.placeholder(tf.float32, [None, 224, 224, img_ch]) params = model.encode(input_holder, training=False) params_holder = tf.placeholder(tf.float32, [None, model.n_params]) points_holder = tf.placeholder(tf.float32, [None, None, FLAGS.dims]) indicators, unused_var = model.decode( params_holder, points_holder, training=False) if (not FLAGS.extract_mesh) or (not FLAGS.surface_metrics): summary_writer = tf.summary.FileWriter(eval_dir) iou_holder = tf.placeholder(tf.float32) iou_summary = tf.summary.scalar('test_iou', iou_holder) logging.info('=> Evaluating ...') last_step = -1 while True: shapenet_stats = utils.init_stats() with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.train_dir, hooks=[], save_checkpoint_steps=None, save_checkpoint_secs=None, save_summaries_steps=None, save_summaries_secs=None, log_step_count_steps=None, max_wait_secs=3600) as mon_sess: step_val = mon_sess.run(global_step) if step_val <= last_step: continue else: last_step = step_val while not mon_sess.should_stop(): batch_val, unused_var, test_iou_val = mon_sess.run( [batch, test_loss, test_iou]) if FLAGS.extract_mesh or FLAGS.surface_metrics: if FLAGS.image_input: input_val = batch_val['image'] else: input_val = batch_val['depth'] mesh = utils.extract_mesh( input_val, params, indicators, input_holder, params_holder, points_holder, mon_sess, FLAGS, ) if FLAGS.trans_dir is not None: utils.transform_mesh(mesh, batch_val['name'], FLAGS.trans_dir) if FLAGS.extract_mesh: utils.save_mesh(mesh, batch_val['name'], eval_dir) if FLAGS.surface_metrics: chamfer, fscore = utils.compute_surface_metrics( mesh, batch_val['name'], FLAGS.mesh_dir) else: chamfer = fscore = 0. example_stats = utils.Stats( iou=test_iou_val[0], chamfer=chamfer, fscore=fscore) utils.update_stats(example_stats, batch_val['name'], shapenet_stats) utils.average_stats(shapenet_stats) if (not FLAGS.extract_mesh) and (not FLAGS.surface_metrics): with tf.Session() as sess: iou_summary_val = sess.run( iou_summary, feed_dict={iou_holder: shapenet_stats['all']['iou']}) summary_writer.add_summary(iou_summary_val, step_val) summary_writer.flush() if FLAGS.surface_metrics: utils.write_stats( shapenet_stats, eval_dir, step_val, ) if FLAGS.eval_once or step_val >= FLAGS.max_steps: break if __name__ == '__main__': tf.app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/layer/tests/graph_convolution_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the graph convolution layers.""" from absl.testing import parameterized import numpy as np import tensorflow as tf import tensorflow_graphics.nn.layer.graph_convolution as gc_layer from tensorflow_graphics.util import test_case def _dense_to_sparse(data): """Convert a numpy array to a tf.SparseTensor.""" indices = np.where(data) return tf.SparseTensor( np.stack(indices, axis=-1), data[indices], dense_shape=data.shape) def _dummy_data(batch_size, num_vertices, num_channels): """Create inputs for feature_steered_convolution.""" if batch_size > 0: data = np.zeros( shape=(batch_size, num_vertices, num_channels), dtype=np.float32) neighbors = _dense_to_sparse( np.tile(np.eye(num_vertices, dtype=np.float32), (batch_size, 1, 1))) else: data = np.zeros(shape=(num_vertices, num_channels), dtype=np.float32) neighbors = _dense_to_sparse(np.eye(num_vertices, dtype=np.float32)) return data, neighbors class GraphConvolutionTestFeatureSteeredConvolutionLayerTests( test_case.TestCase): @parameterized.parameters( (1, 1, 1, 1, 1, False), (4, 2, 3, None, 5, False), (1, 2, 3, 4, 5, True), ) def test_feature_steered_convolution_layer_exception_not_raised_shapes( self, batch_size, num_vertices, in_channels, out_channels, num_weight_matrices, translation_invariant): """Check if the convolution parameters and output have correct shapes.""" data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) name_scope = "test" if tf.executing_eagerly(): layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=translation_invariant, num_weight_matrices=num_weight_matrices, num_output_channels=out_channels, name=name_scope) def _run_convolution(): """Run the appropriate feature steered convolution layer.""" if tf.executing_eagerly(): try: output = layer(inputs=[data, neighbors], sizes=None) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) else: try: output = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=translation_invariant, num_weight_matrices=num_weight_matrices, num_output_channels=out_channels, name=None, var_name=name_scope) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) return output output = _run_convolution() output_shape = output.shape.as_list() out_channels = in_channels if out_channels is None else out_channels self.assertEqual(output_shape[-1], out_channels) self.assertAllEqual(output_shape[:-1], data.shape[:-1]) def _get_var_shape(var_name): """Get the shape of a variable by name.""" if tf.executing_eagerly(): trainable_variables = layer.trainable_variables for tv in trainable_variables: if tv.name == name_scope + "/" + var_name + ":0": return tv.shape.as_list() raise ValueError("Variable not found.") else: with tf.compat.v1.variable_scope(name_scope, reuse=True): variable = tf.compat.v1.get_variable( var_name, initializer=tf.constant(0)) return variable.shape.as_list() self.assertAllEqual(_get_var_shape("u"), [in_channels, num_weight_matrices]) self.assertAllEqual(_get_var_shape("c"), [num_weight_matrices]) self.assertAllEqual(_get_var_shape("b"), [out_channels]) self.assertAllEqual( _get_var_shape("w"), [num_weight_matrices, in_channels, out_channels]) if not translation_invariant: self.assertAllEqual( _get_var_shape("v"), [in_channels, num_weight_matrices]) def test_feature_steered_convolution_layer_initializer(self): """Tests a custom variable initializer.""" data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) initializer = tf.compat.v1.keras.initializers.zeros() if tf.executing_eagerly(): layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=False, initializer=initializer) output = layer(inputs=[data, neighbors], sizes=None) else: out = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=False, initializer=initializer) self.evaluate(tf.compat.v1.global_variables_initializer()) output = self.evaluate(out) # All zeros initializer should result in all zeros output. self.assertAllEqual(output, np.zeros_like(data)) def test_feature_steered_convolution_layer_training(self): """Test a simple training loop.""" # Generate a small valid input for a simple training task. # Four corners of a square. data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) # Desired output is arbitrary. labels = np.reshape([-1.0, -0.5, 0.5, 1.0], (-1, 1)) num_training_iterations = 5 if tf.executing_eagerly(): with tf.GradientTape(persistent=True) as tape: layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=False, num_weight_matrices=1, num_output_channels=1) output = layer(inputs=[data, neighbors], sizes=None) loss = tf.nn.l2_loss(output - labels) trainable_variables = layer.trainable_variables for _ in range(num_training_iterations): grads = tape.gradient(loss, trainable_variables) tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients( zip(grads, trainable_variables)) else: output = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=False, num_weight_matrices=1, num_output_channels=1) train_op = tf.compat.v1.train.GradientDescentOptimizer(1e-4).minimize( tf.nn.l2_loss(output - labels)) with tf.compat.v1.Session() as sess: sess.run(tf.compat.v1.initialize_all_variables()) for _ in range(num_training_iterations): sess.run(train_op) class GraphConvolutionTestDynamicGraphConvolutionKerasLayerTests( test_case.TestCase): @parameterized.parameters( (1, 1, 1, 1, "weighted"), (4, 2, 3, 12, "max"), (1, 2, 3, 4, "max"), ) def test_dynamic_graph_convolution_keras_layer_exception_not_raised_shapes( self, batch_size, num_vertices, in_channels, out_channels, reduction): """Check if the convolution parameters and output have correct shapes.""" if not tf.executing_eagerly(): return data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction=reduction) try: output = layer(inputs=[data, neighbors], sizes=None) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) self.assertAllEqual((batch_size, num_vertices, out_channels), output.shape) @parameterized.parameters( (1, 1, 1, 1, "weighted"), (4, 2, 3, 12, "max"), (1, 2, 3, 4, "max"), ) def test_dynamic_graph_convolution_keras_layer_zero_kernel( self, batch_size, num_vertices, in_channels, out_channels, reduction): """Tests convolution with an all-zeros kernel.""" if not tf.executing_eagerly(): return data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) data = np.random.uniform(size=data.shape).astype(np.float32) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction=reduction, use_bias=False, kernel_initializer=tf.compat.v1.keras.initializers.zeros()) output = layer(inputs=[data, neighbors], sizes=None) self.assertAllEqual( output, np.zeros(shape=(batch_size, num_vertices, out_channels), dtype=np.float32)) @parameterized.parameters((1, 1, 1), (2, 3, 12), (2, 3, 4)) def test_dynamic_graph_convolution_keras_layer_duplicate_features( self, num_vertices, in_channels, out_channels): """Tests convolution when all vertex features are identical.""" if not tf.executing_eagerly(): return data = np.random.uniform(size=(1, in_channels)) data = np.tile(data, (num_vertices, 1)) # Results should be independent of 'neighbors'. neighbors = np.maximum(np.random.randint( 0, 2, size=(num_vertices, num_vertices)), np.eye(num_vertices)) neighbors = _dense_to_sparse(neighbors) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction="max") output = layer(inputs=[data, neighbors], sizes=None) output_tile = tf.tile(output[:1, :], (num_vertices, 1)) self.assertAllEqual(output, output_tile) @parameterized.parameters("weighted", "max") def test_dynamic_graph_convolution_keras_layer_training(self, reduction): """Test a simple training loop.""" if not tf.executing_eagerly(): return # Generate a small valid input for a simple training task. # Four corners of a square. data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) # Desired output is arbitrary. labels = np.reshape([-1.0, -0.5, 0.5, 1.0], (-1, 1)) num_training_iterations = 5 with tf.GradientTape(persistent=True) as tape: layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=2, reduction=reduction) output = layer(inputs=[data, neighbors], sizes=None) loss = tf.nn.l2_loss(output - labels) trainable_variables = layer.trainable_variables for _ in range(num_training_iterations): grads = tape.gradient(loss, trainable_variables) tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients( zip(grads, trainable_variables)) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the graph convolution layers.""" from absl.testing import parameterized import numpy as np import tensorflow as tf import tensorflow_graphics.nn.layer.graph_convolution as gc_layer from tensorflow_graphics.util import test_case def _dense_to_sparse(data): """Convert a numpy array to a tf.SparseTensor.""" indices = np.where(data) return tf.SparseTensor( np.stack(indices, axis=-1), data[indices], dense_shape=data.shape) def _dummy_data(batch_size, num_vertices, num_channels): """Create inputs for feature_steered_convolution.""" if batch_size > 0: data = np.zeros( shape=(batch_size, num_vertices, num_channels), dtype=np.float32) neighbors = _dense_to_sparse( np.tile(np.eye(num_vertices, dtype=np.float32), (batch_size, 1, 1))) else: data = np.zeros(shape=(num_vertices, num_channels), dtype=np.float32) neighbors = _dense_to_sparse(np.eye(num_vertices, dtype=np.float32)) return data, neighbors class GraphConvolutionTestFeatureSteeredConvolutionLayerTests( test_case.TestCase): @parameterized.parameters( (1, 1, 1, 1, 1, False), (4, 2, 3, None, 5, False), (1, 2, 3, 4, 5, True), ) def test_feature_steered_convolution_layer_exception_not_raised_shapes( self, batch_size, num_vertices, in_channels, out_channels, num_weight_matrices, translation_invariant): """Check if the convolution parameters and output have correct shapes.""" data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) name_scope = "test" if tf.executing_eagerly(): layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=translation_invariant, num_weight_matrices=num_weight_matrices, num_output_channels=out_channels, name=name_scope) def _run_convolution(): """Run the appropriate feature steered convolution layer.""" if tf.executing_eagerly(): try: output = layer(inputs=[data, neighbors], sizes=None) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) else: try: output = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=translation_invariant, num_weight_matrices=num_weight_matrices, num_output_channels=out_channels, name=None, var_name=name_scope) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) return output output = _run_convolution() output_shape = output.shape.as_list() out_channels = in_channels if out_channels is None else out_channels self.assertEqual(output_shape[-1], out_channels) self.assertAllEqual(output_shape[:-1], data.shape[:-1]) def _get_var_shape(var_name): """Get the shape of a variable by name.""" if tf.executing_eagerly(): trainable_variables = layer.trainable_variables for tv in trainable_variables: if tv.name == name_scope + "/" + var_name + ":0": return tv.shape.as_list() raise ValueError("Variable not found.") else: with tf.compat.v1.variable_scope(name_scope, reuse=True): variable = tf.compat.v1.get_variable( var_name, initializer=tf.constant(0)) return variable.shape.as_list() self.assertAllEqual(_get_var_shape("u"), [in_channels, num_weight_matrices]) self.assertAllEqual(_get_var_shape("c"), [num_weight_matrices]) self.assertAllEqual(_get_var_shape("b"), [out_channels]) self.assertAllEqual( _get_var_shape("w"), [num_weight_matrices, in_channels, out_channels]) if not translation_invariant: self.assertAllEqual( _get_var_shape("v"), [in_channels, num_weight_matrices]) def test_feature_steered_convolution_layer_initializer(self): """Tests a custom variable initializer.""" data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) initializer = tf.compat.v1.keras.initializers.zeros() if tf.executing_eagerly(): layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=False, initializer=initializer) output = layer(inputs=[data, neighbors], sizes=None) else: out = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=False, initializer=initializer) self.evaluate(tf.compat.v1.global_variables_initializer()) output = self.evaluate(out) # All zeros initializer should result in all zeros output. self.assertAllEqual(output, np.zeros_like(data)) def test_feature_steered_convolution_layer_training(self): """Test a simple training loop.""" # Generate a small valid input for a simple training task. # Four corners of a square. data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) # Desired output is arbitrary. labels = np.reshape([-1.0, -0.5, 0.5, 1.0], (-1, 1)) num_training_iterations = 5 if tf.executing_eagerly(): with tf.GradientTape(persistent=True) as tape: layer = gc_layer.FeatureSteeredConvolutionKerasLayer( translation_invariant=False, num_weight_matrices=1, num_output_channels=1) output = layer(inputs=[data, neighbors], sizes=None) loss = tf.nn.l2_loss(output - labels) trainable_variables = layer.trainable_variables for _ in range(num_training_iterations): grads = tape.gradient(loss, trainable_variables) tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients( zip(grads, trainable_variables)) else: output = gc_layer.feature_steered_convolution_layer( data=data, neighbors=neighbors, sizes=None, translation_invariant=False, num_weight_matrices=1, num_output_channels=1) train_op = tf.compat.v1.train.GradientDescentOptimizer(1e-4).minimize( tf.nn.l2_loss(output - labels)) with tf.compat.v1.Session() as sess: sess.run(tf.compat.v1.initialize_all_variables()) for _ in range(num_training_iterations): sess.run(train_op) class GraphConvolutionTestDynamicGraphConvolutionKerasLayerTests( test_case.TestCase): @parameterized.parameters( (1, 1, 1, 1, "weighted"), (4, 2, 3, 12, "max"), (1, 2, 3, 4, "max"), ) def test_dynamic_graph_convolution_keras_layer_exception_not_raised_shapes( self, batch_size, num_vertices, in_channels, out_channels, reduction): """Check if the convolution parameters and output have correct shapes.""" if not tf.executing_eagerly(): return data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction=reduction) try: output = layer(inputs=[data, neighbors], sizes=None) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) self.assertAllEqual((batch_size, num_vertices, out_channels), output.shape) @parameterized.parameters( (1, 1, 1, 1, "weighted"), (4, 2, 3, 12, "max"), (1, 2, 3, 4, "max"), ) def test_dynamic_graph_convolution_keras_layer_zero_kernel( self, batch_size, num_vertices, in_channels, out_channels, reduction): """Tests convolution with an all-zeros kernel.""" if not tf.executing_eagerly(): return data, neighbors = _dummy_data(batch_size, num_vertices, in_channels) data = np.random.uniform(size=data.shape).astype(np.float32) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction=reduction, use_bias=False, kernel_initializer=tf.compat.v1.keras.initializers.zeros()) output = layer(inputs=[data, neighbors], sizes=None) self.assertAllEqual( output, np.zeros(shape=(batch_size, num_vertices, out_channels), dtype=np.float32)) @parameterized.parameters((1, 1, 1), (2, 3, 12), (2, 3, 4)) def test_dynamic_graph_convolution_keras_layer_duplicate_features( self, num_vertices, in_channels, out_channels): """Tests convolution when all vertex features are identical.""" if not tf.executing_eagerly(): return data = np.random.uniform(size=(1, in_channels)) data = np.tile(data, (num_vertices, 1)) # Results should be independent of 'neighbors'. neighbors = np.maximum(np.random.randint( 0, 2, size=(num_vertices, num_vertices)), np.eye(num_vertices)) neighbors = _dense_to_sparse(neighbors) layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=out_channels, reduction="max") output = layer(inputs=[data, neighbors], sizes=None) output_tile = tf.tile(output[:1, :], (num_vertices, 1)) self.assertAllEqual(output, output_tile) @parameterized.parameters("weighted", "max") def test_dynamic_graph_convolution_keras_layer_training(self, reduction): """Test a simple training loop.""" if not tf.executing_eagerly(): return # Generate a small valid input for a simple training task. # Four corners of a square. data = np.array(((1.0, 1.0), (-1.0, 1.0), (-1.0, -1.0), (1.0, -1.0))) neighbors_indices = np.array(((0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3))) neighbors = tf.SparseTensor( neighbors_indices, np.ones(shape=(12,)) / 3.0, dense_shape=(4, 4)) # Desired output is arbitrary. labels = np.reshape([-1.0, -0.5, 0.5, 1.0], (-1, 1)) num_training_iterations = 5 with tf.GradientTape(persistent=True) as tape: layer = gc_layer.DynamicGraphConvolutionKerasLayer( num_output_channels=2, reduction=reduction) output = layer(inputs=[data, neighbors], sizes=None) loss = tf.nn.l2_loss(output - labels) trainable_variables = layer.trainable_variables for _ in range(num_training_iterations): grads = tape.gradient(loss, trainable_variables) tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients( zip(grads, trainable_variables)) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/modelnet40_makefakes.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Generates fake data for testing.""" import os from absl import app from absl import flags import h5py import numpy as np flags.DEFINE_string("fakes_path", ".", "path where files will be generated") FLAGS = flags.FLAGS def main(argv): """Generates files with the internal structure. Args: argv: the path where to generate the fake files Reference: f = h5py.File("modelnet40_ply_hdf5_2048/ply_data_train0.h5", "r") print(f['data']) # <HDF5 dataset "data": shape(2048, 2048, 3), type "<f4"> print(f['label']) # <HDF5 dataset "label": shape(2048, 1), type "|u1"> """ if len(argv) != 1: raise app.UsageError("One argument required.") for i in range(3): fake_points = np.random.randn(8, 2048, 3).astype(np.float32) fake_label = np.random.uniform(low=0, high=40, size=(8, 1)).astype(np.uint8) path = os.path.join(FLAGS.fakes_path, "ply_data_train{}.h5".format(i)) with h5py.File(path, "w") as h5f: h5f.create_dataset("data", data=fake_points) h5f.create_dataset("label", data=fake_label) for i in range(2): fake_points = np.random.randn(8, 2048, 3).astype(np.float32) fake_label = np.random.uniform(low=0, high=40, size=(8, 1)).astype(np.uint8) path = os.path.join(FLAGS.fakes_path, "ply_data_test{}.h5".format(i)) with h5py.File(path, "w") as h5f: h5f.create_dataset("data", data=fake_points) h5f.create_dataset("label", data=fake_label) if __name__ == "__main__": app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Generates fake data for testing.""" import os from absl import app from absl import flags import h5py import numpy as np flags.DEFINE_string("fakes_path", ".", "path where files will be generated") FLAGS = flags.FLAGS def main(argv): """Generates files with the internal structure. Args: argv: the path where to generate the fake files Reference: f = h5py.File("modelnet40_ply_hdf5_2048/ply_data_train0.h5", "r") print(f['data']) # <HDF5 dataset "data": shape(2048, 2048, 3), type "<f4"> print(f['label']) # <HDF5 dataset "label": shape(2048, 1), type "|u1"> """ if len(argv) != 1: raise app.UsageError("One argument required.") for i in range(3): fake_points = np.random.randn(8, 2048, 3).astype(np.float32) fake_label = np.random.uniform(low=0, high=40, size=(8, 1)).astype(np.uint8) path = os.path.join(FLAGS.fakes_path, "ply_data_train{}.h5".format(i)) with h5py.File(path, "w") as h5f: h5f.create_dataset("data", data=fake_points) h5f.create_dataset("label", data=fake_label) for i in range(2): fake_points = np.random.randn(8, 2048, 3).astype(np.float32) fake_label = np.random.uniform(low=0, high=40, size=(8, 1)).astype(np.uint8) path = os.path.join(FLAGS.fakes_path, "ply_data_test{}.h5".format(i)) with h5py.File(path, "w") as h5f: h5f.create_dataset("data", data=fake_points) h5f.create_dataset("label", data=fake_label) if __name__ == "__main__": app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/features/trimesh_feature.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Triangle mesh feature.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import six import tensorflow.compat.v2 as tf from tensorflow_datasets import features from tensorflow_graphics.io import triangle_mesh class TriangleMesh(features.FeaturesDict): """`FeatureConnector` for triangle meshes. During `_generate_examples`, the feature connector accepts as input any of: * `str`: path to a {obj,stl,ply,glb} triangle mesh. * `trimesh.Trimesh`: A triangle mesh object. * `trimesh.Scene`: A scene object containing multiple TriangleMesh objects. * `dict:` A dictionary containing the vertices and faces of the mesh (see output format below). Output: A dictionary containing: # TODO(b/156112246): Add additional attributes (vertex normals, colors, # texture coordinates). * 'vertices': A `float32` tensor with shape `[N, 3]` denoting the vertex coordinates, where N is the number of vertices in the mesh. * 'faces': An `int64` tensor with shape `[F, 3]` denoting the face vertex indices, where F is the number of faces in the mesh. Note: In case the input specifies a Scene (with multiple meshes), the output will be a single TriangleMesh which combines all the triangle meshes in the scene. """ def __init__(self): super(TriangleMesh, self).__init__({ 'vertices': features.Tensor(shape=(None, 3), dtype=tf.float32), 'faces': features.Tensor(shape=(None, 3), dtype=tf.uint64), }) def encode_example(self, path_or_trianglemesh): """Convert the given triangle mesh into a dict convertible to tf example.""" if isinstance(path_or_trianglemesh, six.string_types): # The parameter is a path. with tf.io.gfile.GFile(path_or_trianglemesh, 'rb') as tmesh_file: features_dict = self._convert_to_trimesh_feature( triangle_mesh.load(tmesh_file)) elif hasattr(path_or_trianglemesh, 'read') and hasattr( path_or_trianglemesh, 'name') and hasattr(path_or_trianglemesh, 'seek'): # The parameter is a file object. path_or_trianglemesh.seek(0) # reset features_dict = self._convert_to_trimesh_feature( triangle_mesh.load(path_or_trianglemesh)) elif isinstance(path_or_trianglemesh, dict): # The parameter is already a Trimesh dictionary. features_dict = path_or_trianglemesh else: # The parameter is a Trimesh or a Scene. features_dict = self._convert_to_trimesh_feature(path_or_trianglemesh) return super(TriangleMesh, self).encode_example(features_dict) def _convert_to_trimesh_feature(self, obj): if isinstance(obj, triangle_mesh.Trimesh): vertices = np.array(obj.vertices) faces = np.array(obj.faces, dtype=np.uint64) elif isinstance(obj, triangle_mesh.Scene): # Concatenate all the vertices and faces of the triangle meshes in the # scene. # TODO(b/156117488): Change to a different merging algorithm to avoid # duplicated vertices. vertices_list = [ np.array(mesh.vertices) for mesh in obj.geometry.values() ] faces_list = np.array([ np.array(mesh.faces, dtype=np.uint64) for mesh in obj.geometry.values() ]) faces_offset = np.cumsum( [vertices.shape[0] for vertices in vertices_list], dtype=np.uint64) faces_list[1:] += faces_offset[:-1] vertices = np.concatenate(vertices_list, axis=0) faces = np.concatenate(faces_list, axis=0) else: raise ValueError('obj should be either a Trimesh or a Scene') return { 'vertices': vertices.astype(np.float32), 'faces': faces, } @classmethod def from_json_content(cls, value) -> 'TriangleMesh': return cls() def to_json_content(self): return {}

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Triangle mesh feature.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import six import tensorflow.compat.v2 as tf from tensorflow_datasets import features from tensorflow_graphics.io import triangle_mesh class TriangleMesh(features.FeaturesDict): """`FeatureConnector` for triangle meshes. During `_generate_examples`, the feature connector accepts as input any of: * `str`: path to a {obj,stl,ply,glb} triangle mesh. * `trimesh.Trimesh`: A triangle mesh object. * `trimesh.Scene`: A scene object containing multiple TriangleMesh objects. * `dict:` A dictionary containing the vertices and faces of the mesh (see output format below). Output: A dictionary containing: # TODO(b/156112246): Add additional attributes (vertex normals, colors, # texture coordinates). * 'vertices': A `float32` tensor with shape `[N, 3]` denoting the vertex coordinates, where N is the number of vertices in the mesh. * 'faces': An `int64` tensor with shape `[F, 3]` denoting the face vertex indices, where F is the number of faces in the mesh. Note: In case the input specifies a Scene (with multiple meshes), the output will be a single TriangleMesh which combines all the triangle meshes in the scene. """ def __init__(self): super(TriangleMesh, self).__init__({ 'vertices': features.Tensor(shape=(None, 3), dtype=tf.float32), 'faces': features.Tensor(shape=(None, 3), dtype=tf.uint64), }) def encode_example(self, path_or_trianglemesh): """Convert the given triangle mesh into a dict convertible to tf example.""" if isinstance(path_or_trianglemesh, six.string_types): # The parameter is a path. with tf.io.gfile.GFile(path_or_trianglemesh, 'rb') as tmesh_file: features_dict = self._convert_to_trimesh_feature( triangle_mesh.load(tmesh_file)) elif hasattr(path_or_trianglemesh, 'read') and hasattr( path_or_trianglemesh, 'name') and hasattr(path_or_trianglemesh, 'seek'): # The parameter is a file object. path_or_trianglemesh.seek(0) # reset features_dict = self._convert_to_trimesh_feature( triangle_mesh.load(path_or_trianglemesh)) elif isinstance(path_or_trianglemesh, dict): # The parameter is already a Trimesh dictionary. features_dict = path_or_trianglemesh else: # The parameter is a Trimesh or a Scene. features_dict = self._convert_to_trimesh_feature(path_or_trianglemesh) return super(TriangleMesh, self).encode_example(features_dict) def _convert_to_trimesh_feature(self, obj): if isinstance(obj, triangle_mesh.Trimesh): vertices = np.array(obj.vertices) faces = np.array(obj.faces, dtype=np.uint64) elif isinstance(obj, triangle_mesh.Scene): # Concatenate all the vertices and faces of the triangle meshes in the # scene. # TODO(b/156117488): Change to a different merging algorithm to avoid # duplicated vertices. vertices_list = [ np.array(mesh.vertices) for mesh in obj.geometry.values() ] faces_list = np.array([ np.array(mesh.faces, dtype=np.uint64) for mesh in obj.geometry.values() ]) faces_offset = np.cumsum( [vertices.shape[0] for vertices in vertices_list], dtype=np.uint64) faces_list[1:] += faces_offset[:-1] vertices = np.concatenate(vertices_list, axis=0) faces = np.concatenate(faces_list, axis=0) else: raise ValueError('obj should be either a Trimesh or a Scene') return { 'vertices': vertices.astype(np.float32), 'faces': faces, } @classmethod def from_json_content(cls, value) -> 'TriangleMesh': return cls() def to_json_content(self): return {}

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/shape.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Shape utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import itertools import numpy as np import six import tensorflow as tf def _broadcast_shape_helper(shape_x, shape_y): """Helper function for is_broadcast_compatible and broadcast_shape. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: Returns None if the shapes are not broadcast compatible, or a list containing the broadcasted dimensions otherwise. """ # To compute the broadcasted dimensions, we zip together shape_x and shape_y, # and pad with 1 to make them the same length. broadcasted_dims = reversed( list( six.moves.zip_longest( reversed(shape_x.dims), reversed(shape_y.dims), fillvalue=tf.compat.v1.Dimension(1)))) # Next we combine the dimensions according to the numpy broadcasting rules. # http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html return_dims = [] for (dim_x, dim_y) in broadcasted_dims: if dim_x.value is None or dim_y.value is None: # One or both dimensions is unknown. If either dimension is greater than # 1, we assume that the program is correct, and the other dimension will # be broadcast to match it. if dim_x.value is not None and dim_x.value > 1: return_dims.append(dim_x) elif dim_y.value is not None and dim_y.value > 1: return_dims.append(dim_y) else: return_dims.append(None) elif dim_x.value == 1: # We will broadcast dim_x to dim_y. return_dims.append(dim_y) elif dim_y.value == 1: # We will broadcast dim_y to dim_x. return_dims.append(dim_x) elif dim_x.value == dim_y.value: # The dimensions are compatible, so output is the same size in that # dimension. return_dims.append(dim_x.merge_with(dim_y)) else: return None return return_dims def is_broadcast_compatible(shape_x, shape_y): """Returns True if `shape_x` and `shape_y` are broadcast compatible. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: True if a shape exists that both `shape_x` and `shape_y` can be broadcasted to. False otherwise. """ if shape_x.ndims is None or shape_y.ndims is None: return False return _broadcast_shape_helper(shape_x, shape_y) is not None def get_broadcasted_shape(shape_x, shape_y): """Returns the common shape for broadcast compatible shapes. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: Returns None if the shapes are not broadcast compatible, or a list containing the broadcasted dimensions otherwise. """ if shape_x.ndims is None or shape_y.ndims is None: return None return _broadcast_shape_helper(shape_x, shape_y) def _check_type(variable, variable_name, expected_type): """Helper function for checking that inputs are of expected types.""" if isinstance(expected_type, (list, tuple)): expected_type_name = 'list or tuple' else: expected_type_name = expected_type.__name__ if not isinstance(variable, expected_type): raise ValueError('{} must be of type {}, but it is {}'.format( variable_name, expected_type_name, type(variable).__name__)) def _fix_axis_dim_pairs(pairs, name): """Helper function to make `pairs` a list if needed.""" if isinstance(pairs[0], int): pairs = [pairs] for pair in pairs: if len(pair) != 2: raise ValueError( '{} must consist of axis-value pairs, but found {}'.format( name, pair)) return pairs def _get_dim(tensor, axis): """Returns dimensionality of a tensor for a given axis.""" return tf.compat.v1.dimension_value(tensor.shape[axis]) def check_static(tensor, has_rank=None, has_rank_greater_than=None, has_rank_less_than=None, has_dim_equals=None, has_dim_greater_than=None, has_dim_less_than=None, tensor_name='tensor'): """Checks static shapes for rank and dimension constraints. This function can be used to check a tensor's shape for multiple rank and dimension constraints at the same time. Args: tensor: Any tensor with a static shape. has_rank: An int or `None`. If not `None`, the function checks if the rank of the `tensor` equals to `has_rank`. has_rank_greater_than: An int or `None`. If not `None`, the function checks if the rank of the `tensor` is greater than `has_rank_greater_than`. has_rank_less_than: An int or `None`. If not `None`, the function checks if the rank of the `tensor` is less than `has_rank_less_than`. has_dim_equals: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] == dim`. has_dim_greater_than: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] > dim`. has_dim_less_than: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] < dim`. tensor_name: A name for `tensor` to be used in the error message if one is thrown. Raises: ValueError: If any input is not of the expected types, or if one of the checks described above fails. """ rank = tensor.shape.ndims def _raise_value_error_for_rank(variable, error_msg): raise ValueError( '{} must have a rank {} {}, but it has rank {} and shape {}'.format( tensor_name, error_msg, variable, rank, tensor.shape.as_list())) def _raise_value_error_for_dim(tensor_name, error_msg, axis, value): raise ValueError( '{} must have {} {} dimensions in axis {}, but it has shape {}'.format( tensor_name, error_msg, value, axis, tensor.shape.as_list())) if has_rank is not None: _check_type(has_rank, 'has_rank', int) if rank != has_rank: _raise_value_error_for_rank(has_rank, 'of') if has_rank_greater_than is not None: _check_type(has_rank_greater_than, 'has_rank_greater_than', int) if rank <= has_rank_greater_than: _raise_value_error_for_rank(has_rank_greater_than, 'greater than') if has_rank_less_than is not None: _check_type(has_rank_less_than, 'has_rank_less_than', int) if rank >= has_rank_less_than: _raise_value_error_for_rank(has_rank_less_than, 'less than') if has_dim_equals is not None: _check_type(has_dim_equals, 'has_dim_equals', (list, tuple)) has_dim_equals = _fix_axis_dim_pairs(has_dim_equals, 'has_dim_equals') for axis, value in has_dim_equals: if _get_dim(tensor, axis) != value: _raise_value_error_for_dim(tensor_name, 'exactly', axis, value) if has_dim_greater_than is not None: _check_type(has_dim_greater_than, 'has_dim_greater_than', (list, tuple)) has_dim_greater_than = _fix_axis_dim_pairs(has_dim_greater_than, 'has_dim_greater_than') for axis, value in has_dim_greater_than: if not _get_dim(tensor, axis) > value: _raise_value_error_for_dim(tensor_name, 'greater than', axis, value) if has_dim_less_than is not None: _check_type(has_dim_less_than, 'has_dim_less_than', (list, tuple)) has_dim_less_than = _fix_axis_dim_pairs(has_dim_less_than, 'has_dim_less_than') for axis, value in has_dim_less_than: if not _get_dim(tensor, axis) < value: _raise_value_error_for_dim(tensor_name, 'less than', axis, value) def _check_tensors(tensors, tensors_name): """Helper function to check the type and length of tensors.""" _check_type(tensors, tensors_name, (list, tuple)) if len(tensors) < 2: raise ValueError('At least 2 tensors are required.') def _check_tensor_axis_lists(tensors, tensors_name, axes, axes_name): """Helper function to check that lengths of `tensors` and `axes` match.""" _check_type(axes, axes_name, (list, tuple)) if len(tensors) != len(axes): raise ValueError( '{} and {} must have the same length, but are {} and {}.'.format( tensors_name, axes_name, len(tensors), len(axes))) def _fix_axes(tensors, axes, allow_negative): """Makes all axes positive and checks for out of bound errors.""" axes = [ axis + tensor.shape.ndims if axis < 0 else axis for tensor, axis in zip(tensors, axes) ] if not all( ((allow_negative or (not allow_negative and axis >= 0)) and axis < tensor.shape.ndims) for tensor, axis in zip(tensors, axes)): rank_axis_pairs = zip([tensor.shape.ndims for tensor in tensors], axes) raise ValueError( 'Some axes are out of bounds. Given rank-axes pairs: {}'.format( [pair for pair in rank_axis_pairs])) return axes def _give_default_names(list_of_objects, name): """Helper function to give default names to objects for error messages.""" return [name + '_' + str(index) for index in range(len(list_of_objects))] def _all_are_equal(list_of_objects): """Helper function to check if all the items in a list are the same.""" if not list_of_objects: return True if isinstance(list_of_objects[0], list): list_of_objects = [tuple(obj) for obj in list_of_objects] return len(set(list_of_objects)) == 1 def _raise_error(tensor_names, batch_shapes): formatted_list = [(name, batch_shape) for name, batch_shape in zip(tensor_names, batch_shapes)] raise ValueError( 'Not all batch dimensions are identical: {}'.format(formatted_list)) def compare_batch_dimensions(tensors, last_axes, broadcast_compatible, initial_axes=0, tensor_names=None): """Compares batch dimensions for tensors with static shapes. Args: tensors: A list or tuple of tensors with static shapes to compare. last_axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the last axis of the batch (with zero based indices). For instance, if there is only a single batch dimension, last axis should be `0`. broadcast_compatible: A 'bool', whether the batch shapes can be broadcast compatible in the numpy sense. initial_axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the first axis of the batch (with zero based indices). Default value is `0`. tensor_names: Names of `tensors` to be used in the error message if one is thrown. If left as `None`, `tensor_i` is used. Raises: ValueError: If inputs have unexpected types, or if given axes are out of bounds, or if the check fails. """ _check_tensors(tensors, 'tensors') if isinstance(initial_axes, int): initial_axes = [initial_axes] * len(tensors) if isinstance(last_axes, int): last_axes = [last_axes] * len(tensors) _check_tensor_axis_lists(tensors, 'tensors', initial_axes, 'initial_axes') _check_tensor_axis_lists(tensors, 'tensors', last_axes, 'last_axes') initial_axes = _fix_axes(tensors, initial_axes, allow_negative=True) last_axes = _fix_axes(tensors, last_axes, allow_negative=True) batch_shapes = [ tensor.shape[init:last + 1] for tensor, init, last in zip(tensors, initial_axes, last_axes) ] if tensor_names is None: tensor_names = _give_default_names(tensors, 'tensor') if not broadcast_compatible: batch_ndims = [batch_shape.ndims for batch_shape in batch_shapes] batch_shapes = [batch_shape.as_list() for batch_shape in batch_shapes] if not _all_are_equal(batch_ndims): # If not all batch shapes have the same length, they cannot be identical. _raise_error(tensor_names, batch_shapes) for dims in zip(*batch_shapes): if _all_are_equal(dims): # Continue if all dimensions are None or have the same value. continue if None not in dims: # If all dimensions are known at this point, they are not identical. _raise_error(tensor_names, batch_shapes) # At this point dims must consist of both None's and int's. if len(set(dims)) != 2: # set(dims) should return (None, some_int). # Otherwise shapes are not identical. _raise_error(tensor_names, batch_shapes) else: if not all( is_broadcast_compatible(shape1, shape2) for shape1, shape2 in itertools.combinations(batch_shapes, 2)): raise ValueError( 'Not all batch dimensions are broadcast-compatible: {}'.format([ (name, batch_shape.as_list()) for name, batch_shape in zip(tensor_names, batch_shapes) ])) def compare_dimensions(tensors, axes, tensor_names=None): """Compares dimensions of tensors with static or dynamic shapes. Args: tensors: A list or tuple of tensors to compare. axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the axis of the tensor being compared. tensor_names: Names of `tensors` to be used in the error message if one is thrown. If left as `None`, their `Tensor.name` fields are used instead. Raises: ValueError: If inputs have unexpected types, or if given axes are out of bounds, or if the check fails. """ _check_tensors(tensors, 'tensors') if isinstance(axes, int): axes = [axes] * len(tensors) _check_tensor_axis_lists(tensors, 'tensors', axes, 'axes') axes = _fix_axes(tensors, axes, allow_negative=False) if tensor_names is None: tensor_names = _give_default_names(tensors, 'tensor') dimensions = [_get_dim(tensor, axis) for tensor, axis in zip(tensors, axes)] if not _all_are_equal(dimensions): raise ValueError('Tensors {} must have the same number of dimensions in ' 'axes {}, but they are {}.'.format( list(tensor_names), list(axes), list(dimensions))) def is_static(tensor_shape): """Checks if the given tensor shape is static.""" if isinstance(tensor_shape, (list, tuple)): return None not in tensor_shape else: return None not in tensor_shape.as_list() def add_batch_dimensions(tensor, tensor_name, batch_shape, last_axis=None): """Broadcasts tensor to match batch dimensions. It will either broadcast to all provided batch dimensions, therefore increasing tensor shape by len(batch_shape) dimensions or will do nothing if batch dimensions already present and equal to expected batch dimensions. Args: tensor: A tensor to broadcast of a shape [A1, ..., An, B1, ..., Bn]. Where [A1, ..., An] is batch dimensions (it is allowed to have no batch dimensions), and [B1, ..., Bn] are other tensor dimensions. If [A1, ..., An] are present but different from values in `batch_shape` the error will be thrown. tensor_name: Name of `tensor` to be used in the error message if one is batch_shape: list of `int` representing desired batch dimensions. last_axis: An `int` corresponding to the last axis of the batch (with zero based indices). For instance, if there is only a single batch dimension, last axis should be `0`. If there is no batch dimensions it must be set to `None`. thrown. Returns: Tensor of a shape `batch_shape` + [B1, ..., Bn] or unmodified tensor if `batch_shape` = [A1, ..., An]. Raises: ValueError if tensor already has batch dimensions different from desired one. """ if last_axis is not None: last_axis = _fix_axes([tensor], [last_axis], allow_negative=True)[0] tensor_batch_shape = tensor.shape.as_list()[:last_axis + 1] if np.array_equal(tensor_batch_shape, batch_shape): return tensor elif tensor_batch_shape: raise ValueError( 'Tensor {} has batch dimensions different from target ' 'one. Found {}, but expected no batch dimensions or {}'.format( tensor_name, tensor.shape[:last_axis + 1], batch_shape)) return tf.broadcast_to(tensor, batch_shape + list(tensor.shape)) # The util functions or classes are not exported. __all__ = []

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Shape utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import itertools import numpy as np import six import tensorflow as tf def _broadcast_shape_helper(shape_x, shape_y): """Helper function for is_broadcast_compatible and broadcast_shape. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: Returns None if the shapes are not broadcast compatible, or a list containing the broadcasted dimensions otherwise. """ # To compute the broadcasted dimensions, we zip together shape_x and shape_y, # and pad with 1 to make them the same length. broadcasted_dims = reversed( list( six.moves.zip_longest( reversed(shape_x.dims), reversed(shape_y.dims), fillvalue=tf.compat.v1.Dimension(1)))) # Next we combine the dimensions according to the numpy broadcasting rules. # http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html return_dims = [] for (dim_x, dim_y) in broadcasted_dims: if dim_x.value is None or dim_y.value is None: # One or both dimensions is unknown. If either dimension is greater than # 1, we assume that the program is correct, and the other dimension will # be broadcast to match it. if dim_x.value is not None and dim_x.value > 1: return_dims.append(dim_x) elif dim_y.value is not None and dim_y.value > 1: return_dims.append(dim_y) else: return_dims.append(None) elif dim_x.value == 1: # We will broadcast dim_x to dim_y. return_dims.append(dim_y) elif dim_y.value == 1: # We will broadcast dim_y to dim_x. return_dims.append(dim_x) elif dim_x.value == dim_y.value: # The dimensions are compatible, so output is the same size in that # dimension. return_dims.append(dim_x.merge_with(dim_y)) else: return None return return_dims def is_broadcast_compatible(shape_x, shape_y): """Returns True if `shape_x` and `shape_y` are broadcast compatible. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: True if a shape exists that both `shape_x` and `shape_y` can be broadcasted to. False otherwise. """ if shape_x.ndims is None or shape_y.ndims is None: return False return _broadcast_shape_helper(shape_x, shape_y) is not None def get_broadcasted_shape(shape_x, shape_y): """Returns the common shape for broadcast compatible shapes. Args: shape_x: A `TensorShape`. shape_y: A `TensorShape`. Returns: Returns None if the shapes are not broadcast compatible, or a list containing the broadcasted dimensions otherwise. """ if shape_x.ndims is None or shape_y.ndims is None: return None return _broadcast_shape_helper(shape_x, shape_y) def _check_type(variable, variable_name, expected_type): """Helper function for checking that inputs are of expected types.""" if isinstance(expected_type, (list, tuple)): expected_type_name = 'list or tuple' else: expected_type_name = expected_type.__name__ if not isinstance(variable, expected_type): raise ValueError('{} must be of type {}, but it is {}'.format( variable_name, expected_type_name, type(variable).__name__)) def _fix_axis_dim_pairs(pairs, name): """Helper function to make `pairs` a list if needed.""" if isinstance(pairs[0], int): pairs = [pairs] for pair in pairs: if len(pair) != 2: raise ValueError( '{} must consist of axis-value pairs, but found {}'.format( name, pair)) return pairs def _get_dim(tensor, axis): """Returns dimensionality of a tensor for a given axis.""" return tf.compat.v1.dimension_value(tensor.shape[axis]) def check_static(tensor, has_rank=None, has_rank_greater_than=None, has_rank_less_than=None, has_dim_equals=None, has_dim_greater_than=None, has_dim_less_than=None, tensor_name='tensor'): """Checks static shapes for rank and dimension constraints. This function can be used to check a tensor's shape for multiple rank and dimension constraints at the same time. Args: tensor: Any tensor with a static shape. has_rank: An int or `None`. If not `None`, the function checks if the rank of the `tensor` equals to `has_rank`. has_rank_greater_than: An int or `None`. If not `None`, the function checks if the rank of the `tensor` is greater than `has_rank_greater_than`. has_rank_less_than: An int or `None`. If not `None`, the function checks if the rank of the `tensor` is less than `has_rank_less_than`. has_dim_equals: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] == dim`. has_dim_greater_than: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] > dim`. has_dim_less_than: Either a tuple or list containing a single pair of `int`s, or a list or tuple containing multiple such pairs. Each pair is in the form (`axis`, `dim`), which means the function should check if `tensor.shape[axis] < dim`. tensor_name: A name for `tensor` to be used in the error message if one is thrown. Raises: ValueError: If any input is not of the expected types, or if one of the checks described above fails. """ rank = tensor.shape.ndims def _raise_value_error_for_rank(variable, error_msg): raise ValueError( '{} must have a rank {} {}, but it has rank {} and shape {}'.format( tensor_name, error_msg, variable, rank, tensor.shape.as_list())) def _raise_value_error_for_dim(tensor_name, error_msg, axis, value): raise ValueError( '{} must have {} {} dimensions in axis {}, but it has shape {}'.format( tensor_name, error_msg, value, axis, tensor.shape.as_list())) if has_rank is not None: _check_type(has_rank, 'has_rank', int) if rank != has_rank: _raise_value_error_for_rank(has_rank, 'of') if has_rank_greater_than is not None: _check_type(has_rank_greater_than, 'has_rank_greater_than', int) if rank <= has_rank_greater_than: _raise_value_error_for_rank(has_rank_greater_than, 'greater than') if has_rank_less_than is not None: _check_type(has_rank_less_than, 'has_rank_less_than', int) if rank >= has_rank_less_than: _raise_value_error_for_rank(has_rank_less_than, 'less than') if has_dim_equals is not None: _check_type(has_dim_equals, 'has_dim_equals', (list, tuple)) has_dim_equals = _fix_axis_dim_pairs(has_dim_equals, 'has_dim_equals') for axis, value in has_dim_equals: if _get_dim(tensor, axis) != value: _raise_value_error_for_dim(tensor_name, 'exactly', axis, value) if has_dim_greater_than is not None: _check_type(has_dim_greater_than, 'has_dim_greater_than', (list, tuple)) has_dim_greater_than = _fix_axis_dim_pairs(has_dim_greater_than, 'has_dim_greater_than') for axis, value in has_dim_greater_than: if not _get_dim(tensor, axis) > value: _raise_value_error_for_dim(tensor_name, 'greater than', axis, value) if has_dim_less_than is not None: _check_type(has_dim_less_than, 'has_dim_less_than', (list, tuple)) has_dim_less_than = _fix_axis_dim_pairs(has_dim_less_than, 'has_dim_less_than') for axis, value in has_dim_less_than: if not _get_dim(tensor, axis) < value: _raise_value_error_for_dim(tensor_name, 'less than', axis, value) def _check_tensors(tensors, tensors_name): """Helper function to check the type and length of tensors.""" _check_type(tensors, tensors_name, (list, tuple)) if len(tensors) < 2: raise ValueError('At least 2 tensors are required.') def _check_tensor_axis_lists(tensors, tensors_name, axes, axes_name): """Helper function to check that lengths of `tensors` and `axes` match.""" _check_type(axes, axes_name, (list, tuple)) if len(tensors) != len(axes): raise ValueError( '{} and {} must have the same length, but are {} and {}.'.format( tensors_name, axes_name, len(tensors), len(axes))) def _fix_axes(tensors, axes, allow_negative): """Makes all axes positive and checks for out of bound errors.""" axes = [ axis + tensor.shape.ndims if axis < 0 else axis for tensor, axis in zip(tensors, axes) ] if not all( ((allow_negative or (not allow_negative and axis >= 0)) and axis < tensor.shape.ndims) for tensor, axis in zip(tensors, axes)): rank_axis_pairs = zip([tensor.shape.ndims for tensor in tensors], axes) raise ValueError( 'Some axes are out of bounds. Given rank-axes pairs: {}'.format( [pair for pair in rank_axis_pairs])) return axes def _give_default_names(list_of_objects, name): """Helper function to give default names to objects for error messages.""" return [name + '_' + str(index) for index in range(len(list_of_objects))] def _all_are_equal(list_of_objects): """Helper function to check if all the items in a list are the same.""" if not list_of_objects: return True if isinstance(list_of_objects[0], list): list_of_objects = [tuple(obj) for obj in list_of_objects] return len(set(list_of_objects)) == 1 def _raise_error(tensor_names, batch_shapes): formatted_list = [(name, batch_shape) for name, batch_shape in zip(tensor_names, batch_shapes)] raise ValueError( 'Not all batch dimensions are identical: {}'.format(formatted_list)) def compare_batch_dimensions(tensors, last_axes, broadcast_compatible, initial_axes=0, tensor_names=None): """Compares batch dimensions for tensors with static shapes. Args: tensors: A list or tuple of tensors with static shapes to compare. last_axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the last axis of the batch (with zero based indices). For instance, if there is only a single batch dimension, last axis should be `0`. broadcast_compatible: A 'bool', whether the batch shapes can be broadcast compatible in the numpy sense. initial_axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the first axis of the batch (with zero based indices). Default value is `0`. tensor_names: Names of `tensors` to be used in the error message if one is thrown. If left as `None`, `tensor_i` is used. Raises: ValueError: If inputs have unexpected types, or if given axes are out of bounds, or if the check fails. """ _check_tensors(tensors, 'tensors') if isinstance(initial_axes, int): initial_axes = [initial_axes] * len(tensors) if isinstance(last_axes, int): last_axes = [last_axes] * len(tensors) _check_tensor_axis_lists(tensors, 'tensors', initial_axes, 'initial_axes') _check_tensor_axis_lists(tensors, 'tensors', last_axes, 'last_axes') initial_axes = _fix_axes(tensors, initial_axes, allow_negative=True) last_axes = _fix_axes(tensors, last_axes, allow_negative=True) batch_shapes = [ tensor.shape[init:last + 1] for tensor, init, last in zip(tensors, initial_axes, last_axes) ] if tensor_names is None: tensor_names = _give_default_names(tensors, 'tensor') if not broadcast_compatible: batch_ndims = [batch_shape.ndims for batch_shape in batch_shapes] batch_shapes = [batch_shape.as_list() for batch_shape in batch_shapes] if not _all_are_equal(batch_ndims): # If not all batch shapes have the same length, they cannot be identical. _raise_error(tensor_names, batch_shapes) for dims in zip(*batch_shapes): if _all_are_equal(dims): # Continue if all dimensions are None or have the same value. continue if None not in dims: # If all dimensions are known at this point, they are not identical. _raise_error(tensor_names, batch_shapes) # At this point dims must consist of both None's and int's. if len(set(dims)) != 2: # set(dims) should return (None, some_int). # Otherwise shapes are not identical. _raise_error(tensor_names, batch_shapes) else: if not all( is_broadcast_compatible(shape1, shape2) for shape1, shape2 in itertools.combinations(batch_shapes, 2)): raise ValueError( 'Not all batch dimensions are broadcast-compatible: {}'.format([ (name, batch_shape.as_list()) for name, batch_shape in zip(tensor_names, batch_shapes) ])) def compare_dimensions(tensors, axes, tensor_names=None): """Compares dimensions of tensors with static or dynamic shapes. Args: tensors: A list or tuple of tensors to compare. axes: An `int` or a list or tuple of `int`s with the same length as `tensors`. If an `int`, it is assumed to be the same for all the tensors. Each entry should correspond to the axis of the tensor being compared. tensor_names: Names of `tensors` to be used in the error message if one is thrown. If left as `None`, their `Tensor.name` fields are used instead. Raises: ValueError: If inputs have unexpected types, or if given axes are out of bounds, or if the check fails. """ _check_tensors(tensors, 'tensors') if isinstance(axes, int): axes = [axes] * len(tensors) _check_tensor_axis_lists(tensors, 'tensors', axes, 'axes') axes = _fix_axes(tensors, axes, allow_negative=False) if tensor_names is None: tensor_names = _give_default_names(tensors, 'tensor') dimensions = [_get_dim(tensor, axis) for tensor, axis in zip(tensors, axes)] if not _all_are_equal(dimensions): raise ValueError('Tensors {} must have the same number of dimensions in ' 'axes {}, but they are {}.'.format( list(tensor_names), list(axes), list(dimensions))) def is_static(tensor_shape): """Checks if the given tensor shape is static.""" if isinstance(tensor_shape, (list, tuple)): return None not in tensor_shape else: return None not in tensor_shape.as_list() def add_batch_dimensions(tensor, tensor_name, batch_shape, last_axis=None): """Broadcasts tensor to match batch dimensions. It will either broadcast to all provided batch dimensions, therefore increasing tensor shape by len(batch_shape) dimensions or will do nothing if batch dimensions already present and equal to expected batch dimensions. Args: tensor: A tensor to broadcast of a shape [A1, ..., An, B1, ..., Bn]. Where [A1, ..., An] is batch dimensions (it is allowed to have no batch dimensions), and [B1, ..., Bn] are other tensor dimensions. If [A1, ..., An] are present but different from values in `batch_shape` the error will be thrown. tensor_name: Name of `tensor` to be used in the error message if one is batch_shape: list of `int` representing desired batch dimensions. last_axis: An `int` corresponding to the last axis of the batch (with zero based indices). For instance, if there is only a single batch dimension, last axis should be `0`. If there is no batch dimensions it must be set to `None`. thrown. Returns: Tensor of a shape `batch_shape` + [B1, ..., Bn] or unmodified tensor if `batch_shape` = [A1, ..., An]. Raises: ValueError if tensor already has batch dimensions different from desired one. """ if last_axis is not None: last_axis = _fix_axes([tensor], [last_axis], allow_negative=True)[0] tensor_batch_shape = tensor.shape.as_list()[:last_axis + 1] if np.array_equal(tensor_batch_shape, batch_shape): return tensor elif tensor_batch_shape: raise ValueError( 'Tensor {} has batch dimensions different from target ' 'one. Found {}, but expected no batch dimensions or {}'.format( tensor_name, tensor.shape[:last_axis + 1], batch_shape)) return tf.broadcast_to(tensor, batch_shape + list(tensor.shape)) # The util functions or classes are not exported. __all__ = []

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/mesh/tests/mesh_test_utils.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Helper routines for mesh unit tests. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def create_single_triangle_mesh(): r"""Creates a single-triangle mesh, in the z=0 plane and facing +z. (0,1) 2 |\ | \ | \ (0,0) 0---1 (1,0) Returns: vertices: A [3, 3] float array faces: A [1, 3] int array """ vertices = np.array( ((0, 0, 0), (1, 0, 0), (0, 1, 0)), dtype=np.float32) faces = np.array(((0, 1, 2),), dtype=np.int32) return vertices, faces def create_square_triangle_mesh(): r"""Creates a square mesh, in the z=0 planse and facing +z. # (0,1) 2---3 (1,1) # |\ /| # | 4 | # |/ \| # (0,0) 0---1 (1,0) Returns: vertices: A [5, 3] float array faces: A [4, 3] int array """ vertices = np.array( ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0.5, 0.5, 0)), dtype=np.float32) faces = np.array( ((0, 1, 4), (1, 3, 4), (3, 2, 4), (2, 0, 4)), dtype=np.int32) return vertices, faces

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Helper routines for mesh unit tests. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def create_single_triangle_mesh(): r"""Creates a single-triangle mesh, in the z=0 plane and facing +z. (0,1) 2 |\ | \ | \ (0,0) 0---1 (1,0) Returns: vertices: A [3, 3] float array faces: A [1, 3] int array """ vertices = np.array( ((0, 0, 0), (1, 0, 0), (0, 1, 0)), dtype=np.float32) faces = np.array(((0, 1, 2),), dtype=np.int32) return vertices, faces def create_square_triangle_mesh(): r"""Creates a square mesh, in the z=0 planse and facing +z. # (0,1) 2---3 (1,1) # |\ /| # | 4 | # |/ \| # (0,0) 0---1 (1,0) Returns: vertices: A [5, 3] float array faces: A [4, 3] int array """ vertices = np.array( ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0.5, 0.5, 0)), dtype=np.float32) faces = np.array( ((0, 1, 4), (1, 3, 4), (3, 2, 4), (2, 0, 4)), dtype=np.int32) return vertices, faces

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/image/color_space/tests/srgb_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for srgb.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np from tensorflow_graphics.image.color_space import linear_rgb from tensorflow_graphics.image.color_space import srgb from tensorflow_graphics.util import test_case class SrgbTest(test_case.TestCase): def test_cycle_linear_rgb_srgb_linear_rgb_for_random_input(self): """Tests loop from linear RGB to sRGB and back for random inputs.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() linear_input = np.random.uniform(size=tensor_shape + [3]) srgb_output = srgb.from_linear_rgb(linear_input) linear_reverse = linear_rgb.from_srgb(srgb_output) self.assertAllClose(linear_input, linear_reverse) @parameterized.parameters( (((0., 0.5, 1.), (0.00312, 0.0031308, 0.00314)), ((0., 0.735357, 1.), (0.04031, 0.04045, 0.040567))),) def test_from_linear_rgb_preset(self, test_inputs, test_outputs): """Tests conversion from linear to sRGB color space for preset inputs.""" self.assert_output_is_correct(srgb.from_linear_rgb, (test_inputs,), (test_outputs,)) def test_from_linear_rgb_jacobian_random(self): """Tests the Jacobian of the from_linear_rgb function for random inputs.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() linear_random_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(srgb.from_linear_rgb, [linear_random_init]) @parameterized.parameters((np.array((0., 0.001, 0.002)),), (np.array( (0.004, 0.005, 1.)),), (np.array((0.00312, 0.004, 0.00314)),)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_linear_rgb_jacobian_preset(self, inputs_init): """Tests the Jacobian of the from_linear_rgb function for preset inputs.""" self.assert_jacobian_is_correct_fn(srgb.from_linear_rgb, [inputs_init]) @parameterized.parameters( ((3,),), ((None, None, None, 3),), ) def test_from_linear_rgb_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(srgb.from_linear_rgb, shape) @parameterized.parameters( ("must have a rank greater than 0", ()), ("must have exactly 3 dimensions in axis -1", (2, 3, 4)), ) def test_from_linear_rgb_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(srgb.from_linear_rgb, error_msg, shape) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for srgb.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np from tensorflow_graphics.image.color_space import linear_rgb from tensorflow_graphics.image.color_space import srgb from tensorflow_graphics.util import test_case class SrgbTest(test_case.TestCase): def test_cycle_linear_rgb_srgb_linear_rgb_for_random_input(self): """Tests loop from linear RGB to sRGB and back for random inputs.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() linear_input = np.random.uniform(size=tensor_shape + [3]) srgb_output = srgb.from_linear_rgb(linear_input) linear_reverse = linear_rgb.from_srgb(srgb_output) self.assertAllClose(linear_input, linear_reverse) @parameterized.parameters( (((0., 0.5, 1.), (0.00312, 0.0031308, 0.00314)), ((0., 0.735357, 1.), (0.04031, 0.04045, 0.040567))),) def test_from_linear_rgb_preset(self, test_inputs, test_outputs): """Tests conversion from linear to sRGB color space for preset inputs.""" self.assert_output_is_correct(srgb.from_linear_rgb, (test_inputs,), (test_outputs,)) def test_from_linear_rgb_jacobian_random(self): """Tests the Jacobian of the from_linear_rgb function for random inputs.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() linear_random_init = np.random.uniform(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(srgb.from_linear_rgb, [linear_random_init]) @parameterized.parameters((np.array((0., 0.001, 0.002)),), (np.array( (0.004, 0.005, 1.)),), (np.array((0.00312, 0.004, 0.00314)),)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_from_linear_rgb_jacobian_preset(self, inputs_init): """Tests the Jacobian of the from_linear_rgb function for preset inputs.""" self.assert_jacobian_is_correct_fn(srgb.from_linear_rgb, [inputs_init]) @parameterized.parameters( ((3,),), ((None, None, None, 3),), ) def test_from_linear_rgb_exception_not_raised(self, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(srgb.from_linear_rgb, shape) @parameterized.parameters( ("must have a rank greater than 0", ()), ("must have exactly 3 dimensions in axis -1", (2, 3, 4)), ) def test_from_linear_rgb_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(srgb.from_linear_rgb, error_msg, shape) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/image/color_space/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/loss/tests/chamfer_distance_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the chamfer distance loss.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.loss import chamfer_distance from tensorflow_graphics.util import test_case def _random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def _random_tensor_shape(): tensor_size = np.random.randint(3) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() def _random_point_sets(): space_dimensions = np.random.randint(3) + 1 batch_shape = _random_tensor_shape() point_set_a_size = np.random.randint(10) + 1 point_set_b_size = np.random.randint(10) + 1 point_set_a_init = np.random.uniform( low=-100.0, high=100.0, size=batch_shape + [point_set_a_size, space_dimensions]) point_set_b_init = np.random.uniform( low=-100.0, high=100.0, size=batch_shape + [point_set_b_size, space_dimensions]) return (point_set_a_init, point_set_b_init) class ChamferDistanceTest(test_case.TestCase): @parameterized.parameters( (((0., 0), (0, 1), (1, 0), (-1, 0)), ((0., 0), (0, 2), (0.7, 0.4), (-0.5, -0.5)), # a[0] -> b[0] (0 + \ # a[1] -> b[2] 0.7**2 + 0.6**2 + \ # a[2] -> b[2] 0.3**2 + 0.4**2 + \ # a[3] -> b[3] 0.5) / 4 + \ # b[0] -> a[0] (0 + \ # b[1] -> a[1] 1 + \ # b[2] -> a[2] 0.3**2 + 0.4**2 + \ # b[3] -> a[3] 0.5) / 4), (((0., 1, 4), (3, 4, 2)), ((2., 2, 2), (2, 3, 4), (3, 2, 2)), # a[0] -> b[1] (8 + \ # a[1] -> b[2] 4) / 2 + \ # b[0] -> a[1] (5 + \ # b[1] -> a[1] 6 + \ # b[2] -> a[1] 4) / 3), ) def test_evaluate_preset(self, point_set_a, point_set_b, expected_distance): tensor_shape = _random_tensor_shape() point_set_a = np.tile(point_set_a, tensor_shape + [1, 1]) point_set_b = np.tile(point_set_b, tensor_shape + [1, 1]) expected = np.tile(expected_distance, tensor_shape) result = chamfer_distance.evaluate(point_set_a, point_set_b) self.assertAllClose(expected, result) def test_chamfer_distance_evaluate_jacobian(self): """Tests the Jacobian of the Chamfer distance loss.""" point_set_a, point_set_b = _random_point_sets() with self.subTest(name="jacobian_wrt_point_set_a"): self.assert_jacobian_is_correct_fn( lambda x: chamfer_distance.evaluate(x, point_set_b), [point_set_a], atol=1e-5) with self.subTest(name="jacobian_wrt_point_set_b"): self.assert_jacobian_is_correct_fn( lambda x: chamfer_distance.evaluate(point_set_a, x), [point_set_b], atol=1e-5) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 3, 5, 3), (2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 3, 5), (2, 4, 5)), ("point_set_b must have exactly 3 dimensions in axis -1,.", (2, 4, 3), (2, 4, 2)), ("point_set_b must have exactly 2 dimensions in axis -1,.", (2, 4, 2), (2, 4, 3)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(chamfer_distance.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 6, 3), (2, 5, 9, 3)), ((None, 2, 6, 2), (4, 2, None, 4, 2)), ((3, 5, 8, 7), (3, 1, 1, 7)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(chamfer_distance.evaluate, shapes) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the chamfer distance loss.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.loss import chamfer_distance from tensorflow_graphics.util import test_case def _random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def _random_tensor_shape(): tensor_size = np.random.randint(3) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() def _random_point_sets(): space_dimensions = np.random.randint(3) + 1 batch_shape = _random_tensor_shape() point_set_a_size = np.random.randint(10) + 1 point_set_b_size = np.random.randint(10) + 1 point_set_a_init = np.random.uniform( low=-100.0, high=100.0, size=batch_shape + [point_set_a_size, space_dimensions]) point_set_b_init = np.random.uniform( low=-100.0, high=100.0, size=batch_shape + [point_set_b_size, space_dimensions]) return (point_set_a_init, point_set_b_init) class ChamferDistanceTest(test_case.TestCase): @parameterized.parameters( (((0., 0), (0, 1), (1, 0), (-1, 0)), ((0., 0), (0, 2), (0.7, 0.4), (-0.5, -0.5)), # a[0] -> b[0] (0 + \ # a[1] -> b[2] 0.7**2 + 0.6**2 + \ # a[2] -> b[2] 0.3**2 + 0.4**2 + \ # a[3] -> b[3] 0.5) / 4 + \ # b[0] -> a[0] (0 + \ # b[1] -> a[1] 1 + \ # b[2] -> a[2] 0.3**2 + 0.4**2 + \ # b[3] -> a[3] 0.5) / 4), (((0., 1, 4), (3, 4, 2)), ((2., 2, 2), (2, 3, 4), (3, 2, 2)), # a[0] -> b[1] (8 + \ # a[1] -> b[2] 4) / 2 + \ # b[0] -> a[1] (5 + \ # b[1] -> a[1] 6 + \ # b[2] -> a[1] 4) / 3), ) def test_evaluate_preset(self, point_set_a, point_set_b, expected_distance): tensor_shape = _random_tensor_shape() point_set_a = np.tile(point_set_a, tensor_shape + [1, 1]) point_set_b = np.tile(point_set_b, tensor_shape + [1, 1]) expected = np.tile(expected_distance, tensor_shape) result = chamfer_distance.evaluate(point_set_a, point_set_b) self.assertAllClose(expected, result) def test_chamfer_distance_evaluate_jacobian(self): """Tests the Jacobian of the Chamfer distance loss.""" point_set_a, point_set_b = _random_point_sets() with self.subTest(name="jacobian_wrt_point_set_a"): self.assert_jacobian_is_correct_fn( lambda x: chamfer_distance.evaluate(x, point_set_b), [point_set_a], atol=1e-5) with self.subTest(name="jacobian_wrt_point_set_b"): self.assert_jacobian_is_correct_fn( lambda x: chamfer_distance.evaluate(point_set_a, x), [point_set_b], atol=1e-5) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 3, 5, 3), (2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 3, 5), (2, 4, 5)), ("point_set_b must have exactly 3 dimensions in axis -1,.", (2, 4, 3), (2, 4, 2)), ("point_set_b must have exactly 2 dimensions in axis -1,.", (2, 4, 2), (2, 4, 3)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(chamfer_distance.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 6, 3), (2, 5, 9, 3)), ((None, 2, 6, 2), (4, 2, None, 4, 2)), ((3, 5, 8, 7), (3, 1, 1, 7)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(chamfer_distance.evaluate, shapes) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/tests/rasterizer_op_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the opengl rasterizer op.""" from absl.testing import parameterized import numpy as np import six import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.rendering.opengl import rasterization_backend from tensorflow_graphics.util import test_case # Empty vertex shader test_vertex_shader = """ #version 450 void main() { } """ # Geometry shader that projects the vertices of visible triangles onto the image # plane. test_geometry_shader = """ #version 450 uniform mat4 view_projection_matrix; layout(points) in; layout(triangle_strip, max_vertices=3) out; out layout(location = 0) vec3 position; out layout(location = 1) vec3 normal; out layout(location = 2) vec2 bar_coord; out layout(location = 3) float tri_id; layout(binding=0) buffer triangular_mesh { float mesh_buffer[]; }; vec3 get_vertex_position(int i) { int o = gl_PrimitiveIDIn * 9 + i * 3; return vec3(mesh_buffer[o + 0], mesh_buffer[o + 1], mesh_buffer[o + 2]); } bool is_back_facing(vec3 v0, vec3 v1, vec3 v2) { vec4 tv0 = view_projection_matrix * vec4(v0, 1.0); vec4 tv1 = view_projection_matrix * vec4(v1, 1.0); vec4 tv2 = view_projection_matrix * vec4(v2, 1.0); tv0 /= tv0.w; tv1 /= tv1.w; tv2 /= tv2.w; vec2 a = (tv1.xy - tv0.xy); vec2 b = (tv2.xy - tv0.xy); return (a.x * b.y - b.x * a.y) <= 0; } void main() { vec3 v0 = get_vertex_position(0); vec3 v1 = get_vertex_position(1); vec3 v2 = get_vertex_position(2); // Cull back-facing triangles. if (is_back_facing(v0, v1, v2)) { return; } normal = normalize(cross(v1 - v0, v2 - v0)); vec3 positions[3] = {v0, v1, v2}; for (int i = 0; i < 3; ++i) { // gl_Position is a pre-defined size 4 output variable gl_Position = view_projection_matrix * vec4(positions[i], 1); bar_coord = vec2(i==0 ? 1 : 0, i==1 ? 1 : 0); tri_id = gl_PrimitiveIDIn; position = positions[i]; EmitVertex(); } EndPrimitive(); } """ # Fragment shader that packs barycentric coordinates, triangle index, and depth # map in a resulting vec4 per pixel. test_fragment_shader = """ #version 450 in layout(location = 0) vec3 position; in layout(location = 1) vec3 normal; in layout(location = 2) vec2 bar_coord; in layout(location = 3) float tri_id; out vec4 output_color; void main() { output_color = vec4(bar_coord, tri_id, position.z); } """ class RasterizerOPTest(test_case.TestCase): def test_rasterize(self): max_depth = 10 min_depth = 2 height = 480 width = 640 camera_origin = (0.0, 0.0, 0.0) camera_up = (0.0, 1.0, 0.0) look_at_point = (0.0, 0.0, 1.0) fov = (60.0 * np.math.pi / 180,) near_plane = (1.0,) far_plane = (10.0,) batch_shape = tf.convert_to_tensor( value=(2, (max_depth - min_depth) // 2), dtype=tf.int32) world_to_camera = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (float(width) / float(height),), near_plane, far_plane) view_projection_matrix = tf.matmul(perspective_matrix, world_to_camera) view_projection_matrix = tf.squeeze(view_projection_matrix) # Generate triangles at different depths and associated ground truth. tris = np.zeros((max_depth - min_depth, 9), dtype=np.float32) gt = np.zeros((max_depth - min_depth, height, width, 2), dtype=np.float32) for idx in range(max_depth - min_depth): tris[idx, :] = (-100.0, 100.0, idx + min_depth, 100.0, 100.0, idx + min_depth, 0.0, -100.0, idx + min_depth) gt[idx, :, :, :] = (0, idx + min_depth) # Broadcast the variables. render_parameters = { "view_projection_matrix": ("mat", tf.broadcast_to( input=view_projection_matrix, shape=tf.concat( values=(batch_shape, tf.shape(input=view_projection_matrix)[-2:]), axis=0))), "triangular_mesh": ("buffer", tf.reshape( tris, shape=tf.concat(values=(batch_shape, (9,)), axis=0))) } # Reshape the ground truth. gt = tf.reshape( gt, shape=tf.concat(values=(batch_shape, (height, width, 2)), axis=0)) render_parameters = list(six.iteritems(render_parameters)) variable_names = [v[0] for v in render_parameters] variable_kinds = [v[1][0] for v in render_parameters] variable_values = [v[1][1] for v in render_parameters] def rasterize(): return rasterization_backend.render_ops.rasterize( num_points=3, variable_names=variable_names, variable_kinds=variable_kinds, variable_values=variable_values, output_resolution=(width, height), vertex_shader=test_vertex_shader, geometry_shader=test_geometry_shader, fragment_shader=test_fragment_shader, ) result = rasterize() self.assertAllClose(result[..., 2:4], gt) @tf.function def check_lazy_shape(): # Within @tf.function, the tensor shape is determined by SetShapeFn # callback. Ensure that the shape of non-batch axes matches that of of # the actual tensor evaluated in eager mode above. lazy_shape = rasterize().shape self.assertEqual(lazy_shape[-3:], list(result.shape)[-3:]) check_lazy_shape() @parameterized.parameters( ("The variable names, kinds, and values must have the same size.", ["var1"], ["buffer", "buffer"], [[1.0], [1.0]], tf.errors.InvalidArgumentError, ValueError), ("The variable names, kinds, and values must have the same size.", ["var1", "var2"], ["buffer"], [[1.0], [1.0]], tf.errors.InvalidArgumentError, ValueError), ("The variable names, kinds, and values must have the same size.", ["var1", "var2"], ["buffer", "buffer"], [[1.0]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid batch", ["var1", "var2"], ["buffer", "buffer"], [[1.0], [[1.0]]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid", ["var1"], ["mat"], [[1.0]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid", ["var1"], ["buffer"], [1.0], tf.errors.InvalidArgumentError, ValueError), ) def test_invalid_variable_inputs(self, error_msg, variable_names, variable_kinds, variable_values, error_eager, error_graph_mode): height = 1 width = 1 empty_shader_code = "#version 450\n void main() { }\n" if tf.executing_eagerly(): error = error_eager else: error = error_graph_mode with self.assertRaisesRegexp(error, error_msg): self.evaluate( rasterization_backend.render_ops.rasterize( num_points=0, variable_names=variable_names, variable_kinds=variable_kinds, variable_values=variable_values, output_resolution=(width, height), vertex_shader=empty_shader_code, geometry_shader=empty_shader_code, fragment_shader=empty_shader_code)) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the opengl rasterizer op.""" from absl.testing import parameterized import numpy as np import six import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.rendering.opengl import rasterization_backend from tensorflow_graphics.util import test_case # Empty vertex shader test_vertex_shader = """ #version 450 void main() { } """ # Geometry shader that projects the vertices of visible triangles onto the image # plane. test_geometry_shader = """ #version 450 uniform mat4 view_projection_matrix; layout(points) in; layout(triangle_strip, max_vertices=3) out; out layout(location = 0) vec3 position; out layout(location = 1) vec3 normal; out layout(location = 2) vec2 bar_coord; out layout(location = 3) float tri_id; layout(binding=0) buffer triangular_mesh { float mesh_buffer[]; }; vec3 get_vertex_position(int i) { int o = gl_PrimitiveIDIn * 9 + i * 3; return vec3(mesh_buffer[o + 0], mesh_buffer[o + 1], mesh_buffer[o + 2]); } bool is_back_facing(vec3 v0, vec3 v1, vec3 v2) { vec4 tv0 = view_projection_matrix * vec4(v0, 1.0); vec4 tv1 = view_projection_matrix * vec4(v1, 1.0); vec4 tv2 = view_projection_matrix * vec4(v2, 1.0); tv0 /= tv0.w; tv1 /= tv1.w; tv2 /= tv2.w; vec2 a = (tv1.xy - tv0.xy); vec2 b = (tv2.xy - tv0.xy); return (a.x * b.y - b.x * a.y) <= 0; } void main() { vec3 v0 = get_vertex_position(0); vec3 v1 = get_vertex_position(1); vec3 v2 = get_vertex_position(2); // Cull back-facing triangles. if (is_back_facing(v0, v1, v2)) { return; } normal = normalize(cross(v1 - v0, v2 - v0)); vec3 positions[3] = {v0, v1, v2}; for (int i = 0; i < 3; ++i) { // gl_Position is a pre-defined size 4 output variable gl_Position = view_projection_matrix * vec4(positions[i], 1); bar_coord = vec2(i==0 ? 1 : 0, i==1 ? 1 : 0); tri_id = gl_PrimitiveIDIn; position = positions[i]; EmitVertex(); } EndPrimitive(); } """ # Fragment shader that packs barycentric coordinates, triangle index, and depth # map in a resulting vec4 per pixel. test_fragment_shader = """ #version 450 in layout(location = 0) vec3 position; in layout(location = 1) vec3 normal; in layout(location = 2) vec2 bar_coord; in layout(location = 3) float tri_id; out vec4 output_color; void main() { output_color = vec4(bar_coord, tri_id, position.z); } """ class RasterizerOPTest(test_case.TestCase): def test_rasterize(self): max_depth = 10 min_depth = 2 height = 480 width = 640 camera_origin = (0.0, 0.0, 0.0) camera_up = (0.0, 1.0, 0.0) look_at_point = (0.0, 0.0, 1.0) fov = (60.0 * np.math.pi / 180,) near_plane = (1.0,) far_plane = (10.0,) batch_shape = tf.convert_to_tensor( value=(2, (max_depth - min_depth) // 2), dtype=tf.int32) world_to_camera = look_at.right_handed(camera_origin, look_at_point, camera_up) perspective_matrix = perspective.right_handed( fov, (float(width) / float(height),), near_plane, far_plane) view_projection_matrix = tf.matmul(perspective_matrix, world_to_camera) view_projection_matrix = tf.squeeze(view_projection_matrix) # Generate triangles at different depths and associated ground truth. tris = np.zeros((max_depth - min_depth, 9), dtype=np.float32) gt = np.zeros((max_depth - min_depth, height, width, 2), dtype=np.float32) for idx in range(max_depth - min_depth): tris[idx, :] = (-100.0, 100.0, idx + min_depth, 100.0, 100.0, idx + min_depth, 0.0, -100.0, idx + min_depth) gt[idx, :, :, :] = (0, idx + min_depth) # Broadcast the variables. render_parameters = { "view_projection_matrix": ("mat", tf.broadcast_to( input=view_projection_matrix, shape=tf.concat( values=(batch_shape, tf.shape(input=view_projection_matrix)[-2:]), axis=0))), "triangular_mesh": ("buffer", tf.reshape( tris, shape=tf.concat(values=(batch_shape, (9,)), axis=0))) } # Reshape the ground truth. gt = tf.reshape( gt, shape=tf.concat(values=(batch_shape, (height, width, 2)), axis=0)) render_parameters = list(six.iteritems(render_parameters)) variable_names = [v[0] for v in render_parameters] variable_kinds = [v[1][0] for v in render_parameters] variable_values = [v[1][1] for v in render_parameters] def rasterize(): return rasterization_backend.render_ops.rasterize( num_points=3, variable_names=variable_names, variable_kinds=variable_kinds, variable_values=variable_values, output_resolution=(width, height), vertex_shader=test_vertex_shader, geometry_shader=test_geometry_shader, fragment_shader=test_fragment_shader, ) result = rasterize() self.assertAllClose(result[..., 2:4], gt) @tf.function def check_lazy_shape(): # Within @tf.function, the tensor shape is determined by SetShapeFn # callback. Ensure that the shape of non-batch axes matches that of of # the actual tensor evaluated in eager mode above. lazy_shape = rasterize().shape self.assertEqual(lazy_shape[-3:], list(result.shape)[-3:]) check_lazy_shape() @parameterized.parameters( ("The variable names, kinds, and values must have the same size.", ["var1"], ["buffer", "buffer"], [[1.0], [1.0]], tf.errors.InvalidArgumentError, ValueError), ("The variable names, kinds, and values must have the same size.", ["var1", "var2"], ["buffer"], [[1.0], [1.0]], tf.errors.InvalidArgumentError, ValueError), ("The variable names, kinds, and values must have the same size.", ["var1", "var2"], ["buffer", "buffer"], [[1.0]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid batch", ["var1", "var2"], ["buffer", "buffer"], [[1.0], [[1.0]]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid", ["var1"], ["mat"], [[1.0]], tf.errors.InvalidArgumentError, ValueError), ("has an invalid", ["var1"], ["buffer"], [1.0], tf.errors.InvalidArgumentError, ValueError), ) def test_invalid_variable_inputs(self, error_msg, variable_names, variable_kinds, variable_values, error_eager, error_graph_mode): height = 1 width = 1 empty_shader_code = "#version 450\n void main() { }\n" if tf.executing_eagerly(): error = error_eager else: error = error_graph_mode with self.assertRaisesRegexp(error, error_msg): self.evaluate( rasterization_backend.render_ops.rasterize( num_points=0, variable_names=variable_names, variable_kinds=variable_kinds, variable_values=variable_values, output_resolution=(width, height), vertex_shader=empty_shader_code, geometry_shader=empty_shader_code, fragment_shader=empty_shader_code)) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/fscore.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the fscore metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.nn.metric import precision as precision_module from tensorflow_graphics.nn.metric import recall as recall_module from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def evaluate(ground_truth, prediction, precision_function=precision_module.evaluate, recall_function=recall_module.evaluate, name=None): """Computes the fscore metric for the given ground truth and predicted labels. The fscore is calculated as 2 * (precision * recall) / (precision + recall) where the precision and recall are evaluated by the given function parameters. The precision and recall functions default to their definition for boolean labels (see https://en.wikipedia.org/wiki/Precision_and_recall for more details). Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth values. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predicted values. precision_function: The function to use for evaluating the precision. Defaults to the precision evaluation for binary ground-truth and predictions. recall_function: The function to use for evaluating the recall. Defaults to the recall evaluation for binary ground-truth and prediction. name: A name for this op. Defaults to "fscore_evaluate". Returns: A tensor of shape `[A1, ..., An]` that stores the fscore metric for the given ground truth labels and predictions. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "fscore_evaluate", [ground_truth, prediction]): ground_truth = tf.convert_to_tensor(value=ground_truth) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) recall = recall_function(ground_truth, prediction) precision = precision_function(ground_truth, prediction) return safe_ops.safe_signed_div(2 * precision * recall, precision + recall) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the fscore metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.nn.metric import precision as precision_module from tensorflow_graphics.nn.metric import recall as recall_module from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def evaluate(ground_truth, prediction, precision_function=precision_module.evaluate, recall_function=recall_module.evaluate, name=None): """Computes the fscore metric for the given ground truth and predicted labels. The fscore is calculated as 2 * (precision * recall) / (precision + recall) where the precision and recall are evaluated by the given function parameters. The precision and recall functions default to their definition for boolean labels (see https://en.wikipedia.org/wiki/Precision_and_recall for more details). Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth values. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predicted values. precision_function: The function to use for evaluating the precision. Defaults to the precision evaluation for binary ground-truth and predictions. recall_function: The function to use for evaluating the recall. Defaults to the recall evaluation for binary ground-truth and prediction. name: A name for this op. Defaults to "fscore_evaluate". Returns: A tensor of shape `[A1, ..., An]` that stores the fscore metric for the given ground truth labels and predictions. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "fscore_evaluate", [ground_truth, prediction]): ground_truth = tf.convert_to_tensor(value=ground_truth) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) recall = recall_function(ground_truth, prediction) precision = precision_function(ground_truth, prediction) return safe_ops.safe_signed_div(2 * precision * recall, precision + recall) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/local_implicit_grid/core/evaluator.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Utility modules for evaluating model from checkpoint. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import ast import numpy as np import tensorflow.compat.v1 as tf from tensorflow.compat.v1.io import gfile from tensorflow_graphics.projects.local_implicit_grid.core import implicit_nets as im from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig from tensorflow_graphics.projects.local_implicit_grid.core import model_g2g as g2g from tensorflow_graphics.projects.local_implicit_grid.core import model_g2v as g2v tf.logging.set_verbosity(tf.logging.ERROR) def parse_param_file(param_file): """Parse parameter file for parameters.""" with gfile.GFile(param_file, 'r') as fh: lines = fh.readlines() d = {} for l in lines: l = l.rstrip('\n') splits = l.split(':') key = splits[0] val_ = splits[1].strip() if not val_: val = '' else: try: val = ast.literal_eval(val_) except (ValueError, SyntaxError): val = str(val_) d[key] = val return d class RefinerEvaluator(object): """Load pretrained refiner and evaluate for a given code. """ def __init__(self, ckpt, codelen, dim=3, out_features=1, num_filters=128, point_batch=20000): self.ckpt = ckpt self.codelen = codelen self.dim = dim self.out_features = out_features self.num_filters = num_filters self.point_batch = point_batch self.graph = tf.Graph() self._init_graph() self.global_step_ = self.global_step.eval(session=self.sess) def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.refiner = im.ImNet(dim=self.dim, in_features=self.codelen, out_features=self.out_features, num_filters=self.num_filters) self.global_step = tf.get_variable('global_step', shape=[], dtype=tf.int64) self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3]) self.lat_ph = tf.placeholder(tf.float32, shape=[self.codelen]) lat = tf.broadcast_to(self.lat_ph[tf.newaxis], [self.point_batch, self.codelen]) code = tf.concat((self.pts_ph, lat), axis=-1) # [pb, 3+c] vals = self.refiner(code, training=False) # [pb, 1] self.vals = tf.squeeze(vals, axis=1) # [pb] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _get_grid_points(self, xmin, xmax, res): x = np.linspace(xmin, xmax, res) xyz = np.meshgrid(*tuple([x] * self.dim), indexing='ij') xyz = np.stack(xyz, axis=-1) xyz = xyz.reshape([-1, self.dim]) return xyz def eval_points(self, lat, points): """Evaluate network at locations specified by points. Args: lat: [self.codelen,] np array, latent code. points: [#v, self.dim] np array, point locations to evaluate. Returns: all_vals: [#v] np array, function values at locations. """ npt = points.shape[0] npb = int(np.ceil(float(npt)/self.point_batch)) all_vals = np.zeros([npt], dtype=np.float32) for idx in range(npb): sid = int(idx * self.point_batch) eid = int(min(npt, sid+self.point_batch)) pts = points[sid:eid] pad_w = self.point_batch - (eid - sid) pts = np.pad(pts, ((0, pad_w), (0, 0)), mode='constant') with self.graph.as_default(): val = self.sess.run(self.vals, feed_dict={self.pts_ph: pts, self.lat_ph: lat}) all_vals[sid:eid] = val[:(eid-sid)] return all_vals def eval_grid(self, lat, xmin=-1.0, xmax=1.0, res=64): """Evaluate network on a grid. Args: lat: [self.codelen,] np array, latent code. xmin: float, minimum coordinate value for grid. xmax: float, maximum coordinate value for grid. res: int, resolution (per dimension) of grid. Returns: grid_val: [res, res, res] np.float32 array, grid of values from query. """ grid_points = self._get_grid_points(xmin=xmin, xmax=xmax, res=res) point_val = self.eval_points(lat, grid_points) grid_val = point_val.reshape([res, res, res]) return grid_val class EncoderEvaluator(object): """Load pretrained grid encoder and evaluate single crops.""" def __init__(self, ckpt, in_grid_res=32, encoder_nf=32, codelen=32, grid_batch=128): """Initialization function. Args: ckpt: str, path to checkpoint. in_grid_res: int, resolution of grid to feed to encoder. encoder_nf: int, number of base filters for encoder. codelen: int, length of output latent code. grid_batch: int, batch size of cut-out grid to evaluate at a time. """ self.ckpt = ckpt self.codelen = codelen self.grid_batch = grid_batch self.in_grid_res = in_grid_res self.encoder_nf = encoder_nf self.graph = tf.Graph() self._init_graph() # creates self.sess def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.encoder = g2v.GridEncoder(in_grid_res=self.in_grid_res, num_filters=self.encoder_nf, codelen=self.codelen, name='g2v') self.grid_ph = tf.placeholder( tf.float32, shape=[None, self.in_grid_res, self.in_grid_res, self.in_grid_res, 1]) self.lats = self.encoder(self.grid_ph, training=False) # [gb, codelen] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def eval_grid(self, grid): """Strided evaluation of full grid into feature grid. Args: grid: [batch, gres, gres, gres, 1] input feature grid. Returns: codes: [batch, codelen] output feature gird. """ # initialize output feature grid niters = int(np.ceil(grid.shape[0] / self.grid_batch)) codes = [] for idx in range(niters): sid = idx * self.grid_batch eid = min(sid+self.grid_batch, grid.shape[0]) c = self.sess.run(self.lats, feed_dict={self.grid_ph: grid[sid:eid]}) codes.append(c) codes = np.concatenate(codes, axis=0) return codes.astype(np.float32) class FullGridEncoderEvaluator(object): """Load pretrained grid encoder and evaluate a full input grid. Performs windowed encoding and outputs an encoded feature grid. """ def __init__(self, ckpt, in_grid_res=32, num_filters=32, codelen=128, grid_batch=128, gres=256, overlap=True): """Initialization function. Args: ckpt: str, path to checkpoint. in_grid_res: int, resolution of grid to feed to encoder. num_filters: int, number of base filters for encoder. codelen: int, length of output latent code. grid_batch: int, batch size of cut-out grid to evaluate at a time. gres: int, resolution of the full grid. overlap: bool, whether to do overlapping or non-overlapping cutout evaluations. """ self.ckpt = ckpt self.codelen = codelen self.grid_batch = grid_batch self.in_grid_res = in_grid_res self.gres = gres self.num_filters = num_filters self.graph = tf.Graph() self._init_graph() self.global_step_ = self.global_step.eval(session=self.sess) if overlap: ijk = np.arange(0, gres-int(in_grid_res/2), int(in_grid_res/2)) self.out_grid_res = ijk.shape[0] else: ijk = np.arange(0, gres, in_grid_res) self.out_grid_res = ijk.shape[0] self.ijk = np.meshgrid(ijk, ijk, ijk, indexing='ij') self.ijk = np.stack(self.ijk, axis=-1).reshape([-1, 3]) def _init_graph(self): """Initialize computation graph for tensorflow.""" with self.graph.as_default(): self.encoder = g2v.GridEncoder( in_grid_res=self.in_grid_res, num_filters=self.num_filters, codelen=self.codelen, name='g2v') self.global_step = tf.get_variable( 'global_step', shape=[], dtype=tf.int64) self.grid_ph = tf.placeholder( tf.float32, shape=[self.gres, self.gres, self.gres]) self.start_ph = tf.placeholder(tf.int32, shape=[self.grid_batch, 3]) self.ingrid = self._batch_slice(self.grid_ph, self.start_ph, self.in_grid_res, self.grid_batch) self.ingrid = self.ingrid[..., tf.newaxis] self.lats = self.encoder(self.ingrid, training=False) # [gb, codelen] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _batch_slice(self, ary, start_ijk, w, batch_size): """Batched slicing of original grid. Args: ary: tensor, rank = 3. start_ijk: [batch_size, 3] tensor, starting index. w: width of cube to extract. batch_size: int, batch size. Returns: batched_slices: [batch_size, w, w, w] tensor, batched slices of ary. """ batch_size = start_ijk.shape[0] ijk = tf.range(w, dtype=tf.int32) slice_idx = tf.meshgrid(ijk, ijk, ijk, indexing='ij') slice_idx = tf.stack( slice_idx, axis=-1) # [in_grid_res, in_grid_res, in_grid_res, 3] slice_idx = tf.broadcast_to(slice_idx[tf.newaxis], [batch_size, w, w, w, 3]) offset = tf.broadcast_to( start_ijk[:, tf.newaxis, tf.newaxis, tf.newaxis, :], [batch_size, w, w, w, 3]) slice_idx += offset # [batch_size, in_grid_res, in_grid_res, in_grid_res, 3] batched_slices = tf.gather_nd(ary, slice_idx) # [batch_size, in_grid_res, in_grid_res, in_grid_res] return batched_slices def eval_grid(self, grid): """Strided evaluation of full grid into feature grid. Args: grid: [gres, gres, gres] input feature grid. Returns: ogrid: [out_grid_res, out_grid_res, out_grid_res, codelen] output feature gird. """ # initialize output feature grid ogrid = np.zeros([self.ijk.shape[0], self.codelen]) niters = np.ceil(self.ijk.shape[0] / self.grid_batch).astype(np.int) for idx in range(niters): sid = idx * self.grid_batch eid = min(sid + self.grid_batch, self.ijk.shape[0]) start_ijk = self.ijk[sid:eid] # pad if last iteration does not have a full batch pad_w = self.grid_batch - start_ijk.shape[0] start_ijk = np.pad(start_ijk, ((0, pad_w), (0, 0)), mode='constant') lats = self.sess.run( self.lats, feed_dict={ self.grid_ph: grid, self.start_ph: start_ijk }) ogrid[sid:eid] = lats[:eid - sid] ogrid = ogrid.reshape( [self.out_grid_res, self.out_grid_res, self.out_grid_res, self.codelen]) return ogrid.astype(np.float32) class LIGEvaluator(object): """Load pretrained grid refiner and evaluate a feature grid. """ def __init__(self, ckpt, size=(15, 15, 15), in_features=32, out_features=1, x_location_max=1, num_filters=32, min_grid_value=(0., 0., 0.), max_grid_value=(1., 1., 1.), net_type='imnet', method='linear', point_batch=20000, scope=''): """Initialization function. Args: ckpt: str, path to checkpoint. size: list or tuple of ints, grid dimension in each dimension. in_features: int, number of input channels. out_features: int, number of output channels. x_location_max: float, relative coordinate range for one voxel. num_filters: int, number of filters for refiner. min_grid_value: tuple, lower bound of query points. max_grid_value: tuple, upper bound of query points. net_type: str, one of occnet/deepsdf. method: str, one of linear/nn. point_batch: int, pseudo batch size for evaluating points. scope: str, scope of imnet layer. """ self.dim = 3 # hardcode for dim = 3 self.ckpt = ckpt self.size = size self.x_location_max = x_location_max self.num_filters = num_filters self.in_features = in_features self.out_features = out_features self.net_type = net_type self.method = method self.point_batch = point_batch self.scope = scope self.min_grid_value = min_grid_value self.max_grid_value = max_grid_value self.graph = tf.Graph() self._init_graph() def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.lig = lig.LocalImplicitGrid(size=self.size, in_features=self.in_features, out_features=self.out_features, num_filters=self.num_filters, net_type=self.net_type, method=self.method, x_location_max=self.x_location_max, min_grid_value=self.min_grid_value, max_grid_value=self.max_grid_value, name='lig') self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3]) self.latgrid_ph = tf.placeholder(tf.float32, shape=[self.size[0], self.size[1], self.size[2], self.in_features]) self.latgrid = self.latgrid_ph[tf.newaxis] self.points = self.pts_ph[tf.newaxis] vals = self.lig(self.latgrid, self.points, training=False) # [1,npts,1] self.vals = tf.squeeze(vals, axis=[0, 2]) # [npts] self.map_dict = self._get_var_mapping(model=self.lig) self.saver = tf.train.Saver(self.map_dict) self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _get_grid_points(self, xmin, xmax, res): x = np.linspace(xmin, xmax, res) xyz = np.meshgrid(*tuple([x] * self.dim), indexing='ij') xyz = np.stack(xyz, axis=-1) xyz = xyz.reshape([-1, self.dim]) return xyz def eval_points(self, latgrid, points): """Evaluate network at locations specified by points. Args: latgrid: [size0, size1, size2, self.codelen] np array, latent code. points: [#v, self.dim] np array, point locations to evaluate. Returns: all_vals: [#v] np array, function values at locations. """ npt = points.shape[0] npb = int(np.ceil(float(npt)/self.point_batch)) all_vals = np.zeros([npt], dtype=np.float32) for idx in range(npb): sid = int(idx * self.point_batch) eid = int(min(npt, sid+self.point_batch)) pts = points[sid:eid] pad_w = self.point_batch - (eid - sid) if pts.shape[0] < self.point_batch: pts_pad = np.tile(pts[0:1], (pad_w, 1)) # repeat the first point in the batch pts = np.concatenate([pts, pts_pad], axis=0) with self.graph.as_default(): val = self.sess.run(self.vals, feed_dict={self.pts_ph: pts, self.latgrid_ph: latgrid}) all_vals[sid:eid] = val[:(eid-sid)] return all_vals def eval_grid(self, latgrid, xmin=0.0, xmax=1.0, res=128): """Evaluate network on a grid. Args: latgrid: [size0, size1, size2, self.codelen] np array, latent code. xmin: float, minimum coordinate value for grid. xmax: float, maximum coordinate value for grid. res: int, resolution (per dimension) of grid. Returns: grid_val: [res, res, res] np.float32 array, grid of values from query. """ grid_points = self._get_grid_points(xmin=xmin, xmax=xmax, res=res) point_val = self.eval_points(latgrid, grid_points) grid_val = point_val.reshape([res, res, res]) return grid_val def _get_var_mapping(self, model): vars_ = model.trainable_variables varnames = [v.name for v in vars_] # .split(':')[0] varnames = [self.scope+v.replace('lig/', '').strip(':0') for v in varnames] map_dict = dict(zip(varnames, vars_)) return map_dict class UNetEvaluator(object): """Load pretrained UNet for generating feature grid for coarse voxel inputs.""" def __init__(self, ckpt, in_grid_res, out_grid_res, num_filters, max_filters, out_features, sph_norm=0.): self.ckpt = ckpt self.in_grid_res = in_grid_res self.out_grid_res = out_grid_res self.num_filters = num_filters self.max_filters = max_filters self.out_features = out_features self.sph_norm = sph_norm self.graph = tf.Graph() self._init_graph() def _init_graph(self): """Initialize computation graph for tensorflow.""" with self.graph.as_default(): self.unet = g2g.UNet3D(in_grid_res=self.in_grid_res, out_grid_res=self.out_grid_res, num_filters=self.num_filters, max_filters=self.max_filters, out_features=self.out_features) self.input_grid_ph = tf.placeholder( tf.float32, [None, None, None]) self.input_grid = self.input_grid_ph[tf.newaxis, ..., tf.newaxis] self.feat_grid = self.unet(self.input_grid) self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def eval_grid(self, input_grid): """Evaluate input grid (no batching). Args: input_grid: [in_grid_res, in_grid_res, in_grid_res] tensor. Returns: [out_grid_res, out_grid_res, out_grid_res, out_features] """ with self.graph.as_default(): feat_grid = self.sess.run(self.feat_grid, feed_dict={self.input_grid_ph: input_grid}) feat_grid = feat_grid[0] if self.sph_norm > 0: feat_grid = (feat_grid / np.linalg.norm(feat_grid, axis=-1, keepdims=True) * self.sph_norm) return feat_grid class SparseLIGEvaluator(object): """Evaluate sparse encoded feature grids.""" def __init__(self, ckpt, num_filters, codelen, origin, grid_shape, part_size, overlap=True, scope=''): self.scope = scope self.overlap = overlap self.ckpt = ckpt self.num_filters = num_filters self.codelen = codelen if overlap: self.res = (np.array(grid_shape) - 1) / 2.0 else: self.res = np.array(grid_shape) - 1 self.res = self.res.astype(np.int32) self.xmin = np.array(origin) self.xmax = self.xmin + self.res * part_size self.part_size = part_size self.lvg = LIGEvaluator(ckpt=ckpt, size=grid_shape, in_features=codelen, out_features=1, x_location_max=2-float(overlap), num_filters=num_filters, min_grid_value=self.xmin, max_grid_value=self.xmax, net_type='imnet', method='linear' if overlap else 'nn', scope=scope) def evaluate_feature_grid(self, feature_grid, mask, res_per_part=4, conservative=False): """Evaluate feature grid. Args: feature_grid: [*grid_size, codelen] np.array, feature grid to evaluate. mask: [*grid_size] bool np.array, mask for feature locations. res_per_part: int, resolution of output evaluation per part. conservative: bool, whether to do conservative evaluations. If true, evalutes a cell if either neighbor is masked. Else, evaluates a cell if all neighbors are masked. Returns: output grid. """ # setup grid eps = 1e-6 s = self.res l = [np.linspace(self.xmin[i]+eps, self.xmax[i]-eps, res_per_part*s[i]) for i in range(3)] xyz = np.stack(np.meshgrid(l[0], l[1], l[2], indexing='ij'), axis=-1).reshape(-1, 3) output_grid = np.ones([res_per_part*s[0], res_per_part*s[1], res_per_part*s[2]], dtype=np.float32).reshape(-1) mask = mask.astype(np.bool) if self.overlap: mask = np.stack([mask[:-1, :-1, :-1], mask[:-1, :-1, 1:], mask[:-1, 1:, :-1], mask[:-1, 1:, 1:], mask[1:, :-1, :-1], mask[1:, :-1, 1:], mask[1:, 1:, :-1], mask[1:, 1:, 1:]], axis=-1) if conservative: mask = np.any(mask, axis=-1) else: mask = np.all(mask, axis=-1) g = np.stack(np.meshgrid(np.arange(mask.shape[0]), np.arange(mask.shape[1]), np.arange(mask.shape[2]), indexing='ij'), axis=-1).reshape(-1, 3) g = g[:, 0]*(mask.shape[1]*mask.shape[2]) + g[:, 1]*mask.shape[2] + g[:, 2] g_valid = g[mask.ravel()] if self.overlap: ijk = np.floor((xyz - self.xmin) / self.part_size * 2).astype(np.int32) else: ijk = np.floor((xyz - self.xmin + 0.5 * self.part_size) / self.part_size).astype(np.int32) ijk_idx = (ijk[:, 0]*(mask.shape[1] * mask.shape[2]) + ijk[:, 1]*mask.shape[2] + ijk[:, 2]) pt_mask = np.isin(ijk_idx, g_valid) output_grid[pt_mask] = self.lvg.eval_points(feature_grid, xyz[pt_mask]) output_grid = output_grid.reshape(res_per_part*s[0], # pylint: disable=too-many-function-args res_per_part*s[1], res_per_part*s[2]) return output_grid

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Utility modules for evaluating model from checkpoint. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import ast import numpy as np import tensorflow.compat.v1 as tf from tensorflow.compat.v1.io import gfile from tensorflow_graphics.projects.local_implicit_grid.core import implicit_nets as im from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig from tensorflow_graphics.projects.local_implicit_grid.core import model_g2g as g2g from tensorflow_graphics.projects.local_implicit_grid.core import model_g2v as g2v tf.logging.set_verbosity(tf.logging.ERROR) def parse_param_file(param_file): """Parse parameter file for parameters.""" with gfile.GFile(param_file, 'r') as fh: lines = fh.readlines() d = {} for l in lines: l = l.rstrip('\n') splits = l.split(':') key = splits[0] val_ = splits[1].strip() if not val_: val = '' else: try: val = ast.literal_eval(val_) except (ValueError, SyntaxError): val = str(val_) d[key] = val return d class RefinerEvaluator(object): """Load pretrained refiner and evaluate for a given code. """ def __init__(self, ckpt, codelen, dim=3, out_features=1, num_filters=128, point_batch=20000): self.ckpt = ckpt self.codelen = codelen self.dim = dim self.out_features = out_features self.num_filters = num_filters self.point_batch = point_batch self.graph = tf.Graph() self._init_graph() self.global_step_ = self.global_step.eval(session=self.sess) def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.refiner = im.ImNet(dim=self.dim, in_features=self.codelen, out_features=self.out_features, num_filters=self.num_filters) self.global_step = tf.get_variable('global_step', shape=[], dtype=tf.int64) self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3]) self.lat_ph = tf.placeholder(tf.float32, shape=[self.codelen]) lat = tf.broadcast_to(self.lat_ph[tf.newaxis], [self.point_batch, self.codelen]) code = tf.concat((self.pts_ph, lat), axis=-1) # [pb, 3+c] vals = self.refiner(code, training=False) # [pb, 1] self.vals = tf.squeeze(vals, axis=1) # [pb] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _get_grid_points(self, xmin, xmax, res): x = np.linspace(xmin, xmax, res) xyz = np.meshgrid(*tuple([x] * self.dim), indexing='ij') xyz = np.stack(xyz, axis=-1) xyz = xyz.reshape([-1, self.dim]) return xyz def eval_points(self, lat, points): """Evaluate network at locations specified by points. Args: lat: [self.codelen,] np array, latent code. points: [#v, self.dim] np array, point locations to evaluate. Returns: all_vals: [#v] np array, function values at locations. """ npt = points.shape[0] npb = int(np.ceil(float(npt)/self.point_batch)) all_vals = np.zeros([npt], dtype=np.float32) for idx in range(npb): sid = int(idx * self.point_batch) eid = int(min(npt, sid+self.point_batch)) pts = points[sid:eid] pad_w = self.point_batch - (eid - sid) pts = np.pad(pts, ((0, pad_w), (0, 0)), mode='constant') with self.graph.as_default(): val = self.sess.run(self.vals, feed_dict={self.pts_ph: pts, self.lat_ph: lat}) all_vals[sid:eid] = val[:(eid-sid)] return all_vals def eval_grid(self, lat, xmin=-1.0, xmax=1.0, res=64): """Evaluate network on a grid. Args: lat: [self.codelen,] np array, latent code. xmin: float, minimum coordinate value for grid. xmax: float, maximum coordinate value for grid. res: int, resolution (per dimension) of grid. Returns: grid_val: [res, res, res] np.float32 array, grid of values from query. """ grid_points = self._get_grid_points(xmin=xmin, xmax=xmax, res=res) point_val = self.eval_points(lat, grid_points) grid_val = point_val.reshape([res, res, res]) return grid_val class EncoderEvaluator(object): """Load pretrained grid encoder and evaluate single crops.""" def __init__(self, ckpt, in_grid_res=32, encoder_nf=32, codelen=32, grid_batch=128): """Initialization function. Args: ckpt: str, path to checkpoint. in_grid_res: int, resolution of grid to feed to encoder. encoder_nf: int, number of base filters for encoder. codelen: int, length of output latent code. grid_batch: int, batch size of cut-out grid to evaluate at a time. """ self.ckpt = ckpt self.codelen = codelen self.grid_batch = grid_batch self.in_grid_res = in_grid_res self.encoder_nf = encoder_nf self.graph = tf.Graph() self._init_graph() # creates self.sess def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.encoder = g2v.GridEncoder(in_grid_res=self.in_grid_res, num_filters=self.encoder_nf, codelen=self.codelen, name='g2v') self.grid_ph = tf.placeholder( tf.float32, shape=[None, self.in_grid_res, self.in_grid_res, self.in_grid_res, 1]) self.lats = self.encoder(self.grid_ph, training=False) # [gb, codelen] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def eval_grid(self, grid): """Strided evaluation of full grid into feature grid. Args: grid: [batch, gres, gres, gres, 1] input feature grid. Returns: codes: [batch, codelen] output feature gird. """ # initialize output feature grid niters = int(np.ceil(grid.shape[0] / self.grid_batch)) codes = [] for idx in range(niters): sid = idx * self.grid_batch eid = min(sid+self.grid_batch, grid.shape[0]) c = self.sess.run(self.lats, feed_dict={self.grid_ph: grid[sid:eid]}) codes.append(c) codes = np.concatenate(codes, axis=0) return codes.astype(np.float32) class FullGridEncoderEvaluator(object): """Load pretrained grid encoder and evaluate a full input grid. Performs windowed encoding and outputs an encoded feature grid. """ def __init__(self, ckpt, in_grid_res=32, num_filters=32, codelen=128, grid_batch=128, gres=256, overlap=True): """Initialization function. Args: ckpt: str, path to checkpoint. in_grid_res: int, resolution of grid to feed to encoder. num_filters: int, number of base filters for encoder. codelen: int, length of output latent code. grid_batch: int, batch size of cut-out grid to evaluate at a time. gres: int, resolution of the full grid. overlap: bool, whether to do overlapping or non-overlapping cutout evaluations. """ self.ckpt = ckpt self.codelen = codelen self.grid_batch = grid_batch self.in_grid_res = in_grid_res self.gres = gres self.num_filters = num_filters self.graph = tf.Graph() self._init_graph() self.global_step_ = self.global_step.eval(session=self.sess) if overlap: ijk = np.arange(0, gres-int(in_grid_res/2), int(in_grid_res/2)) self.out_grid_res = ijk.shape[0] else: ijk = np.arange(0, gres, in_grid_res) self.out_grid_res = ijk.shape[0] self.ijk = np.meshgrid(ijk, ijk, ijk, indexing='ij') self.ijk = np.stack(self.ijk, axis=-1).reshape([-1, 3]) def _init_graph(self): """Initialize computation graph for tensorflow.""" with self.graph.as_default(): self.encoder = g2v.GridEncoder( in_grid_res=self.in_grid_res, num_filters=self.num_filters, codelen=self.codelen, name='g2v') self.global_step = tf.get_variable( 'global_step', shape=[], dtype=tf.int64) self.grid_ph = tf.placeholder( tf.float32, shape=[self.gres, self.gres, self.gres]) self.start_ph = tf.placeholder(tf.int32, shape=[self.grid_batch, 3]) self.ingrid = self._batch_slice(self.grid_ph, self.start_ph, self.in_grid_res, self.grid_batch) self.ingrid = self.ingrid[..., tf.newaxis] self.lats = self.encoder(self.ingrid, training=False) # [gb, codelen] self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _batch_slice(self, ary, start_ijk, w, batch_size): """Batched slicing of original grid. Args: ary: tensor, rank = 3. start_ijk: [batch_size, 3] tensor, starting index. w: width of cube to extract. batch_size: int, batch size. Returns: batched_slices: [batch_size, w, w, w] tensor, batched slices of ary. """ batch_size = start_ijk.shape[0] ijk = tf.range(w, dtype=tf.int32) slice_idx = tf.meshgrid(ijk, ijk, ijk, indexing='ij') slice_idx = tf.stack( slice_idx, axis=-1) # [in_grid_res, in_grid_res, in_grid_res, 3] slice_idx = tf.broadcast_to(slice_idx[tf.newaxis], [batch_size, w, w, w, 3]) offset = tf.broadcast_to( start_ijk[:, tf.newaxis, tf.newaxis, tf.newaxis, :], [batch_size, w, w, w, 3]) slice_idx += offset # [batch_size, in_grid_res, in_grid_res, in_grid_res, 3] batched_slices = tf.gather_nd(ary, slice_idx) # [batch_size, in_grid_res, in_grid_res, in_grid_res] return batched_slices def eval_grid(self, grid): """Strided evaluation of full grid into feature grid. Args: grid: [gres, gres, gres] input feature grid. Returns: ogrid: [out_grid_res, out_grid_res, out_grid_res, codelen] output feature gird. """ # initialize output feature grid ogrid = np.zeros([self.ijk.shape[0], self.codelen]) niters = np.ceil(self.ijk.shape[0] / self.grid_batch).astype(np.int) for idx in range(niters): sid = idx * self.grid_batch eid = min(sid + self.grid_batch, self.ijk.shape[0]) start_ijk = self.ijk[sid:eid] # pad if last iteration does not have a full batch pad_w = self.grid_batch - start_ijk.shape[0] start_ijk = np.pad(start_ijk, ((0, pad_w), (0, 0)), mode='constant') lats = self.sess.run( self.lats, feed_dict={ self.grid_ph: grid, self.start_ph: start_ijk }) ogrid[sid:eid] = lats[:eid - sid] ogrid = ogrid.reshape( [self.out_grid_res, self.out_grid_res, self.out_grid_res, self.codelen]) return ogrid.astype(np.float32) class LIGEvaluator(object): """Load pretrained grid refiner and evaluate a feature grid. """ def __init__(self, ckpt, size=(15, 15, 15), in_features=32, out_features=1, x_location_max=1, num_filters=32, min_grid_value=(0., 0., 0.), max_grid_value=(1., 1., 1.), net_type='imnet', method='linear', point_batch=20000, scope=''): """Initialization function. Args: ckpt: str, path to checkpoint. size: list or tuple of ints, grid dimension in each dimension. in_features: int, number of input channels. out_features: int, number of output channels. x_location_max: float, relative coordinate range for one voxel. num_filters: int, number of filters for refiner. min_grid_value: tuple, lower bound of query points. max_grid_value: tuple, upper bound of query points. net_type: str, one of occnet/deepsdf. method: str, one of linear/nn. point_batch: int, pseudo batch size for evaluating points. scope: str, scope of imnet layer. """ self.dim = 3 # hardcode for dim = 3 self.ckpt = ckpt self.size = size self.x_location_max = x_location_max self.num_filters = num_filters self.in_features = in_features self.out_features = out_features self.net_type = net_type self.method = method self.point_batch = point_batch self.scope = scope self.min_grid_value = min_grid_value self.max_grid_value = max_grid_value self.graph = tf.Graph() self._init_graph() def _init_graph(self): """Initialize computation graph for tensorflow. """ with self.graph.as_default(): self.lig = lig.LocalImplicitGrid(size=self.size, in_features=self.in_features, out_features=self.out_features, num_filters=self.num_filters, net_type=self.net_type, method=self.method, x_location_max=self.x_location_max, min_grid_value=self.min_grid_value, max_grid_value=self.max_grid_value, name='lig') self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3]) self.latgrid_ph = tf.placeholder(tf.float32, shape=[self.size[0], self.size[1], self.size[2], self.in_features]) self.latgrid = self.latgrid_ph[tf.newaxis] self.points = self.pts_ph[tf.newaxis] vals = self.lig(self.latgrid, self.points, training=False) # [1,npts,1] self.vals = tf.squeeze(vals, axis=[0, 2]) # [npts] self.map_dict = self._get_var_mapping(model=self.lig) self.saver = tf.train.Saver(self.map_dict) self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def _get_grid_points(self, xmin, xmax, res): x = np.linspace(xmin, xmax, res) xyz = np.meshgrid(*tuple([x] * self.dim), indexing='ij') xyz = np.stack(xyz, axis=-1) xyz = xyz.reshape([-1, self.dim]) return xyz def eval_points(self, latgrid, points): """Evaluate network at locations specified by points. Args: latgrid: [size0, size1, size2, self.codelen] np array, latent code. points: [#v, self.dim] np array, point locations to evaluate. Returns: all_vals: [#v] np array, function values at locations. """ npt = points.shape[0] npb = int(np.ceil(float(npt)/self.point_batch)) all_vals = np.zeros([npt], dtype=np.float32) for idx in range(npb): sid = int(idx * self.point_batch) eid = int(min(npt, sid+self.point_batch)) pts = points[sid:eid] pad_w = self.point_batch - (eid - sid) if pts.shape[0] < self.point_batch: pts_pad = np.tile(pts[0:1], (pad_w, 1)) # repeat the first point in the batch pts = np.concatenate([pts, pts_pad], axis=0) with self.graph.as_default(): val = self.sess.run(self.vals, feed_dict={self.pts_ph: pts, self.latgrid_ph: latgrid}) all_vals[sid:eid] = val[:(eid-sid)] return all_vals def eval_grid(self, latgrid, xmin=0.0, xmax=1.0, res=128): """Evaluate network on a grid. Args: latgrid: [size0, size1, size2, self.codelen] np array, latent code. xmin: float, minimum coordinate value for grid. xmax: float, maximum coordinate value for grid. res: int, resolution (per dimension) of grid. Returns: grid_val: [res, res, res] np.float32 array, grid of values from query. """ grid_points = self._get_grid_points(xmin=xmin, xmax=xmax, res=res) point_val = self.eval_points(latgrid, grid_points) grid_val = point_val.reshape([res, res, res]) return grid_val def _get_var_mapping(self, model): vars_ = model.trainable_variables varnames = [v.name for v in vars_] # .split(':')[0] varnames = [self.scope+v.replace('lig/', '').strip(':0') for v in varnames] map_dict = dict(zip(varnames, vars_)) return map_dict class UNetEvaluator(object): """Load pretrained UNet for generating feature grid for coarse voxel inputs.""" def __init__(self, ckpt, in_grid_res, out_grid_res, num_filters, max_filters, out_features, sph_norm=0.): self.ckpt = ckpt self.in_grid_res = in_grid_res self.out_grid_res = out_grid_res self.num_filters = num_filters self.max_filters = max_filters self.out_features = out_features self.sph_norm = sph_norm self.graph = tf.Graph() self._init_graph() def _init_graph(self): """Initialize computation graph for tensorflow.""" with self.graph.as_default(): self.unet = g2g.UNet3D(in_grid_res=self.in_grid_res, out_grid_res=self.out_grid_res, num_filters=self.num_filters, max_filters=self.max_filters, out_features=self.out_features) self.input_grid_ph = tf.placeholder( tf.float32, [None, None, None]) self.input_grid = self.input_grid_ph[tf.newaxis, ..., tf.newaxis] self.feat_grid = self.unet(self.input_grid) self.saver = tf.train.Saver() self.sess = tf.Session() self.saver.restore(self.sess, self.ckpt) def eval_grid(self, input_grid): """Evaluate input grid (no batching). Args: input_grid: [in_grid_res, in_grid_res, in_grid_res] tensor. Returns: [out_grid_res, out_grid_res, out_grid_res, out_features] """ with self.graph.as_default(): feat_grid = self.sess.run(self.feat_grid, feed_dict={self.input_grid_ph: input_grid}) feat_grid = feat_grid[0] if self.sph_norm > 0: feat_grid = (feat_grid / np.linalg.norm(feat_grid, axis=-1, keepdims=True) * self.sph_norm) return feat_grid class SparseLIGEvaluator(object): """Evaluate sparse encoded feature grids.""" def __init__(self, ckpt, num_filters, codelen, origin, grid_shape, part_size, overlap=True, scope=''): self.scope = scope self.overlap = overlap self.ckpt = ckpt self.num_filters = num_filters self.codelen = codelen if overlap: self.res = (np.array(grid_shape) - 1) / 2.0 else: self.res = np.array(grid_shape) - 1 self.res = self.res.astype(np.int32) self.xmin = np.array(origin) self.xmax = self.xmin + self.res * part_size self.part_size = part_size self.lvg = LIGEvaluator(ckpt=ckpt, size=grid_shape, in_features=codelen, out_features=1, x_location_max=2-float(overlap), num_filters=num_filters, min_grid_value=self.xmin, max_grid_value=self.xmax, net_type='imnet', method='linear' if overlap else 'nn', scope=scope) def evaluate_feature_grid(self, feature_grid, mask, res_per_part=4, conservative=False): """Evaluate feature grid. Args: feature_grid: [*grid_size, codelen] np.array, feature grid to evaluate. mask: [*grid_size] bool np.array, mask for feature locations. res_per_part: int, resolution of output evaluation per part. conservative: bool, whether to do conservative evaluations. If true, evalutes a cell if either neighbor is masked. Else, evaluates a cell if all neighbors are masked. Returns: output grid. """ # setup grid eps = 1e-6 s = self.res l = [np.linspace(self.xmin[i]+eps, self.xmax[i]-eps, res_per_part*s[i]) for i in range(3)] xyz = np.stack(np.meshgrid(l[0], l[1], l[2], indexing='ij'), axis=-1).reshape(-1, 3) output_grid = np.ones([res_per_part*s[0], res_per_part*s[1], res_per_part*s[2]], dtype=np.float32).reshape(-1) mask = mask.astype(np.bool) if self.overlap: mask = np.stack([mask[:-1, :-1, :-1], mask[:-1, :-1, 1:], mask[:-1, 1:, :-1], mask[:-1, 1:, 1:], mask[1:, :-1, :-1], mask[1:, :-1, 1:], mask[1:, 1:, :-1], mask[1:, 1:, 1:]], axis=-1) if conservative: mask = np.any(mask, axis=-1) else: mask = np.all(mask, axis=-1) g = np.stack(np.meshgrid(np.arange(mask.shape[0]), np.arange(mask.shape[1]), np.arange(mask.shape[2]), indexing='ij'), axis=-1).reshape(-1, 3) g = g[:, 0]*(mask.shape[1]*mask.shape[2]) + g[:, 1]*mask.shape[2] + g[:, 2] g_valid = g[mask.ravel()] if self.overlap: ijk = np.floor((xyz - self.xmin) / self.part_size * 2).astype(np.int32) else: ijk = np.floor((xyz - self.xmin + 0.5 * self.part_size) / self.part_size).astype(np.int32) ijk_idx = (ijk[:, 0]*(mask.shape[1] * mask.shape[2]) + ijk[:, 1]*mask.shape[2] + ijk[:, 2]) pt_mask = np.isin(ijk_idx, g_valid) output_grid[pt_mask] = self.lvg.eval_points(feature_grid, xyz[pt_mask]) output_grid = output_grid.reshape(res_per_part*s[0], # pylint: disable=too-many-function-args res_per_part*s[1], res_per_part*s[2]) return output_grid

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/test_case.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Unit test base class. This class is intended to be used as the unit test base class in TensorFlow Graphics. It implements new methods on top of the TensorFlow TestCase class that are used to simplify the code and check for various kinds of failure. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import warnings from absl import flags from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.util import tfg_flags FLAGS = flags.FLAGS def _max_error(arrays1, arrays2): """Computes maximum elementwise gap between two lists of ndarrays. Computes the maximum elementwise gap between two lists with the same length, of arrays with the same shape. Args: arrays1: a lists of np.ndarrays. arrays2: a lists of np.ndarrays of the same shape as arrays1. Returns: The maximum elementwise absolute difference between the two lists of arrays. """ error = 0 for array1, array2 in zip(arrays1, arrays2): if array1.size or array2.size: # Handle zero size ndarrays correctly error = np.maximum(error, np.fabs(array1 - array2).max()) return error class TestCase(parameterized.TestCase, tf.test.TestCase): """Test case class implementing extra test functionalities.""" def setUp(self): # pylint: disable=invalid-name """Sets the seed for tensorflow and numpy.""" super(TestCase, self).setUp() try: seed = flags.FLAGS.test_random_seed except flags.UnparsedFlagAccessError: seed = 301 # Default seed in case test_random_seed is not defined. tf.compat.v1.set_random_seed(seed) np.random.seed(seed) FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value = True def _remove_dynamic_shapes(self, shapes): for s in shapes: if None in s: return None return shapes def _compute_gradient_error(self, x, y, x_init_value, delta=1e-6): """Computes the gradient error. Args: x: a tensor or list of tensors. y: a tensor. x_init_value: a numpy array of the same shape as "x" representing the initial value of x. delta: (optional) the amount of perturbation. Returns: A tuple (max_error, row, column), with max_error the maxium error between the two Jacobians, and row/column the position of said maximum error. """ x_shape = x.shape.as_list() y_shape = y.shape.as_list() with self.cached_session(): grad = tf.compat.v1.test.compute_gradient(x, x_shape, y, y_shape, x_init_value, delta) if isinstance(grad, tuple): grad = [grad] error = 0 row_max_error = 0 column_max_error = 0 for j_t, j_n in grad: if j_t.size or j_n.size: # Handle zero size tensors correctly diff = np.fabs(j_t - j_n) max_error = np.maximum(error, diff.max()) row_max_error, column_max_error = np.unravel_index( diff.argmax(), diff.shape) return max_error, row_max_error, column_max_error def _create_placeholders_from_shapes(self, shapes, dtypes=None, sparse_tensors=None): """Creates a list of placeholders based on a list of shapes. Args: shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A `bool` list denoting if placeholder is a SparseTensor. This is ignored in eager mode - in eager execution, only dense placeholders will be created. Returns: A list of placeholders. """ if dtypes is None: dtypes = [tf.float32] * len(shapes) if sparse_tensors is None: sparse_tensors = [False] * len(shapes) if tf.executing_eagerly(): placeholders = [ tf.compat.v1.placeholder_with_default( tf.zeros(shape=shape, dtype=dtype), shape=shape) for shape, dtype in zip(shapes, dtypes) ] else: placeholders = [ tf.compat.v1.sparse.placeholder(dtype, shape=shape) if is_sparse else tf.compat.v1.placeholder(shape=shape, dtype=dtype) for shape, dtype, is_sparse in zip(shapes, dtypes, sparse_tensors) ] return placeholders def _tile_tensors(self, tiling, tensors): """Tiles a set of tensors using the tiling information. Args: tiling: A list of integers defining how to tile the tensors. tensors: A list of tensors to tile. Returns: A list of tiled tensors. """ tensors = [ np.tile(tensor, tiling + [1] * len(np.array(tensor).shape)) for tensor in tensors ] return tensors def assert_exception_is_not_raised(self, func, shapes, dtypes=None, sparse_tensors=None, **kwargs): """Runs the function to make sure an exception is not raised. Args: func: A function to exectute. shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A list of `bool` indicating if the inputs are SparseTensors. Defaults to all `False`. This is used for creating SparseTensor placeholders in graph mode. **kwargs: A dict of keyword arguments to be passed to the function. """ if tf.executing_eagerly() and shapes: # If a shape is given in eager mode, the tensor will be initialized with # zeros, which can make some range checks fail for certain functions. # But if only kwargs are passed and shapes is empty, this function # still should run correctly. return placeholders = self._create_placeholders_from_shapes( shapes=shapes, dtypes=dtypes, sparse_tensors=sparse_tensors) try: func(*placeholders, **kwargs) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) def assert_exception_is_raised(self, func, error_msg, shapes, dtypes=None, sparse_tensors=None, **kwargs): """Runs the function to make sure an exception is raised. Args: func: A function to exectute. error_msg: The error message of the exception. shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A list of `bool` indicating if the inputs are SparseTensors. Defaults to all `False`. This is used for creating SparseTensor placeholders in graph mode. **kwargs: A dict of keyword arguments to be passed to the function. """ if tf.executing_eagerly(): # If shapes is an empty list, we can continue with the test. If shapes # has None values, we shoud return. shapes = self._remove_dynamic_shapes(shapes) if shapes is None: return placeholders = self._create_placeholders_from_shapes( shapes=shapes, dtypes=dtypes, sparse_tensors=sparse_tensors) with self.assertRaisesRegexp(ValueError, error_msg): func(*placeholders, **kwargs) def assert_jacobian_is_correct(self, x, x_init, y, atol=1e-6, delta=1e-6): """Tests that the gradient error of y=f(x) is small. Args: x: A tensor. x_init: A numpy array containing the values at which to estimate the gradients of y. y: A tensor. atol: Maximum absolute tolerance in gradient error. delta: The amount of perturbation. """ warnings.warn(( "assert_jacobian_is_correct is deprecated and might get " "removed in a future version please use assert_jacobian_is_correct_fn"), DeprecationWarning) if tf.executing_eagerly(): self.skipTest(reason="Graph mode only test") max_error, _, _ = self._compute_gradient_error(x, y, x_init, delta) self.assertLessEqual(max_error, atol) def assert_jacobian_is_correct_fn(self, f, x, atol=1e-6, delta=1e-6): """Tests that the gradient error of y=f(x) is small. Args: f: the function. x: A list of arguments for the function atol: Maximum absolute tolerance in gradient error. delta: The amount of perturbation. """ # pylint: disable=no-value-for-parameter if tf.executing_eagerly(): max_error = _max_error(*tf.test.compute_gradient(f, x, delta)) else: with self.cached_session(): max_error = _max_error(*tf.test.compute_gradient(f, x, delta)) # pylint: enable=no-value-for-parameter self.assertLessEqual(max_error, atol) def assert_jacobian_is_finite(self, x, x_init, y): """Tests that the Jacobian only contains valid values. The analytical gradients and numerical ones are expected to differ at points where y is not smooth. This function can be used to check that the analytical gradient is not NaN nor Inf. Args: x: A tensor. x_init: A numpy array containing the values at which to estimate the gradients of y. y: A tensor. """ warnings.warn(( "assert_jacobian_is_finite is deprecated and might get " "removed in a future version please use assert_jacobian_is_finite_fn"), DeprecationWarning) if tf.executing_eagerly(): self.skipTest(reason="Graph mode only test") x_shape = x.shape.as_list() y_shape = y.shape.as_list() with tf.compat.v1.Session(): gradient = tf.compat.v1.test.compute_gradient( x, x_shape, y, y_shape, x_init_value=x_init) theoretical_gradient = gradient[0][0] self.assertFalse( np.isnan(theoretical_gradient).any() or np.isinf(theoretical_gradient).any()) def assert_jacobian_is_finite_fn(self, f, x): """Tests that the Jacobian only contains valid values. The analytical gradients and numerical ones are expected to differ at points where f(x) is not smooth. This function can be used to check that the analytical gradient is not 'NaN' nor 'Inf'. Args: f: the function. x: A list of arguments for the function """ if tf.executing_eagerly(): theoretical_gradient, _ = tf.compat.v2.test.compute_gradient(f, x) else: with self.cached_session(): theoretical_gradient, _ = tf.compat.v2.test.compute_gradient(f, x) self.assertNotIn( True, [ np.isnan(element).any() or np.isinf(element).any() for element in theoretical_gradient ], msg="nan or inf elements found in theoretical jacobian.") def assert_output_is_correct(self, func, test_inputs, test_outputs, rtol=1e-3, atol=1e-6, tile=True): """Tests that the function gives the correct result. Args: func: A function to exectute. test_inputs: A tuple or list of test inputs. test_outputs: A tuple or list of test outputs against which the result of calling `func` on `test_inputs` will be compared to. rtol: The relative tolerance used during the comparison. atol: The absolute tolerance used during the comparison. tile: A `bool` indicating whether or not to automatically tile the test inputs and outputs. """ if tile: # Creates a rank 4 list of values between 1 and 10. tensor_tile = np.random.randint(1, 10, size=np.random.randint(4)).tolist() test_inputs = self._tile_tensors(tensor_tile, test_inputs) test_outputs = self._tile_tensors(tensor_tile, test_outputs) test_outputs = [ tf.convert_to_tensor(value=output) for output in test_outputs ] test_outputs = test_outputs[0] if len(test_outputs) == 1 else test_outputs self.assertAllClose(test_outputs, func(*test_inputs), rtol=rtol, atol=atol) def assert_tf_lite_convertible(self, func, shapes, dtypes=None, test_inputs=None): """Runs the tf-lite converter to make sure the function can be exported. Args: func: A function to execute with tf-lite. shapes: A tuple or list of input shapes. dtypes: A list of input types. test_inputs: A tuple or list of inputs. If not provided the test inputs will be randomly generated. """ if tf.executing_eagerly(): # Currently TFLite conversion is not supported in eager mode. self.skipTest(reason="Graph mode only test") # Generate graph with the function given as input. in_tensors = self._create_placeholders_from_shapes(shapes, dtypes) out_tensors = func(*in_tensors) if not isinstance(out_tensors, (list, tuple)): out_tensors = [out_tensors] with tf.compat.v1.Session() as sess: try: sess.run(tf.compat.v1.global_variables_initializer()) # Convert to a TFLite model. converter = tf.compat.v1.lite.TFLiteConverter.from_session( sess, in_tensors, out_tensors) tflite_model = converter.convert() # Load TFLite model and allocate tensors. interpreter = tf.lite.Interpreter(model_content=tflite_model) interpreter.allocate_tensors() # If no test inputs provided then randomly generate inputs. if test_inputs is None: test_inputs = [ np.array(np.random.sample(shape), dtype=np.float32) for shape in shapes ] else: test_inputs = [ np.array(test, dtype=np.float32) for test in test_inputs ] # Evaluate function using TensorFlow. feed_dict = dict(zip(in_tensors, test_inputs)) test_outputs = sess.run(out_tensors, feed_dict) # Set tensors for the TFLite model. input_details = interpreter.get_input_details() for i, test_input in enumerate(test_inputs): index = input_details[i]["index"] interpreter.set_tensor(index, test_input) # Run TFLite model. interpreter.invoke() # Get tensors from the TFLite model and compare with TensorFlow. output_details = interpreter.get_output_details() for o, test_output in enumerate(test_outputs): index = output_details[o]["index"] self.assertAllClose(test_output, interpreter.get_tensor(index)) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) def main(argv=None): """Main function.""" tf.test.main(argv) # The util functions or classes are not exported. __all__ = []

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Unit test base class. This class is intended to be used as the unit test base class in TensorFlow Graphics. It implements new methods on top of the TensorFlow TestCase class that are used to simplify the code and check for various kinds of failure. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import warnings from absl import flags from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.util import tfg_flags FLAGS = flags.FLAGS def _max_error(arrays1, arrays2): """Computes maximum elementwise gap between two lists of ndarrays. Computes the maximum elementwise gap between two lists with the same length, of arrays with the same shape. Args: arrays1: a lists of np.ndarrays. arrays2: a lists of np.ndarrays of the same shape as arrays1. Returns: The maximum elementwise absolute difference between the two lists of arrays. """ error = 0 for array1, array2 in zip(arrays1, arrays2): if array1.size or array2.size: # Handle zero size ndarrays correctly error = np.maximum(error, np.fabs(array1 - array2).max()) return error class TestCase(parameterized.TestCase, tf.test.TestCase): """Test case class implementing extra test functionalities.""" def setUp(self): # pylint: disable=invalid-name """Sets the seed for tensorflow and numpy.""" super(TestCase, self).setUp() try: seed = flags.FLAGS.test_random_seed except flags.UnparsedFlagAccessError: seed = 301 # Default seed in case test_random_seed is not defined. tf.compat.v1.set_random_seed(seed) np.random.seed(seed) FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value = True def _remove_dynamic_shapes(self, shapes): for s in shapes: if None in s: return None return shapes def _compute_gradient_error(self, x, y, x_init_value, delta=1e-6): """Computes the gradient error. Args: x: a tensor or list of tensors. y: a tensor. x_init_value: a numpy array of the same shape as "x" representing the initial value of x. delta: (optional) the amount of perturbation. Returns: A tuple (max_error, row, column), with max_error the maxium error between the two Jacobians, and row/column the position of said maximum error. """ x_shape = x.shape.as_list() y_shape = y.shape.as_list() with self.cached_session(): grad = tf.compat.v1.test.compute_gradient(x, x_shape, y, y_shape, x_init_value, delta) if isinstance(grad, tuple): grad = [grad] error = 0 row_max_error = 0 column_max_error = 0 for j_t, j_n in grad: if j_t.size or j_n.size: # Handle zero size tensors correctly diff = np.fabs(j_t - j_n) max_error = np.maximum(error, diff.max()) row_max_error, column_max_error = np.unravel_index( diff.argmax(), diff.shape) return max_error, row_max_error, column_max_error def _create_placeholders_from_shapes(self, shapes, dtypes=None, sparse_tensors=None): """Creates a list of placeholders based on a list of shapes. Args: shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A `bool` list denoting if placeholder is a SparseTensor. This is ignored in eager mode - in eager execution, only dense placeholders will be created. Returns: A list of placeholders. """ if dtypes is None: dtypes = [tf.float32] * len(shapes) if sparse_tensors is None: sparse_tensors = [False] * len(shapes) if tf.executing_eagerly(): placeholders = [ tf.compat.v1.placeholder_with_default( tf.zeros(shape=shape, dtype=dtype), shape=shape) for shape, dtype in zip(shapes, dtypes) ] else: placeholders = [ tf.compat.v1.sparse.placeholder(dtype, shape=shape) if is_sparse else tf.compat.v1.placeholder(shape=shape, dtype=dtype) for shape, dtype, is_sparse in zip(shapes, dtypes, sparse_tensors) ] return placeholders def _tile_tensors(self, tiling, tensors): """Tiles a set of tensors using the tiling information. Args: tiling: A list of integers defining how to tile the tensors. tensors: A list of tensors to tile. Returns: A list of tiled tensors. """ tensors = [ np.tile(tensor, tiling + [1] * len(np.array(tensor).shape)) for tensor in tensors ] return tensors def assert_exception_is_not_raised(self, func, shapes, dtypes=None, sparse_tensors=None, **kwargs): """Runs the function to make sure an exception is not raised. Args: func: A function to exectute. shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A list of `bool` indicating if the inputs are SparseTensors. Defaults to all `False`. This is used for creating SparseTensor placeholders in graph mode. **kwargs: A dict of keyword arguments to be passed to the function. """ if tf.executing_eagerly() and shapes: # If a shape is given in eager mode, the tensor will be initialized with # zeros, which can make some range checks fail for certain functions. # But if only kwargs are passed and shapes is empty, this function # still should run correctly. return placeholders = self._create_placeholders_from_shapes( shapes=shapes, dtypes=dtypes, sparse_tensors=sparse_tensors) try: func(*placeholders, **kwargs) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) def assert_exception_is_raised(self, func, error_msg, shapes, dtypes=None, sparse_tensors=None, **kwargs): """Runs the function to make sure an exception is raised. Args: func: A function to exectute. error_msg: The error message of the exception. shapes: A tuple or list of the input shapes. dtypes: A list of input types. sparse_tensors: A list of `bool` indicating if the inputs are SparseTensors. Defaults to all `False`. This is used for creating SparseTensor placeholders in graph mode. **kwargs: A dict of keyword arguments to be passed to the function. """ if tf.executing_eagerly(): # If shapes is an empty list, we can continue with the test. If shapes # has None values, we shoud return. shapes = self._remove_dynamic_shapes(shapes) if shapes is None: return placeholders = self._create_placeholders_from_shapes( shapes=shapes, dtypes=dtypes, sparse_tensors=sparse_tensors) with self.assertRaisesRegexp(ValueError, error_msg): func(*placeholders, **kwargs) def assert_jacobian_is_correct(self, x, x_init, y, atol=1e-6, delta=1e-6): """Tests that the gradient error of y=f(x) is small. Args: x: A tensor. x_init: A numpy array containing the values at which to estimate the gradients of y. y: A tensor. atol: Maximum absolute tolerance in gradient error. delta: The amount of perturbation. """ warnings.warn(( "assert_jacobian_is_correct is deprecated and might get " "removed in a future version please use assert_jacobian_is_correct_fn"), DeprecationWarning) if tf.executing_eagerly(): self.skipTest(reason="Graph mode only test") max_error, _, _ = self._compute_gradient_error(x, y, x_init, delta) self.assertLessEqual(max_error, atol) def assert_jacobian_is_correct_fn(self, f, x, atol=1e-6, delta=1e-6): """Tests that the gradient error of y=f(x) is small. Args: f: the function. x: A list of arguments for the function atol: Maximum absolute tolerance in gradient error. delta: The amount of perturbation. """ # pylint: disable=no-value-for-parameter if tf.executing_eagerly(): max_error = _max_error(*tf.test.compute_gradient(f, x, delta)) else: with self.cached_session(): max_error = _max_error(*tf.test.compute_gradient(f, x, delta)) # pylint: enable=no-value-for-parameter self.assertLessEqual(max_error, atol) def assert_jacobian_is_finite(self, x, x_init, y): """Tests that the Jacobian only contains valid values. The analytical gradients and numerical ones are expected to differ at points where y is not smooth. This function can be used to check that the analytical gradient is not NaN nor Inf. Args: x: A tensor. x_init: A numpy array containing the values at which to estimate the gradients of y. y: A tensor. """ warnings.warn(( "assert_jacobian_is_finite is deprecated and might get " "removed in a future version please use assert_jacobian_is_finite_fn"), DeprecationWarning) if tf.executing_eagerly(): self.skipTest(reason="Graph mode only test") x_shape = x.shape.as_list() y_shape = y.shape.as_list() with tf.compat.v1.Session(): gradient = tf.compat.v1.test.compute_gradient( x, x_shape, y, y_shape, x_init_value=x_init) theoretical_gradient = gradient[0][0] self.assertFalse( np.isnan(theoretical_gradient).any() or np.isinf(theoretical_gradient).any()) def assert_jacobian_is_finite_fn(self, f, x): """Tests that the Jacobian only contains valid values. The analytical gradients and numerical ones are expected to differ at points where f(x) is not smooth. This function can be used to check that the analytical gradient is not 'NaN' nor 'Inf'. Args: f: the function. x: A list of arguments for the function """ if tf.executing_eagerly(): theoretical_gradient, _ = tf.compat.v2.test.compute_gradient(f, x) else: with self.cached_session(): theoretical_gradient, _ = tf.compat.v2.test.compute_gradient(f, x) self.assertNotIn( True, [ np.isnan(element).any() or np.isinf(element).any() for element in theoretical_gradient ], msg="nan or inf elements found in theoretical jacobian.") def assert_output_is_correct(self, func, test_inputs, test_outputs, rtol=1e-3, atol=1e-6, tile=True): """Tests that the function gives the correct result. Args: func: A function to exectute. test_inputs: A tuple or list of test inputs. test_outputs: A tuple or list of test outputs against which the result of calling `func` on `test_inputs` will be compared to. rtol: The relative tolerance used during the comparison. atol: The absolute tolerance used during the comparison. tile: A `bool` indicating whether or not to automatically tile the test inputs and outputs. """ if tile: # Creates a rank 4 list of values between 1 and 10. tensor_tile = np.random.randint(1, 10, size=np.random.randint(4)).tolist() test_inputs = self._tile_tensors(tensor_tile, test_inputs) test_outputs = self._tile_tensors(tensor_tile, test_outputs) test_outputs = [ tf.convert_to_tensor(value=output) for output in test_outputs ] test_outputs = test_outputs[0] if len(test_outputs) == 1 else test_outputs self.assertAllClose(test_outputs, func(*test_inputs), rtol=rtol, atol=atol) def assert_tf_lite_convertible(self, func, shapes, dtypes=None, test_inputs=None): """Runs the tf-lite converter to make sure the function can be exported. Args: func: A function to execute with tf-lite. shapes: A tuple or list of input shapes. dtypes: A list of input types. test_inputs: A tuple or list of inputs. If not provided the test inputs will be randomly generated. """ if tf.executing_eagerly(): # Currently TFLite conversion is not supported in eager mode. self.skipTest(reason="Graph mode only test") # Generate graph with the function given as input. in_tensors = self._create_placeholders_from_shapes(shapes, dtypes) out_tensors = func(*in_tensors) if not isinstance(out_tensors, (list, tuple)): out_tensors = [out_tensors] with tf.compat.v1.Session() as sess: try: sess.run(tf.compat.v1.global_variables_initializer()) # Convert to a TFLite model. converter = tf.compat.v1.lite.TFLiteConverter.from_session( sess, in_tensors, out_tensors) tflite_model = converter.convert() # Load TFLite model and allocate tensors. interpreter = tf.lite.Interpreter(model_content=tflite_model) interpreter.allocate_tensors() # If no test inputs provided then randomly generate inputs. if test_inputs is None: test_inputs = [ np.array(np.random.sample(shape), dtype=np.float32) for shape in shapes ] else: test_inputs = [ np.array(test, dtype=np.float32) for test in test_inputs ] # Evaluate function using TensorFlow. feed_dict = dict(zip(in_tensors, test_inputs)) test_outputs = sess.run(out_tensors, feed_dict) # Set tensors for the TFLite model. input_details = interpreter.get_input_details() for i, test_input in enumerate(test_inputs): index = input_details[i]["index"] interpreter.set_tensor(index, test_input) # Run TFLite model. interpreter.invoke() # Get tensors from the TFLite model and compare with TensorFlow. output_details = interpreter.get_output_details() for o, test_output in enumerate(test_outputs): index = output_details[o]["index"] self.assertAllClose(test_output, interpreter.get_tensor(index)) except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % str(e)) def main(argv=None): """Main function.""" tf.test.main(argv) # The util functions or classes are not exported. __all__ = []

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/mesh/tests/utils_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for utils.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np from tensorflow_graphics.geometry.representation.mesh import utils from tensorflow_graphics.util import test_case class UtilsTest(test_case.TestCase): @parameterized.parameters( (np.array(((0, 1, 2),)), [[0, 1], [0, 2], [1, 2]]), (np.array( ((0, 1, 2), (0, 1, 3))), [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3]]), ) def test_extract_undirected_edges_from_triangular_mesh_preset( self, test_inputs, test_outputs): """Tests that the output contain the expected edges.""" edges = utils.extract_unique_edges_from_triangular_mesh( test_inputs, directed_edges=False) edges.sort(axis=1) # Ensure edge tuple ordered by first vertex. self.assertEqual(sorted(edges.tolist()), test_outputs) @parameterized.parameters( (np.array( ((0, 1, 2),)), [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]), (np.array( ((0, 1, 2), (0, 1, 3))), [[0, 1], [0, 2], [0, 3], [1, 0], [1, 2], [1, 3], [2, 0], [2, 1], [3, 0], [3, 1]]), ) def test_extract_directed_edges_from_triangular_mesh_preset( self, test_inputs, test_outputs): """Tests that the output contain the expected edges.""" edges = utils.extract_unique_edges_from_triangular_mesh( test_inputs, directed_edges=True) self.assertEqual(sorted(edges.tolist()), test_outputs) @parameterized.parameters( (1, "'faces' must be a numpy.ndarray."), (np.array((1,)), "must have a rank equal to 2"), (np.array((((1,),),)), "must have a rank equal to 2"), (np.array(((1,),)), "must have exactly 3 dimensions in the last axis"), (np.array(((1, 1),)), "must have exactly 3 dimensions in the last axis"), (np.array( ((1, 1, 1, 1),)), "must have exactly 3 dimensions in the last axis"), ) def test_extract_edges_from_triangular_mesh_raised( self, invalid_input, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.extract_unique_edges_from_triangular_mesh(invalid_input) @parameterized.parameters( (np.array(((0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1))), np.float16, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), (np.array(((0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1))), np.float32, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), (np.array(((0, 1), (0, 2), (0, 3), (1, 0), (1, 2), (1, 3), (2, 0), (2, 1), (3, 0), (3, 1))), np.float64, [1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 0.5, 0.5, 0.5, 0.5]), ) def test_get_degree_based_edge_weights_preset( self, test_inputs, test_dtype, test_outputs): """Tests that the output contain the expected edges.""" weights = utils.get_degree_based_edge_weights(test_inputs, test_dtype) self.assertAllClose(weights.tolist(), test_outputs) @parameterized.parameters( (1, "'edges' must be a numpy.ndarray."), (np.array((1,)), "must have a rank equal to 2"), (np.array((((1,),),)), "must have a rank equal to 2"), (np.array(((1,),)), "must have exactly 2 dimensions in the last axis"), (np.array( ((1, 1, 1),)), "must have exactly 2 dimensions in the last axis"), ) def test_get_degree_based_edge_weights_invalid_edges_raised( self, invalid_input, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.get_degree_based_edge_weights(invalid_input) @parameterized.parameters( (np.bool, "must be a numpy float type"), (np.int, "must be a numpy float type"), (np.complex, "must be a numpy float type"), (np.uint, "must be a numpy float type"), (np.int16, "must be a numpy float type"), ) def test_get_degree_based_edge_weights_dtype_raised( self, invalid_type, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.get_degree_based_edge_weights(np.array(((1, 1),)), invalid_type) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for utils.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np from tensorflow_graphics.geometry.representation.mesh import utils from tensorflow_graphics.util import test_case class UtilsTest(test_case.TestCase): @parameterized.parameters( (np.array(((0, 1, 2),)), [[0, 1], [0, 2], [1, 2]]), (np.array( ((0, 1, 2), (0, 1, 3))), [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3]]), ) def test_extract_undirected_edges_from_triangular_mesh_preset( self, test_inputs, test_outputs): """Tests that the output contain the expected edges.""" edges = utils.extract_unique_edges_from_triangular_mesh( test_inputs, directed_edges=False) edges.sort(axis=1) # Ensure edge tuple ordered by first vertex. self.assertEqual(sorted(edges.tolist()), test_outputs) @parameterized.parameters( (np.array( ((0, 1, 2),)), [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]), (np.array( ((0, 1, 2), (0, 1, 3))), [[0, 1], [0, 2], [0, 3], [1, 0], [1, 2], [1, 3], [2, 0], [2, 1], [3, 0], [3, 1]]), ) def test_extract_directed_edges_from_triangular_mesh_preset( self, test_inputs, test_outputs): """Tests that the output contain the expected edges.""" edges = utils.extract_unique_edges_from_triangular_mesh( test_inputs, directed_edges=True) self.assertEqual(sorted(edges.tolist()), test_outputs) @parameterized.parameters( (1, "'faces' must be a numpy.ndarray."), (np.array((1,)), "must have a rank equal to 2"), (np.array((((1,),),)), "must have a rank equal to 2"), (np.array(((1,),)), "must have exactly 3 dimensions in the last axis"), (np.array(((1, 1),)), "must have exactly 3 dimensions in the last axis"), (np.array( ((1, 1, 1, 1),)), "must have exactly 3 dimensions in the last axis"), ) def test_extract_edges_from_triangular_mesh_raised( self, invalid_input, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.extract_unique_edges_from_triangular_mesh(invalid_input) @parameterized.parameters( (np.array(((0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1))), np.float16, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), (np.array(((0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1))), np.float32, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]), (np.array(((0, 1), (0, 2), (0, 3), (1, 0), (1, 2), (1, 3), (2, 0), (2, 1), (3, 0), (3, 1))), np.float64, [1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 1.0 / 3, 0.5, 0.5, 0.5, 0.5]), ) def test_get_degree_based_edge_weights_preset( self, test_inputs, test_dtype, test_outputs): """Tests that the output contain the expected edges.""" weights = utils.get_degree_based_edge_weights(test_inputs, test_dtype) self.assertAllClose(weights.tolist(), test_outputs) @parameterized.parameters( (1, "'edges' must be a numpy.ndarray."), (np.array((1,)), "must have a rank equal to 2"), (np.array((((1,),),)), "must have a rank equal to 2"), (np.array(((1,),)), "must have exactly 2 dimensions in the last axis"), (np.array( ((1, 1, 1),)), "must have exactly 2 dimensions in the last axis"), ) def test_get_degree_based_edge_weights_invalid_edges_raised( self, invalid_input, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.get_degree_based_edge_weights(invalid_input) @parameterized.parameters( (np.bool, "must be a numpy float type"), (np.int, "must be a numpy float type"), (np.complex, "must be a numpy float type"), (np.uint, "must be a numpy float type"), (np.int16, "must be a numpy float type"), ) def test_get_degree_based_edge_weights_dtype_raised( self, invalid_type, error_msg): """Tests that the shape exceptions are properly raised.""" with self.assertRaisesRegexp(ValueError, error_msg): utils.get_degree_based_edge_weights(np.array(((1, 1),)), invalid_type) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/voxels/tests/absorption_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for absoprtion voxel rendering.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.rendering.voxels import absorption from tensorflow_graphics.rendering.voxels.tests import test_helpers from tensorflow_graphics.util import test_case class AbsorptionTest(test_case.TestCase): @parameterized.parameters( (0, (8, 16, 6, 1)), (1, (12, 8, 16, 6, 3)), ) def test_render_shape_exception_not_raised(self, axis, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(absorption.render, shape, axis=axis) @parameterized.parameters( ("must have a rank greater than 3", 2, (3,)), ("must have a rank greater than 3", 2, (16, 6, 3)), ("'axis' needs to be 0, 1 or 2", 5, (8, 16, 6, 1)), ) def test_render_shape_exception_raised(self, error_msg, axis, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(absorption.render, error_msg, shape, axis=axis) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_render_jacobian_random(self): """Tests the Jacobian of render.""" voxels_init = test_helpers.generate_random_test_voxels_render() absorption_factor_init = np.float64(np.random.uniform(low=0.1, high=2.0)) cell_size_init = np.float64(np.random.uniform(low=0.1, high=2.0)) self.assert_jacobian_is_correct_fn( absorption.render, [voxels_init, absorption_factor_init, cell_size_init]) def test_render_preset(self): """Checks that render returns the expected value.""" x_voxels_init, y_images_init = test_helpers.generate_preset_test_voxels_absorption_render( ) voxels = tf.convert_to_tensor(value=x_voxels_init) y_images = tf.convert_to_tensor(value=y_images_init) y = absorption.render(voxels, absorption_factor=0.1, cell_size=0.2) self.assertAllClose(y_images, y) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for absoprtion voxel rendering.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.rendering.voxels import absorption from tensorflow_graphics.rendering.voxels.tests import test_helpers from tensorflow_graphics.util import test_case class AbsorptionTest(test_case.TestCase): @parameterized.parameters( (0, (8, 16, 6, 1)), (1, (12, 8, 16, 6, 3)), ) def test_render_shape_exception_not_raised(self, axis, *shape): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(absorption.render, shape, axis=axis) @parameterized.parameters( ("must have a rank greater than 3", 2, (3,)), ("must have a rank greater than 3", 2, (16, 6, 3)), ("'axis' needs to be 0, 1 or 2", 5, (8, 16, 6, 1)), ) def test_render_shape_exception_raised(self, error_msg, axis, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(absorption.render, error_msg, shape, axis=axis) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_render_jacobian_random(self): """Tests the Jacobian of render.""" voxels_init = test_helpers.generate_random_test_voxels_render() absorption_factor_init = np.float64(np.random.uniform(low=0.1, high=2.0)) cell_size_init = np.float64(np.random.uniform(low=0.1, high=2.0)) self.assert_jacobian_is_correct_fn( absorption.render, [voxels_init, absorption_factor_init, cell_size_init]) def test_render_preset(self): """Checks that render returns the expected value.""" x_voxels_init, y_images_init = test_helpers.generate_preset_test_voxels_absorption_render( ) voxels = tf.convert_to_tensor(value=x_voxels_init) y_images = tf.convert_to_tensor(value=y_images_init) y = absorption.render(voxels, absorption_factor=0.1, cell_size=0.2) self.assertAllClose(y_images, y) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/tests/vector_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for vector.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation.tests import test_data as td from tensorflow_graphics.math import vector from tensorflow_graphics.util import test_case class VectorTest(test_case.TestCase): @parameterized.parameters( ((None, 3), (None, 3)),) def test_cross_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.cross, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis", (1,), (3,)), ("must have exactly 3 dimensions in axis", (3,), (2,)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3)), ) def test_cross_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.cross, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) def test_cross_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the dot product.""" self.assert_jacobian_is_correct_fn(vector.cross, [u_init, v_init]) def test_cross_jacobian_random(self): """Test the Jacobian of the dot product.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.cross, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (-td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (-td.AXIS_3D_Z,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (-td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ) def test_cross_preset(self, test_inputs, test_outputs): """Tests the cross product of predefined axes.""" self.assert_output_is_correct(vector.cross, test_inputs, test_outputs) def test_cross_random(self): """Tests the cross product function.""" tensor_size = np.random.randint(1, 4) tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist() axis = np.random.randint(tensor_size) tensor_shape[axis] = 3 # pylint: disable=invalid-sequence-index u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) self.assertAllClose( vector.cross(u, v, axis=axis), np.cross(u, v, axis=axis)) @parameterized.parameters( ((None,), (None,)), ((None, None), (None, None)), ) def test_dot_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.dot, shapes) @parameterized.parameters( ("must have the same number of dimensions", (None, 1), (None, 2)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3)), ) def test_dot_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.dot, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) def test_dot_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the dot product.""" self.assert_jacobian_is_correct_fn(vector.dot, [u_init, v_init]) def test_dot_jacobian_random(self): """Tests the Jacobian of the dot product.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.dot, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_0), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (1.,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (1.,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (1.,)), ) def test_dot_preset(self, test_inputs, test_outputs): """Tests the dot product of predefined axes.""" def func(u, v): return tf.squeeze(vector.dot(u, v), axis=-1) self.assert_output_is_correct(func, test_inputs, test_outputs) def test_dot_random(self): """Tests the dot product function.""" tensor_size = np.random.randint(2, 4) tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist() axis = np.random.randint(tensor_size) u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) dot = tf.linalg.tensor_diag_part(tf.tensordot(u, v, axes=[[axis], [axis]])) dot = tf.expand_dims(dot, axis=axis) self.assertAllClose(vector.dot(u, v, axis=axis), dot) @parameterized.parameters( ((None,), (None,)), ((None, None), (None, None)), ((1,), (1,)), ((1, 1), (1, 1)), ) def test_reflect_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.reflect, shapes) @parameterized.parameters( ("must have the same number of dimensions", (None, 1), (None, 2)), ("Not all batch dimensions are broadcast-compatible.", (2, 2), (3, 2)), ) def test_reflect_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.reflect, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_reflect_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the reflect function.""" self.assert_jacobian_is_correct_fn(vector.reflect, [u_init, v_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_reflect_jacobian_random(self): """Tests the Jacobian of the reflect function.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.reflect, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (-td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (-td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (-td.AXIS_3D_Z,)), ) def test_reflect_preset(self, test_inputs, test_outputs): """Tests the reflect function of predefined axes.""" self.assert_output_is_correct(vector.reflect, test_inputs, test_outputs) def test_reflect_random(self): """Tests that calling reflect twice give an identity transform.""" tensor_size = np.random.randint(2, 4) tensor_shape = np.random.randint(2, 3, size=tensor_size).tolist() axis = np.random.randint(tensor_size) u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) v /= np.linalg.norm(v, axis=axis, keepdims=True) u_new = vector.reflect(u, v, axis=axis) u_new = vector.reflect(u_new, v, axis=axis) self.assertAllClose(u_new, u) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for vector.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.transformation.tests import test_data as td from tensorflow_graphics.math import vector from tensorflow_graphics.util import test_case class VectorTest(test_case.TestCase): @parameterized.parameters( ((None, 3), (None, 3)),) def test_cross_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.cross, shapes) @parameterized.parameters( ("must have exactly 3 dimensions in axis", (1,), (3,)), ("must have exactly 3 dimensions in axis", (3,), (2,)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3)), ) def test_cross_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.cross, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) def test_cross_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the dot product.""" self.assert_jacobian_is_correct_fn(vector.cross, [u_init, v_init]) def test_cross_jacobian_random(self): """Test the Jacobian of the dot product.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.cross, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (-td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (-td.AXIS_3D_Z,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (-td.AXIS_3D_X,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ) def test_cross_preset(self, test_inputs, test_outputs): """Tests the cross product of predefined axes.""" self.assert_output_is_correct(vector.cross, test_inputs, test_outputs) def test_cross_random(self): """Tests the cross product function.""" tensor_size = np.random.randint(1, 4) tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist() axis = np.random.randint(tensor_size) tensor_shape[axis] = 3 # pylint: disable=invalid-sequence-index u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) self.assertAllClose( vector.cross(u, v, axis=axis), np.cross(u, v, axis=axis)) @parameterized.parameters( ((None,), (None,)), ((None, None), (None, None)), ) def test_dot_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.dot, shapes) @parameterized.parameters( ("must have the same number of dimensions", (None, 1), (None, 2)), ("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3)), ) def test_dot_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.dot, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) def test_dot_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the dot product.""" self.assert_jacobian_is_correct_fn(vector.dot, [u_init, v_init]) def test_dot_jacobian_random(self): """Tests the Jacobian of the dot product.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.dot, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_0), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (1.,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (1.,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (0.,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (1.,)), ) def test_dot_preset(self, test_inputs, test_outputs): """Tests the dot product of predefined axes.""" def func(u, v): return tf.squeeze(vector.dot(u, v), axis=-1) self.assert_output_is_correct(func, test_inputs, test_outputs) def test_dot_random(self): """Tests the dot product function.""" tensor_size = np.random.randint(2, 4) tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist() axis = np.random.randint(tensor_size) u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) dot = tf.linalg.tensor_diag_part(tf.tensordot(u, v, axes=[[axis], [axis]])) dot = tf.expand_dims(dot, axis=axis) self.assertAllClose(vector.dot(u, v, axis=axis), dot) @parameterized.parameters( ((None,), (None,)), ((None, None), (None, None)), ((1,), (1,)), ((1, 1), (1, 1)), ) def test_reflect_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(vector.reflect, shapes) @parameterized.parameters( ("must have the same number of dimensions", (None, 1), (None, 2)), ("Not all batch dimensions are broadcast-compatible.", (2, 2), (3, 2)), ) def test_reflect_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(vector.reflect, error_msg, shapes) @parameterized.parameters( (td.AXIS_3D_0, td.AXIS_3D_0), (td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_X, td.AXIS_3D_X), (td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y, td.AXIS_3D_Y), (td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z, td.AXIS_3D_Z), ) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_reflect_jacobian_preset(self, u_init, v_init): """Tests the Jacobian of the reflect function.""" self.assert_jacobian_is_correct_fn(vector.reflect, [u_init, v_init]) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_reflect_jacobian_random(self): """Tests the Jacobian of the reflect function.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() u_init = np.random.random(size=tensor_shape + [3]) v_init = np.random.random(size=tensor_shape + [3]) self.assert_jacobian_is_correct_fn(vector.reflect, [u_init, v_init]) @parameterized.parameters( ((td.AXIS_3D_0, td.AXIS_3D_X), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Y), (td.AXIS_3D_0,)), ((td.AXIS_3D_0, td.AXIS_3D_Z), (td.AXIS_3D_0,)), ((td.AXIS_3D_X, td.AXIS_3D_X), (-td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.AXIS_3D_Y), (td.AXIS_3D_X,)), ((td.AXIS_3D_X, td.AXIS_3D_Z), (td.AXIS_3D_X,)), ((td.AXIS_3D_Y, td.AXIS_3D_X), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_Y), (-td.AXIS_3D_Y,)), ((td.AXIS_3D_Y, td.AXIS_3D_Z), (td.AXIS_3D_Y,)), ((td.AXIS_3D_Z, td.AXIS_3D_X), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Z, td.AXIS_3D_Y), (td.AXIS_3D_Z,)), ((td.AXIS_3D_Z, td.AXIS_3D_Z), (-td.AXIS_3D_Z,)), ) def test_reflect_preset(self, test_inputs, test_outputs): """Tests the reflect function of predefined axes.""" self.assert_output_is_correct(vector.reflect, test_inputs, test_outputs) def test_reflect_random(self): """Tests that calling reflect twice give an identity transform.""" tensor_size = np.random.randint(2, 4) tensor_shape = np.random.randint(2, 3, size=tensor_size).tolist() axis = np.random.randint(tensor_size) u = np.random.random(size=tensor_shape) v = np.random.random(size=tensor_shape) v /= np.linalg.norm(v, axis=axis, keepdims=True) u_new = vector.reflect(u, v, axis=axis) u_new = vector.reflect(u_new, v, axis=axis) self.assertAllClose(u_new, u) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/precision.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the precision metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _cast_to_int(prediction): return tf.cast(x=prediction, dtype=tf.int32) def evaluate(ground_truth, prediction, classes=None, reduce_average=True, prediction_to_category_function=_cast_to_int, name=None): """Computes the precision metric for the given ground truth and predictions. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth labels. Will be cast to int32. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predictions (which can be continuous). classes: An integer or a list/tuple of integers representing the classes for which the precision will be evaluated. In case 'classes' is 'None', the number of classes will be inferred from the given labels and the precision will be calculated for each of the classes. Defaults to 'None'. reduce_average: Whether to calculate the average of the precision for each class and return a single precision value. Defaults to true. prediction_to_category_function: A function to associate a `prediction` to a category. Defaults to rounding down the value of the prediction to the nearest integer value. name: A name for this op. Defaults to "precision_evaluate". Returns: A tensor of shape `[A1, ..., An, C]`, where the last axis represents the precision calculated for each of the requested classes. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "precision_evaluate", [ground_truth, prediction]): ground_truth = tf.cast( x=tf.convert_to_tensor(value=ground_truth), dtype=tf.int32) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) prediction = prediction_to_category_function(prediction) if classes is None: num_classes = tf.math.maximum( tf.math.reduce_max(input_tensor=ground_truth), tf.math.reduce_max(input_tensor=prediction)) + 1 classes = tf.range(num_classes) else: classes = tf.convert_to_tensor(value=classes) # Make sure classes is a tensor of rank 1. classes = tf.reshape(classes, [1]) if tf.rank(classes) == 0 else classes # Create a confusion matrix for each of the classes (with dimensions # [A1, ..., An, C, N]). classes = tf.expand_dims(classes, -1) ground_truth_per_class = tf.equal(tf.expand_dims(ground_truth, -2), classes) prediction_per_class = tf.equal(tf.expand_dims(prediction, -2), classes) # Calculate the precision for each of the classes. true_positives = tf.math.reduce_sum( input_tensor=tf.cast( x=tf.math.logical_and(ground_truth_per_class, prediction_per_class), dtype=tf.float32), axis=-1) total_predicted_positives = tf.math.reduce_sum( input_tensor=tf.cast(x=prediction_per_class, dtype=tf.float32), axis=-1) precision_per_class = safe_ops.safe_signed_div(true_positives, total_predicted_positives) if reduce_average: return tf.math.reduce_mean(input_tensor=precision_per_class, axis=-1) else: return precision_per_class # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the precision metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _cast_to_int(prediction): return tf.cast(x=prediction, dtype=tf.int32) def evaluate(ground_truth, prediction, classes=None, reduce_average=True, prediction_to_category_function=_cast_to_int, name=None): """Computes the precision metric for the given ground truth and predictions. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Args: ground_truth: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the ground truth labels. Will be cast to int32. prediction: A tensor of shape `[A1, ..., An, N]`, where the last axis represents the predictions (which can be continuous). classes: An integer or a list/tuple of integers representing the classes for which the precision will be evaluated. In case 'classes' is 'None', the number of classes will be inferred from the given labels and the precision will be calculated for each of the classes. Defaults to 'None'. reduce_average: Whether to calculate the average of the precision for each class and return a single precision value. Defaults to true. prediction_to_category_function: A function to associate a `prediction` to a category. Defaults to rounding down the value of the prediction to the nearest integer value. name: A name for this op. Defaults to "precision_evaluate". Returns: A tensor of shape `[A1, ..., An, C]`, where the last axis represents the precision calculated for each of the requested classes. Raises: ValueError: if the shape of `ground_truth`, `prediction` is not supported. """ with tf.compat.v1.name_scope(name, "precision_evaluate", [ground_truth, prediction]): ground_truth = tf.cast( x=tf.convert_to_tensor(value=ground_truth), dtype=tf.int32) prediction = tf.convert_to_tensor(value=prediction) shape.compare_batch_dimensions( tensors=(ground_truth, prediction), tensor_names=("ground_truth", "prediction"), last_axes=-1, broadcast_compatible=True) prediction = prediction_to_category_function(prediction) if classes is None: num_classes = tf.math.maximum( tf.math.reduce_max(input_tensor=ground_truth), tf.math.reduce_max(input_tensor=prediction)) + 1 classes = tf.range(num_classes) else: classes = tf.convert_to_tensor(value=classes) # Make sure classes is a tensor of rank 1. classes = tf.reshape(classes, [1]) if tf.rank(classes) == 0 else classes # Create a confusion matrix for each of the classes (with dimensions # [A1, ..., An, C, N]). classes = tf.expand_dims(classes, -1) ground_truth_per_class = tf.equal(tf.expand_dims(ground_truth, -2), classes) prediction_per_class = tf.equal(tf.expand_dims(prediction, -2), classes) # Calculate the precision for each of the classes. true_positives = tf.math.reduce_sum( input_tensor=tf.cast( x=tf.math.logical_and(ground_truth_per_class, prediction_per_class), dtype=tf.float32), axis=-1) total_predicted_positives = tf.math.reduce_sum( input_tensor=tf.cast(x=prediction_per_class, dtype=tf.float32), axis=-1) precision_per_class = safe_ops.safe_signed_div(true_positives, total_predicted_positives) if reduce_average: return tf.math.reduce_mean(input_tensor=precision_per_class, axis=-1) else: return precision_per_class # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/mesh/tests/sampler_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for uniform mesh sampler.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation.mesh import sampler from tensorflow_graphics.geometry.representation.mesh.tests import mesh_test_utils from tensorflow_graphics.util import test_case class MeshSamplerTest(test_case.TestCase): def setUp(self): """Sets up default parameters.""" super(MeshSamplerTest, self).setUp() self._test_sigma_compare_tolerance = 4.0 def compare_poisson_equivalence(self, expected, actual): """Performs equivalence check on Poisson-distributed random variables.""" delta = np.sqrt(expected) * self._test_sigma_compare_tolerance self.assertAllClose(expected, actual, atol=delta) # Tests for generate_random_face_indices @parameterized.parameters( (((), (2,)), (tf.int32, tf.float32)), (((), (None, 1)), (tf.int64, tf.float32)), (((), (None, 3, 4)), (tf.int64, tf.float64)), ) def test_random_face_indices_exception_not_raised(self, shapes, dtypes): """Tests that shape exceptions are not raised for random_face_indices.""" self.assert_exception_is_not_raised(sampler.generate_random_face_indices, shapes, dtypes) @parameterized.parameters( ("face_weights must have a rank greater than 0.", (), ()), ("num_samples must have a rank of 0.", (None,), (1, 2)), ("num_samples must have a rank of 0.", (4, 2), (1,)), ) def test_random_face_indices_shape_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for random_face_indices.""" self.assert_exception_is_raised( sampler.generate_random_face_indices, error_msg, shapes) def test_negative_weights_random_face_indices_exception(self): """Test for exception for random_face_indices with negative weights.""" face_wts = np.array([0.1, -0.1], dtype=np.float32) num_samples = 10 error_msg = "Condition x >= y did not hold." with self.assertRaisesRegexp(tf.errors.InvalidArgumentError, error_msg): sampler.generate_random_face_indices(num_samples, face_weights=face_wts) @parameterized.parameters( ((0., 0.), 10, (5, 5)), ((0., 0.0, 0.001), 100, (0, 0, 100)), ((0.1, 0.2, 0.3), 1000, (167, 333, 500)), ) def test_random_face_indices(self, face_weights, num_samples, expected): """Test for generate_random_face_indices.""" face_weights = np.array(face_weights, dtype=np.float32) expected = np.array(expected, dtype=np.intp) sample_faces = sampler.generate_random_face_indices( num_samples, face_weights) self.assertEqual(sample_faces.shape[0], num_samples) self.compare_poisson_equivalence(expected, tf.math.bincount(sample_faces)) # Tests for generate_random_barycentric_coordinates @parameterized.parameters( ((1,), (tf.int32)), ((None,), (tf.int64)), ) def test_random_barycentric_coordinates_exception_not_raised( self, shapes, dtypes): """Tests that shape exceptions are not raised for random_barycentric_coordinates.""" self.assert_exception_is_not_raised( sampler.generate_random_barycentric_coordinates, shapes, dtypes) @parameterized.parameters( ("sample_shape must have a rank of 1.", ()), ("sample_shape must have a rank of 1.", (4, None)), ) def test_random_barycentric_coordinates_shape_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for random_barycentric_coordinates.""" self.assert_exception_is_raised( sampler.generate_random_barycentric_coordinates, error_msg, shapes) @parameterized.parameters( ((5,),), ((10, 1, 3),), ) def test_random_barycentric_coordinates(self, sample_shape): """Test for generate_random_barycentric_coordinates.""" sample_shape = np.array(sample_shape, dtype=np.intp) random_coordinates = sampler.generate_random_barycentric_coordinates( sample_shape=sample_shape) coordinate_sum = tf.reduce_sum(input_tensor=random_coordinates, axis=-1) expected_coordinate_sum = np.ones(shape=sample_shape) self.assertAllClose(expected_coordinate_sum, coordinate_sum) # Tests for weighted_random_sample_triangle_mesh @parameterized.parameters( (((4, 3), (5, 3), (), (5,)), (tf.float32, tf.int32, tf.int32, tf.float32)), (((None, 3), (None, 3), (), (None,)), (tf.float32, tf.int32, tf.int32, tf.float32)), (((3, None, 3), (3, None, 3), (), (3, None)), (tf.float32, tf.int64, tf.int64, tf.float64)), (((3, 6, 5), (3, 5, 3), (), (3, 5)), (tf.float64, tf.int32, tf.int32, tf.float32)), ) def test_weighted_sampler_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised for weighted sampler.""" self.assert_exception_is_not_raised( sampler.weighted_random_sample_triangle_mesh, shapes, dtypes) @parameterized.parameters( ("vertex_attributes must have a rank greater than 1.", (3,), (None, 3), (), (None, 3)), ("faces must have a rank greater than 1.", (5, 2), (None,), (), (None,)), ("face_weights must have a rank greater than 0.", (1, None, 3), (None, 3), (), ()), ("Not all batch dimensions are identical", (4, 4, 2), (3, 5, 3), (), (3, 5)), ("Not all batch dimensions are identical", (4, 2), (5, 3), (), (4,)), ) def test_weighted_sampler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for weighted sampler.""" self.assert_exception_is_raised( sampler.weighted_random_sample_triangle_mesh, error_msg, shapes) def test_weighted_sampler_negative_weights(self): """Test for exception with negative weights.""" vertices, faces = mesh_test_utils.create_square_triangle_mesh() face_wts = np.array([-0.3, 0.1, 0.5, 0.6], dtype=np.float32) num_samples = 10 error_msg = "Condition x >= y did not hold." with self.assertRaisesRegexp(tf.errors.InvalidArgumentError, error_msg): sampler.weighted_random_sample_triangle_mesh( vertices, faces, num_samples, face_weights=face_wts) def test_weighted_random_sample(self): """Test for provided face weights.""" faces = np.array([[0, 1, 2], [2, 1, 3]], dtype=np.int32) vertex_attributes = np.array([[0.], [0.], [1.], [1.]], dtype=np.float32) # Equal face weights, mean of sampled attributes = 0.5. expected_mean = np.array([0.5], dtype=np.float32) sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertex_attributes, faces, num_samples=1000000, face_weights=(0.5, 0.5)) self.assertAllClose( expected_mean, tf.reduce_mean(input_tensor=sample_pts, axis=-2), atol=1e-3) # Face weights biased towards second face, mean > 0.5 sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertex_attributes, faces, num_samples=1000000, face_weights=(0.2, 0.8)) self.assertGreater( tf.reduce_mean(input_tensor=sample_pts, axis=-2), expected_mean) def test_weighted_sampler_jacobian_random(self): """Test the Jacobian of weighted triangle random sampler.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) face_weights = np.random.uniform( size=index_init.shape[:index_init.ndim - 1]) weights_tensor = tf.convert_to_tensor(value=face_weights) num_samples = np.random.randint(10, 100) def sampler_fn(vertices): sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertices, index_tensor, num_samples, weights_tensor, seed=[0, 1], stateless=True) return sample_pts self.assert_jacobian_is_correct_fn( sampler_fn, [vertex_init], atol=1e-4, delta=1e-4) # Tests for area_weighted_random_sample_triangle_mesh @parameterized.parameters( (((4, 3), (5, 3), ()), (tf.float32, tf.int32, tf.int32)), (((None, 3), (None, 3), ()), (tf.float32, tf.int32, tf.int32)), (((3, None, 3), (3, None, 3), ()), (tf.float32, tf.int64, tf.int64)), # Test for vertex attributes + positions (((3, 6, 5), (3, 5, 3), (), (3, 6, 3)), (tf.float64, tf.int32, tf.int32, tf.float32)), ) def test_area_sampler_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised for area weighted sampler.""" self.assert_exception_is_not_raised( sampler.area_weighted_random_sample_triangle_mesh, shapes, dtypes) @parameterized.parameters( ("vertices must have a rank greater than 1.", (3,), (None, 3), ()), ("vertices must have greater than 2 dimensions in axis -1.", (5, 2), (None, 3), ()), ("vertex_positions must have exactly 3 dimensions in axis -1.", (5, 3), (None, 3), (), (3, 2)), ) def test_area_sampler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for area weighted sampler.""" self.assert_exception_is_raised( sampler.area_weighted_random_sample_triangle_mesh, error_msg, shapes) def test_area_sampler_distribution(self): """Test for area weighted sampler distribution.""" vertices, faces = mesh_test_utils.create_single_triangle_mesh() vertices = np.repeat(np.expand_dims(vertices, axis=0), 3, axis=0) faces = np.repeat(np.expand_dims(faces, axis=0), 3, axis=0) num_samples = 5000 sample_pts, _ = sampler.area_weighted_random_sample_triangle_mesh( vertices, faces, num_samples) for i in range(3): samples = sample_pts[i, ...] self.assertEqual(samples.shape[-2], num_samples) # Test distribution in 4 quadrants of [0,1]x[0,1] v = samples[:, :2] < [0.5, 0.5] not_v = tf.logical_not(v) quad00 = tf.math.count_nonzero(tf.reduce_all(input_tensor=v, axis=-1)) quad11 = tf.math.count_nonzero(tf.reduce_all(input_tensor=not_v, axis=-1)) quad01 = tf.math.count_nonzero(tf.reduce_all( input_tensor=tf.stack((v[:, 0], not_v[:, 1]), axis=1), axis=-1)) quad10 = tf.math.count_nonzero(tf.reduce_all( input_tensor=tf.stack((not_v[:, 0], v[:, 1]), axis=1), axis=-1)) counts = tf.stack((quad00, quad01, quad10, quad11), axis=0) expected = np.array( [num_samples / 2, num_samples / 4, num_samples / 4, 0], dtype=np.float32) self.compare_poisson_equivalence(expected, counts) def test_face_distribution(self): """Test for distribution of face indices with area weighted sampler.""" vertices, faces = mesh_test_utils.create_square_triangle_mesh() num_samples = 1000 _, sample_faces = sampler.area_weighted_random_sample_triangle_mesh( vertices, faces, num_samples) # All points should be approx poisson distributed among the 4 faces. self.assertEqual(sample_faces.shape[0], num_samples) num_faces = faces.shape[0] expected = np.array([num_samples / num_faces] * num_faces, dtype=np.intp) self.compare_poisson_equivalence(expected, tf.math.bincount(sample_faces)) def test_area_sampler_jacobian_random(self): """Test the Jacobian of area weighted triangle random sampler.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) num_samples = np.random.randint(10, 100) def sampler_fn(vertices): sample_pts, _ = sampler.area_weighted_random_sample_triangle_mesh( vertices, index_tensor, num_samples, seed=[0, 1], stateless=True) return sample_pts self.assert_jacobian_is_correct_fn( sampler_fn, [vertex_init], atol=1e-4, delta=1e-4) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for uniform mesh sampler.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation.mesh import sampler from tensorflow_graphics.geometry.representation.mesh.tests import mesh_test_utils from tensorflow_graphics.util import test_case class MeshSamplerTest(test_case.TestCase): def setUp(self): """Sets up default parameters.""" super(MeshSamplerTest, self).setUp() self._test_sigma_compare_tolerance = 4.0 def compare_poisson_equivalence(self, expected, actual): """Performs equivalence check on Poisson-distributed random variables.""" delta = np.sqrt(expected) * self._test_sigma_compare_tolerance self.assertAllClose(expected, actual, atol=delta) # Tests for generate_random_face_indices @parameterized.parameters( (((), (2,)), (tf.int32, tf.float32)), (((), (None, 1)), (tf.int64, tf.float32)), (((), (None, 3, 4)), (tf.int64, tf.float64)), ) def test_random_face_indices_exception_not_raised(self, shapes, dtypes): """Tests that shape exceptions are not raised for random_face_indices.""" self.assert_exception_is_not_raised(sampler.generate_random_face_indices, shapes, dtypes) @parameterized.parameters( ("face_weights must have a rank greater than 0.", (), ()), ("num_samples must have a rank of 0.", (None,), (1, 2)), ("num_samples must have a rank of 0.", (4, 2), (1,)), ) def test_random_face_indices_shape_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for random_face_indices.""" self.assert_exception_is_raised( sampler.generate_random_face_indices, error_msg, shapes) def test_negative_weights_random_face_indices_exception(self): """Test for exception for random_face_indices with negative weights.""" face_wts = np.array([0.1, -0.1], dtype=np.float32) num_samples = 10 error_msg = "Condition x >= y did not hold." with self.assertRaisesRegexp(tf.errors.InvalidArgumentError, error_msg): sampler.generate_random_face_indices(num_samples, face_weights=face_wts) @parameterized.parameters( ((0., 0.), 10, (5, 5)), ((0., 0.0, 0.001), 100, (0, 0, 100)), ((0.1, 0.2, 0.3), 1000, (167, 333, 500)), ) def test_random_face_indices(self, face_weights, num_samples, expected): """Test for generate_random_face_indices.""" face_weights = np.array(face_weights, dtype=np.float32) expected = np.array(expected, dtype=np.intp) sample_faces = sampler.generate_random_face_indices( num_samples, face_weights) self.assertEqual(sample_faces.shape[0], num_samples) self.compare_poisson_equivalence(expected, tf.math.bincount(sample_faces)) # Tests for generate_random_barycentric_coordinates @parameterized.parameters( ((1,), (tf.int32)), ((None,), (tf.int64)), ) def test_random_barycentric_coordinates_exception_not_raised( self, shapes, dtypes): """Tests that shape exceptions are not raised for random_barycentric_coordinates.""" self.assert_exception_is_not_raised( sampler.generate_random_barycentric_coordinates, shapes, dtypes) @parameterized.parameters( ("sample_shape must have a rank of 1.", ()), ("sample_shape must have a rank of 1.", (4, None)), ) def test_random_barycentric_coordinates_shape_exception_raised( self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for random_barycentric_coordinates.""" self.assert_exception_is_raised( sampler.generate_random_barycentric_coordinates, error_msg, shapes) @parameterized.parameters( ((5,),), ((10, 1, 3),), ) def test_random_barycentric_coordinates(self, sample_shape): """Test for generate_random_barycentric_coordinates.""" sample_shape = np.array(sample_shape, dtype=np.intp) random_coordinates = sampler.generate_random_barycentric_coordinates( sample_shape=sample_shape) coordinate_sum = tf.reduce_sum(input_tensor=random_coordinates, axis=-1) expected_coordinate_sum = np.ones(shape=sample_shape) self.assertAllClose(expected_coordinate_sum, coordinate_sum) # Tests for weighted_random_sample_triangle_mesh @parameterized.parameters( (((4, 3), (5, 3), (), (5,)), (tf.float32, tf.int32, tf.int32, tf.float32)), (((None, 3), (None, 3), (), (None,)), (tf.float32, tf.int32, tf.int32, tf.float32)), (((3, None, 3), (3, None, 3), (), (3, None)), (tf.float32, tf.int64, tf.int64, tf.float64)), (((3, 6, 5), (3, 5, 3), (), (3, 5)), (tf.float64, tf.int32, tf.int32, tf.float32)), ) def test_weighted_sampler_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised for weighted sampler.""" self.assert_exception_is_not_raised( sampler.weighted_random_sample_triangle_mesh, shapes, dtypes) @parameterized.parameters( ("vertex_attributes must have a rank greater than 1.", (3,), (None, 3), (), (None, 3)), ("faces must have a rank greater than 1.", (5, 2), (None,), (), (None,)), ("face_weights must have a rank greater than 0.", (1, None, 3), (None, 3), (), ()), ("Not all batch dimensions are identical", (4, 4, 2), (3, 5, 3), (), (3, 5)), ("Not all batch dimensions are identical", (4, 2), (5, 3), (), (4,)), ) def test_weighted_sampler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for weighted sampler.""" self.assert_exception_is_raised( sampler.weighted_random_sample_triangle_mesh, error_msg, shapes) def test_weighted_sampler_negative_weights(self): """Test for exception with negative weights.""" vertices, faces = mesh_test_utils.create_square_triangle_mesh() face_wts = np.array([-0.3, 0.1, 0.5, 0.6], dtype=np.float32) num_samples = 10 error_msg = "Condition x >= y did not hold." with self.assertRaisesRegexp(tf.errors.InvalidArgumentError, error_msg): sampler.weighted_random_sample_triangle_mesh( vertices, faces, num_samples, face_weights=face_wts) def test_weighted_random_sample(self): """Test for provided face weights.""" faces = np.array([[0, 1, 2], [2, 1, 3]], dtype=np.int32) vertex_attributes = np.array([[0.], [0.], [1.], [1.]], dtype=np.float32) # Equal face weights, mean of sampled attributes = 0.5. expected_mean = np.array([0.5], dtype=np.float32) sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertex_attributes, faces, num_samples=1000000, face_weights=(0.5, 0.5)) self.assertAllClose( expected_mean, tf.reduce_mean(input_tensor=sample_pts, axis=-2), atol=1e-3) # Face weights biased towards second face, mean > 0.5 sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertex_attributes, faces, num_samples=1000000, face_weights=(0.2, 0.8)) self.assertGreater( tf.reduce_mean(input_tensor=sample_pts, axis=-2), expected_mean) def test_weighted_sampler_jacobian_random(self): """Test the Jacobian of weighted triangle random sampler.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) face_weights = np.random.uniform( size=index_init.shape[:index_init.ndim - 1]) weights_tensor = tf.convert_to_tensor(value=face_weights) num_samples = np.random.randint(10, 100) def sampler_fn(vertices): sample_pts, _ = sampler.weighted_random_sample_triangle_mesh( vertices, index_tensor, num_samples, weights_tensor, seed=[0, 1], stateless=True) return sample_pts self.assert_jacobian_is_correct_fn( sampler_fn, [vertex_init], atol=1e-4, delta=1e-4) # Tests for area_weighted_random_sample_triangle_mesh @parameterized.parameters( (((4, 3), (5, 3), ()), (tf.float32, tf.int32, tf.int32)), (((None, 3), (None, 3), ()), (tf.float32, tf.int32, tf.int32)), (((3, None, 3), (3, None, 3), ()), (tf.float32, tf.int64, tf.int64)), # Test for vertex attributes + positions (((3, 6, 5), (3, 5, 3), (), (3, 6, 3)), (tf.float64, tf.int32, tf.int32, tf.float32)), ) def test_area_sampler_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised for area weighted sampler.""" self.assert_exception_is_not_raised( sampler.area_weighted_random_sample_triangle_mesh, shapes, dtypes) @parameterized.parameters( ("vertices must have a rank greater than 1.", (3,), (None, 3), ()), ("vertices must have greater than 2 dimensions in axis -1.", (5, 2), (None, 3), ()), ("vertex_positions must have exactly 3 dimensions in axis -1.", (5, 3), (None, 3), (), (3, 2)), ) def test_area_sampler_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised for area weighted sampler.""" self.assert_exception_is_raised( sampler.area_weighted_random_sample_triangle_mesh, error_msg, shapes) def test_area_sampler_distribution(self): """Test for area weighted sampler distribution.""" vertices, faces = mesh_test_utils.create_single_triangle_mesh() vertices = np.repeat(np.expand_dims(vertices, axis=0), 3, axis=0) faces = np.repeat(np.expand_dims(faces, axis=0), 3, axis=0) num_samples = 5000 sample_pts, _ = sampler.area_weighted_random_sample_triangle_mesh( vertices, faces, num_samples) for i in range(3): samples = sample_pts[i, ...] self.assertEqual(samples.shape[-2], num_samples) # Test distribution in 4 quadrants of [0,1]x[0,1] v = samples[:, :2] < [0.5, 0.5] not_v = tf.logical_not(v) quad00 = tf.math.count_nonzero(tf.reduce_all(input_tensor=v, axis=-1)) quad11 = tf.math.count_nonzero(tf.reduce_all(input_tensor=not_v, axis=-1)) quad01 = tf.math.count_nonzero(tf.reduce_all( input_tensor=tf.stack((v[:, 0], not_v[:, 1]), axis=1), axis=-1)) quad10 = tf.math.count_nonzero(tf.reduce_all( input_tensor=tf.stack((not_v[:, 0], v[:, 1]), axis=1), axis=-1)) counts = tf.stack((quad00, quad01, quad10, quad11), axis=0) expected = np.array( [num_samples / 2, num_samples / 4, num_samples / 4, 0], dtype=np.float32) self.compare_poisson_equivalence(expected, counts) def test_face_distribution(self): """Test for distribution of face indices with area weighted sampler.""" vertices, faces = mesh_test_utils.create_square_triangle_mesh() num_samples = 1000 _, sample_faces = sampler.area_weighted_random_sample_triangle_mesh( vertices, faces, num_samples) # All points should be approx poisson distributed among the 4 faces. self.assertEqual(sample_faces.shape[0], num_samples) num_faces = faces.shape[0] expected = np.array([num_samples / num_faces] * num_faces, dtype=np.intp) self.compare_poisson_equivalence(expected, tf.math.bincount(sample_faces)) def test_area_sampler_jacobian_random(self): """Test the Jacobian of area weighted triangle random sampler.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) num_samples = np.random.randint(10, 100) def sampler_fn(vertices): sample_pts, _ = sampler.area_weighted_random_sample_triangle_mesh( vertices, index_tensor, num_samples, seed=[0, 1], stateless=True) return sample_pts self.assert_jacobian_is_correct_fn( sampler_fn, [vertex_init], atol=1e-4, delta=1e-4) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/doc.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Query environment variable for documentation building.""" import os def _import_tfg_docs(): """Checks if __init__.py imports should be executed (for buildling docs).""" return os.getenv("TFG_DOC_IMPORTS", "0") == "1" def enable_tfg_doc_imports(): """Re-enables the imports in the __init__.py so that docs can be built.""" os.environ["TFG_DOC_IMPORTS"] = "1"

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Query environment variable for documentation building.""" import os def _import_tfg_docs(): """Checks if __init__.py imports should be executed (for buildling docs).""" return os.getenv("TFG_DOC_IMPORTS", "0") == "1" def enable_tfg_doc_imports(): """Re-enables the imports in the __init__.py so that docs can be built.""" os.environ["TFG_DOC_IMPORTS"] = "1"

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/neural_voxel_renderer/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Neural voxel renderer module."""

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Neural voxel renderer module."""

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/camera/perspective.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""This module implements perspective camera functionalities. The perspective camera model, also referred to as pinhole camera model, is defined using a focal length \\((f_x, f_y)\\) and a principal point \\((c_x, c_y)\\). The perspective camera model can be written as a calibration matrix $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix}, $$ also referred to as the intrinsic parameter matrix. The camera focal length \\((f_x, f_y)\\), defined in pixels, is the physical focal length divided by the physical size of a camera pixel. The physical focal length is the distance between the camera center and the image plane. The principal point is the intersection of the camera axis with the image plane. The camera axis is the line perpendicular to the image plane starting at the optical center. More details about perspective cameras can be found on [this page.] (http://ksimek.github.io/2013/08/13/intrinsic/) Note: The current implementation does not take into account distortion or skew parameters. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def parameters_from_right_handed(projection_matrix, name=None): """Recovers the parameters used to contruct a right handed projection matrix. Note: In the following, A1 to An are optional batch dimensions. Args: projection_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, containing matrices of right handed perspective-view frustum. name: A name for this op. Defaults to 'perspective_parameters_from_right_handed'. Raises: InvalidArgumentError: if `projection_matrix` is not of the expected shape. Returns: Tuple of 4 tensors of shape `[A1, ..., An, 1]`, where the first tensor represents the vertical field of view used to contruct `projection_matrix, the second tensor represents the ascpect ratio used to construct `projection_matrix`, and the third and fourth parameters repectively represent the near and far clipping planes used to construct `projection_matrix`. """ with tf.compat.v1.name_scope(name, "perspective_parameters_from_right_handed", [projection_matrix]): projection_matrix = tf.convert_to_tensor(value=projection_matrix) shape.check_static( tensor=projection_matrix, tensor_name="projection_matrix", has_rank_greater_than=1, has_dim_equals=((-2, 4), (-1, 4))) inverse_tan_half_vertical_field_of_view = projection_matrix[..., 1, 1:2] vertical_field_of_view = 2.0 * tf.atan( 1.0 / inverse_tan_half_vertical_field_of_view) aspect_ratio = inverse_tan_half_vertical_field_of_view / projection_matrix[ ..., 0, 0:1] a = projection_matrix[..., 2, 2:3] b = projection_matrix[..., 2, 3:4] far = b / (a + 1.0) near = (a + 1.0) / (a - 1.0) * far return vertical_field_of_view, aspect_ratio, near, far def right_handed(vertical_field_of_view, aspect_ratio, near, far, name=None): """Generates the matrix for a right handed perspective projection. Note: In the following, A1 to An are optional batch dimensions. Args: vertical_field_of_view: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the vertical field of view of the frustum expressed in radians. Note that values for `vertical_field_of_view` must be in the range (0,pi). aspect_ratio: A tensor of shape `[A1, ..., An, 1]`, where the last dimension stores the width over height ratio of the frustum. Note that values for `aspect_ratio` must be non-negative. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be greater than those of `near`. name: A name for this op. Defaults to 'perspective_right_handed'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: if the all the inputs are not of the same shape. Returns: A tensor of shape `[A1, ..., An, 4, 4]`, containing matrices of right handed perspective-view frustum. """ with tf.compat.v1.name_scope( name, "perspective_right_handed", [vertical_field_of_view, aspect_ratio, near, far]): vertical_field_of_view = tf.convert_to_tensor(value=vertical_field_of_view) aspect_ratio = tf.convert_to_tensor(value=aspect_ratio) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=vertical_field_of_view, tensor_name="vertical_field_of_view", has_dim_equals=(-1, 1)) shape.check_static( tensor=aspect_ratio, tensor_name="aspect_ratio", has_dim_equals=(-1, 1)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(vertical_field_of_view, aspect_ratio, near, far), last_axes=-2, tensor_names=("vertical_field_of_view", "aspect_ratio", "near", "far"), broadcast_compatible=False) vertical_field_of_view = asserts.assert_all_in_range( vertical_field_of_view, 0.0, math.pi, open_bounds=True) aspect_ratio = asserts.assert_all_above(aspect_ratio, 0.0, open_bound=True) near = asserts.assert_all_above(near, 0.0, open_bound=True) far = asserts.assert_all_above(far, near, open_bound=True) inverse_tan_half_vertical_field_of_view = 1.0 / tf.tan( vertical_field_of_view * 0.5) zero = tf.zeros_like(inverse_tan_half_vertical_field_of_view) one = tf.ones_like(inverse_tan_half_vertical_field_of_view) near_minus_far = near - far matrix = tf.concat( (inverse_tan_half_vertical_field_of_view / aspect_ratio, zero, zero, zero, zero, inverse_tan_half_vertical_field_of_view, zero, zero, zero, zero, (far + near) / near_minus_far, 2.0 * far * near / near_minus_far, zero, zero, -one, zero), axis=-1) matrix_shape = tf.shape(input=matrix) output_shape = tf.concat((matrix_shape[:-1], (4, 4)), axis=-1) return tf.reshape(matrix, shape=output_shape) def intrinsics_from_matrix(matrix, name=None): r"""Extracts intrinsic parameters from a calibration matrix. Extracts the focal length \\((f_x, f_y)\\) and the principal point \\((c_x, c_y)\\) from a camera calibration matrix $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix}. $$ Note: In the following, A1 to An are optional batch dimensions. Args: matrix: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions represent a camera calibration matrix. name: A name for this op that defaults to "perspective_intrinsics_from_matrix". Returns: Tuple of two tensors, each one of shape `[A1, ..., An, 2]`. The first tensor represents the focal length, and the second one the principle point. Raises: ValueError: If the shape of `matrix` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_intrinsics_from_matrix", [matrix]): matrix = tf.convert_to_tensor(value=matrix) shape.check_static( tensor=matrix, tensor_name="matrix", has_rank_greater_than=1, has_dim_equals=((-1, 3), (-2, 3))) fx = matrix[..., 0, 0] fy = matrix[..., 1, 1] cx = matrix[..., 0, 2] cy = matrix[..., 1, 2] focal = tf.stack((fx, fy), axis=-1) principal_point = tf.stack((cx, cy), axis=-1) return focal, principal_point def matrix_from_intrinsics(focal, principal_point, name=None): r"""Builds calibration matrix from intrinsic parameters. Builds the camera calibration matrix as $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix} $$ from the focal length \\((f_x, f_y)\\) and the principal point \\((c_x, c_y)\\). Note: In the following, A1 to An are optional batch dimensions. Args: focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_matrix_from_intrinsics". Returns: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions represent a camera calibration matrix. Raises: ValueError: If the shape of `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_matrix_from_intrinsics", [focal, principal_point]): focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(focal, principal_point), tensor_names=("focal", "principal_point"), last_axes=-2, broadcast_compatible=False) fx, fy = tf.unstack(focal, axis=-1) cx, cy = tf.unstack(principal_point, axis=-1) zero = tf.zeros_like(fx) one = tf.ones_like(fx) matrix = tf.stack((fx, zero, cx, zero, fy, cy, zero, zero, one), axis=-1) # pyformat: disable matrix_shape = tf.shape(input=matrix) output_shape = tf.concat((matrix_shape[:-1], (3, 3)), axis=-1) return tf.reshape(matrix, shape=output_shape) def project(point_3d, focal, principal_point, name=None): r"""Projects a 3d point onto the 2d camera plane. Projects a 3d point \\((x, y, z)\\) to a 2d point \\((x', y')\\) onto the image plane with $$ \begin{matrix} x' = \frac{f_x}{z}x + c_x, & y' = \frac{f_y}{z}y + c_y, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. Note: In the following, A1 to An are optional batch dimensions that must be broadcast compatible. Args: point_3d: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d point to project. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_project". Returns: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point. Raises: ValueError: If the shape of `point_3d`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_project", [point_3d, focal, principal_point]): point_3d = tf.convert_to_tensor(value=point_3d) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_3d, tensor_name="point_3d", has_dim_equals=(-1, 3)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_3d, focal, principal_point), tensor_names=("point_3d", "focal", "principal_point"), last_axes=-2, broadcast_compatible=True) point_2d, depth = tf.split(point_3d, (2, 1), axis=-1) point_2d *= safe_ops.safe_signed_div(focal, depth) point_2d += principal_point return point_2d def ray(point_2d, focal, principal_point, name=None): r"""Computes the 3d ray for a 2d point (the z component of the ray is 1). Computes the 3d ray \\((r_x, r_y, 1)\\) from the camera center to a 2d point \\((x', y')\\) on the image plane with $$ \begin{matrix} r_x = \frac{(x' - c_x)}{f_x}, & r_y = \frac{(y' - c_y)}{f_y}, & z = 1, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. The camera optical center is assumed to be at \\((0, 0, 0)\\). Note: In the following, A1 to An are optional batch dimensions that must be broadcast compatible. Args: point_2d: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_ray". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d ray. Raises: ValueError: If the shape of `point_2d`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_ray", [point_2d, focal, principal_point]): point_2d = tf.convert_to_tensor(value=point_2d) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_2d, focal, principal_point), tensor_names=("point_2d", "focal", "principal_point"), last_axes=-2, broadcast_compatible=True) point_2d -= principal_point point_2d = safe_ops.safe_signed_div(point_2d, focal) padding = [[0, 0] for _ in point_2d.shape] padding[-1][-1] = 1 return tf.pad( tensor=point_2d, paddings=padding, mode="CONSTANT", constant_values=1.0) def unproject(point_2d, depth, focal, principal_point, name=None): r"""Unprojects a 2d point in 3d. Unprojects a 2d point \\((x', y')\\) to a 3d point \\((x, y, z)\\) knowing the depth \\(z\\) with $$ \begin{matrix} x = \frac{z (x' - c_x)}{f_x}, & y = \frac{z(y' - c_y)}{f_y}, & z = z, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. Note: In the following, A1 to An are optional batch dimensions. Args: point_2d: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point to unproject. depth: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the depth of a 2d point. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_unproject". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d point. Raises: ValueError: If the shape of `point_2d`, `depth`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_unproject", [point_2d, depth, focal, principal_point]): point_2d = tf.convert_to_tensor(value=point_2d) depth = tf.convert_to_tensor(value=depth) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2)) shape.check_static( tensor=depth, tensor_name="depth", has_dim_equals=(-1, 1)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_2d, depth, focal, principal_point), tensor_names=("point_2d", "depth", "focal", "principal_point"), last_axes=-2, broadcast_compatible=False) point_2d -= principal_point point_2d *= safe_ops.safe_signed_div(depth, focal) return tf.concat((point_2d, depth), axis=-1) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""This module implements perspective camera functionalities. The perspective camera model, also referred to as pinhole camera model, is defined using a focal length \\((f_x, f_y)\\) and a principal point \\((c_x, c_y)\\). The perspective camera model can be written as a calibration matrix $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix}, $$ also referred to as the intrinsic parameter matrix. The camera focal length \\((f_x, f_y)\\), defined in pixels, is the physical focal length divided by the physical size of a camera pixel. The physical focal length is the distance between the camera center and the image plane. The principal point is the intersection of the camera axis with the image plane. The camera axis is the line perpendicular to the image plane starting at the optical center. More details about perspective cameras can be found on [this page.] (http://ksimek.github.io/2013/08/13/intrinsic/) Note: The current implementation does not take into account distortion or skew parameters. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def parameters_from_right_handed(projection_matrix, name=None): """Recovers the parameters used to contruct a right handed projection matrix. Note: In the following, A1 to An are optional batch dimensions. Args: projection_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, containing matrices of right handed perspective-view frustum. name: A name for this op. Defaults to 'perspective_parameters_from_right_handed'. Raises: InvalidArgumentError: if `projection_matrix` is not of the expected shape. Returns: Tuple of 4 tensors of shape `[A1, ..., An, 1]`, where the first tensor represents the vertical field of view used to contruct `projection_matrix, the second tensor represents the ascpect ratio used to construct `projection_matrix`, and the third and fourth parameters repectively represent the near and far clipping planes used to construct `projection_matrix`. """ with tf.compat.v1.name_scope(name, "perspective_parameters_from_right_handed", [projection_matrix]): projection_matrix = tf.convert_to_tensor(value=projection_matrix) shape.check_static( tensor=projection_matrix, tensor_name="projection_matrix", has_rank_greater_than=1, has_dim_equals=((-2, 4), (-1, 4))) inverse_tan_half_vertical_field_of_view = projection_matrix[..., 1, 1:2] vertical_field_of_view = 2.0 * tf.atan( 1.0 / inverse_tan_half_vertical_field_of_view) aspect_ratio = inverse_tan_half_vertical_field_of_view / projection_matrix[ ..., 0, 0:1] a = projection_matrix[..., 2, 2:3] b = projection_matrix[..., 2, 3:4] far = b / (a + 1.0) near = (a + 1.0) / (a - 1.0) * far return vertical_field_of_view, aspect_ratio, near, far def right_handed(vertical_field_of_view, aspect_ratio, near, far, name=None): """Generates the matrix for a right handed perspective projection. Note: In the following, A1 to An are optional batch dimensions. Args: vertical_field_of_view: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the vertical field of view of the frustum expressed in radians. Note that values for `vertical_field_of_view` must be in the range (0,pi). aspect_ratio: A tensor of shape `[A1, ..., An, 1]`, where the last dimension stores the width over height ratio of the frustum. Note that values for `aspect_ratio` must be non-negative. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be greater than those of `near`. name: A name for this op. Defaults to 'perspective_right_handed'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: if the all the inputs are not of the same shape. Returns: A tensor of shape `[A1, ..., An, 4, 4]`, containing matrices of right handed perspective-view frustum. """ with tf.compat.v1.name_scope( name, "perspective_right_handed", [vertical_field_of_view, aspect_ratio, near, far]): vertical_field_of_view = tf.convert_to_tensor(value=vertical_field_of_view) aspect_ratio = tf.convert_to_tensor(value=aspect_ratio) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=vertical_field_of_view, tensor_name="vertical_field_of_view", has_dim_equals=(-1, 1)) shape.check_static( tensor=aspect_ratio, tensor_name="aspect_ratio", has_dim_equals=(-1, 1)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(vertical_field_of_view, aspect_ratio, near, far), last_axes=-2, tensor_names=("vertical_field_of_view", "aspect_ratio", "near", "far"), broadcast_compatible=False) vertical_field_of_view = asserts.assert_all_in_range( vertical_field_of_view, 0.0, math.pi, open_bounds=True) aspect_ratio = asserts.assert_all_above(aspect_ratio, 0.0, open_bound=True) near = asserts.assert_all_above(near, 0.0, open_bound=True) far = asserts.assert_all_above(far, near, open_bound=True) inverse_tan_half_vertical_field_of_view = 1.0 / tf.tan( vertical_field_of_view * 0.5) zero = tf.zeros_like(inverse_tan_half_vertical_field_of_view) one = tf.ones_like(inverse_tan_half_vertical_field_of_view) near_minus_far = near - far matrix = tf.concat( (inverse_tan_half_vertical_field_of_view / aspect_ratio, zero, zero, zero, zero, inverse_tan_half_vertical_field_of_view, zero, zero, zero, zero, (far + near) / near_minus_far, 2.0 * far * near / near_minus_far, zero, zero, -one, zero), axis=-1) matrix_shape = tf.shape(input=matrix) output_shape = tf.concat((matrix_shape[:-1], (4, 4)), axis=-1) return tf.reshape(matrix, shape=output_shape) def intrinsics_from_matrix(matrix, name=None): r"""Extracts intrinsic parameters from a calibration matrix. Extracts the focal length \\((f_x, f_y)\\) and the principal point \\((c_x, c_y)\\) from a camera calibration matrix $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix}. $$ Note: In the following, A1 to An are optional batch dimensions. Args: matrix: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions represent a camera calibration matrix. name: A name for this op that defaults to "perspective_intrinsics_from_matrix". Returns: Tuple of two tensors, each one of shape `[A1, ..., An, 2]`. The first tensor represents the focal length, and the second one the principle point. Raises: ValueError: If the shape of `matrix` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_intrinsics_from_matrix", [matrix]): matrix = tf.convert_to_tensor(value=matrix) shape.check_static( tensor=matrix, tensor_name="matrix", has_rank_greater_than=1, has_dim_equals=((-1, 3), (-2, 3))) fx = matrix[..., 0, 0] fy = matrix[..., 1, 1] cx = matrix[..., 0, 2] cy = matrix[..., 1, 2] focal = tf.stack((fx, fy), axis=-1) principal_point = tf.stack((cx, cy), axis=-1) return focal, principal_point def matrix_from_intrinsics(focal, principal_point, name=None): r"""Builds calibration matrix from intrinsic parameters. Builds the camera calibration matrix as $$ \mathbf{C} = \begin{bmatrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \\ \end{bmatrix} $$ from the focal length \\((f_x, f_y)\\) and the principal point \\((c_x, c_y)\\). Note: In the following, A1 to An are optional batch dimensions. Args: focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_matrix_from_intrinsics". Returns: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions represent a camera calibration matrix. Raises: ValueError: If the shape of `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_matrix_from_intrinsics", [focal, principal_point]): focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(focal, principal_point), tensor_names=("focal", "principal_point"), last_axes=-2, broadcast_compatible=False) fx, fy = tf.unstack(focal, axis=-1) cx, cy = tf.unstack(principal_point, axis=-1) zero = tf.zeros_like(fx) one = tf.ones_like(fx) matrix = tf.stack((fx, zero, cx, zero, fy, cy, zero, zero, one), axis=-1) # pyformat: disable matrix_shape = tf.shape(input=matrix) output_shape = tf.concat((matrix_shape[:-1], (3, 3)), axis=-1) return tf.reshape(matrix, shape=output_shape) def project(point_3d, focal, principal_point, name=None): r"""Projects a 3d point onto the 2d camera plane. Projects a 3d point \\((x, y, z)\\) to a 2d point \\((x', y')\\) onto the image plane with $$ \begin{matrix} x' = \frac{f_x}{z}x + c_x, & y' = \frac{f_y}{z}y + c_y, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. Note: In the following, A1 to An are optional batch dimensions that must be broadcast compatible. Args: point_3d: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d point to project. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_project". Returns: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point. Raises: ValueError: If the shape of `point_3d`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_project", [point_3d, focal, principal_point]): point_3d = tf.convert_to_tensor(value=point_3d) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_3d, tensor_name="point_3d", has_dim_equals=(-1, 3)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_3d, focal, principal_point), tensor_names=("point_3d", "focal", "principal_point"), last_axes=-2, broadcast_compatible=True) point_2d, depth = tf.split(point_3d, (2, 1), axis=-1) point_2d *= safe_ops.safe_signed_div(focal, depth) point_2d += principal_point return point_2d def ray(point_2d, focal, principal_point, name=None): r"""Computes the 3d ray for a 2d point (the z component of the ray is 1). Computes the 3d ray \\((r_x, r_y, 1)\\) from the camera center to a 2d point \\((x', y')\\) on the image plane with $$ \begin{matrix} r_x = \frac{(x' - c_x)}{f_x}, & r_y = \frac{(y' - c_y)}{f_y}, & z = 1, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. The camera optical center is assumed to be at \\((0, 0, 0)\\). Note: In the following, A1 to An are optional batch dimensions that must be broadcast compatible. Args: point_2d: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_ray". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d ray. Raises: ValueError: If the shape of `point_2d`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_ray", [point_2d, focal, principal_point]): point_2d = tf.convert_to_tensor(value=point_2d) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_2d, focal, principal_point), tensor_names=("point_2d", "focal", "principal_point"), last_axes=-2, broadcast_compatible=True) point_2d -= principal_point point_2d = safe_ops.safe_signed_div(point_2d, focal) padding = [[0, 0] for _ in point_2d.shape] padding[-1][-1] = 1 return tf.pad( tensor=point_2d, paddings=padding, mode="CONSTANT", constant_values=1.0) def unproject(point_2d, depth, focal, principal_point, name=None): r"""Unprojects a 2d point in 3d. Unprojects a 2d point \\((x', y')\\) to a 3d point \\((x, y, z)\\) knowing the depth \\(z\\) with $$ \begin{matrix} x = \frac{z (x' - c_x)}{f_x}, & y = \frac{z(y' - c_y)}{f_y}, & z = z, \end{matrix} $$ where \\((f_x, f_y)\\) is the focal length and \\((c_x, c_y)\\) the principal point. Note: In the following, A1 to An are optional batch dimensions. Args: point_2d: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a 2d point to unproject. depth: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the depth of a 2d point. focal: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera focal length. principal_point: A tensor of shape `[A1, ..., An, 2]`, where the last dimension represents a camera principal point. name: A name for this op that defaults to "perspective_unproject". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a 3d point. Raises: ValueError: If the shape of `point_2d`, `depth`, `focal`, or `principal_point` is not supported. """ with tf.compat.v1.name_scope(name, "perspective_unproject", [point_2d, depth, focal, principal_point]): point_2d = tf.convert_to_tensor(value=point_2d) depth = tf.convert_to_tensor(value=depth) focal = tf.convert_to_tensor(value=focal) principal_point = tf.convert_to_tensor(value=principal_point) shape.check_static( tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2)) shape.check_static( tensor=depth, tensor_name="depth", has_dim_equals=(-1, 1)) shape.check_static( tensor=focal, tensor_name="focal", has_dim_equals=(-1, 2)) shape.check_static( tensor=principal_point, tensor_name="principal_point", has_dim_equals=(-1, 2)) shape.compare_batch_dimensions( tensors=(point_2d, depth, focal, principal_point), tensor_names=("point_2d", "depth", "focal", "principal_point"), last_axes=-2, broadcast_compatible=False) point_2d -= principal_point point_2d *= safe_ops.safe_signed_div(depth, focal) return tf.concat((point_2d, depth), axis=-1) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/math.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements math routines used by OpenGL.""" import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def model_to_eye(point_model_space, camera_position, look_at_point, up_vector, name=None): """Transforms points from model to eye coordinates. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_model_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in model space. camera_position: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D position of the camera. look_at_point: A tensor of shape `[A1, ..., An, 3]`, with the last dimension storing the position where the camera is looking at. up_vector: A tensor of shape `[A1, ..., An, 3]`, where the last dimension defines the up vector of the camera. name: A name for this op. Defaults to 'model_to_eye'. Raises: ValueError: if the all the inputs are not of the same shape, or if any input of of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_model_space` in eye coordinates. """ with tf.compat.v1.name_scope( name, "model_to_eye", [point_model_space, camera_position, look_at_point, up_vector]): point_model_space = tf.convert_to_tensor(value=point_model_space) camera_position = tf.convert_to_tensor(value=camera_position) look_at_point = tf.convert_to_tensor(value=look_at_point) up_vector = tf.convert_to_tensor(value=up_vector) shape.check_static( tensor=point_model_space, tensor_name="point_model_space", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(point_model_space, camera_position), last_axes=-2, tensor_names=("point_model_space", "camera_position"), broadcast_compatible=True) model_to_eye_matrix = look_at.right_handed(camera_position, look_at_point, up_vector) batch_shape = tf.shape(input=point_model_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_model_space.dtype) point_model_space = tf.concat((point_model_space, one), axis=-1) point_model_space = tf.expand_dims(point_model_space, axis=-1) res = tf.squeeze(tf.matmul(model_to_eye_matrix, point_model_space), axis=-1) return res[..., :-1] def eye_to_clip(point_eye_space, vertical_field_of_view, aspect_ratio, near, far, name=None): """Transforms points from eye to clip space. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_eye_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in eye coordinates. vertical_field_of_view: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the vertical field of view of the frustum. Note that values for `vertical_field_of_view` must be in the range ]0,pi[. aspect_ratio: A tensor of shape `[A1, ..., An, 1]`, where the last dimension stores the width over height ratio of the frustum. Note that values for `aspect_ratio` must be non-negative. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be non-negative. name: A name for this op. Defaults to 'eye_to_clip'. Raises: ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 4]`, containing `point_eye_space` in homogeneous clip coordinates. """ with tf.compat.v1.name_scope( name, "eye_to_clip", [point_eye_space, vertical_field_of_view, aspect_ratio, near, far]): point_eye_space = tf.convert_to_tensor(value=point_eye_space) vertical_field_of_view = tf.convert_to_tensor(value=vertical_field_of_view) aspect_ratio = tf.convert_to_tensor(value=aspect_ratio) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=point_eye_space, tensor_name="point_eye_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=vertical_field_of_view, tensor_name="vertical_field_of_view", has_dim_equals=(-1, 1)) shape.check_static( tensor=aspect_ratio, tensor_name="aspect_ratio", has_dim_equals=(-1, 1)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(point_eye_space, vertical_field_of_view, aspect_ratio, near, far), last_axes=-2, tensor_names=("point_eye_space", "vertical_field_of_view", "aspect_ratio", "near", "far"), broadcast_compatible=True) perspective_matrix = perspective.right_handed(vertical_field_of_view, aspect_ratio, near, far) batch_shape = tf.shape(input=point_eye_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_eye_space.dtype) point_eye_space = tf.concat((point_eye_space, one), axis=-1) point_eye_space = tf.expand_dims(point_eye_space, axis=-1) return tf.squeeze(tf.matmul(perspective_matrix, point_eye_space), axis=-1) def clip_to_ndc(point_clip_space, name=None): """Transforms points from clip to normalized device coordinates (ndc). Note: In the following, A1 to An are optional batch dimensions. Args: point_clip_space: A tensor of shape `[A1, ..., An, 4]`, where the last dimension represents points in clip space. name: A name for this op. Defaults to 'clip_to_ndc'. Raises: ValueError: If `point_clip_space` is not of size 4 in its last dimension. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_clip_space` in normalized device coordinates. """ with tf.compat.v1.name_scope(name, "clip_to_ndc", [point_clip_space]): point_clip_space = tf.convert_to_tensor(value=point_clip_space) shape.check_static( tensor=point_clip_space, tensor_name="point_clip_space", has_dim_equals=(-1, 4)) w = point_clip_space[..., -1:] return point_clip_space[..., :3] / w def ndc_to_screen(point_ndc_space, lower_left_corner, screen_dimensions, near, far, name=None): """Transforms points from normalized device coordinates to screen coordinates. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible between `point_ndc_space` and the other variables. Args: point_ndc_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents points in normalized device coordinates. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be greater than those of `near`. name: A name for this op. Defaults to 'ndc_to_screen'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_ndc_space` in screen coordinates. """ with tf.compat.v1.name_scope( name, "ndc_to_screen", [point_ndc_space, lower_left_corner, screen_dimensions, near, far]): point_ndc_space = tf.convert_to_tensor(value=point_ndc_space) lower_left_corner = tf.convert_to_tensor(value=lower_left_corner) screen_dimensions = tf.convert_to_tensor(value=screen_dimensions) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=point_ndc_space, tensor_name="point_ndc_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=lower_left_corner, tensor_name="lower_left_corner", has_dim_equals=(-1, 2)) shape.check_static( tensor=screen_dimensions, tensor_name="screen_dimensions", has_dim_equals=(-1, 2)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(lower_left_corner, screen_dimensions, near, far), last_axes=-2, tensor_names=("lower_left_corner", "screen_dimensions", "near", "far"), broadcast_compatible=False) shape.compare_batch_dimensions( tensors=(point_ndc_space, near), last_axes=-2, tensor_names=("point_ndc_space", "near"), broadcast_compatible=True) screen_dimensions = asserts.assert_all_above( screen_dimensions, 0.0, open_bound=True) near = asserts.assert_all_above(near, 0.0, open_bound=True) far = asserts.assert_all_above(far, near, open_bound=True) ndc_to_screen_factor = tf.concat( (screen_dimensions, far - near), axis=-1) / 2.0 screen_center = tf.concat( (lower_left_corner + screen_dimensions / 2.0, (near + far) / 2.0), axis=-1) return ndc_to_screen_factor * point_ndc_space + screen_center def model_to_screen(point_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Transforms points from model to screen coordinates. Note: Please refer to http://www.songho.ca/opengl/gl_transform.html for an in-depth review of this pipeline. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_model_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in model space. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'model_to_screen'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tuple of two tensors, respectively of shape `[A1, ..., An, 3]` and `[A1, ..., An, 1]`, where the first tensor containing the projection of `point_model_space` in screen coordinates, and the second represents the 'w' component of `point_model_space` in clip space. """ with tf.compat.v1.name_scope(name, "model_to_screen", [ point_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): point_model_space = tf.convert_to_tensor(value=point_model_space) model_to_eye_matrix = tf.convert_to_tensor(value=model_to_eye_matrix) perspective_matrix = tf.convert_to_tensor(value=perspective_matrix) shape.check_static( tensor=point_model_space, tensor_name="point_model_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=model_to_eye_matrix, tensor_name="model_to_eye_matrix", has_dim_equals=((-1, 4), (-2, 4))) shape.check_static( tensor=perspective_matrix, tensor_name="perspective_matrix", has_dim_equals=((-1, 4), (-2, 4))) shape.compare_batch_dimensions( tensors=(point_model_space, model_to_eye_matrix, perspective_matrix), last_axes=(-2, -3, -3), tensor_names=("point_model_space", "model_to_eye_matrix", "perspective_matrix"), broadcast_compatible=True) batch_shape = tf.shape(input=point_model_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_model_space.dtype) point_model_space = tf.concat((point_model_space, one), axis=-1) point_model_space = tf.expand_dims(point_model_space, axis=-1) view_projection_matrix = tf.linalg.matmul(perspective_matrix, model_to_eye_matrix) _, _, near, far = perspective.parameters_from_right_handed( perspective_matrix) point_clip_space = tf.squeeze( tf.matmul(view_projection_matrix, point_model_space), axis=-1) point_ndc_space = clip_to_ndc(point_clip_space) point_screen_space = ndc_to_screen(point_ndc_space, lower_left_corner, screen_dimensions, near, far) return point_screen_space, point_clip_space[..., 3:4] def perspective_correct_barycentrics(triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Computes perspective correct barycentrics. Note: In the following, A1 to An are optional batch dimensions. Args: triangle_vertices_model_space: A tensor of shape `[A1, ..., An, 3, 3]`, where the last dimension represents the vertices of a triangle in model space. pixel_position: A tensor of shape `[A1, ..., An, 2]`, where the last dimension stores the position (in pixels) where the interpolation is requested. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'perspective_correct_barycentrics'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing perspective correct barycentric coordinates. """ with tf.compat.v1.name_scope(name, "perspective_correct_barycentrics", [ triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): pixel_position = tf.convert_to_tensor(value=pixel_position) triangle_vertices_model_space = tf.convert_to_tensor( value=triangle_vertices_model_space) shape.check_static( tensor=pixel_position, tensor_name="pixel_position", has_dim_equals=(-1, 2)) shape.check_static( tensor=triangle_vertices_model_space, tensor_name="triangle_vertices_model_space", has_dim_equals=((-2, 3), (-1, 3))) lower_left_corner = tf.convert_to_tensor(value=lower_left_corner) screen_dimensions = tf.convert_to_tensor(value=screen_dimensions) lower_left_corner = shape.add_batch_dimensions( lower_left_corner, "lower_left_corner", model_to_eye_matrix.shape[:-2], last_axis=-2) screen_dimensions = shape.add_batch_dimensions( screen_dimensions, "screen_dimensions", model_to_eye_matrix.shape[:-2], last_axis=-2) vertices_screen, vertices_w = model_to_screen(triangle_vertices_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) vertices_w = tf.squeeze(vertices_w, axis=-1) pixel_position = tf.expand_dims(pixel_position, axis=-2) barycentric_coordinates, _ = weighted.get_barycentric_coordinates( vertices_screen[..., :2], pixel_position) barycentric_coordinates = tf.squeeze(barycentric_coordinates, axis=-2) coeffs = barycentric_coordinates / vertices_w return tf.linalg.normalize(coeffs, ord=1, axis=-1)[0] def interpolate_attributes(attribute, barycentric, name=None): """Interpolates attributes using barycentric weights. Note: In the following, A1 to An are optional batch dimensions. Args: attribute: A tensor of shape `[A1, ..., An, 3, B]`, where the last dimension stores a per-vertex `B`-dimensional attribute. barycentric: A tensor of shape `[A1, ..., An, 3]`, where the last dimension contains barycentric coordinates. name: A name for this op. Defaults to 'interpolate_attributes'. Returns: A tensor of shape `[A1, ..., An, B]`, containing interpolated attributes. """ with tf.compat.v1.name_scope(name, "interpolate_attributes", (attribute, barycentric)): attribute = tf.convert_to_tensor(value=attribute) barycentric = tf.convert_to_tensor(value=barycentric) shape.check_static( tensor=attribute, tensor_name="attribute", has_dim_equals=(-2, 3)) shape.check_static( tensor=barycentric, tensor_name="barycentric", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(attribute, barycentric), last_axes=(-2, -1), tensor_names=("attribute", "barycentric"), broadcast_compatible=True) barycentric = asserts.assert_normalized(barycentric, order=1) return tf.reduce_sum( input_tensor=tf.expand_dims(barycentric, axis=-1) * attribute, axis=-2) def perspective_correct_interpolation(triangle_vertices_model_space, attribute, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Returns perspective corrected interpolation of attributes over triangles. Note: In the following, A1 to An are optional batch dimensions. Args: triangle_vertices_model_space: A tensor of shape `[A1, ..., An, 3, 3]`, where the last dimension represents the vertices of a triangle in model space. attribute: A tensor of shape `[A1, ..., An, 3, B]`, where the last dimension stores a per-vertex `B`-dimensional attribute. pixel_position: A tensor of shape `[A1, ..., An, 2]`, where the last dimension stores the position (in pixels) where the interpolation is requested. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'perspective_correct_interpolation'. Raises: tf.errors.InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, B]`, containing interpolated attributes. """ with tf.compat.v1.name_scope(name, "perspective_correct_interpolation", [ triangle_vertices_model_space, attribute, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): barycentric = perspective_correct_barycentrics( triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) return interpolate_attributes(attribute, barycentric) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements math routines used by OpenGL.""" import tensorflow as tf from tensorflow_graphics.geometry.transformation import look_at from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.rendering.camera import perspective from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def model_to_eye(point_model_space, camera_position, look_at_point, up_vector, name=None): """Transforms points from model to eye coordinates. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_model_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in model space. camera_position: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D position of the camera. look_at_point: A tensor of shape `[A1, ..., An, 3]`, with the last dimension storing the position where the camera is looking at. up_vector: A tensor of shape `[A1, ..., An, 3]`, where the last dimension defines the up vector of the camera. name: A name for this op. Defaults to 'model_to_eye'. Raises: ValueError: if the all the inputs are not of the same shape, or if any input of of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_model_space` in eye coordinates. """ with tf.compat.v1.name_scope( name, "model_to_eye", [point_model_space, camera_position, look_at_point, up_vector]): point_model_space = tf.convert_to_tensor(value=point_model_space) camera_position = tf.convert_to_tensor(value=camera_position) look_at_point = tf.convert_to_tensor(value=look_at_point) up_vector = tf.convert_to_tensor(value=up_vector) shape.check_static( tensor=point_model_space, tensor_name="point_model_space", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(point_model_space, camera_position), last_axes=-2, tensor_names=("point_model_space", "camera_position"), broadcast_compatible=True) model_to_eye_matrix = look_at.right_handed(camera_position, look_at_point, up_vector) batch_shape = tf.shape(input=point_model_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_model_space.dtype) point_model_space = tf.concat((point_model_space, one), axis=-1) point_model_space = tf.expand_dims(point_model_space, axis=-1) res = tf.squeeze(tf.matmul(model_to_eye_matrix, point_model_space), axis=-1) return res[..., :-1] def eye_to_clip(point_eye_space, vertical_field_of_view, aspect_ratio, near, far, name=None): """Transforms points from eye to clip space. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_eye_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in eye coordinates. vertical_field_of_view: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents the vertical field of view of the frustum. Note that values for `vertical_field_of_view` must be in the range ]0,pi[. aspect_ratio: A tensor of shape `[A1, ..., An, 1]`, where the last dimension stores the width over height ratio of the frustum. Note that values for `aspect_ratio` must be non-negative. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be non-negative. name: A name for this op. Defaults to 'eye_to_clip'. Raises: ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 4]`, containing `point_eye_space` in homogeneous clip coordinates. """ with tf.compat.v1.name_scope( name, "eye_to_clip", [point_eye_space, vertical_field_of_view, aspect_ratio, near, far]): point_eye_space = tf.convert_to_tensor(value=point_eye_space) vertical_field_of_view = tf.convert_to_tensor(value=vertical_field_of_view) aspect_ratio = tf.convert_to_tensor(value=aspect_ratio) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=point_eye_space, tensor_name="point_eye_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=vertical_field_of_view, tensor_name="vertical_field_of_view", has_dim_equals=(-1, 1)) shape.check_static( tensor=aspect_ratio, tensor_name="aspect_ratio", has_dim_equals=(-1, 1)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(point_eye_space, vertical_field_of_view, aspect_ratio, near, far), last_axes=-2, tensor_names=("point_eye_space", "vertical_field_of_view", "aspect_ratio", "near", "far"), broadcast_compatible=True) perspective_matrix = perspective.right_handed(vertical_field_of_view, aspect_ratio, near, far) batch_shape = tf.shape(input=point_eye_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_eye_space.dtype) point_eye_space = tf.concat((point_eye_space, one), axis=-1) point_eye_space = tf.expand_dims(point_eye_space, axis=-1) return tf.squeeze(tf.matmul(perspective_matrix, point_eye_space), axis=-1) def clip_to_ndc(point_clip_space, name=None): """Transforms points from clip to normalized device coordinates (ndc). Note: In the following, A1 to An are optional batch dimensions. Args: point_clip_space: A tensor of shape `[A1, ..., An, 4]`, where the last dimension represents points in clip space. name: A name for this op. Defaults to 'clip_to_ndc'. Raises: ValueError: If `point_clip_space` is not of size 4 in its last dimension. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_clip_space` in normalized device coordinates. """ with tf.compat.v1.name_scope(name, "clip_to_ndc", [point_clip_space]): point_clip_space = tf.convert_to_tensor(value=point_clip_space) shape.check_static( tensor=point_clip_space, tensor_name="point_clip_space", has_dim_equals=(-1, 4)) w = point_clip_space[..., -1:] return point_clip_space[..., :3] / w def ndc_to_screen(point_ndc_space, lower_left_corner, screen_dimensions, near, far, name=None): """Transforms points from normalized device coordinates to screen coordinates. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible between `point_ndc_space` and the other variables. Args: point_ndc_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents points in normalized device coordinates. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. near: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the near clipping plane. Note that values for `near` must be non-negative. far: A tensor of shape `[A1, ..., An, 1]`, where the last dimension captures the distance between the viewer and the far clipping plane. Note that values for `far` must be greater than those of `near`. name: A name for this op. Defaults to 'ndc_to_screen'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing `point_ndc_space` in screen coordinates. """ with tf.compat.v1.name_scope( name, "ndc_to_screen", [point_ndc_space, lower_left_corner, screen_dimensions, near, far]): point_ndc_space = tf.convert_to_tensor(value=point_ndc_space) lower_left_corner = tf.convert_to_tensor(value=lower_left_corner) screen_dimensions = tf.convert_to_tensor(value=screen_dimensions) near = tf.convert_to_tensor(value=near) far = tf.convert_to_tensor(value=far) shape.check_static( tensor=point_ndc_space, tensor_name="point_ndc_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=lower_left_corner, tensor_name="lower_left_corner", has_dim_equals=(-1, 2)) shape.check_static( tensor=screen_dimensions, tensor_name="screen_dimensions", has_dim_equals=(-1, 2)) shape.check_static(tensor=near, tensor_name="near", has_dim_equals=(-1, 1)) shape.check_static(tensor=far, tensor_name="far", has_dim_equals=(-1, 1)) shape.compare_batch_dimensions( tensors=(lower_left_corner, screen_dimensions, near, far), last_axes=-2, tensor_names=("lower_left_corner", "screen_dimensions", "near", "far"), broadcast_compatible=False) shape.compare_batch_dimensions( tensors=(point_ndc_space, near), last_axes=-2, tensor_names=("point_ndc_space", "near"), broadcast_compatible=True) screen_dimensions = asserts.assert_all_above( screen_dimensions, 0.0, open_bound=True) near = asserts.assert_all_above(near, 0.0, open_bound=True) far = asserts.assert_all_above(far, near, open_bound=True) ndc_to_screen_factor = tf.concat( (screen_dimensions, far - near), axis=-1) / 2.0 screen_center = tf.concat( (lower_left_corner + screen_dimensions / 2.0, (near + far) / 2.0), axis=-1) return ndc_to_screen_factor * point_ndc_space + screen_center def model_to_screen(point_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Transforms points from model to screen coordinates. Note: Please refer to http://www.songho.ca/opengl/gl_transform.html for an in-depth review of this pipeline. Note: In the following, A1 to An are optional batch dimensions which must be broadcast compatible. Args: point_model_space: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the 3D points in model space. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'model_to_screen'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tuple of two tensors, respectively of shape `[A1, ..., An, 3]` and `[A1, ..., An, 1]`, where the first tensor containing the projection of `point_model_space` in screen coordinates, and the second represents the 'w' component of `point_model_space` in clip space. """ with tf.compat.v1.name_scope(name, "model_to_screen", [ point_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): point_model_space = tf.convert_to_tensor(value=point_model_space) model_to_eye_matrix = tf.convert_to_tensor(value=model_to_eye_matrix) perspective_matrix = tf.convert_to_tensor(value=perspective_matrix) shape.check_static( tensor=point_model_space, tensor_name="point_model_space", has_dim_equals=(-1, 3)) shape.check_static( tensor=model_to_eye_matrix, tensor_name="model_to_eye_matrix", has_dim_equals=((-1, 4), (-2, 4))) shape.check_static( tensor=perspective_matrix, tensor_name="perspective_matrix", has_dim_equals=((-1, 4), (-2, 4))) shape.compare_batch_dimensions( tensors=(point_model_space, model_to_eye_matrix, perspective_matrix), last_axes=(-2, -3, -3), tensor_names=("point_model_space", "model_to_eye_matrix", "perspective_matrix"), broadcast_compatible=True) batch_shape = tf.shape(input=point_model_space)[:-1] one = tf.ones( shape=tf.concat((batch_shape, (1,)), axis=-1), dtype=point_model_space.dtype) point_model_space = tf.concat((point_model_space, one), axis=-1) point_model_space = tf.expand_dims(point_model_space, axis=-1) view_projection_matrix = tf.linalg.matmul(perspective_matrix, model_to_eye_matrix) _, _, near, far = perspective.parameters_from_right_handed( perspective_matrix) point_clip_space = tf.squeeze( tf.matmul(view_projection_matrix, point_model_space), axis=-1) point_ndc_space = clip_to_ndc(point_clip_space) point_screen_space = ndc_to_screen(point_ndc_space, lower_left_corner, screen_dimensions, near, far) return point_screen_space, point_clip_space[..., 3:4] def perspective_correct_barycentrics(triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Computes perspective correct barycentrics. Note: In the following, A1 to An are optional batch dimensions. Args: triangle_vertices_model_space: A tensor of shape `[A1, ..., An, 3, 3]`, where the last dimension represents the vertices of a triangle in model space. pixel_position: A tensor of shape `[A1, ..., An, 2]`, where the last dimension stores the position (in pixels) where the interpolation is requested. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'perspective_correct_barycentrics'. Raises: InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, 3]`, containing perspective correct barycentric coordinates. """ with tf.compat.v1.name_scope(name, "perspective_correct_barycentrics", [ triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): pixel_position = tf.convert_to_tensor(value=pixel_position) triangle_vertices_model_space = tf.convert_to_tensor( value=triangle_vertices_model_space) shape.check_static( tensor=pixel_position, tensor_name="pixel_position", has_dim_equals=(-1, 2)) shape.check_static( tensor=triangle_vertices_model_space, tensor_name="triangle_vertices_model_space", has_dim_equals=((-2, 3), (-1, 3))) lower_left_corner = tf.convert_to_tensor(value=lower_left_corner) screen_dimensions = tf.convert_to_tensor(value=screen_dimensions) lower_left_corner = shape.add_batch_dimensions( lower_left_corner, "lower_left_corner", model_to_eye_matrix.shape[:-2], last_axis=-2) screen_dimensions = shape.add_batch_dimensions( screen_dimensions, "screen_dimensions", model_to_eye_matrix.shape[:-2], last_axis=-2) vertices_screen, vertices_w = model_to_screen(triangle_vertices_model_space, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) vertices_w = tf.squeeze(vertices_w, axis=-1) pixel_position = tf.expand_dims(pixel_position, axis=-2) barycentric_coordinates, _ = weighted.get_barycentric_coordinates( vertices_screen[..., :2], pixel_position) barycentric_coordinates = tf.squeeze(barycentric_coordinates, axis=-2) coeffs = barycentric_coordinates / vertices_w return tf.linalg.normalize(coeffs, ord=1, axis=-1)[0] def interpolate_attributes(attribute, barycentric, name=None): """Interpolates attributes using barycentric weights. Note: In the following, A1 to An are optional batch dimensions. Args: attribute: A tensor of shape `[A1, ..., An, 3, B]`, where the last dimension stores a per-vertex `B`-dimensional attribute. barycentric: A tensor of shape `[A1, ..., An, 3]`, where the last dimension contains barycentric coordinates. name: A name for this op. Defaults to 'interpolate_attributes'. Returns: A tensor of shape `[A1, ..., An, B]`, containing interpolated attributes. """ with tf.compat.v1.name_scope(name, "interpolate_attributes", (attribute, barycentric)): attribute = tf.convert_to_tensor(value=attribute) barycentric = tf.convert_to_tensor(value=barycentric) shape.check_static( tensor=attribute, tensor_name="attribute", has_dim_equals=(-2, 3)) shape.check_static( tensor=barycentric, tensor_name="barycentric", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(attribute, barycentric), last_axes=(-2, -1), tensor_names=("attribute", "barycentric"), broadcast_compatible=True) barycentric = asserts.assert_normalized(barycentric, order=1) return tf.reduce_sum( input_tensor=tf.expand_dims(barycentric, axis=-1) * attribute, axis=-2) def perspective_correct_interpolation(triangle_vertices_model_space, attribute, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner=(0.0, 0.0), name=None): """Returns perspective corrected interpolation of attributes over triangles. Note: In the following, A1 to An are optional batch dimensions. Args: triangle_vertices_model_space: A tensor of shape `[A1, ..., An, 3, 3]`, where the last dimension represents the vertices of a triangle in model space. attribute: A tensor of shape `[A1, ..., An, 3, B]`, where the last dimension stores a per-vertex `B`-dimensional attribute. pixel_position: A tensor of shape `[A1, ..., An, 2]`, where the last dimension stores the position (in pixels) where the interpolation is requested. model_to_eye_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from model to eye coordinates. perspective_matrix: A tensor of shape `[A1, ..., An, 4, 4]`, where the last two dimension represent matrices to transform points from eye to clip coordinates. screen_dimensions: A tensor of shape `[A1, ..., An, 2]`, where the last dimension is expressed in pixels and captures the width and the height (in pixels) of the screen. lower_left_corner: A tensor of shape `[A1, ..., An, 2]`, where the last dimension captures the position (in pixels) of the lower left corner of the screen. name: A name for this op. Defaults to 'perspective_correct_interpolation'. Raises: tf.errors.InvalidArgumentError: if any input contains data not in the specified range of valid values. ValueError: If any input is of an unsupported shape. Returns: A tensor of shape `[A1, ..., An, B]`, containing interpolated attributes. """ with tf.compat.v1.name_scope(name, "perspective_correct_interpolation", [ triangle_vertices_model_space, attribute, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner ]): barycentric = perspective_correct_barycentrics( triangle_vertices_model_space, pixel_position, model_to_eye_matrix, perspective_matrix, screen_dimensions, lower_left_corner) return interpolate_attributes(attribute, barycentric) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/mesh/tests/normals_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for normals.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation.mesh import normals from tensorflow_graphics.util import test_case class MeshTest(test_case.TestCase): @parameterized.parameters( (((None, 3), (None, 3)), (tf.float32, tf.int32)), (((3, 6, 3), (3, 5, 4)), (tf.float32, tf.int32)), ) def test_gather_faces_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.gather_faces, shapes, dtypes) @parameterized.parameters( ("Not all batch dimensions are identical", (3, 5, 4, 4), (1, 2, 4, 4)), ("Not all batch dimensions are identical", (5, 4, 4), (1, 2, 4, 4)), ("Not all batch dimensions are identical", (3, 5, 4, 4), (2, 4, 4)), ("vertices must have a rank greater than 1", (4,), (1, 2, 4, 4)), ("indices must have a rank greater than 1", (3, 5, 4, 4), (4,)), ) def test_gather_faces_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.gather_faces, error_msg, shapes) def test_gather_faces_jacobian_random(self): """Test the Jacobian of the face extraction function.""" tensor_size = np.random.randint(2, 5) tensor_shape = np.random.randint(1, 5, size=tensor_size).tolist() vertex_init = np.random.random(size=tensor_shape) indices_init = np.random.randint(0, tensor_shape[-2], size=tensor_shape) indices_tensor = tf.convert_to_tensor(value=indices_init) def gather_faces(vertex_tensor): return normals.gather_faces(vertex_tensor, indices_tensor) self.assert_jacobian_is_correct_fn(gather_faces, [vertex_init]) @parameterized.parameters( ((((0.,), (1.,)), ((1, 0),)), ((((1.,), (0.,)),),)), ((((0., 1.), (2., 3.)), ((1, 0),)), ((((2., 3.), (0., 1.)),),)), ((((0., 1., 2.), (3., 4., 5.)), ((1, 0),)), ((((3., 4., 5.), (0., 1., 2.)),),)), ) def test_gather_faces_preset(self, test_inputs, test_outputs): """Tests the extraction of mesh faces.""" self.assert_output_is_correct( normals.gather_faces, test_inputs, test_outputs, tile=False) def test_gather_faces_random(self): """Tests the extraction of mesh faces.""" tensor_size = np.random.randint(3, 5) tensor_shape = np.random.randint(1, 5, size=tensor_size).tolist() vertices = np.random.random(size=tensor_shape) indices = np.arange(tensor_shape[-2]) indices = indices.reshape([1] * (tensor_size - 1) + [-1]) indices = np.tile(indices, tensor_shape[:-2] + [1, 1]) expected = np.expand_dims(vertices, -3) self.assertAllClose( normals.gather_faces(vertices, indices), expected, rtol=1e-3) @parameterized.parameters( (((None, 4, 3),), (tf.float32,)), (((4, 3),), (tf.float32,)), (((3, 4, 3),), (tf.float32,)), ) def test_face_normals_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.face_normals, shapes, dtypes) @parameterized.parameters( ("faces must have a rank greater than 1.", (3,)), ("faces must have greater than 2 dimensions in axis -2", (2, 3)), ("faces must have exactly 3 dimensions in axis -1.", (5, 2)), ) def test_face_normals_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.face_normals, error_msg, shapes) def test_face_normals_jacobian_random(self): """Test the Jacobian of the face normals function.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() tensor_vertex_shape = list(tensor_out_shape) tensor_vertex_shape[-1] *= 3 tensor_index_shape = tensor_out_shape[-1] vertex_init = np.random.random(size=tensor_vertex_shape + [3]) index_init = np.arange(tensor_vertex_shape[-1]) np.random.shuffle(index_init) index_init = np.reshape(index_init, newshape=[1] * \ (tensor_vertex_size - 1) + \ [tensor_index_shape, 3]) index_init = np.tile(index_init, tensor_vertex_shape[:-1] + [1, 1]) index_tensor = tf.convert_to_tensor(value=index_init) def face_normals(vertex_tensor): face_tensor = normals.gather_faces(vertex_tensor, index_tensor) return normals.face_normals(face_tensor) self.assert_jacobian_is_correct_fn( face_normals, [vertex_init], atol=1e-4, delta=1e-9) @parameterized.parameters( ((((0., 0., 0.), (1., 0., 0.), (0., 1., 0.)), ((0, 1, 2),)), (((0., 0., 1.),),)), ((((0., 0., 0.), (0., 0., 1.), (1., 0., 0.)), ((0, 1, 2),)), (((0., 1., 0.),),)), ((((0., 0., 0.), (0., 1., 0.), (0., 0., 1.)), ((0, 1, 2),)), (((1., 0., 0.),),)), ((((0., -2., -2.), (0, -2., 2.), (0., 2., 2.), (0., 2., -2.)), ((0, 1, 2, 3),)), (((-1., 0., 0.),),)), ) def test_face_normals_preset(self, test_inputs, test_outputs): """Tests the computation of mesh face normals.""" faces = normals.gather_faces(*test_inputs[:2]) test_inputs = [faces] + list(test_inputs[2:]) self.assert_output_is_correct( normals.face_normals, test_inputs, test_outputs, tile=False) def test_face_normals_random(self): """Tests the computation of mesh face normals in each axis.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() tensor_vertex_shape = list(tensor_out_shape) tensor_vertex_shape[-1] *= 3 tensor_index_shape = tensor_out_shape[-1] for i in range(3): vertices = np.random.random(size=tensor_vertex_shape + [3]) indices = np.arange(tensor_vertex_shape[-1]) np.random.shuffle(indices) indices = np.reshape(indices, newshape=[1] * (tensor_vertex_size - 1) \ + [tensor_index_shape, 3]) indices = np.tile(indices, tensor_vertex_shape[:-1] + [1, 1]) vertices[..., i] = 0. expected = np.zeros(shape=tensor_out_shape + [3], dtype=vertices.dtype) expected[..., i] = 1. faces = normals.gather_faces(vertices, indices) self.assertAllClose( tf.abs(normals.face_normals(faces)), expected, rtol=1e-3) @parameterized.parameters( (((4, 3), (5, 3)), (tf.float32, tf.int32)), (((None, 3), (None, 3)), (tf.float32, tf.int32)), (((3, None, 3), (3, None, 5)), (tf.float32, tf.int32)), (((3, 6, 3), (3, 5, 5)), (tf.float32, tf.int32)), ) def test_vertex_normals_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.vertex_normals, shapes, dtypes) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (3, 5, 4, 3), (1, 2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (2, 200, 3), (4, 100, 3)), ("Not all batch dimensions are broadcast-compatible.", (5, 4, 3), (1, 2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 5, 4, 3), (2, 4, 3)), ("vertices must have a rank greater than 1.", (3,), (1, 2, 4, 3)), ("indices must have a rank greater than 1.", (3, 5, 4, 3), (3,)), ("vertices must have exactly 3 dimensions in axis -1.", (3, 5, 4, 2), (3, 5, 4, 3)), ("indices must have greater than 2 dimensions in axis -1.", (3, 5, 4, 3), (3, 5, 4, 2)), ("'indices' must have specified batch dimensions.", (None, 6, 3), (None, 5, 5)), ) def test_vertex_normals_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.vertex_normals, error_msg, shapes) def test_vertex_normals_jacobian_random(self): """Test the Jacobian of the vertex normals function.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) def vertex_normals(vertex_tensor): return normals.vertex_normals(vertex_tensor, index_tensor) self.assert_jacobian_is_correct_fn(vertex_normals, [vertex_init]) @parameterized.parameters( (((((-1., -1., 1.), (-1., 1., 1.), (-1., -1., -1.), (-1., 1., -1.), (1., -1., 1.), (1., 1., 1.), (1., -1., -1.), (1., 1., -1.)),), (((1, 2, 0), (3, 6, 2), (7, 4, 6), (5, 0, 4), (6, 0, 2), (3, 5, 7), (1, 3, 2), (3, 7, 6), (7, 5, 4), (5, 1, 0), (6, 4, 0), (3, 1, 5)),)), ((((-0.3333333134651184, -0.6666666269302368, 0.6666666269302368), (-0.8164965510368347, 0.40824827551841736, 0.40824827551841736), (-0.8164965510368347, -0.40824827551841736, -0.40824827551841736), (-0.3333333134651184, 0.6666666269302368, -0.6666666269302368), (0.8164965510368347, -0.40824827551841736, 0.40824827551841736), (0.3333333134651184, 0.6666666269302368, 0.6666666269302368), (0.3333333134651184, -0.6666666269302368, -0.6666666269302368), (0.8164965510368347, 0.40824827551841736, -0.40824827551841736)),),)), ) def test_vertex_normals_preset(self, test_inputs, test_outputs): """Tests the computation of vertex normals.""" self.assert_output_is_correct( normals.vertex_normals, test_inputs, test_outputs, tile=False) def test_vertex_normals_random(self): """Tests the computation of vertex normals for a regular octahedral.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() with self.subTest(name="triangular_faces"): vertex_on_axes = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_on_axes = vertex_on_axes.reshape([1] * tensor_vertex_size + [6, 3]) index_init = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) index_init = index_init.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(index_init, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_on_axes * vertex_scale expected = vertex_on_axes * (vertex_scale * 0. + 1.) vertex_tensor = tf.convert_to_tensor(value=vertex_init) index_tensor = tf.convert_to_tensor(value=index_init) self.assertAllClose( normals.vertex_normals(vertex_tensor, index_tensor), expected) with self.subTest(name="polygon_faces"): num_vertices = np.random.randint(4, 8) poly_vertices = [] rad_step = np.pi * 2. / num_vertices for i in range(num_vertices): poly_vertices.append([np.cos(i * rad_step), np.sin(i * rad_step), 0]) vertex_init = np.array(poly_vertices, dtype=np.float32) vertex_init = vertex_init.reshape([1] * tensor_vertex_size + [-1, 3]) vertex_init = vertex_init * vertex_scale index_init = np.arange(num_vertices, dtype=np.int32) index_init = index_init.reshape([1] * tensor_vertex_size + [1, -1]) index_init = np.tile(index_init, tensor_out_shape + [1, 1]) expected = np.array((0., 0., 1.), dtype=np.float32) expected = expected.reshape([1] * tensor_vertex_size + [1, 3]) expected = np.tile(expected, tensor_out_shape + [num_vertices, 1]) vertex_tensor = tf.convert_to_tensor(value=vertex_init) index_tensor = tf.convert_to_tensor(value=index_init) self.assertAllClose( normals.vertex_normals(vertex_tensor, index_tensor), expected) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for normals.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation.mesh import normals from tensorflow_graphics.util import test_case class MeshTest(test_case.TestCase): @parameterized.parameters( (((None, 3), (None, 3)), (tf.float32, tf.int32)), (((3, 6, 3), (3, 5, 4)), (tf.float32, tf.int32)), ) def test_gather_faces_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.gather_faces, shapes, dtypes) @parameterized.parameters( ("Not all batch dimensions are identical", (3, 5, 4, 4), (1, 2, 4, 4)), ("Not all batch dimensions are identical", (5, 4, 4), (1, 2, 4, 4)), ("Not all batch dimensions are identical", (3, 5, 4, 4), (2, 4, 4)), ("vertices must have a rank greater than 1", (4,), (1, 2, 4, 4)), ("indices must have a rank greater than 1", (3, 5, 4, 4), (4,)), ) def test_gather_faces_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.gather_faces, error_msg, shapes) def test_gather_faces_jacobian_random(self): """Test the Jacobian of the face extraction function.""" tensor_size = np.random.randint(2, 5) tensor_shape = np.random.randint(1, 5, size=tensor_size).tolist() vertex_init = np.random.random(size=tensor_shape) indices_init = np.random.randint(0, tensor_shape[-2], size=tensor_shape) indices_tensor = tf.convert_to_tensor(value=indices_init) def gather_faces(vertex_tensor): return normals.gather_faces(vertex_tensor, indices_tensor) self.assert_jacobian_is_correct_fn(gather_faces, [vertex_init]) @parameterized.parameters( ((((0.,), (1.,)), ((1, 0),)), ((((1.,), (0.,)),),)), ((((0., 1.), (2., 3.)), ((1, 0),)), ((((2., 3.), (0., 1.)),),)), ((((0., 1., 2.), (3., 4., 5.)), ((1, 0),)), ((((3., 4., 5.), (0., 1., 2.)),),)), ) def test_gather_faces_preset(self, test_inputs, test_outputs): """Tests the extraction of mesh faces.""" self.assert_output_is_correct( normals.gather_faces, test_inputs, test_outputs, tile=False) def test_gather_faces_random(self): """Tests the extraction of mesh faces.""" tensor_size = np.random.randint(3, 5) tensor_shape = np.random.randint(1, 5, size=tensor_size).tolist() vertices = np.random.random(size=tensor_shape) indices = np.arange(tensor_shape[-2]) indices = indices.reshape([1] * (tensor_size - 1) + [-1]) indices = np.tile(indices, tensor_shape[:-2] + [1, 1]) expected = np.expand_dims(vertices, -3) self.assertAllClose( normals.gather_faces(vertices, indices), expected, rtol=1e-3) @parameterized.parameters( (((None, 4, 3),), (tf.float32,)), (((4, 3),), (tf.float32,)), (((3, 4, 3),), (tf.float32,)), ) def test_face_normals_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.face_normals, shapes, dtypes) @parameterized.parameters( ("faces must have a rank greater than 1.", (3,)), ("faces must have greater than 2 dimensions in axis -2", (2, 3)), ("faces must have exactly 3 dimensions in axis -1.", (5, 2)), ) def test_face_normals_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.face_normals, error_msg, shapes) def test_face_normals_jacobian_random(self): """Test the Jacobian of the face normals function.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() tensor_vertex_shape = list(tensor_out_shape) tensor_vertex_shape[-1] *= 3 tensor_index_shape = tensor_out_shape[-1] vertex_init = np.random.random(size=tensor_vertex_shape + [3]) index_init = np.arange(tensor_vertex_shape[-1]) np.random.shuffle(index_init) index_init = np.reshape(index_init, newshape=[1] * \ (tensor_vertex_size - 1) + \ [tensor_index_shape, 3]) index_init = np.tile(index_init, tensor_vertex_shape[:-1] + [1, 1]) index_tensor = tf.convert_to_tensor(value=index_init) def face_normals(vertex_tensor): face_tensor = normals.gather_faces(vertex_tensor, index_tensor) return normals.face_normals(face_tensor) self.assert_jacobian_is_correct_fn( face_normals, [vertex_init], atol=1e-4, delta=1e-9) @parameterized.parameters( ((((0., 0., 0.), (1., 0., 0.), (0., 1., 0.)), ((0, 1, 2),)), (((0., 0., 1.),),)), ((((0., 0., 0.), (0., 0., 1.), (1., 0., 0.)), ((0, 1, 2),)), (((0., 1., 0.),),)), ((((0., 0., 0.), (0., 1., 0.), (0., 0., 1.)), ((0, 1, 2),)), (((1., 0., 0.),),)), ((((0., -2., -2.), (0, -2., 2.), (0., 2., 2.), (0., 2., -2.)), ((0, 1, 2, 3),)), (((-1., 0., 0.),),)), ) def test_face_normals_preset(self, test_inputs, test_outputs): """Tests the computation of mesh face normals.""" faces = normals.gather_faces(*test_inputs[:2]) test_inputs = [faces] + list(test_inputs[2:]) self.assert_output_is_correct( normals.face_normals, test_inputs, test_outputs, tile=False) def test_face_normals_random(self): """Tests the computation of mesh face normals in each axis.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() tensor_vertex_shape = list(tensor_out_shape) tensor_vertex_shape[-1] *= 3 tensor_index_shape = tensor_out_shape[-1] for i in range(3): vertices = np.random.random(size=tensor_vertex_shape + [3]) indices = np.arange(tensor_vertex_shape[-1]) np.random.shuffle(indices) indices = np.reshape(indices, newshape=[1] * (tensor_vertex_size - 1) \ + [tensor_index_shape, 3]) indices = np.tile(indices, tensor_vertex_shape[:-1] + [1, 1]) vertices[..., i] = 0. expected = np.zeros(shape=tensor_out_shape + [3], dtype=vertices.dtype) expected[..., i] = 1. faces = normals.gather_faces(vertices, indices) self.assertAllClose( tf.abs(normals.face_normals(faces)), expected, rtol=1e-3) @parameterized.parameters( (((4, 3), (5, 3)), (tf.float32, tf.int32)), (((None, 3), (None, 3)), (tf.float32, tf.int32)), (((3, None, 3), (3, None, 5)), (tf.float32, tf.int32)), (((3, 6, 3), (3, 5, 5)), (tf.float32, tf.int32)), ) def test_vertex_normals_exception_not_raised(self, shapes, dtypes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(normals.vertex_normals, shapes, dtypes) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (3, 5, 4, 3), (1, 2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (2, 200, 3), (4, 100, 3)), ("Not all batch dimensions are broadcast-compatible.", (5, 4, 3), (1, 2, 4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 5, 4, 3), (2, 4, 3)), ("vertices must have a rank greater than 1.", (3,), (1, 2, 4, 3)), ("indices must have a rank greater than 1.", (3, 5, 4, 3), (3,)), ("vertices must have exactly 3 dimensions in axis -1.", (3, 5, 4, 2), (3, 5, 4, 3)), ("indices must have greater than 2 dimensions in axis -1.", (3, 5, 4, 3), (3, 5, 4, 2)), ("'indices' must have specified batch dimensions.", (None, 6, 3), (None, 5, 5)), ) def test_vertex_normals_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(normals.vertex_normals, error_msg, shapes) def test_vertex_normals_jacobian_random(self): """Test the Jacobian of the vertex normals function.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() vertex_axis = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_axis = vertex_axis.reshape([1] * tensor_vertex_size + [6, 3]) faces = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) faces = faces.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(faces, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_axis * vertex_scale index_tensor = tf.convert_to_tensor(value=index_init) def vertex_normals(vertex_tensor): return normals.vertex_normals(vertex_tensor, index_tensor) self.assert_jacobian_is_correct_fn(vertex_normals, [vertex_init]) @parameterized.parameters( (((((-1., -1., 1.), (-1., 1., 1.), (-1., -1., -1.), (-1., 1., -1.), (1., -1., 1.), (1., 1., 1.), (1., -1., -1.), (1., 1., -1.)),), (((1, 2, 0), (3, 6, 2), (7, 4, 6), (5, 0, 4), (6, 0, 2), (3, 5, 7), (1, 3, 2), (3, 7, 6), (7, 5, 4), (5, 1, 0), (6, 4, 0), (3, 1, 5)),)), ((((-0.3333333134651184, -0.6666666269302368, 0.6666666269302368), (-0.8164965510368347, 0.40824827551841736, 0.40824827551841736), (-0.8164965510368347, -0.40824827551841736, -0.40824827551841736), (-0.3333333134651184, 0.6666666269302368, -0.6666666269302368), (0.8164965510368347, -0.40824827551841736, 0.40824827551841736), (0.3333333134651184, 0.6666666269302368, 0.6666666269302368), (0.3333333134651184, -0.6666666269302368, -0.6666666269302368), (0.8164965510368347, 0.40824827551841736, -0.40824827551841736)),),)), ) def test_vertex_normals_preset(self, test_inputs, test_outputs): """Tests the computation of vertex normals.""" self.assert_output_is_correct( normals.vertex_normals, test_inputs, test_outputs, tile=False) def test_vertex_normals_random(self): """Tests the computation of vertex normals for a regular octahedral.""" tensor_vertex_size = np.random.randint(1, 3) tensor_out_shape = np.random.randint(1, 5, size=tensor_vertex_size) tensor_out_shape = tensor_out_shape.tolist() with self.subTest(name="triangular_faces"): vertex_on_axes = np.array(((0., 0., 1), (1., 0., 0.), (0., 1., 0.), (0., 0., -1.), (-1., 0., 0.), (0., -1., 0.)), dtype=np.float32) vertex_on_axes = vertex_on_axes.reshape([1] * tensor_vertex_size + [6, 3]) index_init = np.array(((0, 1, 2), (0, 2, 4), (0, 4, 5), (0, 5, 1), (3, 2, 1), (3, 4, 2), (3, 5, 4), (3, 1, 5)), dtype=np.int32) index_init = index_init.reshape([1] * tensor_vertex_size + [8, 3]) index_init = np.tile(index_init, tensor_out_shape + [1, 1]) vertex_scale = np.random.uniform(0.5, 5., tensor_out_shape + [1] * 2) vertex_init = vertex_on_axes * vertex_scale expected = vertex_on_axes * (vertex_scale * 0. + 1.) vertex_tensor = tf.convert_to_tensor(value=vertex_init) index_tensor = tf.convert_to_tensor(value=index_init) self.assertAllClose( normals.vertex_normals(vertex_tensor, index_tensor), expected) with self.subTest(name="polygon_faces"): num_vertices = np.random.randint(4, 8) poly_vertices = [] rad_step = np.pi * 2. / num_vertices for i in range(num_vertices): poly_vertices.append([np.cos(i * rad_step), np.sin(i * rad_step), 0]) vertex_init = np.array(poly_vertices, dtype=np.float32) vertex_init = vertex_init.reshape([1] * tensor_vertex_size + [-1, 3]) vertex_init = vertex_init * vertex_scale index_init = np.arange(num_vertices, dtype=np.int32) index_init = index_init.reshape([1] * tensor_vertex_size + [1, -1]) index_init = np.tile(index_init, tensor_out_shape + [1, 1]) expected = np.array((0., 0., 1.), dtype=np.float32) expected = expected.reshape([1] * tensor_vertex_size + [1, 3]) expected = np.tile(expected, tensor_out_shape + [num_vertices, 1]) vertex_tensor = tf.convert_to_tensor(value=vertex_init) index_tensor = tf.convert_to_tensor(value=index_init) self.assertAllClose( normals.vertex_normals(vertex_tensor, index_tensor), expected) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/notebooks/resources/triangulated_stripe.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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 of a flat rectangular surface.""" import numpy as np vertices = ( (-1.8, 0.0, 0.0), (-1.6, 0.0, 0.0), (-1.4, 0.0, 0.0), (-1.2, 0.0, 0.0), (-1.0, 0.0, 0.0), (-0.8, 0.0, 0.0), (-0.6, 0.0, 0.0), (-0.4, 0.0, 0.0), (-0.2, 0.0, 0.0), (0.0, 0.0, 0.0), (0.2, 0.0, 0.0), (0.4, 0.0, 0.0), (0.6, 0.0, 0.0), (0.8, 0.0, 0.0), (1.0, 0.0, 0.0), (1.2, 0.0, 0.0), (1.4, 0.0, 0.0), (1.6, 0.0, 0.0), (1.8, 0.0, 0.0), (-1.8, 1.0, 0.0), (-1.6, 1.0, 0.0), (-1.4, 1.0, 0.0), (-1.2, 1.0, 0.0), (-1.0, 1.0, 0.0), (-0.8, 1.0, 0.0), (-0.6, 1.0, 0.0), (-0.4, 1.0, 0.0), (-0.2, 1.0, 0.0), (0.0, 1.0, 0.0), (0.2, 1.0, 0.0), (0.4, 1.0, 0.0), (0.6, 1.0, 0.0), (0.8, 1.0, 0.0), (1.0, 1.0, 0.0), (1.2, 1.0, 0.0), (1.4, 1.0, 0.0), (1.6, 1.0, 0.0), (1.8, 1.0, 0.0), ) vertices = np.array(vertices) faces = ( (0, 1, 19), (1, 20, 19), (1, 2, 20), (2, 21, 20), (2, 3, 21), (3, 22, 21), (3, 4, 22), (4, 23, 22), (4, 5, 23), (5, 24, 23), (5, 6, 24), (6, 25, 24), (6, 7, 25), (7, 26, 25), (7, 8, 26), (8, 27, 26), (8, 9, 27), (9, 28, 27), (9, 10, 28), (10, 29, 28), (10, 11, 29), (11, 30, 29), (11, 12, 30), (12, 31, 30), (12, 13, 31), (13, 32, 31), (13, 14, 32), (14, 33, 32), (14, 15, 33), (15, 34, 33), (15, 16, 34), (16, 35, 34), (16, 17, 35), (17, 36, 35), (17, 18, 36), (18, 37, 36), ) faces = np.array(faces) mesh = {'vertices': vertices, 'faces': faces}

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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 of a flat rectangular surface.""" import numpy as np vertices = ( (-1.8, 0.0, 0.0), (-1.6, 0.0, 0.0), (-1.4, 0.0, 0.0), (-1.2, 0.0, 0.0), (-1.0, 0.0, 0.0), (-0.8, 0.0, 0.0), (-0.6, 0.0, 0.0), (-0.4, 0.0, 0.0), (-0.2, 0.0, 0.0), (0.0, 0.0, 0.0), (0.2, 0.0, 0.0), (0.4, 0.0, 0.0), (0.6, 0.0, 0.0), (0.8, 0.0, 0.0), (1.0, 0.0, 0.0), (1.2, 0.0, 0.0), (1.4, 0.0, 0.0), (1.6, 0.0, 0.0), (1.8, 0.0, 0.0), (-1.8, 1.0, 0.0), (-1.6, 1.0, 0.0), (-1.4, 1.0, 0.0), (-1.2, 1.0, 0.0), (-1.0, 1.0, 0.0), (-0.8, 1.0, 0.0), (-0.6, 1.0, 0.0), (-0.4, 1.0, 0.0), (-0.2, 1.0, 0.0), (0.0, 1.0, 0.0), (0.2, 1.0, 0.0), (0.4, 1.0, 0.0), (0.6, 1.0, 0.0), (0.8, 1.0, 0.0), (1.0, 1.0, 0.0), (1.2, 1.0, 0.0), (1.4, 1.0, 0.0), (1.6, 1.0, 0.0), (1.8, 1.0, 0.0), ) vertices = np.array(vertices) faces = ( (0, 1, 19), (1, 20, 19), (1, 2, 20), (2, 21, 20), (2, 3, 21), (3, 22, 21), (3, 4, 22), (4, 23, 22), (4, 5, 23), (5, 24, 23), (5, 6, 24), (6, 25, 24), (6, 7, 25), (7, 26, 25), (7, 8, 26), (8, 27, 26), (8, 9, 27), (9, 28, 27), (9, 10, 28), (10, 29, 28), (10, 11, 29), (11, 30, 29), (11, 12, 30), (12, 31, 30), (12, 13, 31), (13, 32, 31), (13, 14, 32), (14, 33, 32), (14, 15, 33), (15, 34, 33), (15, 16, 34), (16, 35, 34), (16, 17, 35), (17, 36, 35), (17, 18, 36), (18, 37, 36), ) faces = np.array(faces) mesh = {'vertices': vertices, 'faces': faces}

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/optimizer/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/representation/tests/ray_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""Tests for ray.""" import sys from absl import flags from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation import ray from tensorflow_graphics.util import test_case FLAGS = flags.FLAGS class RayTest(test_case.TestCase): def _generate_random_example(self): num_cameras = 4 num_keypoints = 3 batch_size = 2 self.points_values = np.random.random_sample((batch_size, num_keypoints, 3)) points_expanded_values = np.expand_dims(self.points_values, axis=-2) startpoints_values = np.random.random_sample( (batch_size, num_keypoints, num_cameras, 3)) difference = points_expanded_values - startpoints_values difference_norm = np.sqrt((difference * difference).sum(axis=-1)) direction = difference / np.expand_dims(difference_norm, axis=-1) self.startpoints_values = points_expanded_values - 0.5 * direction self.endpoints_values = points_expanded_values + 0.5 * direction self.weights_values = np.ones((batch_size, num_keypoints, num_cameras)) # Wrap these with identies because some assert_* ops look at the constant # tensor values and mark these as unfeedable. self.points = tf.identity(tf.convert_to_tensor(value=self.points_values)) self.startpoints = tf.identity( tf.convert_to_tensor(value=self.startpoints_values)) self.endpoints = tf.identity( tf.convert_to_tensor(value=self.endpoints_values)) self.weights = tf.identity(tf.convert_to_tensor(value=self.weights_values)) @parameterized.parameters( ("Not all batch dimensions are identical.", (4, 3), (5, 3), (4,)), ("must have exactly 3 dimensions in axis", (4, 2), (4, 2), (4,)), ("must have a rank greater than 1", (3,), (3,), (None,)), ("must have greater than 1 dimensions in axis -2", (1, 3), (1, 3), (1,)), ("Not all batch dimensions are identical.", (2, 4, 3), (2, 4, 3), (2, 5)), ) def test_triangulate_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(ray.triangulate, error_msg, shapes) @parameterized.parameters( ((4, 3), (4, 3), (4,)), ((5, 4, 3), (5, 4, 3), (5, 4)), ((6, 5, 4, 3), (6, 5, 4, 3), (6, 5, 4)), ) def test_triangulate_exception_is_not_raised(self, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_not_raised(ray.triangulate, shapes) def test_triangulate_jacobian_is_correct(self): """Tests that Jacobian is correct.""" self._generate_random_example() self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(x, self.endpoints, self.weights), [self.startpoints_values]) self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(self.startpoints, x, self.weights), [self.endpoints_values]) self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(self.startpoints, self.endpoints, x), [self.weights_values]) def test_triangulate_jacobian_is_finite(self): """Tests that Jacobian is finite.""" self._generate_random_example() self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(x, self.endpoints, self.weights), [self.startpoints_values]) self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(self.startpoints, x, self.weights), [self.endpoints_values]) self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(self.startpoints, self.endpoints, x), [self.weights_values]) def test_triangulate_random(self): """Tests that original points are recovered by triangualtion.""" self._generate_random_example() test_inputs = (self.startpoints, self.endpoints, self.weights) test_outputs = (self.points_values,) self.assert_output_is_correct( ray.triangulate, test_inputs, test_outputs, rtol=1e-05, atol=1e-08, tile=False) def test_negative_weights_exception_raised(self): """Tests that exceptions are properly raised.""" self._generate_random_example() self.weights = -1.0 * tf.ones_like(self.weights, dtype=tf.float64) with self.assertRaises(tf.errors.InvalidArgumentError): points = ray.triangulate(self.startpoints, self.endpoints, self.weights) self.evaluate(points) def test_less_that_two_nonzero_weights_exception_raised(self): """Tests that exceptions are properly raised.""" self._generate_random_example() self.weights = tf.convert_to_tensor( value=np.array([[[1., 1., 0., 0.], [1., 1., 0., 0.], [1., 1., 0., 0.]], [[1., 1., 0., 0.], [1., 1., 0., 0.], [1., 0., 0., 0.]]], dtype=np.float64)) with self.assertRaises(tf.errors.InvalidArgumentError): points = ray.triangulate(self.startpoints, self.endpoints, self.weights) self.evaluate(points) @parameterized.parameters( ("must have exactly 3 dimensions in axis 0", (2,), (1,), (3,), (3,)), ("must have a rank of 1", (2, 3), (1,), (3,), (3,)), ("must have exactly 1 dimensions in axis 0", (3,), (2,), (3,), (3,)), ("must have a rank of 1", (3,), (2, 1), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (3,), (2,)), ("Not all batch dimensions are identical.", (3,), (1,), (3,), (2, 3)), ) def test_intersection_ray_sphere_shape_raised(self, error_msg, *shapes): """tests that exceptions are raised when shapes are not supported.""" self.assert_exception_is_raised(ray.intersection_ray_sphere, error_msg, shapes) @parameterized.parameters( ((3,), (1,), (3,), (3,)), ((3), (1), (None, 3), (None, 3)), ) def test_intersection_ray_sphere_shape_not_raised(self, *shapes): """Tests that the shape exceptions are not raised on supported shapes.""" self.assert_exception_is_not_raised(ray.intersection_ray_sphere, shapes) def test_intersection_ray_sphere_exception_raised(self): """Tests that exceptions are properly raised.""" sphere_center = np.random.uniform(size=(3,)) point_on_ray = np.random.uniform(size=(3,)) sample_ray = np.random.uniform(size=(3,)) normalized_sample_ray = sample_ray / np.linalg.norm(sample_ray, axis=-1) positive_sphere_radius = np.random.uniform( sys.float_info.epsilon, 1.0, size=(1,)) negative_sphere_radius = np.random.uniform(-1.0, 0.0, size=(1,)) with self.subTest(name="positive_radius"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( ray.intersection_ray_sphere(sphere_center, negative_sphere_radius, normalized_sample_ray, point_on_ray)) with self.subTest(name="normalized_ray"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( ray.intersection_ray_sphere(sphere_center, positive_sphere_radius, sample_ray, point_on_ray)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_intersection_ray_sphere_jacobian_random(self): """Test the Jacobian of the intersection_ray_sphere function.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() sphere_center_init = np.random.uniform(0.0, 1.0, size=(3,)) sphere_radius_init = np.random.uniform(10.0, 11.0, size=(1,)) ray_init = np.random.uniform(size=tensor_shape + [3]) ray_init /= np.linalg.norm(ray_init, axis=-1, keepdims=True) point_on_ray_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [3]) def intersection_ray_sphere_position(sphere_center, sphere_radius, input_ray, point_on_ray): y_p, _ = ray.intersection_ray_sphere(sphere_center, sphere_radius, input_ray, point_on_ray) return y_p def intersection_ray_sphere_normal(sphere_center, sphere_radius, input_ray, point_on_ray): _, y_n = ray.intersection_ray_sphere(sphere_center, sphere_radius, input_ray, point_on_ray) return y_n self.assert_jacobian_is_correct_fn( intersection_ray_sphere_position, [sphere_center_init, sphere_radius_init, ray_init, point_on_ray_init]) self.assert_jacobian_is_correct_fn( intersection_ray_sphere_normal, [sphere_center_init, sphere_radius_init, ray_init, point_on_ray_init]) @parameterized.parameters( (((0.0, 0.0, 3.0), (1.0,), (0.0, 0.0, 1.0), (0.0, 0.0, 0.0)), (((0.0, 0.0, 2.0), (0.0, 0.0, 4.0)), ((0.0, 0.0, -1.0), (0.0, 0.0, 1.0)))), (((0.0, 0.0, 3.0), (1.0,), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0)), (((1.0, 0.0, 3.0), (1.0, 0.0, 3.0)), ((1.0, 0.0, 0.0), (1.0, 0.0, 0.0)))), ) def test_intersection_ray_sphere_preset(self, test_inputs, test_outputs): self.assert_output_is_correct( ray.intersection_ray_sphere, test_inputs, test_outputs, tile=False) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. r"""Tests for ray.""" import sys from absl import flags from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.geometry.representation import ray from tensorflow_graphics.util import test_case FLAGS = flags.FLAGS class RayTest(test_case.TestCase): def _generate_random_example(self): num_cameras = 4 num_keypoints = 3 batch_size = 2 self.points_values = np.random.random_sample((batch_size, num_keypoints, 3)) points_expanded_values = np.expand_dims(self.points_values, axis=-2) startpoints_values = np.random.random_sample( (batch_size, num_keypoints, num_cameras, 3)) difference = points_expanded_values - startpoints_values difference_norm = np.sqrt((difference * difference).sum(axis=-1)) direction = difference / np.expand_dims(difference_norm, axis=-1) self.startpoints_values = points_expanded_values - 0.5 * direction self.endpoints_values = points_expanded_values + 0.5 * direction self.weights_values = np.ones((batch_size, num_keypoints, num_cameras)) # Wrap these with identies because some assert_* ops look at the constant # tensor values and mark these as unfeedable. self.points = tf.identity(tf.convert_to_tensor(value=self.points_values)) self.startpoints = tf.identity( tf.convert_to_tensor(value=self.startpoints_values)) self.endpoints = tf.identity( tf.convert_to_tensor(value=self.endpoints_values)) self.weights = tf.identity(tf.convert_to_tensor(value=self.weights_values)) @parameterized.parameters( ("Not all batch dimensions are identical.", (4, 3), (5, 3), (4,)), ("must have exactly 3 dimensions in axis", (4, 2), (4, 2), (4,)), ("must have a rank greater than 1", (3,), (3,), (None,)), ("must have greater than 1 dimensions in axis -2", (1, 3), (1, 3), (1,)), ("Not all batch dimensions are identical.", (2, 4, 3), (2, 4, 3), (2, 5)), ) def test_triangulate_exception_raised(self, error_msg, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_raised(ray.triangulate, error_msg, shapes) @parameterized.parameters( ((4, 3), (4, 3), (4,)), ((5, 4, 3), (5, 4, 3), (5, 4)), ((6, 5, 4, 3), (6, 5, 4, 3), (6, 5, 4)), ) def test_triangulate_exception_is_not_raised(self, *shapes): """Tests that the shape exceptions are properly raised.""" self.assert_exception_is_not_raised(ray.triangulate, shapes) def test_triangulate_jacobian_is_correct(self): """Tests that Jacobian is correct.""" self._generate_random_example() self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(x, self.endpoints, self.weights), [self.startpoints_values]) self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(self.startpoints, x, self.weights), [self.endpoints_values]) self.assert_jacobian_is_correct_fn( lambda x: ray.triangulate(self.startpoints, self.endpoints, x), [self.weights_values]) def test_triangulate_jacobian_is_finite(self): """Tests that Jacobian is finite.""" self._generate_random_example() self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(x, self.endpoints, self.weights), [self.startpoints_values]) self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(self.startpoints, x, self.weights), [self.endpoints_values]) self.assert_jacobian_is_finite_fn( lambda x: ray.triangulate(self.startpoints, self.endpoints, x), [self.weights_values]) def test_triangulate_random(self): """Tests that original points are recovered by triangualtion.""" self._generate_random_example() test_inputs = (self.startpoints, self.endpoints, self.weights) test_outputs = (self.points_values,) self.assert_output_is_correct( ray.triangulate, test_inputs, test_outputs, rtol=1e-05, atol=1e-08, tile=False) def test_negative_weights_exception_raised(self): """Tests that exceptions are properly raised.""" self._generate_random_example() self.weights = -1.0 * tf.ones_like(self.weights, dtype=tf.float64) with self.assertRaises(tf.errors.InvalidArgumentError): points = ray.triangulate(self.startpoints, self.endpoints, self.weights) self.evaluate(points) def test_less_that_two_nonzero_weights_exception_raised(self): """Tests that exceptions are properly raised.""" self._generate_random_example() self.weights = tf.convert_to_tensor( value=np.array([[[1., 1., 0., 0.], [1., 1., 0., 0.], [1., 1., 0., 0.]], [[1., 1., 0., 0.], [1., 1., 0., 0.], [1., 0., 0., 0.]]], dtype=np.float64)) with self.assertRaises(tf.errors.InvalidArgumentError): points = ray.triangulate(self.startpoints, self.endpoints, self.weights) self.evaluate(points) @parameterized.parameters( ("must have exactly 3 dimensions in axis 0", (2,), (1,), (3,), (3,)), ("must have a rank of 1", (2, 3), (1,), (3,), (3,)), ("must have exactly 1 dimensions in axis 0", (3,), (2,), (3,), (3,)), ("must have a rank of 1", (3,), (2, 1), (3,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (2,), (3,)), ("must have exactly 3 dimensions in axis -1", (3,), (1,), (3,), (2,)), ("Not all batch dimensions are identical.", (3,), (1,), (3,), (2, 3)), ) def test_intersection_ray_sphere_shape_raised(self, error_msg, *shapes): """tests that exceptions are raised when shapes are not supported.""" self.assert_exception_is_raised(ray.intersection_ray_sphere, error_msg, shapes) @parameterized.parameters( ((3,), (1,), (3,), (3,)), ((3), (1), (None, 3), (None, 3)), ) def test_intersection_ray_sphere_shape_not_raised(self, *shapes): """Tests that the shape exceptions are not raised on supported shapes.""" self.assert_exception_is_not_raised(ray.intersection_ray_sphere, shapes) def test_intersection_ray_sphere_exception_raised(self): """Tests that exceptions are properly raised.""" sphere_center = np.random.uniform(size=(3,)) point_on_ray = np.random.uniform(size=(3,)) sample_ray = np.random.uniform(size=(3,)) normalized_sample_ray = sample_ray / np.linalg.norm(sample_ray, axis=-1) positive_sphere_radius = np.random.uniform( sys.float_info.epsilon, 1.0, size=(1,)) negative_sphere_radius = np.random.uniform(-1.0, 0.0, size=(1,)) with self.subTest(name="positive_radius"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( ray.intersection_ray_sphere(sphere_center, negative_sphere_radius, normalized_sample_ray, point_on_ray)) with self.subTest(name="normalized_ray"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( ray.intersection_ray_sphere(sphere_center, positive_sphere_radius, sample_ray, point_on_ray)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_intersection_ray_sphere_jacobian_random(self): """Test the Jacobian of the intersection_ray_sphere function.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() sphere_center_init = np.random.uniform(0.0, 1.0, size=(3,)) sphere_radius_init = np.random.uniform(10.0, 11.0, size=(1,)) ray_init = np.random.uniform(size=tensor_shape + [3]) ray_init /= np.linalg.norm(ray_init, axis=-1, keepdims=True) point_on_ray_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [3]) def intersection_ray_sphere_position(sphere_center, sphere_radius, input_ray, point_on_ray): y_p, _ = ray.intersection_ray_sphere(sphere_center, sphere_radius, input_ray, point_on_ray) return y_p def intersection_ray_sphere_normal(sphere_center, sphere_radius, input_ray, point_on_ray): _, y_n = ray.intersection_ray_sphere(sphere_center, sphere_radius, input_ray, point_on_ray) return y_n self.assert_jacobian_is_correct_fn( intersection_ray_sphere_position, [sphere_center_init, sphere_radius_init, ray_init, point_on_ray_init]) self.assert_jacobian_is_correct_fn( intersection_ray_sphere_normal, [sphere_center_init, sphere_radius_init, ray_init, point_on_ray_init]) @parameterized.parameters( (((0.0, 0.0, 3.0), (1.0,), (0.0, 0.0, 1.0), (0.0, 0.0, 0.0)), (((0.0, 0.0, 2.0), (0.0, 0.0, 4.0)), ((0.0, 0.0, -1.0), (0.0, 0.0, 1.0)))), (((0.0, 0.0, 3.0), (1.0,), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0)), (((1.0, 0.0, 3.0), (1.0, 0.0, 3.0)), ((1.0, 0.0, 0.0), (1.0, 0.0, 0.0)))), ) def test_intersection_ray_sphere_preset(self, test_inputs, test_outputs): self.assert_output_is_correct( ray.intersection_ray_sphere, test_inputs, test_outputs, tile=False) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/tensorboard/mesh_visualizer/tf_mesh_dashboard/mesh-viewer.js

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ /** * @fileoverview MeshViewer aims to provide 3D rendering capabilities. */ var vz_mesh; (function(vz_mesh) { class MeshViewer extends THREE.EventDispatcher { /** * MeshViewer constructor. Initializes the component and underlying objects. * @param {string} runColor Run color to use in case when colors are absent. */ constructor(runColor) { super(); /** @type {!THREE.Mesh} Last rendered mesh. */ this._lastMesh = null; this._clock = new THREE.Clock(); /** @type {!Object} Contains width and height of the canvas. */ this._canvasSize = null; this._runColor = runColor; /** @type {!Object} Describes what layers must be rendered in addition to a mesh or a point cloud layers. */ this._layersConfig = null; } // TODO(b/130030314) replace with some thirdparty library call. /** * Returns true if the specified value is an object. * @param {?} val Variable to test. * @private * @return {boolean} Whether variable is an object. */ _isObject(val) { var type = typeof val; // We're interested in objects representing dictionaries only. Everything // else is "not mergeable", so we consider it as primitive types. return type == 'object' && val != null && !Array.isArray(val); } /** * Merges two configs together. * @param {!Object} userConfig User configuration has higher priority. * @param {!Object} defaultConfig Default configuration has lower priority and * will be overridden by any conflicting keys from userConfig. * @private * @return {!Object} Merged dictionary from two configuration dictionaries. */ _applyDefaults(userConfig, defaultConfig) { let mergedConfig = {}; const configs = [userConfig, defaultConfig]; for (let i = 0; i < configs.length; i++) { const config = configs[i]; for (let key in config) { const is_key_present = key in mergedConfig; if (this._isObject(config[key])) { mergedConfig[key] = this._applyDefaults(mergedConfig[key] || {}, config[key]); } else if (!is_key_present) { mergedConfig[key] = config[key]; } } } return mergedConfig; } /** * Creates additional layers to render on top of a mesh or a point cloud * layers. * @private */ _createLayers() { if (!this._layersConfig || !this._scene || !this._lastMesh) return; if (this._layersConfig.showBoundingBox) { var box = new THREE.BoxHelper(this._lastMesh, "rgb(0, 0, 255)"); this._scene.add(box); } if (this._layersConfig.showAxes) { var axesHelper = new THREE.AxesHelper(5); this._scene.add(axesHelper); } } /** * Sets layers config. * @param {!Object} layersConfig Config object describing what layers should * be rendered. */ setLayersConfig(layersConfig) { this._layersConfig = this._applyDefaults( layersConfig, this._layersConfig || {}); } /** * Creates scene, camera and renderer. * @param {!Object} config Scene rendering configuration. * @param {!HTMLDOMElement} domElement The HTML element used for event listeners. * @private */ _createWorld(config, domElement) { if (this.isReady()) { // keep world objects as singleton objects. return; } this._scene = new THREE.Scene(); var camera = new THREE[config.camera.cls]( config.camera.fov, this._canvasSize.width / this._canvasSize.height, config.camera.near, config.camera.far); this._camera = camera; var camControls = new THREE.OrbitControls(camera, domElement); camControls.lookSpeed = 0.4; camControls.movementSpeed = 20; camControls.noFly = true; camControls.lookVertical = true; camControls.constrainVertical = true; camControls.verticalMin = 1.0; camControls.verticalMax = 2.0; camControls.addEventListener( 'change', this._onCameraPositionChange.bind(this)); this._cameraControls = camControls; this._renderer = new THREE.WebGLRenderer({antialias: true}); this._renderer.setPixelRatio(window.devicePixelRatio); this._renderer.setSize(this._canvasSize.width, this._canvasSize.height); this._renderer.setClearColor(0xffffff, 1); } /** * Clears scene from any 3D geometry. */ _clearScene() { while (this._scene.children.length > 0) { this._scene.remove(this._scene.children[0]); } } /** * Returns underlying renderer. * @public */ getRenderer() { return this._renderer; } /** * Returns underlying camera controls. * @public */ getCameraControls() { return this._cameraControls; } /** * Returns true when all underlying components were initialized. * @public */ isReady() { return !!this._camera && !!this._cameraControls; } /** * Returns current camera position. * @public */ getCameraPosition() { return { far: this._camera.far, position: this._camera.position.clone(), target: this._cameraControls.target.clone() }; } /** * Sets new canvas size. * @param {!Object} canvasSize Contains current canvas width and height. * @public */ setCanvasSize(canvasSize) { this._canvasSize = canvasSize; } /** * Renders component into the browser. * @public */ draw() { // Cancel any previous requests to perform redraw. if (this._animationFrameIndex) { cancelAnimationFrame(this._animationFrameIndex); } this._camera.aspect = this._canvasSize.width / this._canvasSize.height; this._camera.updateProjectionMatrix(); this._renderer.setSize(this._canvasSize.width, this._canvasSize.height); const animate = function () { var delta = this._clock.getDelta(); this._cameraControls.update(delta); this._animationFrameIndex = requestAnimationFrame(animate); this._renderer.render(this._scene, this._camera); }.bind(this); animate(); } /** * Updates the scene. * @param {!Object} currentStep Step datum. * @param {!HTMLDOMElement} domElement The HTML element used for event listeners. * @public */ updateScene(currentStep, domElement) { let config = {}; if ('config' in currentStep && currentStep.config) { config = JSON.parse(currentStep.config); } // This event is an opportunity for UI-responsible component (parent) to set // proper canvas size. this.dispatchEvent({type:'beforeUpdateScene'}); const default_config = { camera: {cls: 'PerspectiveCamera', fov: 75, near: 0.1, far: 1000}, lights: [ {cls: 'AmbientLight', color: '#ffffff', intensity: 0.75}, { cls: 'DirectionalLight', color: '#ffffff', intensity: 0.75, position: [0, -1, 2] } ] }; config = this._applyDefaults(config, default_config); this._createWorld(config, domElement); this._clearScene(); this._createLights(this._scene, config); this._createGeometry(currentStep, config); this._createLayers(); this.draw(); } /** * Sets camera to default position and zoom. * @param {?THREE.Mesh} mesh Mesh to fit into viewport. * @public */ resetView(mesh) { if (!this.isReady()) return; this._cameraControls.reset(); if (!mesh && this._lastMesh) { mesh = this._lastMesh; } if (mesh) { this._fitObjectToViewport(mesh); // Store last mesh in case of resetView method called due to some events. this._lastMesh = mesh; } this._cameraControls.update(); } /** * Creates geometry for current step data. * @param {!Object} currentStep Step datum. * @param {!Object} config Scene rendering configuration. * @private */ _createGeometry(currentStep, config) { const mesh = currentStep.mesh; if (mesh.vertices && mesh.faces && mesh.faces.length) { this._createMesh(mesh, config); } else { this._createPointCloud(mesh, config); } } /** * Creates point cloud geometry for current step data. * @param {!Object} pointCloudData Object with point cloud data. * @param {!Object} config Scene rendering configuration. * @private */ _createPointCloud(pointCloudData, config) { const points = pointCloudData.vertices; const colors = pointCloudData.colors; let defaultConfig = { material: { cls: 'PointsMaterial', size: 0.005 } }; // Determine what colors will be used. if (colors && colors.length == points.length) { defaultConfig.material['vertexColors'] = THREE.VertexColors; } else { defaultConfig.material['color'] = this._runColor; } const pc_config = this._applyDefaults(config, defaultConfig); var geometry = new THREE.Geometry(); points.forEach(function(point) { var p = new THREE.Vector3(point[0], point[1], point[2]); const scale = 1.; p.x = point[0] * scale; p.y = point[1] * scale; p.z = point[2] * scale; geometry.vertices.push(p); }); if (colors && colors.length == points.length) { colors.forEach(function (color) { const c = new THREE.Color( color[0] / 255., color[1] / 255., color[2] / 255.); geometry.colors.push(c); }); } var material = new THREE[pc_config.material.cls](pc_config.material); var mesh = new THREE.Points(geometry, material); this._scene.add(mesh); this._lastMesh = mesh; } /** * Creates mesh geometry for current step data. * @param {!THREE.Vector3} position Position of the camera. * @param {number} far Camera frustum far plane. * @param {!THREE.Vector3} target Point in space for camera to look at. * @public */ setCameraViewpoint(position, far, target) { this._silent = true; this._camera.far = far; this._camera.position.set(position.x, position.y, position.z); this._camera.lookAt(target.clone()); this._camera.updateProjectionMatrix(); this._cameraControls.target = target.clone(); this._cameraControls.update(); this._silent = false; } /** * Triggered when camera position changed. * @private */ _onCameraPositionChange(event) { if (this._silent) return; this.dispatchEvent({type:'cameraPositionChange', event: event}); } /** * Positions camera on such distance from the object that the whole object is * visible. * @param {!THREE.Mesh} mesh Mesh to fit into viewport. * @private */ _fitObjectToViewport(mesh) { // Small offset multiplicator to avoid edges of mesh touching edges of // viewport. const offset = 1.25; const boundingBox = new THREE.Box3(); boundingBox.setFromObject(mesh); const center = boundingBox.center(); const size = boundingBox.size(); const max_dim = Math.max(size.x, size.y, size.z); const fov = this._camera.fov * (Math.PI / 180); let camera_z = Math.abs(max_dim / (2 * Math.tan(fov / 2))) * offset; const min_z = boundingBox.min.z; // Make sure that even after arbitrary rotation mesh won't be clipped. const camera_to_far_edge = (min_z < 0) ? -min_z + camera_z : camera_z - min_z; // Set camera position and orientation. this.setCameraViewpoint( {x: center.x, y: center.y, z: camera_z}, camera_to_far_edge * 3, center); } /** * Creates mesh geometry for current step data. * @param {!Object} meshData Object with mesh data. * @param {!Object} config Scene rendering configuration. * @private */ _createMesh(meshData, config) { const vertices = meshData.vertices; const faces = meshData.faces; const colors = meshData.colors; const mesh_config = this._applyDefaults(config, { material: { cls: 'MeshStandardMaterial', color: '#a0a0a0', roughness: 1, metalness: 0, } }); let geometry = new THREE.Geometry(); vertices.forEach(function(point) { let p = new THREE.Vector3(point[0], point[1], point[2]); const scale = 1.; p.x = point[0] * scale; p.y = point[1] * scale; p.z = point[2] * scale; geometry.vertices.push(p); }); faces.forEach(function(face_indices) { let face = new THREE.Face3(face_indices[0], face_indices[1], face_indices[2]); if (colors && colors.length) { const face_colors = [ colors[face_indices[0]], colors[face_indices[1]], colors[face_indices[2]] ]; for (let i = 0; i < face_colors.length; i++) { const vertex_color = face_colors[i]; let color = new THREE.Color( vertex_color[0] / 255., vertex_color[1] / 255., vertex_color[2] / 255.); face.vertexColors.push(color); } } geometry.faces.push(face); }); if (colors && colors.length) { mesh_config.material = mesh_config.material || {}; mesh_config.material.vertexColors = THREE.VertexColors; } geometry.center(); geometry.computeBoundingSphere(); geometry.computeVertexNormals(); let material = new THREE[mesh_config.material.cls](mesh_config.material); let mesh = new THREE.Mesh(geometry, material); mesh.castShadow = true; mesh.receiveShadow = true; this._scene.add(mesh); this._lastMesh = mesh; } /** * Creates lights for a given scene based on passed configuration. * @param {!Scene} scene Scene object to add lights to. * @param {!Object} config Scene rendering configuration. * @private */ _createLights(scene, config) { for (let i = 0; i < config.lights.length; i++) { const light_config = config.lights[i]; let light = new THREE[light_config.cls]( light_config.color, light_config.intensity); if (light_config.position) { light.position.set( light_config.position[0], light_config.position[1], light_config.position[2]); } scene.add(light); } } } // end of MeshViewer class. vz_mesh.MeshViewer = MeshViewer; })(vz_mesh || (vz_mesh = {}));

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ /** * @fileoverview MeshViewer aims to provide 3D rendering capabilities. */ var vz_mesh; (function(vz_mesh) { class MeshViewer extends THREE.EventDispatcher { /** * MeshViewer constructor. Initializes the component and underlying objects. * @param {string} runColor Run color to use in case when colors are absent. */ constructor(runColor) { super(); /** @type {!THREE.Mesh} Last rendered mesh. */ this._lastMesh = null; this._clock = new THREE.Clock(); /** @type {!Object} Contains width and height of the canvas. */ this._canvasSize = null; this._runColor = runColor; /** @type {!Object} Describes what layers must be rendered in addition to a mesh or a point cloud layers. */ this._layersConfig = null; } // TODO(b/130030314) replace with some thirdparty library call. /** * Returns true if the specified value is an object. * @param {?} val Variable to test. * @private * @return {boolean} Whether variable is an object. */ _isObject(val) { var type = typeof val; // We're interested in objects representing dictionaries only. Everything // else is "not mergeable", so we consider it as primitive types. return type == 'object' && val != null && !Array.isArray(val); } /** * Merges two configs together. * @param {!Object} userConfig User configuration has higher priority. * @param {!Object} defaultConfig Default configuration has lower priority and * will be overridden by any conflicting keys from userConfig. * @private * @return {!Object} Merged dictionary from two configuration dictionaries. */ _applyDefaults(userConfig, defaultConfig) { let mergedConfig = {}; const configs = [userConfig, defaultConfig]; for (let i = 0; i < configs.length; i++) { const config = configs[i]; for (let key in config) { const is_key_present = key in mergedConfig; if (this._isObject(config[key])) { mergedConfig[key] = this._applyDefaults(mergedConfig[key] || {}, config[key]); } else if (!is_key_present) { mergedConfig[key] = config[key]; } } } return mergedConfig; } /** * Creates additional layers to render on top of a mesh or a point cloud * layers. * @private */ _createLayers() { if (!this._layersConfig || !this._scene || !this._lastMesh) return; if (this._layersConfig.showBoundingBox) { var box = new THREE.BoxHelper(this._lastMesh, "rgb(0, 0, 255)"); this._scene.add(box); } if (this._layersConfig.showAxes) { var axesHelper = new THREE.AxesHelper(5); this._scene.add(axesHelper); } } /** * Sets layers config. * @param {!Object} layersConfig Config object describing what layers should * be rendered. */ setLayersConfig(layersConfig) { this._layersConfig = this._applyDefaults( layersConfig, this._layersConfig || {}); } /** * Creates scene, camera and renderer. * @param {!Object} config Scene rendering configuration. * @param {!HTMLDOMElement} domElement The HTML element used for event listeners. * @private */ _createWorld(config, domElement) { if (this.isReady()) { // keep world objects as singleton objects. return; } this._scene = new THREE.Scene(); var camera = new THREE[config.camera.cls]( config.camera.fov, this._canvasSize.width / this._canvasSize.height, config.camera.near, config.camera.far); this._camera = camera; var camControls = new THREE.OrbitControls(camera, domElement); camControls.lookSpeed = 0.4; camControls.movementSpeed = 20; camControls.noFly = true; camControls.lookVertical = true; camControls.constrainVertical = true; camControls.verticalMin = 1.0; camControls.verticalMax = 2.0; camControls.addEventListener( 'change', this._onCameraPositionChange.bind(this)); this._cameraControls = camControls; this._renderer = new THREE.WebGLRenderer({antialias: true}); this._renderer.setPixelRatio(window.devicePixelRatio); this._renderer.setSize(this._canvasSize.width, this._canvasSize.height); this._renderer.setClearColor(0xffffff, 1); } /** * Clears scene from any 3D geometry. */ _clearScene() { while (this._scene.children.length > 0) { this._scene.remove(this._scene.children[0]); } } /** * Returns underlying renderer. * @public */ getRenderer() { return this._renderer; } /** * Returns underlying camera controls. * @public */ getCameraControls() { return this._cameraControls; } /** * Returns true when all underlying components were initialized. * @public */ isReady() { return !!this._camera && !!this._cameraControls; } /** * Returns current camera position. * @public */ getCameraPosition() { return { far: this._camera.far, position: this._camera.position.clone(), target: this._cameraControls.target.clone() }; } /** * Sets new canvas size. * @param {!Object} canvasSize Contains current canvas width and height. * @public */ setCanvasSize(canvasSize) { this._canvasSize = canvasSize; } /** * Renders component into the browser. * @public */ draw() { // Cancel any previous requests to perform redraw. if (this._animationFrameIndex) { cancelAnimationFrame(this._animationFrameIndex); } this._camera.aspect = this._canvasSize.width / this._canvasSize.height; this._camera.updateProjectionMatrix(); this._renderer.setSize(this._canvasSize.width, this._canvasSize.height); const animate = function () { var delta = this._clock.getDelta(); this._cameraControls.update(delta); this._animationFrameIndex = requestAnimationFrame(animate); this._renderer.render(this._scene, this._camera); }.bind(this); animate(); } /** * Updates the scene. * @param {!Object} currentStep Step datum. * @param {!HTMLDOMElement} domElement The HTML element used for event listeners. * @public */ updateScene(currentStep, domElement) { let config = {}; if ('config' in currentStep && currentStep.config) { config = JSON.parse(currentStep.config); } // This event is an opportunity for UI-responsible component (parent) to set // proper canvas size. this.dispatchEvent({type:'beforeUpdateScene'}); const default_config = { camera: {cls: 'PerspectiveCamera', fov: 75, near: 0.1, far: 1000}, lights: [ {cls: 'AmbientLight', color: '#ffffff', intensity: 0.75}, { cls: 'DirectionalLight', color: '#ffffff', intensity: 0.75, position: [0, -1, 2] } ] }; config = this._applyDefaults(config, default_config); this._createWorld(config, domElement); this._clearScene(); this._createLights(this._scene, config); this._createGeometry(currentStep, config); this._createLayers(); this.draw(); } /** * Sets camera to default position and zoom. * @param {?THREE.Mesh} mesh Mesh to fit into viewport. * @public */ resetView(mesh) { if (!this.isReady()) return; this._cameraControls.reset(); if (!mesh && this._lastMesh) { mesh = this._lastMesh; } if (mesh) { this._fitObjectToViewport(mesh); // Store last mesh in case of resetView method called due to some events. this._lastMesh = mesh; } this._cameraControls.update(); } /** * Creates geometry for current step data. * @param {!Object} currentStep Step datum. * @param {!Object} config Scene rendering configuration. * @private */ _createGeometry(currentStep, config) { const mesh = currentStep.mesh; if (mesh.vertices && mesh.faces && mesh.faces.length) { this._createMesh(mesh, config); } else { this._createPointCloud(mesh, config); } } /** * Creates point cloud geometry for current step data. * @param {!Object} pointCloudData Object with point cloud data. * @param {!Object} config Scene rendering configuration. * @private */ _createPointCloud(pointCloudData, config) { const points = pointCloudData.vertices; const colors = pointCloudData.colors; let defaultConfig = { material: { cls: 'PointsMaterial', size: 0.005 } }; // Determine what colors will be used. if (colors && colors.length == points.length) { defaultConfig.material['vertexColors'] = THREE.VertexColors; } else { defaultConfig.material['color'] = this._runColor; } const pc_config = this._applyDefaults(config, defaultConfig); var geometry = new THREE.Geometry(); points.forEach(function(point) { var p = new THREE.Vector3(point[0], point[1], point[2]); const scale = 1.; p.x = point[0] * scale; p.y = point[1] * scale; p.z = point[2] * scale; geometry.vertices.push(p); }); if (colors && colors.length == points.length) { colors.forEach(function (color) { const c = new THREE.Color( color[0] / 255., color[1] / 255., color[2] / 255.); geometry.colors.push(c); }); } var material = new THREE[pc_config.material.cls](pc_config.material); var mesh = new THREE.Points(geometry, material); this._scene.add(mesh); this._lastMesh = mesh; } /** * Creates mesh geometry for current step data. * @param {!THREE.Vector3} position Position of the camera. * @param {number} far Camera frustum far plane. * @param {!THREE.Vector3} target Point in space for camera to look at. * @public */ setCameraViewpoint(position, far, target) { this._silent = true; this._camera.far = far; this._camera.position.set(position.x, position.y, position.z); this._camera.lookAt(target.clone()); this._camera.updateProjectionMatrix(); this._cameraControls.target = target.clone(); this._cameraControls.update(); this._silent = false; } /** * Triggered when camera position changed. * @private */ _onCameraPositionChange(event) { if (this._silent) return; this.dispatchEvent({type:'cameraPositionChange', event: event}); } /** * Positions camera on such distance from the object that the whole object is * visible. * @param {!THREE.Mesh} mesh Mesh to fit into viewport. * @private */ _fitObjectToViewport(mesh) { // Small offset multiplicator to avoid edges of mesh touching edges of // viewport. const offset = 1.25; const boundingBox = new THREE.Box3(); boundingBox.setFromObject(mesh); const center = boundingBox.center(); const size = boundingBox.size(); const max_dim = Math.max(size.x, size.y, size.z); const fov = this._camera.fov * (Math.PI / 180); let camera_z = Math.abs(max_dim / (2 * Math.tan(fov / 2))) * offset; const min_z = boundingBox.min.z; // Make sure that even after arbitrary rotation mesh won't be clipped. const camera_to_far_edge = (min_z < 0) ? -min_z + camera_z : camera_z - min_z; // Set camera position and orientation. this.setCameraViewpoint( {x: center.x, y: center.y, z: camera_z}, camera_to_far_edge * 3, center); } /** * Creates mesh geometry for current step data. * @param {!Object} meshData Object with mesh data. * @param {!Object} config Scene rendering configuration. * @private */ _createMesh(meshData, config) { const vertices = meshData.vertices; const faces = meshData.faces; const colors = meshData.colors; const mesh_config = this._applyDefaults(config, { material: { cls: 'MeshStandardMaterial', color: '#a0a0a0', roughness: 1, metalness: 0, } }); let geometry = new THREE.Geometry(); vertices.forEach(function(point) { let p = new THREE.Vector3(point[0], point[1], point[2]); const scale = 1.; p.x = point[0] * scale; p.y = point[1] * scale; p.z = point[2] * scale; geometry.vertices.push(p); }); faces.forEach(function(face_indices) { let face = new THREE.Face3(face_indices[0], face_indices[1], face_indices[2]); if (colors && colors.length) { const face_colors = [ colors[face_indices[0]], colors[face_indices[1]], colors[face_indices[2]] ]; for (let i = 0; i < face_colors.length; i++) { const vertex_color = face_colors[i]; let color = new THREE.Color( vertex_color[0] / 255., vertex_color[1] / 255., vertex_color[2] / 255.); face.vertexColors.push(color); } } geometry.faces.push(face); }); if (colors && colors.length) { mesh_config.material = mesh_config.material || {}; mesh_config.material.vertexColors = THREE.VertexColors; } geometry.center(); geometry.computeBoundingSphere(); geometry.computeVertexNormals(); let material = new THREE[mesh_config.material.cls](mesh_config.material); let mesh = new THREE.Mesh(geometry, material); mesh.castShadow = true; mesh.receiveShadow = true; this._scene.add(mesh); this._lastMesh = mesh; } /** * Creates lights for a given scene based on passed configuration. * @param {!Scene} scene Scene object to add lights to. * @param {!Object} config Scene rendering configuration. * @private */ _createLights(scene, config) { for (let i = 0; i < config.lights.length; i++) { const light_config = config.lights[i]; let light = new THREE[light_config.cls]( light_config.color, light_config.intensity); if (light_config.position) { light.position.set( light_config.position[0], light_config.position[1], light_config.position[2]); } scene.add(light); } } } // end of MeshViewer class. vz_mesh.MeshViewer = MeshViewer; })(vz_mesh || (vz_mesh = {}));

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """`tensorflow_graphics.datasets.modelnet40` module.""" from tensorflow_graphics.datasets.modelnet40.modelnet40 import ModelNet40 __all__ = [ "ModelNet40", ]

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """`tensorflow_graphics.datasets.modelnet40` module.""" from tensorflow_graphics.datasets.modelnet40.modelnet40 import ModelNet40 __all__ = [ "ModelNet40", ]

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/notebooks/resources/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """resources module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from tensorflow_graphics.notebooks.resources import tfg_simplified_logo from tensorflow_graphics.notebooks.resources import triangulated_stripe # The resources module is not exported. __all__ = []

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """resources module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys from tensorflow_graphics.notebooks.resources import tfg_simplified_logo from tensorflow_graphics.notebooks.resources import triangulated_stripe # The resources module is not exported. __all__ = []

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/voxels/visual_hull.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the visual hull voxel rendering.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def render(voxels, axis=2, name=None): """Renders the visual hull of a voxel grid, as described in ["Escaping Plato's Cave: 3D Shape From Adversarial Rendering" (Henzler 2019)](https://github.com/henzler/platonicgan). Note: In the following, A1 to An are optional batch dimensions. Args: voxels: A tensor of shape `[A1, ..., An, Vx, Vy, Vz, Vd]`, where Vx, Vy, Vz are the dimensions of the voxel grid and Vd the dimension of the information stored in each voxel (e.g. 3 for RGB color). axis: An index to the projection axis (0 for X, 1 for Y or 2 for Z). name: A name for this op. Defaults to "visual_hull_render". Returns: A tensor of shape `[A1, ..., An, Vx, Vy, Vd]` representing images of size (Vx,Vy). Raises: ValueError: If the shape of the input tensors are not supported. """ with tf.compat.v1.name_scope(name, "visual_hull_render", [voxels]): voxels = tf.convert_to_tensor(value=voxels) shape.check_static( tensor=voxels, tensor_name="voxels", has_rank_greater_than=3) if axis not in [0, 1, 2]: raise ValueError("'axis' needs to be 0, 1 or 2") image = tf.reduce_sum(input_tensor=voxels, axis=axis - 4) image = tf.ones_like(image) - tf.math.exp(-image) return image # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the visual hull voxel rendering.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def render(voxels, axis=2, name=None): """Renders the visual hull of a voxel grid, as described in ["Escaping Plato's Cave: 3D Shape From Adversarial Rendering" (Henzler 2019)](https://github.com/henzler/platonicgan). Note: In the following, A1 to An are optional batch dimensions. Args: voxels: A tensor of shape `[A1, ..., An, Vx, Vy, Vz, Vd]`, where Vx, Vy, Vz are the dimensions of the voxel grid and Vd the dimension of the information stored in each voxel (e.g. 3 for RGB color). axis: An index to the projection axis (0 for X, 1 for Y or 2 for Z). name: A name for this op. Defaults to "visual_hull_render". Returns: A tensor of shape `[A1, ..., An, Vx, Vy, Vd]` representing images of size (Vx,Vy). Raises: ValueError: If the shape of the input tensors are not supported. """ with tf.compat.v1.name_scope(name, "visual_hull_render", [voxels]): voxels = tf.convert_to_tensor(value=voxels) shape.check_static( tensor=voxels, tensor_name="voxels", has_rank_greater_than=3) if axis not in [0, 1, 2]: raise ValueError("'axis' needs to be 0, 1 or 2") image = tf.reduce_sum(input_tensor=voxels, axis=axis - 4) image = tf.ones_like(image) - tf.math.exp(-image) return image # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/rasterizer.cc

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "rasterizer.h" Rasterizer::Rasterizer( std::unique_ptr<gl_utils::Program>&& program, std::unique_ptr<gl_utils::RenderTargets>&& render_targets, float clear_red, float clear_green, float clear_blue, float clear_alpha, float clear_depth, bool enable_cull_face) : program_(std::move(program)), render_targets_(std::move(render_targets)), clear_red_(clear_red), clear_green_(clear_green), clear_blue_(clear_blue), clear_alpha_(clear_alpha), clear_depth_(clear_depth), enable_cull_face_(enable_cull_face) {} Rasterizer::~Rasterizer() {} void Rasterizer::Reset() { program_.reset(); render_targets_.reset(); for (auto&& buffer : shader_storage_buffers_) buffer.second.reset(); } tensorflow::Status Rasterizer::Render(int num_points, absl::Span<float> result) { return RenderImpl(num_points, result); } tensorflow::Status Rasterizer::Render(int num_points, absl::Span<unsigned char> result) { return RenderImpl(num_points, result); } tensorflow::Status Rasterizer::SetUniformMatrix( const std::string& name, int num_columns, int num_rows, bool transpose, absl::Span<const float> matrix) { if (size_t(num_rows * num_columns) != matrix.size()) return TFG_INTERNAL_ERROR("num_rows * num_columns != matrix.size()"); typedef void (*setter_fn)(GLint location, GLsizei count, GLboolean transpose, const GLfloat* value); static const auto type_mapping = std::unordered_map<int, std::tuple<int, int, setter_fn>>({ {GL_FLOAT_MAT2, std::make_tuple(2, 2, glUniformMatrix2fv)}, {GL_FLOAT_MAT3, std::make_tuple(3, 3, glUniformMatrix3fv)}, {GL_FLOAT_MAT4, std::make_tuple(4, 4, glUniformMatrix4fv)}, {GL_FLOAT_MAT2x3, std::make_tuple(2, 3, glUniformMatrix2x3fv)}, {GL_FLOAT_MAT2x4, std::make_tuple(2, 4, glUniformMatrix2x4fv)}, {GL_FLOAT_MAT3x2, std::make_tuple(3, 2, glUniformMatrix3x2fv)}, {GL_FLOAT_MAT3x4, std::make_tuple(3, 4, glUniformMatrix3x4fv)}, {GL_FLOAT_MAT4x2, std::make_tuple(4, 2, glUniformMatrix4x2fv)}, {GL_FLOAT_MAT4x3, std::make_tuple(4, 3, glUniformMatrix4x3fv)}, }); GLint uniform_type; GLenum property = GL_TYPE; TF_RETURN_IF_ERROR(program_->GetResourceProperty( name, GL_UNIFORM, 1, &property, 1, &uniform_type)); // Is a resource active under that name? if (uniform_type == GLint(GL_INVALID_INDEX)) return TFG_INTERNAL_ERROR("GL_INVALID_INDEX"); auto type_info = type_mapping.find(uniform_type); if (type_info == type_mapping.end()) return TFG_INTERNAL_ERROR("Unsupported type"); if (std::get<0>(type_info->second) != num_columns || std::get<1>(type_info->second) != num_rows) return TFG_INTERNAL_ERROR("Invalid dimensions"); GLint uniform_location; property = GL_LOCATION; TF_RETURN_IF_ERROR(program_->GetResourceProperty( name, GL_UNIFORM, 1, &property, 1, &uniform_location)); TF_RETURN_IF_ERROR(program_->Use()); auto program_cleanup = MakeCleanup([this]() { return program_->Detach(); }); // Specify the value of the uniform in the current program. TFG_RETURN_IF_GL_ERROR(std::get<2>(type_info->second)( uniform_location, 1, transpose ? GL_TRUE : GL_FALSE, matrix.data())); // Cleanup the program; no program is active at this point. return tensorflow::Status::OK(); }

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "rasterizer.h" Rasterizer::Rasterizer( std::unique_ptr<gl_utils::Program>&& program, std::unique_ptr<gl_utils::RenderTargets>&& render_targets, float clear_red, float clear_green, float clear_blue, float clear_alpha, float clear_depth, bool enable_cull_face) : program_(std::move(program)), render_targets_(std::move(render_targets)), clear_red_(clear_red), clear_green_(clear_green), clear_blue_(clear_blue), clear_alpha_(clear_alpha), clear_depth_(clear_depth), enable_cull_face_(enable_cull_face) {} Rasterizer::~Rasterizer() {} void Rasterizer::Reset() { program_.reset(); render_targets_.reset(); for (auto&& buffer : shader_storage_buffers_) buffer.second.reset(); } tensorflow::Status Rasterizer::Render(int num_points, absl::Span<float> result) { return RenderImpl(num_points, result); } tensorflow::Status Rasterizer::Render(int num_points, absl::Span<unsigned char> result) { return RenderImpl(num_points, result); } tensorflow::Status Rasterizer::SetUniformMatrix( const std::string& name, int num_columns, int num_rows, bool transpose, absl::Span<const float> matrix) { if (size_t(num_rows * num_columns) != matrix.size()) return TFG_INTERNAL_ERROR("num_rows * num_columns != matrix.size()"); typedef void (*setter_fn)(GLint location, GLsizei count, GLboolean transpose, const GLfloat* value); static const auto type_mapping = std::unordered_map<int, std::tuple<int, int, setter_fn>>({ {GL_FLOAT_MAT2, std::make_tuple(2, 2, glUniformMatrix2fv)}, {GL_FLOAT_MAT3, std::make_tuple(3, 3, glUniformMatrix3fv)}, {GL_FLOAT_MAT4, std::make_tuple(4, 4, glUniformMatrix4fv)}, {GL_FLOAT_MAT2x3, std::make_tuple(2, 3, glUniformMatrix2x3fv)}, {GL_FLOAT_MAT2x4, std::make_tuple(2, 4, glUniformMatrix2x4fv)}, {GL_FLOAT_MAT3x2, std::make_tuple(3, 2, glUniformMatrix3x2fv)}, {GL_FLOAT_MAT3x4, std::make_tuple(3, 4, glUniformMatrix3x4fv)}, {GL_FLOAT_MAT4x2, std::make_tuple(4, 2, glUniformMatrix4x2fv)}, {GL_FLOAT_MAT4x3, std::make_tuple(4, 3, glUniformMatrix4x3fv)}, }); GLint uniform_type; GLenum property = GL_TYPE; TF_RETURN_IF_ERROR(program_->GetResourceProperty( name, GL_UNIFORM, 1, &property, 1, &uniform_type)); // Is a resource active under that name? if (uniform_type == GLint(GL_INVALID_INDEX)) return TFG_INTERNAL_ERROR("GL_INVALID_INDEX"); auto type_info = type_mapping.find(uniform_type); if (type_info == type_mapping.end()) return TFG_INTERNAL_ERROR("Unsupported type"); if (std::get<0>(type_info->second) != num_columns || std::get<1>(type_info->second) != num_rows) return TFG_INTERNAL_ERROR("Invalid dimensions"); GLint uniform_location; property = GL_LOCATION; TF_RETURN_IF_ERROR(program_->GetResourceProperty( name, GL_UNIFORM, 1, &property, 1, &uniform_location)); TF_RETURN_IF_ERROR(program_->Use()); auto program_cleanup = MakeCleanup([this]() { return program_->Detach(); }); // Specify the value of the uniform in the current program. TFG_RETURN_IF_GL_ERROR(std::get<2>(type_info->second)( uniform_location, 1, transpose ? GL_TRUE : GL_FALSE, matrix.data())); // Cleanup the program; no program is active at this point. return tensorflow::Status::OK(); }

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/interpolation/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Interpolation module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.math.interpolation import bspline from tensorflow_graphics.math.interpolation import slerp from tensorflow_graphics.math.interpolation import trilinear from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.math. __all__ = _export_api.get_modules()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Interpolation module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.math.interpolation import bspline from tensorflow_graphics.math.interpolation import slerp from tensorflow_graphics.math.interpolation import trilinear from tensorflow_graphics.math.interpolation import weighted from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.math. __all__ = _export_api.get_modules()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/intersection_over_union.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the intersection-over-union metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def evaluate(ground_truth_labels, predicted_labels, grid_size=1, name=None): """Computes the Intersection-Over-Union metric for the given ground truth and predicted labels. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible, and G1 to Gm are the grid dimensions. Args: ground_truth_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where the last m axes represent a grid of ground truth attributes. Each attribute can either be 0 or 1. predicted_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where the last m axes represent a grid of predicted attributes. Each attribute can either be 0 or 1. grid_size: The number of grid dimensions. Defaults to 1. name: A name for this op. Defaults to "intersection_over_union_evaluate". Returns: A tensor of shape `[A1, ..., An]` that stores the intersection-over-union metric of the given ground truth labels and predictions. Raises: ValueError: if the shape of `ground_truth_labels`, `predicted_labels` is not supported. """ with tf.compat.v1.name_scope(name, "intersection_over_union_evaluate", [ground_truth_labels, predicted_labels]): ground_truth_labels = tf.convert_to_tensor(value=ground_truth_labels) predicted_labels = tf.convert_to_tensor(value=predicted_labels) shape.compare_batch_dimensions( tensors=(ground_truth_labels, predicted_labels), tensor_names=("ground_truth_labels", "predicted_labels"), last_axes=-grid_size, broadcast_compatible=True) ground_truth_labels = asserts.assert_binary(ground_truth_labels) predicted_labels = asserts.assert_binary(predicted_labels) sum_ground_truth = tf.math.reduce_sum( input_tensor=ground_truth_labels, axis=range(-grid_size, 0)) sum_predictions = tf.math.reduce_sum( input_tensor=predicted_labels, axis=range(-grid_size, 0)) intersection = tf.math.reduce_sum( input_tensor=ground_truth_labels * predicted_labels, axis=range(-grid_size, 0)) union = sum_ground_truth + sum_predictions - intersection return tf.compat.v1.where( tf.math.equal(union, 0), tf.ones_like(union), intersection / union) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the intersection-over-union metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import shape def evaluate(ground_truth_labels, predicted_labels, grid_size=1, name=None): """Computes the Intersection-Over-Union metric for the given ground truth and predicted labels. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible, and G1 to Gm are the grid dimensions. Args: ground_truth_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where the last m axes represent a grid of ground truth attributes. Each attribute can either be 0 or 1. predicted_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where the last m axes represent a grid of predicted attributes. Each attribute can either be 0 or 1. grid_size: The number of grid dimensions. Defaults to 1. name: A name for this op. Defaults to "intersection_over_union_evaluate". Returns: A tensor of shape `[A1, ..., An]` that stores the intersection-over-union metric of the given ground truth labels and predictions. Raises: ValueError: if the shape of `ground_truth_labels`, `predicted_labels` is not supported. """ with tf.compat.v1.name_scope(name, "intersection_over_union_evaluate", [ground_truth_labels, predicted_labels]): ground_truth_labels = tf.convert_to_tensor(value=ground_truth_labels) predicted_labels = tf.convert_to_tensor(value=predicted_labels) shape.compare_batch_dimensions( tensors=(ground_truth_labels, predicted_labels), tensor_names=("ground_truth_labels", "predicted_labels"), last_axes=-grid_size, broadcast_compatible=True) ground_truth_labels = asserts.assert_binary(ground_truth_labels) predicted_labels = asserts.assert_binary(predicted_labels) sum_ground_truth = tf.math.reduce_sum( input_tensor=ground_truth_labels, axis=range(-grid_size, 0)) sum_predictions = tf.math.reduce_sum( input_tensor=predicted_labels, axis=range(-grid_size, 0)) intersection = tf.math.reduce_sum( input_tensor=ground_truth_labels * predicted_labels, axis=range(-grid_size, 0)) union = sum_ground_truth + sum_predictions - intersection return tf.compat.v1.where( tf.math.equal(union, 0), tf.ones_like(union), intersection / union) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/convolution/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Convolution module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.geometry.convolution import graph_convolution from tensorflow_graphics.geometry.convolution import graph_pooling from tensorflow_graphics.geometry.convolution import utils from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.geometry.convolution. __all__ = _export_api.get_modules()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Convolution module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.geometry.convolution import graph_convolution from tensorflow_graphics.geometry.convolution import graph_pooling from tensorflow_graphics.geometry.convolution import utils from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.geometry.convolution. __all__ = _export_api.get_modules()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/fakes/modelnet40_ply_hdf5_2048/ply_data_train1.h5

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tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/nasa/eval.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Reconstruction Evaluation.""" from os import path import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.nasa.lib import datasets from tensorflow_graphics.projects.nasa.lib import models from tensorflow_graphics.projects.nasa.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def build_eval_graph(input_fn, model_fn, hparams): """Build the evaluation computation graph.""" dataset = input_fn(None) batch = dataset.make_one_shot_iterator().get_next() batch_holder = { "transform": tf.placeholder( tf.float32, [1, 1, hparams.n_parts, hparams.n_dims + 1, hparams.n_dims + 1]), "joint": tf.placeholder(tf.float32, [1, 1, hparams.n_parts, hparams.n_dims]), "point": tf.placeholder(tf.float32, [1, 1, None, hparams.n_dims]), "label": tf.placeholder(tf.float32, [1, 1, None, 1]), } latent_holder, latent, occ = model_fn(batch_holder, None, None, "gen_mesh") # Eval Summary iou_holder = tf.placeholder(tf.float32, []) best_holder = tf.placeholder(tf.float32, []) tf.summary.scalar("IoU", iou_holder) tf.summary.scalar("Best_IoU", best_holder) return { "batch_holder": batch_holder, "latent_holder": latent_holder, "latent": latent, "occ": occ, "batch": batch, "iou_holder": iou_holder, "best_holder": best_holder, "merged_summary": tf.summary.merge_all(), } def evaluate(hook_dict, ckpt, saver, best_iou, hparams): """Evaluate a checkpoint on the whole test set.""" batch = hook_dict["batch"] merged_summary = hook_dict["merged_summary"] iou_holder = hook_dict["iou_holder"] best_holder = hook_dict["best_holder"] batch_holder = hook_dict["batch_holder"] latent_holder = hook_dict["latent_holder"] latent = hook_dict["latent"] occ = hook_dict["occ"] global_step = utils.parse_global_step(ckpt) assignment_map = { "shape/": "shape/", } tf.train.init_from_checkpoint(ckpt, assignment_map) init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) accum_iou = 0. example_cnt = 0 while True: try: batch_val = sess.run(batch) feed_dict = { batch_holder["transform"]: batch_val["transform"], batch_holder["joint"]: batch_val["joint"], } iou = utils.compute_iou(sess, feed_dict, latent_holder, batch_holder["point"], latent, occ[:, -1:], batch_val["points"], batch_val["labels"], hparams) accum_iou += iou example_cnt += 1 if hparams.gen_mesh_only > 0: # Generate meshes for evaluation unused_var = utils.save_mesh( sess, feed_dict, latent_holder, batch_holder["point"], latent, occ, batch_val, hparams, ) logging.info("Generated mesh No.{}".format(example_cnt)) except tf.errors.OutOfRangeError: accum_iou /= example_cnt if best_iou < accum_iou: best_iou = accum_iou saver.save(sess, path.join(hparams.train_dir, "best", "model.ckpt"), global_step) summary = sess.run( merged_summary, utils.make_summary_feed_dict( iou_holder, accum_iou, best_holder, best_iou, )) # If only generating meshes for the sequence, we can determinate the # evaluation after the first full loop over the test set. if hparams.gen_mesh_only: exit(0) break return summary, global_step def main(unused_argv): tf.random.set_random_seed(20200823) np.random.seed(20200823) input_fn = datasets.get_dataset("test", FLAGS) model_fn = models.get_model(FLAGS) best_iou = 0. with tf.summary.FileWriter(path.join(FLAGS.train_dir, "eval")) as eval_writer: hook_dict = build_eval_graph(input_fn, model_fn, FLAGS) saver = tf.train.Saver() for ckpt in tf.train.checkpoints_iterator(FLAGS.train_dir, timeout=1800): summary, global_step = evaluate(hook_dict, ckpt, saver, best_iou, FLAGS) eval_writer.add_summary(summary, global_step) eval_writer.flush() if __name__ == "__main__": tf.app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Reconstruction Evaluation.""" from os import path import numpy as np import tensorflow.compat.v1 as tf from tensorflow_graphics.projects.nasa.lib import datasets from tensorflow_graphics.projects.nasa.lib import models from tensorflow_graphics.projects.nasa.lib import utils tf.disable_eager_execution() flags = tf.app.flags logging = tf.logging tf.logging.set_verbosity(tf.logging.INFO) utils.define_flags() FLAGS = flags.FLAGS def build_eval_graph(input_fn, model_fn, hparams): """Build the evaluation computation graph.""" dataset = input_fn(None) batch = dataset.make_one_shot_iterator().get_next() batch_holder = { "transform": tf.placeholder( tf.float32, [1, 1, hparams.n_parts, hparams.n_dims + 1, hparams.n_dims + 1]), "joint": tf.placeholder(tf.float32, [1, 1, hparams.n_parts, hparams.n_dims]), "point": tf.placeholder(tf.float32, [1, 1, None, hparams.n_dims]), "label": tf.placeholder(tf.float32, [1, 1, None, 1]), } latent_holder, latent, occ = model_fn(batch_holder, None, None, "gen_mesh") # Eval Summary iou_holder = tf.placeholder(tf.float32, []) best_holder = tf.placeholder(tf.float32, []) tf.summary.scalar("IoU", iou_holder) tf.summary.scalar("Best_IoU", best_holder) return { "batch_holder": batch_holder, "latent_holder": latent_holder, "latent": latent, "occ": occ, "batch": batch, "iou_holder": iou_holder, "best_holder": best_holder, "merged_summary": tf.summary.merge_all(), } def evaluate(hook_dict, ckpt, saver, best_iou, hparams): """Evaluate a checkpoint on the whole test set.""" batch = hook_dict["batch"] merged_summary = hook_dict["merged_summary"] iou_holder = hook_dict["iou_holder"] best_holder = hook_dict["best_holder"] batch_holder = hook_dict["batch_holder"] latent_holder = hook_dict["latent_holder"] latent = hook_dict["latent"] occ = hook_dict["occ"] global_step = utils.parse_global_step(ckpt) assignment_map = { "shape/": "shape/", } tf.train.init_from_checkpoint(ckpt, assignment_map) init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) accum_iou = 0. example_cnt = 0 while True: try: batch_val = sess.run(batch) feed_dict = { batch_holder["transform"]: batch_val["transform"], batch_holder["joint"]: batch_val["joint"], } iou = utils.compute_iou(sess, feed_dict, latent_holder, batch_holder["point"], latent, occ[:, -1:], batch_val["points"], batch_val["labels"], hparams) accum_iou += iou example_cnt += 1 if hparams.gen_mesh_only > 0: # Generate meshes for evaluation unused_var = utils.save_mesh( sess, feed_dict, latent_holder, batch_holder["point"], latent, occ, batch_val, hparams, ) logging.info("Generated mesh No.{}".format(example_cnt)) except tf.errors.OutOfRangeError: accum_iou /= example_cnt if best_iou < accum_iou: best_iou = accum_iou saver.save(sess, path.join(hparams.train_dir, "best", "model.ckpt"), global_step) summary = sess.run( merged_summary, utils.make_summary_feed_dict( iou_holder, accum_iou, best_holder, best_iou, )) # If only generating meshes for the sequence, we can determinate the # evaluation after the first full loop over the test set. if hparams.gen_mesh_only: exit(0) break return summary, global_step def main(unused_argv): tf.random.set_random_seed(20200823) np.random.seed(20200823) input_fn = datasets.get_dataset("test", FLAGS) model_fn = models.get_model(FLAGS) best_iou = 0. with tf.summary.FileWriter(path.join(FLAGS.train_dir, "eval")) as eval_writer: hook_dict = build_eval_graph(input_fn, model_fn, FLAGS) saver = tf.train.Saver() for ckpt in tf.train.checkpoints_iterator(FLAGS.train_dir, timeout=1800): summary, global_step = evaluate(hook_dict, ckpt, saver, best_iou, FLAGS) eval_writer.add_summary(summary, global_step) eval_writer.flush() if __name__ == "__main__": tf.app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/io/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/tests/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/notebooks/6dof_alignment.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X2Fj4S3r0p1A" }, "source": [ "##### Copyright 2019 Google LLC.\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": {}, "colab_type": "code", "id": "Okg-R95R1CaX" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "eeObMQsw1R_p" }, "source": [ "# Object pose alignment\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6S5QwOCcgwGI" }, "source": [ "Precisely estimating the pose of objects is fundamental to many industries. For instance, in augmented and virtual reality, it allows users to modify the state of some variable by interacting with these objects (e.g. volume controlled by a mug on the user's desk).\n", "\n", "This notebook illustrates how to use [Tensorflow Graphics](https://github.com/tensorflow/graphics) to estimate the rotation and translation of known 3D objects. \n", "\n", "\n", "\n", "\n", "This capability is illustrated by two different demos:\n", "1. **Machine learning** demo illustrating how to train a simple neural network capable of precisely estimating the rotation and translation of a given object with respect to a reference pose.\n", "2. **Mathematical optimization** demo that takes a different approach to the problem; does not use machine learning.\n", "\n", "**Note**: The easiest way to use this tutorial is as a Colab notebook, which allows you to dive in with no setup." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PNQ29y8Q4_cH" }, "source": [ "## Setup & Imports\n", "If Tensorflow Graphics is not installed on your system, the following cell can install the Tensorflow Graphics package for you." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "26AvKq8MJRGl" }, "outputs": [], "source": [ "!pip install tensorflow_graphics" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UkPKOuyJKuKM" }, "source": [ "Now that Tensorflow Graphics is installed, let's import everything needed to run the demos contained in this notebook." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "KlBviBxue7n0" }, "outputs": [], "source": [ "import time\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "from tensorflow_graphics.geometry.transformation import quaternion\n", "from tensorflow_graphics.math import vector\n", "from tensorflow_graphics.notebooks import threejs_visualization\n", "from tensorflow_graphics.notebooks.resources import tfg_simplified_logo\n", "\n", "tf.compat.v1.enable_v2_behavior()\n", "\n", "# Loads the Tensorflow Graphics simplified logo.\n", "vertices = tfg_simplified_logo.mesh['vertices'].astype(np.float32)\n", "faces = tfg_simplified_logo.mesh['faces']\n", "num_vertices = vertices.shape[0]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pD2ldsUvaP5V" }, "source": [ "## 1. Machine Learning\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UmFx3BqjNWrN" }, "source": [ "### Model definition\n", "Given the 3D position of all the vertices of a known mesh, we would like a network that is capable of predicting the rotation parametrized by a quaternion (4 dimensional vector), and translation (3 dimensional vector) of this mesh with respect to a reference pose. Let's now create a very simple 3-layer fully connected network, and a loss for the task. Note that this model is very simple and definitely not optimal, which is out of scope for this notebook." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "5_EP7GzQb6sn" }, "outputs": [], "source": [ "# Constructs the model.\n", "model = keras.Sequential()\n", "model.add(layers.Flatten(input_shape=(num_vertices, 3)))\n", "model.add(layers.Dense(64, activation=tf.nn.tanh))\n", "model.add(layers.Dense(64, activation=tf.nn.relu))\n", "model.add(layers.Dense(7))\n", "\n", "\n", "def pose_estimation_loss(y_true, y_pred):\n", " \"\"\"Pose estimation loss used for training.\n", "\n", " This loss measures the average of squared distance between some vertices\n", " of the mesh in 'rest pose' and the transformed mesh to which the predicted\n", " inverse pose is applied. Comparing this loss with a regular L2 loss on the\n", " quaternion and translation values is left as exercise to the interested\n", " reader.\n", "\n", " Args:\n", " y_true: The ground-truth value.\n", " y_pred: The prediction we want to evaluate the loss for.\n", "\n", " Returns:\n", " A scalar value containing the loss described in the description above.\n", " \"\"\"\n", " # y_true.shape : (batch, 7)\n", " y_true_q, y_true_t = tf.split(y_true, (4, 3), axis=-1)\n", " # y_pred.shape : (batch, 7)\n", " y_pred_q, y_pred_t = tf.split(y_pred, (4, 3), axis=-1)\n", "\n", " # vertices.shape: (num_vertices, 3)\n", " # corners.shape:(num_vertices, 1, 3)\n", " corners = tf.expand_dims(vertices, axis=1)\n", "\n", " # transformed_corners.shape: (num_vertices, batch, 3)\n", " # q and t shapes get pre-pre-padded with 1's following standard broadcast rules.\n", " transformed_corners = quaternion.rotate(corners, y_pred_q) + y_pred_t\n", "\n", " # recovered_corners.shape: (num_vertices, batch, 3)\n", " recovered_corners = quaternion.rotate(transformed_corners - y_true_t,\n", " quaternion.inverse(y_true_q))\n", "\n", " # vertex_error.shape: (num_vertices, batch)\n", " vertex_error = tf.reduce_sum((recovered_corners - corners)**2, axis=-1)\n", "\n", " return tf.reduce_mean(vertex_error)\n", "\n", "\n", "optimizer = keras.optimizers.Adam()\n", "model.compile(loss=pose_estimation_loss, optimizer=optimizer)\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VPJeqKp3OaE0" }, "source": [ "### Data generation\n", "Now that we have a model defined, we need data to train it. For each sample in the training set, a random 3D rotation and 3D translation are sampled and applied to the vertices of our object. Each training sample consists of all the transformed vertices and the inverse rotation and translation that would allow to revert the rotation and translation applied to the sample." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "jtaNzJKBGi8s" }, "outputs": [], "source": [ "def generate_training_data(num_samples):\n", " # random_angles.shape: (num_samples, 3)\n", " random_angles = np.random.uniform(-np.pi, np.pi,\n", " (num_samples, 3)).astype(np.float32)\n", "\n", " # random_quaternion.shape: (num_samples, 4)\n", " random_quaternion = quaternion.from_euler(random_angles)\n", "\n", " # random_translation.shape: (num_samples, 3)\n", " random_translation = np.random.uniform(-2.0, 2.0,\n", " (num_samples, 3)).astype(np.float32)\n", "\n", " # data.shape : (num_samples, num_vertices, 3)\n", " data = quaternion.rotate(vertices[tf.newaxis, :, :],\n", " random_quaternion[:, tf.newaxis, :]\n", " ) + random_translation[:, tf.newaxis, :]\n", "\n", " # target.shape : (num_samples, 4+3)\n", " target = tf.concat((random_quaternion, random_translation), axis=-1)\n", "\n", " return np.array(data), np.array(target)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "E3MIRZrQfPJa" }, "outputs": [], "source": [ "num_samples = 10000\n", "\n", "data, target = generate_training_data(num_samples)\n", "\n", "print(data.shape) # (num_samples, num_vertices, 3): the vertices\n", "print(target.shape) # (num_samples, 4+3): the quaternion and translation" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "R5yebHu3OOgJ" }, "source": [ "### Training\n", "At this point, everything is in place to start training the neural network!" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "vquVVhxGC70_" }, "outputs": [], "source": [ "# Callback allowing to display the progression of the training task.\n", "class ProgressTracker(keras.callbacks.Callback):\n", "\n", " def __init__(self, num_epochs, step=5):\n", " self.num_epochs = num_epochs\n", " self.current_epoch = 0.\n", " self.step = step\n", " self.last_percentage_report = 0\n", "\n", " def on_epoch_end(self, batch, logs={}):\n", " self.current_epoch += 1.\n", " training_percentage = int(self.current_epoch * 100.0 / self.num_epochs)\n", " if training_percentage - self.last_percentage_report >= self.step:\n", " print('Training ' + str(\n", " training_percentage) + '% complete. Training loss: ' + str(\n", " logs.get('loss')) + ' | Validation loss: ' + str(\n", " logs.get('val_loss')))\n", " self.last_percentage_report = training_percentage" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "TtntLH1NDAZV" }, "outputs": [], "source": [ "reduce_lr_callback = keras.callbacks.ReduceLROnPlateau(\n", " monitor='val_loss',\n", " factor=0.5,\n", " patience=10,\n", " verbose=0,\n", " mode='auto',\n", " min_delta=0.0001,\n", " cooldown=0,\n", " min_lr=0)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "l0r2AuWsWpb-" }, "outputs": [], "source": [ "# google internal 1", "\n", "# Everything is now in place to train.\n", "EPOCHS = 100\n", "pt = ProgressTracker(EPOCHS)\n", "history = model.fit(\n", " data,\n", " target,\n", " epochs=EPOCHS,\n", " validation_split=0.2,\n", " verbose=0,\n", " batch_size=32,\n", " callbacks=[reduce_lr_callback, pt])\n", "\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.ylim([0, 1])\n", "plt.legend(['loss', 'val loss'], loc='upper left')\n", "plt.xlabel('Train epoch')\n", "_ = plt.ylabel('Error [mean square distance]')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cqYThAKGjhRU" }, "source": [ "### Testing\n", "The network is now trained and ready to use!\n", "The displayed results consist of two images. The first image contains the object in 'rest pose' (pastel lemon color) and the rotated and translated object (pastel honeydew color). This effectively allows to observe how **different** the two configurations are. The second image also shows the object in rest pose, but this time the transformation predicted by our trained neural network is applied to the rotated and translated version. Hopefully, the two objects are now in a very **similar** pose.\n", "\n", "**Note**: press play multiple times to sample different test cases. You will notice that sometimes the scale of the object is off. This comes from the fact that quaternions can encode scale. Using a quaternion of unit norm would result in not changing the scale of the result. We let the interested reader experiment with adding this constraint either in the network architecture, or in the loss function. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bMUOcMsf5sia" }, "source": [ "Start with a helper function to apply a quaternion and a translation:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "3CuzLFUR2TQc" }, "outputs": [], "source": [ "def transform_points(target_points, quaternion_variable, translation_variable):\n", " return quaternion.rotate(target_points,\n", " quaternion_variable) + translation_variable" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EJgJX6DO5zQO" }, "source": [ "Define a `threejs` viewer for the transformed shape: " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "ijibJe1p2Kgk" }, "outputs": [], "source": [ "class Viewer(object):\n", "\n", " def __init__(self, my_vertices):\n", " my_vertices = np.asarray(my_vertices)\n", " context = threejs_visualization.build_context()\n", " light1 = context.THREE.PointLight.new_object(0x808080)\n", " light1.position.set(10., 10., 10.)\n", " light2 = context.THREE.AmbientLight.new_object(0x808080)\n", " lights = (light1, light2)\n", "\n", " material = context.THREE.MeshLambertMaterial.new_object({\n", " 'color': 0xfffacd,\n", " })\n", "\n", " material_deformed = context.THREE.MeshLambertMaterial.new_object({\n", " 'color': 0xf0fff0,\n", " })\n", "\n", " camera = threejs_visualization.build_perspective_camera(\n", " field_of_view=30, position=(10.0, 10.0, 10.0))\n", "\n", " mesh = {'vertices': vertices, 'faces': faces, 'material': material}\n", " transformed_mesh = {\n", " 'vertices': my_vertices,\n", " 'faces': faces,\n", " 'material': material_deformed\n", " }\n", " geometries = threejs_visualization.triangular_mesh_renderer(\n", " [mesh, transformed_mesh],\n", " lights=lights,\n", " camera=camera,\n", " width=400,\n", " height=400)\n", "\n", " self.geometries = geometries\n", "\n", " def update(self, transformed_points):\n", " self.geometries[1].getAttribute('position').copyArray(\n", " transformed_points.numpy().ravel().tolist())\n", " self.geometries[1].getAttribute('position').needsUpdate = True" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vt8dw1VE58VH" }, "source": [ "Define a random rotation and translation:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "dQBZ22DSzaMu" }, "outputs": [], "source": [ "def get_random_transform():\n", " # Forms a random translation\n", " with tf.name_scope('translation_variable'):\n", " random_translation = tf.Variable(\n", " np.random.uniform(-2.0, 2.0, (3,)), dtype=tf.float32)\n", "\n", " # Forms a random quaternion\n", " hi = np.pi\n", " lo = -hi\n", " random_angles = np.random.uniform(lo, hi, (3,)).astype(np.float32)\n", " with tf.name_scope('rotation_variable'):\n", " random_quaternion = tf.Variable(quaternion.from_euler(random_angles))\n", "\n", " return random_quaternion, random_translation\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_9FfPxsj6S8J" }, "source": [ "Run the model to predict the transformation parameters, and visualize the result:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "N70Swvie6IWd" }, "outputs": [], "source": [ "random_quaternion, random_translation = get_random_transform()\n", "\n", "initial_orientation = transform_points(vertices, random_quaternion,\n", " random_translation).numpy()\n", "viewer = Viewer(initial_orientation)\n", "\n", "predicted_transformation = model.predict(initial_orientation[tf.newaxis, :, :])\n", "\n", "predicted_inverse_q = quaternion.inverse(predicted_transformation[0, 0:4])\n", "predicted_inverse_t = -predicted_transformation[0, 4:]\n", "\n", "predicted_aligned = quaternion.rotate(initial_orientation + predicted_inverse_t,\n", " predicted_inverse_q)\n", "\n", "viewer = Viewer(predicted_aligned)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o6Aut-yJJApf" }, "source": [ "## 2. Mathematical optimization\n", "Here the problem is tackled using mathematical optimization, which is another traditional way to approach the problem of object pose estimation. Given correspondences between the object in 'rest pose' (pastel lemon color) and its rotated and translated counter part (pastel honeydew color), the problem can be formulated as a minimization problem. The loss function can for instance be defined as the sum of Euclidean distances between the corresponding points using the current estimate of the rotation and translation of the transformed object. One can then compute the derivative of the rotation and translation parameters with respect to this loss function, and follow the gradient direction until convergence. The following cell closely follows that procedure, and uses gradient descent to align the two objects. It is worth noting that although the results are good, there are more efficient ways to solve this specific problem. The interested reader is referred to the Kabsch algorithm for further details.\n", "\n", "**Note**: press play multiple times to sample different test cases. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6vWEO0846b8h" }, "source": [ "Define the `loss` and `gradient` functions:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "b8_TYq-31l3z" }, "outputs": [], "source": [ "def loss(target_points, quaternion_variable, translation_variable):\n", " transformed_points = transform_points(target_points, quaternion_variable,\n", " translation_variable)\n", " error = (vertices - transformed_points) / num_vertices\n", " return vector.dot(error, error)\n", "\n", "\n", "def gradient_loss(target_points, quaternion, translation):\n", " with tf.GradientTape() as tape:\n", " loss_value = loss(target_points, quaternion, translation)\n", " return tape.gradient(loss_value, [quaternion, translation])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9y8DLxc26h0g" }, "source": [ "Create the optimizer." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "1YzO2Dpy4oDa" }, "outputs": [], "source": [ "learning_rate = 0.05\n", "with tf.name_scope('optimization'):\n", " optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "uPdMbQvO6prf" }, "source": [ "Initialize the random transformation, run the optimization and animate the result." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "vw9RMRPVz0YL" }, "outputs": [], "source": [ "random_quaternion, random_translation = get_random_transform()\n", "\n", "transformed_points = transform_points(vertices, random_quaternion,\n", " random_translation)\n", "\n", "viewer = Viewer(transformed_points)\n", "\n", "nb_iterations = 100\n", "for it in range(nb_iterations):\n", " gradients_loss = gradient_loss(vertices, random_quaternion,\n", " random_translation)\n", " optimizer.apply_gradients(\n", " zip(gradients_loss, (random_quaternion, random_translation)))\n", " transformed_points = transform_points(vertices, random_quaternion,\n", " random_translation)\n", "\n", " viewer.update(transformed_points)\n", " time.sleep(0.1)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "6dof alignment.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X2Fj4S3r0p1A" }, "source": [ "##### Copyright 2019 Google LLC.\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": {}, "colab_type": "code", "id": "Okg-R95R1CaX" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "eeObMQsw1R_p" }, "source": [ "# Object pose alignment\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6S5QwOCcgwGI" }, "source": [ "Precisely estimating the pose of objects is fundamental to many industries. For instance, in augmented and virtual reality, it allows users to modify the state of some variable by interacting with these objects (e.g. volume controlled by a mug on the user's desk).\n", "\n", "This notebook illustrates how to use [Tensorflow Graphics](https://github.com/tensorflow/graphics) to estimate the rotation and translation of known 3D objects. \n", "\n", "\n", "\n", "\n", "This capability is illustrated by two different demos:\n", "1. **Machine learning** demo illustrating how to train a simple neural network capable of precisely estimating the rotation and translation of a given object with respect to a reference pose.\n", "2. **Mathematical optimization** demo that takes a different approach to the problem; does not use machine learning.\n", "\n", "**Note**: The easiest way to use this tutorial is as a Colab notebook, which allows you to dive in with no setup." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PNQ29y8Q4_cH" }, "source": [ "## Setup & Imports\n", "If Tensorflow Graphics is not installed on your system, the following cell can install the Tensorflow Graphics package for you." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "26AvKq8MJRGl" }, "outputs": [], "source": [ "!pip install tensorflow_graphics" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UkPKOuyJKuKM" }, "source": [ "Now that Tensorflow Graphics is installed, let's import everything needed to run the demos contained in this notebook." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "KlBviBxue7n0" }, "outputs": [], "source": [ "import time\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "from tensorflow_graphics.geometry.transformation import quaternion\n", "from tensorflow_graphics.math import vector\n", "from tensorflow_graphics.notebooks import threejs_visualization\n", "from tensorflow_graphics.notebooks.resources import tfg_simplified_logo\n", "\n", "tf.compat.v1.enable_v2_behavior()\n", "\n", "# Loads the Tensorflow Graphics simplified logo.\n", "vertices = tfg_simplified_logo.mesh['vertices'].astype(np.float32)\n", "faces = tfg_simplified_logo.mesh['faces']\n", "num_vertices = vertices.shape[0]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pD2ldsUvaP5V" }, "source": [ "## 1. Machine Learning\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UmFx3BqjNWrN" }, "source": [ "### Model definition\n", "Given the 3D position of all the vertices of a known mesh, we would like a network that is capable of predicting the rotation parametrized by a quaternion (4 dimensional vector), and translation (3 dimensional vector) of this mesh with respect to a reference pose. Let's now create a very simple 3-layer fully connected network, and a loss for the task. Note that this model is very simple and definitely not optimal, which is out of scope for this notebook." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "5_EP7GzQb6sn" }, "outputs": [], "source": [ "# Constructs the model.\n", "model = keras.Sequential()\n", "model.add(layers.Flatten(input_shape=(num_vertices, 3)))\n", "model.add(layers.Dense(64, activation=tf.nn.tanh))\n", "model.add(layers.Dense(64, activation=tf.nn.relu))\n", "model.add(layers.Dense(7))\n", "\n", "\n", "def pose_estimation_loss(y_true, y_pred):\n", " \"\"\"Pose estimation loss used for training.\n", "\n", " This loss measures the average of squared distance between some vertices\n", " of the mesh in 'rest pose' and the transformed mesh to which the predicted\n", " inverse pose is applied. Comparing this loss with a regular L2 loss on the\n", " quaternion and translation values is left as exercise to the interested\n", " reader.\n", "\n", " Args:\n", " y_true: The ground-truth value.\n", " y_pred: The prediction we want to evaluate the loss for.\n", "\n", " Returns:\n", " A scalar value containing the loss described in the description above.\n", " \"\"\"\n", " # y_true.shape : (batch, 7)\n", " y_true_q, y_true_t = tf.split(y_true, (4, 3), axis=-1)\n", " # y_pred.shape : (batch, 7)\n", " y_pred_q, y_pred_t = tf.split(y_pred, (4, 3), axis=-1)\n", "\n", " # vertices.shape: (num_vertices, 3)\n", " # corners.shape:(num_vertices, 1, 3)\n", " corners = tf.expand_dims(vertices, axis=1)\n", "\n", " # transformed_corners.shape: (num_vertices, batch, 3)\n", " # q and t shapes get pre-pre-padded with 1's following standard broadcast rules.\n", " transformed_corners = quaternion.rotate(corners, y_pred_q) + y_pred_t\n", "\n", " # recovered_corners.shape: (num_vertices, batch, 3)\n", " recovered_corners = quaternion.rotate(transformed_corners - y_true_t,\n", " quaternion.inverse(y_true_q))\n", "\n", " # vertex_error.shape: (num_vertices, batch)\n", " vertex_error = tf.reduce_sum((recovered_corners - corners)**2, axis=-1)\n", "\n", " return tf.reduce_mean(vertex_error)\n", "\n", "\n", "optimizer = keras.optimizers.Adam()\n", "model.compile(loss=pose_estimation_loss, optimizer=optimizer)\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VPJeqKp3OaE0" }, "source": [ "### Data generation\n", "Now that we have a model defined, we need data to train it. For each sample in the training set, a random 3D rotation and 3D translation are sampled and applied to the vertices of our object. Each training sample consists of all the transformed vertices and the inverse rotation and translation that would allow to revert the rotation and translation applied to the sample." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "jtaNzJKBGi8s" }, "outputs": [], "source": [ "def generate_training_data(num_samples):\n", " # random_angles.shape: (num_samples, 3)\n", " random_angles = np.random.uniform(-np.pi, np.pi,\n", " (num_samples, 3)).astype(np.float32)\n", "\n", " # random_quaternion.shape: (num_samples, 4)\n", " random_quaternion = quaternion.from_euler(random_angles)\n", "\n", " # random_translation.shape: (num_samples, 3)\n", " random_translation = np.random.uniform(-2.0, 2.0,\n", " (num_samples, 3)).astype(np.float32)\n", "\n", " # data.shape : (num_samples, num_vertices, 3)\n", " data = quaternion.rotate(vertices[tf.newaxis, :, :],\n", " random_quaternion[:, tf.newaxis, :]\n", " ) + random_translation[:, tf.newaxis, :]\n", "\n", " # target.shape : (num_samples, 4+3)\n", " target = tf.concat((random_quaternion, random_translation), axis=-1)\n", "\n", " return np.array(data), np.array(target)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "E3MIRZrQfPJa" }, "outputs": [], "source": [ "num_samples = 10000\n", "\n", "data, target = generate_training_data(num_samples)\n", "\n", "print(data.shape) # (num_samples, num_vertices, 3): the vertices\n", "print(target.shape) # (num_samples, 4+3): the quaternion and translation" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "R5yebHu3OOgJ" }, "source": [ "### Training\n", "At this point, everything is in place to start training the neural network!" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "vquVVhxGC70_" }, "outputs": [], "source": [ "# Callback allowing to display the progression of the training task.\n", "class ProgressTracker(keras.callbacks.Callback):\n", "\n", " def __init__(self, num_epochs, step=5):\n", " self.num_epochs = num_epochs\n", " self.current_epoch = 0.\n", " self.step = step\n", " self.last_percentage_report = 0\n", "\n", " def on_epoch_end(self, batch, logs={}):\n", " self.current_epoch += 1.\n", " training_percentage = int(self.current_epoch * 100.0 / self.num_epochs)\n", " if training_percentage - self.last_percentage_report >= self.step:\n", " print('Training ' + str(\n", " training_percentage) + '% complete. Training loss: ' + str(\n", " logs.get('loss')) + ' | Validation loss: ' + str(\n", " logs.get('val_loss')))\n", " self.last_percentage_report = training_percentage" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "TtntLH1NDAZV" }, "outputs": [], "source": [ "reduce_lr_callback = keras.callbacks.ReduceLROnPlateau(\n", " monitor='val_loss',\n", " factor=0.5,\n", " patience=10,\n", " verbose=0,\n", " mode='auto',\n", " min_delta=0.0001,\n", " cooldown=0,\n", " min_lr=0)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "l0r2AuWsWpb-" }, "outputs": [], "source": [ "# google internal 1", "\n", "# Everything is now in place to train.\n", "EPOCHS = 100\n", "pt = ProgressTracker(EPOCHS)\n", "history = model.fit(\n", " data,\n", " target,\n", " epochs=EPOCHS,\n", " validation_split=0.2,\n", " verbose=0,\n", " batch_size=32,\n", " callbacks=[reduce_lr_callback, pt])\n", "\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.ylim([0, 1])\n", "plt.legend(['loss', 'val loss'], loc='upper left')\n", "plt.xlabel('Train epoch')\n", "_ = plt.ylabel('Error [mean square distance]')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cqYThAKGjhRU" }, "source": [ "### Testing\n", "The network is now trained and ready to use!\n", "The displayed results consist of two images. The first image contains the object in 'rest pose' (pastel lemon color) and the rotated and translated object (pastel honeydew color). This effectively allows to observe how **different** the two configurations are. The second image also shows the object in rest pose, but this time the transformation predicted by our trained neural network is applied to the rotated and translated version. Hopefully, the two objects are now in a very **similar** pose.\n", "\n", "**Note**: press play multiple times to sample different test cases. You will notice that sometimes the scale of the object is off. This comes from the fact that quaternions can encode scale. Using a quaternion of unit norm would result in not changing the scale of the result. We let the interested reader experiment with adding this constraint either in the network architecture, or in the loss function. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bMUOcMsf5sia" }, "source": [ "Start with a helper function to apply a quaternion and a translation:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "3CuzLFUR2TQc" }, "outputs": [], "source": [ "def transform_points(target_points, quaternion_variable, translation_variable):\n", " return quaternion.rotate(target_points,\n", " quaternion_variable) + translation_variable" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EJgJX6DO5zQO" }, "source": [ "Define a `threejs` viewer for the transformed shape: " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "ijibJe1p2Kgk" }, "outputs": [], "source": [ "class Viewer(object):\n", "\n", " def __init__(self, my_vertices):\n", " my_vertices = np.asarray(my_vertices)\n", " context = threejs_visualization.build_context()\n", " light1 = context.THREE.PointLight.new_object(0x808080)\n", " light1.position.set(10., 10., 10.)\n", " light2 = context.THREE.AmbientLight.new_object(0x808080)\n", " lights = (light1, light2)\n", "\n", " material = context.THREE.MeshLambertMaterial.new_object({\n", " 'color': 0xfffacd,\n", " })\n", "\n", " material_deformed = context.THREE.MeshLambertMaterial.new_object({\n", " 'color': 0xf0fff0,\n", " })\n", "\n", " camera = threejs_visualization.build_perspective_camera(\n", " field_of_view=30, position=(10.0, 10.0, 10.0))\n", "\n", " mesh = {'vertices': vertices, 'faces': faces, 'material': material}\n", " transformed_mesh = {\n", " 'vertices': my_vertices,\n", " 'faces': faces,\n", " 'material': material_deformed\n", " }\n", " geometries = threejs_visualization.triangular_mesh_renderer(\n", " [mesh, transformed_mesh],\n", " lights=lights,\n", " camera=camera,\n", " width=400,\n", " height=400)\n", "\n", " self.geometries = geometries\n", "\n", " def update(self, transformed_points):\n", " self.geometries[1].getAttribute('position').copyArray(\n", " transformed_points.numpy().ravel().tolist())\n", " self.geometries[1].getAttribute('position').needsUpdate = True" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vt8dw1VE58VH" }, "source": [ "Define a random rotation and translation:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "dQBZ22DSzaMu" }, "outputs": [], "source": [ "def get_random_transform():\n", " # Forms a random translation\n", " with tf.name_scope('translation_variable'):\n", " random_translation = tf.Variable(\n", " np.random.uniform(-2.0, 2.0, (3,)), dtype=tf.float32)\n", "\n", " # Forms a random quaternion\n", " hi = np.pi\n", " lo = -hi\n", " random_angles = np.random.uniform(lo, hi, (3,)).astype(np.float32)\n", " with tf.name_scope('rotation_variable'):\n", " random_quaternion = tf.Variable(quaternion.from_euler(random_angles))\n", "\n", " return random_quaternion, random_translation\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_9FfPxsj6S8J" }, "source": [ "Run the model to predict the transformation parameters, and visualize the result:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "N70Swvie6IWd" }, "outputs": [], "source": [ "random_quaternion, random_translation = get_random_transform()\n", "\n", "initial_orientation = transform_points(vertices, random_quaternion,\n", " random_translation).numpy()\n", "viewer = Viewer(initial_orientation)\n", "\n", "predicted_transformation = model.predict(initial_orientation[tf.newaxis, :, :])\n", "\n", "predicted_inverse_q = quaternion.inverse(predicted_transformation[0, 0:4])\n", "predicted_inverse_t = -predicted_transformation[0, 4:]\n", "\n", "predicted_aligned = quaternion.rotate(initial_orientation + predicted_inverse_t,\n", " predicted_inverse_q)\n", "\n", "viewer = Viewer(predicted_aligned)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o6Aut-yJJApf" }, "source": [ "## 2. Mathematical optimization\n", "Here the problem is tackled using mathematical optimization, which is another traditional way to approach the problem of object pose estimation. Given correspondences between the object in 'rest pose' (pastel lemon color) and its rotated and translated counter part (pastel honeydew color), the problem can be formulated as a minimization problem. The loss function can for instance be defined as the sum of Euclidean distances between the corresponding points using the current estimate of the rotation and translation of the transformed object. One can then compute the derivative of the rotation and translation parameters with respect to this loss function, and follow the gradient direction until convergence. The following cell closely follows that procedure, and uses gradient descent to align the two objects. It is worth noting that although the results are good, there are more efficient ways to solve this specific problem. The interested reader is referred to the Kabsch algorithm for further details.\n", "\n", "**Note**: press play multiple times to sample different test cases. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6vWEO0846b8h" }, "source": [ "Define the `loss` and `gradient` functions:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "b8_TYq-31l3z" }, "outputs": [], "source": [ "def loss(target_points, quaternion_variable, translation_variable):\n", " transformed_points = transform_points(target_points, quaternion_variable,\n", " translation_variable)\n", " error = (vertices - transformed_points) / num_vertices\n", " return vector.dot(error, error)\n", "\n", "\n", "def gradient_loss(target_points, quaternion, translation):\n", " with tf.GradientTape() as tape:\n", " loss_value = loss(target_points, quaternion, translation)\n", " return tape.gradient(loss_value, [quaternion, translation])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9y8DLxc26h0g" }, "source": [ "Create the optimizer." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "1YzO2Dpy4oDa" }, "outputs": [], "source": [ "learning_rate = 0.05\n", "with tf.name_scope('optimization'):\n", " optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "uPdMbQvO6prf" }, "source": [ "Initialize the random transformation, run the optimization and animate the result." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "vw9RMRPVz0YL" }, "outputs": [], "source": [ "random_quaternion, random_translation = get_random_transform()\n", "\n", "transformed_points = transform_points(vertices, random_quaternion,\n", " random_translation)\n", "\n", "viewer = Viewer(transformed_points)\n", "\n", "nb_iterations = 100\n", "for it in range(nb_iterations):\n", " gradients_loss = gradient_loss(vertices, random_quaternion,\n", " random_translation)\n", " optimizer.apply_gradients(\n", " zip(gradients_loss, (random_quaternion, random_translation)))\n", " transformed_points = transform_points(vertices, random_quaternion,\n", " random_translation)\n", "\n", " viewer.update(transformed_points)\n", " time.sleep(0.1)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "6dof alignment.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/modelnet40_run.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Simple demo of modelnet40 dataset. See: https://www.tensorflow.org/datasets/api_docs/python/tfds/load """ from absl import app from tensorflow_graphics.datasets.modelnet40 import ModelNet40 def main(_): ds_train, info = ModelNet40.load( split="train", data_dir="~/tensorflow_dataset", with_info=True) for example in ds_train.take(1): points = example["points"] label = example["label"] # --- example accessing data print("points.shape=", points.shape) print("label.shape", label.shape) # --- example accessing info print("Example of string='{}' to ID#={}".format( "airplane", info.features["label"].str2int("airplane"))) print("Example of ID#={} to string='{}'".format( 12, info.features["label"].int2str(12))) if __name__ == "__main__": app.run(main)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Simple demo of modelnet40 dataset. See: https://www.tensorflow.org/datasets/api_docs/python/tfds/load """ from absl import app from tensorflow_graphics.datasets.modelnet40 import ModelNet40 def main(_): ds_train, info = ModelNet40.load( split="train", data_dir="~/tensorflow_dataset", with_info=True) for example in ds_train.take(1): points = example["points"] label = example["label"] # --- example accessing data print("points.shape=", points.shape) print("label.shape", label.shape) # --- example accessing info print("Example of string='{}' to ID#={}".format( "airplane", info.features["label"].str2int("airplane"))) print("Example of ID#={} to string='{}'".format( 12, info.features["label"].int2str(12))) if __name__ == "__main__": app.run(main)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/nasa/lib/datasets.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Dataset implementations.""" from os import path import tensorflow.compat.v1 as tf tf.disable_eager_execution() def get_dataset(split, hparams): return dataset_dict[hparams.dataset](split, hparams) def amass(split, hparams): """Construct an AMASS data loader.""" def _input_fn(params): # pylint: disable=unused-argument # Dataset constants. n_bbox = 100000 n_surf = 100000 n_points = n_bbox + n_surf n_vert = 6890 n_frames = 1 # Parse parameters for global configurations. n_dims = hparams.n_dims data_dir = hparams.data_dir sample_bbox = hparams.sample_bbox sample_surf = hparams.sample_surf batch_size = hparams.batch_size subject = hparams.subject motion = hparams.motion n_parts = hparams.n_parts def _parse_tfrecord(serialized_example): fs = tf.parse_single_example( serialized_example, features={ 'point': tf.FixedLenFeature([n_frames * n_points * n_dims], tf.float32), 'label': tf.FixedLenFeature([n_frames * n_points * 1], tf.float32), 'vert': tf.FixedLenFeature([n_frames * n_vert * n_dims], tf.float32), 'weight': tf.FixedLenFeature([n_frames * n_vert * n_parts], tf.float32), 'transform': tf.FixedLenFeature( [n_frames * n_parts * (n_dims + 1) * (n_dims + 1)], tf.float32), 'joint': tf.FixedLenFeature([n_frames * n_parts * n_dims], tf.float32), 'name': tf.FixedLenFeature([], tf.string), }) fs['point'] = tf.reshape(fs['point'], [n_frames, n_points, n_dims]) fs['label'] = tf.reshape(fs['label'], [n_frames, n_points, 1]) fs['vert'] = tf.reshape(fs['vert'], [n_frames, n_vert, n_dims]) fs['weight'] = tf.reshape(fs['weight'], [n_frames, n_vert, n_parts]) fs['transform'] = tf.reshape(fs['transform'], [n_frames, n_parts, n_dims + 1, n_dims + 1]) fs['joint'] = tf.reshape(fs['joint'], [n_frames, n_parts, n_dims]) return fs def _sample_frame_points(fs): feature = {} for k, v in fs.items(): feature[k] = v points = feature['point'][0] labels = feature['label'][0] sample_points = [] sample_labels = [] if sample_bbox > 0: indices_bbox = tf.random.uniform([sample_bbox], minval=0, maxval=n_bbox, dtype=tf.int32) bbox_samples = tf.gather(points[:n_bbox], indices_bbox, axis=0) bbox_labels = tf.gather(labels[:n_bbox], indices_bbox, axis=0) sample_points.append(bbox_samples) sample_labels.append(bbox_labels) if sample_surf > 0: indices_surf = tf.random.uniform([sample_surf], minval=0, maxval=n_surf, dtype=tf.int32) surf_samples = tf.gather( points[n_bbox:n_bbox + n_surf], indices_surf, axis=0) surf_labels = tf.gather( labels[n_bbox:n_bbox + n_surf], indices_surf, axis=0) sample_points.append(surf_samples) sample_labels.append(surf_labels) points = tf.concat(sample_points, axis=0) point_labels = tf.concat(sample_labels, axis=0) feature['point'] = tf.expand_dims(points, axis=0) feature['label'] = tf.expand_dims(point_labels, axis=0) return feature def _sample_eval_points(fs): feature = {} feature['transform'] = fs['transform'] feature['points'] = fs['point'][:, :n_bbox] feature['labels'] = fs['label'][:, :n_bbox] feature['name'] = fs['name'] feature['vert'] = fs['vert'] feature['weight'] = fs['weight'] feature['joint'] = fs['joint'] return feature data_split = 'train' all_motions = list(x for x in range(10)) if split == 'train': file_pattern = [ path.join(data_dir, '{0}-{1:02d}-{2:02d}-*'.format(data_split, subject, x)) for x in all_motions if x != motion ] else: file_pattern = [ path.join(data_dir, '{0}-{1:02d}-{2:02d}-*'.format(data_split, subject, motion)) ] data_files = tf.gfile.Glob(file_pattern) if not data_files: raise IOError('{} did not match any files'.format(file_pattern)) filenames = tf.data.Dataset.list_files(file_pattern, shuffle=True) data = filenames.interleave( lambda x: tf.data.TFRecordDataset([x]), num_parallel_calls=tf.data.experimental.AUTOTUNE) data = data.map( _parse_tfrecord, num_parallel_calls=tf.data.experimental.AUTOTUNE).cache() if split == 'train': data = data.map( _sample_frame_points, num_parallel_calls=tf.data.experimental.AUTOTUNE) else: data = data.map( _sample_eval_points, num_parallel_calls=tf.data.experimental.AUTOTUNE) if split == 'train': data = data.shuffle(int(batch_size * 2.5)).repeat(-1) else: batch_size = 1 return data.batch( batch_size, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE) return _input_fn dataset_dict = { 'amass': amass, }

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Dataset implementations.""" from os import path import tensorflow.compat.v1 as tf tf.disable_eager_execution() def get_dataset(split, hparams): return dataset_dict[hparams.dataset](split, hparams) def amass(split, hparams): """Construct an AMASS data loader.""" def _input_fn(params): # pylint: disable=unused-argument # Dataset constants. n_bbox = 100000 n_surf = 100000 n_points = n_bbox + n_surf n_vert = 6890 n_frames = 1 # Parse parameters for global configurations. n_dims = hparams.n_dims data_dir = hparams.data_dir sample_bbox = hparams.sample_bbox sample_surf = hparams.sample_surf batch_size = hparams.batch_size subject = hparams.subject motion = hparams.motion n_parts = hparams.n_parts def _parse_tfrecord(serialized_example): fs = tf.parse_single_example( serialized_example, features={ 'point': tf.FixedLenFeature([n_frames * n_points * n_dims], tf.float32), 'label': tf.FixedLenFeature([n_frames * n_points * 1], tf.float32), 'vert': tf.FixedLenFeature([n_frames * n_vert * n_dims], tf.float32), 'weight': tf.FixedLenFeature([n_frames * n_vert * n_parts], tf.float32), 'transform': tf.FixedLenFeature( [n_frames * n_parts * (n_dims + 1) * (n_dims + 1)], tf.float32), 'joint': tf.FixedLenFeature([n_frames * n_parts * n_dims], tf.float32), 'name': tf.FixedLenFeature([], tf.string), }) fs['point'] = tf.reshape(fs['point'], [n_frames, n_points, n_dims]) fs['label'] = tf.reshape(fs['label'], [n_frames, n_points, 1]) fs['vert'] = tf.reshape(fs['vert'], [n_frames, n_vert, n_dims]) fs['weight'] = tf.reshape(fs['weight'], [n_frames, n_vert, n_parts]) fs['transform'] = tf.reshape(fs['transform'], [n_frames, n_parts, n_dims + 1, n_dims + 1]) fs['joint'] = tf.reshape(fs['joint'], [n_frames, n_parts, n_dims]) return fs def _sample_frame_points(fs): feature = {} for k, v in fs.items(): feature[k] = v points = feature['point'][0] labels = feature['label'][0] sample_points = [] sample_labels = [] if sample_bbox > 0: indices_bbox = tf.random.uniform([sample_bbox], minval=0, maxval=n_bbox, dtype=tf.int32) bbox_samples = tf.gather(points[:n_bbox], indices_bbox, axis=0) bbox_labels = tf.gather(labels[:n_bbox], indices_bbox, axis=0) sample_points.append(bbox_samples) sample_labels.append(bbox_labels) if sample_surf > 0: indices_surf = tf.random.uniform([sample_surf], minval=0, maxval=n_surf, dtype=tf.int32) surf_samples = tf.gather( points[n_bbox:n_bbox + n_surf], indices_surf, axis=0) surf_labels = tf.gather( labels[n_bbox:n_bbox + n_surf], indices_surf, axis=0) sample_points.append(surf_samples) sample_labels.append(surf_labels) points = tf.concat(sample_points, axis=0) point_labels = tf.concat(sample_labels, axis=0) feature['point'] = tf.expand_dims(points, axis=0) feature['label'] = tf.expand_dims(point_labels, axis=0) return feature def _sample_eval_points(fs): feature = {} feature['transform'] = fs['transform'] feature['points'] = fs['point'][:, :n_bbox] feature['labels'] = fs['label'][:, :n_bbox] feature['name'] = fs['name'] feature['vert'] = fs['vert'] feature['weight'] = fs['weight'] feature['joint'] = fs['joint'] return feature data_split = 'train' all_motions = list(x for x in range(10)) if split == 'train': file_pattern = [ path.join(data_dir, '{0}-{1:02d}-{2:02d}-*'.format(data_split, subject, x)) for x in all_motions if x != motion ] else: file_pattern = [ path.join(data_dir, '{0}-{1:02d}-{2:02d}-*'.format(data_split, subject, motion)) ] data_files = tf.gfile.Glob(file_pattern) if not data_files: raise IOError('{} did not match any files'.format(file_pattern)) filenames = tf.data.Dataset.list_files(file_pattern, shuffle=True) data = filenames.interleave( lambda x: tf.data.TFRecordDataset([x]), num_parallel_calls=tf.data.experimental.AUTOTUNE) data = data.map( _parse_tfrecord, num_parallel_calls=tf.data.experimental.AUTOTUNE).cache() if split == 'train': data = data.map( _sample_frame_points, num_parallel_calls=tf.data.experimental.AUTOTUNE) else: data = data.map( _sample_eval_points, num_parallel_calls=tf.data.experimental.AUTOTUNE) if split == 'train': data = data.shuffle(int(batch_size * 2.5)).repeat(-1) else: batch_size = 1 return data.batch( batch_size, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE) return _input_fn dataset_dict = { 'amass': amass, }

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./.git/hooks/pre-receive.sample

#!/bin/sh # # An example hook script to make use of push options. # The example simply echoes all push options that start with 'echoback=' # and rejects all pushes when the "reject" push option is used. # # To enable this hook, rename this file to "pre-receive". if test -n "$GIT_PUSH_OPTION_COUNT" then i=0 while test "$i" -lt "$GIT_PUSH_OPTION_COUNT" do eval "value=\$GIT_PUSH_OPTION_$i" case "$value" in echoback=*) echo "echo from the pre-receive-hook: ${value#*=}" >&2 ;; reject) exit 1 esac i=$((i + 1)) done fi

#!/bin/sh # # An example hook script to make use of push options. # The example simply echoes all push options that start with 'echoback=' # and rejects all pushes when the "reject" push option is used. # # To enable this hook, rename this file to "pre-receive". if test -n "$GIT_PUSH_OPTION_COUNT" then i=0 while test "$i" -lt "$GIT_PUSH_OPTION_COUNT" do eval "value=\$GIT_PUSH_OPTION_$i" case "$value" in echoback=*) echo "echo from the pre-receive-hook: ${value#*=}" >&2 ;; reject) exit 1 esac i=$((i + 1)) done fi

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./.github/workflows/tfg-nigthly-pypi.yml

# Publishes tfg-nightly name: Deploy tfg-nightly to pypi on: schedule: # * is a special character in YAML so you have to quote this string # runs daily at 00:30 am - cron: '30 0 * * *' jobs: deploy: if: github.repository == 'tensorflow/graphics' # prevents action from running on forks runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: 3.8 - name: Install system dependencies run: | sudo xargs apt-get update sudo xargs apt-get -y install < requirements.unix - name: Install pip requirements run: | python -m pip install --upgrade pip pip install -U -r requirements.txt pip install -U pytest pip install -U setuptools wheel pip install -U twine - name: Build ops run: | bazel build tensorflow_graphics/... --define=BASEDIR=$(pwd) --sandbox_writable_path=$(pwd) bazel clean --expunge - name: Run python tests env: MESA_GL_VERSION_OVERRIDE: 4.5 MESA_GLSL_VERSION_OVERRIDE: 450 run: | pytest tensorflow_graphics - name: Build pip package and install run: | python setup.py sdist bdist_wheel --nightly pip install dist/*.whl - name: Test install run: | cd $(mktemp -d) && python -c 'import tensorflow_graphics as tfg' - name: Publish to PyPi # https://pypi.org/project/tfg-nightly env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | twine upload dist/*

# Publishes tfg-nightly name: Deploy tfg-nightly to pypi on: schedule: # * is a special character in YAML so you have to quote this string # runs daily at 00:30 am - cron: '30 0 * * *' jobs: deploy: if: github.repository == 'tensorflow/graphics' # prevents action from running on forks runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: 3.8 - name: Install system dependencies run: | sudo xargs apt-get update sudo xargs apt-get -y install < requirements.unix - name: Install pip requirements run: | python -m pip install --upgrade pip pip install -U -r requirements.txt pip install -U pytest pip install -U setuptools wheel pip install -U twine - name: Build ops run: | bazel build tensorflow_graphics/... --define=BASEDIR=$(pwd) --sandbox_writable_path=$(pwd) bazel clean --expunge - name: Run python tests env: MESA_GL_VERSION_OVERRIDE: 4.5 MESA_GLSL_VERSION_OVERRIDE: 450 run: | pytest tensorflow_graphics - name: Build pip package and install run: | python setup.py sdist bdist_wheel --nightly pip install dist/*.whl - name: Test install run: | cd $(mktemp -d) && python -c 'import tensorflow_graphics as tfg' - name: Publish to PyPi # https://pypi.org/project/tfg-nightly env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | twine upload dist/*

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/shapenet/fakes/all.csv

000656,02691156,02690373,9550774ad1c19b24a5a118bd15e6e34f,train 000080,02691156,02690373,a98038807a61926abce962d6c4b37336,train 026597,03001627,04331277,a800bd725fe116447a84e76181a9e08f,train 006718,02691156,03335030,3d5354863690ac7eca27bba175814d1,test 003380,02691156,02690373,7eff60e0d72800b8ca8607f540cc62ba,val

000656,02691156,02690373,9550774ad1c19b24a5a118bd15e6e34f,train 000080,02691156,02690373,a98038807a61926abce962d6c4b37336,train 026597,03001627,04331277,a800bd725fe116447a84e76181a9e08f,train 006718,02691156,03335030,3d5354863690ac7eca27bba175814d1,test 003380,02691156,02690373,7eff60e0d72800b8ca8607f540cc62ba,val

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/egl_offscreen_context.cc

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "egl_offscreen_context.h" #include <EGL/egl.h> #include "egl_util.h" #include "macros.h" #include "cleanup.h" #include "tensorflow/core/lib/core/status.h" EGLOffscreenContext::EGLOffscreenContext(EGLContext context, EGLDisplay display, EGLSurface pixel_buffer_surface) : context_(context), display_(display), pixel_buffer_surface_(pixel_buffer_surface) {} EGLOffscreenContext::~EGLOffscreenContext() { TF_CHECK_OK(Destroy()); } tensorflow::Status EGLOffscreenContext::Create( std::unique_ptr<EGLOffscreenContext>* egl_offscreen_context) { constexpr std::array<int, 13> kDefaultConfigurationAttributes = { EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_RENDERABLE_TYPE, EGL_OPENGL_ES2_BIT, EGL_BLUE_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_RED_SIZE, 8, EGL_DEPTH_SIZE, 24, EGL_NONE // The array must be terminated with that value. }; constexpr std::array<int, 3> kDefaultContextAttributes = { EGL_CONTEXT_CLIENT_VERSION, 2, EGL_NONE, }; return Create(0, 0, EGL_OPENGL_API, kDefaultConfigurationAttributes.data(), kDefaultContextAttributes.data(), egl_offscreen_context); } tensorflow::Status EGLOffscreenContext::Create( const int pixel_buffer_width, const int pixel_buffer_height, const EGLenum rendering_api, const EGLint* configuration_attributes, const EGLint* context_attributes, std::unique_ptr<EGLOffscreenContext>* egl_offscreen_context) { // Create an EGL display at device index 0. EGLDisplay display; display = CreateInitializedEGLDisplay(); if (display == EGL_NO_DISPLAY) return TFG_INTERNAL_ERROR("EGL_NO_DISPLAY"); auto initialize_cleanup = MakeCleanup([display]() { TerminateInitializedEGLDisplay(display); }); // Set the current rendering API. EGLBoolean success; success = eglBindAPI(rendering_api); if (success == false) return TFG_INTERNAL_ERROR("eglBindAPI"); // Build a frame buffer configuration. EGLConfig frame_buffer_configuration; EGLint returned_num_configs; const EGLint kRequestedNumConfigs = 1; TFG_RETURN_IF_EGL_ERROR( success = eglChooseConfig(display, configuration_attributes, &frame_buffer_configuration, kRequestedNumConfigs, &returned_num_configs)); if (!success || returned_num_configs != kRequestedNumConfigs) return TFG_INTERNAL_ERROR("returned_num_configs != kRequestedNumConfigs"); // Create a pixel buffer surface. EGLint pixel_buffer_attributes[] = { EGL_WIDTH, pixel_buffer_width, EGL_HEIGHT, pixel_buffer_height, EGL_NONE, }; EGLSurface pixel_buffer_surface; TFG_RETURN_IF_EGL_ERROR( pixel_buffer_surface = eglCreatePbufferSurface( display, frame_buffer_configuration, pixel_buffer_attributes)); auto surface_cleanup = MakeCleanup([display, pixel_buffer_surface]() { eglDestroySurface(display, pixel_buffer_surface); }); // Create the EGL rendering context. EGLContext context; TFG_RETURN_IF_EGL_ERROR( context = eglCreateContext(display, frame_buffer_configuration, EGL_NO_CONTEXT, context_attributes)); if (context == EGL_NO_CONTEXT) return TFG_INTERNAL_ERROR("EGL_NO_CONTEXT"); initialize_cleanup.release(); surface_cleanup.release(); *egl_offscreen_context = std::unique_ptr<EGLOffscreenContext>( new EGLOffscreenContext(context, display, pixel_buffer_surface)); return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::Destroy() { TF_RETURN_IF_ERROR(Release()); if (eglDestroyContext(display_, context_) == false) { return TFG_INTERNAL_ERROR("an error occured in eglDestroyContext."); } if (eglDestroySurface(display_, pixel_buffer_surface_) == false) { return TFG_INTERNAL_ERROR("an error occured in eglDestroySurface."); } if (TerminateInitializedEGLDisplay(display_) == false) { return TFG_INTERNAL_ERROR( "an error occured in TerminateInitializedEGLDisplay."); } return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::MakeCurrent() const { TFG_RETURN_IF_EGL_ERROR(eglMakeCurrent(display_, pixel_buffer_surface_, pixel_buffer_surface_, context_)); return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::Release() { if (context_ != EGL_NO_CONTEXT && context_ == eglGetCurrentContext()) { TFG_RETURN_IF_EGL_ERROR(eglMakeCurrent(display_, EGL_NO_SURFACE, EGL_NO_SURFACE, EGL_NO_CONTEXT)); } return tensorflow::Status::OK(); }

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "egl_offscreen_context.h" #include <EGL/egl.h> #include "egl_util.h" #include "macros.h" #include "cleanup.h" #include "tensorflow/core/lib/core/status.h" EGLOffscreenContext::EGLOffscreenContext(EGLContext context, EGLDisplay display, EGLSurface pixel_buffer_surface) : context_(context), display_(display), pixel_buffer_surface_(pixel_buffer_surface) {} EGLOffscreenContext::~EGLOffscreenContext() { TF_CHECK_OK(Destroy()); } tensorflow::Status EGLOffscreenContext::Create( std::unique_ptr<EGLOffscreenContext>* egl_offscreen_context) { constexpr std::array<int, 13> kDefaultConfigurationAttributes = { EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_RENDERABLE_TYPE, EGL_OPENGL_ES2_BIT, EGL_BLUE_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_RED_SIZE, 8, EGL_DEPTH_SIZE, 24, EGL_NONE // The array must be terminated with that value. }; constexpr std::array<int, 3> kDefaultContextAttributes = { EGL_CONTEXT_CLIENT_VERSION, 2, EGL_NONE, }; return Create(0, 0, EGL_OPENGL_API, kDefaultConfigurationAttributes.data(), kDefaultContextAttributes.data(), egl_offscreen_context); } tensorflow::Status EGLOffscreenContext::Create( const int pixel_buffer_width, const int pixel_buffer_height, const EGLenum rendering_api, const EGLint* configuration_attributes, const EGLint* context_attributes, std::unique_ptr<EGLOffscreenContext>* egl_offscreen_context) { // Create an EGL display at device index 0. EGLDisplay display; display = CreateInitializedEGLDisplay(); if (display == EGL_NO_DISPLAY) return TFG_INTERNAL_ERROR("EGL_NO_DISPLAY"); auto initialize_cleanup = MakeCleanup([display]() { TerminateInitializedEGLDisplay(display); }); // Set the current rendering API. EGLBoolean success; success = eglBindAPI(rendering_api); if (success == false) return TFG_INTERNAL_ERROR("eglBindAPI"); // Build a frame buffer configuration. EGLConfig frame_buffer_configuration; EGLint returned_num_configs; const EGLint kRequestedNumConfigs = 1; TFG_RETURN_IF_EGL_ERROR( success = eglChooseConfig(display, configuration_attributes, &frame_buffer_configuration, kRequestedNumConfigs, &returned_num_configs)); if (!success || returned_num_configs != kRequestedNumConfigs) return TFG_INTERNAL_ERROR("returned_num_configs != kRequestedNumConfigs"); // Create a pixel buffer surface. EGLint pixel_buffer_attributes[] = { EGL_WIDTH, pixel_buffer_width, EGL_HEIGHT, pixel_buffer_height, EGL_NONE, }; EGLSurface pixel_buffer_surface; TFG_RETURN_IF_EGL_ERROR( pixel_buffer_surface = eglCreatePbufferSurface( display, frame_buffer_configuration, pixel_buffer_attributes)); auto surface_cleanup = MakeCleanup([display, pixel_buffer_surface]() { eglDestroySurface(display, pixel_buffer_surface); }); // Create the EGL rendering context. EGLContext context; TFG_RETURN_IF_EGL_ERROR( context = eglCreateContext(display, frame_buffer_configuration, EGL_NO_CONTEXT, context_attributes)); if (context == EGL_NO_CONTEXT) return TFG_INTERNAL_ERROR("EGL_NO_CONTEXT"); initialize_cleanup.release(); surface_cleanup.release(); *egl_offscreen_context = std::unique_ptr<EGLOffscreenContext>( new EGLOffscreenContext(context, display, pixel_buffer_surface)); return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::Destroy() { TF_RETURN_IF_ERROR(Release()); if (eglDestroyContext(display_, context_) == false) { return TFG_INTERNAL_ERROR("an error occured in eglDestroyContext."); } if (eglDestroySurface(display_, pixel_buffer_surface_) == false) { return TFG_INTERNAL_ERROR("an error occured in eglDestroySurface."); } if (TerminateInitializedEGLDisplay(display_) == false) { return TFG_INTERNAL_ERROR( "an error occured in TerminateInitializedEGLDisplay."); } return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::MakeCurrent() const { TFG_RETURN_IF_EGL_ERROR(eglMakeCurrent(display_, pixel_buffer_surface_, pixel_buffer_surface_, context_)); return tensorflow::Status::OK(); } tensorflow::Status EGLOffscreenContext::Release() { if (context_ != EGL_NO_CONTEXT && context_ == eglGetCurrentContext()) { TFG_RETURN_IF_EGL_ERROR(eglMakeCurrent(display_, EGL_NO_SURFACE, EGL_NO_SURFACE, EGL_NO_CONTEXT)); } return tensorflow::Status::OK(); }

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/tests/asserts_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for asserts.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import test_case def _pick_random_vector(): """Creates a random vector with a random shape.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() return np.random.normal(size=tensor_shape + [4]) class AssertsTest(test_case.TestCase): @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_normalized_exception_not_raised(self, dtype): """Checks that assert_normalized raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) norm_vector = vector / tf.norm(tensor=vector, axis=-1, keepdims=True) self.assert_exception_is_not_raised( asserts.assert_normalized, shapes=[], vector=norm_vector) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_normalized_exception_raised(self, dtype): """Checks that assert_normalized raises exceptions for invalid input.""" vector = _pick_random_vector() + 10.0 vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = tf.abs(vector) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_normalized(vector)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_normalized_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_normalized(vector_input) self.assertIs(vector_input, vector_output) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_at_least_k_non_zero_entries_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_at_least_k_non_zero_entries(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters( (None, None), (1e-3, tf.float16), (4e-19, tf.float32), (4e-154, tf.float64), ) def test_assert_nonzero_norm_exception_not_raised(self, value, dtype): """Checks that assert_nonzero_norm works for values above eps.""" if value is None: vector = _pick_random_vector() + 10.0 vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = tf.abs(vector) else: vector = tf.constant((value,), dtype=dtype) self.assert_exception_is_not_raised( asserts.assert_nonzero_norm, shapes=[], vector=vector) @parameterized.parameters( (1e-4, tf.float16), (1e-38, tf.float32), (1e-308, tf.float64), ) def test_assert_nonzero_norm_exception_raised(self, value, dtype): """Checks that assert_nonzero_norm fails for values below eps.""" vector = tf.constant((value,), dtype=dtype) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_nonzero_norm(vector)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_nonzero_norm_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_nonzero_norm(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_above_exception_not_raised(self, dtype): """Checks that assert_all_above raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= -tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector + eps ones_vector = -tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=inside_vector, minval=-1.0, open_bound=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=inside_vector, minval=-1.0, open_bound=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=ones_vector, minval=-1.0, open_bound=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_above_exception_raised(self, dtype): """Checks that assert_all_above raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= -tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector - eps ones_vector = -tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(outside_vector, -1.0, open_bound=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(outside_vector, -1.0, open_bound=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(ones_vector, -1.0, open_bound=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_above_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_above(vector_input, 1.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_below_exception_not_raised(self, dtype): """Checks that assert_all_below raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector - eps ones_vector = tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=inside_vector, maxval=1.0, open_bound=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=inside_vector, maxval=1.0, open_bound=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=ones_vector, maxval=1.0, open_bound=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_below_exception_raised(self, dtype): """Checks that assert_all_below raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector + eps ones_vector = tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(outside_vector, 1.0, open_bound=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(outside_vector, 1.0, open_bound=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(ones_vector, 1.0, open_bound=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_below_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_below(vector_input, 0.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_in_range_exception_not_raised(self, dtype): """Checks that assert_all_in_range raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector - eps ones_vector = tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=inside_vector, minval=-1.0, maxval=1.0, open_bounds=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=inside_vector, minval=-1.0, maxval=1.0, open_bounds=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=ones_vector, minval=-1.0, maxval=1.0, open_bounds=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_in_range_exception_raised(self, dtype): """Checks that assert_all_in_range raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector + eps ones_vector = tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( outside_vector, -1.0, 1.0, open_bounds=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( outside_vector, -1.0, 1.0, open_bounds=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( ones_vector, -1.0, 1.0, open_bounds=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_in_range_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_in_range(vector_input, -1.0, 1.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_select_eps_for_division(self, dtype): """Checks that select_eps_for_division does not cause Inf values.""" a = tf.constant(1.0, dtype=dtype) eps = asserts.select_eps_for_division(dtype) self.assert_exception_is_not_raised( asserts.assert_no_infs_or_nans, shapes=[], tensor=a / eps) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_select_eps_for_addition(self, dtype): """Checks that select_eps_for_addition returns large enough eps.""" a = tf.constant(1.0, dtype=dtype) eps = asserts.select_eps_for_addition(dtype) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(tf.compat.v1.assert_equal(a, a + eps)) @parameterized.parameters((np.NaN,), (np.inf,)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_no_infs_or_nans_passthrough(self, value): """Checks that the assert is a passthrough when the flag is False.""" vector_input = (value,) vector_output = asserts.assert_no_infs_or_nans(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters((np.NaN,), (np.inf,)) def test_assert_no_infs_or_nans_raises_exception_for_nan(self, value): """Checks that the assert works for `Inf` or `NaN` values.""" vector_input = (value,) with self.assertRaisesRegex( # pylint: disable=g-error-prone-assert-raises tf.errors.InvalidArgumentError, "Inf or NaN detected."): self.evaluate(asserts.assert_no_infs_or_nans(vector_input)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_binary_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_binary(vector_input) self.assertIs(vector_input, vector_output) # pylint: disable=g-error-prone-assert-raises @parameterized.parameters(tf.float16, tf.float32, tf.float64, tf.int16, tf.int32, tf.int64) def test_assert_binary_exception_raised(self, dtype): """Checks that assert_binary raises exceptions for invalid input.""" tensor_size = np.random.randint(3) + 1 tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() num_elements = np.prod(tensor_shape) # Vector with all ones except for a single negative entry. vector_with_negative = np.ones(num_elements) vector_with_negative[np.random.randint(num_elements)] = -1 vector_with_negative = vector_with_negative.reshape(tensor_shape) vector_with_negative = tf.convert_to_tensor( value=vector_with_negative, dtype=dtype) # Vector with all zeros except for a single 0.5 (or 2 in case dtype=int). vector = np.zeros(num_elements) vector[np.random.randint(num_elements)] = 2 vector = vector.reshape(tensor_shape) vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector - tf.compat.v1.div(vector, 4) * 3 with self.subTest(name="has_negative_number"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_binary(vector_with_negative)) with self.subTest(name="has_non_binary_number"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_binary(vector)) @parameterized.parameters(tf.float16, tf.float32, tf.float64, tf.int16, tf.int32, tf.int64) def test_assert_binary_exception_not_raised(self, dtype): """Checks that assert_binary raises no exceptions for valid input.""" tensor_size = np.random.randint(3) + 1 tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() # Vector with random zeros and ones. vector = np.random.randint(2, size=tensor_shape) vector = tf.convert_to_tensor(value=vector, dtype=dtype) self.assert_exception_is_not_raised( asserts.assert_binary, shapes=[], tensor=vector) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for asserts.""" from absl.testing import flagsaver from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.util import asserts from tensorflow_graphics.util import test_case def _pick_random_vector(): """Creates a random vector with a random shape.""" tensor_size = np.random.randint(3) tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() return np.random.normal(size=tensor_shape + [4]) class AssertsTest(test_case.TestCase): @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_normalized_exception_not_raised(self, dtype): """Checks that assert_normalized raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) norm_vector = vector / tf.norm(tensor=vector, axis=-1, keepdims=True) self.assert_exception_is_not_raised( asserts.assert_normalized, shapes=[], vector=norm_vector) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_normalized_exception_raised(self, dtype): """Checks that assert_normalized raises exceptions for invalid input.""" vector = _pick_random_vector() + 10.0 vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = tf.abs(vector) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_normalized(vector)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_normalized_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_normalized(vector_input) self.assertIs(vector_input, vector_output) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_at_least_k_non_zero_entries_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_at_least_k_non_zero_entries(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters( (None, None), (1e-3, tf.float16), (4e-19, tf.float32), (4e-154, tf.float64), ) def test_assert_nonzero_norm_exception_not_raised(self, value, dtype): """Checks that assert_nonzero_norm works for values above eps.""" if value is None: vector = _pick_random_vector() + 10.0 vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = tf.abs(vector) else: vector = tf.constant((value,), dtype=dtype) self.assert_exception_is_not_raised( asserts.assert_nonzero_norm, shapes=[], vector=vector) @parameterized.parameters( (1e-4, tf.float16), (1e-38, tf.float32), (1e-308, tf.float64), ) def test_assert_nonzero_norm_exception_raised(self, value, dtype): """Checks that assert_nonzero_norm fails for values below eps.""" vector = tf.constant((value,), dtype=dtype) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_nonzero_norm(vector)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_nonzero_norm_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_nonzero_norm(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_above_exception_not_raised(self, dtype): """Checks that assert_all_above raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= -tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector + eps ones_vector = -tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=inside_vector, minval=-1.0, open_bound=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=inside_vector, minval=-1.0, open_bound=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_above, shapes=[], vector=ones_vector, minval=-1.0, open_bound=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_above_exception_raised(self, dtype): """Checks that assert_all_above raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= -tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector - eps ones_vector = -tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(outside_vector, -1.0, open_bound=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(outside_vector, -1.0, open_bound=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_above(ones_vector, -1.0, open_bound=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_above_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_above(vector_input, 1.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_below_exception_not_raised(self, dtype): """Checks that assert_all_below raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector - eps ones_vector = tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=inside_vector, maxval=1.0, open_bound=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=inside_vector, maxval=1.0, open_bound=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_below, shapes=[], vector=ones_vector, maxval=1.0, open_bound=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_below_exception_raised(self, dtype): """Checks that assert_all_below raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector + eps ones_vector = tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(outside_vector, 1.0, open_bound=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(outside_vector, 1.0, open_bound=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_below(ones_vector, 1.0, open_bound=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_below_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_below(vector_input, 0.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_in_range_exception_not_raised(self, dtype): """Checks that assert_all_in_range raises no exceptions for valid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) inside_vector = vector - eps ones_vector = tf.ones_like(vector) with self.subTest(name="inside_and_open_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=inside_vector, minval=-1.0, maxval=1.0, open_bounds=True) with self.subTest(name="inside_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=inside_vector, minval=-1.0, maxval=1.0, open_bounds=False) with self.subTest(name="exact_and_close_bounds"): self.assert_exception_is_not_raised( asserts.assert_all_in_range, shapes=[], vector=ones_vector, minval=-1.0, maxval=1.0, open_bounds=False) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_assert_all_in_range_exception_raised(self, dtype): """Checks that assert_all_in_range raises exceptions for invalid input.""" vector = _pick_random_vector() vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector * vector vector /= tf.reduce_max(input_tensor=vector, axis=-1, keepdims=True) eps = asserts.select_eps_for_addition(dtype) outside_vector = vector + eps ones_vector = tf.ones_like(vector) with self.subTest(name="outside_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( outside_vector, -1.0, 1.0, open_bounds=True)) with self.subTest(name="outside_and_close_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( outside_vector, -1.0, 1.0, open_bounds=False)) with self.subTest(name="exact_and_open_bounds"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate( asserts.assert_all_in_range( ones_vector, -1.0, 1.0, open_bounds=True)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_all_in_range_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_all_in_range(vector_input, -1.0, 1.0) self.assertIs(vector_input, vector_output) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_select_eps_for_division(self, dtype): """Checks that select_eps_for_division does not cause Inf values.""" a = tf.constant(1.0, dtype=dtype) eps = asserts.select_eps_for_division(dtype) self.assert_exception_is_not_raised( asserts.assert_no_infs_or_nans, shapes=[], tensor=a / eps) @parameterized.parameters(tf.float16, tf.float32, tf.float64) def test_select_eps_for_addition(self, dtype): """Checks that select_eps_for_addition returns large enough eps.""" a = tf.constant(1.0, dtype=dtype) eps = asserts.select_eps_for_addition(dtype) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(tf.compat.v1.assert_equal(a, a + eps)) @parameterized.parameters((np.NaN,), (np.inf,)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_no_infs_or_nans_passthrough(self, value): """Checks that the assert is a passthrough when the flag is False.""" vector_input = (value,) vector_output = asserts.assert_no_infs_or_nans(vector_input) self.assertIs(vector_input, vector_output) @parameterized.parameters((np.NaN,), (np.inf,)) def test_assert_no_infs_or_nans_raises_exception_for_nan(self, value): """Checks that the assert works for `Inf` or `NaN` values.""" vector_input = (value,) with self.assertRaisesRegex( # pylint: disable=g-error-prone-assert-raises tf.errors.InvalidArgumentError, "Inf or NaN detected."): self.evaluate(asserts.assert_no_infs_or_nans(vector_input)) @flagsaver.flagsaver(tfg_add_asserts_to_graph=False) def test_assert_binary_passthrough(self): """Checks that the assert is a passthrough when the flag is False.""" vector_input = _pick_random_vector() vector_output = asserts.assert_binary(vector_input) self.assertIs(vector_input, vector_output) # pylint: disable=g-error-prone-assert-raises @parameterized.parameters(tf.float16, tf.float32, tf.float64, tf.int16, tf.int32, tf.int64) def test_assert_binary_exception_raised(self, dtype): """Checks that assert_binary raises exceptions for invalid input.""" tensor_size = np.random.randint(3) + 1 tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() num_elements = np.prod(tensor_shape) # Vector with all ones except for a single negative entry. vector_with_negative = np.ones(num_elements) vector_with_negative[np.random.randint(num_elements)] = -1 vector_with_negative = vector_with_negative.reshape(tensor_shape) vector_with_negative = tf.convert_to_tensor( value=vector_with_negative, dtype=dtype) # Vector with all zeros except for a single 0.5 (or 2 in case dtype=int). vector = np.zeros(num_elements) vector[np.random.randint(num_elements)] = 2 vector = vector.reshape(tensor_shape) vector = tf.convert_to_tensor(value=vector, dtype=dtype) vector = vector - tf.compat.v1.div(vector, 4) * 3 with self.subTest(name="has_negative_number"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_binary(vector_with_negative)) with self.subTest(name="has_non_binary_number"): with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(asserts.assert_binary(vector)) @parameterized.parameters(tf.float16, tf.float32, tf.float64, tf.int16, tf.int32, tf.int64) def test_assert_binary_exception_not_raised(self, dtype): """Checks that assert_binary raises no exceptions for valid input.""" tensor_size = np.random.randint(3) + 1 tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist() # Vector with random zeros and ones. vector = np.random.randint(2, size=tensor_shape) vector = tf.convert_to_tensor(value=vector, dtype=dtype) self.assert_exception_is_not_raised( asserts.assert_binary, shapes=[], tensor=vector) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/modelnet40_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Tests the ModelNet40 dataset with fake data.""" import os import tensorflow_datasets as tfds from tensorflow_graphics.datasets import modelnet40 class ModelNet40Test(tfds.testing.DatasetBuilderTestCase): """Tests the ModelNet40 dataset with fake data.""" DATASET_CLASS = modelnet40.ModelNet40 SPLITS = { "train": 24, # Number of fake train example "test": 16, # Number of fake test example } # If you are calling `download/download_and_extract` with a dict, like: # dl_manager.download({'some_key': 'http://a.org/out.txt', ...}) # then the tests needs to provide the fake output paths relative to the # fake data directory DL_EXTRACT_RESULT = "" EXAMPLE_DIR = os.path.join(os.path.dirname(__file__), "fakes") # SKIP_CHECKSUMS = True if __name__ == "__main__": tfds.testing.test_main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Tests the ModelNet40 dataset with fake data.""" import os import tensorflow_datasets as tfds from tensorflow_graphics.datasets import modelnet40 class ModelNet40Test(tfds.testing.DatasetBuilderTestCase): """Tests the ModelNet40 dataset with fake data.""" DATASET_CLASS = modelnet40.ModelNet40 SPLITS = { "train": 24, # Number of fake train example "test": 16, # Number of fake test example } # If you are calling `download/download_and_extract` with a dict, like: # dl_manager.download({'some_key': 'http://a.org/out.txt', ...}) # then the tests needs to provide the fake output paths relative to the # fake data directory DL_EXTRACT_RESULT = "" EXAMPLE_DIR = os.path.join(os.path.dirname(__file__), "fakes") # SKIP_CHECKSUMS = True if __name__ == "__main__": tfds.testing.test_main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./.pylintrc

[MASTER] # Specify a configuration file. #rcfile= # Python code to execute, usually for sys.path manipulation such as # pygtk.require(). #init-hook= # Profiled execution. profile=no # Add files or directories to the blacklist. They should be base names, not # paths. ignore=CVS, __pycache__, .git, .tox, .pytest_cache, tensorflow_graphics/projects/* # Pickle collected data for later comparisons. persistent=yes # Use multiple processes to speed up Pylint. jobs=4 # List of plugins (as comma separated values of python modules names) to load, # usually to register additional checkers. load-plugins= [MESSAGES CONTROL] # Enable the message, report, category or checker with the given id(s). You can # either give multiple identifier separated by comma (,) or put this option # multiple time. See also the "--disable" option for examples. enable=indexing-exception,old-raise-syntax # Disable the message, report, category or checker with the given id(s). You # can either give multiple identifiers separated by comma (,) or put this # option multiple times (only on the command line, not in the configuration # file where it should appear only once).You can also use "--disable=all" to # disable everything first and then reenable specific checks. For example, if # you want to run only the similarities checker, you can use "--disable=all # --enable=similarities". If you want to run only the classes checker, but have # no Warning level messages displayed, use"--disable=all --enable=classes # --disable=W" disable=design, similarities, no-self-use, attribute-defined-outside-init, locally-disabled, star-args, pointless-except, bad-option-value, global-statement, fixme, suppressed-message, useless-suppression, locally-enabled, no-member, no-name-in-module, import-error, unsubscriptable-object, unbalanced-tuple-unpacking, undefined-variable, not-context-manager, E1130, # (TFG) Invalid-unary-operand-type for Numpy array R1705, # (TFG) Unnecessary "else" after "return" (no-else-return) R1720, # (TFG) Unnecessary "else" after "raise" (no-else-raise) R1721, # (TFG) Unnecessary use of a comprehension (unnecessary-comprehension) # Set the cache size for astng objects. cache-size=500 [REPORTS] # Set the output format. Available formats are text, parseable, colorized, msvs # (visual studio) and html. You can also give a reporter class, eg # mypackage.mymodule.MyReporterClass. output-format=text # Put messages in a separate file for each module / package specified on the # command line instead of printing them on stdout. Reports (if any) will be # written in a file name "pylint_global.[txt|html]". files-output=no # Tells whether to display a full report or only the messages reports=no # Python expression which should return a note less than 10 (10 is the highest # note). You have access to the variables errors warning, statement which # respectively contain the number of errors / warnings messages and the total # number of statements analyzed. This is used by the global evaluation report # (RP0004). evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) # Add a comment according to your evaluation note. This is used by the global # evaluation report (RP0004). comment=no # Template used to display messages. This is a python new-style format string # used to format the message information. See doc for all details #msg-template= [TYPECHECK] # Tells whether missing members accessed in mixin class should be ignored. A # mixin class is detected if its name ends with "mixin" (case insensitive). ignore-mixin-members=yes # List of classes names for which member attributes should not be checked # (useful for classes with attributes dynamically set). ignored-classes=SQLObject # When zope mode is activated, add a predefined set of Zope acquired attributes # to generated-members. zope=no # List of members which are set dynamically and missed by pylint inference # system, and so shouldn't trigger E0201 when accessed. Python regular # expressions are accepted. generated-members=REQUEST,acl_users,aq_parent # List of decorators that create context managers from functions, such as # contextlib.contextmanager. contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager [VARIABLES] # Tells whether we should check for unused import in __init__ files. init-import=no # A regular expression matching the beginning of the name of dummy variables # (i.e. not used). dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) # List of additional names supposed to be defined in builtins. 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[MASTER] # Specify a configuration file. #rcfile= # Python code to execute, usually for sys.path manipulation such as # pygtk.require(). #init-hook= # Profiled execution. profile=no # Add files or directories to the blacklist. They should be base names, not # paths. ignore=CVS, __pycache__, .git, .tox, .pytest_cache, tensorflow_graphics/projects/* # Pickle collected data for later comparisons. persistent=yes # Use multiple processes to speed up Pylint. jobs=4 # List of plugins (as comma separated values of python modules names) to load, # usually to register additional checkers. load-plugins= [MESSAGES CONTROL] # Enable the message, report, category or checker with the given id(s). You can # either give multiple identifier separated by comma (,) or put this option # multiple time. See also the "--disable" option for examples. enable=indexing-exception,old-raise-syntax # Disable the message, report, category or checker with the given id(s). 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This is used for # instance to not check methods defines in Zope's Interface base class. ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by # List of method names used to declare (i.e. assign) instance attributes. defining-attr-methods=__init__,__new__,setUp # List of valid names for the first argument in a class method. valid-classmethod-first-arg=cls,class_ # List of valid names for the first argument in a metaclass class method. valid-metaclass-classmethod-first-arg=mcs [DESIGN] # Maximum number of arguments for function / method max-args=5 # Argument names that match this expression will be ignored. 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Defaults to # "Exception" overgeneral-exceptions=Exception,StandardError,BaseException [AST] # Maximum line length for lambdas short-func-length=1 # List of module members that should be marked as deprecated. # All of the string functions are listed in 4.1.4 Deprecated string functions # in the Python 2.4 docs. deprecated-members=string.atof,string.atoi,string.atol,string.capitalize,string.expandtabs,string.find,string.rfind,string.index,string.rindex,string.count,string.lower,string.split,string.rsplit,string.splitfields,string.join,string.joinfields,string.lstrip,string.rstrip,string.strip,string.swapcase,string.translate,string.upper,string.ljust,string.rjust,string.center,string.zfill,string.replace,sys.exitfunc [DOCSTRING] # List of exceptions that do not need to be mentioned in the Raises section of # a docstring. ignore-exceptions=AssertionError,NotImplementedError,StopIteration,TypeError [TOKENS] # Number of spaces of indent required when the last token on the preceding line # is an open (, [, or {. indent-after-paren=4 [GOOGLE LINES] # Regexp for a proper copyright notice. copyright=Copyright \d{4} The TensorFlow Authors\. +All [Rr]ights [Rr]eserved\.

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/projects/local_implicit_grid/core/postprocess.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Postprocess to remove interior backface from reconstruction artifact. """ import numpy as np from scipy import sparse from scipy import spatial import trimesh def merge_meshes(mesh_list): """Merge a list of individual meshes into a single mesh.""" verts = [] faces = [] nv = 0 for m in mesh_list: verts.append(m.vertices) faces.append(m.faces + nv) nv += m.vertices.shape[0] v = np.concatenate(verts, axis=0) f = np.concatenate(faces, axis=0) merged_mesh = trimesh.Trimesh(v, f) return merged_mesh def average_onto_vertex(mesh, per_face_attrib): """Average per-face attribute onto vertices.""" assert per_face_attrib.shape[0] == mesh.faces.shape[0] assert len(per_face_attrib.shape) == 1 c = np.concatenate([[0], per_face_attrib], axis=0) v2f_orig = mesh.vertex_faces.copy() v2f = v2f_orig.copy() v2f += 1 per_vert_sum = np.sum(c[v2f], axis=1) per_vert_count = np.sum(np.logical_not(v2f == 0), axis=1) per_vert_attrib = per_vert_sum / per_vert_count return per_vert_attrib def average_onto_face(mesh, per_vert_attrib): """Average per-vert attribute onto faces.""" assert per_vert_attrib.shape[0] == mesh.vertices.shape[0] assert len(per_vert_attrib.shape) == 1 per_face_attrib = per_vert_attrib[mesh.faces] per_face_attrib = np.mean(per_face_attrib, axis=1) return per_face_attrib def remove_backface(mesh, pc, k=3, lap_iter=50, lap_val=0.50, area_threshold=1, verbose=False): """Remove the interior backface resulting from reconstruction artifacts. Args: mesh: trimesh instance. mesh recon. from lig that may contain backface. pc: np.array of shape [n, 6], original input point cloud. k: int, number of nearest neighbor for pooling sign. lap_iter: int, number of laplacian smoothing iterations. lap_val: float, lambda value for laplacian smoothing of cosine distance. area_threshold: float, minimum area connected components to preserve. verbose: bool, verbose print. Returns: mesh_new: trimesh instance. new mesh with backface removed. """ mesh.remove_degenerate_faces() v, n = pc[:, :3], pc[:, 3:] # build cKDTree to accelerate nearest point search if verbose: print("Building KDTree...") tree_pc = spatial.cKDTree(data=v) # for each vertex, find nearest point in input point cloud if verbose: print("{}-nearest neighbor search...".format(k)) _, idx = tree_pc.query(mesh.vertices, k=k, n_jobs=-1) # slice out the nn points n_nn = n[idx] # shape: [#v_query, k, dim] # find dot products. if verbose: print("Computing norm alignment...") n_v = mesh.vertex_normals[:, None, :] # shape: [#v_query, 1, dim] per_vert_norm_alignment = np.sum(n_nn * n_v, axis=-1) # [#v_query, k] per_vert_norm_alignment = np.mean(per_vert_norm_alignment, axis=-1) # laplacian smoothing of per vertex normal alignment if verbose: print("Computing laplacian smoothing...") lap = trimesh.smoothing.laplacian_calculation(mesh) dlap = lap.shape[0] op = sparse.eye(dlap) + lap_val * (lap - sparse.eye(dlap)) for _ in range(lap_iter): per_vert_norm_alignment = op.dot(per_vert_norm_alignment) # average onto face per_face_norm_alignment = average_onto_face(mesh, per_vert_norm_alignment) # remove faces with per_face_norm_alignment < 0 if verbose: print("Removing backfaces...") ff = mesh.faces[per_face_norm_alignment > -0.75] mesh_new = trimesh.Trimesh(mesh.vertices, ff) mesh_new.remove_unreferenced_vertices() if verbose: print("Cleaning up...") mesh_list = mesh_new.split(only_watertight=False) # filter out small floating junk from backface areas = [m.area for m in mesh_list] threshold = min(np.max(areas)/5, area_threshold) mesh_list = [m for m in mesh_list if m.area > threshold] mesh_new = merge_meshes(mesh_list) # fill small holes mesh_new.fill_holes() return mesh_new

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """Postprocess to remove interior backface from reconstruction artifact. """ import numpy as np from scipy import sparse from scipy import spatial import trimesh def merge_meshes(mesh_list): """Merge a list of individual meshes into a single mesh.""" verts = [] faces = [] nv = 0 for m in mesh_list: verts.append(m.vertices) faces.append(m.faces + nv) nv += m.vertices.shape[0] v = np.concatenate(verts, axis=0) f = np.concatenate(faces, axis=0) merged_mesh = trimesh.Trimesh(v, f) return merged_mesh def average_onto_vertex(mesh, per_face_attrib): """Average per-face attribute onto vertices.""" assert per_face_attrib.shape[0] == mesh.faces.shape[0] assert len(per_face_attrib.shape) == 1 c = np.concatenate([[0], per_face_attrib], axis=0) v2f_orig = mesh.vertex_faces.copy() v2f = v2f_orig.copy() v2f += 1 per_vert_sum = np.sum(c[v2f], axis=1) per_vert_count = np.sum(np.logical_not(v2f == 0), axis=1) per_vert_attrib = per_vert_sum / per_vert_count return per_vert_attrib def average_onto_face(mesh, per_vert_attrib): """Average per-vert attribute onto faces.""" assert per_vert_attrib.shape[0] == mesh.vertices.shape[0] assert len(per_vert_attrib.shape) == 1 per_face_attrib = per_vert_attrib[mesh.faces] per_face_attrib = np.mean(per_face_attrib, axis=1) return per_face_attrib def remove_backface(mesh, pc, k=3, lap_iter=50, lap_val=0.50, area_threshold=1, verbose=False): """Remove the interior backface resulting from reconstruction artifacts. Args: mesh: trimesh instance. mesh recon. from lig that may contain backface. pc: np.array of shape [n, 6], original input point cloud. k: int, number of nearest neighbor for pooling sign. lap_iter: int, number of laplacian smoothing iterations. lap_val: float, lambda value for laplacian smoothing of cosine distance. area_threshold: float, minimum area connected components to preserve. verbose: bool, verbose print. Returns: mesh_new: trimesh instance. new mesh with backface removed. """ mesh.remove_degenerate_faces() v, n = pc[:, :3], pc[:, 3:] # build cKDTree to accelerate nearest point search if verbose: print("Building KDTree...") tree_pc = spatial.cKDTree(data=v) # for each vertex, find nearest point in input point cloud if verbose: print("{}-nearest neighbor search...".format(k)) _, idx = tree_pc.query(mesh.vertices, k=k, n_jobs=-1) # slice out the nn points n_nn = n[idx] # shape: [#v_query, k, dim] # find dot products. if verbose: print("Computing norm alignment...") n_v = mesh.vertex_normals[:, None, :] # shape: [#v_query, 1, dim] per_vert_norm_alignment = np.sum(n_nn * n_v, axis=-1) # [#v_query, k] per_vert_norm_alignment = np.mean(per_vert_norm_alignment, axis=-1) # laplacian smoothing of per vertex normal alignment if verbose: print("Computing laplacian smoothing...") lap = trimesh.smoothing.laplacian_calculation(mesh) dlap = lap.shape[0] op = sparse.eye(dlap) + lap_val * (lap - sparse.eye(dlap)) for _ in range(lap_iter): per_vert_norm_alignment = op.dot(per_vert_norm_alignment) # average onto face per_face_norm_alignment = average_onto_face(mesh, per_vert_norm_alignment) # remove faces with per_face_norm_alignment < 0 if verbose: print("Removing backfaces...") ff = mesh.faces[per_face_norm_alignment > -0.75] mesh_new = trimesh.Trimesh(mesh.vertices, ff) mesh_new.remove_unreferenced_vertices() if verbose: print("Cleaning up...") mesh_list = mesh_new.split(only_watertight=False) # filter out small floating junk from backface areas = [m.area for m in mesh_list] threshold = min(np.max(areas)/5, area_threshold) mesh_list = [m for m in mesh_list if m.area > threshold] mesh_new = merge_meshes(mesh_list) # fill small holes mesh_new.fill_holes() return mesh_new

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/transformation/tests/test_data.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Module with test data for transformation tests.""" import numpy as np ANGLE_0 = np.array((0.,)) ANGLE_45 = np.array((np.pi / 4.,)) ANGLE_90 = np.array((np.pi / 2.,)) ANGLE_180 = np.array((np.pi,)) AXIS_2D_0 = np.array((0., 0.)) AXIS_2D_X = np.array((1., 0.)) AXIS_2D_Y = np.array((0., 1.)) def _rotation_2d_x(angle): """Creates a 2d rotation matrix. Args: angle: The angle. Returns: The 2d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), -np.sin(angle)), (np.sin(angle), np.cos(angle)))) # pyformat: disable MAT_2D_ID = np.eye(2) MAT_2D_45 = _rotation_2d_x(ANGLE_45) MAT_2D_90 = _rotation_2d_x(ANGLE_90) MAT_2D_180 = _rotation_2d_x(ANGLE_180) AXIS_3D_0 = np.array((0., 0., 0.)) AXIS_3D_X = np.array((1., 0., 0.)) AXIS_3D_Y = np.array((0., 1., 0.)) AXIS_3D_Z = np.array((0., 0., 1.)) def _axis_angle_to_quaternion(axis, angle): """Converts an axis-angle representation to a quaternion. Args: axis: The axis of rotation. angle: The angle. Returns: The quaternion. """ quat = np.zeros(4) quat[0:3] = axis * np.sin(0.5 * angle) quat[3] = np.cos(0.5 * angle) return quat QUAT_ID = _axis_angle_to_quaternion(AXIS_3D_0, ANGLE_0) QUAT_X_45 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_45) QUAT_X_90 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_90) QUAT_X_180 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_180) QUAT_Y_45 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_45) QUAT_Y_90 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_90) QUAT_Y_180 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_180) QUAT_Z_45 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_45) QUAT_Z_90 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_90) QUAT_Z_180 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_180) def _rotation_3d_x(angle): """Creates a 3d rotation matrix around the x axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((1., 0., 0.), (0., np.cos(angle), -np.sin(angle)), (0., np.sin(angle), np.cos(angle)))) # pyformat: disable def _rotation_3d_y(angle): """Creates a 3d rotation matrix around the y axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), 0., np.sin(angle)), (0., 1., 0.), (-np.sin(angle), 0., np.cos(angle)))) # pyformat: disable def _rotation_3d_z(angle): """Creates a 3d rotation matrix around the z axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), -np.sin(angle), 0.), (np.sin(angle), np.cos(angle), 0.), (0., 0., 1.))) # pyformat: disable MAT_3D_ID = np.eye(3) MAT_3D_X_45 = _rotation_3d_x(ANGLE_45) MAT_3D_X_90 = _rotation_3d_x(ANGLE_90) MAT_3D_X_180 = _rotation_3d_x(ANGLE_180) MAT_3D_Y_45 = _rotation_3d_y(ANGLE_45) MAT_3D_Y_90 = _rotation_3d_y(ANGLE_90) MAT_3D_Y_180 = _rotation_3d_y(ANGLE_180) MAT_3D_Z_45 = _rotation_3d_z(ANGLE_45) MAT_3D_Z_90 = _rotation_3d_z(ANGLE_90) MAT_3D_Z_180 = _rotation_3d_z(ANGLE_180)

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Module with test data for transformation tests.""" import numpy as np ANGLE_0 = np.array((0.,)) ANGLE_45 = np.array((np.pi / 4.,)) ANGLE_90 = np.array((np.pi / 2.,)) ANGLE_180 = np.array((np.pi,)) AXIS_2D_0 = np.array((0., 0.)) AXIS_2D_X = np.array((1., 0.)) AXIS_2D_Y = np.array((0., 1.)) def _rotation_2d_x(angle): """Creates a 2d rotation matrix. Args: angle: The angle. Returns: The 2d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), -np.sin(angle)), (np.sin(angle), np.cos(angle)))) # pyformat: disable MAT_2D_ID = np.eye(2) MAT_2D_45 = _rotation_2d_x(ANGLE_45) MAT_2D_90 = _rotation_2d_x(ANGLE_90) MAT_2D_180 = _rotation_2d_x(ANGLE_180) AXIS_3D_0 = np.array((0., 0., 0.)) AXIS_3D_X = np.array((1., 0., 0.)) AXIS_3D_Y = np.array((0., 1., 0.)) AXIS_3D_Z = np.array((0., 0., 1.)) def _axis_angle_to_quaternion(axis, angle): """Converts an axis-angle representation to a quaternion. Args: axis: The axis of rotation. angle: The angle. Returns: The quaternion. """ quat = np.zeros(4) quat[0:3] = axis * np.sin(0.5 * angle) quat[3] = np.cos(0.5 * angle) return quat QUAT_ID = _axis_angle_to_quaternion(AXIS_3D_0, ANGLE_0) QUAT_X_45 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_45) QUAT_X_90 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_90) QUAT_X_180 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_180) QUAT_Y_45 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_45) QUAT_Y_90 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_90) QUAT_Y_180 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_180) QUAT_Z_45 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_45) QUAT_Z_90 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_90) QUAT_Z_180 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_180) def _rotation_3d_x(angle): """Creates a 3d rotation matrix around the x axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((1., 0., 0.), (0., np.cos(angle), -np.sin(angle)), (0., np.sin(angle), np.cos(angle)))) # pyformat: disable def _rotation_3d_y(angle): """Creates a 3d rotation matrix around the y axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), 0., np.sin(angle)), (0., 1., 0.), (-np.sin(angle), 0., np.cos(angle)))) # pyformat: disable def _rotation_3d_z(angle): """Creates a 3d rotation matrix around the z axis. Args: angle: The angle. Returns: The 3d rotation matrix. """ angle = angle.item() return np.array(((np.cos(angle), -np.sin(angle), 0.), (np.sin(angle), np.cos(angle), 0.), (0., 0., 1.))) # pyformat: disable MAT_3D_ID = np.eye(3) MAT_3D_X_45 = _rotation_3d_x(ANGLE_45) MAT_3D_X_90 = _rotation_3d_x(ANGLE_90) MAT_3D_X_180 = _rotation_3d_x(ANGLE_180) MAT_3D_Y_45 = _rotation_3d_y(ANGLE_45) MAT_3D_Y_90 = _rotation_3d_y(ANGLE_90) MAT_3D_Y_180 = _rotation_3d_y(ANGLE_180) MAT_3D_Z_45 = _rotation_3d_z(ANGLE_45) MAT_3D_Z_90 = _rotation_3d_z(ANGLE_90) MAT_3D_Z_180 = _rotation_3d_z(ANGLE_180)

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./requirements.unix

libopenexr-dev libgles2-mesa-dev libc-ares-dev

libopenexr-dev libgles2-mesa-dev libc-ares-dev

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/opensource_only.files

tensorflow_graphics/rendering/opengl/BUILD

tensorflow_graphics/rendering/opengl/BUILD

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/math/optimizer/__init__.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Optimizer module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.math.optimizer import levenberg_marquardt from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.math. __all__ = _export_api.get_modules()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Optimizer module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow_graphics.math.optimizer import levenberg_marquardt from tensorflow_graphics.util import export_api as _export_api # API contains submodules of tensorflow_graphics.math. __all__ = _export_api.get_modules()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/nn/metric/tests/precision_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the precision metric.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.metric import precision from tensorflow_graphics.util import test_case def random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def random_tensor_shape(): tensor_size = np.random.randint(5) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() class PrecisionTest(test_case.TestCase): @parameterized.parameters( # Precision = 0.5. ((0, 1, 1, 1, 1), (1, 1, 0, 0, 0), 0.5), # Precision = 1. ((0, 0, 0, 1, 1, 1, 0, 1), (0, 0, 0, 1, 1, 1, 0, 1), 1), # All-0 predictions, returns 0. ((0, 1, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0), 0), # All-0 ground truth, returns 0. ((0, 0, 0, 0, 0, 0), (0, 0, 0, 1, 0, 1), 0), ) def test_evaluate_preset(self, ground_truth, predictions, expected_precision): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_precision, tensor_shape) result = precision.evaluate( ground_truth_labels, predicted_labels, classes=[1]) self.assertAllClose(expected, result) @parameterized.parameters( # Precision for classes 2, 3: [0.5, 0.25] ((2, 0, 3, 1, 2, 2), (2, 3, 3, 2, 3, 3), [2, 3], False, [0.5, 0.25]), # Average precision for classes 2, 3: 0.375 ((2, 0, 3, 1, 2, 2), (2, 3, 3, 2, 3, 3), [2, 3], True, 0.375), # Precision for all classes: [0, 0, 0.5, 1] ((1, 2, 3, 3, 1, 1, 2), (0, 2, 0, 3, 0, 2, 0), None, False, [0, 0, 0.5, 1]), # Average precision for all classes: 1.5 / 4 (0.375) ((1, 2, 3, 3, 1, 1, 2), (0, 2, 0, 3, 0, 2, 0), None, True, 1.5 / 4), ) def test_evaluate_preset_multiclass(self, ground_truth, predictions, classes, reduce_average, expected_precision): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_precision, tensor_shape + ([1] if not reduce_average else [])) result = precision.evaluate(ground_truth_labels, predicted_labels, classes, reduce_average) self.assertAllClose(expected, result) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 5, 3), (4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 4), (2, 4, 5)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(precision.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 3), (2, 5, 1)), ((None, 2, 6), (4, 2, None)), ((3, 1, 1, 2), (3, 5, 8, 2)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(precision.evaluate, shapes) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for the precision metric.""" from absl.testing import parameterized import numpy as np from tensorflow_graphics.nn.metric import precision from tensorflow_graphics.util import test_case def random_tensor(tensor_shape): return np.random.uniform(low=0.0, high=1.0, size=tensor_shape) def random_tensor_shape(): tensor_size = np.random.randint(5) + 1 return np.random.randint(1, 10, size=(tensor_size)).tolist() class PrecisionTest(test_case.TestCase): @parameterized.parameters( # Precision = 0.5. ((0, 1, 1, 1, 1), (1, 1, 0, 0, 0), 0.5), # Precision = 1. ((0, 0, 0, 1, 1, 1, 0, 1), (0, 0, 0, 1, 1, 1, 0, 1), 1), # All-0 predictions, returns 0. ((0, 1, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0), 0), # All-0 ground truth, returns 0. ((0, 0, 0, 0, 0, 0), (0, 0, 0, 1, 0, 1), 0), ) def test_evaluate_preset(self, ground_truth, predictions, expected_precision): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_precision, tensor_shape) result = precision.evaluate( ground_truth_labels, predicted_labels, classes=[1]) self.assertAllClose(expected, result) @parameterized.parameters( # Precision for classes 2, 3: [0.5, 0.25] ((2, 0, 3, 1, 2, 2), (2, 3, 3, 2, 3, 3), [2, 3], False, [0.5, 0.25]), # Average precision for classes 2, 3: 0.375 ((2, 0, 3, 1, 2, 2), (2, 3, 3, 2, 3, 3), [2, 3], True, 0.375), # Precision for all classes: [0, 0, 0.5, 1] ((1, 2, 3, 3, 1, 1, 2), (0, 2, 0, 3, 0, 2, 0), None, False, [0, 0, 0.5, 1]), # Average precision for all classes: 1.5 / 4 (0.375) ((1, 2, 3, 3, 1, 1, 2), (0, 2, 0, 3, 0, 2, 0), None, True, 1.5 / 4), ) def test_evaluate_preset_multiclass(self, ground_truth, predictions, classes, reduce_average, expected_precision): tensor_shape = random_tensor_shape() ground_truth_labels = np.tile(ground_truth, tensor_shape + [1]) predicted_labels = np.tile(predictions, tensor_shape + [1]) expected = np.tile(expected_precision, tensor_shape + ([1] if not reduce_average else [])) result = precision.evaluate(ground_truth_labels, predicted_labels, classes, reduce_average) self.assertAllClose(expected, result) @parameterized.parameters( ("Not all batch dimensions are broadcast-compatible.", (1, 5, 3), (4, 3)), ("Not all batch dimensions are broadcast-compatible.", (3, 4), (2, 4, 5)), ) def test_evaluate_shape_exception_raised(self, error_msg, *shape): """Tests that the shape exception is raised.""" self.assert_exception_is_raised(precision.evaluate, error_msg, shape) @parameterized.parameters( ((1, 5, 3), (2, 5, 1)), ((None, 2, 6), (4, 2, None)), ((3, 1, 1, 2), (3, 5, 8, 2)), ) def test_evaluate_shape_exception_not_raised(self, *shapes): """Tests that the shape exceptions are not raised.""" self.assert_exception_is_not_raised(precision.evaluate, shapes) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/opengl/egl_util.cc

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "egl_util.h" #include <EGL/egl.h> #include <EGL/eglext.h> #include <ios> #include <iostream> #include <mutex> #include <thread> #include <unordered_map> namespace { // Maximum number of EGL devices to query. Currently the maximum number of // devices a machine could have is 16, but we allow space for 32. constexpr int kMaxDevices = 32; // Helper function for loading EGL extensions. template <typename T> T LoadEGLFunction(const char* func_name) { if (T func = reinterpret_cast<T>(eglGetProcAddress(func_name))) { return func; } else { std::cerr << "Failed to load EGL function " << func_name << "\n"; return nullptr; } } // Mutex used to lock the display_reference_map and eglInitialize/egTerminate // calls. std::mutex* get_display_mutex() { static std::mutex* display_reference_mutex = new std::mutex(); return display_reference_mutex; } std::unordered_map<EGLDisplay, int>* get_display_reference_map() { static std::unordered_map<EGLDisplay, int>* display_reference_map = new std::unordered_map<EGLDisplay, int>(); return display_reference_map; } void IncrementDisplayRefCount(EGLDisplay display) { auto* display_map = get_display_reference_map(); auto iter_inserted = display_map->emplace(display, 0).first; ++iter_inserted->second; } // Helper to decrement reference count for provided EGLDisplay. Returns the // reference count after decrementing the provided counter. If the EGLDisplay is // not found, return -1. int DecrementDisplayRefCount(EGLDisplay display) { auto* display_map = get_display_reference_map(); auto it = display_map->find(display); if (it != display_map->end()) { int ref_count = --it->second; if (ref_count == 0) display_map->erase(it); return ref_count; } else { return -1; } } EGLBoolean TerminateInitializedEGLDisplayNoLock(EGLDisplay display) { if (display == EGL_NO_DISPLAY) { return eglTerminate(display); } int ref_count = DecrementDisplayRefCount(display); if (ref_count == 0) { return eglTerminate(display); } else if (ref_count > 0) { return EGL_TRUE; } else { std::cerr << "Could not find EGLDisplay Reference count! Either we didn't " "create EGLDisplay with CreateInitializedEGLDisplay() or we " "have already terminated the display.\n"; return EGL_FALSE; } } } // namespace extern "C" EGLDisplay CreateInitializedEGLDisplayAtIndex(int device_index) { // Load EGL extension functions for querying EGL devices manually. This // extension isn't officially supported in EGL 1.4, so try and manually // load them using eglGetProcAddress. auto eglQueryDevicesEXT = LoadEGLFunction<PFNEGLQUERYDEVICESEXTPROC>("eglQueryDevicesEXT"); if (eglQueryDevicesEXT == nullptr) return EGL_NO_DISPLAY; auto eglGetPlatformDisplayEXT = LoadEGLFunction<PFNEGLGETPLATFORMDISPLAYEXTPROC>( "eglGetPlatformDisplayEXT"); if (eglGetPlatformDisplayEXT == nullptr) return EGL_NO_DISPLAY; EGLDeviceEXT egl_devices[kMaxDevices]; EGLint num_devices = 0; auto egl_error = eglGetError(); if (!eglQueryDevicesEXT(kMaxDevices, egl_devices, &num_devices) || egl_error != EGL_SUCCESS) { std::cerr << "eglQueryDevicesEXT Failed. EGL error " << std::hex << eglGetError() << "\n"; return EGL_NO_DISPLAY; } // Go through each device and try to initialize the display. for (EGLint i = 0; i < num_devices; ++i) { // First try and get a valid EGL display. auto display = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, egl_devices[i], nullptr); if (eglGetError() == EGL_SUCCESS && display != EGL_NO_DISPLAY) { // Aquire lock before calling eglInitialize() and incrementing ref count. std::lock_guard<std::mutex> display_guard(*get_display_mutex()); // Now try to initialize the display. This can fail when we don't have // access to the device. int major, minor; EGLBoolean initialized = eglInitialize(display, &major, &minor); if (eglGetError() == EGL_SUCCESS && initialized == EGL_TRUE) { IncrementDisplayRefCount(display); if (--device_index < 0) { return display; } else { TerminateInitializedEGLDisplayNoLock(display); } } } } std::cerr << "Failed to create and initialize a valid EGL display! " << "Devices tried: " << num_devices << "\n"; return EGL_NO_DISPLAY; } extern "C" EGLDisplay CreateInitializedEGLDisplay() { return CreateInitializedEGLDisplayAtIndex(0); } extern "C" EGLBoolean TerminateInitializedEGLDisplay(EGLDisplay display) { // Acquire lock before terminating and decrementing display ref count. std::lock_guard<std::mutex> display_guard(*get_display_mutex()); return TerminateInitializedEGLDisplayNoLock(display); } extern "C" void ShutDownEGLSubsystem() { delete get_display_reference_map(); delete get_display_mutex(); }

/* Copyright 2020 The TensorFlow Authors 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. ==============================================================================*/ #include "egl_util.h" #include <EGL/egl.h> #include <EGL/eglext.h> #include <ios> #include <iostream> #include <mutex> #include <thread> #include <unordered_map> namespace { // Maximum number of EGL devices to query. Currently the maximum number of // devices a machine could have is 16, but we allow space for 32. constexpr int kMaxDevices = 32; // Helper function for loading EGL extensions. template <typename T> T LoadEGLFunction(const char* func_name) { if (T func = reinterpret_cast<T>(eglGetProcAddress(func_name))) { return func; } else { std::cerr << "Failed to load EGL function " << func_name << "\n"; return nullptr; } } // Mutex used to lock the display_reference_map and eglInitialize/egTerminate // calls. std::mutex* get_display_mutex() { static std::mutex* display_reference_mutex = new std::mutex(); return display_reference_mutex; } std::unordered_map<EGLDisplay, int>* get_display_reference_map() { static std::unordered_map<EGLDisplay, int>* display_reference_map = new std::unordered_map<EGLDisplay, int>(); return display_reference_map; } void IncrementDisplayRefCount(EGLDisplay display) { auto* display_map = get_display_reference_map(); auto iter_inserted = display_map->emplace(display, 0).first; ++iter_inserted->second; } // Helper to decrement reference count for provided EGLDisplay. Returns the // reference count after decrementing the provided counter. If the EGLDisplay is // not found, return -1. int DecrementDisplayRefCount(EGLDisplay display) { auto* display_map = get_display_reference_map(); auto it = display_map->find(display); if (it != display_map->end()) { int ref_count = --it->second; if (ref_count == 0) display_map->erase(it); return ref_count; } else { return -1; } } EGLBoolean TerminateInitializedEGLDisplayNoLock(EGLDisplay display) { if (display == EGL_NO_DISPLAY) { return eglTerminate(display); } int ref_count = DecrementDisplayRefCount(display); if (ref_count == 0) { return eglTerminate(display); } else if (ref_count > 0) { return EGL_TRUE; } else { std::cerr << "Could not find EGLDisplay Reference count! Either we didn't " "create EGLDisplay with CreateInitializedEGLDisplay() or we " "have already terminated the display.\n"; return EGL_FALSE; } } } // namespace extern "C" EGLDisplay CreateInitializedEGLDisplayAtIndex(int device_index) { // Load EGL extension functions for querying EGL devices manually. This // extension isn't officially supported in EGL 1.4, so try and manually // load them using eglGetProcAddress. auto eglQueryDevicesEXT = LoadEGLFunction<PFNEGLQUERYDEVICESEXTPROC>("eglQueryDevicesEXT"); if (eglQueryDevicesEXT == nullptr) return EGL_NO_DISPLAY; auto eglGetPlatformDisplayEXT = LoadEGLFunction<PFNEGLGETPLATFORMDISPLAYEXTPROC>( "eglGetPlatformDisplayEXT"); if (eglGetPlatformDisplayEXT == nullptr) return EGL_NO_DISPLAY; EGLDeviceEXT egl_devices[kMaxDevices]; EGLint num_devices = 0; auto egl_error = eglGetError(); if (!eglQueryDevicesEXT(kMaxDevices, egl_devices, &num_devices) || egl_error != EGL_SUCCESS) { std::cerr << "eglQueryDevicesEXT Failed. EGL error " << std::hex << eglGetError() << "\n"; return EGL_NO_DISPLAY; } // Go through each device and try to initialize the display. for (EGLint i = 0; i < num_devices; ++i) { // First try and get a valid EGL display. auto display = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, egl_devices[i], nullptr); if (eglGetError() == EGL_SUCCESS && display != EGL_NO_DISPLAY) { // Aquire lock before calling eglInitialize() and incrementing ref count. std::lock_guard<std::mutex> display_guard(*get_display_mutex()); // Now try to initialize the display. This can fail when we don't have // access to the device. int major, minor; EGLBoolean initialized = eglInitialize(display, &major, &minor); if (eglGetError() == EGL_SUCCESS && initialized == EGL_TRUE) { IncrementDisplayRefCount(display); if (--device_index < 0) { return display; } else { TerminateInitializedEGLDisplayNoLock(display); } } } } std::cerr << "Failed to create and initialize a valid EGL display! " << "Devices tried: " << num_devices << "\n"; return EGL_NO_DISPLAY; } extern "C" EGLDisplay CreateInitializedEGLDisplay() { return CreateInitializedEGLDisplayAtIndex(0); } extern "C" EGLBoolean TerminateInitializedEGLDisplay(EGLDisplay display) { // Acquire lock before terminating and decrementing display ref count. std::lock_guard<std::mutex> display_guard(*get_display_mutex()); return TerminateInitializedEGLDisplayNoLock(display); } extern "C" void ShutDownEGLSubsystem() { delete get_display_reference_map(); delete get_display_mutex(); }

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/rendering/reflectance/blinn_phong.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the Blinn-Phong specular reflectance. For a derivation of the normalization factor ensuring energy conservation, we refer the interested reader to: Fabian Giesen. "Derivation of Phong and Blinn-Phong BRDF normalization factors". 2009 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _brdf_normalization_factor(shininess): """Returns the normalization factor needed to ensure energy conservation.""" numerator = (shininess + 2.0) * (shininess + 4.0) denominator = 8.0 * math.pi * ( tf.pow(tf.constant(2.0, dtype=shininess.dtype), -shininess / 2.0) + shininess) return safe_ops.safe_signed_div(numerator, denominator) def brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo, brdf_normalization=True, name=None): """Evaluates the specular brdf of the Blinn-Phong model. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Note: The gradient of this function is not smooth when the dot product of the normal with any light is 0.0. Args: direction_incoming_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized incoming light vector. direction_outgoing_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized outgoing light vector. surface_normal: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized surface normal. shininess: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents a non-negative shininess coefficient. albedo: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents albedo with values in [0,1]. brdf_normalization: A `bool` indicating whether normalization should be applied to enforce the energy conservation property of BRDFs. Note that `brdf_normalization` must be set to False in order to use the original Blinn-Phong specular model. name: A name for this op. Defaults to "blinn_phong_brdf". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the amount of light reflected in the outgoing light direction. Raises: ValueError: if the shape of `direction_incoming_light`, `direction_outgoing_light`, `surface_normal`, `shininess` or `albedo` is not supported. InvalidArgumentError: if not all of shininess values are non-negative, or if at least one element of `albedo` is outside of [0,1]. """ with tf.compat.v1.name_scope(name, "blinn_phong_brdf", [ direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo ]): direction_incoming_light = tf.convert_to_tensor( value=direction_incoming_light) direction_outgoing_light = tf.convert_to_tensor( value=direction_outgoing_light) surface_normal = tf.convert_to_tensor(value=surface_normal) shininess = tf.convert_to_tensor(value=shininess) albedo = tf.convert_to_tensor(value=albedo) shape.check_static( tensor=direction_incoming_light, tensor_name="direction_incoming_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=direction_outgoing_light, tensor_name="direction_outgoing_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_normal, tensor_name="surface_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=shininess, tensor_name="shininess", has_dim_equals=(-1, 1)) shape.check_static( tensor=albedo, tensor_name="albedo", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo), tensor_names=("direction_incoming_light", "direction_outgoing_light", "surface_normal", "shininess", "albedo"), last_axes=-2, broadcast_compatible=True) direction_incoming_light = asserts.assert_normalized( direction_incoming_light) direction_outgoing_light = asserts.assert_normalized( direction_outgoing_light) surface_normal = asserts.assert_normalized(surface_normal) albedo = asserts.assert_all_in_range(albedo, 0.0, 1.0, open_bounds=False) shininess = asserts.assert_all_above(shininess, 0.0, open_bound=False) # Checks whether the incoming or outgoing light point behind the surface. dot_incoming_light_surface_normal = vector.dot(-direction_incoming_light, surface_normal) dot_outgoing_light_surface_normal = vector.dot(direction_outgoing_light, surface_normal) min_dot = tf.minimum(dot_incoming_light_surface_normal, dot_outgoing_light_surface_normal) difference_outgoing_incoming = ( direction_outgoing_light - direction_incoming_light) difference_outgoing_incoming = tf.math.l2_normalize( difference_outgoing_incoming, axis=-1) cos_alpha = vector.dot( surface_normal, difference_outgoing_incoming, axis=-1) cos_alpha = tf.maximum(cos_alpha, tf.zeros_like(cos_alpha)) blinn_phong_model = albedo * tf.pow(cos_alpha, shininess) if brdf_normalization: blinn_phong_model *= _brdf_normalization_factor(shininess) common_shape = shape.get_broadcasted_shape(min_dot.shape, blinn_phong_model.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) blinn_phong_model = tf.broadcast_to(blinn_phong_model, common_shape) return tf.compat.v1.where(condition, blinn_phong_model, tf.zeros_like(blinn_phong_model)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """This module implements the Blinn-Phong specular reflectance. For a derivation of the normalization factor ensuring energy conservation, we refer the interested reader to: Fabian Giesen. "Derivation of Phong and Blinn-Phong BRDF normalization factors". 2009 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import tensorflow as tf from tensorflow_graphics.math import vector from tensorflow_graphics.util import asserts from tensorflow_graphics.util import export_api from tensorflow_graphics.util import safe_ops from tensorflow_graphics.util import shape def _brdf_normalization_factor(shininess): """Returns the normalization factor needed to ensure energy conservation.""" numerator = (shininess + 2.0) * (shininess + 4.0) denominator = 8.0 * math.pi * ( tf.pow(tf.constant(2.0, dtype=shininess.dtype), -shininess / 2.0) + shininess) return safe_ops.safe_signed_div(numerator, denominator) def brdf(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo, brdf_normalization=True, name=None): """Evaluates the specular brdf of the Blinn-Phong model. Note: In the following, A1 to An are optional batch dimensions, which must be broadcast compatible. Note: The gradient of this function is not smooth when the dot product of the normal with any light is 0.0. Args: direction_incoming_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized incoming light vector. direction_outgoing_light: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized outgoing light vector. surface_normal: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents a normalized surface normal. shininess: A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents a non-negative shininess coefficient. albedo: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents albedo with values in [0,1]. brdf_normalization: A `bool` indicating whether normalization should be applied to enforce the energy conservation property of BRDFs. Note that `brdf_normalization` must be set to False in order to use the original Blinn-Phong specular model. name: A name for this op. Defaults to "blinn_phong_brdf". Returns: A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents the amount of light reflected in the outgoing light direction. Raises: ValueError: if the shape of `direction_incoming_light`, `direction_outgoing_light`, `surface_normal`, `shininess` or `albedo` is not supported. InvalidArgumentError: if not all of shininess values are non-negative, or if at least one element of `albedo` is outside of [0,1]. """ with tf.compat.v1.name_scope(name, "blinn_phong_brdf", [ direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo ]): direction_incoming_light = tf.convert_to_tensor( value=direction_incoming_light) direction_outgoing_light = tf.convert_to_tensor( value=direction_outgoing_light) surface_normal = tf.convert_to_tensor(value=surface_normal) shininess = tf.convert_to_tensor(value=shininess) albedo = tf.convert_to_tensor(value=albedo) shape.check_static( tensor=direction_incoming_light, tensor_name="direction_incoming_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=direction_outgoing_light, tensor_name="direction_outgoing_light", has_dim_equals=(-1, 3)) shape.check_static( tensor=surface_normal, tensor_name="surface_normal", has_dim_equals=(-1, 3)) shape.check_static( tensor=shininess, tensor_name="shininess", has_dim_equals=(-1, 1)) shape.check_static( tensor=albedo, tensor_name="albedo", has_dim_equals=(-1, 3)) shape.compare_batch_dimensions( tensors=(direction_incoming_light, direction_outgoing_light, surface_normal, shininess, albedo), tensor_names=("direction_incoming_light", "direction_outgoing_light", "surface_normal", "shininess", "albedo"), last_axes=-2, broadcast_compatible=True) direction_incoming_light = asserts.assert_normalized( direction_incoming_light) direction_outgoing_light = asserts.assert_normalized( direction_outgoing_light) surface_normal = asserts.assert_normalized(surface_normal) albedo = asserts.assert_all_in_range(albedo, 0.0, 1.0, open_bounds=False) shininess = asserts.assert_all_above(shininess, 0.0, open_bound=False) # Checks whether the incoming or outgoing light point behind the surface. dot_incoming_light_surface_normal = vector.dot(-direction_incoming_light, surface_normal) dot_outgoing_light_surface_normal = vector.dot(direction_outgoing_light, surface_normal) min_dot = tf.minimum(dot_incoming_light_surface_normal, dot_outgoing_light_surface_normal) difference_outgoing_incoming = ( direction_outgoing_light - direction_incoming_light) difference_outgoing_incoming = tf.math.l2_normalize( difference_outgoing_incoming, axis=-1) cos_alpha = vector.dot( surface_normal, difference_outgoing_incoming, axis=-1) cos_alpha = tf.maximum(cos_alpha, tf.zeros_like(cos_alpha)) blinn_phong_model = albedo * tf.pow(cos_alpha, shininess) if brdf_normalization: blinn_phong_model *= _brdf_normalization_factor(shininess) common_shape = shape.get_broadcasted_shape(min_dot.shape, blinn_phong_model.shape) d_val = lambda dim: 1 if dim is None else tf.compat.v1.dimension_value(dim) common_shape = [d_val(dim) for dim in common_shape] condition = tf.broadcast_to(tf.greater_equal(min_dot, 0.0), common_shape) blinn_phong_model = tf.broadcast_to(blinn_phong_model, common_shape) return tf.compat.v1.where(condition, blinn_phong_model, tf.zeros_like(blinn_phong_model)) # API contains all public functions and classes. __all__ = export_api.get_functions_and_classes()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/util/tests/test_case_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for shape utility functions.""" import unittest from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.util import test_case class TestCaseTest(test_case.TestCase): def _dummy_tf_lite_compatible_function(self, data): """Executes a simple supported function to test TFLite conversion.""" data = tf.convert_to_tensor(value=data) return 2.0 * data def _dummy_tf_lite_incompatible_function(self, data): """Executes a simple unsupported function to test TFLite conversion.""" del data # Unused return 2.0 * tf.ones(shape=[2] * 10) @parameterized.parameters(None, (((1.0,),),)) def test_assert_tf_lite_convertible_exception_not_raised(self, test_inputs): """Tests that assert_tf_lite_convertible succeeds with a simple function.""" tc = test_case.TestCase(methodName="assert_tf_lite_convertible") # We can't use self.assert_exception_is_not_raised here because we need to # use `shapes` as both a named argument and a kwarg. try: tc.assert_tf_lite_convertible( func=self._dummy_tf_lite_compatible_function, shapes=((1,),), test_inputs=test_inputs) except unittest.SkipTest as e: # Forwarding SkipTest exception in order to skip the test. raise e except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % type(e)) @parameterized.parameters(None, (((1.0,),),)) def test_assert_tf_lite_convertible_exception_raised(self, test_inputs): """Tests that assert_tf_lite_convertible succeeds with a simple function.""" # TODO(b/131912561): TFLite conversion throws SIGABRT instead of Exception. return # pylint: disable=unreachable # This code should be able to catch exceptions correctly once TFLite bug # is fixed. tc = test_case.TestCase(methodName="assert_tf_lite_convertible") with self.assertRaises(Exception): tc.assert_tf_lite_convertible( func=self._dummy_tf_lite_incompatible_function, shapes=((1,),), test_inputs=test_inputs) # pylint: enable=unreachable def _dummy_failing_function(self, data): """Fails instantly.""" del data # Unused raise ValueError("Fail.") def test_assert_exception_is_not_raised_raises_exception(self): """Tests that assert_exception_is_not_raised raises exception.""" if tf.executing_eagerly(): # In eager mode placeholders are assigned zeros by default, which fails # for various tests. Therefore this function can only be tested in graph # mode. return tc = test_case.TestCase(methodName="assert_exception_is_not_raised") with self.assertRaises(AssertionError): tc.assert_exception_is_not_raised( self._dummy_failing_function, shapes=((1,),)) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for shape utility functions.""" import unittest from absl.testing import parameterized import tensorflow as tf from tensorflow_graphics.util import test_case class TestCaseTest(test_case.TestCase): def _dummy_tf_lite_compatible_function(self, data): """Executes a simple supported function to test TFLite conversion.""" data = tf.convert_to_tensor(value=data) return 2.0 * data def _dummy_tf_lite_incompatible_function(self, data): """Executes a simple unsupported function to test TFLite conversion.""" del data # Unused return 2.0 * tf.ones(shape=[2] * 10) @parameterized.parameters(None, (((1.0,),),)) def test_assert_tf_lite_convertible_exception_not_raised(self, test_inputs): """Tests that assert_tf_lite_convertible succeeds with a simple function.""" tc = test_case.TestCase(methodName="assert_tf_lite_convertible") # We can't use self.assert_exception_is_not_raised here because we need to # use `shapes` as both a named argument and a kwarg. try: tc.assert_tf_lite_convertible( func=self._dummy_tf_lite_compatible_function, shapes=((1,),), test_inputs=test_inputs) except unittest.SkipTest as e: # Forwarding SkipTest exception in order to skip the test. raise e except Exception as e: # pylint: disable=broad-except self.fail("Exception raised: %s" % type(e)) @parameterized.parameters(None, (((1.0,),),)) def test_assert_tf_lite_convertible_exception_raised(self, test_inputs): """Tests that assert_tf_lite_convertible succeeds with a simple function.""" # TODO(b/131912561): TFLite conversion throws SIGABRT instead of Exception. return # pylint: disable=unreachable # This code should be able to catch exceptions correctly once TFLite bug # is fixed. tc = test_case.TestCase(methodName="assert_tf_lite_convertible") with self.assertRaises(Exception): tc.assert_tf_lite_convertible( func=self._dummy_tf_lite_incompatible_function, shapes=((1,),), test_inputs=test_inputs) # pylint: enable=unreachable def _dummy_failing_function(self, data): """Fails instantly.""" del data # Unused raise ValueError("Fail.") def test_assert_exception_is_not_raised_raises_exception(self): """Tests that assert_exception_is_not_raised raises exception.""" if tf.executing_eagerly(): # In eager mode placeholders are assigned zeros by default, which fails # for various tests. Therefore this function can only be tested in graph # mode. return tc = test_case.TestCase(methodName="assert_exception_is_not_raised") with self.assertRaises(AssertionError): tc.assert_exception_is_not_raised( self._dummy_failing_function, shapes=((1,),)) if __name__ == "__main__": test_case.main()

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/modelnet40/fakes/modelnet40_ply_hdf5_2048/train_files.txt

data/modelnet40_ply_hdf5_2048/ply_data_train0.h5 data/modelnet40_ply_hdf5_2048/ply_data_train1.h5 data/modelnet40_ply_hdf5_2048/ply_data_train2.h5

data/modelnet40_ply_hdf5_2048/ply_data_train0.h5 data/modelnet40_ply_hdf5_2048/ply_data_train1.h5 data/modelnet40_ply_hdf5_2048/ply_data_train2.h5

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./.git/hooks/pre-commit.sample

#!/bin/sh # # An example hook script to verify what is about to be committed. # Called by "git commit" with no arguments. The hook should # exit with non-zero status after issuing an appropriate message if # it wants to stop the commit. # # To enable this hook, rename this file to "pre-commit". if git rev-parse --verify HEAD >/dev/null 2>&1 then against=HEAD else # Initial commit: diff against an empty tree object against=$(git hash-object -t tree /dev/null) fi # If you want to allow non-ASCII filenames set this variable to true. allownonascii=$(git config --bool hooks.allownonascii) # Redirect output to stderr. exec 1>&2 # Cross platform projects tend to avoid non-ASCII filenames; prevent # them from being added to the repository. We exploit the fact that the # printable range starts at the space character and ends with tilde. if [ "$allownonascii" != "true" ] && # Note that the use of brackets around a tr range is ok here, (it's # even required, for portability to Solaris 10's /usr/bin/tr), since # the square bracket bytes happen to fall in the designated range. test $(git diff --cached --name-only --diff-filter=A -z $against | LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 then cat <<\EOF Error: Attempt to add a non-ASCII file name. This can cause problems if you want to work with people on other platforms. To be portable it is advisable to rename the file. If you know what you are doing you can disable this check using: git config hooks.allownonascii true EOF exit 1 fi # If there are whitespace errors, print the offending file names and fail. exec git diff-index --check --cached $against --

#!/bin/sh # # An example hook script to verify what is about to be committed. # Called by "git commit" with no arguments. The hook should # exit with non-zero status after issuing an appropriate message if # it wants to stop the commit. # # To enable this hook, rename this file to "pre-commit". if git rev-parse --verify HEAD >/dev/null 2>&1 then against=HEAD else # Initial commit: diff against an empty tree object against=$(git hash-object -t tree /dev/null) fi # If you want to allow non-ASCII filenames set this variable to true. allownonascii=$(git config --bool hooks.allownonascii) # Redirect output to stderr. exec 1>&2 # Cross platform projects tend to avoid non-ASCII filenames; prevent # them from being added to the repository. We exploit the fact that the # printable range starts at the space character and ends with tilde. if [ "$allownonascii" != "true" ] && # Note that the use of brackets around a tr range is ok here, (it's # even required, for portability to Solaris 10's /usr/bin/tr), since # the square bracket bytes happen to fall in the designated range. test $(git diff --cached --name-only --diff-filter=A -z $against | LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 then cat <<\EOF Error: Attempt to add a non-ASCII file name. This can cause problems if you want to work with people on other platforms. To be portable it is advisable to rename the file. If you know what you are doing you can disable this check using: git config hooks.allownonascii true EOF exit 1 fi # If there are whitespace errors, print the offending file names and fail. exec git diff-index --check --cached $against --

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/io/triangle_mesh.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """A thin wrapper around the trimesh library for loading triangle meshes.""" import os import tensorflow as tf import trimesh from trimesh import Scene from trimesh import Trimesh # TODO(b/156115314): Revisit the library for loading the triangle meshes. class GFileResolver(trimesh.visual.resolvers.Resolver): """A resolver using gfile for accessing other assets in the mesh directory.""" def __init__(self, path): if tf.io.gfile.isdir(path): self.directory = path elif tf.io.gfile.exists(path): self.directory = os.path.dirname(path) else: raise ValueError('path is not a file or directory') def get(self, name): with tf.io.gfile.GFile(os.path.join(self.directory, name), 'rb') as f: data = f.read() return data def load(file_obj, file_type=None, **kwargs): """Loads a triangle mesh from the given GFile/file path. Args: file_obj: A tf.io.gfile.GFile object or a string specifying the mesh file path. file_type: A string specifying the type of the file (e.g. 'obj', 'stl'). If not specified the file_type will be inferred from the file name. **kwargs: Additional arguments that should be passed to trimesh.load(). Returns: A trimesh.Trimesh or trimesh.Scene. """ if isinstance(file_obj, str): with tf.io.gfile.GFile(file_obj, 'r') as f: if file_type is None: file_type = trimesh.util.split_extension(file_obj) return trimesh.load( file_obj=f, file_type=file_type, resolver=GFileResolver(file_obj), **kwargs) if trimesh.util.is_file(file_obj): if not hasattr(file_obj, 'name') or not file_obj.name: raise ValueError( 'file_obj must have attribute "name". Try passing the file name instead.' ) if file_type is None: file_type = trimesh.util.split_extension(file_obj.name) return trimesh.load( file_obj=file_obj, file_type=file_type, resolver=GFileResolver(file_obj.name), **kwargs) raise ValueError('file_obj should be either a file object or a string') __all__ = ['load', 'Trimesh', 'Scene']

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. # Lint as: python3 """A thin wrapper around the trimesh library for loading triangle meshes.""" import os import tensorflow as tf import trimesh from trimesh import Scene from trimesh import Trimesh # TODO(b/156115314): Revisit the library for loading the triangle meshes. class GFileResolver(trimesh.visual.resolvers.Resolver): """A resolver using gfile for accessing other assets in the mesh directory.""" def __init__(self, path): if tf.io.gfile.isdir(path): self.directory = path elif tf.io.gfile.exists(path): self.directory = os.path.dirname(path) else: raise ValueError('path is not a file or directory') def get(self, name): with tf.io.gfile.GFile(os.path.join(self.directory, name), 'rb') as f: data = f.read() return data def load(file_obj, file_type=None, **kwargs): """Loads a triangle mesh from the given GFile/file path. Args: file_obj: A tf.io.gfile.GFile object or a string specifying the mesh file path. file_type: A string specifying the type of the file (e.g. 'obj', 'stl'). If not specified the file_type will be inferred from the file name. **kwargs: Additional arguments that should be passed to trimesh.load(). Returns: A trimesh.Trimesh or trimesh.Scene. """ if isinstance(file_obj, str): with tf.io.gfile.GFile(file_obj, 'r') as f: if file_type is None: file_type = trimesh.util.split_extension(file_obj) return trimesh.load( file_obj=f, file_type=file_type, resolver=GFileResolver(file_obj), **kwargs) if trimesh.util.is_file(file_obj): if not hasattr(file_obj, 'name') or not file_obj.name: raise ValueError( 'file_obj must have attribute "name". Try passing the file name instead.' ) if file_type is None: file_type = trimesh.util.split_extension(file_obj.name) return trimesh.load( file_obj=file_obj, file_type=file_type, resolver=GFileResolver(file_obj.name), **kwargs) raise ValueError('file_obj should be either a file object or a string') __all__ = ['load', 'Trimesh', 'Scene']

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/datasets/pix3d/checksums.tsv

http://pix3d.csail.mit.edu/data/pix3d.zip 3783261880 16dc7d5028d4064f4c3319966add05d9c7a87413fdd60f8a979c6df27224052c

http://pix3d.csail.mit.edu/data/pix3d.zip 3783261880 16dc7d5028d4064f4c3319966add05d9c7a87413fdd60f8a979c6df27224052c

tensorflow/graphics

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

copybara-service[bot]

9d257ad4a72ccf65e4349910b9fff7c0a5648073

f683a9a5794bade30ede447339394e84b44acc0b

Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2.. Migrate tensorflow_graphics/geometry/transformation to using TensorFlow 2. Following changes are made to the library code: - tf.compat.v1.name_scope -> tf.name_scope - tf.compat.v1.where. -> tf.where - tf.compat.v1.assert_equal -> tf.debugging.assert_equal - tf.compat.v1.dimension_value -> tf.compat.dimension_value Following changes are made to the test code: - Remove tf.compat.v1.get_variable() - Remove tf.compat.v1.global_variables_initializer() - Remove tf.compat.v1.Session() except for a couple of places using assert_jacobian_is_finite() that depends on TensorFlow v1 libraries

./tensorflow_graphics/geometry/transformation/tests/rotation_matrix_common_test.py

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for rotation_matrix_common.""" from absl.testing import parameterized from tensorflow_graphics.geometry.transformation import rotation_matrix_common from tensorflow_graphics.util import test_case class RotationMatrixCommonTest(test_case.TestCase): @parameterized.parameters( ((2, 2),), ((3, 3),), ((4, 4),), ((None, 2, 2),), ((None, 3, 3),), ((None, 4, 4),), ) def test_is_valid_exception_not_raised(self, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_not_raised(rotation_matrix_common.is_valid, shape) @parameterized.parameters( ("must have a rank greater than 1", (2,)), ("must have the same number of dimensions in axes", (1, 2)), ("must have the same number of dimensions in axes", (None, 2)), ) def test_is_valid_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(rotation_matrix_common.is_valid, error_msg, shape) if __name__ == "__main__": test_case.main()

# Copyright 2020 The TensorFlow Authors # # 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 # # https://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. """Tests for rotation_matrix_common.""" from absl.testing import parameterized from tensorflow_graphics.geometry.transformation import rotation_matrix_common from tensorflow_graphics.util import test_case class RotationMatrixCommonTest(test_case.TestCase): @parameterized.parameters( ((2, 2),), ((3, 3),), ((4, 4),), ((None, 2, 2),), ((None, 3, 3),), ((None, 4, 4),), ) def test_is_valid_exception_not_raised(self, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_not_raised(rotation_matrix_common.is_valid, shape) @parameterized.parameters( ("must have a rank greater than 1", (2,)), ("must have the same number of dimensions in axes", (1, 2)), ("must have the same number of dimensions in axes", (None, 2)), ) def test_is_valid_exception_raised(self, error_msg, *shape): """Tests that the shape exceptions are raised.""" self.assert_exception_is_raised(rotation_matrix_common.is_valid, error_msg, shape) if __name__ == "__main__": test_case.main()