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tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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.txt | tensorflow >= 2.2.0
tensorflow-addons >= 0.10.0
tensorflow-datasets >= 2.0.0
absl-py >= 0.6.1
h5py >= 2.10.0
matplotlib >= 2.2.5
numpy >= 1.15.4
psutil >= 5.7.0
scipy >= 1.1.0
tqdm >= 4.45.0
OpenEXR >= 1.3.2
termcolor >= 1.1.0
trimesh >= 2.37.22
# Required by trimesh.
networkx
| tensorflow >= 2.2.0
tensorflow-addons >= 0.10.0
tensorflow-datasets >= 2.0.0
absl-py >= 0.6.1
h5py >= 2.10.0
matplotlib >= 2.2.5
numpy >= 1.15.4
psutil >= 5.7.0
scipy >= 1.1.0
tqdm >= 4.45.0
OpenEXR >= 1.3.2
termcolor >= 1.1.0
trimesh >= 2.37.22
# Required by trimesh.
networkx
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/test_helpers.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.
"""Test helpers for the voxels module."""
import numpy as np
def generate_random_test_voxels_render():
"""Generates random test for the voxels rendering functions."""
batch_shape = np.random.randint(1, 3)
voxels_shape = np.random.randint(2, 8, size=(3)).tolist()
signals_dimension = np.random.randint(2, 4)
random_voxels = np.random.uniform(size=[batch_shape] + voxels_shape +
[signals_dimension])
return random_voxels
def generate_preset_test_voxels_visual_hull_render():
"""Generates preset test for the visual hull voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.77686984, 0.59343034, 0.59343034],
[0.45118836, 0.87754357, 0.32967995]],
[[0.19748120, 0.63940506, 0.67372021],
[0.91107838, 0.73286470, 0.57683792],
[0.64654532, 0.85772593, 0.82795514]],
[[0.15633518, 0.28107627, 0.42305019],
[0.91107838, 0.73286470, 0.57683792],
[0.69272126, 0.86330457, 0.57258507]],
[[0.86466472, 0, 0],
[0.82271559, 0.50341470, 0.67372021],
[0.82093385, 0.77909002, 0.58521709]]],
[[[0, 0, 0.63212055],
[0.73552274, 0.77236231, 0.65006225],
[0.48829142, 0.81175293, 0.74842145]],
[[0, 0.950212931, 0],
[0.75092470, 0.22894841, 0.64654532],
[0.63940506, 0.92792154, 0.67044104]],
[[0, 0, 0],
[0.89771579, 0.87381422, 0.65699148],
[0.52288608, 0.89460078, 0.52763345]],
[[0.64654532, 0.58104845, 0.30926567],
[0.72746821, 0.76776373, 0.79607439],
[0.724729, 0.844327, 0.676967]]]]) # pyformat: disable
return voxels, images
def generate_preset_test_voxels_absorption_render():
"""Generates preset test for the absorption voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.6175, 0.413375, 0.43],
[0.27325, 0.7525, 0.2]],
[[0.107375, 0.453075, 0.481625],
[0.7919875, 0.54112625, 0.383775],
[0.4523725, 0.736325, 0.70984]],
[[0.085, 0.165, 0.275],
[0.7919875, 0.54112625, 0.383775],
[0.5212, 0.737375, 0.38]],
[[0.75, 0, 0],
[0.664084, 0.336275, 0.466],
[0.64637875, 0.593425, 0.391625]]],
[[[0, 0, 0.5],
[0.5597, 0.59340875, 0.4478125],
[0.3052, 0.653475, 0.5447]],
[[0, 0.875, 0],
[0.59275, 0.124575, 0.472],
[0.4463875, 0.826425, 0.46804]],
[[0, 0, 0],
[0.76438, 0.7207, 0.44976],
[0.351055, 0.7713925, 0.3484]],
[[0.51, 0.435, 0.185],
[0.53624, 0.58452, 0.6264125],
[0.5294, 0.6985, 0.512425]]]]) # pyformat: disable
return voxels, images
def generate_preset_test_voxels_emission_absorption_render():
"""Generates preset test for the emission absorption voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.19553845, 0.27123076, 0.82],
[0.08, 0.39999998, 0.4]],
[[0.10144142, 0.46858389, 0.8065],
[0.47932099, 0.41181099, 0.6751],
[0.22078022, 0.23262935, 1.11352]],
[[0.0935, 0.18149999, 0.55],
[0.47932099, 0.41181099, 0.6751],
[0.30814825, 0.43694864, 0.67]],
[[0, 0, 0],
[0.5677705, 0.17392569, 0.766],
[0.48741499, 0.44055107, 0.6865]]],
[[[0, 0, 1],
[0.28019208, 0.40287539, 0.7525],
[0.13121746, 0.42573205, 0.8461]],
[[0, 0, 0],
[0.16451199, 0.064448, 0.848],
[0.24191167, 0.69841443, 0.77812]],
[[0, 0, 0],
[0.56974806, 0.50646416, 0.74728],
[0.09611898, 0.32276643, 0.6436]],
[[0.0148, 0.32189999, 0.37],
[0.3099809, 0.33312645, 0.9433],
[0.55598098, 0.41542985, 0.9224]]]]) # pyformat: disable
return voxels, images
| # 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.
"""Test helpers for the voxels module."""
import numpy as np
def generate_random_test_voxels_render():
"""Generates random test for the voxels rendering functions."""
batch_shape = np.random.randint(1, 3)
voxels_shape = np.random.randint(2, 8, size=(3)).tolist()
signals_dimension = np.random.randint(2, 4)
random_voxels = np.random.uniform(size=[batch_shape] + voxels_shape +
[signals_dimension])
return random_voxels
def generate_preset_test_voxels_visual_hull_render():
"""Generates preset test for the visual hull voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.77686984, 0.59343034, 0.59343034],
[0.45118836, 0.87754357, 0.32967995]],
[[0.19748120, 0.63940506, 0.67372021],
[0.91107838, 0.73286470, 0.57683792],
[0.64654532, 0.85772593, 0.82795514]],
[[0.15633518, 0.28107627, 0.42305019],
[0.91107838, 0.73286470, 0.57683792],
[0.69272126, 0.86330457, 0.57258507]],
[[0.86466472, 0, 0],
[0.82271559, 0.50341470, 0.67372021],
[0.82093385, 0.77909002, 0.58521709]]],
[[[0, 0, 0.63212055],
[0.73552274, 0.77236231, 0.65006225],
[0.48829142, 0.81175293, 0.74842145]],
[[0, 0.950212931, 0],
[0.75092470, 0.22894841, 0.64654532],
[0.63940506, 0.92792154, 0.67044104]],
[[0, 0, 0],
[0.89771579, 0.87381422, 0.65699148],
[0.52288608, 0.89460078, 0.52763345]],
[[0.64654532, 0.58104845, 0.30926567],
[0.72746821, 0.76776373, 0.79607439],
[0.724729, 0.844327, 0.676967]]]]) # pyformat: disable
return voxels, images
def generate_preset_test_voxels_absorption_render():
"""Generates preset test for the absorption voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.6175, 0.413375, 0.43],
[0.27325, 0.7525, 0.2]],
[[0.107375, 0.453075, 0.481625],
[0.7919875, 0.54112625, 0.383775],
[0.4523725, 0.736325, 0.70984]],
[[0.085, 0.165, 0.275],
[0.7919875, 0.54112625, 0.383775],
[0.5212, 0.737375, 0.38]],
[[0.75, 0, 0],
[0.664084, 0.336275, 0.466],
[0.64637875, 0.593425, 0.391625]]],
[[[0, 0, 0.5],
[0.5597, 0.59340875, 0.4478125],
[0.3052, 0.653475, 0.5447]],
[[0, 0.875, 0],
[0.59275, 0.124575, 0.472],
[0.4463875, 0.826425, 0.46804]],
[[0, 0, 0],
[0.76438, 0.7207, 0.44976],
[0.351055, 0.7713925, 0.3484]],
[[0.51, 0.435, 0.185],
[0.53624, 0.58452, 0.6264125],
[0.5294, 0.6985, 0.512425]]]]) # pyformat: disable
return voxels, images
def generate_preset_test_voxels_emission_absorption_render():
"""Generates preset test for the emission absorption voxels rendering function."""
voxels = np.array([[[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.3, 0.7, 0.1], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.3, 0.2, 0]]],
[[[0.15, 0.69, 0.57], [0.07, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.17, 0.01, 1.22], [0.2, 1, 0.4], [0.67, 0.94, 0.14]]],
[[[0, 0, 0], [0.17, 0.33, 0.55], [0, 0, 0]],
[[0.71, 0.61, 0.43], [1, 0.1, 0], [0.71, 0.61, 0.43]],
[[0.1, 0.9, 0], [0.2, 1, 0.4], [0.88, 0.09, 0.45]]],
[[[1, 0, 0], [0, 0, 0], [1, 0, 0]],
[[0.88, 0.09, 0.5], [0.71, 0.61, 0.4], [0.14, 0, 0.22]],
[[0.71, 0.61, 0.45], [0.71, 0.7, 0.43], [0.3, 0.2, 0]]]],
[[[[0, 0, 0], [0, 0, 0], [0, 0, 1]],
[[0.3, 0.7, 0.1], [0.15, 0.69, 0.5], [0.88, 0.09, 0.45]],
[[0.07, 0.33, 0.55], [0.2, 1, 0.4], [0.4, 0.34, 0.43]]],
[[[0, 1, 0], [0, 1, 0], [0, 1, 0]],
[[0.19, 0.06, 0.24], [1, 0.1, 0], [0.2, 0.1, 0.8]],
[[0.67, 0.94, 0.14], [0.2, 1, 0.4], [0.15, 0.69, 0.57]]],
[[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0.74, 0.67, 0.4], [0.64, 0.8, 0.19], [0.9, 0.6, 0.48]],
[[0.1, 0.9, 0], [0.02, 0.37, 0.56], [0.62, 0.98, 0.19]]],
[[[0.04, 0.87, 0.37], [0, 0, 0], [1, 0, 0]],
[[0.3, 0.7, 0.1], [0.24, 0.12, 0.7], [0.76, 0.64, 0.79]],
[[0.7, 0.2, 0.2], [0.4, 1, 0.9], [0.19, 0.66, 0.03]]]]])
images = np.array([[[[0, 0, 0],
[0.19553845, 0.27123076, 0.82],
[0.08, 0.39999998, 0.4]],
[[0.10144142, 0.46858389, 0.8065],
[0.47932099, 0.41181099, 0.6751],
[0.22078022, 0.23262935, 1.11352]],
[[0.0935, 0.18149999, 0.55],
[0.47932099, 0.41181099, 0.6751],
[0.30814825, 0.43694864, 0.67]],
[[0, 0, 0],
[0.5677705, 0.17392569, 0.766],
[0.48741499, 0.44055107, 0.6865]]],
[[[0, 0, 1],
[0.28019208, 0.40287539, 0.7525],
[0.13121746, 0.42573205, 0.8461]],
[[0, 0, 0],
[0.16451199, 0.064448, 0.848],
[0.24191167, 0.69841443, 0.77812]],
[[0, 0, 0],
[0.56974806, 0.50646416, 0.74728],
[0.09611898, 0.32276643, 0.6436]],
[[0.0148, 0.32189999, 0.37],
[0.3099809, 0.33312645, 0.9433],
[0.55598098, 0.41542985, 0.9224]]]]) # pyformat: disable
return voxels, images
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/reconstruction.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 reconstructing scenes.
"""
import os
import numpy as np
from skimage import measure
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import evaluator
from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig
from tensorflow_graphics.projects.local_implicit_grid.core import point_utils as pt
class LIGOptimizer(object):
"""Class for using optimization to acquire feature grid."""
def __init__(self, ckpt, origin, grid_shape, part_size, occ_idx,
indep_pt_loss=True, overlap=True, alpha_lat=1e-2, npts=2048,
init_std=1e-2, learning_rate=1e-3, var_prefix='', nows=False):
self.ckpt = ckpt
self.ckpt_dir = os.path.dirname(ckpt)
self.params = self._load_params(self.ckpt_dir)
self.origin = origin
self.grid_shape = grid_shape
self.part_size = part_size
self.occ_idx = occ_idx
self.init_std = init_std
self.learning_rate = learning_rate
self.var_prefix = var_prefix
self.nows = nows
self.xmin = self.origin
if overlap:
true_shape = (np.array(grid_shape) - 1) / 2.0
self.xmax = self.origin + true_shape * part_size
else:
self.xmax = self.origin + (np.array(grid_shape) - 1) * part_size
_, sj, sk = self.grid_shape
self.occ_idx_flat = (self.occ_idx[:, 0]*(sj*sk)+
self.occ_idx[:, 1]*sk+self.occ_idx[:, 2])
self.indep_pt_loss = indep_pt_loss
self.overlap = overlap
self.alpha_lat = alpha_lat
self.npts = int(npts)
self._init_graph()
def _load_params(self, ckpt_dir):
param_file = os.path.join(ckpt_dir, 'params.txt')
params = evaluator.parse_param_file(param_file)
return params
def _init_graph(self):
"""Initialize computation graph for tensorflow.
"""
self.graph = tf.Graph()
with self.graph.as_default():
self.point_coords_ph = tf.placeholder(
tf.float32,
shape=[1, self.npts, 3]) # placeholder
self.point_values_ph = tf.placeholder(
tf.float32,
shape=[1, self.npts, 1]) # placeholder
self.point_coords = self.point_coords_ph
self.point_values = self.point_values_ph
self.liggrid = lig.LocalImplicitGrid(
size=self.grid_shape,
in_features=self.params['codelen'],
out_features=1,
num_filters=self.params['refiner_nf'],
net_type='imnet',
method='linear' if self.overlap else 'nn',
x_location_max=(1.0 if self.overlap else 2.0),
name='lig',
interp=(not self.indep_pt_loss),
min_grid_value=self.xmin,
max_grid_value=self.xmax)
si, sj, sk = self.grid_shape
self.occ_idx_flat_ = tf.convert_to_tensor(
self.occ_idx_flat[:, np.newaxis])
self.shape_ = tf.constant([si*sj*sk, self.params['codelen']],
dtype=tf.int64)
self.feat_sparse_ = tf.Variable(
(tf.random.normal(shape=[self.occ_idx.shape[0],
self.params['codelen']]) *
self.init_std),
trainable=True,
name='feat_sparse')
self.feat_grid = tf.scatter_nd(self.occ_idx_flat_,
self.feat_sparse_,
self.shape_)
self.feat_grid = tf.reshape(self.feat_grid,
[1, si, sj, sk, self.params['codelen']])
self.feat_norm = tf.norm(self.feat_sparse_, axis=-1)
if self.indep_pt_loss:
self.preds, self.weights = self.liggrid(self.feat_grid,
self.point_coords,
training=True)
# preds: [b, n, 8, 1], weights: [b, n, 8]
self.preds_interp = tf.reduce_sum(
tf.expand_dims(self.weights, axis=-1)*self.preds,
axis=2) # [b, n, 1]
self.preds = tf.concat([self.preds,
self.preds_interp[:, :, tf.newaxis, :]],
axis=2) # preds: [b, n, 9, 1]
self.point_values = tf.broadcast_to(
self.point_values[:, :, tf.newaxis, :],
shape=self.preds.shape) # [b, n, 9, 1]
else:
self.preds = self.liggrid(self.feat_grid,
self.point_coords,
training=True) # [b, n, 1]
self.labels_01 = (self.point_values+1) / 2 # turn labels to 0, 1 labels
self.loss_pt = tf.losses.sigmoid_cross_entropy(
self.labels_01,
logits=self.preds,
reduction=tf.losses.Reduction.NONE)
self.loss_lat = tf.reduce_mean(self.feat_norm) * self.alpha_lat
self.loss = tf.reduce_mean(self.loss_pt) + self.loss_lat
# compute accuracy metric
if self.indep_pt_loss:
self.pvalue = tf.sign(self.point_values[:, :, -1, 0])
self.ppred = tf.sign(self.preds[:, :, -1, 0])
else:
self.pvalue = tf.sign(self.point_values[..., 0])
self.ppred = tf.sign(self.preds[:, :, 0])
self.accu = tf.reduce_sum(tf.cast(
tf.logical_or(tf.logical_and(self.pvalue > 0, self.ppred > 0),
tf.logical_and(self.pvalue < 0, self.ppred < 0)),
tf.float32)) / float(self.npts)
# get optimizer
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
self.fgrid_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='feat_sparse')
self.train_op = self.optimizer.minimize(
self.loss,
global_step=tf.train.get_or_create_global_step(),
var_list=[self.fgrid_vars])
self.map_dict = self._get_var_mapping(model=self.liggrid,
scope=self.var_prefix)
self.sess = tf.Session()
if not self.nows:
self.saver = tf.train.Saver(self.map_dict)
self.saver.restore(self.sess, self.ckpt)
self._initialize_uninitialized(self.sess)
def _get_var_mapping(self, model, scope=''):
vars_ = model.trainable_variables
varnames = [v.name for v in vars_] # .split(':')[0]
varnames = [scope+v.replace('lig/', '').strip(':0') for v in varnames]
map_dict = dict(zip(varnames, vars_))
return map_dict
def _initialize_uninitialized(self, sess):
global_vars = tf.global_variables()
is_not_initialized = sess.run(
[tf.is_variable_initialized(var) for var in global_vars])
not_initialized_vars = [v for (v, f) in zip(global_vars,
is_not_initialized) if not f]
if not_initialized_vars:
sess.run(tf.variables_initializer(not_initialized_vars))
def optimize_feat_grid(self, point_coords, point_vals, steps=10000,
print_every_n_steps=1000):
"""Optimize feature grid.
Args:
point_coords: [npts, 3] point coordinates.
point_vals: [npts, 1] point values.
steps: int, number of steps for gradient descent.
print_every_n_steps: int, print every n steps.
Returns:
"""
print_every_n_steps = int(print_every_n_steps)
point_coords = point_coords.copy()
point_vals = np.sign(point_vals.copy())
if point_coords.ndim == 3:
point_coords = point_coords[0]
if point_vals.ndim == 3:
point_vals = point_vals[0]
elif point_vals.ndim == 1:
point_vals = point_vals[:, np.newaxis]
# clip
point_coords = np.clip(point_coords, self.xmin, self.xmax)
# shuffle points
seq = np.random.permutation(point_coords.shape[0])
point_coords = point_coords[seq]
point_vals = point_vals[seq]
point_coords = point_coords[np.newaxis]
point_vals = point_vals[np.newaxis]
# random point sampling function
def random_point_sample():
sid = np.random.choice(point_coords.shape[1]-self.npts+1)
eid = sid + self.npts
return point_coords[:, sid:eid], point_vals[:, sid:eid]
with self.graph.as_default():
for i in range(steps):
pc, pv = random_point_sample()
accu_, loss_, _ = self.sess.run([self.accu, self.loss, self.train_op],
feed_dict={
self.point_coords_ph: pc,
self.point_values_ph: pv})
if i % print_every_n_steps == 0:
print('Step [{:6d}] Accu: {:5.4f} Loss: {:5.4f}'.format(i,
accu_, loss_))
@property
def feature_grid(self):
with self.graph.as_default():
return self.sess.run(self.feat_grid)
def occupancy_sparse_to_dense(occ_idx, grid_shape):
dense = np.zeros(grid_shape, dtype=np.bool).ravel()
occ_idx_f = (occ_idx[:, 0] * grid_shape[1] * grid_shape[2] +
occ_idx[:, 1] * grid_shape[2] + occ_idx[:, 2])
dense[occ_idx_f] = True
dense = np.reshape(dense, grid_shape)
return dense
def get_in_out_from_samples(mesh, npoints, sample_factor=10, std=0.01):
"""Get in/out point samples from a given mesh.
Args:
mesh: trimesh mesh. Original mesh to sample points from.
npoints: int, number of points to sample on the mesh surface.
sample_factor: int, number of samples to pick per surface point.
std: float, std of samples to generate.
Returns:
surface_samples: [npoints, 6], where first 3 dims are xyz, last 3 dims are
normals (nx, ny, nz).
"""
surface_point_samples, fid = mesh.sample(int(npoints), return_index=True)
surface_point_normals = mesh.face_normals[fid]
offsets = np.random.randn(int(npoints), sample_factor, 1) * std
near_surface_samples = (surface_point_samples[:, np.newaxis, :] +
surface_point_normals[:, np.newaxis, :] * offsets)
near_surface_samples = np.concatenate([near_surface_samples, offsets],
axis=-1)
near_surface_samples = near_surface_samples.reshape([-1, 4])
surface_samples = np.concatenate([surface_point_samples,
surface_point_normals], axis=-1)
return surface_samples, near_surface_samples
def get_in_out_from_ray(points_from_ray, sample_factor=10, std=0.01):
"""Get sample points from points from ray.
Args:
points_from_ray: [npts, 6], where first 3 dims are xyz, last 3 are ray dir.
sample_factor: int, number of samples to pick per surface point.
std: float, std of samples to generate.
Returns:
near_surface_samples: [npts*sample_factor, 4], where last dimension is
distance to surface point.
"""
surface_point_samples = points_from_ray[:, :3]
surface_point_normals = points_from_ray[:, 3:]
# make sure normals are normalized to unit length
n = surface_point_normals
surface_point_normals = n / (np.linalg.norm(n, axis=1, keepdims=True)+1e-8)
npoints = points_from_ray.shape[0]
offsets = np.random.randn(npoints, sample_factor, 1) * std
near_surface_samples = (surface_point_samples[:, np.newaxis, :] +
surface_point_normals[:, np.newaxis, :] * offsets)
near_surface_samples = np.concatenate([near_surface_samples, offsets],
axis=-1)
near_surface_samples = near_surface_samples.reshape([-1, 4])
return near_surface_samples
def intrinsics_from_matrix(int_mat):
return (int_mat[0, 0], int_mat[1, 1], int_mat[0, 2], int_mat[1, 2])
def encode_decoder_one_scene(near_surface_samples, ckpt_dir, part_size,
overlap, indep_pt_loss,
xmin=np.zeros(3),
xmax=np.ones(3),
res_per_part=16, npts=4096, init_std=1e-4,
learning_rate=1e-3, steps=10000, nows=False,
verbose=False):
"""Wrapper function for encoding and decoding one scene.
Args:
near_surface_samples: [npts*sample_factor, 4], where last dimension is
distance to surface point.
ckpt_dir: str, path to checkpoint directory to use.
part_size: float, size of each part to use when autodecoding.
overlap: bool, whether to use overlapping encoding.
indep_pt_loss: bool, whether to use independent point loss in optimization.
xmin: np.array of len 3, lower coordinates of the domain bounds.
xmax: np.array of len 3, upper coordinates of the domain bounds.
res_per_part: int, resolution of output evaluation per part.
npts: int, number of points to use per step when doing gradient descent.
init_std: float, std to use when initializing seed.
learning_rate: float, learning rate for doing gradient descent.
steps: int, number of optimization steps to take.
nows: bool, no warmstarting from checkpoint. use random codebook.
verbose: bool, verbose mode.
Returns:
v: float32 np.array, vertices of reconstructed mesh.
f: int32 np.array, faces of reconstructed mesh.
feat_grid: float32 np.array, feature grid.
mask: bool np.array, mask of occupied cells.
"""
ckpt = tf.train.latest_checkpoint(ckpt_dir)
np.random.shuffle(near_surface_samples)
param_file = os.path.join(ckpt_dir, 'params.txt')
params = evaluator.parse_param_file(param_file)
_, occ_idx, grid_shape = pt.np_get_occupied_idx(
near_surface_samples[:100000, :3],
xmin=xmin-0.5*part_size, xmax=xmax+0.5*part_size, crop_size=part_size,
ntarget=1, overlap=overlap, normalize_crops=False, return_shape=True)
npts = min(npts, near_surface_samples.shape[0])
if verbose: print('LIG shape: {}'.format(grid_shape))
if verbose: print('Optimizing latent codes in LIG...')
goptim = LIGOptimizer(
ckpt, origin=xmin, grid_shape=grid_shape, part_size=part_size,
occ_idx=occ_idx, indep_pt_loss=indep_pt_loss, overlap=overlap,
alpha_lat=params['alpha_lat'], npts=npts, init_std=init_std,
learning_rate=learning_rate, var_prefix='', nows=nows)
goptim.optimize_feat_grid(near_surface_samples[:, :3],
near_surface_samples[:, 3:], steps=steps)
mask = occupancy_sparse_to_dense(occ_idx, grid_shape)
# evaluate mesh for the current crop
if verbose: print('Extracting mesh from LIG...')
svg = evaluator.SparseLIGEvaluator(
ckpt, num_filters=params['refiner_nf'],
codelen=params['codelen'], origin=xmin,
grid_shape=grid_shape, part_size=part_size,
overlap=overlap, scope='')
feat_grid = goptim.feature_grid[0]
out_grid = svg.evaluate_feature_grid(feat_grid,
mask=mask,
res_per_part=res_per_part)
v, f, _, _ = measure.marching_cubes_lewiner(out_grid, 0)
v *= (part_size / float(res_per_part) *
float(out_grid.shape[0]) / (float(out_grid.shape[0])-1))
v += xmin
return v, f, feat_grid, mask
| # 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 reconstructing scenes.
"""
import os
import numpy as np
from skimage import measure
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import evaluator
from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig
from tensorflow_graphics.projects.local_implicit_grid.core import point_utils as pt
class LIGOptimizer(object):
"""Class for using optimization to acquire feature grid."""
def __init__(self, ckpt, origin, grid_shape, part_size, occ_idx,
indep_pt_loss=True, overlap=True, alpha_lat=1e-2, npts=2048,
init_std=1e-2, learning_rate=1e-3, var_prefix='', nows=False):
self.ckpt = ckpt
self.ckpt_dir = os.path.dirname(ckpt)
self.params = self._load_params(self.ckpt_dir)
self.origin = origin
self.grid_shape = grid_shape
self.part_size = part_size
self.occ_idx = occ_idx
self.init_std = init_std
self.learning_rate = learning_rate
self.var_prefix = var_prefix
self.nows = nows
self.xmin = self.origin
if overlap:
true_shape = (np.array(grid_shape) - 1) / 2.0
self.xmax = self.origin + true_shape * part_size
else:
self.xmax = self.origin + (np.array(grid_shape) - 1) * part_size
_, sj, sk = self.grid_shape
self.occ_idx_flat = (self.occ_idx[:, 0]*(sj*sk)+
self.occ_idx[:, 1]*sk+self.occ_idx[:, 2])
self.indep_pt_loss = indep_pt_loss
self.overlap = overlap
self.alpha_lat = alpha_lat
self.npts = int(npts)
self._init_graph()
def _load_params(self, ckpt_dir):
param_file = os.path.join(ckpt_dir, 'params.txt')
params = evaluator.parse_param_file(param_file)
return params
def _init_graph(self):
"""Initialize computation graph for tensorflow.
"""
self.graph = tf.Graph()
with self.graph.as_default():
self.point_coords_ph = tf.placeholder(
tf.float32,
shape=[1, self.npts, 3]) # placeholder
self.point_values_ph = tf.placeholder(
tf.float32,
shape=[1, self.npts, 1]) # placeholder
self.point_coords = self.point_coords_ph
self.point_values = self.point_values_ph
self.liggrid = lig.LocalImplicitGrid(
size=self.grid_shape,
in_features=self.params['codelen'],
out_features=1,
num_filters=self.params['refiner_nf'],
net_type='imnet',
method='linear' if self.overlap else 'nn',
x_location_max=(1.0 if self.overlap else 2.0),
name='lig',
interp=(not self.indep_pt_loss),
min_grid_value=self.xmin,
max_grid_value=self.xmax)
si, sj, sk = self.grid_shape
self.occ_idx_flat_ = tf.convert_to_tensor(
self.occ_idx_flat[:, np.newaxis])
self.shape_ = tf.constant([si*sj*sk, self.params['codelen']],
dtype=tf.int64)
self.feat_sparse_ = tf.Variable(
(tf.random.normal(shape=[self.occ_idx.shape[0],
self.params['codelen']]) *
self.init_std),
trainable=True,
name='feat_sparse')
self.feat_grid = tf.scatter_nd(self.occ_idx_flat_,
self.feat_sparse_,
self.shape_)
self.feat_grid = tf.reshape(self.feat_grid,
[1, si, sj, sk, self.params['codelen']])
self.feat_norm = tf.norm(self.feat_sparse_, axis=-1)
if self.indep_pt_loss:
self.preds, self.weights = self.liggrid(self.feat_grid,
self.point_coords,
training=True)
# preds: [b, n, 8, 1], weights: [b, n, 8]
self.preds_interp = tf.reduce_sum(
tf.expand_dims(self.weights, axis=-1)*self.preds,
axis=2) # [b, n, 1]
self.preds = tf.concat([self.preds,
self.preds_interp[:, :, tf.newaxis, :]],
axis=2) # preds: [b, n, 9, 1]
self.point_values = tf.broadcast_to(
self.point_values[:, :, tf.newaxis, :],
shape=self.preds.shape) # [b, n, 9, 1]
else:
self.preds = self.liggrid(self.feat_grid,
self.point_coords,
training=True) # [b, n, 1]
self.labels_01 = (self.point_values+1) / 2 # turn labels to 0, 1 labels
self.loss_pt = tf.losses.sigmoid_cross_entropy(
self.labels_01,
logits=self.preds,
reduction=tf.losses.Reduction.NONE)
self.loss_lat = tf.reduce_mean(self.feat_norm) * self.alpha_lat
self.loss = tf.reduce_mean(self.loss_pt) + self.loss_lat
# compute accuracy metric
if self.indep_pt_loss:
self.pvalue = tf.sign(self.point_values[:, :, -1, 0])
self.ppred = tf.sign(self.preds[:, :, -1, 0])
else:
self.pvalue = tf.sign(self.point_values[..., 0])
self.ppred = tf.sign(self.preds[:, :, 0])
self.accu = tf.reduce_sum(tf.cast(
tf.logical_or(tf.logical_and(self.pvalue > 0, self.ppred > 0),
tf.logical_and(self.pvalue < 0, self.ppred < 0)),
tf.float32)) / float(self.npts)
# get optimizer
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
self.fgrid_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='feat_sparse')
self.train_op = self.optimizer.minimize(
self.loss,
global_step=tf.train.get_or_create_global_step(),
var_list=[self.fgrid_vars])
self.map_dict = self._get_var_mapping(model=self.liggrid,
scope=self.var_prefix)
self.sess = tf.Session()
if not self.nows:
self.saver = tf.train.Saver(self.map_dict)
self.saver.restore(self.sess, self.ckpt)
self._initialize_uninitialized(self.sess)
def _get_var_mapping(self, model, scope=''):
vars_ = model.trainable_variables
varnames = [v.name for v in vars_] # .split(':')[0]
varnames = [scope+v.replace('lig/', '').strip(':0') for v in varnames]
map_dict = dict(zip(varnames, vars_))
return map_dict
def _initialize_uninitialized(self, sess):
global_vars = tf.global_variables()
is_not_initialized = sess.run(
[tf.is_variable_initialized(var) for var in global_vars])
not_initialized_vars = [v for (v, f) in zip(global_vars,
is_not_initialized) if not f]
if not_initialized_vars:
sess.run(tf.variables_initializer(not_initialized_vars))
def optimize_feat_grid(self, point_coords, point_vals, steps=10000,
print_every_n_steps=1000):
"""Optimize feature grid.
Args:
point_coords: [npts, 3] point coordinates.
point_vals: [npts, 1] point values.
steps: int, number of steps for gradient descent.
print_every_n_steps: int, print every n steps.
Returns:
"""
print_every_n_steps = int(print_every_n_steps)
point_coords = point_coords.copy()
point_vals = np.sign(point_vals.copy())
if point_coords.ndim == 3:
point_coords = point_coords[0]
if point_vals.ndim == 3:
point_vals = point_vals[0]
elif point_vals.ndim == 1:
point_vals = point_vals[:, np.newaxis]
# clip
point_coords = np.clip(point_coords, self.xmin, self.xmax)
# shuffle points
seq = np.random.permutation(point_coords.shape[0])
point_coords = point_coords[seq]
point_vals = point_vals[seq]
point_coords = point_coords[np.newaxis]
point_vals = point_vals[np.newaxis]
# random point sampling function
def random_point_sample():
sid = np.random.choice(point_coords.shape[1]-self.npts+1)
eid = sid + self.npts
return point_coords[:, sid:eid], point_vals[:, sid:eid]
with self.graph.as_default():
for i in range(steps):
pc, pv = random_point_sample()
accu_, loss_, _ = self.sess.run([self.accu, self.loss, self.train_op],
feed_dict={
self.point_coords_ph: pc,
self.point_values_ph: pv})
if i % print_every_n_steps == 0:
print('Step [{:6d}] Accu: {:5.4f} Loss: {:5.4f}'.format(i,
accu_, loss_))
@property
def feature_grid(self):
with self.graph.as_default():
return self.sess.run(self.feat_grid)
def occupancy_sparse_to_dense(occ_idx, grid_shape):
dense = np.zeros(grid_shape, dtype=np.bool).ravel()
occ_idx_f = (occ_idx[:, 0] * grid_shape[1] * grid_shape[2] +
occ_idx[:, 1] * grid_shape[2] + occ_idx[:, 2])
dense[occ_idx_f] = True
dense = np.reshape(dense, grid_shape)
return dense
def get_in_out_from_samples(mesh, npoints, sample_factor=10, std=0.01):
"""Get in/out point samples from a given mesh.
Args:
mesh: trimesh mesh. Original mesh to sample points from.
npoints: int, number of points to sample on the mesh surface.
sample_factor: int, number of samples to pick per surface point.
std: float, std of samples to generate.
Returns:
surface_samples: [npoints, 6], where first 3 dims are xyz, last 3 dims are
normals (nx, ny, nz).
"""
surface_point_samples, fid = mesh.sample(int(npoints), return_index=True)
surface_point_normals = mesh.face_normals[fid]
offsets = np.random.randn(int(npoints), sample_factor, 1) * std
near_surface_samples = (surface_point_samples[:, np.newaxis, :] +
surface_point_normals[:, np.newaxis, :] * offsets)
near_surface_samples = np.concatenate([near_surface_samples, offsets],
axis=-1)
near_surface_samples = near_surface_samples.reshape([-1, 4])
surface_samples = np.concatenate([surface_point_samples,
surface_point_normals], axis=-1)
return surface_samples, near_surface_samples
def get_in_out_from_ray(points_from_ray, sample_factor=10, std=0.01):
"""Get sample points from points from ray.
Args:
points_from_ray: [npts, 6], where first 3 dims are xyz, last 3 are ray dir.
sample_factor: int, number of samples to pick per surface point.
std: float, std of samples to generate.
Returns:
near_surface_samples: [npts*sample_factor, 4], where last dimension is
distance to surface point.
"""
surface_point_samples = points_from_ray[:, :3]
surface_point_normals = points_from_ray[:, 3:]
# make sure normals are normalized to unit length
n = surface_point_normals
surface_point_normals = n / (np.linalg.norm(n, axis=1, keepdims=True)+1e-8)
npoints = points_from_ray.shape[0]
offsets = np.random.randn(npoints, sample_factor, 1) * std
near_surface_samples = (surface_point_samples[:, np.newaxis, :] +
surface_point_normals[:, np.newaxis, :] * offsets)
near_surface_samples = np.concatenate([near_surface_samples, offsets],
axis=-1)
near_surface_samples = near_surface_samples.reshape([-1, 4])
return near_surface_samples
def intrinsics_from_matrix(int_mat):
return (int_mat[0, 0], int_mat[1, 1], int_mat[0, 2], int_mat[1, 2])
def encode_decoder_one_scene(near_surface_samples, ckpt_dir, part_size,
overlap, indep_pt_loss,
xmin=np.zeros(3),
xmax=np.ones(3),
res_per_part=16, npts=4096, init_std=1e-4,
learning_rate=1e-3, steps=10000, nows=False,
verbose=False):
"""Wrapper function for encoding and decoding one scene.
Args:
near_surface_samples: [npts*sample_factor, 4], where last dimension is
distance to surface point.
ckpt_dir: str, path to checkpoint directory to use.
part_size: float, size of each part to use when autodecoding.
overlap: bool, whether to use overlapping encoding.
indep_pt_loss: bool, whether to use independent point loss in optimization.
xmin: np.array of len 3, lower coordinates of the domain bounds.
xmax: np.array of len 3, upper coordinates of the domain bounds.
res_per_part: int, resolution of output evaluation per part.
npts: int, number of points to use per step when doing gradient descent.
init_std: float, std to use when initializing seed.
learning_rate: float, learning rate for doing gradient descent.
steps: int, number of optimization steps to take.
nows: bool, no warmstarting from checkpoint. use random codebook.
verbose: bool, verbose mode.
Returns:
v: float32 np.array, vertices of reconstructed mesh.
f: int32 np.array, faces of reconstructed mesh.
feat_grid: float32 np.array, feature grid.
mask: bool np.array, mask of occupied cells.
"""
ckpt = tf.train.latest_checkpoint(ckpt_dir)
np.random.shuffle(near_surface_samples)
param_file = os.path.join(ckpt_dir, 'params.txt')
params = evaluator.parse_param_file(param_file)
_, occ_idx, grid_shape = pt.np_get_occupied_idx(
near_surface_samples[:100000, :3],
xmin=xmin-0.5*part_size, xmax=xmax+0.5*part_size, crop_size=part_size,
ntarget=1, overlap=overlap, normalize_crops=False, return_shape=True)
npts = min(npts, near_surface_samples.shape[0])
if verbose: print('LIG shape: {}'.format(grid_shape))
if verbose: print('Optimizing latent codes in LIG...')
goptim = LIGOptimizer(
ckpt, origin=xmin, grid_shape=grid_shape, part_size=part_size,
occ_idx=occ_idx, indep_pt_loss=indep_pt_loss, overlap=overlap,
alpha_lat=params['alpha_lat'], npts=npts, init_std=init_std,
learning_rate=learning_rate, var_prefix='', nows=nows)
goptim.optimize_feat_grid(near_surface_samples[:, :3],
near_surface_samples[:, 3:], steps=steps)
mask = occupancy_sparse_to_dense(occ_idx, grid_shape)
# evaluate mesh for the current crop
if verbose: print('Extracting mesh from LIG...')
svg = evaluator.SparseLIGEvaluator(
ckpt, num_filters=params['refiner_nf'],
codelen=params['codelen'], origin=xmin,
grid_shape=grid_shape, part_size=part_size,
overlap=overlap, scope='')
feat_grid = goptim.feature_grid[0]
out_grid = svg.evaluate_feature_grid(feat_grid,
mask=mask,
res_per_part=res_per_part)
v, f, _, _ = measure.marching_cubes_lewiner(out_grid, 0)
v *= (part_size / float(res_per_part) *
float(out_grid.shape[0]) / (float(out_grid.shape[0])-1))
v += xmin
return v, f, feat_grid, mask
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/__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.
"""Camera module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_graphics.rendering.camera import orthographic
from tensorflow_graphics.rendering.camera import perspective
from tensorflow_graphics.rendering.camera import quadratic_radial_distortion
from tensorflow_graphics.util import export_api as _export_api
# API contains submodules of tensorflow_graphics.rendering.camera.
__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.
"""Camera module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_graphics.rendering.camera import orthographic
from tensorflow_graphics.rendering.camera import perspective
from tensorflow_graphics.rendering.camera import quadratic_radial_distortion
from tensorflow_graphics.util import export_api as _export_api
# API contains submodules of tensorflow_graphics.rendering.camera.
__all__ = _export_api.get_modules()
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/_book.yaml | upper_tabs:
# Tabs left of dropdown menu
- include: /_upper_tabs_left.yaml
- include: /api_docs/_upper_tabs_api.yaml
# Dropdown menu
- name: Resources
path: /resources
is_default: true
menu:
- include: /resources/_menu_toc.yaml
lower_tabs:
# Subsite tabs
other:
- name: Guide & Tutorials
contents:
- title: Overview
path: /graphics/overview
- title: Install
path: /graphics/install
- title: Contributing
path: https://github.com/tensorflow/graphics/blob/master/CONTRIBUTING.md
status: external
- title: Debug
path: /graphics/debug_mode
- title: TensorBoard
path: /graphics/tensorboard
- heading: Tutorials
- title: 6DOF alignment
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb
status: external
- title: Camera intrinsics optimization
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb
status: external
- title: Interpolation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb
status: external
- title: Reflectance
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb
status: external
- title: Non-rigid deformation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb
status: external
- title: Spherical harmonics rendering
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb
status: external
- title: Environment map optimization
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb
status: external
- title: Semantic mesh segmentation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb
status: external
- name: API
skip_translation: true
contents:
- include: /graphics/api_docs/python/tfg/_toc.yaml
- include: /_upper_tabs_right.yaml
| upper_tabs:
# Tabs left of dropdown menu
- include: /_upper_tabs_left.yaml
- include: /api_docs/_upper_tabs_api.yaml
# Dropdown menu
- name: Resources
path: /resources
is_default: true
menu:
- include: /resources/_menu_toc.yaml
lower_tabs:
# Subsite tabs
other:
- name: Guide & Tutorials
contents:
- title: Overview
path: /graphics/overview
- title: Install
path: /graphics/install
- title: Contributing
path: https://github.com/tensorflow/graphics/blob/master/CONTRIBUTING.md
status: external
- title: Debug
path: /graphics/debug_mode
- title: TensorBoard
path: /graphics/tensorboard
- heading: Tutorials
- title: 6DOF alignment
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb
status: external
- title: Camera intrinsics optimization
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb
status: external
- title: Interpolation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb
status: external
- title: Reflectance
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb
status: external
- title: Non-rigid deformation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb
status: external
- title: Spherical harmonics rendering
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb
status: external
- title: Environment map optimization
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb
status: external
- title: Semantic mesh segmentation
path: https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb
status: external
- name: API
skip_translation: true
contents:
- include: /graphics/api_docs/python/tfg/_toc.yaml
- include: /_upper_tabs_right.yaml
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/requirements.txt | absl-py>=0.7.1
numpy>=1.16.4
plyfile>=0.7.1
scipy>=1.3.1
scikit-image>==0.15.0
trimesh>=3.2.12
tensorflow>=1.14.0
| absl-py>=0.7.1
numpy>=1.16.4
plyfile>=0.7.1
scipy>=1.3.1
scikit-image>==0.15.0
trimesh>=3.2.12
tensorflow>=1.14.0
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/pointnet/helpers.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.
"""A collection of training helper utilities."""
from __future__ import print_function
import argparse
import os
import tempfile
import time
import tensorflow as tf
import termcolor
class ArgumentParser(argparse.ArgumentParser):
"""Argument parser with default flags, and tensorboard helpers."""
def __init__(self, *args, **kwargs):
argparse.ArgumentParser.__init__(self, *args, **kwargs)
# --- Query default logdir
random_logdir = tempfile.mkdtemp(prefix="tensorboard_")
default_logdir = os.environ.get("TENSORBOARD_DEFAULT_LOGDIR", random_logdir)
# --- Add the default options
self.add("--logdir", default_logdir, help="tensorboard dir")
self.add("--tensorboard", True, help="should generate summaries?")
self.add("--assert_gpu", True, help="asserts on missing GPU accelerator")
self.add("--tf_quiet", True, help="no verbose tf startup")
def add(self, name, default, **kwargs):
"""More compact argumentparser 'add' flag method."""
helpstring = kwargs["help"] if "help" in kwargs else ""
metavar = kwargs["metavar"] if "metavar" in kwargs else name
# --- Fixes problems with bool arguments
def str2bool(string):
if isinstance(string, bool):
return str
if string.lower() in ("true", "yes"):
return True
if string.lower() in ("false", "no"):
return False
raise argparse.ArgumentTypeError("Bad value for boolean flag")
mytype = type(default)
if isinstance(default, bool):
mytype = str2bool
self.add_argument(
name, metavar=metavar, default=default, help=helpstring, type=mytype)
def parse_args(self, args=None, namespace=None):
"""WARNING: programmatically changes the logdir flags."""
flags = super(ArgumentParser, self).parse_args(args)
# --- setup automatic logdir (timestamp)
if "timestamp" in flags.logdir:
timestamp = time.strftime("%a%d_%H:%M:%S") # "Tue19_12:02:26"
flags.logdir = flags.logdir.replace("timestamp", timestamp)
if flags.tf_quiet:
set_tensorflow_log_level(3)
if flags.assert_gpu:
assert_gpu_available()
# --- ensure logdir ends in /
if flags.logdir[-1] != "/":
flags.logdir += "/"
return flags
def assert_gpu_available():
"""Verifies a GPU accelerator is available."""
physical_devices = tf.config.list_physical_devices("GPU")
num_gpus = len(physical_devices)
assert num_gpus >= 1, "execution requires one GPU"
def set_tensorflow_log_level(level=3):
"""Sets the log level of TensorFlow."""
os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(level)
def summary_command(parser, flags, log_to_file=True, log_to_summary=True):
"""Cache the command used to reproduce experiment in summary folder."""
if not flags.tensorboard:
return
exec_string = "python " + parser.prog + " \\\n"
nflags = len(vars(flags))
for i, arg in enumerate(vars(flags)):
exec_string += " --{} ".format(arg)
exec_string += "{}".format(getattr(flags, arg))
if i + 1 < nflags:
exec_string += " \\\n"
exec_string += "\n"
if log_to_file:
with tf.io.gfile.GFile(
os.path.join(flags.logdir, "command.txt"), mode="w") as fid:
fid.write(exec_string)
if log_to_summary and flags.tensorboard:
tf.summary.text("command", exec_string, step=0)
def setup_tensorboard(flags):
"""Creates summary writers, and setups default tensorboard paths."""
if not flags.tensorboard:
return
# --- Do not allow experiment with same name
assert (not tf.io.gfile.exists(flags.logdir) or
not tf.io.gfile.listdir(flags.logdir)), \
"CRITICAL: folder {} already exists".format(flags.logdir)
# --- Log where summary can be found
print("View results with: ")
termcolor.cprint(" tensorboard --logdir {}".format(flags.logdir), "red")
writer = tf.summary.create_file_writer(flags.logdir, flush_millis=10000)
writer.set_as_default()
# --- Log dir name tweak for "hypertune"
log_dir = ""
trial_id = int(os.environ.get("CLOUD_ML_TRIAL_ID", 0))
if trial_id != 0:
if log_dir.endswith(os.sep):
log_dir = log_dir[:-1] # removes trailing "/"
log_dir += "_trial{0:03d}/".format(trial_id)
def handle_keyboard_interrupt(flags):
"""Informs user how to delete stale summaries."""
print("Keyboard interrupt by user")
if flags.logdir.startswith("gs://"):
bucketpath = flags.logdir[5:]
print("Delete these summaries with: ")
termcolor.cprint(" gsutil rm -rf {}".format(flags.logdir), "red")
baseurl = " https://pantheon.google.com/storage/browser/{}"
print("Or by visiting: ")
termcolor.cprint(baseurl.format(bucketpath), "red")
else:
print("Delete these summaries with: ")
termcolor.cprint(" rm -rf {}".format(flags.logdir), "red")
| # 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.
"""A collection of training helper utilities."""
from __future__ import print_function
import argparse
import os
import tempfile
import time
import tensorflow as tf
import termcolor
class ArgumentParser(argparse.ArgumentParser):
"""Argument parser with default flags, and tensorboard helpers."""
def __init__(self, *args, **kwargs):
argparse.ArgumentParser.__init__(self, *args, **kwargs)
# --- Query default logdir
random_logdir = tempfile.mkdtemp(prefix="tensorboard_")
default_logdir = os.environ.get("TENSORBOARD_DEFAULT_LOGDIR", random_logdir)
# --- Add the default options
self.add("--logdir", default_logdir, help="tensorboard dir")
self.add("--tensorboard", True, help="should generate summaries?")
self.add("--assert_gpu", True, help="asserts on missing GPU accelerator")
self.add("--tf_quiet", True, help="no verbose tf startup")
def add(self, name, default, **kwargs):
"""More compact argumentparser 'add' flag method."""
helpstring = kwargs["help"] if "help" in kwargs else ""
metavar = kwargs["metavar"] if "metavar" in kwargs else name
# --- Fixes problems with bool arguments
def str2bool(string):
if isinstance(string, bool):
return str
if string.lower() in ("true", "yes"):
return True
if string.lower() in ("false", "no"):
return False
raise argparse.ArgumentTypeError("Bad value for boolean flag")
mytype = type(default)
if isinstance(default, bool):
mytype = str2bool
self.add_argument(
name, metavar=metavar, default=default, help=helpstring, type=mytype)
def parse_args(self, args=None, namespace=None):
"""WARNING: programmatically changes the logdir flags."""
flags = super(ArgumentParser, self).parse_args(args)
# --- setup automatic logdir (timestamp)
if "timestamp" in flags.logdir:
timestamp = time.strftime("%a%d_%H:%M:%S") # "Tue19_12:02:26"
flags.logdir = flags.logdir.replace("timestamp", timestamp)
if flags.tf_quiet:
set_tensorflow_log_level(3)
if flags.assert_gpu:
assert_gpu_available()
# --- ensure logdir ends in /
if flags.logdir[-1] != "/":
flags.logdir += "/"
return flags
def assert_gpu_available():
"""Verifies a GPU accelerator is available."""
physical_devices = tf.config.list_physical_devices("GPU")
num_gpus = len(physical_devices)
assert num_gpus >= 1, "execution requires one GPU"
def set_tensorflow_log_level(level=3):
"""Sets the log level of TensorFlow."""
os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(level)
def summary_command(parser, flags, log_to_file=True, log_to_summary=True):
"""Cache the command used to reproduce experiment in summary folder."""
if not flags.tensorboard:
return
exec_string = "python " + parser.prog + " \\\n"
nflags = len(vars(flags))
for i, arg in enumerate(vars(flags)):
exec_string += " --{} ".format(arg)
exec_string += "{}".format(getattr(flags, arg))
if i + 1 < nflags:
exec_string += " \\\n"
exec_string += "\n"
if log_to_file:
with tf.io.gfile.GFile(
os.path.join(flags.logdir, "command.txt"), mode="w") as fid:
fid.write(exec_string)
if log_to_summary and flags.tensorboard:
tf.summary.text("command", exec_string, step=0)
def setup_tensorboard(flags):
"""Creates summary writers, and setups default tensorboard paths."""
if not flags.tensorboard:
return
# --- Do not allow experiment with same name
assert (not tf.io.gfile.exists(flags.logdir) or
not tf.io.gfile.listdir(flags.logdir)), \
"CRITICAL: folder {} already exists".format(flags.logdir)
# --- Log where summary can be found
print("View results with: ")
termcolor.cprint(" tensorboard --logdir {}".format(flags.logdir), "red")
writer = tf.summary.create_file_writer(flags.logdir, flush_millis=10000)
writer.set_as_default()
# --- Log dir name tweak for "hypertune"
log_dir = ""
trial_id = int(os.environ.get("CLOUD_ML_TRIAL_ID", 0))
if trial_id != 0:
if log_dir.endswith(os.sep):
log_dir = log_dir[:-1] # removes trailing "/"
log_dir += "_trial{0:03d}/".format(trial_id)
def handle_keyboard_interrupt(flags):
"""Informs user how to delete stale summaries."""
print("Keyboard interrupt by user")
if flags.logdir.startswith("gs://"):
bucketpath = flags.logdir[5:]
print("Delete these summaries with: ")
termcolor.cprint(" gsutil rm -rf {}".format(flags.logdir), "red")
baseurl = " https://pantheon.google.com/storage/browser/{}"
print("Or by visiting: ")
termcolor.cprint(baseurl.format(bucketpath), "red")
else:
print("Delete these summaries with: ")
termcolor.cprint(" rm -rf {}".format(flags.logdir), "red")
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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
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tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/fixed_masks/0045.png | PNG
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tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/rasterization_backend_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.
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.geometry.representation import grid
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.rendering.opengl import rasterization_backend
from tensorflow_graphics.util import test_case
_IMAGE_HEIGHT = 5
_IMAGE_WIDTH = 7
_TRIANGLE_SIZE = 2.0
def _generate_vertices_and_view_matrices():
camera_origin = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))
camera_up = ((0.0, 1.0, 0.0), (0.0, 1.0, 0.0))
look_at_point = ((0.0, 0.0, 1.0), (0.0, 0.0, -1.0))
field_of_view = ((60 * np.math.pi / 180,), (60 * np.math.pi / 180,))
near_plane = ((0.01,), (0.01,))
far_plane = ((400.0,), (400.0,))
aspect_ratio = ((float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
(float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),))
# Construct the view projection matrix.
world_to_camera = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(field_of_view, aspect_ratio,
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix, world_to_camera)
depth = 1.0
vertices = (((-10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE,
depth), (10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE, depth),
(0.0, -10.0 * _TRIANGLE_SIZE, depth)),
((-_TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE, depth),
(0.0, 0.0, depth)))
return vertices, view_projection_matrix
def _proxy_rasterize(vertices, triangles, view_projection_matrices):
return rasterization_backend.rasterize(vertices, triangles,
view_projection_matrices,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
class RasterizationBackendTest(test_case.TestCase):
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 2), (17, 3),
(2, 6, 4, 4)),
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 3), (17, 2),
(2, 6, 4, 4)),
("must have a rank of 2", (2, 6, 32, 3), (3, 17, 2), (2, 6, 4, 4)),
("must have exactly 4 dimensions in axis -1", (2, 6, 32, 3), (17, 3),
(2, 6, 4, 3)),
("must have exactly 4 dimensions in axis -2", (2, 6, 32, 3), (17, 3),
(2, 6, 3, 4)),
("Not all batch dimensions are broadcast-compatible", (3, 6, 32, 3),
(17, 3), (5, 6, 4, 4)),
)
def test_rasterize_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(_proxy_rasterize, error_msg, shapes)
@parameterized.parameters(
(((32, 3), (17, 3), (4, 4)), (tf.float32, tf.int32, tf.float32)),
(((None, 32, 3), (17, 3), (None, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
(((None, 9, 32, 3), (17, 3), (None, 9, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
)
def test_rasterize_exception_not_raised(self, shapes, dtypes):
self.assert_exception_is_not_raised(
_proxy_rasterize, shapes=shapes, dtypes=dtypes)
def test_rasterize_batch_vertices_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
predicted_fb = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix[0],
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
mask = predicted_fb.foreground_mask
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
gt_layer_1 = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1), np.float32)
gt_layer_1[_IMAGE_HEIGHT // 2:, _IMAGE_WIDTH // 2:, 0] = 1.0
self.assertAllEqual(mask[1, ...], gt_layer_1)
def test_rasterize_batch_view_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
predicted_fb = rasterization_backend.rasterize(
vertices[0], triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(predicted_fb.foreground_mask[0, ...],
tf.ones_like(predicted_fb.foreground_mask[0, ...]))
self.assertAllEqual(predicted_fb.foreground_mask[1, ...],
tf.zeros_like(predicted_fb.foreground_mask[1, ...]))
def test_rasterize_preset(self):
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)
field_of_view = (60 * np.math.pi / 180,)
near_plane = (0.01,)
far_plane = (400.0,)
# Construct the view projection matrix.
model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(
field_of_view, (float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix,
model_to_eye_matrix)
depth = 1.0
vertices = ((-2.0 * _TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE,
depth), (0.0, 0.0, depth),
(0.0, -_TRIANGLE_SIZE, depth))
triangles = np.array(((1, 2, 0), (0, 2, 3)), np.int32)
predicted_fb = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
with self.subTest(name="triangle_index"):
groundtruth_triangle_index = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_triangle_index[..., :_IMAGE_WIDTH // 2, 0] = 0
groundtruth_triangle_index[:_IMAGE_HEIGHT // 2, _IMAGE_WIDTH // 2:, 0] = 1
self.assertAllEqual(groundtruth_triangle_index, predicted_fb.triangle_id)
with self.subTest(name="mask"):
groundtruth_mask = np.ones((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_mask[..., :_IMAGE_WIDTH // 2, 0] = 0
self.assertAllEqual(groundtruth_mask, predicted_fb.foreground_mask)
attributes = np.array(
((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))).astype(np.float32)
perspective_correct_interpolation = lambda geometry, pixels: glm.perspective_correct_interpolation( # pylint: disable=g-long-lambda,line-too-long
geometry, attributes, pixels, model_to_eye_matrix, perspective_matrix,
np.array((_IMAGE_WIDTH, _IMAGE_HEIGHT)).astype(np.float32),
np.array((0.0, 0.0)).astype(np.float32))
with self.subTest(name="barycentric_coordinates_triangle_0"):
geometry_0 = tf.gather(vertices, triangles[0, :])
pixels_0 = tf.transpose(
grid.generate((3.5, 2.5), (6.5, 4.5), (4, 3)), perm=(1, 0, 2))
barycentrics_gt_0 = perspective_correct_interpolation(
geometry_0, pixels_0)
self.assertAllClose(
barycentrics_gt_0,
predicted_fb.barycentrics.value[2:, 3:, :],
atol=1e-3)
with self.subTest(name="barycentric_coordinates_triangle_1"):
geometry_1 = tf.gather(vertices, triangles[1, :])
pixels_1 = tf.transpose(
grid.generate((3.5, 0.5), (6.5, 1.5), (4, 2)), perm=(1, 0, 2))
barycentrics_gt_1 = perspective_correct_interpolation(
geometry_1, pixels_1)
self.assertAllClose(
barycentrics_gt_1,
predicted_fb.barycentrics.value[0:2, 3:, :],
atol=1e-3)
| # 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.
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.geometry.representation import grid
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.rendering.opengl import rasterization_backend
from tensorflow_graphics.util import test_case
_IMAGE_HEIGHT = 5
_IMAGE_WIDTH = 7
_TRIANGLE_SIZE = 2.0
def _generate_vertices_and_view_matrices():
camera_origin = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))
camera_up = ((0.0, 1.0, 0.0), (0.0, 1.0, 0.0))
look_at_point = ((0.0, 0.0, 1.0), (0.0, 0.0, -1.0))
field_of_view = ((60 * np.math.pi / 180,), (60 * np.math.pi / 180,))
near_plane = ((0.01,), (0.01,))
far_plane = ((400.0,), (400.0,))
aspect_ratio = ((float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
(float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),))
# Construct the view projection matrix.
world_to_camera = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(field_of_view, aspect_ratio,
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix, world_to_camera)
depth = 1.0
vertices = (((-10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE,
depth), (10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE, depth),
(0.0, -10.0 * _TRIANGLE_SIZE, depth)),
((-_TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE, depth),
(0.0, 0.0, depth)))
return vertices, view_projection_matrix
def _proxy_rasterize(vertices, triangles, view_projection_matrices):
return rasterization_backend.rasterize(vertices, triangles,
view_projection_matrices,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
class RasterizationBackendTest(test_case.TestCase):
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 2), (17, 3),
(2, 6, 4, 4)),
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 3), (17, 2),
(2, 6, 4, 4)),
("must have a rank of 2", (2, 6, 32, 3), (3, 17, 2), (2, 6, 4, 4)),
("must have exactly 4 dimensions in axis -1", (2, 6, 32, 3), (17, 3),
(2, 6, 4, 3)),
("must have exactly 4 dimensions in axis -2", (2, 6, 32, 3), (17, 3),
(2, 6, 3, 4)),
("Not all batch dimensions are broadcast-compatible", (3, 6, 32, 3),
(17, 3), (5, 6, 4, 4)),
)
def test_rasterize_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(_proxy_rasterize, error_msg, shapes)
@parameterized.parameters(
(((32, 3), (17, 3), (4, 4)), (tf.float32, tf.int32, tf.float32)),
(((None, 32, 3), (17, 3), (None, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
(((None, 9, 32, 3), (17, 3), (None, 9, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
)
def test_rasterize_exception_not_raised(self, shapes, dtypes):
self.assert_exception_is_not_raised(
_proxy_rasterize, shapes=shapes, dtypes=dtypes)
def test_rasterize_batch_vertices_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
predicted_fb = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix[0],
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
mask = predicted_fb.foreground_mask
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
gt_layer_1 = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1), np.float32)
gt_layer_1[_IMAGE_HEIGHT // 2:, _IMAGE_WIDTH // 2:, 0] = 1.0
self.assertAllEqual(mask[1, ...], gt_layer_1)
def test_rasterize_batch_view_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
predicted_fb = rasterization_backend.rasterize(
vertices[0], triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(predicted_fb.foreground_mask[0, ...],
tf.ones_like(predicted_fb.foreground_mask[0, ...]))
self.assertAllEqual(predicted_fb.foreground_mask[1, ...],
tf.zeros_like(predicted_fb.foreground_mask[1, ...]))
def test_rasterize_preset(self):
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)
field_of_view = (60 * np.math.pi / 180,)
near_plane = (0.01,)
far_plane = (400.0,)
# Construct the view projection matrix.
model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(
field_of_view, (float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix,
model_to_eye_matrix)
depth = 1.0
vertices = ((-2.0 * _TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE,
depth), (0.0, 0.0, depth),
(0.0, -_TRIANGLE_SIZE, depth))
triangles = np.array(((1, 2, 0), (0, 2, 3)), np.int32)
predicted_fb = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
with self.subTest(name="triangle_index"):
groundtruth_triangle_index = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_triangle_index[..., :_IMAGE_WIDTH // 2, 0] = 0
groundtruth_triangle_index[:_IMAGE_HEIGHT // 2, _IMAGE_WIDTH // 2:, 0] = 1
self.assertAllEqual(groundtruth_triangle_index, predicted_fb.triangle_id)
with self.subTest(name="mask"):
groundtruth_mask = np.ones((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_mask[..., :_IMAGE_WIDTH // 2, 0] = 0
self.assertAllEqual(groundtruth_mask, predicted_fb.foreground_mask)
attributes = np.array(
((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))).astype(np.float32)
perspective_correct_interpolation = lambda geometry, pixels: glm.perspective_correct_interpolation( # pylint: disable=g-long-lambda,line-too-long
geometry, attributes, pixels, model_to_eye_matrix, perspective_matrix,
np.array((_IMAGE_WIDTH, _IMAGE_HEIGHT)).astype(np.float32),
np.array((0.0, 0.0)).astype(np.float32))
with self.subTest(name="barycentric_coordinates_triangle_0"):
geometry_0 = tf.gather(vertices, triangles[0, :])
pixels_0 = tf.transpose(
grid.generate((3.5, 2.5), (6.5, 4.5), (4, 3)), perm=(1, 0, 2))
barycentrics_gt_0 = perspective_correct_interpolation(
geometry_0, pixels_0)
self.assertAllClose(
barycentrics_gt_0,
predicted_fb.barycentrics.value[2:, 3:, :],
atol=1e-3)
with self.subTest(name="barycentric_coordinates_triangle_1"):
geometry_1 = tf.gather(vertices, triangles[1, :])
pixels_1 = tf.transpose(
grid.generate((3.5, 0.5), (6.5, 1.5), (4, 2)), perm=(1, 0, 2))
barycentrics_gt_1 = perspective_correct_interpolation(
geometry_1, pixels_1)
self.assertAllClose(
barycentrics_gt_1,
predicted_fb.barycentrics.value[0:2, 3:, :],
atol=1e-3)
| -1 |
tensorflow/graphics | 486 | 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] | "2021-01-29T04:02:31Z" | "2021-02-07T22:38:58Z" | 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/linear_rgb_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 LinearRGBTest(test_case.TestCase):
def test_cycle_srgb_linear_rgb_srgb_for_random_input(self):
"""Tests loop from sRGB to linear RGB and back for random inputs."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
srgb_input = np.random.uniform(size=tensor_shape + [3])
linear_output = linear_rgb.from_srgb(srgb_input)
srgb_recovered = srgb.from_linear_rgb(linear_output)
self.assertAllClose(srgb_input, srgb_recovered)
@parameterized.parameters(
(((0., 0.5, 1.), (0.0404, 0.04045, 0.0405)),
((0., 0.214041, 1.), (0.003127, 0.003131, 0.003135))),)
def test_from_srgb_preset(self, test_inputs, test_outputs):
"""Tests conversion from sRGB to linear RGB space for preset inputs."""
self.assert_output_is_correct(linear_rgb.from_srgb, (test_inputs,),
(test_outputs,))
def test_from_srgb_jacobian_random(self):
"""Tests the Jacobian of the from_srgb function for random inputs."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
srgb_random_init = np.random.uniform(size=tensor_shape + [3])
self.assert_jacobian_is_correct_fn(linear_rgb.from_srgb, [srgb_random_init])
@parameterized.parameters(
(np.array((0., 0.01, 0.02)),), (np.array((0.05, 0.06, 1.)),),
(np.array((0.01, 0.04, 0.06)),))
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_srgb_jacobian_preset(self, inputs_init):
"""Tests the Jacobian of the from_srgb function for preset inputs."""
self.assert_jacobian_is_correct_fn(linear_rgb.from_srgb, [inputs_init])
@parameterized.parameters(
((3,),),
((None, None, None, 3),),
)
def test_from_srgb_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(linear_rgb.from_srgb, shape)
@parameterized.parameters(
("must have a rank greater than 0", ()),
("must have exactly 3 dimensions in axis -1", (2, 3, 4)),
)
def test_from_srgb_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(linear_rgb.from_srgb, 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 LinearRGBTest(test_case.TestCase):
def test_cycle_srgb_linear_rgb_srgb_for_random_input(self):
"""Tests loop from sRGB to linear RGB and back for random inputs."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
srgb_input = np.random.uniform(size=tensor_shape + [3])
linear_output = linear_rgb.from_srgb(srgb_input)
srgb_recovered = srgb.from_linear_rgb(linear_output)
self.assertAllClose(srgb_input, srgb_recovered)
@parameterized.parameters(
(((0., 0.5, 1.), (0.0404, 0.04045, 0.0405)),
((0., 0.214041, 1.), (0.003127, 0.003131, 0.003135))),)
def test_from_srgb_preset(self, test_inputs, test_outputs):
"""Tests conversion from sRGB to linear RGB space for preset inputs."""
self.assert_output_is_correct(linear_rgb.from_srgb, (test_inputs,),
(test_outputs,))
def test_from_srgb_jacobian_random(self):
"""Tests the Jacobian of the from_srgb function for random inputs."""
tensor_size = np.random.randint(3)
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
srgb_random_init = np.random.uniform(size=tensor_shape + [3])
self.assert_jacobian_is_correct_fn(linear_rgb.from_srgb, [srgb_random_init])
@parameterized.parameters(
(np.array((0., 0.01, 0.02)),), (np.array((0.05, 0.06, 1.)),),
(np.array((0.01, 0.04, 0.06)),))
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_srgb_jacobian_preset(self, inputs_init):
"""Tests the Jacobian of the from_srgb function for preset inputs."""
self.assert_jacobian_is_correct_fn(linear_rgb.from_srgb, [inputs_init])
@parameterized.parameters(
((3,),),
((None, None, None, 3),),
)
def test_from_srgb_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(linear_rgb.from_srgb, shape)
@parameterized.parameters(
("must have a rank greater than 0", ()),
("must have exactly 3 dimensions in axis -1", (2, 3, 4)),
)
def test_from_srgb_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(linear_rgb.from_srgb, error_msg, shape)
if __name__ == "__main__":
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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)))
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()
| 1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/opengl/rasterization_backend.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.
"""OpenGL rasterization backend for TF Graphics."""
import tensorflow as tf
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
# pylint: disable=g-import-not-at-top
try:
from tensorflow_graphics.rendering.opengl import gen_rasterizer_op as render_ops
except ImportError:
import os
dir_path = os.path.dirname(os.path.abspath(__file__))
render_ops = tf.load_op_library(os.path.join(dir_path, "rasterizer_op.so"))
# pylint: enable=g-import-not-at-top
def _dim_value(dim):
return 1 if dim is None else tf.compat.v1.dimension_value(dim)
# Empty vertex shader; all the work happens in the geometry shader.
vertex_shader = """
#version 430
void main() { }
"""
# Geometry shader that projects the vertices of visible triangles onto the image
# plane.
geometry_shader = """
#version 430
uniform mat4 view_projection_matrix;
layout(points) in;
layout(triangle_strip, max_vertices=3) out;
out layout(location = 0) vec2 barycentric_coordinates;
out layout(location = 1) float triangle_index;
layout(binding=0) buffer triangular_mesh { float mesh_buffer[]; };
vec3 get_vertex_position(int vertex_index) {
// Triangles are packed as 3 consecuitve vertices, each with 3 coordinates.
int offset = gl_PrimitiveIDIn * 9 + vertex_index * 3;
return vec3(mesh_buffer[offset], mesh_buffer[offset + 1],
mesh_buffer[offset + 2]);
}
void main() {
vec3 positions[3] = {get_vertex_position(0), get_vertex_position(1),
get_vertex_position(2)};
vec4 projected_vertices[3] = {
view_projection_matrix * vec4(positions[0], 1.0),
view_projection_matrix * vec4(positions[1], 1.0),
view_projection_matrix * vec4(positions[2], 1.0)};
for (int i = 0; i < 3; ++i) {
// gl_Position is a pre-defined size 4 output variable.
gl_Position = projected_vertices[i];
barycentric_coordinates = vec2(i==0 ? 1.0 : 0.0, i==1 ? 1.0 : 0.0);
triangle_index = gl_PrimitiveIDIn;
EmitVertex();
}
EndPrimitive();
}
"""
# Fragment shader that packs barycentric coordinates, and triangle index.
fragment_shader = """
#version 430
in layout(location = 0) vec2 barycentric_coordinates;
in layout(location = 1) float triangle_index;
out vec4 output_color;
void main() {
output_color = vec4(round(triangle_index + 1.0), barycentric_coordinates, 1.0);
}
"""
def rasterize(vertices,
triangles,
view_projection_matrices,
image_size,
name=None):
"""Rasterizes the scene.
This rasterizer estimates which triangle is associated with each pixel using
OpenGL.
Note:
In the following, A1 to An are optional batch dimensions which must be
broadcast compatible for inputs `vertices` and `view_projection_matrices`.
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 `scene_vertices`
view_projection_matrices: A tensor of shape `[A1, ..., An, 4, 4]` containing
batches of view projection matrices
image_size: An tuple of integers (width, height) containing the dimensions
in pixels of the rasterized image.
name: A name for this op. Defaults to 'rasterization_backend_rasterize'.
Returns:
A tuple of 3 elements. The first one of shape `[A1, ..., An, H, W, 1]`
representing the triangle index associated with each pixel. If no triangle
is associated to a pixel, the index is set to -1.
The second element in the tuple is of shape `[A1, ..., An, H, W, 3]` and
correspond to barycentric coordinates per pixel. The last element in the
tuple is of shape `[A1, ..., An, H, W]` and stores a value of `0` of the
pixel is assciated with the background, and `1` with the foreground.
"""
with tf.compat.v1.name_scope(name, "rasterization_backend_rasterize",
(vertices, triangles, view_projection_matrices)):
vertices = tf.convert_to_tensor(value=vertices)
triangles = tf.convert_to_tensor(value=triangles)
view_projection_matrices = tf.convert_to_tensor(
value=view_projection_matrices)
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=view_projection_matrices,
tensor_name="view_projection_matrices",
has_rank_greater_than=1,
has_dim_equals=((-1, 4), (-2, 4)))
shape.compare_batch_dimensions(
tensors=(vertices, view_projection_matrices),
tensor_names=("vertices", "view_projection_matrices"),
last_axes=(-3, -3),
broadcast_compatible=True)
common_batch_shape = shape.get_broadcasted_shape(
vertices.shape[:-2], view_projection_matrices.shape[:-2])
common_batch_shape = [_dim_value(dim) for dim in common_batch_shape]
vertices = tf.broadcast_to(vertices,
common_batch_shape + vertices.shape[-2:])
view_projection_matrices = tf.broadcast_to(view_projection_matrices,
common_batch_shape + [4, 4])
geometry = tf.gather(vertices, triangles, axis=-2)
rasterized = render_ops.rasterize(
num_points=geometry.shape[-3],
alpha_clear=0.0,
enable_cull_face=True,
variable_names=("view_projection_matrix", "triangular_mesh"),
variable_kinds=("mat", "buffer"),
variable_values=(view_projection_matrices,
tf.reshape(geometry, shape=common_batch_shape + [-1])),
output_resolution=image_size,
vertex_shader=vertex_shader,
geometry_shader=geometry_shader,
fragment_shader=fragment_shader)
triangle_index = tf.cast(rasterized[..., 0], tf.int32) - 1
barycentric_coordinates = rasterized[..., 1:3]
barycentric_coordinates = tf.concat(
(barycentric_coordinates, 1.0 - barycentric_coordinates[..., 0:1] -
barycentric_coordinates[..., 1:2]),
axis=-1)
mask = tf.cast(rasterized[..., 3], tf.int32)
return triangle_index, barycentric_coordinates, mask
# 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.
"""OpenGL rasterization backend for TF Graphics."""
import tensorflow as tf
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
# pylint: disable=g-import-not-at-top
try:
from tensorflow_graphics.rendering.opengl import gen_rasterizer_op as render_ops
except ImportError:
import os
dir_path = os.path.dirname(os.path.abspath(__file__))
render_ops = tf.load_op_library(os.path.join(dir_path, "rasterizer_op.so"))
# pylint: enable=g-import-not-at-top
def _dim_value(dim):
return 1 if dim is None else tf.compat.v1.dimension_value(dim)
# Empty vertex shader; all the work happens in the geometry shader.
vertex_shader = """
#version 430
void main() { }
"""
# Geometry shader that projects the vertices of visible triangles onto the image
# plane.
geometry_shader = """
#version 430
uniform mat4 view_projection_matrix;
layout(points) in;
layout(triangle_strip, max_vertices=3) out;
out layout(location = 0) vec2 barycentric_coordinates;
out layout(location = 1) float triangle_index;
layout(binding=0) buffer triangular_mesh { float mesh_buffer[]; };
vec3 get_vertex_position(int vertex_index) {
// Triangles are packed as 3 consecuitve vertices, each with 3 coordinates.
int offset = gl_PrimitiveIDIn * 9 + vertex_index * 3;
return vec3(mesh_buffer[offset], mesh_buffer[offset + 1],
mesh_buffer[offset + 2]);
}
void main() {
vec3 positions[3] = {get_vertex_position(0), get_vertex_position(1),
get_vertex_position(2)};
vec4 projected_vertices[3] = {
view_projection_matrix * vec4(positions[0], 1.0),
view_projection_matrix * vec4(positions[1], 1.0),
view_projection_matrix * vec4(positions[2], 1.0)};
for (int i = 0; i < 3; ++i) {
// gl_Position is a pre-defined size 4 output variable.
gl_Position = projected_vertices[i];
barycentric_coordinates = vec2(i==0 ? 1.0 : 0.0, i==1 ? 1.0 : 0.0);
triangle_index = gl_PrimitiveIDIn;
EmitVertex();
}
EndPrimitive();
}
"""
# Fragment shader that packs barycentric coordinates, and triangle index.
fragment_shader = """
#version 430
in layout(location = 0) vec2 barycentric_coordinates;
in layout(location = 1) float triangle_index;
out vec4 output_color;
void main() {
output_color = vec4(round(triangle_index + 1.0), barycentric_coordinates, 1.0);
}
"""
def rasterize(vertices,
triangles,
view_projection_matrices,
image_size,
name=None):
"""Rasterizes the scene.
This rasterizer estimates which triangle is associated with each pixel using
OpenGL.
Note:
In the following, A1 to An are optional batch dimensions which must be
broadcast compatible for inputs `vertices` and `view_projection_matrices`.
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 `scene_vertices`
view_projection_matrices: A tensor of shape `[A1, ..., An, 4, 4]` containing
batches of view projection matrices
image_size: An tuple of integers (width, height) containing the dimensions
in pixels of the rasterized image.
name: A name for this op. Defaults to 'rasterization_backend_rasterize'.
Returns:
A tuple of 3 elements. The first one of shape `[A1, ..., An, H, W, 1]`
representing the triangle index associated with each pixel. If no triangle
is associated to a pixel, the index is set to -1.
The second element in the tuple is of shape `[A1, ..., An, H, W, 3]` and
correspond to barycentric coordinates per pixel. The last element in the
tuple is of shape `[A1, ..., An, H, W, 1]` and stores a value of `0` of the
pixel is assciated with the background, and `1` with the foreground.
"""
with tf.compat.v1.name_scope(name, "rasterization_backend_rasterize",
(vertices, triangles, view_projection_matrices)):
vertices = tf.convert_to_tensor(value=vertices)
triangles = tf.convert_to_tensor(value=triangles)
view_projection_matrices = tf.convert_to_tensor(
value=view_projection_matrices)
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=view_projection_matrices,
tensor_name="view_projection_matrices",
has_rank_greater_than=1,
has_dim_equals=((-1, 4), (-2, 4)))
shape.compare_batch_dimensions(
tensors=(vertices, view_projection_matrices),
tensor_names=("vertices", "view_projection_matrices"),
last_axes=(-3, -3),
broadcast_compatible=True)
common_batch_shape = shape.get_broadcasted_shape(
vertices.shape[:-2], view_projection_matrices.shape[:-2])
common_batch_shape = [_dim_value(dim) for dim in common_batch_shape]
vertices = tf.broadcast_to(vertices,
common_batch_shape + vertices.shape[-2:])
view_projection_matrices = tf.broadcast_to(view_projection_matrices,
common_batch_shape + [4, 4])
geometry = tf.gather(vertices, triangles, axis=-2)
rasterized = render_ops.rasterize(
num_points=geometry.shape[-3],
alpha_clear=0.0,
enable_cull_face=True,
variable_names=("view_projection_matrix", "triangular_mesh"),
variable_kinds=("mat", "buffer"),
variable_values=(view_projection_matrices,
tf.reshape(geometry, shape=common_batch_shape + [-1])),
output_resolution=image_size,
vertex_shader=vertex_shader,
geometry_shader=geometry_shader,
fragment_shader=fragment_shader)
triangle_index = tf.cast(rasterized[..., 0], tf.int32) - 1
# Slicing of the tensor will result in all batch dimensions being
# `None` for tensorflow graph mode, therefore we have to fix it in order to
# have explicit shape.
width, height = image_size
triangle_index = tf.reshape(triangle_index,
common_batch_shape + [height, width, 1])
barycentric_coordinates = rasterized[..., 1:3]
barycentric_coordinates = tf.concat(
(barycentric_coordinates, 1.0 - barycentric_coordinates[..., 0:1] -
barycentric_coordinates[..., 1:2]),
axis=-1)
mask = tf.cast(rasterized[..., 3], tf.int32)
mask = tf.reshape(mask, common_batch_shape + [height, width, 1])
return triangle_index, barycentric_coordinates, mask
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| 1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/opengl/tests/rasterization_backend_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.
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.geometry.representation import grid
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.rendering.opengl import rasterization_backend
from tensorflow_graphics.util import test_case
_IMAGE_HEIGHT = 5
_IMAGE_WIDTH = 7
_TRIANGLE_SIZE = 2.0
def _generate_vertices_and_view_matrices():
camera_origin = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))
camera_up = ((0.0, 1.0, 0.0), (0.0, 1.0, 0.0))
look_at_point = ((0.0, 0.0, 1.0), (0.0, 0.0, -1.0))
field_of_view = ((60 * np.math.pi / 180,), (60 * np.math.pi / 180,))
near_plane = ((0.01,), (0.01,))
far_plane = ((400.0,), (400.0,))
aspect_ratio = ((float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
(float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),))
# Construct the view projection matrix.
world_to_camera = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(field_of_view, aspect_ratio,
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix, world_to_camera)
depth = 1.0
vertices = (((-10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE,
depth), (10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE, depth),
(0.0, -10.0 * _TRIANGLE_SIZE, depth)),
((-_TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE, depth),
(0.0, 0.0, depth)))
return vertices, view_projection_matrix
def _proxy_rasterize(vertices, triangles, view_projection_matrices):
return rasterization_backend.rasterize(vertices, triangles,
view_projection_matrices,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
class RasterizationBackendTest(test_case.TestCase):
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 2), (17, 3),
(2, 6, 4, 4)),
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 3), (17, 2),
(2, 6, 4, 4)),
("must have a rank of 2", (2, 6, 32, 3), (3, 17, 2), (2, 6, 4, 4)),
("must have exactly 4 dimensions in axis -1", (2, 6, 32, 3), (17, 3),
(2, 6, 4, 3)),
("must have exactly 4 dimensions in axis -2", (2, 6, 32, 3), (17, 3),
(2, 6, 3, 4)),
("Not all batch dimensions are broadcast-compatible", (3, 6, 32, 3),
(17, 3), (5, 6, 4, 4)),
)
def test_rasterize_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(_proxy_rasterize, error_msg, shapes)
@parameterized.parameters(
(((32, 3), (17, 3), (4, 4)), (tf.float32, tf.int32, tf.float32)),
(((None, 32, 3), (17, 3), (None, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
(((None, 9, 32, 3), (17, 3), (None, 9, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
)
def test_rasterize_exception_not_raised(self, shapes, dtypes):
self.assert_exception_is_not_raised(
_proxy_rasterize, shapes=shapes, dtypes=dtypes)
def test_rasterize_batch_vertices_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
_, _, mask = rasterization_backend.rasterize(vertices, triangles,
view_projection_matrix[0],
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
gt_layer_1 = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH), np.float32)
gt_layer_1[_IMAGE_HEIGHT // 2:, _IMAGE_WIDTH // 2:] = 1.0
self.assertAllEqual(mask[1, ...], gt_layer_1)
def test_rasterize_batch_view_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
_, _, mask = rasterization_backend.rasterize(vertices[0], triangles,
view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
self.assertAllEqual(mask[1, ...], tf.zeros_like(mask[1, ...]))
def test_rasterize_preset(self):
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)
field_of_view = (60 * np.math.pi / 180,)
near_plane = (0.01,)
far_plane = (400.0,)
# Construct the view projection matrix.
model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(
field_of_view, (float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix,
model_to_eye_matrix)
depth = 1.0
vertices = ((-2.0 * _TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE,
depth), (0.0, 0.0, depth),
(0.0, -_TRIANGLE_SIZE, depth))
triangles = np.array(((1, 2, 0), (0, 2, 3)), np.int32)
predicted_triangle_index, predicted_barycentrics, predicted_mask = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
with self.subTest(name="triangle_index"):
groundtruth_triangle_index = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH),
dtype=np.int32)
groundtruth_triangle_index[..., :_IMAGE_WIDTH // 2] = -1
groundtruth_triangle_index[:_IMAGE_HEIGHT // 2, _IMAGE_WIDTH // 2:] = 1
self.assertAllEqual(groundtruth_triangle_index, predicted_triangle_index)
with self.subTest(name="mask"):
groundtruth_mask = np.ones((_IMAGE_HEIGHT, _IMAGE_WIDTH), dtype=np.int32)
groundtruth_mask[..., :_IMAGE_WIDTH // 2] = 0
self.assertAllEqual(groundtruth_mask, predicted_mask)
attributes = np.array(
((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))).astype(np.float32)
perspective_correct_interpolation = lambda geometry, pixels: glm.perspective_correct_interpolation( # pylint: disable=g-long-lambda,line-too-long
geometry, attributes, pixels, model_to_eye_matrix, perspective_matrix,
np.array((_IMAGE_WIDTH, _IMAGE_HEIGHT)).astype(np.float32),
np.array((0.0, 0.0)).astype(np.float32))
with self.subTest(name="barycentric_coordinates_triangle_0"):
geometry_0 = tf.gather(vertices, triangles[0, :])
pixels_0 = tf.transpose(
grid.generate((3.5, 2.5), (6.5, 4.5), (4, 3)), perm=(1, 0, 2))
barycentrics_gt_0 = perspective_correct_interpolation(
geometry_0, pixels_0)
self.assertAllClose(
barycentrics_gt_0, predicted_barycentrics[2:, 3:, :], atol=1e-3)
with self.subTest(name="barycentric_coordinates_triangle_1"):
geometry_1 = tf.gather(vertices, triangles[1, :])
pixels_1 = tf.transpose(
grid.generate((3.5, 0.5), (6.5, 1.5), (4, 2)), perm=(1, 0, 2))
barycentrics_gt_1 = perspective_correct_interpolation(
geometry_1, pixels_1)
self.assertAllClose(
barycentrics_gt_1, predicted_barycentrics[0:2, 3:, :], atol=1e-3)
| # 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.
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.geometry.representation import grid
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.rendering.opengl import rasterization_backend
from tensorflow_graphics.util import test_case
_IMAGE_HEIGHT = 5
_IMAGE_WIDTH = 7
_TRIANGLE_SIZE = 2.0
def _generate_vertices_and_view_matrices():
camera_origin = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))
camera_up = ((0.0, 1.0, 0.0), (0.0, 1.0, 0.0))
look_at_point = ((0.0, 0.0, 1.0), (0.0, 0.0, -1.0))
field_of_view = ((60 * np.math.pi / 180,), (60 * np.math.pi / 180,))
near_plane = ((0.01,), (0.01,))
far_plane = ((400.0,), (400.0,))
aspect_ratio = ((float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
(float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),))
# Construct the view projection matrix.
world_to_camera = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(field_of_view, aspect_ratio,
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix, world_to_camera)
depth = 1.0
vertices = (((-10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE,
depth), (10.0 * _TRIANGLE_SIZE, 10.0 * _TRIANGLE_SIZE, depth),
(0.0, -10.0 * _TRIANGLE_SIZE, depth)),
((-_TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE, depth),
(0.0, 0.0, depth)))
return vertices, view_projection_matrix
def _proxy_rasterize(vertices, triangles, view_projection_matrices):
return rasterization_backend.rasterize(vertices, triangles,
view_projection_matrices,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
class RasterizationBackendTest(test_case.TestCase):
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 2), (17, 3),
(2, 6, 4, 4)),
("must have exactly 3 dimensions in axis -1", (2, 6, 32, 3), (17, 2),
(2, 6, 4, 4)),
("must have a rank of 2", (2, 6, 32, 3), (3, 17, 2), (2, 6, 4, 4)),
("must have exactly 4 dimensions in axis -1", (2, 6, 32, 3), (17, 3),
(2, 6, 4, 3)),
("must have exactly 4 dimensions in axis -2", (2, 6, 32, 3), (17, 3),
(2, 6, 3, 4)),
("Not all batch dimensions are broadcast-compatible", (3, 6, 32, 3),
(17, 3), (5, 6, 4, 4)),
)
def test_rasterize_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(_proxy_rasterize, error_msg, shapes)
@parameterized.parameters(
(((32, 3), (17, 3), (4, 4)), (tf.float32, tf.int32, tf.float32)),
(((None, 32, 3), (17, 3), (None, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
(((None, 9, 32, 3), (17, 3), (None, 9, 4, 4)),
(tf.float32, tf.int32, tf.float32)),
)
def test_rasterize_exception_not_raised(self, shapes, dtypes):
self.assert_exception_is_not_raised(
_proxy_rasterize, shapes=shapes, dtypes=dtypes)
def test_rasterize_batch_vertices_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
_, _, mask = rasterization_backend.rasterize(vertices, triangles,
view_projection_matrix[0],
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
gt_layer_1 = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1), np.float32)
gt_layer_1[_IMAGE_HEIGHT // 2:, _IMAGE_WIDTH // 2:, 0] = 1.0
self.assertAllEqual(mask[1, ...], gt_layer_1)
def test_rasterize_batch_view_only(self):
triangles = np.array(((0, 1, 2),), np.int32)
vertices, view_projection_matrix = _generate_vertices_and_view_matrices()
_, _, mask = rasterization_backend.rasterize(vertices[0], triangles,
view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
self.assertAllEqual(mask[0, ...], tf.ones_like(mask[0, ...]))
self.assertAllEqual(mask[1, ...], tf.zeros_like(mask[1, ...]))
def test_rasterize_preset(self):
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)
field_of_view = (60 * np.math.pi / 180,)
near_plane = (0.01,)
far_plane = (400.0,)
# Construct the view projection matrix.
model_to_eye_matrix = look_at.right_handed(camera_origin, look_at_point,
camera_up)
perspective_matrix = perspective.right_handed(
field_of_view, (float(_IMAGE_WIDTH) / float(_IMAGE_HEIGHT),),
near_plane, far_plane)
view_projection_matrix = tf.linalg.matmul(perspective_matrix,
model_to_eye_matrix)
depth = 1.0
vertices = ((-2.0 * _TRIANGLE_SIZE, 0.0, depth), (0.0, _TRIANGLE_SIZE,
depth), (0.0, 0.0, depth),
(0.0, -_TRIANGLE_SIZE, depth))
triangles = np.array(((1, 2, 0), (0, 2, 3)), np.int32)
predicted_triangle_index, predicted_barycentrics, predicted_mask = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix,
(_IMAGE_WIDTH, _IMAGE_HEIGHT))
with self.subTest(name="triangle_index"):
groundtruth_triangle_index = np.zeros((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_triangle_index[..., :_IMAGE_WIDTH // 2, 0] = -1
groundtruth_triangle_index[:_IMAGE_HEIGHT // 2, _IMAGE_WIDTH // 2:, 0] = 1
self.assertAllEqual(groundtruth_triangle_index, predicted_triangle_index)
with self.subTest(name="mask"):
groundtruth_mask = np.ones((_IMAGE_HEIGHT, _IMAGE_WIDTH, 1),
dtype=np.int32)
groundtruth_mask[..., :_IMAGE_WIDTH // 2, 0] = 0
self.assertAllEqual(groundtruth_mask, predicted_mask)
attributes = np.array(
((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))).astype(np.float32)
perspective_correct_interpolation = lambda geometry, pixels: glm.perspective_correct_interpolation( # pylint: disable=g-long-lambda,line-too-long
geometry, attributes, pixels, model_to_eye_matrix, perspective_matrix,
np.array((_IMAGE_WIDTH, _IMAGE_HEIGHT)).astype(np.float32),
np.array((0.0, 0.0)).astype(np.float32))
with self.subTest(name="barycentric_coordinates_triangle_0"):
geometry_0 = tf.gather(vertices, triangles[0, :])
pixels_0 = tf.transpose(
grid.generate((3.5, 2.5), (6.5, 4.5), (4, 3)), perm=(1, 0, 2))
barycentrics_gt_0 = perspective_correct_interpolation(
geometry_0, pixels_0)
self.assertAllClose(
barycentrics_gt_0, predicted_barycentrics[2:, 3:, :], atol=1e-3)
with self.subTest(name="barycentric_coordinates_triangle_1"):
geometry_1 = tf.gather(vertices, triangles[1, :])
pixels_1 = tf.transpose(
grid.generate((3.5, 0.5), (6.5, 1.5), (4, 2)), perm=(1, 0, 2))
barycentrics_gt_1 = perspective_correct_interpolation(
geometry_1, pixels_1)
self.assertAllClose(
barycentrics_gt_1, predicted_barycentrics[0:2, 3:, :], atol=1e-3)
| 1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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]` 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)
triangle_index, _, mask = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix, image_size_backend,
backend)
outputs = {"mask": mask, "triangle_indices": triangle_index}
batch_shape = triangle_index.shape[:-3]
batch_shape = [_dim_value(dim) for dim in batch_shape]
vertices = tf.gather(vertices, triangles, axis=-2)
# Gather does not work on negative indices, which is the case for the pixel
# associated to the background.
triangle_index = triangle_index * mask
vertices_per_pixel = tf.gather(
vertices, triangle_index, axis=-3, 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(tf.expand_dims(mask, axis=-1), 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, triangle_index, 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)
triangle_index, _, mask = rasterization_backend.rasterize(
vertices, triangles, view_projection_matrix, image_size_backend,
backend)
outputs = {"mask": mask, "triangle_indices": triangle_index}
vertices = tf.gather(vertices, triangles, axis=-2)
# Gather does not work on negative indices, which is the case for the pixel
# associated to the background.
triangle_index = triangle_index * mask
# Extract batch shape in order to make sure it is preserved after `gather`
# operation.
batch_shape = triangle_index.shape[:-3]
batch_shape = [_dim_value(dim) for dim in batch_shape]
# Remove last dimension of `triangle_index` in order to make it compatible
# with gather operations.
triangle_index_lean = tf.squeeze(triangle_index, axis=-1)
vertices_per_pixel = tf.gather(
vertices, triangle_index_lean, axis=-3, 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(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, triangle_index_lean,
len(batch_shape))
return outputs
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| 1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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 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()
# 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__ = []
| 1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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]
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/nasa/lib/models.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.
"""Model Implementations."""
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.nasa.lib import model_utils
tf.disable_eager_execution()
def get_model(hparams):
return model_dict[hparams.model](hparams)
def nasa(hparams):
"""Construct the model function of NASA."""
# Parse model parameters from global configurations.
n_parts = hparams.n_parts
n_dims = hparams.n_dims
transform_dims = (n_dims + 1)**2 # Using homogeneous coordinates.
lr = hparams.lr
level_set = hparams.level_set
label_w = hparams.label_w
minimal_w = hparams.minimal_w
sample_vert = hparams.sample_vert
sample_bbox = hparams.sample_bbox
def _model_fn(features, labels, mode, params=None):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
batch_size = features['point'].shape[0]
n_sample_frames = features['point'].shape[1]
accum_size = batch_size * n_sample_frames
if params == 'gen_mesh':
latent_output = tf.constant([0, 0, 0], dtype=tf.float32)
latent_holder = tf.placeholder(tf.float32, latent_output.shape)
# Decode the tranformed shapes and compute the losses
with tf.variable_scope('shape/decode', reuse=tf.AUTO_REUSE):
transform = tf.reshape(features['transform'],
[accum_size, n_parts, transform_dims])
joint = tf.reshape(features['joint'], [accum_size, n_parts, n_dims])
points = features['point']
n_points = tf.shape(points)[2]
points = tf.reshape(points, [accum_size, n_points, n_dims])
if is_training:
labels = tf.reshape(features['label'], [accum_size, n_points, 1])
predictions, parts = model_utils.nasa_indicator(
points, transform, joint, hparams, need_transformation=True)
indicator_loss = model_utils.compute_l2_indicator_loss(
labels, predictions)
minimal_loss = tf.reduce_mean(tf.square(parts[..., :sample_bbox, :]))
part_points = tf.reshape(features['vert'], [accum_size, -1, n_dims])
part_weight = tf.reshape(features['weight'], [accum_size, -1, n_parts])
if sample_vert > 0: # If 0, use all vertices.
n_vert = part_points.shape[1]
sample_indices = tf.random.uniform([accum_size, sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
part_points = tf.gather(
part_points, sample_indices, axis=1, batch_dims=1)
part_weight = tf.gather(
part_weight, sample_indices, axis=1, batch_dims=1)
unused_var, pred_parts = model_utils.nasa_indicator(
part_points, transform, joint, hparams, need_transformation=True)
part_label = tf.argmax(part_weight, axis=-1)
part_label = tf.one_hot(
part_label, depth=n_parts, axis=-1, dtype=tf.float32) * level_set
part_label = tf.expand_dims(
tf.transpose(part_label, [0, 2, 1]), axis=-1)
label_loss = model_utils.compute_l2_indicator_loss(
part_label, pred_parts)
else:
n_points = tf.shape(features['point'])[2]
points = tf.reshape(features['point'], [accum_size, n_points, n_dims])
predictions, parts = model_utils.nasa_indicator(
points,
transform,
joint,
hparams,
need_transformation=True,
noise=labels)
if params == 'gen_mesh':
return latent_holder, latent_output, tf.concat(
[parts, tf.expand_dims(predictions, axis=1)], axis=1)
tf.summary.scalar('indicator', indicator_loss)
loss = indicator_loss
if label_w > 0:
tf.summary.scalar('label', label_loss)
indicator_loss += label_loss * label_w
if minimal_w > 0:
tf.summary.scalar('minimal', minimal_loss)
indicator_loss += minimal_loss * minimal_w
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
indicator_loss, global_step=global_step, name='optimizer_shape')
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
return _model_fn
model_dict = {
'nasa': nasa,
}
| # 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.
"""Model Implementations."""
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.nasa.lib import model_utils
tf.disable_eager_execution()
def get_model(hparams):
return model_dict[hparams.model](hparams)
def nasa(hparams):
"""Construct the model function of NASA."""
# Parse model parameters from global configurations.
n_parts = hparams.n_parts
n_dims = hparams.n_dims
transform_dims = (n_dims + 1)**2 # Using homogeneous coordinates.
lr = hparams.lr
level_set = hparams.level_set
label_w = hparams.label_w
minimal_w = hparams.minimal_w
sample_vert = hparams.sample_vert
sample_bbox = hparams.sample_bbox
def _model_fn(features, labels, mode, params=None):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
batch_size = features['point'].shape[0]
n_sample_frames = features['point'].shape[1]
accum_size = batch_size * n_sample_frames
if params == 'gen_mesh':
latent_output = tf.constant([0, 0, 0], dtype=tf.float32)
latent_holder = tf.placeholder(tf.float32, latent_output.shape)
# Decode the tranformed shapes and compute the losses
with tf.variable_scope('shape/decode', reuse=tf.AUTO_REUSE):
transform = tf.reshape(features['transform'],
[accum_size, n_parts, transform_dims])
joint = tf.reshape(features['joint'], [accum_size, n_parts, n_dims])
points = features['point']
n_points = tf.shape(points)[2]
points = tf.reshape(points, [accum_size, n_points, n_dims])
if is_training:
labels = tf.reshape(features['label'], [accum_size, n_points, 1])
predictions, parts = model_utils.nasa_indicator(
points, transform, joint, hparams, need_transformation=True)
indicator_loss = model_utils.compute_l2_indicator_loss(
labels, predictions)
minimal_loss = tf.reduce_mean(tf.square(parts[..., :sample_bbox, :]))
part_points = tf.reshape(features['vert'], [accum_size, -1, n_dims])
part_weight = tf.reshape(features['weight'], [accum_size, -1, n_parts])
if sample_vert > 0: # If 0, use all vertices.
n_vert = part_points.shape[1]
sample_indices = tf.random.uniform([accum_size, sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
part_points = tf.gather(
part_points, sample_indices, axis=1, batch_dims=1)
part_weight = tf.gather(
part_weight, sample_indices, axis=1, batch_dims=1)
unused_var, pred_parts = model_utils.nasa_indicator(
part_points, transform, joint, hparams, need_transformation=True)
part_label = tf.argmax(part_weight, axis=-1)
part_label = tf.one_hot(
part_label, depth=n_parts, axis=-1, dtype=tf.float32) * level_set
part_label = tf.expand_dims(
tf.transpose(part_label, [0, 2, 1]), axis=-1)
label_loss = model_utils.compute_l2_indicator_loss(
part_label, pred_parts)
else:
n_points = tf.shape(features['point'])[2]
points = tf.reshape(features['point'], [accum_size, n_points, n_dims])
predictions, parts = model_utils.nasa_indicator(
points,
transform,
joint,
hparams,
need_transformation=True,
noise=labels)
if params == 'gen_mesh':
return latent_holder, latent_output, tf.concat(
[parts, tf.expand_dims(predictions, axis=1)], axis=1)
tf.summary.scalar('indicator', indicator_loss)
loss = indicator_loss
if label_w > 0:
tf.summary.scalar('label', label_loss)
indicator_loss += label_loss * label_w
if minimal_w > 0:
tf.summary.scalar('minimal', minimal_loss)
indicator_loss += minimal_loss * minimal_w
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
indicator_loss, global_step=global_step, name='optimizer_shape')
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
return _model_fn
model_dict = {
'nasa': nasa,
}
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/camera/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.
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/camera/orthographic.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 orthographic camera functionalities.
An orthographic camera represents three-dimensional objects in two dimensions
by parallel projection, in which the projection lines are parallel to the
camera axis. The camera axis is the line perpendicular to the image plane
starting at the camera center.
"""
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 project(point_3d, 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
$$
\begin{matrix}
x' = x, & y' = y.
\end{matrix}
$$
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_3d: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a 3d point to project.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_project", [point_3d]):
point_3d = tf.convert_to_tensor(value=point_3d)
shape.check_static(
tensor=point_3d, tensor_name="point_3d", has_dim_equals=(-1, 3))
point_xy, _ = tf.compat.v1.split(point_3d, (2, 1), axis=-1)
return point_xy
def ray(point_2d, 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)\\) for a 2d point \\((x', y')\\) on the
image plane. For an orthographic camera the rays are constant over the image
plane with
$$
\begin{matrix}
r_x = 0, & r_y = 0, & z = 1.
\end{matrix}
$$
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.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_ray", [point_2d]):
point_2d = tf.convert_to_tensor(value=point_2d)
shape.check_static(
tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2))
ones = tf.ones_like(point_2d[..., :1])
# point_2d is multiplied by zero to ensure it has defined gradients.
return tf.concat((point_2d * 0.0, ones), axis=-1)
def unproject(point_2d, depth, name=None):
r"""Unprojects a 2d point in 3d.
Unprojects a 2d point \\((x', y')\\) to a 3d point \\((x, y, z)\\) given its
depth \\(z\\), with
$$
\begin{matrix}
x = x', & y = y', & z = z.
\end{matrix}
$$
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.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_unproject",
[point_2d, depth]):
point_2d = tf.convert_to_tensor(value=point_2d)
depth = tf.convert_to_tensor(value=depth)
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.compare_batch_dimensions(
tensors=(point_2d, depth),
tensor_names=("point_2d", "depth"),
last_axes=-2,
broadcast_compatible=False)
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 orthographic camera functionalities.
An orthographic camera represents three-dimensional objects in two dimensions
by parallel projection, in which the projection lines are parallel to the
camera axis. The camera axis is the line perpendicular to the image plane
starting at the camera center.
"""
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 project(point_3d, 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
$$
\begin{matrix}
x' = x, & y' = y.
\end{matrix}
$$
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_3d: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a 3d point to project.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_project", [point_3d]):
point_3d = tf.convert_to_tensor(value=point_3d)
shape.check_static(
tensor=point_3d, tensor_name="point_3d", has_dim_equals=(-1, 3))
point_xy, _ = tf.compat.v1.split(point_3d, (2, 1), axis=-1)
return point_xy
def ray(point_2d, 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)\\) for a 2d point \\((x', y')\\) on the
image plane. For an orthographic camera the rays are constant over the image
plane with
$$
\begin{matrix}
r_x = 0, & r_y = 0, & z = 1.
\end{matrix}
$$
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.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_ray", [point_2d]):
point_2d = tf.convert_to_tensor(value=point_2d)
shape.check_static(
tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2))
ones = tf.ones_like(point_2d[..., :1])
# point_2d is multiplied by zero to ensure it has defined gradients.
return tf.concat((point_2d * 0.0, ones), axis=-1)
def unproject(point_2d, depth, name=None):
r"""Unprojects a 2d point in 3d.
Unprojects a 2d point \\((x', y')\\) to a 3d point \\((x, y, z)\\) given its
depth \\(z\\), with
$$
\begin{matrix}
x = x', & y = y', & z = z.
\end{matrix}
$$
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.
name: A name for this op that defaults to "orthographic_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` is not supported.
"""
with tf.compat.v1.name_scope(name, "orthographic_unproject",
[point_2d, depth]):
point_2d = tf.convert_to_tensor(value=point_2d)
depth = tf.convert_to_tensor(value=depth)
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.compare_batch_dimensions(
tensors=(point_2d, depth),
tensor_names=("point_2d", "depth"),
last_axes=-2,
broadcast_compatible=False)
return tf.concat((point_2d, depth), axis=-1)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/datasets/features/voxel_feature_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 for tensorflow_graphics.datasets.features.voxel_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import voxel_feature
_TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), 'test_data')
class VoxelGridFeatureTest(tfds.testing.FeatureExpectationsTestCase):
"""Test Cases for VoxelGrid FeatureConnector."""
def test_voxel(self):
"""Tests voxel I/O and encoding/decoding to DatasetFeature."""
mat_file_path = os.path.join(_TEST_DATA_DIR, 'cube.mat')
expected_voxel = np.zeros((16, 16, 16), dtype=np.float32)
expected_voxel[4:12, 4:12, 4:12] = 1.
mat_dict = {'path': mat_file_path, 'key': 'voxels'}
raising_inputs = {'path': mat_file_path, 'foo': 'voxels'}
wrong_key = {'path': mat_file_path, 'key': 'foo'}
wrong_path = {'path': '/somewhere/wrong', 'key': 'voxels'}
wrong_dim = np.ones((1, 1, 1, 1))
self.assertFeature(
feature=voxel_feature.VoxelGrid((16, 16, 16)),
shape=(16, 16, 16),
dtype=tf.float32,
tests=[
# mat file
tfds.testing.FeatureExpectationItem(
value=mat_dict,
expected=expected_voxel,
),
# Voxel Grid
tfds.testing.FeatureExpectationItem(
value=expected_voxel,
expected=expected_voxel,
),
tfds.testing.FeatureExpectationItem(
value=raising_inputs,
raise_cls=ValueError,
raise_msg='Missing keys in provided dictionary!',
),
tfds.testing.FeatureExpectationItem(
value=wrong_key,
raise_cls=ValueError,
raise_msg='Key `foo` not found in .mat file',
),
tfds.testing.FeatureExpectationItem(
value=wrong_path,
raise_cls=FileNotFoundError,
raise_msg='File `/somewhere/wrong` does not exist.',
),
tfds.testing.FeatureExpectationItem(
value=wrong_dim,
raise_cls=ValueError,
raise_msg='Only 3D Voxel Grids are supported.',
),
],
)
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 for tensorflow_graphics.datasets.features.voxel_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import voxel_feature
_TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), 'test_data')
class VoxelGridFeatureTest(tfds.testing.FeatureExpectationsTestCase):
"""Test Cases for VoxelGrid FeatureConnector."""
def test_voxel(self):
"""Tests voxel I/O and encoding/decoding to DatasetFeature."""
mat_file_path = os.path.join(_TEST_DATA_DIR, 'cube.mat')
expected_voxel = np.zeros((16, 16, 16), dtype=np.float32)
expected_voxel[4:12, 4:12, 4:12] = 1.
mat_dict = {'path': mat_file_path, 'key': 'voxels'}
raising_inputs = {'path': mat_file_path, 'foo': 'voxels'}
wrong_key = {'path': mat_file_path, 'key': 'foo'}
wrong_path = {'path': '/somewhere/wrong', 'key': 'voxels'}
wrong_dim = np.ones((1, 1, 1, 1))
self.assertFeature(
feature=voxel_feature.VoxelGrid((16, 16, 16)),
shape=(16, 16, 16),
dtype=tf.float32,
tests=[
# mat file
tfds.testing.FeatureExpectationItem(
value=mat_dict,
expected=expected_voxel,
),
# Voxel Grid
tfds.testing.FeatureExpectationItem(
value=expected_voxel,
expected=expected_voxel,
),
tfds.testing.FeatureExpectationItem(
value=raising_inputs,
raise_cls=ValueError,
raise_msg='Missing keys in provided dictionary!',
),
tfds.testing.FeatureExpectationItem(
value=wrong_key,
raise_cls=ValueError,
raise_msg='Key `foo` not found in .mat file',
),
tfds.testing.FeatureExpectationItem(
value=wrong_path,
raise_cls=FileNotFoundError,
raise_msg='File `/somewhere/wrong` does not exist.',
),
tfds.testing.FeatureExpectationItem(
value=wrong_dim,
raise_cls=ValueError,
raise_msg='Only 3D Voxel Grids are supported.',
),
],
)
if __name__ == '__main__':
tfds.testing.test_main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/local_implicit_grid/core/local_implicit_grid_layer.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
"""Local Implicit Grid layer implemented in Tensorflow.
"""
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import implicit_nets
from tensorflow_graphics.projects.local_implicit_grid.core import regular_grid_interpolation
layers = tf.keras.layers
class LocalImplicitGrid(layers.Layer):
"""Local Implicit Grid layer.
"""
def __init__(self,
size=(32, 32, 32),
in_features=16,
out_features=1,
x_location_max=1,
num_filters=128,
net_type="imnet",
method="linear",
interp=True,
min_grid_value=(0, 0, 0),
max_grid_value=(1, 1, 1),
name="lvoxgrid"):
"""Initialization function.
Args:
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.
net_type: str, one of occnet/deepsdf.
method: str, one of linear/nn.
interp: bool, interp final results across neighbors (only in linear mode).
min_grid_value: tuple, lower bound of query points.
max_grid_value: tuple, upper bound of query points.
name: str, name of the layer.
"""
super(LocalImplicitGrid, self).__init__(name=name)
# Print warning if x_location_max and method do not match
if not ((x_location_max == 1 and method == "linear") or
(x_location_max == 2 and method == "nn")):
raise ValueError("Bad combination of x_location_max and method.")
self.cin = in_features
self.cout = out_features
self.dim = len(size)
self.x_location_max = x_location_max
self.interp = interp
self.min_grid_value = min_grid_value
self.max_grid_value = max_grid_value
self.num_filters = num_filters
if self.dim not in [2, 3]:
raise ValueError("`size` must be tuple or list of len 2 or 3.")
if net_type == "imnet":
self.net = implicit_nets.ImNet(
in_features=in_features, num_filters=num_filters)
elif net_type == "deepsdf":
self.net = implicit_nets.DeepSDF(
in_features=in_features, num_filters=num_filters)
else:
raise NotImplementedError
if method not in ["linear", "nn"]:
raise ValueError("`method` must be `linear` or `nn`.")
self.method = method
self.size_tensor = tf.constant(size)
self.size = size
if size[0] == size[1] == size[2] == 1:
self.cubesize = None
else:
self.cubesize = tf.constant([1/(r-1) for r in size], dtype=tf.float32)
def call(self, grid, pts, training=False):
"""Forward method for Learnable Voxel Grid.
Args:
grid: `[batch_size, *self.size, in_features]` tensor, input feature grid.
pts: `[batch_size, num_points, dim]` tensor, coordinates of points that
are within the range (0, 1).
training: bool, flag indicating training phase.
Returns:
outputs: `[batch_size, num_points, out_features]` tensor, continuous
function field value at locations specified at pts.
Raises:
RuntimeError: dimensions of grid does not match that of self.
"""
# assert that dimensions match
grid = tf.ensure_shape(grid, (None, self.size[0], self.size[1],
self.size[2], self.cin))
pts = tf.ensure_shape(pts, (None, None, self.dim))
lat, weights, xloc = self._interp(grid, pts)
outputs = self._eval_net(lat, weights, xloc, training=training)
return outputs
def _interp(self, grid, pts):
"""Interpolation function to get local latent code, weights & relative loc.
Args:
grid: `[batch_size, *self.size, in_features]` tensor, input feature grid.
pts: `[batch_size, num_points, dim]` tensor, coordinates of points that
are within the range (0, 1).
Returns:
lat: `[batch_size, num_points, 2**dim, in_features]` tensor, neighbor
latent codes for each input point.
weights: `[batch_size, num_points, 2**dim]` tensor, bi/tri-linear
interpolation weights for each neighbor.
xloc: `[batch_size, num_points, 2**dim, dim]`tensor, relative coordinates.
"""
lat, weights, xloc = regular_grid_interpolation.get_interp_coefficients(
grid,
pts,
min_grid_value=self.min_grid_value,
max_grid_value=self.max_grid_value)
xloc *= self.x_location_max
return lat, weights, xloc
def _eval_net(self, lat, weights, xloc, training=False):
"""Evaluate function values by querying shared dense network.
Args:
lat: `[batch_size, num_points, 2**dim, in_features]` tensor, neighbor
latent codes for each input point.
weights: `[batch_size, num_points, 2**dim]` tensor, bi/tri-linear
interpolation weights for each neighbor.
xloc: `[batch_size, num_points, 2**dim, dim]`tensor, relative coordinates.
training: bool, flag indicating training phase.
Returns:
values: `[batch_size, num_point, out_features]` tensor, query values.
"""
nb, np, nn, nc = lat.get_shape().as_list()
nd = self.dim
if self.method == "linear":
inputs = tf.concat([xloc, lat], axis=-1)
# `[batch_size, num_points, 2**dim, dim+in_features]`
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, nn, self.cout])
# `[batch_size, num_points, 2**dim, out_features]`
if self.interp:
values = tf.reduce_sum(tf.expand_dims(weights, axis=-1)*values, axis=2)
# `[batch_size, num_points out_features]`
else:
values = (values, weights)
else: # nearest neighbor
nid = tf.cast(tf.argmax(weights, axis=-1), tf.int32)
# [batch_size, num_points]
bid = tf.broadcast_to(tf.range(nb, dtype=tf.int32)[:, tf.newaxis],
[nb, np])
pid = tf.broadcast_to(tf.range(np, dtype=tf.int32)[tf.newaxis, :],
[nb, np])
gather_id = tf.stack((bid, pid, nid), axis=-1)
lat_ = tf.gather_nd(lat, gather_id) # [batch_size, num_points, in_feat]
xloc_ = tf.gather_nd(xloc, gather_id) # [batch_size, num_points, dim]
inputs = tf.concat([xloc_, lat_], axis=-1)
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, self.cout])
# `[batch_size, num_points, out_features]`
return values
| # 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
"""Local Implicit Grid layer implemented in Tensorflow.
"""
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import implicit_nets
from tensorflow_graphics.projects.local_implicit_grid.core import regular_grid_interpolation
layers = tf.keras.layers
class LocalImplicitGrid(layers.Layer):
"""Local Implicit Grid layer.
"""
def __init__(self,
size=(32, 32, 32),
in_features=16,
out_features=1,
x_location_max=1,
num_filters=128,
net_type="imnet",
method="linear",
interp=True,
min_grid_value=(0, 0, 0),
max_grid_value=(1, 1, 1),
name="lvoxgrid"):
"""Initialization function.
Args:
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.
net_type: str, one of occnet/deepsdf.
method: str, one of linear/nn.
interp: bool, interp final results across neighbors (only in linear mode).
min_grid_value: tuple, lower bound of query points.
max_grid_value: tuple, upper bound of query points.
name: str, name of the layer.
"""
super(LocalImplicitGrid, self).__init__(name=name)
# Print warning if x_location_max and method do not match
if not ((x_location_max == 1 and method == "linear") or
(x_location_max == 2 and method == "nn")):
raise ValueError("Bad combination of x_location_max and method.")
self.cin = in_features
self.cout = out_features
self.dim = len(size)
self.x_location_max = x_location_max
self.interp = interp
self.min_grid_value = min_grid_value
self.max_grid_value = max_grid_value
self.num_filters = num_filters
if self.dim not in [2, 3]:
raise ValueError("`size` must be tuple or list of len 2 or 3.")
if net_type == "imnet":
self.net = implicit_nets.ImNet(
in_features=in_features, num_filters=num_filters)
elif net_type == "deepsdf":
self.net = implicit_nets.DeepSDF(
in_features=in_features, num_filters=num_filters)
else:
raise NotImplementedError
if method not in ["linear", "nn"]:
raise ValueError("`method` must be `linear` or `nn`.")
self.method = method
self.size_tensor = tf.constant(size)
self.size = size
if size[0] == size[1] == size[2] == 1:
self.cubesize = None
else:
self.cubesize = tf.constant([1/(r-1) for r in size], dtype=tf.float32)
def call(self, grid, pts, training=False):
"""Forward method for Learnable Voxel Grid.
Args:
grid: `[batch_size, *self.size, in_features]` tensor, input feature grid.
pts: `[batch_size, num_points, dim]` tensor, coordinates of points that
are within the range (0, 1).
training: bool, flag indicating training phase.
Returns:
outputs: `[batch_size, num_points, out_features]` tensor, continuous
function field value at locations specified at pts.
Raises:
RuntimeError: dimensions of grid does not match that of self.
"""
# assert that dimensions match
grid = tf.ensure_shape(grid, (None, self.size[0], self.size[1],
self.size[2], self.cin))
pts = tf.ensure_shape(pts, (None, None, self.dim))
lat, weights, xloc = self._interp(grid, pts)
outputs = self._eval_net(lat, weights, xloc, training=training)
return outputs
def _interp(self, grid, pts):
"""Interpolation function to get local latent code, weights & relative loc.
Args:
grid: `[batch_size, *self.size, in_features]` tensor, input feature grid.
pts: `[batch_size, num_points, dim]` tensor, coordinates of points that
are within the range (0, 1).
Returns:
lat: `[batch_size, num_points, 2**dim, in_features]` tensor, neighbor
latent codes for each input point.
weights: `[batch_size, num_points, 2**dim]` tensor, bi/tri-linear
interpolation weights for each neighbor.
xloc: `[batch_size, num_points, 2**dim, dim]`tensor, relative coordinates.
"""
lat, weights, xloc = regular_grid_interpolation.get_interp_coefficients(
grid,
pts,
min_grid_value=self.min_grid_value,
max_grid_value=self.max_grid_value)
xloc *= self.x_location_max
return lat, weights, xloc
def _eval_net(self, lat, weights, xloc, training=False):
"""Evaluate function values by querying shared dense network.
Args:
lat: `[batch_size, num_points, 2**dim, in_features]` tensor, neighbor
latent codes for each input point.
weights: `[batch_size, num_points, 2**dim]` tensor, bi/tri-linear
interpolation weights for each neighbor.
xloc: `[batch_size, num_points, 2**dim, dim]`tensor, relative coordinates.
training: bool, flag indicating training phase.
Returns:
values: `[batch_size, num_point, out_features]` tensor, query values.
"""
nb, np, nn, nc = lat.get_shape().as_list()
nd = self.dim
if self.method == "linear":
inputs = tf.concat([xloc, lat], axis=-1)
# `[batch_size, num_points, 2**dim, dim+in_features]`
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, nn, self.cout])
# `[batch_size, num_points, 2**dim, out_features]`
if self.interp:
values = tf.reduce_sum(tf.expand_dims(weights, axis=-1)*values, axis=2)
# `[batch_size, num_points out_features]`
else:
values = (values, weights)
else: # nearest neighbor
nid = tf.cast(tf.argmax(weights, axis=-1), tf.int32)
# [batch_size, num_points]
bid = tf.broadcast_to(tf.range(nb, dtype=tf.int32)[:, tf.newaxis],
[nb, np])
pid = tf.broadcast_to(tf.range(np, dtype=tf.int32)[tf.newaxis, :],
[nb, np])
gather_id = tf.stack((bid, pid, nid), axis=-1)
lat_ = tf.gather_nd(lat, gather_id) # [batch_size, num_points, in_feat]
xloc_ = tf.gather_nd(xloc, gather_id) # [batch_size, num_points, dim]
inputs = tf.concat([xloc_, lat_], axis=-1)
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, self.cout])
# `[batch_size, num_points, out_features]`
return values
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/util/export_api.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.
"""API export functions used to create the automated documentation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
def get_functions_and_classes():
"""Extracts a list of public functions and classes for the API generation.
Returns:
A list of function and class names.
"""
caller = inspect.stack()[1]
module = inspect.getmodule(caller[0])
return [
obj_name for obj_name, obj in inspect.getmembers(module)
if inspect.isfunction(obj) or
inspect.isclass(obj) and not obj_name.startswith("_")
]
def get_modules():
"""Extracts a list of public modules for the API generation.
Returns:
A list of module names.
"""
caller = inspect.stack()[1]
module = inspect.getmodule(caller[0])
return [
obj_name for obj_name, obj in inspect.getmembers(module)
if inspect.ismodule(obj) and obj.__name__.rsplit(".", 1)[0] ==
module.__name__ and not obj_name.startswith("_")
]
# 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.
"""API export functions used to create the automated documentation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
def get_functions_and_classes():
"""Extracts a list of public functions and classes for the API generation.
Returns:
A list of function and class names.
"""
caller = inspect.stack()[1]
module = inspect.getmodule(caller[0])
return [
obj_name for obj_name, obj in inspect.getmembers(module)
if inspect.isfunction(obj) or
inspect.isclass(obj) and not obj_name.startswith("_")
]
def get_modules():
"""Extracts a list of public modules for the API generation.
Returns:
A list of module names.
"""
caller = inspect.stack()[1]
module = inspect.getmodule(caller[0])
return [
obj_name for obj_name, obj in inspect.getmembers(module)
if inspect.ismodule(obj) and obj.__name__.rsplit(".", 1)[0] ==
module.__name__ and not obj_name.startswith("_")
]
# The util functions or classes are not exported.
__all__ = []
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/datasets/features/camera_feature_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 for tensorflow_graphics.datasets.features.camera_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import camera_feature
class CameraFeatureTest(tfds.testing.FeatureExpectationsTestCase):
"""Test Cases for Camera FeatureConnector."""
def __get_camera_params(self):
pose = {'R': np.eye(3).astype(np.float32),
't': np.zeros(3).astype(np.float32)}
f = 35.
optical_center = (640 / 2, 480 / 2)
return pose, f, optical_center
def test_simple_camera(self):
"""Tests camera parameters with fixed focal length, no skew and no aspect ratio."""
expected_pose, expected_f, expected_center = self.__get_camera_params()
expected_intrinsics = np.asarray([[expected_f, 0, expected_center[0]],
[0, expected_f, expected_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f, 'optical_center': expected_center,
'pose': expected_pose}
lookat_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {
'look_at': np.array([0, 0, -1], dtype=np.float32),
'up': np.array([0, 1, 0], dtype=np.float32),
'position': np.array([0, 0, 0], dtype=np.float32)
}
}
raising_pose_entry = {
'f': expected_f,
'optical_center': expected_center,
'pose': np.eye(4)
}
raising_pose_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {'rot': np.eye(3), 'trans': np.zeros(3)}
}
raising_lookat_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {
'l': np.array([0, 0, -1], dtype=np.float32),
'up': np.array([0, 1, 0], dtype=np.float32),
'C': np.array([0, 0, 0], dtype=np.float32)
}
}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
tfds.testing.FeatureExpectationItem(
value=lookat_inputs,
expected=expected_camera
),
tfds.testing.FeatureExpectationItem(
value=raising_pose_inputs,
raise_cls=ValueError,
raise_msg='Wrong keys for pose feature provided'
),
tfds.testing.FeatureExpectationItem(
value=raising_lookat_inputs,
raise_cls=ValueError,
raise_msg='Wrong keys for pose feature provided'
),
tfds.testing.FeatureExpectationItem(
value=raising_pose_entry,
raise_cls=ValueError,
raise_msg='Pose needs to be a dictionary'
),
],
)
def test_camera_with_aspect_ratio_and_skew(self):
"""Tests camera parameters with fixed focal length, aspect_ratio and skew."""
expected_pose, expected_f, expected_center = self.__get_camera_params()
expected_aspect_ratio = expected_center[0] / expected_center[1]
expected_skew = 0.6
expected_intrinsics = np.asarray(
[[expected_f, expected_skew, expected_center[0]],
[0, expected_aspect_ratio * expected_f, expected_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f,
'optical_center': expected_center,
'skew': expected_skew,
'aspect_ratio': expected_aspect_ratio,
'pose': expected_pose}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
],
)
def test_full_camera_calibration_matrix(self):
"""Tests camera parameters with different focal length per camera axis and skew."""
expected_pose, _, expected_optical_center = self.__get_camera_params()
expected_skew = 0.6
expected_f = (35., 40.)
expected_intrinsics = np.array(
[[expected_f[0], expected_skew, expected_optical_center[0]],
[0, expected_f[1], expected_optical_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f,
'optical_center': expected_optical_center,
'skew': expected_skew, 'pose': expected_pose}
raising_inputs = {'f': expected_f,
'aspect_ratio': 1.5,
'optical_center': expected_optical_center,
'skew': expected_skew, 'pose': expected_pose}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
tfds.testing.FeatureExpectationItem(
value=raising_inputs,
raise_cls=ValueError,
raise_msg='If aspect ratio is provided, f needs to '
'be a single float',
),
],
)
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 for tensorflow_graphics.datasets.features.camera_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import camera_feature
class CameraFeatureTest(tfds.testing.FeatureExpectationsTestCase):
"""Test Cases for Camera FeatureConnector."""
def __get_camera_params(self):
pose = {'R': np.eye(3).astype(np.float32),
't': np.zeros(3).astype(np.float32)}
f = 35.
optical_center = (640 / 2, 480 / 2)
return pose, f, optical_center
def test_simple_camera(self):
"""Tests camera parameters with fixed focal length, no skew and no aspect ratio."""
expected_pose, expected_f, expected_center = self.__get_camera_params()
expected_intrinsics = np.asarray([[expected_f, 0, expected_center[0]],
[0, expected_f, expected_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f, 'optical_center': expected_center,
'pose': expected_pose}
lookat_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {
'look_at': np.array([0, 0, -1], dtype=np.float32),
'up': np.array([0, 1, 0], dtype=np.float32),
'position': np.array([0, 0, 0], dtype=np.float32)
}
}
raising_pose_entry = {
'f': expected_f,
'optical_center': expected_center,
'pose': np.eye(4)
}
raising_pose_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {'rot': np.eye(3), 'trans': np.zeros(3)}
}
raising_lookat_inputs = {
'f': expected_f,
'optical_center': expected_center,
'pose': {
'l': np.array([0, 0, -1], dtype=np.float32),
'up': np.array([0, 1, 0], dtype=np.float32),
'C': np.array([0, 0, 0], dtype=np.float32)
}
}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
tfds.testing.FeatureExpectationItem(
value=lookat_inputs,
expected=expected_camera
),
tfds.testing.FeatureExpectationItem(
value=raising_pose_inputs,
raise_cls=ValueError,
raise_msg='Wrong keys for pose feature provided'
),
tfds.testing.FeatureExpectationItem(
value=raising_lookat_inputs,
raise_cls=ValueError,
raise_msg='Wrong keys for pose feature provided'
),
tfds.testing.FeatureExpectationItem(
value=raising_pose_entry,
raise_cls=ValueError,
raise_msg='Pose needs to be a dictionary'
),
],
)
def test_camera_with_aspect_ratio_and_skew(self):
"""Tests camera parameters with fixed focal length, aspect_ratio and skew."""
expected_pose, expected_f, expected_center = self.__get_camera_params()
expected_aspect_ratio = expected_center[0] / expected_center[1]
expected_skew = 0.6
expected_intrinsics = np.asarray(
[[expected_f, expected_skew, expected_center[0]],
[0, expected_aspect_ratio * expected_f, expected_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f,
'optical_center': expected_center,
'skew': expected_skew,
'aspect_ratio': expected_aspect_ratio,
'pose': expected_pose}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
],
)
def test_full_camera_calibration_matrix(self):
"""Tests camera parameters with different focal length per camera axis and skew."""
expected_pose, _, expected_optical_center = self.__get_camera_params()
expected_skew = 0.6
expected_f = (35., 40.)
expected_intrinsics = np.array(
[[expected_f[0], expected_skew, expected_optical_center[0]],
[0, expected_f[1], expected_optical_center[1]],
[0, 0, 1]], dtype=np.float32)
expected_camera = {'pose': expected_pose, 'intrinsics': expected_intrinsics}
inputs = {'f': expected_f,
'optical_center': expected_optical_center,
'skew': expected_skew, 'pose': expected_pose}
raising_inputs = {'f': expected_f,
'aspect_ratio': 1.5,
'optical_center': expected_optical_center,
'skew': expected_skew, 'pose': expected_pose}
self.assertFeature(
feature=camera_feature.Camera(),
shape={
'pose': {
'R': (3, 3),
't': (3,)
},
'intrinsics': (3, 3)
},
dtype={
'pose': {
'R': tf.float32,
't': tf.float32
},
'intrinsics': tf.float32
},
tests=[
tfds.testing.FeatureExpectationItem(
value=inputs,
expected=expected_camera,
),
tfds.testing.FeatureExpectationItem(
value=raising_inputs,
raise_cls=ValueError,
raise_msg='If aspect ratio is provided, f needs to '
'be a single float',
),
],
)
if __name__ == '__main__':
tfds.testing.test_main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/convolution/tests/graph_pooling_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 tensorflow_graphics.geometry.convolution.tests.graph_pooling."""
# pylint: disable=protected-access
import itertools
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
import tensorflow_graphics.geometry.convolution.graph_pooling as gp
from tensorflow_graphics.geometry.convolution.tests import utils_test
from tensorflow_graphics.util import test_case
def _dense_to_sparse(data):
"""Convert a numpy array to a tf.SparseTensor."""
return utils_test._dense_to_sparse(data)
def _batch_sparse_eye(batch_shape, num_vertices, dtype):
"""Generate a batch of identity matrices."""
eye = np.eye(num_vertices, dtype=dtype)
num_batch_dims = len(batch_shape)
expand_shape = np.concatenate((np.ones(
num_batch_dims, dtype=np.int32), (num_vertices, num_vertices)),
axis=0)
eye = np.reshape(eye, expand_shape)
tile_shape = np.concatenate((batch_shape, (1, 1)), axis=0)
return _dense_to_sparse(np.tile(eye, tile_shape))
class GraphPoolingTestPoolTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_pool_exception_raised_types(self, err_msg, data_type, pool_map_type,
sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.pool(data, pool_map, sizes)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
)
def test_pool_exception_raised_shapes(self, err_msg, data_shape,
pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.pool(data, pool_map, sizes)
def test_pool_exception_raised_algorithm(self):
"""Tests the correct exception is raised for an invalid algorithm."""
data = np.ones(shape=(2, 2))
pool_map = _dense_to_sparse(np.ones(shape=(2, 2)))
with self.assertRaisesRegexp(
ValueError, 'The pooling method must be "weighted" or "max"'):
gp.pool(data, pool_map, sizes=None, algorithm='mean')
@parameterized.parameters(
((2, 3), 4, 3, np.float32),
((1,), 6, 1, np.float32),
((4, 1, 3), 9, 7, np.float64),
((2, 8, 4, 6), 19, 11, np.float64),
)
def test_pool_identity(self, batch_shape, num_vertices, num_features,
data_type):
"""Tests graph pooling with identity maps."""
data_shape = np.concatenate((batch_shape, (num_vertices, num_features)))
data = np.random.uniform(size=data_shape).astype(data_type)
pool_map = _batch_sparse_eye(batch_shape, num_vertices, data_type)
pooled_max = gp.pool(data, pool_map, sizes=None, algorithm='max', name=None)
pooled_weighted = gp.pool(
data, pool_map, sizes=None, algorithm='weighted', name=None)
self.assertAllClose(pooled_max, data)
self.assertAllClose(pooled_weighted, data)
def test_pool_preset_padded(self):
"""Tests pooling with preset data and padding."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
pooled_max = gp.pool(data, pool_map, sizes, algorithm='max')
pooled_weighted = gp.pool(data, pool_map, sizes, algorithm='weighted')
true_max = (((2., 3.), (4., 5.), (0., 0.)), ((6., 7.), (8., 9.), (10.,
11.)))
true_weighted = (((1., 2.), (4., 5.), (0., 0.)), ((6., 7.), (8., 9.),
(10., 11.)))
self.assertAllClose(pooled_max, true_max)
self.assertAllClose(pooled_weighted, true_weighted)
def test_pool_preset(self):
"""Tests pooling with preset data."""
pool_map = np.array(((0.5, 0.5, 0., 0.), (0., 0., 0.5, 0.5)),
dtype=np.float32)
pool_map = _dense_to_sparse(pool_map)
data = np.reshape(np.arange(8).astype(np.float32), (4, 2))
max_true = data[(1, 3), :]
max_weighted = (data[(0, 2), :] + max_true) * 0.5
pooled_max = gp.pool(data, pool_map, sizes=None, algorithm='max', name=None)
pooled_weighted = gp.pool(
data, pool_map, sizes=None, algorithm='weighted', name=None)
self.assertAllClose(pooled_max, max_true)
self.assertAllClose(pooled_weighted, max_weighted)
@parameterized.parameters((20, 10, 3), (2, 1, 1), (2, 5, 4), (2, 1, 3))
def test_pool_random(self, num_input_vertices, num_output_vertices,
num_features):
"""Tests pooling with random inputs."""
pool_map = 0.001 + np.random.uniform(
size=(num_output_vertices, num_input_vertices))
data = np.random.uniform(size=(num_input_vertices, num_features))
true_weighted = np.matmul(pool_map, data)
true_max = np.tile(
np.max(data, axis=0, keepdims=True), (num_output_vertices, 1))
pool_map = _dense_to_sparse(pool_map)
with self.subTest(name='max'):
pooled_max = gp.pool(data, pool_map, None, algorithm='max')
self.assertAllClose(pooled_max, true_max)
with self.subTest(name='weighted'):
pooled_weighted = gp.pool(data, pool_map, None, algorithm='weighted')
self.assertAllClose(pooled_weighted, true_weighted)
def test_pool_jacobian(self):
"""Tests the jacobian is correct."""
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 5, 3))
pool_map = np.random.uniform(size=(2, 3, 5))
data_init[0, -1, :] = 0.
pool_map[0, -1, :] = 0.
pool_map = _dense_to_sparse(pool_map)
def gp_pool(data, algorithm):
return gp.pool(data, pool_map, sizes, algorithm=algorithm)
with self.subTest(name='max'):
self.assert_jacobian_is_correct_fn(lambda data: gp_pool(data, 'max'),
[data_init])
with self.subTest(name='weighted'):
self.assert_jacobian_is_correct_fn(lambda data: gp_pool(data, 'weighted'),
[data_init])
class GraphPoolingTestUnpoolTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_unpool_exception_raised_types(self, err_msg, data_type,
pool_map_type, sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.unpool(data, pool_map, sizes)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
('data must have a rank less than 6', (2, 3, 4, 5, 3, 2),
(2, 3, 4, 5, 3, 3), None),
)
def test_unpool_exception_raised_shapes(self, err_msg, data_shape,
pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.unpool(data, pool_map, sizes)
@parameterized.parameters(
((2, 3), 4, 3, np.float32),
((1,), 6, 1, np.float32),
((4, 1, 3), 9, 7, np.float64),
((2, 8, 4), 19, 11, np.float64),
)
def test_unpool_identity(self, batch_shape, num_vertices, num_features,
data_type):
"""Tests graph unpooling with identity maps."""
data_shape = np.concatenate((batch_shape, (num_vertices, num_features)))
data = np.random.uniform(size=data_shape).astype(data_type)
pool_map = _batch_sparse_eye(batch_shape, num_vertices, data_type)
unpooled = gp.unpool(data, pool_map, sizes=None)
self.assertAllClose(unpooled, data)
def test_unpool_preset_padded(self):
"""Tests pooling with preset data and padding."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
data[0, -1, :] = 0.
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
unpooled = gp.unpool(data, pool_map, sizes)
true = (((0., 1.), (0., 1.), (2., 3.)), ((6., 7.), (8., 9.), (10., 11.)))
self.assertAllClose(unpooled, true)
@parameterized.parameters((20, 4), (2, 1), (12, 4), (6, 3))
def test_unpool_random(self, num_vertices, num_features):
"""Tests pooling with random data inputs."""
output_vertices = num_vertices // 2
pool_map = np.zeros(shape=(output_vertices, num_vertices), dtype=np.float32)
for i in range(output_vertices):
pool_map[i, (i * 2, i * 2 + 1)] = (0.5, 0.5)
data = np.random.uniform(size=(output_vertices,
num_features)).astype(np.float32)
unpooled = gp.unpool(
data, _dense_to_sparse(pool_map), sizes=None, name=None)
with self.subTest(name='direct_unpool'):
true = np.zeros(shape=(num_vertices, num_features)).astype(np.float32)
true[0::2, :] = data
true[1::2, :] = data
self.assertAllClose(unpooled, true)
with self.subTest(name='permute_pool_map'):
permutation = np.random.permutation(num_vertices)
pool_map_permute = pool_map[:, permutation]
unpooled_permute = gp.unpool(data, _dense_to_sparse(pool_map_permute),
None)
true_permute = true[permutation, :]
self.assertAllClose(unpooled_permute, true_permute)
def test_unpool_jacobian_random(self):
"""Tests the jacobian is correct."""
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 3, 6))
pool_map = np.random.uniform(size=(2, 3, 5))
data_init[0, -1, :] = 0.
pool_map[0, -1, :] = 0.
pool_map = _dense_to_sparse(pool_map)
def gp_unpool(data):
return gp.unpool(data, pool_map, sizes)
self.assert_jacobian_is_correct_fn(gp_unpool, [data_init])
class GraphPoolingUpsampleTransposeConvolutionTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_upsample_transposed_convolution_exception_raised_types(
self, err_msg, data_type, pool_map_type, sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.upsample_transposed_convolution(
data, pool_map, sizes, kernel_size=1, transposed_convolution_op=None)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
('data must have a rank less than 6', (2, 3, 4, 5, 3, 2),
(2, 3, 4, 5, 3, 3), None),
)
def test_upsample_transposed_convolution_exception_raised_shapes(
self, err_msg, data_shape, pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.upsample_transposed_convolution(
data, pool_map, sizes, kernel_size=1, transposed_convolution_op=None)
def test_upsample_transposed_convolution_exception_raised_callable(self):
"""Tests the correct exception is raised for a invalid convolution op."""
data = np.ones((5, 3))
pool_map = _dense_to_sparse(np.eye(5))
err_msg = "'transposed_convolution_op' must be callable."
with self.assertRaisesRegexp(TypeError, err_msg):
gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=1,
transposed_convolution_op=1)
@parameterized.parameters((1, 1, 1, np.float32), (5, 3, 1, np.float32),
(3, 6, 15, np.float64))
def test_upsample_transposed_convolution_zero_kernel(self, num_vertices,
num_features,
kernel_size, data_type):
"""Tests the upsampling with a zero kernel."""
data = np.random.uniform(size=(num_vertices,
num_features)).astype(data_type)
pool_map = np.zeros(
shape=(num_vertices, num_vertices * kernel_size), dtype=data_type)
for i in range(num_vertices):
pool_map[i, np.arange(kernel_size * i, kernel_size *
(i + 1))] = (1.0 / kernel_size)
pool_map = _dense_to_sparse(pool_map)
# Transposed convolution op with a zero kernel.
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_features,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
use_bias=False,
kernel_initializer=tf.compat.v1.keras.initializers.zeros())
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(
tf.shape(input=upsampled), (num_vertices * kernel_size, num_features))
self.assertAllEqual(upsampled, tf.zeros_like(upsampled))
@parameterized.parameters(
itertools.product((3,), (6,), (3,), range(3), range(6), range(6)),)
def test_upsample_transposed_convolution_selector_kernel_random(
self, num_vertices, num_features, kernel_size, kernel_index,
feature1_index, feature2_index):
"""Tests the upsampling with an indicator kernel."""
data = np.random.uniform(size=(num_vertices,
num_features)).astype(np.float32)
pool_map = np.zeros(
shape=(num_vertices, num_vertices * kernel_size), dtype=np.float32)
for i in range(num_vertices):
pool_map[i, np.arange(kernel_size * i, kernel_size *
(i + 1))] = (1.0 / kernel_size)
pool_map = _dense_to_sparse(pool_map)
selection = np.zeros(
shape=(1, kernel_size, num_features, num_features), dtype=np.float32)
selection[0, kernel_index, feature1_index, feature2_index] = 1.
initializer = tf.compat.v1.constant_initializer(value=selection)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_features,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
use_bias=False,
kernel_initializer=initializer)
true = np.zeros(
shape=(num_vertices * kernel_size, num_features), dtype=np.float32)
input_column = feature2_index
output_column = feature1_index
output_row_start = kernel_index
true[output_row_start::kernel_size, output_column] = (data[:, input_column])
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(upsampled, true)
def test_upsample_transposed_convolution_preset_padded(self):
"""Tests upsampling with presets."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
data[0, -1, :] = 0.
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
kernel = np.ones(shape=(1, 2, 2, 2), dtype=np.float32)
initializer = tf.compat.v1.constant_initializer(value=kernel)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=2,
kernel_size=(1, 2),
strides=(1, 2),
padding='valid',
use_bias=False,
kernel_initializer=initializer)
# Convolving with an all-ones kernel is equal to summation of the input.
data_sum = np.tile(np.sum(data, axis=-1, keepdims=True), (1, 1, 2))
true = np.zeros(shape=(2, 3, 2), dtype=np.float32)
true[0, :, :] = data_sum[0, (0, 0, 1), :]
true[1, :, :] = data_sum[1, :, :]
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=sizes,
kernel_size=2,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(upsampled.shape, (2, 3, 2))
self.assertAllClose(upsampled, true)
def test_upsample_transposed_convolution_jacobian_random(self):
"""Tests the jacobian is correct."""
num_filters = 6
kernel_size = 1
data_init = np.random.uniform(size=(2, 5, num_filters))
pool_map = _batch_sparse_eye((2,), 5, np.float64)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_filters,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
dtype='float64')
# Calling the upsample_transposed_convolution to create the variables
# in the transposed_convoution.
gp.upsample_transposed_convolution(
data_init,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
def gp_upsample_transposed_convolution(data):
return gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
self.assert_jacobian_is_correct_fn(gp_upsample_transposed_convolution,
[data_init])
def test_upsample_transposed_convolution_jacobian_random_padding(self):
"""Tests the jacobian is correct with padded data."""
num_filters = 6
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 3, num_filters))
data_init[0, -1, :] = 0.
pool_map = np.array(
(((0.5, 0.5, 0., 0., 0.), (0., 0., 0.5, 0.5, 0.), (0., 0., 0., 0., 0.)),
((1., 0., 0., 0., 0.), (0., 1. / 3., 1. / 3., 1. / 3., 0.),
(0., 0., 0., 0., 1.))),
dtype=data_init.dtype)
pool_map = _dense_to_sparse(pool_map)
kernel_size = 2
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_filters,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
dtype='float64')
# Calling the upsample_transposed_convolution to create the variables
# in the transposed_convoution.
gp.upsample_transposed_convolution(
data_init,
pool_map,
sizes=sizes,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
def gp_upsample_transposed_convolution(data):
return gp.upsample_transposed_convolution(
data,
pool_map,
sizes=sizes,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
self.assert_jacobian_is_correct_fn(gp_upsample_transposed_convolution,
[data_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 tensorflow_graphics.geometry.convolution.tests.graph_pooling."""
# pylint: disable=protected-access
import itertools
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
import tensorflow_graphics.geometry.convolution.graph_pooling as gp
from tensorflow_graphics.geometry.convolution.tests import utils_test
from tensorflow_graphics.util import test_case
def _dense_to_sparse(data):
"""Convert a numpy array to a tf.SparseTensor."""
return utils_test._dense_to_sparse(data)
def _batch_sparse_eye(batch_shape, num_vertices, dtype):
"""Generate a batch of identity matrices."""
eye = np.eye(num_vertices, dtype=dtype)
num_batch_dims = len(batch_shape)
expand_shape = np.concatenate((np.ones(
num_batch_dims, dtype=np.int32), (num_vertices, num_vertices)),
axis=0)
eye = np.reshape(eye, expand_shape)
tile_shape = np.concatenate((batch_shape, (1, 1)), axis=0)
return _dense_to_sparse(np.tile(eye, tile_shape))
class GraphPoolingTestPoolTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_pool_exception_raised_types(self, err_msg, data_type, pool_map_type,
sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.pool(data, pool_map, sizes)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
)
def test_pool_exception_raised_shapes(self, err_msg, data_shape,
pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.pool(data, pool_map, sizes)
def test_pool_exception_raised_algorithm(self):
"""Tests the correct exception is raised for an invalid algorithm."""
data = np.ones(shape=(2, 2))
pool_map = _dense_to_sparse(np.ones(shape=(2, 2)))
with self.assertRaisesRegexp(
ValueError, 'The pooling method must be "weighted" or "max"'):
gp.pool(data, pool_map, sizes=None, algorithm='mean')
@parameterized.parameters(
((2, 3), 4, 3, np.float32),
((1,), 6, 1, np.float32),
((4, 1, 3), 9, 7, np.float64),
((2, 8, 4, 6), 19, 11, np.float64),
)
def test_pool_identity(self, batch_shape, num_vertices, num_features,
data_type):
"""Tests graph pooling with identity maps."""
data_shape = np.concatenate((batch_shape, (num_vertices, num_features)))
data = np.random.uniform(size=data_shape).astype(data_type)
pool_map = _batch_sparse_eye(batch_shape, num_vertices, data_type)
pooled_max = gp.pool(data, pool_map, sizes=None, algorithm='max', name=None)
pooled_weighted = gp.pool(
data, pool_map, sizes=None, algorithm='weighted', name=None)
self.assertAllClose(pooled_max, data)
self.assertAllClose(pooled_weighted, data)
def test_pool_preset_padded(self):
"""Tests pooling with preset data and padding."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
pooled_max = gp.pool(data, pool_map, sizes, algorithm='max')
pooled_weighted = gp.pool(data, pool_map, sizes, algorithm='weighted')
true_max = (((2., 3.), (4., 5.), (0., 0.)), ((6., 7.), (8., 9.), (10.,
11.)))
true_weighted = (((1., 2.), (4., 5.), (0., 0.)), ((6., 7.), (8., 9.),
(10., 11.)))
self.assertAllClose(pooled_max, true_max)
self.assertAllClose(pooled_weighted, true_weighted)
def test_pool_preset(self):
"""Tests pooling with preset data."""
pool_map = np.array(((0.5, 0.5, 0., 0.), (0., 0., 0.5, 0.5)),
dtype=np.float32)
pool_map = _dense_to_sparse(pool_map)
data = np.reshape(np.arange(8).astype(np.float32), (4, 2))
max_true = data[(1, 3), :]
max_weighted = (data[(0, 2), :] + max_true) * 0.5
pooled_max = gp.pool(data, pool_map, sizes=None, algorithm='max', name=None)
pooled_weighted = gp.pool(
data, pool_map, sizes=None, algorithm='weighted', name=None)
self.assertAllClose(pooled_max, max_true)
self.assertAllClose(pooled_weighted, max_weighted)
@parameterized.parameters((20, 10, 3), (2, 1, 1), (2, 5, 4), (2, 1, 3))
def test_pool_random(self, num_input_vertices, num_output_vertices,
num_features):
"""Tests pooling with random inputs."""
pool_map = 0.001 + np.random.uniform(
size=(num_output_vertices, num_input_vertices))
data = np.random.uniform(size=(num_input_vertices, num_features))
true_weighted = np.matmul(pool_map, data)
true_max = np.tile(
np.max(data, axis=0, keepdims=True), (num_output_vertices, 1))
pool_map = _dense_to_sparse(pool_map)
with self.subTest(name='max'):
pooled_max = gp.pool(data, pool_map, None, algorithm='max')
self.assertAllClose(pooled_max, true_max)
with self.subTest(name='weighted'):
pooled_weighted = gp.pool(data, pool_map, None, algorithm='weighted')
self.assertAllClose(pooled_weighted, true_weighted)
def test_pool_jacobian(self):
"""Tests the jacobian is correct."""
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 5, 3))
pool_map = np.random.uniform(size=(2, 3, 5))
data_init[0, -1, :] = 0.
pool_map[0, -1, :] = 0.
pool_map = _dense_to_sparse(pool_map)
def gp_pool(data, algorithm):
return gp.pool(data, pool_map, sizes, algorithm=algorithm)
with self.subTest(name='max'):
self.assert_jacobian_is_correct_fn(lambda data: gp_pool(data, 'max'),
[data_init])
with self.subTest(name='weighted'):
self.assert_jacobian_is_correct_fn(lambda data: gp_pool(data, 'weighted'),
[data_init])
class GraphPoolingTestUnpoolTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_unpool_exception_raised_types(self, err_msg, data_type,
pool_map_type, sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.unpool(data, pool_map, sizes)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
('data must have a rank less than 6', (2, 3, 4, 5, 3, 2),
(2, 3, 4, 5, 3, 3), None),
)
def test_unpool_exception_raised_shapes(self, err_msg, data_shape,
pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.unpool(data, pool_map, sizes)
@parameterized.parameters(
((2, 3), 4, 3, np.float32),
((1,), 6, 1, np.float32),
((4, 1, 3), 9, 7, np.float64),
((2, 8, 4), 19, 11, np.float64),
)
def test_unpool_identity(self, batch_shape, num_vertices, num_features,
data_type):
"""Tests graph unpooling with identity maps."""
data_shape = np.concatenate((batch_shape, (num_vertices, num_features)))
data = np.random.uniform(size=data_shape).astype(data_type)
pool_map = _batch_sparse_eye(batch_shape, num_vertices, data_type)
unpooled = gp.unpool(data, pool_map, sizes=None)
self.assertAllClose(unpooled, data)
def test_unpool_preset_padded(self):
"""Tests pooling with preset data and padding."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
data[0, -1, :] = 0.
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
unpooled = gp.unpool(data, pool_map, sizes)
true = (((0., 1.), (0., 1.), (2., 3.)), ((6., 7.), (8., 9.), (10., 11.)))
self.assertAllClose(unpooled, true)
@parameterized.parameters((20, 4), (2, 1), (12, 4), (6, 3))
def test_unpool_random(self, num_vertices, num_features):
"""Tests pooling with random data inputs."""
output_vertices = num_vertices // 2
pool_map = np.zeros(shape=(output_vertices, num_vertices), dtype=np.float32)
for i in range(output_vertices):
pool_map[i, (i * 2, i * 2 + 1)] = (0.5, 0.5)
data = np.random.uniform(size=(output_vertices,
num_features)).astype(np.float32)
unpooled = gp.unpool(
data, _dense_to_sparse(pool_map), sizes=None, name=None)
with self.subTest(name='direct_unpool'):
true = np.zeros(shape=(num_vertices, num_features)).astype(np.float32)
true[0::2, :] = data
true[1::2, :] = data
self.assertAllClose(unpooled, true)
with self.subTest(name='permute_pool_map'):
permutation = np.random.permutation(num_vertices)
pool_map_permute = pool_map[:, permutation]
unpooled_permute = gp.unpool(data, _dense_to_sparse(pool_map_permute),
None)
true_permute = true[permutation, :]
self.assertAllClose(unpooled_permute, true_permute)
def test_unpool_jacobian_random(self):
"""Tests the jacobian is correct."""
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 3, 6))
pool_map = np.random.uniform(size=(2, 3, 5))
data_init[0, -1, :] = 0.
pool_map[0, -1, :] = 0.
pool_map = _dense_to_sparse(pool_map)
def gp_unpool(data):
return gp.unpool(data, pool_map, sizes)
self.assert_jacobian_is_correct_fn(gp_unpool, [data_init])
class GraphPoolingUpsampleTransposeConvolutionTests(test_case.TestCase):
@parameterized.parameters(
("'sizes' must have an integer type.", np.float32, np.float32,
np.float32),
("'data' must have a float type.", np.int32, np.float32, np.int32),
("'pool_map' and 'data' must have the same type.", np.float32, np.float64,
np.int32))
def test_upsample_transposed_convolution_exception_raised_types(
self, err_msg, data_type, pool_map_type, sizes_type):
"""Tests the correct exceptions are raised for invalid types."""
data = np.ones((2, 3, 3), dtype=data_type)
pool_map = _dense_to_sparse(np.ones((2, 3, 3), dtype=pool_map_type))
sizes = np.array(((1, 2), (2, 3)), dtype=sizes_type)
with self.assertRaisesRegexp(TypeError, err_msg):
gp.upsample_transposed_convolution(
data, pool_map, sizes, kernel_size=1, transposed_convolution_op=None)
@parameterized.parameters(
('data must have a rank greater than 1', (3,), (3,), None),
('pool_map must have a rank of 2', (3, 3), (3,), None),
('sizes must have a rank of 3', (4, 5, 3, 2), (4, 5, 3, 3), (3, 2)),
('data must have a rank less than 6', (2, 3, 4, 5, 3, 2),
(2, 3, 4, 5, 3, 3), None),
)
def test_upsample_transposed_convolution_exception_raised_shapes(
self, err_msg, data_shape, pool_map_shape, sizes_shape):
"""Tests the correct exceptions are raised for invalid shapes."""
data = np.ones(data_shape, dtype=np.float32)
pool_map = _dense_to_sparse(np.ones(pool_map_shape, dtype=np.float32))
if sizes_shape is not None:
sizes = np.ones(sizes_shape, dtype=np.int32)
else:
sizes = None
with self.assertRaisesRegexp(ValueError, err_msg):
gp.upsample_transposed_convolution(
data, pool_map, sizes, kernel_size=1, transposed_convolution_op=None)
def test_upsample_transposed_convolution_exception_raised_callable(self):
"""Tests the correct exception is raised for a invalid convolution op."""
data = np.ones((5, 3))
pool_map = _dense_to_sparse(np.eye(5))
err_msg = "'transposed_convolution_op' must be callable."
with self.assertRaisesRegexp(TypeError, err_msg):
gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=1,
transposed_convolution_op=1)
@parameterized.parameters((1, 1, 1, np.float32), (5, 3, 1, np.float32),
(3, 6, 15, np.float64))
def test_upsample_transposed_convolution_zero_kernel(self, num_vertices,
num_features,
kernel_size, data_type):
"""Tests the upsampling with a zero kernel."""
data = np.random.uniform(size=(num_vertices,
num_features)).astype(data_type)
pool_map = np.zeros(
shape=(num_vertices, num_vertices * kernel_size), dtype=data_type)
for i in range(num_vertices):
pool_map[i, np.arange(kernel_size * i, kernel_size *
(i + 1))] = (1.0 / kernel_size)
pool_map = _dense_to_sparse(pool_map)
# Transposed convolution op with a zero kernel.
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_features,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
use_bias=False,
kernel_initializer=tf.compat.v1.keras.initializers.zeros())
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(
tf.shape(input=upsampled), (num_vertices * kernel_size, num_features))
self.assertAllEqual(upsampled, tf.zeros_like(upsampled))
@parameterized.parameters(
itertools.product((3,), (6,), (3,), range(3), range(6), range(6)),)
def test_upsample_transposed_convolution_selector_kernel_random(
self, num_vertices, num_features, kernel_size, kernel_index,
feature1_index, feature2_index):
"""Tests the upsampling with an indicator kernel."""
data = np.random.uniform(size=(num_vertices,
num_features)).astype(np.float32)
pool_map = np.zeros(
shape=(num_vertices, num_vertices * kernel_size), dtype=np.float32)
for i in range(num_vertices):
pool_map[i, np.arange(kernel_size * i, kernel_size *
(i + 1))] = (1.0 / kernel_size)
pool_map = _dense_to_sparse(pool_map)
selection = np.zeros(
shape=(1, kernel_size, num_features, num_features), dtype=np.float32)
selection[0, kernel_index, feature1_index, feature2_index] = 1.
initializer = tf.compat.v1.constant_initializer(value=selection)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_features,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
use_bias=False,
kernel_initializer=initializer)
true = np.zeros(
shape=(num_vertices * kernel_size, num_features), dtype=np.float32)
input_column = feature2_index
output_column = feature1_index
output_row_start = kernel_index
true[output_row_start::kernel_size, output_column] = (data[:, input_column])
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(upsampled, true)
def test_upsample_transposed_convolution_preset_padded(self):
"""Tests upsampling with presets."""
data = np.reshape(np.arange(12).astype(np.float32), (2, 3, 2))
data[0, -1, :] = 0.
sizes = ((2, 3), (3, 3))
pool_map = _dense_to_sparse(
np.array((((0.5, 0.5, 0.), (0., 0., 1.), (0., 0., 0.)),
((1., 0., 0.), (0., 1., 0.), (0., 0., 1.))),
dtype=np.float32))
kernel = np.ones(shape=(1, 2, 2, 2), dtype=np.float32)
initializer = tf.compat.v1.constant_initializer(value=kernel)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=2,
kernel_size=(1, 2),
strides=(1, 2),
padding='valid',
use_bias=False,
kernel_initializer=initializer)
# Convolving with an all-ones kernel is equal to summation of the input.
data_sum = np.tile(np.sum(data, axis=-1, keepdims=True), (1, 1, 2))
true = np.zeros(shape=(2, 3, 2), dtype=np.float32)
true[0, :, :] = data_sum[0, (0, 0, 1), :]
true[1, :, :] = data_sum[1, :, :]
upsampled = gp.upsample_transposed_convolution(
data,
pool_map,
sizes=sizes,
kernel_size=2,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(upsampled.shape, (2, 3, 2))
self.assertAllClose(upsampled, true)
def test_upsample_transposed_convolution_jacobian_random(self):
"""Tests the jacobian is correct."""
num_filters = 6
kernel_size = 1
data_init = np.random.uniform(size=(2, 5, num_filters))
pool_map = _batch_sparse_eye((2,), 5, np.float64)
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_filters,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
dtype='float64')
# Calling the upsample_transposed_convolution to create the variables
# in the transposed_convoution.
gp.upsample_transposed_convolution(
data_init,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
def gp_upsample_transposed_convolution(data):
return gp.upsample_transposed_convolution(
data,
pool_map,
sizes=None,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
self.assert_jacobian_is_correct_fn(gp_upsample_transposed_convolution,
[data_init])
def test_upsample_transposed_convolution_jacobian_random_padding(self):
"""Tests the jacobian is correct with padded data."""
num_filters = 6
sizes = ((2, 4), (3, 5))
data_init = np.random.uniform(size=(2, 3, num_filters))
data_init[0, -1, :] = 0.
pool_map = np.array(
(((0.5, 0.5, 0., 0., 0.), (0., 0., 0.5, 0.5, 0.), (0., 0., 0., 0., 0.)),
((1., 0., 0., 0., 0.), (0., 1. / 3., 1. / 3., 1. / 3., 0.),
(0., 0., 0., 0., 1.))),
dtype=data_init.dtype)
pool_map = _dense_to_sparse(pool_map)
kernel_size = 2
transposed_convolution_op = tf.keras.layers.Conv2DTranspose(
filters=num_filters,
kernel_size=(1, kernel_size),
strides=(1, kernel_size),
padding='valid',
dtype='float64')
# Calling the upsample_transposed_convolution to create the variables
# in the transposed_convoution.
gp.upsample_transposed_convolution(
data_init,
pool_map,
sizes=sizes,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
# Initializes variables of the transpose conv layer.
self.evaluate(tf.compat.v1.global_variables_initializer())
def gp_upsample_transposed_convolution(data):
return gp.upsample_transposed_convolution(
data,
pool_map,
sizes=sizes,
kernel_size=kernel_size,
transposed_convolution_op=transposed_convolution_op)
self.assert_jacobian_is_correct_fn(gp_upsample_transposed_convolution,
[data_init])
if __name__ == '__main__':
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/tests/look_at_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 lookAt functions."""
from absl.testing import parameterized
import numpy as np
from tensorflow_graphics.geometry.transformation import look_at
from tensorflow_graphics.util import test_case
class LookAtTest(test_case.TestCase):
def test_look_at_right_handed_preset(self):
"""Tests that look_at_right_handed generates expected results."""
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 = look_at.right_handed(camera_position, look_at_point, up_vector)
gt = (((-1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, -1.0, 0.0),
(0.0, 0.0, 0.0, 1.0)),
((4.08248186e-01, -8.16496551e-01, 4.08248395e-01, -2.98023224e-08),
(-7.07106888e-01, 1.19209290e-07, 7.07106769e-01, -1.41421378e-01),
(-5.77350318e-01, -5.77350318e-01, -5.77350318e-01,
3.46410215e-01), (0.0, 0.0, 0.0, 1.0)))
self.assertAllClose(pred, gt)
@parameterized.parameters(
((3,), (3,), (3,)),
((None, 3), (None, 3), (None, 3)),
((None, 2, 3), (None, 2, 3), (None, 2, 3)),
)
def test_look_at_right_handed_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(look_at.right_handed, shapes)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,)),
("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,)),
("must have exactly 3 dimensions in axis -1", (3,), (3,), (1,)),
("Not all batch dimensions are identical", (3,), (3, 3), (3, 3)),
)
def test_look_at_right_handed_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(look_at.right_handed, error_msg, shapes)
def test_look_at_right_handed_jacobian_preset(self):
"""Tests the Jacobian of look_at_right_handed."""
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(
look_at.right_handed,
[camera_position_init, look_at_init, up_vector_init])
def test_look_at_right_handed_jacobian_random(self):
"""Tests the Jacobian of look_at_right_handed."""
tensor_size = np.random.randint(1, 3)
tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist()
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(
look_at.right_handed,
[camera_position_init, look_at_init, up_vector_init])
| # 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 lookAt functions."""
from absl.testing import parameterized
import numpy as np
from tensorflow_graphics.geometry.transformation import look_at
from tensorflow_graphics.util import test_case
class LookAtTest(test_case.TestCase):
def test_look_at_right_handed_preset(self):
"""Tests that look_at_right_handed generates expected results."""
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 = look_at.right_handed(camera_position, look_at_point, up_vector)
gt = (((-1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, -1.0, 0.0),
(0.0, 0.0, 0.0, 1.0)),
((4.08248186e-01, -8.16496551e-01, 4.08248395e-01, -2.98023224e-08),
(-7.07106888e-01, 1.19209290e-07, 7.07106769e-01, -1.41421378e-01),
(-5.77350318e-01, -5.77350318e-01, -5.77350318e-01,
3.46410215e-01), (0.0, 0.0, 0.0, 1.0)))
self.assertAllClose(pred, gt)
@parameterized.parameters(
((3,), (3,), (3,)),
((None, 3), (None, 3), (None, 3)),
((None, 2, 3), (None, 2, 3), (None, 2, 3)),
)
def test_look_at_right_handed_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(look_at.right_handed, shapes)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2,), (3,), (3,)),
("must have exactly 3 dimensions in axis -1", (3,), (2,), (3,)),
("must have exactly 3 dimensions in axis -1", (3,), (3,), (1,)),
("Not all batch dimensions are identical", (3,), (3, 3), (3, 3)),
)
def test_look_at_right_handed_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(look_at.right_handed, error_msg, shapes)
def test_look_at_right_handed_jacobian_preset(self):
"""Tests the Jacobian of look_at_right_handed."""
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(
look_at.right_handed,
[camera_position_init, look_at_init, up_vector_init])
def test_look_at_right_handed_jacobian_random(self):
"""Tests the Jacobian of look_at_right_handed."""
tensor_size = np.random.randint(1, 3)
tensor_shape = np.random.randint(1, 5, size=(tensor_size)).tolist()
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(
look_at.right_handed,
[camera_position_init, look_at_init, up_vector_init])
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/representation/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.
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/representation/ray.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 ray utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 triangulate(startpoints, endpoints, weights, name=None):
"""Triangulates 3d points by miminizing the sum of squared distances to rays.
The rays are defined by their start points and endpoints. At least two rays
are required to triangulate any given point. Contrary to the standard
reprojection-error metric, the sum of squared distances to rays can be
minimized in a closed form.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
startpoints: A tensor of ray start points with shape `[A1, ..., An, V, 3]`,
the number of rays V around which the solution points live should be
greater or equal to 2, otherwise triangulation is impossible.
endpoints: A tensor of ray endpoints with shape `[A1, ..., An, V, 3]`, the
number of rays V around which the solution points live should be greater
or equal to 2, otherwise triangulation is impossible. The `endpoints`
tensor should have the same shape as the `startpoints` tensor.
weights: A tensor of ray weights (certainties) with shape `[A1, ..., An,
V]`. Weights should have all positive entries. Weight should have at least
two non-zero entries for each point (at least two rays should have
certainties > 0).
name: A name for this op. The default value of None means "ray_triangulate".
Returns:
A tensor of triangulated points with shape `[A1, ..., An, 3]`.
Raises:
ValueError: If the shape of the arguments is not supported.
"""
with tf.compat.v1.name_scope(name, "ray_triangulate",
[startpoints, endpoints, weights]):
startpoints = tf.convert_to_tensor(value=startpoints)
endpoints = tf.convert_to_tensor(value=endpoints)
weights = tf.convert_to_tensor(value=weights)
shape.check_static(
tensor=startpoints,
tensor_name="startpoints",
has_rank_greater_than=1,
has_dim_equals=(-1, 3),
has_dim_greater_than=(-2, 1))
shape.check_static(
tensor=endpoints,
tensor_name="endpoints",
has_rank_greater_than=1,
has_dim_equals=(-1, 3),
has_dim_greater_than=(-2, 1))
shape.compare_batch_dimensions(
tensors=(startpoints, endpoints, weights),
last_axes=(-2, -2, -1),
broadcast_compatible=False)
weights = asserts.assert_all_above(weights, 0.0, open_bound=False)
weights = asserts.assert_at_least_k_non_zero_entries(weights, k=2)
left_hand_side_list = []
right_hand_side_list = []
# TODO(b/130892100): Replace the inefficient for loop and add comments here.
for ray_id in range(weights.shape[-1]):
weights_single_ray = weights[..., ray_id]
startpoints_single_ray = startpoints[..., ray_id, :]
endpoints_singleview = endpoints[..., ray_id, :]
ray = endpoints_singleview - startpoints_single_ray
ray = tf.nn.l2_normalize(ray, axis=-1)
ray_x, ray_y, ray_z = tf.unstack(ray, axis=-1)
zeros = tf.zeros_like(ray_x)
cross_product_matrix = tf.stack(
(zeros, -ray_z, ray_y, ray_z, zeros, -ray_x, -ray_y, ray_x, zeros),
axis=-1)
cross_product_matrix_shape = tf.concat(
(tf.shape(input=cross_product_matrix)[:-1], (3, 3)), axis=-1)
cross_product_matrix = tf.reshape(
cross_product_matrix, shape=cross_product_matrix_shape)
weights_single_ray = tf.expand_dims(weights_single_ray, axis=-1)
weights_single_ray = tf.expand_dims(weights_single_ray, axis=-1)
left_hand_side = weights_single_ray * cross_product_matrix
left_hand_side_list.append(left_hand_side)
dot_product = tf.matmul(cross_product_matrix,
tf.expand_dims(startpoints_single_ray, axis=-1))
right_hand_side = weights_single_ray * dot_product
right_hand_side_list.append(right_hand_side)
left_hand_side_multi_rays = tf.concat(left_hand_side_list, axis=-2)
right_hand_side_multi_rays = tf.concat(right_hand_side_list, axis=-2)
points = tf.linalg.lstsq(left_hand_side_multi_rays,
right_hand_side_multi_rays)
points = tf.squeeze(points, axis=-1)
return points
# TODO(b/130893491): Add batch support for radii and return [A1, ... , 3, 2].
def intersection_ray_sphere(sphere_center,
sphere_radius,
ray,
point_on_ray,
name=None):
"""Finds positions and surface normals where the sphere and the ray intersect.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
sphere_center: A tensor of shape `[3]` representing the 3d sphere center.
sphere_radius: A tensor of shape `[1]` containing a strictly positive value
defining the radius of the sphere.
ray: A tensor of shape `[A1, ..., An, 3]` containing normalized 3D vectors.
point_on_ray: A tensor of shape `[A1, ..., An, 3]`.
name: A name for this op. The default value of None means
"ray_intersection_ray_sphere".
Returns:
A tensor of shape `[2, A1, ..., An, 3]` containing the position of the
intersections, and a tensor of shape `[2, A1, ..., An, 3]` the associated
surface normals at that point. Both tensors contain NaNs when there is no
intersections. The first dimension of the returned tensor provides access to
the first and second intersections of the ray with the sphere.
Raises:
ValueError: if the shape of `sphere_center`, `sphere_radius`, `ray` or
`point_on_ray` is not supported.
tf.errors.InvalidArgumentError: If `ray` is not normalized.
"""
with tf.compat.v1.name_scope(
name, "ray_intersection_ray_sphere",
[sphere_center, sphere_radius, ray, point_on_ray]):
sphere_center = tf.convert_to_tensor(value=sphere_center)
sphere_radius = tf.convert_to_tensor(value=sphere_radius)
ray = tf.convert_to_tensor(value=ray)
point_on_ray = tf.convert_to_tensor(value=point_on_ray)
shape.check_static(
tensor=sphere_center,
tensor_name="sphere_center",
has_rank=1,
has_dim_equals=(0, 3))
shape.check_static(
tensor=sphere_radius,
tensor_name="sphere_radius",
has_rank=1,
has_dim_equals=(0, 1))
shape.check_static(tensor=ray, tensor_name="ray", has_dim_equals=(-1, 3))
shape.check_static(
tensor=point_on_ray, tensor_name="point_on_ray", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(ray, point_on_ray),
last_axes=(-2, -2),
broadcast_compatible=False)
sphere_radius = asserts.assert_all_above(
sphere_radius, 0.0, open_bound=True)
ray = asserts.assert_normalized(ray)
vector_sphere_center_to_point_on_ray = sphere_center - point_on_ray
distance_sphere_center_to_point_on_ray = tf.norm(
tensor=vector_sphere_center_to_point_on_ray, axis=-1, keepdims=True)
distance_projection_sphere_center_on_ray = vector.dot(
vector_sphere_center_to_point_on_ray, ray)
closest_distance_sphere_center_to_ray = tf.sqrt(
tf.square(distance_sphere_center_to_point_on_ray) -
tf.pow(distance_projection_sphere_center_on_ray, 2))
half_secant_length = tf.sqrt(
tf.square(sphere_radius) -
tf.square(closest_distance_sphere_center_to_ray))
distances = tf.stack(
(distance_projection_sphere_center_on_ray - half_secant_length,
distance_projection_sphere_center_on_ray + half_secant_length),
axis=0)
intersections_points = distances * ray + point_on_ray
normals = tf.math.l2_normalize(
intersections_points - sphere_center, axis=-1)
return intersections_points, normals
# 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 ray utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 triangulate(startpoints, endpoints, weights, name=None):
"""Triangulates 3d points by miminizing the sum of squared distances to rays.
The rays are defined by their start points and endpoints. At least two rays
are required to triangulate any given point. Contrary to the standard
reprojection-error metric, the sum of squared distances to rays can be
minimized in a closed form.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
startpoints: A tensor of ray start points with shape `[A1, ..., An, V, 3]`,
the number of rays V around which the solution points live should be
greater or equal to 2, otherwise triangulation is impossible.
endpoints: A tensor of ray endpoints with shape `[A1, ..., An, V, 3]`, the
number of rays V around which the solution points live should be greater
or equal to 2, otherwise triangulation is impossible. The `endpoints`
tensor should have the same shape as the `startpoints` tensor.
weights: A tensor of ray weights (certainties) with shape `[A1, ..., An,
V]`. Weights should have all positive entries. Weight should have at least
two non-zero entries for each point (at least two rays should have
certainties > 0).
name: A name for this op. The default value of None means "ray_triangulate".
Returns:
A tensor of triangulated points with shape `[A1, ..., An, 3]`.
Raises:
ValueError: If the shape of the arguments is not supported.
"""
with tf.compat.v1.name_scope(name, "ray_triangulate",
[startpoints, endpoints, weights]):
startpoints = tf.convert_to_tensor(value=startpoints)
endpoints = tf.convert_to_tensor(value=endpoints)
weights = tf.convert_to_tensor(value=weights)
shape.check_static(
tensor=startpoints,
tensor_name="startpoints",
has_rank_greater_than=1,
has_dim_equals=(-1, 3),
has_dim_greater_than=(-2, 1))
shape.check_static(
tensor=endpoints,
tensor_name="endpoints",
has_rank_greater_than=1,
has_dim_equals=(-1, 3),
has_dim_greater_than=(-2, 1))
shape.compare_batch_dimensions(
tensors=(startpoints, endpoints, weights),
last_axes=(-2, -2, -1),
broadcast_compatible=False)
weights = asserts.assert_all_above(weights, 0.0, open_bound=False)
weights = asserts.assert_at_least_k_non_zero_entries(weights, k=2)
left_hand_side_list = []
right_hand_side_list = []
# TODO(b/130892100): Replace the inefficient for loop and add comments here.
for ray_id in range(weights.shape[-1]):
weights_single_ray = weights[..., ray_id]
startpoints_single_ray = startpoints[..., ray_id, :]
endpoints_singleview = endpoints[..., ray_id, :]
ray = endpoints_singleview - startpoints_single_ray
ray = tf.nn.l2_normalize(ray, axis=-1)
ray_x, ray_y, ray_z = tf.unstack(ray, axis=-1)
zeros = tf.zeros_like(ray_x)
cross_product_matrix = tf.stack(
(zeros, -ray_z, ray_y, ray_z, zeros, -ray_x, -ray_y, ray_x, zeros),
axis=-1)
cross_product_matrix_shape = tf.concat(
(tf.shape(input=cross_product_matrix)[:-1], (3, 3)), axis=-1)
cross_product_matrix = tf.reshape(
cross_product_matrix, shape=cross_product_matrix_shape)
weights_single_ray = tf.expand_dims(weights_single_ray, axis=-1)
weights_single_ray = tf.expand_dims(weights_single_ray, axis=-1)
left_hand_side = weights_single_ray * cross_product_matrix
left_hand_side_list.append(left_hand_side)
dot_product = tf.matmul(cross_product_matrix,
tf.expand_dims(startpoints_single_ray, axis=-1))
right_hand_side = weights_single_ray * dot_product
right_hand_side_list.append(right_hand_side)
left_hand_side_multi_rays = tf.concat(left_hand_side_list, axis=-2)
right_hand_side_multi_rays = tf.concat(right_hand_side_list, axis=-2)
points = tf.linalg.lstsq(left_hand_side_multi_rays,
right_hand_side_multi_rays)
points = tf.squeeze(points, axis=-1)
return points
# TODO(b/130893491): Add batch support for radii and return [A1, ... , 3, 2].
def intersection_ray_sphere(sphere_center,
sphere_radius,
ray,
point_on_ray,
name=None):
"""Finds positions and surface normals where the sphere and the ray intersect.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
sphere_center: A tensor of shape `[3]` representing the 3d sphere center.
sphere_radius: A tensor of shape `[1]` containing a strictly positive value
defining the radius of the sphere.
ray: A tensor of shape `[A1, ..., An, 3]` containing normalized 3D vectors.
point_on_ray: A tensor of shape `[A1, ..., An, 3]`.
name: A name for this op. The default value of None means
"ray_intersection_ray_sphere".
Returns:
A tensor of shape `[2, A1, ..., An, 3]` containing the position of the
intersections, and a tensor of shape `[2, A1, ..., An, 3]` the associated
surface normals at that point. Both tensors contain NaNs when there is no
intersections. The first dimension of the returned tensor provides access to
the first and second intersections of the ray with the sphere.
Raises:
ValueError: if the shape of `sphere_center`, `sphere_radius`, `ray` or
`point_on_ray` is not supported.
tf.errors.InvalidArgumentError: If `ray` is not normalized.
"""
with tf.compat.v1.name_scope(
name, "ray_intersection_ray_sphere",
[sphere_center, sphere_radius, ray, point_on_ray]):
sphere_center = tf.convert_to_tensor(value=sphere_center)
sphere_radius = tf.convert_to_tensor(value=sphere_radius)
ray = tf.convert_to_tensor(value=ray)
point_on_ray = tf.convert_to_tensor(value=point_on_ray)
shape.check_static(
tensor=sphere_center,
tensor_name="sphere_center",
has_rank=1,
has_dim_equals=(0, 3))
shape.check_static(
tensor=sphere_radius,
tensor_name="sphere_radius",
has_rank=1,
has_dim_equals=(0, 1))
shape.check_static(tensor=ray, tensor_name="ray", has_dim_equals=(-1, 3))
shape.check_static(
tensor=point_on_ray, tensor_name="point_on_ray", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(ray, point_on_ray),
last_axes=(-2, -2),
broadcast_compatible=False)
sphere_radius = asserts.assert_all_above(
sphere_radius, 0.0, open_bound=True)
ray = asserts.assert_normalized(ray)
vector_sphere_center_to_point_on_ray = sphere_center - point_on_ray
distance_sphere_center_to_point_on_ray = tf.norm(
tensor=vector_sphere_center_to_point_on_ray, axis=-1, keepdims=True)
distance_projection_sphere_center_on_ray = vector.dot(
vector_sphere_center_to_point_on_ray, ray)
closest_distance_sphere_center_to_ray = tf.sqrt(
tf.square(distance_sphere_center_to_point_on_ray) -
tf.pow(distance_projection_sphere_center_on_ray, 2))
half_secant_length = tf.sqrt(
tf.square(sphere_radius) -
tf.square(closest_distance_sphere_center_to_ray))
distances = tf.stack(
(distance_projection_sphere_center_on_ray - half_secant_length,
distance_projection_sphere_center_on_ray + half_secant_length),
axis=0)
intersections_points = distances * ray + point_on_ray
normals = tf.math.l2_normalize(
intersections_points - sphere_center, axis=-1)
return intersections_points, normals
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/local_implicit_grid/resample_geometry.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
"""Compute point samples from a mesh after normalizing its scale."""
import os
from absl import app
from absl import flags
import numpy as np
from tensorflow_graphics.projects.local_implicit_grid.core import point_utils as pu
import trimesh
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
flags.DEFINE_string('input_mesh', '',
'Input geometry file. Must be a trimesh supported type')
flags.DEFINE_string('output_ply', '', 'Samples points ply file.')
flags.DEFINE_float('sampling_density', 2e-4,
'Approx surface area based point sampling density.')
FLAGS = flags.FLAGS
def normalize_mesh(mesh, in_place=True):
"""Rescales vertex positions to lie inside unit cube."""
scale = 1.0 / np.max(mesh.bounds[1, :] - mesh.bounds[0, :])
centroid = mesh.centroid
scaled_vertices = (mesh.vertices - centroid) * scale
if in_place:
scaled_mesh = mesh
scaled_mesh.vertices = scaled_vertices
else:
scaled_mesh = mesh.copy()
scaled_mesh.vertices = scaled_vertices
scaled_mesh.fix_normals()
return scaled_mesh
def sample_mesh(mesh):
"""Samples oriented points from a mesh."""
num_samples = int(mesh.area / FLAGS.sampling_density)
sample_pts, sample_face_ids = trimesh.sample.sample_surface(mesh, num_samples)
sample_normals = mesh.face_normals[sample_face_ids]
return sample_pts, sample_normals
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if not FLAGS.input_mesh:
raise IOError('--input_mesh must be specified.')
if not FLAGS.output_ply:
raise IOError('--output_ply must be specified.')
mesh = trimesh.load(FLAGS.input_mesh)
mesh = normalize_mesh(mesh)
sample_pts, sample_normals = sample_mesh(mesh)
print('Computed {} samples from mesh.'.format(sample_pts.shape[0]))
print('Writing sampled points to {}'.format(FLAGS.output_ply))
pu.write_point_ply(FLAGS.output_ply, sample_pts, sample_normals)
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
"""Compute point samples from a mesh after normalizing its scale."""
import os
from absl import app
from absl import flags
import numpy as np
from tensorflow_graphics.projects.local_implicit_grid.core import point_utils as pu
import trimesh
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
flags.DEFINE_string('input_mesh', '',
'Input geometry file. Must be a trimesh supported type')
flags.DEFINE_string('output_ply', '', 'Samples points ply file.')
flags.DEFINE_float('sampling_density', 2e-4,
'Approx surface area based point sampling density.')
FLAGS = flags.FLAGS
def normalize_mesh(mesh, in_place=True):
"""Rescales vertex positions to lie inside unit cube."""
scale = 1.0 / np.max(mesh.bounds[1, :] - mesh.bounds[0, :])
centroid = mesh.centroid
scaled_vertices = (mesh.vertices - centroid) * scale
if in_place:
scaled_mesh = mesh
scaled_mesh.vertices = scaled_vertices
else:
scaled_mesh = mesh.copy()
scaled_mesh.vertices = scaled_vertices
scaled_mesh.fix_normals()
return scaled_mesh
def sample_mesh(mesh):
"""Samples oriented points from a mesh."""
num_samples = int(mesh.area / FLAGS.sampling_density)
sample_pts, sample_face_ids = trimesh.sample.sample_surface(mesh, num_samples)
sample_normals = mesh.face_normals[sample_face_ids]
return sample_pts, sample_normals
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if not FLAGS.input_mesh:
raise IOError('--input_mesh must be specified.')
if not FLAGS.output_ply:
raise IOError('--output_ply must be specified.')
mesh = trimesh.load(FLAGS.input_mesh)
mesh = normalize_mesh(mesh)
sample_pts, sample_normals = sample_mesh(mesh)
print('Computed {} samples from mesh.'.format(sample_pts.shape[0]))
print('Writing sampled points to {}'.format(FLAGS.output_ply))
pu.write_point_ply(FLAGS.output_ply, sample_pts, sample_normals)
if __name__ == '__main__':
app.run(main)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/cvxnet/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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os import path
import tensorflow.compat.v1 as tf
def get_dataset(data_name, split, args):
return dataset_dict[data_name](split, args)
def shapenet(split, args):
"""ShapeNet Dataset.
Args:
split: string, the split of the dataset, either "train" or "test".
args: tf.app.flags.FLAGS, configurations.
Returns:
dataset: tf.data.Dataset, the shapenet dataset.
"""
total_points = 100000
data_dir = args.data_dir
sample_bbx = args.sample_bbx
if split != "train":
sample_bbx = total_points
sample_surf = args.sample_surf
if split != "train":
sample_surf = 0
image_h = args.image_h
image_w = args.image_w
image_d = args.image_d
n_views = args.n_views
depth_h = args.depth_h
depth_w = args.depth_w
depth_d = args.depth_d
batch_size = args.batch_size if split == "train" else 1
dims = args.dims
def _parser(example):
fs = tf.parse_single_example(
example,
features={
"rgb":
tf.FixedLenFeature([n_views * image_h * image_w * image_d],
tf.float32),
"depth":
tf.FixedLenFeature([depth_d * depth_h * depth_w], tf.float32),
"bbox_samples":
tf.FixedLenFeature([total_points * (dims + 1)], tf.float32),
"surf_samples":
tf.FixedLenFeature([total_points * (dims + 1)], tf.float32),
"name":
tf.FixedLenFeature([], tf.string),
})
fs["rgb"] = tf.reshape(fs["rgb"], [n_views, image_h, image_w, image_d])
fs["depth"] = tf.reshape(fs["depth"], [depth_d, depth_h, depth_w, 1])
fs["bbox_samples"] = tf.reshape(fs["bbox_samples"],
[total_points, dims + 1])
fs["surf_samples"] = tf.reshape(fs["surf_samples"],
[total_points, dims + 1])
return fs
def _sampler(example):
image = tf.gather(
example["rgb"],
tf.random.uniform((),
minval=0,
maxval=n_views if split == "train" else 1,
dtype=tf.int32),
axis=0)
image = tf.image.resize_bilinear(tf.expand_dims(image, axis=0), [224, 224])
depth = example["depth"] / 1000.
sample_points = []
sample_labels = []
if sample_bbx > 0:
if split == "train":
indices_bbx = tf.random.uniform([sample_bbx],
minval=0,
maxval=total_points,
dtype=tf.int32)
bbx_samples = tf.gather(example["bbox_samples"], indices_bbx, axis=0)
else:
bbx_samples = example["bbox_samples"]
bbx_points, bbx_labels = tf.split(bbx_samples, [3, 1], axis=-1)
sample_points.append(bbx_points)
sample_labels.append(bbx_labels)
if sample_surf > 0:
indices_surf = tf.random.uniform([sample_surf],
minval=0,
maxval=total_points,
dtype=tf.int32)
surf_samples = tf.gather(example["surf_samples"], indices_surf, axis=0)
surf_points, surf_labels = tf.split(surf_samples, [3, 1], axis=-1)
sample_points.append(surf_points)
sample_labels.append(surf_labels)
points = tf.concat(sample_points, axis=0)
point_labels = tf.cast(tf.concat(sample_labels, axis=0) <= 0., tf.float32)
image = tf.reshape(image, [224, 224, image_d])
depth = tf.reshape(depth, [depth_d, depth_h, depth_w])
depth = tf.transpose(depth, [1, 2, 0])
points = tf.reshape(points, [sample_bbx + sample_surf, 3])
point_labels = tf.reshape(point_labels, [sample_bbx + sample_surf, 1])
return {
"image": image,
"depth": depth,
"point": points,
"point_label": point_labels,
"name": example["name"],
}
data_pattern = path.join(data_dir, "{}-{}-*".format(args.obj_class, split))
data_files = tf.gfile.Glob(data_pattern)
if not data_files:
raise ValueError("{} did not match any files".format(data_pattern))
file_count = len(data_files)
filenames = tf.data.Dataset.list_files(data_pattern, shuffle=True)
data = filenames.interleave(
lambda x: tf.data.TFRecordDataset([x]),
cycle_length=file_count,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
data = data.map(_parser, num_parallel_calls=tf.data.experimental.AUTOTUNE)
data = data.map(_sampler, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if split == "train":
data = data.shuffle(batch_size * 5).repeat(-1)
return data.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
dataset_dict = {
"shapenet": shapenet,
}
| # 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os import path
import tensorflow.compat.v1 as tf
def get_dataset(data_name, split, args):
return dataset_dict[data_name](split, args)
def shapenet(split, args):
"""ShapeNet Dataset.
Args:
split: string, the split of the dataset, either "train" or "test".
args: tf.app.flags.FLAGS, configurations.
Returns:
dataset: tf.data.Dataset, the shapenet dataset.
"""
total_points = 100000
data_dir = args.data_dir
sample_bbx = args.sample_bbx
if split != "train":
sample_bbx = total_points
sample_surf = args.sample_surf
if split != "train":
sample_surf = 0
image_h = args.image_h
image_w = args.image_w
image_d = args.image_d
n_views = args.n_views
depth_h = args.depth_h
depth_w = args.depth_w
depth_d = args.depth_d
batch_size = args.batch_size if split == "train" else 1
dims = args.dims
def _parser(example):
fs = tf.parse_single_example(
example,
features={
"rgb":
tf.FixedLenFeature([n_views * image_h * image_w * image_d],
tf.float32),
"depth":
tf.FixedLenFeature([depth_d * depth_h * depth_w], tf.float32),
"bbox_samples":
tf.FixedLenFeature([total_points * (dims + 1)], tf.float32),
"surf_samples":
tf.FixedLenFeature([total_points * (dims + 1)], tf.float32),
"name":
tf.FixedLenFeature([], tf.string),
})
fs["rgb"] = tf.reshape(fs["rgb"], [n_views, image_h, image_w, image_d])
fs["depth"] = tf.reshape(fs["depth"], [depth_d, depth_h, depth_w, 1])
fs["bbox_samples"] = tf.reshape(fs["bbox_samples"],
[total_points, dims + 1])
fs["surf_samples"] = tf.reshape(fs["surf_samples"],
[total_points, dims + 1])
return fs
def _sampler(example):
image = tf.gather(
example["rgb"],
tf.random.uniform((),
minval=0,
maxval=n_views if split == "train" else 1,
dtype=tf.int32),
axis=0)
image = tf.image.resize_bilinear(tf.expand_dims(image, axis=0), [224, 224])
depth = example["depth"] / 1000.
sample_points = []
sample_labels = []
if sample_bbx > 0:
if split == "train":
indices_bbx = tf.random.uniform([sample_bbx],
minval=0,
maxval=total_points,
dtype=tf.int32)
bbx_samples = tf.gather(example["bbox_samples"], indices_bbx, axis=0)
else:
bbx_samples = example["bbox_samples"]
bbx_points, bbx_labels = tf.split(bbx_samples, [3, 1], axis=-1)
sample_points.append(bbx_points)
sample_labels.append(bbx_labels)
if sample_surf > 0:
indices_surf = tf.random.uniform([sample_surf],
minval=0,
maxval=total_points,
dtype=tf.int32)
surf_samples = tf.gather(example["surf_samples"], indices_surf, axis=0)
surf_points, surf_labels = tf.split(surf_samples, [3, 1], axis=-1)
sample_points.append(surf_points)
sample_labels.append(surf_labels)
points = tf.concat(sample_points, axis=0)
point_labels = tf.cast(tf.concat(sample_labels, axis=0) <= 0., tf.float32)
image = tf.reshape(image, [224, 224, image_d])
depth = tf.reshape(depth, [depth_d, depth_h, depth_w])
depth = tf.transpose(depth, [1, 2, 0])
points = tf.reshape(points, [sample_bbx + sample_surf, 3])
point_labels = tf.reshape(point_labels, [sample_bbx + sample_surf, 1])
return {
"image": image,
"depth": depth,
"point": points,
"point_label": point_labels,
"name": example["name"],
}
data_pattern = path.join(data_dir, "{}-{}-*".format(args.obj_class, split))
data_files = tf.gfile.Glob(data_pattern)
if not data_files:
raise ValueError("{} did not match any files".format(data_pattern))
file_count = len(data_files)
filenames = tf.data.Dataset.list_files(data_pattern, shuffle=True)
data = filenames.interleave(
lambda x: tf.data.TFRecordDataset([x]),
cycle_length=file_count,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
data = data.map(_parser, num_parallel_calls=tf.data.experimental.AUTOTUNE)
data = data.map(_sampler, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if split == "train":
data = data.shuffle(batch_size * 5).repeat(-1)
return data.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
dataset_dict = {
"shapenet": shapenet,
}
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/euler.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 modules implements Euler angles functionalities.
The Euler angles are defined using a vector $$[\theta, \gamma, \beta]^T \in
\mathbb{R}^3$$, where $$\theta$$ is the angle about $$x$$, $$\gamma$$ the angle
about $$y$$, and $$\beta$$ is the angle about $$z$$
More details about Euler angles can be found on [this page.]
(https://en.wikipedia.org/wiki/Euler_angles)
Note: The angles are defined in radians.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from tensorflow_graphics.geometry.transformation import quaternion
from tensorflow_graphics.geometry.transformation import rotation_matrix_3d
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 from_axis_angle(axis, angle, name=None):
"""Converts axis-angle to Euler angles.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
axis: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a normalized axis.
angle: A tensor of shape `[A1, ..., An, 1]`, where the last dimension
represents an angle.
name: A name for this op that defaults to "euler_from_axis_angle".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
"""
with tf.compat.v1.name_scope(name, "euler_from_axis_angle", [axis, angle]):
return from_quaternion(quaternion.from_axis_angle(axis, angle))
def from_quaternion(quaternions, name=None):
"""Converts quaternions to Euler angles.
Args:
quaternions: A tensor of shape `[A1, ..., An, 4]`, where the last dimension
represents a normalized quaternion.
name: A name for this op that defaults to "euler_from_quaternion".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
"""
def general_case(r00, r10, r21, r22, r20, eps_addition):
"""Handles the general case."""
theta_y = -tf.asin(r20)
sign_cos_theta_y = safe_ops.nonzero_sign(tf.cos(theta_y))
r00 = safe_ops.nonzero_sign(r00) * eps_addition + r00
r22 = safe_ops.nonzero_sign(r22) * eps_addition + r22
theta_z = tf.atan2(r10 * sign_cos_theta_y, r00 * sign_cos_theta_y)
theta_x = tf.atan2(r21 * sign_cos_theta_y, r22 * sign_cos_theta_y)
return tf.stack((theta_x, theta_y, theta_z), axis=-1)
def gimbal_lock(r01, r02, r20, eps_addition):
"""Handles Gimbal locks."""
sign_r20 = safe_ops.nonzero_sign(r20)
r02 = safe_ops.nonzero_sign(r02) * eps_addition + r02
theta_x = tf.atan2(-sign_r20 * r01, -sign_r20 * r02)
theta_y = -sign_r20 * tf.constant(math.pi / 2.0, dtype=r20.dtype)
theta_z = tf.zeros_like(theta_x)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
with tf.compat.v1.name_scope(name, "euler_from_quaternion", [quaternions]):
quaternions = tf.convert_to_tensor(value=quaternions)
shape.check_static(
tensor=quaternions,
tensor_name="quaternions",
has_dim_equals=(-1, 4))
x, y, z, w = tf.unstack(quaternions, axis=-1)
tx = safe_ops.safe_shrink(2.0 * x, -2.0, 2.0, True)
ty = safe_ops.safe_shrink(2.0 * y, -2.0, 2.0, True)
tz = safe_ops.safe_shrink(2.0 * z, -2.0, 2.0, True)
twx = tx * w
twy = ty * w
twz = tz * w
txx = tx * x
txy = ty * x
txz = tz * x
tyy = ty * y
tyz = tz * y
tzz = tz * z
# The following is clipped due to numerical instabilities that can take some
# enties outside the [-1;1] range.
r00 = safe_ops.safe_shrink(1.0 - (tyy + tzz), -1.0, 1.0, True)
r10 = safe_ops.safe_shrink(txy + twz, -1.0, 1.0, True)
r21 = safe_ops.safe_shrink(tyz + twx, -1.0, 1.0, True)
r22 = safe_ops.safe_shrink(1.0 - (txx + tyy), -1.0, 1.0, True)
r20 = safe_ops.safe_shrink(txz - twy, -1.0, 1.0, True)
r01 = safe_ops.safe_shrink(txy - twz, -1.0, 1.0, True)
r02 = safe_ops.safe_shrink(txz + twy, -1.0, 1.0, True)
eps_addition = asserts.select_eps_for_addition(quaternions.dtype)
general_solution = general_case(r00, r10, r21, r22, r20, eps_addition)
gimbal_solution = gimbal_lock(r01, r02, r20, eps_addition)
# The general solution is unstable close to the Gimbal lock, and the gimbal
# solution is not toooff in these cases.
is_gimbal = tf.less(tf.abs(tf.abs(r20) - 1.0), 1.0e-6)
gimbal_mask = tf.stack((is_gimbal, is_gimbal, is_gimbal), axis=-1)
return tf.compat.v1.where(gimbal_mask, gimbal_solution, general_solution)
def from_rotation_matrix(rotation_matrix, name=None):
"""Converts rotation matrices to Euler angles.
The rotation matrices are assumed to have been constructed by rotation around
the $$x$$, then $$y$$, and finally the $$z$$ axis.
Note:
There is an infinite number of solutions to this problem. There are
Gimbal locks when abs(rotation_matrix(2,0)) == 1, which are not handled.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
rotation_matrix: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two
dimensions represent a rotation matrix.
name: A name for this op that defaults to "euler_from_rotation_matrix".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
Raises:
ValueError: If the shape of `rotation_matrix` is not supported.
"""
def general_case(rotation_matrix, r20, eps_addition):
"""Handles the general case."""
theta_y = -tf.asin(r20)
sign_cos_theta_y = safe_ops.nonzero_sign(tf.cos(theta_y))
r00 = rotation_matrix[..., 0, 0]
r10 = rotation_matrix[..., 1, 0]
r21 = rotation_matrix[..., 2, 1]
r22 = rotation_matrix[..., 2, 2]
r00 = safe_ops.nonzero_sign(r00) * eps_addition + r00
r22 = safe_ops.nonzero_sign(r22) * eps_addition + r22
# cos_theta_y evaluates to 0 on Gimbal locks, in which case the output of
# this function will not be used.
theta_z = tf.atan2(r10 * sign_cos_theta_y, r00 * sign_cos_theta_y)
theta_x = tf.atan2(r21 * sign_cos_theta_y, r22 * sign_cos_theta_y)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
def gimbal_lock(rotation_matrix, r20, eps_addition):
"""Handles Gimbal locks."""
r01 = rotation_matrix[..., 0, 1]
r02 = rotation_matrix[..., 0, 2]
sign_r20 = safe_ops.nonzero_sign(r20)
r02 = safe_ops.nonzero_sign(r02) * eps_addition + r02
theta_x = tf.atan2(-sign_r20 * r01, -sign_r20 * r02)
theta_y = -sign_r20 * tf.constant(math.pi / 2.0, dtype=r20.dtype)
theta_z = tf.zeros_like(theta_x)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
with tf.compat.v1.name_scope(name, "euler_from_rotation_matrix",
[rotation_matrix]):
rotation_matrix = tf.convert_to_tensor(value=rotation_matrix)
shape.check_static(
tensor=rotation_matrix,
tensor_name="rotation_matrix",
has_rank_greater_than=1,
has_dim_equals=((-1, 3), (-2, 3)))
rotation_matrix = rotation_matrix_3d.assert_rotation_matrix_normalized(
rotation_matrix)
r20 = rotation_matrix[..., 2, 0]
eps_addition = asserts.select_eps_for_addition(rotation_matrix.dtype)
general_solution = general_case(rotation_matrix, r20, eps_addition)
gimbal_solution = gimbal_lock(rotation_matrix, r20, eps_addition)
is_gimbal = tf.equal(tf.abs(r20), 1)
gimbal_mask = tf.stack((is_gimbal, is_gimbal, is_gimbal), axis=-1)
return tf.compat.v1.where(gimbal_mask, gimbal_solution, general_solution)
def inverse(euler_angle, name=None):
"""Computes the angles that would inverse a transformation by euler_angle.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
euler_angle: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three Euler angles.
name: A name for this op that defaults to "euler_inverse".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
Raises:
ValueError: If the shape of `euler_angle` is not supported.
"""
with tf.compat.v1.name_scope(name, "euler_inverse", [euler_angle]):
euler_angle = tf.convert_to_tensor(value=euler_angle)
shape.check_static(
tensor=euler_angle,
tensor_name="euler_angle",
has_dim_equals=(-1, 3))
return -euler_angle
# 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 modules implements Euler angles functionalities.
The Euler angles are defined using a vector $$[\theta, \gamma, \beta]^T \in
\mathbb{R}^3$$, where $$\theta$$ is the angle about $$x$$, $$\gamma$$ the angle
about $$y$$, and $$\beta$$ is the angle about $$z$$
More details about Euler angles can be found on [this page.]
(https://en.wikipedia.org/wiki/Euler_angles)
Note: The angles are defined in radians.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from tensorflow_graphics.geometry.transformation import quaternion
from tensorflow_graphics.geometry.transformation import rotation_matrix_3d
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 from_axis_angle(axis, angle, name=None):
"""Converts axis-angle to Euler angles.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
axis: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a normalized axis.
angle: A tensor of shape `[A1, ..., An, 1]`, where the last dimension
represents an angle.
name: A name for this op that defaults to "euler_from_axis_angle".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
"""
with tf.compat.v1.name_scope(name, "euler_from_axis_angle", [axis, angle]):
return from_quaternion(quaternion.from_axis_angle(axis, angle))
def from_quaternion(quaternions, name=None):
"""Converts quaternions to Euler angles.
Args:
quaternions: A tensor of shape `[A1, ..., An, 4]`, where the last dimension
represents a normalized quaternion.
name: A name for this op that defaults to "euler_from_quaternion".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
"""
def general_case(r00, r10, r21, r22, r20, eps_addition):
"""Handles the general case."""
theta_y = -tf.asin(r20)
sign_cos_theta_y = safe_ops.nonzero_sign(tf.cos(theta_y))
r00 = safe_ops.nonzero_sign(r00) * eps_addition + r00
r22 = safe_ops.nonzero_sign(r22) * eps_addition + r22
theta_z = tf.atan2(r10 * sign_cos_theta_y, r00 * sign_cos_theta_y)
theta_x = tf.atan2(r21 * sign_cos_theta_y, r22 * sign_cos_theta_y)
return tf.stack((theta_x, theta_y, theta_z), axis=-1)
def gimbal_lock(r01, r02, r20, eps_addition):
"""Handles Gimbal locks."""
sign_r20 = safe_ops.nonzero_sign(r20)
r02 = safe_ops.nonzero_sign(r02) * eps_addition + r02
theta_x = tf.atan2(-sign_r20 * r01, -sign_r20 * r02)
theta_y = -sign_r20 * tf.constant(math.pi / 2.0, dtype=r20.dtype)
theta_z = tf.zeros_like(theta_x)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
with tf.compat.v1.name_scope(name, "euler_from_quaternion", [quaternions]):
quaternions = tf.convert_to_tensor(value=quaternions)
shape.check_static(
tensor=quaternions,
tensor_name="quaternions",
has_dim_equals=(-1, 4))
x, y, z, w = tf.unstack(quaternions, axis=-1)
tx = safe_ops.safe_shrink(2.0 * x, -2.0, 2.0, True)
ty = safe_ops.safe_shrink(2.0 * y, -2.0, 2.0, True)
tz = safe_ops.safe_shrink(2.0 * z, -2.0, 2.0, True)
twx = tx * w
twy = ty * w
twz = tz * w
txx = tx * x
txy = ty * x
txz = tz * x
tyy = ty * y
tyz = tz * y
tzz = tz * z
# The following is clipped due to numerical instabilities that can take some
# enties outside the [-1;1] range.
r00 = safe_ops.safe_shrink(1.0 - (tyy + tzz), -1.0, 1.0, True)
r10 = safe_ops.safe_shrink(txy + twz, -1.0, 1.0, True)
r21 = safe_ops.safe_shrink(tyz + twx, -1.0, 1.0, True)
r22 = safe_ops.safe_shrink(1.0 - (txx + tyy), -1.0, 1.0, True)
r20 = safe_ops.safe_shrink(txz - twy, -1.0, 1.0, True)
r01 = safe_ops.safe_shrink(txy - twz, -1.0, 1.0, True)
r02 = safe_ops.safe_shrink(txz + twy, -1.0, 1.0, True)
eps_addition = asserts.select_eps_for_addition(quaternions.dtype)
general_solution = general_case(r00, r10, r21, r22, r20, eps_addition)
gimbal_solution = gimbal_lock(r01, r02, r20, eps_addition)
# The general solution is unstable close to the Gimbal lock, and the gimbal
# solution is not toooff in these cases.
is_gimbal = tf.less(tf.abs(tf.abs(r20) - 1.0), 1.0e-6)
gimbal_mask = tf.stack((is_gimbal, is_gimbal, is_gimbal), axis=-1)
return tf.compat.v1.where(gimbal_mask, gimbal_solution, general_solution)
def from_rotation_matrix(rotation_matrix, name=None):
"""Converts rotation matrices to Euler angles.
The rotation matrices are assumed to have been constructed by rotation around
the $$x$$, then $$y$$, and finally the $$z$$ axis.
Note:
There is an infinite number of solutions to this problem. There are
Gimbal locks when abs(rotation_matrix(2,0)) == 1, which are not handled.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
rotation_matrix: A tensor of shape `[A1, ..., An, 3, 3]`, where the last two
dimensions represent a rotation matrix.
name: A name for this op that defaults to "euler_from_rotation_matrix".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
Raises:
ValueError: If the shape of `rotation_matrix` is not supported.
"""
def general_case(rotation_matrix, r20, eps_addition):
"""Handles the general case."""
theta_y = -tf.asin(r20)
sign_cos_theta_y = safe_ops.nonzero_sign(tf.cos(theta_y))
r00 = rotation_matrix[..., 0, 0]
r10 = rotation_matrix[..., 1, 0]
r21 = rotation_matrix[..., 2, 1]
r22 = rotation_matrix[..., 2, 2]
r00 = safe_ops.nonzero_sign(r00) * eps_addition + r00
r22 = safe_ops.nonzero_sign(r22) * eps_addition + r22
# cos_theta_y evaluates to 0 on Gimbal locks, in which case the output of
# this function will not be used.
theta_z = tf.atan2(r10 * sign_cos_theta_y, r00 * sign_cos_theta_y)
theta_x = tf.atan2(r21 * sign_cos_theta_y, r22 * sign_cos_theta_y)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
def gimbal_lock(rotation_matrix, r20, eps_addition):
"""Handles Gimbal locks."""
r01 = rotation_matrix[..., 0, 1]
r02 = rotation_matrix[..., 0, 2]
sign_r20 = safe_ops.nonzero_sign(r20)
r02 = safe_ops.nonzero_sign(r02) * eps_addition + r02
theta_x = tf.atan2(-sign_r20 * r01, -sign_r20 * r02)
theta_y = -sign_r20 * tf.constant(math.pi / 2.0, dtype=r20.dtype)
theta_z = tf.zeros_like(theta_x)
angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
return angles
with tf.compat.v1.name_scope(name, "euler_from_rotation_matrix",
[rotation_matrix]):
rotation_matrix = tf.convert_to_tensor(value=rotation_matrix)
shape.check_static(
tensor=rotation_matrix,
tensor_name="rotation_matrix",
has_rank_greater_than=1,
has_dim_equals=((-1, 3), (-2, 3)))
rotation_matrix = rotation_matrix_3d.assert_rotation_matrix_normalized(
rotation_matrix)
r20 = rotation_matrix[..., 2, 0]
eps_addition = asserts.select_eps_for_addition(rotation_matrix.dtype)
general_solution = general_case(rotation_matrix, r20, eps_addition)
gimbal_solution = gimbal_lock(rotation_matrix, r20, eps_addition)
is_gimbal = tf.equal(tf.abs(r20), 1)
gimbal_mask = tf.stack((is_gimbal, is_gimbal, is_gimbal), axis=-1)
return tf.compat.v1.where(gimbal_mask, gimbal_solution, general_solution)
def inverse(euler_angle, name=None):
"""Computes the angles that would inverse a transformation by euler_angle.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
euler_angle: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three Euler angles.
name: A name for this op that defaults to "euler_inverse".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
the three Euler angles.
Raises:
ValueError: If the shape of `euler_angle` is not supported.
"""
with tf.compat.v1.name_scope(name, "euler_inverse", [euler_angle]):
euler_angle = tf.convert_to_tensor(value=euler_angle)
shape.check_static(
tensor=euler_angle,
tensor_name="euler_angle",
has_dim_equals=(-1, 3))
return -euler_angle
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/tests/axis_angle_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 axis-angle."""
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_helpers
from tensorflow_graphics.util import test_case
class AxisAngleTest(test_case.TestCase):
@parameterized.parameters(
((3,),),
((None, 3),),
)
def test_from_euler_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.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(axis_angle.from_euler, error_msg, shapes)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_euler_jacobian_random(self):
"""Test the Jacobian of the from_euler function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN at 0.
"""
x_init = test_helpers.generate_random_test_euler_angles()
self.assert_jacobian_is_finite_fn(lambda x: axis_angle.from_euler(x)[0],
[x_init])
self.assert_jacobian_is_finite_fn(lambda x: axis_angle.from_euler(x)[1],
[x_init])
def test_from_euler_random(self):
"""Tests that from_euler allows to perform the expect rotation of points."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
tensor_shape = random_euler_angles.shape[:-1]
random_point = np.random.normal(size=tensor_shape + (3,))
random_matrix = rotation_matrix_3d.from_euler(random_euler_angles)
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
rotated_with_matrix = rotation_matrix_3d.rotate(random_point, random_matrix)
rotated_with_axis_angle = axis_angle.rotate(random_point, random_axis,
random_angle)
self.assertAllClose(rotated_with_matrix, rotated_with_axis_angle)
@parameterized.parameters(
((3,),),
((None, 3),),
((2, 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(
axis_angle.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(
axis_angle.from_euler_with_small_angles_approximation, error_msg,
shapes)
def test_from_euler_normalized_preset(self):
"""Tests that from_euler allows build normalized axis-angles."""
euler_angles = test_helpers.generate_preset_test_euler_angles()
axis, angle = axis_angle.from_euler(euler_angles)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_euler_normalized_random(self):
"""Tests that from_euler allows build normalized axis-angles."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
self.assertAllEqual(
axis_angle.is_normalized(random_axis, random_angle),
np.ones(shape=random_angle.shape))
def test_from_euler_with_small_angles_approximation_random(self):
# Only generate small angles. For a test tolerance of 1e-3, 0.23 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=-0.23, max_angle=0.23)
exact_axis_angle = axis_angle.from_euler(random_euler_angles)
approximate_axis_angle = (
axis_angle.from_euler_with_small_angles_approximation(
random_euler_angles))
self.assertAllClose(exact_axis_angle, approximate_axis_angle, atol=1e-3)
@parameterized.parameters(
((4,),),
((None, 4),),
((2, 4),),
)
def test_from_quaternion_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.from_quaternion, shape)
@parameterized.parameters(
("must have exactly 4 dimensions in axis -1", (None,)),)
def test_from_quaternion_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.from_quaternion, error_msg,
shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_quaternion_jacobian_random(self):
"""Test the Jacobian of the from_quaternion function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN a 0.
"""
x_init = test_helpers.generate_random_test_quaternions()
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_quaternion(x)[0], [x_init])
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_quaternion(x)[1], [x_init])
def test_from_quaternion_normalized_preset(self):
"""Tests that from_quaternion returns normalized axis-angles."""
euler_angles = test_helpers.generate_preset_test_euler_angles()
quat = quaternion.from_euler(euler_angles)
axis, angle = axis_angle.from_quaternion(quat)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_quaternion_normalized_random(self):
"""Tests that from_quaternion returns normalized axis-angles."""
random_quaternions = test_helpers.generate_random_test_quaternions()
random_axis, random_angle = axis_angle.from_quaternion(random_quaternions)
self.assertAllEqual(
axis_angle.is_normalized(random_axis, random_angle),
np.ones(random_angle.shape))
def test_from_quaternion_preset(self):
"""Tests that axis_angle.from_quaternion produces the expected result."""
preset_euler_angles = test_helpers.generate_preset_test_euler_angles()
preset_quaternions = quaternion.from_euler(preset_euler_angles)
preset_axis_angle = axis_angle.from_euler(preset_euler_angles)
self.assertAllClose(
preset_axis_angle,
axis_angle.from_quaternion(preset_quaternions),
rtol=1e-3)
def test_from_quaternion_random(self):
"""Tests that axis_angle.from_quaternion produces the expected result."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
random_quaternions = quaternion.from_euler(random_euler_angles)
random_axis_angle = axis_angle.from_euler(random_euler_angles)
self.assertAllClose(
random_axis_angle,
axis_angle.from_quaternion(random_quaternions),
rtol=1e-3)
@parameterized.parameters(
((3, 3),),
((None, 3, 3),),
)
def test_from_rotation_matrix_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.from_rotation_matrix, 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_from_rotation_matrix_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.from_rotation_matrix, error_msg,
shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_rotation_matrix_jacobian_random(self):
"""Test the Jacobian of the from_rotation_matrix function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN a 0.
"""
x_init = test_helpers.generate_random_test_rotation_matrix_3d()
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_rotation_matrix(x)[0], [x_init])
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_rotation_matrix(x)[1], [x_init])
def test_from_rotation_matrix_normalized_preset(self):
"""Tests that from_rotation_matrix returns normalized axis-angles."""
preset_euler_angles = test_helpers.generate_preset_test_euler_angles()
matrix = rotation_matrix_3d.from_euler(preset_euler_angles)
axis, angle = axis_angle.from_rotation_matrix(matrix)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_rotation_matrix_normalized_random(self):
"""Tests that from_rotation_matrix returns normalized axis-angles."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
matrix = rotation_matrix_3d.from_euler(random_euler_angles)
axis, angle = axis_angle.from_rotation_matrix(matrix)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_rotation_matrix_random(self):
"""Tests rotation around Z axis."""
def get_rotation_matrix_around_z(angle_rad):
return np.array([
[np.cos(angle_rad), -np.sin(angle_rad), 0],
[np.sin(angle_rad), np.cos(angle_rad), 0],
[0, 0, 1],
])
tensor_size = np.random.randint(10)
angle = (
np.array([
np.deg2rad(np.random.randint(720) - 360) for _ in range(tensor_size)
]).reshape((tensor_size, 1)))
rotation_matrix = [get_rotation_matrix_around_z(i[0]) for i in angle]
rotation_matrix = np.array(rotation_matrix).reshape((tensor_size, 3, 3))
tf_axis, tf_angle = axis_angle.from_rotation_matrix(rotation_matrix)
axis = np.tile([[0., 0., 1.]], (angle.shape[0], 1))
tf_quat_gt = quaternion.from_axis_angle(axis, angle)
tf_quat = quaternion.from_axis_angle(tf_axis, tf_angle)
# Compare quaternions since axis orientation and angle ambiguity will
# lead to more complex comparisons.
for quat_gt, quat in zip(self.evaluate(tf_quat_gt), self.evaluate(tf_quat)):
# Remember that q=-q for any quaternion.
pos = np.allclose(quat_gt, quat)
neg = np.allclose(quat_gt, -quat)
self.assertTrue(pos or neg)
@parameterized.parameters(
((3,), (1,)),
((None, 3), (None, 1)),
((2, 3), (2, 1)),
((1, 3), (1,)),
((3,), (1, 1)),
)
def test_inverse_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.inverse, shape)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (None,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (None,)),
)
def test_inverse_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.inverse, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_inverse_jacobian_preset(self):
"""Test the Jacobian of the inverse function."""
x_axis_init, x_angle_init = test_helpers.generate_preset_test_axis_angle()
if tf.executing_eagerly():
# Because axis is returned as is, gradient calculation fails in graph mode
# but not in eager mode. This is a side effect of having a graph rather
# than a problem of the function.
with self.subTest("axis"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x, x_angle_init)[0], [x_axis_init])
with self.subTest("angle"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x_axis_init, x)[1], [x_angle_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_inverse_jacobian_random(self):
"""Test the Jacobian of the inverse function."""
x_axis_init, x_angle_init = test_helpers.generate_random_test_axis_angle()
if tf.executing_eagerly():
# Because axis is returned as is, gradient calculation fails in graph mode
# but not in eager mode. This is a side effect of having a graph rather
# than a problem of the function.
with self.subTest("axis"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(1.0 * x, x_angle_init)[0],
[x_axis_init])
with self.subTest("angle"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x_axis_init, x)[1], [x_angle_init])
def test_inverse_normalized_random(self):
"""Tests that axis-angle inversion return a normalized axis-angle."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
inverse_axis, inverse_angle = axis_angle.inverse(random_axis, random_angle)
self.assertAllEqual(
axis_angle.is_normalized(inverse_axis, inverse_angle),
np.ones(random_angle.shape))
def test_inverse_random(self):
"""Tests axis-angle inversion."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
inverse_axis, inverse_angle = axis_angle.inverse(random_axis, random_angle)
self.assertAllClose(inverse_axis, random_axis, rtol=1e-3)
self.assertAllClose(inverse_angle, -random_angle, rtol=1e-3)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (None,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (None,)),
)
def test_is_normalized_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.is_normalized, error_msg, shape)
def test_is_normalized_random(self):
"""Tests that is_normalized works as intended."""
# Samples normalized axis-angles.
random_euler_angles = test_helpers.generate_random_test_euler_angles()
with self.subTest(name=("is_normalized")):
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
pred = axis_angle.is_normalized(random_axis, random_angle)
self.assertAllEqual(np.ones(shape=random_angle.shape, dtype=bool), pred)
with self.subTest(name=("is_not_normalized")):
random_axis *= 1.01
pred = axis_angle.is_normalized(random_axis, random_angle)
self.assertAllEqual(np.zeros(shape=random_angle.shape, dtype=bool), pred)
@parameterized.parameters(
((3,), (3,), (1,)),
((None, 3), (None, 3), (None, 1)),
((2, 3), (2, 3), (2, 1)),
((3,), (1, 3), (1, 2, 1)),
((1, 2, 3), (1, 3), (1,)),
((3,), (1, 3), (1,)),
)
def test_rotate_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.rotate, shapes)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2,), (3,), (1,)),
("must have exactly 3 dimensions in axis -1", (3,), (2,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (3,), (2,)),
)
def test_rotate_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.rotate, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_rotate_jacobian_preset(self):
"""Test the Jacobian of the rotate function."""
x_axis_init, x_angle_init = test_helpers.generate_preset_test_axis_angle()
x_point_init = np.random.uniform(size=x_axis_init.shape)
self.assert_jacobian_is_correct_fn(
axis_angle.rotate, [x_point_init, x_axis_init, x_angle_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_rotate_jacobian_random(self):
"""Test the Jacobian of the rotate function."""
x_axis_init, x_angle_init = test_helpers.generate_random_test_axis_angle()
x_point_init = np.random.uniform(size=x_axis_init.shape)
self.assert_jacobian_is_correct_fn(
axis_angle.rotate, [x_point_init, x_axis_init, x_angle_init])
def test_rotate_random(self):
"""Tests that the rotate provide the same results as quaternion.rotate."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
tensor_shape = random_angle.shape[:-1]
random_point = np.random.normal(size=tensor_shape + (3,))
random_quaternion = quaternion.from_axis_angle(random_axis, random_angle)
ground_truth = quaternion.rotate(random_point, random_quaternion)
prediction = axis_angle.rotate(random_point, random_axis, random_angle)
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 axis-angle."""
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_helpers
from tensorflow_graphics.util import test_case
class AxisAngleTest(test_case.TestCase):
@parameterized.parameters(
((3,),),
((None, 3),),
)
def test_from_euler_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.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(axis_angle.from_euler, error_msg, shapes)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_euler_jacobian_random(self):
"""Test the Jacobian of the from_euler function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN at 0.
"""
x_init = test_helpers.generate_random_test_euler_angles()
self.assert_jacobian_is_finite_fn(lambda x: axis_angle.from_euler(x)[0],
[x_init])
self.assert_jacobian_is_finite_fn(lambda x: axis_angle.from_euler(x)[1],
[x_init])
def test_from_euler_random(self):
"""Tests that from_euler allows to perform the expect rotation of points."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
tensor_shape = random_euler_angles.shape[:-1]
random_point = np.random.normal(size=tensor_shape + (3,))
random_matrix = rotation_matrix_3d.from_euler(random_euler_angles)
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
rotated_with_matrix = rotation_matrix_3d.rotate(random_point, random_matrix)
rotated_with_axis_angle = axis_angle.rotate(random_point, random_axis,
random_angle)
self.assertAllClose(rotated_with_matrix, rotated_with_axis_angle)
@parameterized.parameters(
((3,),),
((None, 3),),
((2, 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(
axis_angle.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(
axis_angle.from_euler_with_small_angles_approximation, error_msg,
shapes)
def test_from_euler_normalized_preset(self):
"""Tests that from_euler allows build normalized axis-angles."""
euler_angles = test_helpers.generate_preset_test_euler_angles()
axis, angle = axis_angle.from_euler(euler_angles)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_euler_normalized_random(self):
"""Tests that from_euler allows build normalized axis-angles."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
self.assertAllEqual(
axis_angle.is_normalized(random_axis, random_angle),
np.ones(shape=random_angle.shape))
def test_from_euler_with_small_angles_approximation_random(self):
# Only generate small angles. For a test tolerance of 1e-3, 0.23 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=-0.23, max_angle=0.23)
exact_axis_angle = axis_angle.from_euler(random_euler_angles)
approximate_axis_angle = (
axis_angle.from_euler_with_small_angles_approximation(
random_euler_angles))
self.assertAllClose(exact_axis_angle, approximate_axis_angle, atol=1e-3)
@parameterized.parameters(
((4,),),
((None, 4),),
((2, 4),),
)
def test_from_quaternion_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.from_quaternion, shape)
@parameterized.parameters(
("must have exactly 4 dimensions in axis -1", (None,)),)
def test_from_quaternion_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.from_quaternion, error_msg,
shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_quaternion_jacobian_random(self):
"""Test the Jacobian of the from_quaternion function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN a 0.
"""
x_init = test_helpers.generate_random_test_quaternions()
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_quaternion(x)[0], [x_init])
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_quaternion(x)[1], [x_init])
def test_from_quaternion_normalized_preset(self):
"""Tests that from_quaternion returns normalized axis-angles."""
euler_angles = test_helpers.generate_preset_test_euler_angles()
quat = quaternion.from_euler(euler_angles)
axis, angle = axis_angle.from_quaternion(quat)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_quaternion_normalized_random(self):
"""Tests that from_quaternion returns normalized axis-angles."""
random_quaternions = test_helpers.generate_random_test_quaternions()
random_axis, random_angle = axis_angle.from_quaternion(random_quaternions)
self.assertAllEqual(
axis_angle.is_normalized(random_axis, random_angle),
np.ones(random_angle.shape))
def test_from_quaternion_preset(self):
"""Tests that axis_angle.from_quaternion produces the expected result."""
preset_euler_angles = test_helpers.generate_preset_test_euler_angles()
preset_quaternions = quaternion.from_euler(preset_euler_angles)
preset_axis_angle = axis_angle.from_euler(preset_euler_angles)
self.assertAllClose(
preset_axis_angle,
axis_angle.from_quaternion(preset_quaternions),
rtol=1e-3)
def test_from_quaternion_random(self):
"""Tests that axis_angle.from_quaternion produces the expected result."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
random_quaternions = quaternion.from_euler(random_euler_angles)
random_axis_angle = axis_angle.from_euler(random_euler_angles)
self.assertAllClose(
random_axis_angle,
axis_angle.from_quaternion(random_quaternions),
rtol=1e-3)
@parameterized.parameters(
((3, 3),),
((None, 3, 3),),
)
def test_from_rotation_matrix_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.from_rotation_matrix, 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_from_rotation_matrix_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.from_rotation_matrix, error_msg,
shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_from_rotation_matrix_jacobian_random(self):
"""Test the Jacobian of the from_rotation_matrix function.
Note:
Preset angles are not tested as the gradient of tf.norm is NaN a 0.
"""
x_init = test_helpers.generate_random_test_rotation_matrix_3d()
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_rotation_matrix(x)[0], [x_init])
self.assert_jacobian_is_finite_fn(
lambda x: axis_angle.from_rotation_matrix(x)[1], [x_init])
def test_from_rotation_matrix_normalized_preset(self):
"""Tests that from_rotation_matrix returns normalized axis-angles."""
preset_euler_angles = test_helpers.generate_preset_test_euler_angles()
matrix = rotation_matrix_3d.from_euler(preset_euler_angles)
axis, angle = axis_angle.from_rotation_matrix(matrix)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_rotation_matrix_normalized_random(self):
"""Tests that from_rotation_matrix returns normalized axis-angles."""
random_euler_angles = test_helpers.generate_random_test_euler_angles()
matrix = rotation_matrix_3d.from_euler(random_euler_angles)
axis, angle = axis_angle.from_rotation_matrix(matrix)
self.assertAllEqual(
axis_angle.is_normalized(axis, angle), np.ones(angle.shape, dtype=bool))
def test_from_rotation_matrix_random(self):
"""Tests rotation around Z axis."""
def get_rotation_matrix_around_z(angle_rad):
return np.array([
[np.cos(angle_rad), -np.sin(angle_rad), 0],
[np.sin(angle_rad), np.cos(angle_rad), 0],
[0, 0, 1],
])
tensor_size = np.random.randint(10)
angle = (
np.array([
np.deg2rad(np.random.randint(720) - 360) for _ in range(tensor_size)
]).reshape((tensor_size, 1)))
rotation_matrix = [get_rotation_matrix_around_z(i[0]) for i in angle]
rotation_matrix = np.array(rotation_matrix).reshape((tensor_size, 3, 3))
tf_axis, tf_angle = axis_angle.from_rotation_matrix(rotation_matrix)
axis = np.tile([[0., 0., 1.]], (angle.shape[0], 1))
tf_quat_gt = quaternion.from_axis_angle(axis, angle)
tf_quat = quaternion.from_axis_angle(tf_axis, tf_angle)
# Compare quaternions since axis orientation and angle ambiguity will
# lead to more complex comparisons.
for quat_gt, quat in zip(self.evaluate(tf_quat_gt), self.evaluate(tf_quat)):
# Remember that q=-q for any quaternion.
pos = np.allclose(quat_gt, quat)
neg = np.allclose(quat_gt, -quat)
self.assertTrue(pos or neg)
@parameterized.parameters(
((3,), (1,)),
((None, 3), (None, 1)),
((2, 3), (2, 1)),
((1, 3), (1,)),
((3,), (1, 1)),
)
def test_inverse_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.inverse, shape)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (None,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (None,)),
)
def test_inverse_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.inverse, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_inverse_jacobian_preset(self):
"""Test the Jacobian of the inverse function."""
x_axis_init, x_angle_init = test_helpers.generate_preset_test_axis_angle()
if tf.executing_eagerly():
# Because axis is returned as is, gradient calculation fails in graph mode
# but not in eager mode. This is a side effect of having a graph rather
# than a problem of the function.
with self.subTest("axis"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x, x_angle_init)[0], [x_axis_init])
with self.subTest("angle"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x_axis_init, x)[1], [x_angle_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_inverse_jacobian_random(self):
"""Test the Jacobian of the inverse function."""
x_axis_init, x_angle_init = test_helpers.generate_random_test_axis_angle()
if tf.executing_eagerly():
# Because axis is returned as is, gradient calculation fails in graph mode
# but not in eager mode. This is a side effect of having a graph rather
# than a problem of the function.
with self.subTest("axis"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(1.0 * x, x_angle_init)[0],
[x_axis_init])
with self.subTest("angle"):
self.assert_jacobian_is_correct_fn(
lambda x: axis_angle.inverse(x_axis_init, x)[1], [x_angle_init])
def test_inverse_normalized_random(self):
"""Tests that axis-angle inversion return a normalized axis-angle."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
inverse_axis, inverse_angle = axis_angle.inverse(random_axis, random_angle)
self.assertAllEqual(
axis_angle.is_normalized(inverse_axis, inverse_angle),
np.ones(random_angle.shape))
def test_inverse_random(self):
"""Tests axis-angle inversion."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
inverse_axis, inverse_angle = axis_angle.inverse(random_axis, random_angle)
self.assertAllClose(inverse_axis, random_axis, rtol=1e-3)
self.assertAllClose(inverse_angle, -random_angle, rtol=1e-3)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (None,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (None,)),
)
def test_is_normalized_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.is_normalized, error_msg, shape)
def test_is_normalized_random(self):
"""Tests that is_normalized works as intended."""
# Samples normalized axis-angles.
random_euler_angles = test_helpers.generate_random_test_euler_angles()
with self.subTest(name=("is_normalized")):
random_axis, random_angle = axis_angle.from_euler(random_euler_angles)
pred = axis_angle.is_normalized(random_axis, random_angle)
self.assertAllEqual(np.ones(shape=random_angle.shape, dtype=bool), pred)
with self.subTest(name=("is_not_normalized")):
random_axis *= 1.01
pred = axis_angle.is_normalized(random_axis, random_angle)
self.assertAllEqual(np.zeros(shape=random_angle.shape, dtype=bool), pred)
@parameterized.parameters(
((3,), (3,), (1,)),
((None, 3), (None, 3), (None, 1)),
((2, 3), (2, 3), (2, 1)),
((3,), (1, 3), (1, 2, 1)),
((1, 2, 3), (1, 3), (1,)),
((3,), (1, 3), (1,)),
)
def test_rotate_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(axis_angle.rotate, shapes)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (2,), (3,), (1,)),
("must have exactly 3 dimensions in axis -1", (3,), (2,), (1,)),
("must have exactly 1 dimensions in axis -1", (3,), (3,), (2,)),
)
def test_rotate_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(axis_angle.rotate, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_rotate_jacobian_preset(self):
"""Test the Jacobian of the rotate function."""
x_axis_init, x_angle_init = test_helpers.generate_preset_test_axis_angle()
x_point_init = np.random.uniform(size=x_axis_init.shape)
self.assert_jacobian_is_correct_fn(
axis_angle.rotate, [x_point_init, x_axis_init, x_angle_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_rotate_jacobian_random(self):
"""Test the Jacobian of the rotate function."""
x_axis_init, x_angle_init = test_helpers.generate_random_test_axis_angle()
x_point_init = np.random.uniform(size=x_axis_init.shape)
self.assert_jacobian_is_correct_fn(
axis_angle.rotate, [x_point_init, x_axis_init, x_angle_init])
def test_rotate_random(self):
"""Tests that the rotate provide the same results as quaternion.rotate."""
random_axis, random_angle = test_helpers.generate_random_test_axis_angle()
tensor_shape = random_angle.shape[:-1]
random_point = np.random.normal(size=tensor_shape + (3,))
random_quaternion = quaternion.from_axis_angle(random_axis, random_angle)
ground_truth = quaternion.rotate(random_point, random_quaternion)
prediction = axis_angle.rotate(random_point, random_axis, random_angle)
self.assertAllClose(ground_truth, prediction, rtol=1e-6)
if __name__ == "__main__":
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/nasa/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.
"""General helper functions."""
from os import path
import numpy as np
from skimage import measure
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.cvxnet.lib.libmise import mise
from tensorflow_graphics.projects.nasa.lib import datasets
from tensorflow_graphics.projects.nasa.lib import models
import tensorflow_probability as tfp
from tqdm import trange
import trimesh
tf.disable_eager_execution()
tfd = tfp.distributions
def define_flags():
"""Define command line flags."""
flags = tf.app.flags
# Dataset Parameters
flags.DEFINE_enum("dataset", "amass",
list(k for k in datasets.dataset_dict.keys()),
"Name of the dataset.")
flags.DEFINE_string("data_dir", None, "Directory to load data from.")
flags.mark_flag_as_required("data_dir")
flags.DEFINE_integer("sample_bbox", 1024, "Number of bbox samples.")
flags.DEFINE_integer("sample_surf", 1024, "Number of surface samples.")
flags.DEFINE_integer("batch_size", 12, "Batch size.")
flags.DEFINE_integer("motion", 0, "Index of the motion for evaluation.")
flags.DEFINE_integer("subject", 0, "Index of the subject for training.")
# Model Parameters
flags.DEFINE_enum("model", "nasa", list(k for k in models.model_dict.keys()),
"Name of the model.")
flags.DEFINE_integer("n_parts", 24, "Number of parts.")
flags.DEFINE_integer("total_dim", 960,
"Dimension of the latent vector (in total).")
flags.DEFINE_bool("shared_decoder", False, "Whether to use shared decoder.")
flags.DEFINE_float("soft_blend", 5., "The constant to blend parts.")
flags.DEFINE_bool("projection", True,
"Whether to use projected shape features.")
flags.DEFINE_float("level_set", 0.5, "The value of the level_set.")
flags.DEFINE_integer("n_dims", 3, "The dimension of the query points.")
# Training Parameters
flags.DEFINE_float("lr", 1e-4, "Learning rate")
flags.DEFINE_string("train_dir", None, "Training directory.")
flags.mark_flag_as_required("train_dir")
flags.DEFINE_integer("max_steps", 200000, "Number of optimization steps.")
flags.DEFINE_integer("save_every", 5000,
"Number of steps to save checkpoint.")
flags.DEFINE_integer("summary_every", 500,
"Number of steps to save checkpoint.")
flags.DEFINE_float("label_w", 0.5, "Weight of labed vertices loss.")
flags.DEFINE_float("minimal_w", 0.05, "Weight of minimal loss.")
flags.DEFINE_bool("use_vert", True,
"Whether to use vertices on the mesh for training.")
flags.DEFINE_bool("use_joint", True,
"Whether to use joint-based transformation.")
flags.DEFINE_integer("sample_vert", 2048, "Number of vertex samples.")
# Evalulation Parameters
flags.DEFINE_bool("gen_mesh_only", False, "Whether to generate meshes only.")
# Tracking Parameters
flags.DEFINE_float("theta_lr", 5e-4, "Learning rate")
flags.DEFINE_integer("max_steps_per_frame", 1792,
"Number of optimization steps for tracking each frame.")
flags.DEFINE_enum("gradient_type", "reparam", ["vanilla", "reparam"],
"Type of gradient to use in theta optimization.")
flags.DEFINE_integer("sample_track_vert", 1024,
"Number of vertex samples for tracking each frame.")
flags.DEFINE_integer("n_noisy_samples", 8,
"Number of noisy samples per vertex")
flags.DEFINE_float("bandwidth", 1e-2, "Bandwidth of the gaussian noises.")
flags.DEFINE_bool(
"left_trans", False,
"Whether to use left side transformation (True) or right side (False).")
flags.DEFINE_string("joint_data", None, "Path to load joint data.")
flags.DEFINE_float("glue_w", 20., "Weight of length constraint loss.")
flags.DEFINE_float("trans_range", 1., "The range of allowed translations.")
def gen_mesh(sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
idx=0):
"""Generating meshes given a trained NASA model."""
scale = 1.1 # Scale of the padded bbox regarding the tight one.
level_set = hparams.level_set
latent_val = sess.run(latent, feed_dict)
mesh_extractor = mise.MISE(32, 3, level_set)
points = mesh_extractor.query()
gt_verts = batch_val["vert"].reshape([-1, 3])
gt_bbox = np.stack([gt_verts.min(axis=0), gt_verts.max(axis=0)], axis=0)
gt_center = (gt_bbox[0] + gt_bbox[1]) * 0.5
gt_scale = (gt_bbox[1] - gt_bbox[0]).max()
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) * scale
points = points * gt_scale + gt_center
n_points = points.shape[1]
values = []
for i in range(0, n_points,
100000): # Add this to prevent OOM due to points overload.
feed_dict[latent_holder] = latent_val
feed_dict[point_holder] = np.expand_dims(points[:, i:i + 100000], axis=1)
value = sess.run(occ[:, idx], feed_dict)
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()
try:
value_grid = np.pad(value_grid, 1, "constant", constant_values=-1e6)
verts, faces, normals, unused_var = measure.marching_cubes_lewiner(
value_grid, min(level_set, value_grid.max()))
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 = scale * (verts - 0.5)
verts = verts * gt_scale + gt_center
faces = np.stack([faces[..., 1], faces[..., 0], faces[..., 2]], axis=-1)
mesh = trimesh.Trimesh(vertices=verts, faces=faces)
return mesh
except: # pylint: disable=bare-except
return None
def save_mesh(sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
pth="meshes"):
"""Generate and save meshes to disk given a trained NASA model."""
name = batch_val["name"][0].decode("utf-8")
subject, motion, frame = amass_name_helper(name)
pth = path.join(hparams.train_dir, pth, frame)
if not tf.io.gfile.isdir(pth):
tf.io.gfile.makedirs(pth)
start = hparams.n_parts
for i in range(start, hparams.n_parts + 1):
mesh_model = gen_mesh(
sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
idx=i)
mesh_name = "full_pred.obj"
if mesh_model is not None:
with tf.io.gfile.GFile(path.join(pth, mesh_name), "w") as fout:
mesh_model.export(fout, file_type="obj")
return subject, motion, frame, mesh_model
def save_pointcloud(data, hparams, pth="pointcloud"):
"""Save pointcloud to disk."""
name = data["name"][0].decode("utf-8")
unused_subject, unused_motion, frame = amass_name_helper(name)
pth = path.join(hparams.train_dir, pth, frame)
if not tf.io.gfile.isdir(pth):
tf.io.gfile.makedirs(pth)
mesh_name = "pointcloud.obj"
with tf.io.gfile.GFile(path.join(pth, mesh_name), "w") as fout:
pointcloud = data["vert"].reshape([-1, 3])
for v in pointcloud:
fout.write("v {0} {1} {2}\n".format(*v.tolist()))
def amass_name_helper(name):
name, frame = name.split("-")
subject = name[:5]
motion = name[6:]
return subject, motion, frame
def make_summary_feed_dict(
iou_hook,
iou,
best_hook,
best_iou,
):
feed_dict = {}
feed_dict[iou_hook] = iou
feed_dict[best_hook] = best_iou
return feed_dict
def parse_global_step(ckpt):
basename = path.basename(ckpt)
return int(basename.split("-")[-1])
def compute_iou(sess, feed_dict, latent_holder, point_holder, latent, occ,
point, label, hparams):
"""Compute IoU."""
iou = 0.
eps = 1e-9
latent_val = sess.run(latent, feed_dict)
n_points = point.shape[2]
preds = []
for start in range(0, n_points, 100000):
feed_dict[point_holder] = point[:, :, start:start + 100000]
feed_dict[latent_holder] = latent_val
pred = sess.run(occ, feed_dict)
preds.append(pred)
pred = np.concatenate(preds, axis=2)
pred = (pred >= hparams.level_set).astype(np.float32)
label = (label[:, :1] >= 0.5).astype(np.float32).squeeze(axis=1)
iou += np.sum(pred * label) / np.maximum(np.sum(np.maximum(pred, label)), eps)
return iou
def compute_glue_loss(connect, end_pts, inv_transforms, inv_first_frame_trans,
joints, hparams):
"""Compute the prior term as a glue loss."""
n_dims = hparams.n_dims
# Invert the transformation
r_inv = inv_transforms[..., :n_dims, :n_dims]
t_inv = inv_transforms[..., :n_dims, -1:]
r = tf.transpose(r_inv, [0, 2, 1])
t = -tf.matmul(r, t_inv)
transforms = tf.concat(
[tf.concat([r, t], axis=-1), inv_transforms[..., -1:, :]], axis=-2)
transforms = tf.matmul(transforms, inv_first_frame_trans)
# Compute transformations of father joints and apply it to vectors from frame0
father_transforms = tf.reduce_sum(
tf.expand_dims(transforms, axis=1) *
connect.reshape([hparams.n_parts, hparams.n_parts, 1, 1]),
axis=0)
end_pts_homo = tf.expand_dims(
tf.concat([end_pts, tf.ones_like(end_pts[..., :1])], axis=-1), axis=-1)
end_pts_transformed = tf.matmul(father_transforms, end_pts_homo)
end_pts_transformed = tf.squeeze(end_pts_transformed, axis=-1)[..., :n_dims]
# Compute vectors in current configuration
pred_links = tf.reshape(joints, [hparams.n_parts, n_dims])
# Compute distance between links and transformed vectors
return tf.reduce_sum(tf.square(pred_links - end_pts_transformed))
def vanilla_theta_gradient(model_fn, batch_holder, hparams):
"""A vanilla gradient estimator for the pose, theta."""
latent_holder, latent, occ_eval = model_fn(batch_holder, None, None,
"gen_mesh")
if hparams.sample_vert > 0:
points = batch_holder["point"]
weights = batch_holder["weight"]
n_vert = tf.shape(points)[2]
sample_indices = tf.random.uniform([1, 1, hparams.sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
points = tf.gather(points, sample_indices, axis=2, batch_dims=2)
weights = tf.gather(weights, sample_indices, axis=2, batch_dims=2)
batch_holder["point"] = points
batch_holder["weight"] = weights
unused_var0, unused_var1, occ = model_fn(batch_holder, None, None, "gen_mesh")
return latent_holder, latent, occ_eval, tf.reduce_mean(
tf.square(occ - hparams.level_set))
def reparam_theta_gradient(model_fn, batch_holder, hparams):
"""A gradient estimaor for the pose, theta, using the reparam trick."""
sigma = hparams.bandwidth
n_samples = hparams.n_noisy_samples
latent_holder, latent, occ_eval = model_fn(batch_holder, None, None,
"gen_mesh")
if hparams.sample_vert > 0:
points = batch_holder["point"]
weights = batch_holder["weight"]
n_vert = tf.shape(points)[2]
sample_indices = tf.random.uniform([1, 1, hparams.sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
points = tf.gather(points, sample_indices, axis=2, batch_dims=2)
weights = tf.gather(weights, sample_indices, axis=2, batch_dims=2)
batch_holder["point"] = points
batch_holder["weight"] = weights
dist = tfd.Normal(loc=0., scale=sigma)
n_pts = hparams.sample_vert if hparams.sample_vert > 0 else hparams.n_vert
noises = dist.sample((1, hparams.n_parts, n_pts, n_samples, hparams.n_dims))
unused_var0, unused_var1, occ = model_fn(batch_holder, noises, None,
"gen_mesh")
occ = tf.reshape(occ, [1, hparams.n_parts + 1, -1, n_samples, 1])
occ = tf.reduce_mean(occ[:, hparams.n_parts:], axis=3)
return latent_holder, latent, occ_eval, tf.reduce_mean(
tf.square(occ - hparams.level_set))
def optimize_theta(feed_dict, loss, reset_op, train_op, rec_loss, glue_loss,
sess, k, hparams):
"""Optimize the pose, theta, during tracking."""
sess.run(reset_op)
loss_val = 0
glue_val = 0
with trange(hparams.max_steps_per_frame) as t:
for unused_i in t:
loss_val, unused_var, rec_val, glue_val = sess.run(
[loss, train_op, rec_loss, glue_loss], feed_dict)
t.set_description("Frame_{0} {1:.4f}|{2:.4f}".format(
k, rec_val, glue_val))
return loss_val, glue_val
| # 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.
"""General helper functions."""
from os import path
import numpy as np
from skimage import measure
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.cvxnet.lib.libmise import mise
from tensorflow_graphics.projects.nasa.lib import datasets
from tensorflow_graphics.projects.nasa.lib import models
import tensorflow_probability as tfp
from tqdm import trange
import trimesh
tf.disable_eager_execution()
tfd = tfp.distributions
def define_flags():
"""Define command line flags."""
flags = tf.app.flags
# Dataset Parameters
flags.DEFINE_enum("dataset", "amass",
list(k for k in datasets.dataset_dict.keys()),
"Name of the dataset.")
flags.DEFINE_string("data_dir", None, "Directory to load data from.")
flags.mark_flag_as_required("data_dir")
flags.DEFINE_integer("sample_bbox", 1024, "Number of bbox samples.")
flags.DEFINE_integer("sample_surf", 1024, "Number of surface samples.")
flags.DEFINE_integer("batch_size", 12, "Batch size.")
flags.DEFINE_integer("motion", 0, "Index of the motion for evaluation.")
flags.DEFINE_integer("subject", 0, "Index of the subject for training.")
# Model Parameters
flags.DEFINE_enum("model", "nasa", list(k for k in models.model_dict.keys()),
"Name of the model.")
flags.DEFINE_integer("n_parts", 24, "Number of parts.")
flags.DEFINE_integer("total_dim", 960,
"Dimension of the latent vector (in total).")
flags.DEFINE_bool("shared_decoder", False, "Whether to use shared decoder.")
flags.DEFINE_float("soft_blend", 5., "The constant to blend parts.")
flags.DEFINE_bool("projection", True,
"Whether to use projected shape features.")
flags.DEFINE_float("level_set", 0.5, "The value of the level_set.")
flags.DEFINE_integer("n_dims", 3, "The dimension of the query points.")
# Training Parameters
flags.DEFINE_float("lr", 1e-4, "Learning rate")
flags.DEFINE_string("train_dir", None, "Training directory.")
flags.mark_flag_as_required("train_dir")
flags.DEFINE_integer("max_steps", 200000, "Number of optimization steps.")
flags.DEFINE_integer("save_every", 5000,
"Number of steps to save checkpoint.")
flags.DEFINE_integer("summary_every", 500,
"Number of steps to save checkpoint.")
flags.DEFINE_float("label_w", 0.5, "Weight of labed vertices loss.")
flags.DEFINE_float("minimal_w", 0.05, "Weight of minimal loss.")
flags.DEFINE_bool("use_vert", True,
"Whether to use vertices on the mesh for training.")
flags.DEFINE_bool("use_joint", True,
"Whether to use joint-based transformation.")
flags.DEFINE_integer("sample_vert", 2048, "Number of vertex samples.")
# Evalulation Parameters
flags.DEFINE_bool("gen_mesh_only", False, "Whether to generate meshes only.")
# Tracking Parameters
flags.DEFINE_float("theta_lr", 5e-4, "Learning rate")
flags.DEFINE_integer("max_steps_per_frame", 1792,
"Number of optimization steps for tracking each frame.")
flags.DEFINE_enum("gradient_type", "reparam", ["vanilla", "reparam"],
"Type of gradient to use in theta optimization.")
flags.DEFINE_integer("sample_track_vert", 1024,
"Number of vertex samples for tracking each frame.")
flags.DEFINE_integer("n_noisy_samples", 8,
"Number of noisy samples per vertex")
flags.DEFINE_float("bandwidth", 1e-2, "Bandwidth of the gaussian noises.")
flags.DEFINE_bool(
"left_trans", False,
"Whether to use left side transformation (True) or right side (False).")
flags.DEFINE_string("joint_data", None, "Path to load joint data.")
flags.DEFINE_float("glue_w", 20., "Weight of length constraint loss.")
flags.DEFINE_float("trans_range", 1., "The range of allowed translations.")
def gen_mesh(sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
idx=0):
"""Generating meshes given a trained NASA model."""
scale = 1.1 # Scale of the padded bbox regarding the tight one.
level_set = hparams.level_set
latent_val = sess.run(latent, feed_dict)
mesh_extractor = mise.MISE(32, 3, level_set)
points = mesh_extractor.query()
gt_verts = batch_val["vert"].reshape([-1, 3])
gt_bbox = np.stack([gt_verts.min(axis=0), gt_verts.max(axis=0)], axis=0)
gt_center = (gt_bbox[0] + gt_bbox[1]) * 0.5
gt_scale = (gt_bbox[1] - gt_bbox[0]).max()
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) * scale
points = points * gt_scale + gt_center
n_points = points.shape[1]
values = []
for i in range(0, n_points,
100000): # Add this to prevent OOM due to points overload.
feed_dict[latent_holder] = latent_val
feed_dict[point_holder] = np.expand_dims(points[:, i:i + 100000], axis=1)
value = sess.run(occ[:, idx], feed_dict)
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()
try:
value_grid = np.pad(value_grid, 1, "constant", constant_values=-1e6)
verts, faces, normals, unused_var = measure.marching_cubes_lewiner(
value_grid, min(level_set, value_grid.max()))
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 = scale * (verts - 0.5)
verts = verts * gt_scale + gt_center
faces = np.stack([faces[..., 1], faces[..., 0], faces[..., 2]], axis=-1)
mesh = trimesh.Trimesh(vertices=verts, faces=faces)
return mesh
except: # pylint: disable=bare-except
return None
def save_mesh(sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
pth="meshes"):
"""Generate and save meshes to disk given a trained NASA model."""
name = batch_val["name"][0].decode("utf-8")
subject, motion, frame = amass_name_helper(name)
pth = path.join(hparams.train_dir, pth, frame)
if not tf.io.gfile.isdir(pth):
tf.io.gfile.makedirs(pth)
start = hparams.n_parts
for i in range(start, hparams.n_parts + 1):
mesh_model = gen_mesh(
sess,
feed_dict,
latent_holder,
point_holder,
latent,
occ,
batch_val,
hparams,
idx=i)
mesh_name = "full_pred.obj"
if mesh_model is not None:
with tf.io.gfile.GFile(path.join(pth, mesh_name), "w") as fout:
mesh_model.export(fout, file_type="obj")
return subject, motion, frame, mesh_model
def save_pointcloud(data, hparams, pth="pointcloud"):
"""Save pointcloud to disk."""
name = data["name"][0].decode("utf-8")
unused_subject, unused_motion, frame = amass_name_helper(name)
pth = path.join(hparams.train_dir, pth, frame)
if not tf.io.gfile.isdir(pth):
tf.io.gfile.makedirs(pth)
mesh_name = "pointcloud.obj"
with tf.io.gfile.GFile(path.join(pth, mesh_name), "w") as fout:
pointcloud = data["vert"].reshape([-1, 3])
for v in pointcloud:
fout.write("v {0} {1} {2}\n".format(*v.tolist()))
def amass_name_helper(name):
name, frame = name.split("-")
subject = name[:5]
motion = name[6:]
return subject, motion, frame
def make_summary_feed_dict(
iou_hook,
iou,
best_hook,
best_iou,
):
feed_dict = {}
feed_dict[iou_hook] = iou
feed_dict[best_hook] = best_iou
return feed_dict
def parse_global_step(ckpt):
basename = path.basename(ckpt)
return int(basename.split("-")[-1])
def compute_iou(sess, feed_dict, latent_holder, point_holder, latent, occ,
point, label, hparams):
"""Compute IoU."""
iou = 0.
eps = 1e-9
latent_val = sess.run(latent, feed_dict)
n_points = point.shape[2]
preds = []
for start in range(0, n_points, 100000):
feed_dict[point_holder] = point[:, :, start:start + 100000]
feed_dict[latent_holder] = latent_val
pred = sess.run(occ, feed_dict)
preds.append(pred)
pred = np.concatenate(preds, axis=2)
pred = (pred >= hparams.level_set).astype(np.float32)
label = (label[:, :1] >= 0.5).astype(np.float32).squeeze(axis=1)
iou += np.sum(pred * label) / np.maximum(np.sum(np.maximum(pred, label)), eps)
return iou
def compute_glue_loss(connect, end_pts, inv_transforms, inv_first_frame_trans,
joints, hparams):
"""Compute the prior term as a glue loss."""
n_dims = hparams.n_dims
# Invert the transformation
r_inv = inv_transforms[..., :n_dims, :n_dims]
t_inv = inv_transforms[..., :n_dims, -1:]
r = tf.transpose(r_inv, [0, 2, 1])
t = -tf.matmul(r, t_inv)
transforms = tf.concat(
[tf.concat([r, t], axis=-1), inv_transforms[..., -1:, :]], axis=-2)
transforms = tf.matmul(transforms, inv_first_frame_trans)
# Compute transformations of father joints and apply it to vectors from frame0
father_transforms = tf.reduce_sum(
tf.expand_dims(transforms, axis=1) *
connect.reshape([hparams.n_parts, hparams.n_parts, 1, 1]),
axis=0)
end_pts_homo = tf.expand_dims(
tf.concat([end_pts, tf.ones_like(end_pts[..., :1])], axis=-1), axis=-1)
end_pts_transformed = tf.matmul(father_transforms, end_pts_homo)
end_pts_transformed = tf.squeeze(end_pts_transformed, axis=-1)[..., :n_dims]
# Compute vectors in current configuration
pred_links = tf.reshape(joints, [hparams.n_parts, n_dims])
# Compute distance between links and transformed vectors
return tf.reduce_sum(tf.square(pred_links - end_pts_transformed))
def vanilla_theta_gradient(model_fn, batch_holder, hparams):
"""A vanilla gradient estimator for the pose, theta."""
latent_holder, latent, occ_eval = model_fn(batch_holder, None, None,
"gen_mesh")
if hparams.sample_vert > 0:
points = batch_holder["point"]
weights = batch_holder["weight"]
n_vert = tf.shape(points)[2]
sample_indices = tf.random.uniform([1, 1, hparams.sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
points = tf.gather(points, sample_indices, axis=2, batch_dims=2)
weights = tf.gather(weights, sample_indices, axis=2, batch_dims=2)
batch_holder["point"] = points
batch_holder["weight"] = weights
unused_var0, unused_var1, occ = model_fn(batch_holder, None, None, "gen_mesh")
return latent_holder, latent, occ_eval, tf.reduce_mean(
tf.square(occ - hparams.level_set))
def reparam_theta_gradient(model_fn, batch_holder, hparams):
"""A gradient estimaor for the pose, theta, using the reparam trick."""
sigma = hparams.bandwidth
n_samples = hparams.n_noisy_samples
latent_holder, latent, occ_eval = model_fn(batch_holder, None, None,
"gen_mesh")
if hparams.sample_vert > 0:
points = batch_holder["point"]
weights = batch_holder["weight"]
n_vert = tf.shape(points)[2]
sample_indices = tf.random.uniform([1, 1, hparams.sample_vert],
minval=0,
maxval=n_vert,
dtype=tf.int32)
points = tf.gather(points, sample_indices, axis=2, batch_dims=2)
weights = tf.gather(weights, sample_indices, axis=2, batch_dims=2)
batch_holder["point"] = points
batch_holder["weight"] = weights
dist = tfd.Normal(loc=0., scale=sigma)
n_pts = hparams.sample_vert if hparams.sample_vert > 0 else hparams.n_vert
noises = dist.sample((1, hparams.n_parts, n_pts, n_samples, hparams.n_dims))
unused_var0, unused_var1, occ = model_fn(batch_holder, noises, None,
"gen_mesh")
occ = tf.reshape(occ, [1, hparams.n_parts + 1, -1, n_samples, 1])
occ = tf.reduce_mean(occ[:, hparams.n_parts:], axis=3)
return latent_holder, latent, occ_eval, tf.reduce_mean(
tf.square(occ - hparams.level_set))
def optimize_theta(feed_dict, loss, reset_op, train_op, rec_loss, glue_loss,
sess, k, hparams):
"""Optimize the pose, theta, during tracking."""
sess.run(reset_op)
loss_val = 0
glue_val = 0
with trange(hparams.max_steps_per_frame) as t:
for unused_i in t:
loss_val, unused_var, rec_val, glue_val = sess.run(
[loss, train_op, rec_loss, glue_loss], feed_dict)
t.set_description("Frame_{0} {1:.4f}|{2:.4f}".format(
k, rec_val, glue_val))
return loss_val, glue_val
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/local_implicit_grid/core/model_g2g.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
"""Model for voxel grid-to-grid encoding."""
import math
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig
layers = tf.keras.layers
class ResBlock3D(layers.Layer):
"""3D convolutional Residue Block.
Maintains same resolution.
"""
def __init__(self, neck_channels, out_channels, final_relu=True):
"""Initialization.
Args:
neck_channels: int, number of channels in bottleneck layer.
out_channels: int, number of output channels.
final_relu: bool, add relu to the last layer.
"""
super(ResBlock3D, self).__init__()
self.neck_channels = neck_channels
self.out_channels = out_channels
self.conv1 = layers.Conv3D(neck_channels, kernel_size=1, strides=1)
self.conv2 = layers.Conv3D(
neck_channels, kernel_size=3, strides=1, padding='same')
self.conv3 = layers.Conv3D(out_channels, kernel_size=1, strides=1)
self.bn1 = layers.BatchNormalization(axis=-1)
self.bn2 = layers.BatchNormalization(axis=-1)
self.bn3 = layers.BatchNormalization(axis=-1)
self.shortcut = layers.Conv3D(out_channels, kernel_size=1, strides=1)
self.final_relu = final_relu
def call(self, x, training=False):
identity = x
x = self.conv1(x)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = tf.nn.relu(x)
x = self.conv3(x)
x = self.bn3(x, training=training)
x += self.shortcut(identity)
if self.final_relu:
x = tf.nn.relu(x)
return x
class UNet3D(tf.keras.layers.Layer):
"""UNet that consumes even dimension grid and outputs even dimension grid."""
def __init__(self,
in_grid_res=32,
out_grid_res=16,
num_filters=16,
max_filters=512,
out_features=32,
name='unet3d'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
max_filters: int, max number of feature layers at any resolution.
out_features: int, number of output feature channels.
name: str, name of the layer.
Raises:
ValueError: if in_grid_res or out_grid_res is not powers of 2.
"""
super(UNet3D, self).__init__(name=name)
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
# assert dimensions acceptable
if math.log(out_grid_res, 2) % 1 != 0 or math.log(in_grid_res, 2) % 1 != 0:
raise ValueError('in_grid_res and out_grid_res must be 2**n.')
self.num_in_level = math.log(self.in_grid_res, 2)
self.num_out_level = math.log(self.out_grid_res, 2)
self.num_in_level = int(self.num_in_level) # number of input levels
self.num_out_level = int(self.num_out_level) # number of output levels
self._create_layers()
def _create_layers(self):
num_filter_down = [
self.num_filters * (2**(i + 1)) for i in range(self.num_in_level)
]
# num. features in downward path
num_filter_down = [
n if n <= self.max_filters else self.max_filters
for n in num_filter_down
]
num_filter_up = num_filter_down[::-1][:self.num_out_level]
self.num_filter_down = num_filter_down
self.num_filter_up = num_filter_up
self.conv_in = ResBlock3D(self.num_filters, self.num_filters)
self.conv_out = ResBlock3D(
self.out_features, self.out_features, final_relu=False)
self.down_modules = [ResBlock3D(int(n / 2), n) for n in num_filter_down]
self.up_modules = [ResBlock3D(n, n) for n in num_filter_up]
self.dnpool = layers.MaxPool3D((2, 2, 2))
self.upsamp = layers.UpSampling3D((2, 2, 2))
self.up_final = layers.UpSampling3D((2, 2, 2))
def call(self, x, training=False):
"""Forward method.
Args:
x: `[batch, in_grid_res, in_grid_res, in_grid_res, in_features]` tensor,
input voxel grid.
training: bool, flag indicating whether model is in training mode.
Returns:
`[batch, out_grid_res, out_grid_res, out_grid_res, out_features]` tensor,
output voxel grid.
"""
x = self.conv_in(x)
x_dns = [x]
for mod in self.down_modules:
x_ = self.dnpool(mod(x_dns[-1], training=training))
x_dns.append(x_)
x_ups = [x_dns.pop(-1)]
for mod in self.up_modules:
x_ = tf.concat([self.upsamp(x_ups[-1]), x_dns.pop(-1)], axis=-1)
x_ = mod(x_, training=training)
x_ups.append(x_)
x = self.conv_out(x_ups[-1])
return x
class UNet3DOdd(tf.keras.layers.Layer):
"""UNet that consumes even dimension grid and outputs odd dimension grid."""
def __init__(self,
in_grid_res=32,
out_grid_res=15,
num_filters=16,
max_filters=512,
out_features=32,
name='unet3dodd'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
max_filters: int, max number of feature layers at any resolution.
out_features: int, number of output feature channels.
name: str, name of the layer.
Raises:
ValueError: if in_grid_res or out_grid_res are not 2**n or 2**n-1 for
some positive integer n.
"""
super(UNet3DOdd, self).__init__(name=name)
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
# assert dimensions acceptable
if math.log(out_grid_res + 1, 2) % 1 != 0 or math.log(in_grid_res,
2) % 1 != 0:
raise ValueError(
'in_grid_res must be 2**n, out_grid_res must be 2**n-1.')
self.num_in_level = math.log(self.in_grid_res, 2)
self.num_out_level = math.log(self.out_grid_res + 1, 2)
self.num_in_level = int(self.num_in_level) # number of input levels
self.num_out_level = int(self.num_out_level) # number of output levels
self._create_layers()
def _create_layers(self):
num_filter_down = [
self.num_filters * (2**(i + 1)) for i in range(self.num_in_level)
]
# num. features in downward path
num_filter_down = [
n if n <= self.max_filters else self.max_filters
for n in num_filter_down
]
num_filter_up = num_filter_down[::-1][1:self.num_out_level]
self.num_filter_down = num_filter_down
self.num_filter_up = num_filter_up
self.conv_in = ResBlock3D(self.num_filters, self.num_filters)
self.conv_out = ResBlock3D(
self.out_features, self.out_features, final_relu=False)
self.down_modules = [ResBlock3D(int(n / 2), n) for n in num_filter_down]
self.up_modules = [ResBlock3D(n, n) for n in num_filter_up]
self.dnpool = layers.MaxPool3D((2, 2, 2))
self.upsamp = layers.UpSampling3D((2, 2, 2))
self.up_final = layers.UpSampling3D((2, 2, 2))
def call(self, x, training=False):
"""Forward method.
Args:
x: `[batch, in_grid_res, in_grid_res, in_grid_res, in_features]` tensor,
input voxel grid.
training: bool, flag indicating whether model is in training mode.
Returns:
`[batch, out_grid_res, out_grid_res, out_grid_res, out_features]` tensor,
output voxel grid.
"""
x = self.conv_in(x)
x_dns = [x]
for mod in self.down_modules:
x_ = self.dnpool(mod(x_dns[-1], training=training))
x_dns.append(x_)
x_ups = [x_dns.pop(-1)]
for mod in self.up_modules:
x_ = tf.concat([self.upsamp(x_ups[-1]), x_dns.pop(-1)], axis=-1)
x_ = mod(x_, training=training)
x_ups.append(x_)
# odd layer
x = self.upsamp(x_ups[-1])[:, :-1, :-1, :-1, :]
x = self.conv_out(x)
return x
class ModelG2G(tf.keras.Model):
"""Grid-to-Grid Model with U-Net skip connections."""
def __init__(self,
in_grid_res=32,
out_grid_res=8,
num_filters=256,
codelen=128,
out_features=1,
net_type='imnet',
name='g2g'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
codelen: int, length of local latent codes.
out_features: int, number of output feature channels.
net_type: str, implicit function network architecture. imnet/deepsdf.
name: str, name of the layer.
Raises:
NotImplementedError: if net_type is not imnet or deepsdf.
ValueError: if in_grid_res or out_grid_res is not powers of 2.
"""
super(ModelG2G, self).__init__(name=name)
if math.log(out_grid_res, 2) % 1 != 0 or math.log(in_grid_res, 2) % 1 != 0:
raise ValueError('in_grid_res and out_grid_res must be powers of 2.')
if net_type not in ['imnet', 'deepsdf']:
raise NotImplementedError
self.codelen = codelen
self.out_features = out_features
self.in_grid_res = in_grid_res
self.out_grid_res = out_grid_res
self.num_filters = num_filters
self.net_type = net_type
self.outgrid = None
self.unet = UNet3D(
in_grid_res=in_grid_res,
out_grid_res=out_grid_res,
num_filters=num_filters,
out_features=codelen)
self.lig = lig.LocalImplicitGrid(
size=(out_grid_res, out_grid_res, out_grid_res),
in_features=codelen,
out_features=out_features,
net_type=net_type)
def call(self, voxgrid, pts, training=False):
"""Forward method.
Args:
voxgrid: `[batch, inres, inres, inres, nc]` tensor, input voxel grid.
pts: `[batch, num_points, 3]` tensor, input query points.
training: bool, flag indicating whether model is in training mode.
Returns:
vals: `[batch, num_points, 3]` tensor, predicted values at query points.
"""
self.outgrid = self.unet(voxgrid, training=training)
val = self.lig(self.outgrid, pts, training=training)
return val
| # 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
"""Model for voxel grid-to-grid encoding."""
import math
import tensorflow.compat.v1 as tf
from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer as lig
layers = tf.keras.layers
class ResBlock3D(layers.Layer):
"""3D convolutional Residue Block.
Maintains same resolution.
"""
def __init__(self, neck_channels, out_channels, final_relu=True):
"""Initialization.
Args:
neck_channels: int, number of channels in bottleneck layer.
out_channels: int, number of output channels.
final_relu: bool, add relu to the last layer.
"""
super(ResBlock3D, self).__init__()
self.neck_channels = neck_channels
self.out_channels = out_channels
self.conv1 = layers.Conv3D(neck_channels, kernel_size=1, strides=1)
self.conv2 = layers.Conv3D(
neck_channels, kernel_size=3, strides=1, padding='same')
self.conv3 = layers.Conv3D(out_channels, kernel_size=1, strides=1)
self.bn1 = layers.BatchNormalization(axis=-1)
self.bn2 = layers.BatchNormalization(axis=-1)
self.bn3 = layers.BatchNormalization(axis=-1)
self.shortcut = layers.Conv3D(out_channels, kernel_size=1, strides=1)
self.final_relu = final_relu
def call(self, x, training=False):
identity = x
x = self.conv1(x)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = tf.nn.relu(x)
x = self.conv3(x)
x = self.bn3(x, training=training)
x += self.shortcut(identity)
if self.final_relu:
x = tf.nn.relu(x)
return x
class UNet3D(tf.keras.layers.Layer):
"""UNet that consumes even dimension grid and outputs even dimension grid."""
def __init__(self,
in_grid_res=32,
out_grid_res=16,
num_filters=16,
max_filters=512,
out_features=32,
name='unet3d'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
max_filters: int, max number of feature layers at any resolution.
out_features: int, number of output feature channels.
name: str, name of the layer.
Raises:
ValueError: if in_grid_res or out_grid_res is not powers of 2.
"""
super(UNet3D, self).__init__(name=name)
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
# assert dimensions acceptable
if math.log(out_grid_res, 2) % 1 != 0 or math.log(in_grid_res, 2) % 1 != 0:
raise ValueError('in_grid_res and out_grid_res must be 2**n.')
self.num_in_level = math.log(self.in_grid_res, 2)
self.num_out_level = math.log(self.out_grid_res, 2)
self.num_in_level = int(self.num_in_level) # number of input levels
self.num_out_level = int(self.num_out_level) # number of output levels
self._create_layers()
def _create_layers(self):
num_filter_down = [
self.num_filters * (2**(i + 1)) for i in range(self.num_in_level)
]
# num. features in downward path
num_filter_down = [
n if n <= self.max_filters else self.max_filters
for n in num_filter_down
]
num_filter_up = num_filter_down[::-1][:self.num_out_level]
self.num_filter_down = num_filter_down
self.num_filter_up = num_filter_up
self.conv_in = ResBlock3D(self.num_filters, self.num_filters)
self.conv_out = ResBlock3D(
self.out_features, self.out_features, final_relu=False)
self.down_modules = [ResBlock3D(int(n / 2), n) for n in num_filter_down]
self.up_modules = [ResBlock3D(n, n) for n in num_filter_up]
self.dnpool = layers.MaxPool3D((2, 2, 2))
self.upsamp = layers.UpSampling3D((2, 2, 2))
self.up_final = layers.UpSampling3D((2, 2, 2))
def call(self, x, training=False):
"""Forward method.
Args:
x: `[batch, in_grid_res, in_grid_res, in_grid_res, in_features]` tensor,
input voxel grid.
training: bool, flag indicating whether model is in training mode.
Returns:
`[batch, out_grid_res, out_grid_res, out_grid_res, out_features]` tensor,
output voxel grid.
"""
x = self.conv_in(x)
x_dns = [x]
for mod in self.down_modules:
x_ = self.dnpool(mod(x_dns[-1], training=training))
x_dns.append(x_)
x_ups = [x_dns.pop(-1)]
for mod in self.up_modules:
x_ = tf.concat([self.upsamp(x_ups[-1]), x_dns.pop(-1)], axis=-1)
x_ = mod(x_, training=training)
x_ups.append(x_)
x = self.conv_out(x_ups[-1])
return x
class UNet3DOdd(tf.keras.layers.Layer):
"""UNet that consumes even dimension grid and outputs odd dimension grid."""
def __init__(self,
in_grid_res=32,
out_grid_res=15,
num_filters=16,
max_filters=512,
out_features=32,
name='unet3dodd'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
max_filters: int, max number of feature layers at any resolution.
out_features: int, number of output feature channels.
name: str, name of the layer.
Raises:
ValueError: if in_grid_res or out_grid_res are not 2**n or 2**n-1 for
some positive integer n.
"""
super(UNet3DOdd, self).__init__(name=name)
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
# assert dimensions acceptable
if math.log(out_grid_res + 1, 2) % 1 != 0 or math.log(in_grid_res,
2) % 1 != 0:
raise ValueError(
'in_grid_res must be 2**n, out_grid_res must be 2**n-1.')
self.num_in_level = math.log(self.in_grid_res, 2)
self.num_out_level = math.log(self.out_grid_res + 1, 2)
self.num_in_level = int(self.num_in_level) # number of input levels
self.num_out_level = int(self.num_out_level) # number of output levels
self._create_layers()
def _create_layers(self):
num_filter_down = [
self.num_filters * (2**(i + 1)) for i in range(self.num_in_level)
]
# num. features in downward path
num_filter_down = [
n if n <= self.max_filters else self.max_filters
for n in num_filter_down
]
num_filter_up = num_filter_down[::-1][1:self.num_out_level]
self.num_filter_down = num_filter_down
self.num_filter_up = num_filter_up
self.conv_in = ResBlock3D(self.num_filters, self.num_filters)
self.conv_out = ResBlock3D(
self.out_features, self.out_features, final_relu=False)
self.down_modules = [ResBlock3D(int(n / 2), n) for n in num_filter_down]
self.up_modules = [ResBlock3D(n, n) for n in num_filter_up]
self.dnpool = layers.MaxPool3D((2, 2, 2))
self.upsamp = layers.UpSampling3D((2, 2, 2))
self.up_final = layers.UpSampling3D((2, 2, 2))
def call(self, x, training=False):
"""Forward method.
Args:
x: `[batch, in_grid_res, in_grid_res, in_grid_res, in_features]` tensor,
input voxel grid.
training: bool, flag indicating whether model is in training mode.
Returns:
`[batch, out_grid_res, out_grid_res, out_grid_res, out_features]` tensor,
output voxel grid.
"""
x = self.conv_in(x)
x_dns = [x]
for mod in self.down_modules:
x_ = self.dnpool(mod(x_dns[-1], training=training))
x_dns.append(x_)
x_ups = [x_dns.pop(-1)]
for mod in self.up_modules:
x_ = tf.concat([self.upsamp(x_ups[-1]), x_dns.pop(-1)], axis=-1)
x_ = mod(x_, training=training)
x_ups.append(x_)
# odd layer
x = self.upsamp(x_ups[-1])[:, :-1, :-1, :-1, :]
x = self.conv_out(x)
return x
class ModelG2G(tf.keras.Model):
"""Grid-to-Grid Model with U-Net skip connections."""
def __init__(self,
in_grid_res=32,
out_grid_res=8,
num_filters=256,
codelen=128,
out_features=1,
net_type='imnet',
name='g2g'):
"""Initialization.
Args:
in_grid_res: int, input grid resolution, must be powers of 2.
out_grid_res: int, output grid resolution, must be powers of 2.
num_filters: int, number of feature layers at smallest grid resolution.
codelen: int, length of local latent codes.
out_features: int, number of output feature channels.
net_type: str, implicit function network architecture. imnet/deepsdf.
name: str, name of the layer.
Raises:
NotImplementedError: if net_type is not imnet or deepsdf.
ValueError: if in_grid_res or out_grid_res is not powers of 2.
"""
super(ModelG2G, self).__init__(name=name)
if math.log(out_grid_res, 2) % 1 != 0 or math.log(in_grid_res, 2) % 1 != 0:
raise ValueError('in_grid_res and out_grid_res must be powers of 2.')
if net_type not in ['imnet', 'deepsdf']:
raise NotImplementedError
self.codelen = codelen
self.out_features = out_features
self.in_grid_res = in_grid_res
self.out_grid_res = out_grid_res
self.num_filters = num_filters
self.net_type = net_type
self.outgrid = None
self.unet = UNet3D(
in_grid_res=in_grid_res,
out_grid_res=out_grid_res,
num_filters=num_filters,
out_features=codelen)
self.lig = lig.LocalImplicitGrid(
size=(out_grid_res, out_grid_res, out_grid_res),
in_features=codelen,
out_features=out_features,
net_type=net_type)
def call(self, voxgrid, pts, training=False):
"""Forward method.
Args:
voxgrid: `[batch, inres, inres, inres, nc]` tensor, input voxel grid.
pts: `[batch, num_points, 3]` tensor, input query points.
training: bool, flag indicating whether model is in training mode.
Returns:
vals: `[batch, num_points, 3]` tensor, predicted values at query points.
"""
self.outgrid = self.unet(voxgrid, training=training)
val = self.lig(self.outgrid, pts, training=training)
return val
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/local_implicit_grid/core/point_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.
# Lint as: python3
"""Additional data utilities for point preprocessing.
"""
import numpy as np
from plyfile import PlyData
from plyfile import PlyElement
def read_point_ply(filename):
"""Load point cloud from ply file.
Args:
filename: str, filename for ply file to load.
Returns:
v: np.array of shape [#v, 3], vertex coordinates
n: np.array of shape [#v, 3], vertex normals
"""
pd = PlyData.read(filename)['vertex']
v = np.array(np.stack([pd[i] for i in ['x', 'y', 'z']], axis=-1))
n = np.array(np.stack([pd[i] for i in ['nx', 'ny', 'nz']], axis=-1))
return v, n
def write_point_ply(filename, v, n):
"""Write point cloud to ply file.
Args:
filename: str, filename for ply file to load.
v: np.array of shape [#v, 3], vertex coordinates
n: np.array of shape [#v, 3], vertex normals
"""
vn = np.concatenate([v, n], axis=1)
vn = [tuple(vn[i]) for i in range(vn.shape[0])]
vn = np.array(vn, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4')])
el = PlyElement.describe(vn, 'vertex')
PlyData([el]).write(filename)
def np_pad_points(points, ntarget):
"""Pad point cloud to required size.
If number of points is larger than ntarget, take ntarget random samples.
If number of points is smaller than ntarget, pad by repeating last point.
Args:
points: `[npoints, nchannel]` np array, where first 3 channels are xyz.
ntarget: int, number of target channels.
Returns:
result: `[ntarget, nchannel]` np array, padded points to ntarget numbers.
"""
if points.shape[0] < ntarget:
mult = np.ceil(float(ntarget)/float(points.shape[0])) - 1
rand_pool = np.tile(points, [int(mult), 1])
nextra = ntarget-points.shape[0]
extra_idx = np.random.choice(rand_pool.shape[0], nextra, replace=False)
extra_pts = rand_pool[extra_idx]
points_out = np.concatenate([points, extra_pts], axis=0)
else:
idx_choice = np.random.choice(points.shape[0],
size=ntarget,
replace=False)
points_out = points[idx_choice]
return points_out
def np_gather_ijk_index(arr, index):
arr_flat = arr.reshape(-1, arr.shape[-1])
_, j, k, _ = arr.shape
index_transform = index[:, 0]*j*k+index[:, 1]*k+index[:, 2]
return arr_flat[index_transform]
def np_shifted_crop(v, idx_grid, shift, crop_size, ntarget):
"""Create a shifted crop."""
nchannel = v.shape[1]
vxyz = v[:, :3] - shift * crop_size * 0.5
vall = v.copy()
point_idxs = np.arange(v.shape[0])
point_grid_idx = np.floor(vxyz / crop_size).astype(np.int32)
valid_mask = np.ones(point_grid_idx.shape[0]).astype(np.bool)
for i in range(3):
valid_mask = np.logical_and(valid_mask, point_grid_idx[:, i] >= 0)
valid_mask = np.logical_and(valid_mask,
point_grid_idx[:, i] < idx_grid.shape[i])
point_grid_idx = point_grid_idx[valid_mask]
# translate to global grid index
point_grid_idx = np_gather_ijk_index(idx_grid, point_grid_idx)
vall = vall[valid_mask]
point_idxs = point_idxs[valid_mask]
crop_indices, revidx = np.unique(point_grid_idx, axis=0,
return_inverse=True)
ncrops = crop_indices.shape[0]
sortarr = np.argsort(revidx)
revidx_sorted = revidx[sortarr]
vall_sorted = vall[sortarr]
point_idxs_sorted = point_idxs[sortarr]
bins = np.searchsorted(revidx_sorted, np.arange(ncrops))
bins = list(bins) + [v.shape[0]]
sid = bins[0:-1]
eid = bins[1:]
# initialize outputs
point_crops = np.zeros([ncrops, ntarget, nchannel])
crop_point_idxs = []
# extract crops and pad
for i, (s, e) in enumerate(zip(sid, eid)):
cropped_points = vall_sorted[s:e]
crop_point_idx = point_idxs_sorted[s:e]
crop_point_idxs.append(crop_point_idx)
if cropped_points.shape[0] < ntarget:
padded_points = np_pad_points(cropped_points, ntarget=ntarget)
else:
choice_idx = np.random.choice(cropped_points.shape[0],
ntarget, replace=False)
padded_points = cropped_points[choice_idx]
point_crops[i] = padded_points
return point_crops, crop_indices, crop_point_idxs
def np_get_occupied_idx(v,
xmin=(0., 0., 0.),
xmax=(1., 1., 1.),
crop_size=.125, ntarget=2048,
overlap=True, normalize_crops=False,
return_shape=False, return_crop_point_idxs=False):
"""Get crop indices for point clouds."""
v = v.copy()-xmin
xmin = np.array(xmin)
xmax = np.array(xmax)
r = (xmax-xmin)/crop_size
r = np.ceil(r)
rr = r.astype(np.int32) if not overlap else (2*r-1).astype(np.int32)
# create index grid
idx_grid = np.stack(np.meshgrid(np.arange(rr[0]),
np.arange(rr[1]),
np.arange(rr[2]), indexing='ij'), axis=-1)
# [rr[0], rr[1], rr[2], 3]
shift_idxs = np.stack(
np.meshgrid(np.arange(int(overlap)+1),
np.arange(int(overlap)+1),
np.arange(int(overlap)+1), indexing='ij'), axis=-1)
shift_idxs = np.reshape(shift_idxs, [-1, 3])
point_crops = []
crop_indices = []
crop_point_idxs = []
for i in range(shift_idxs.shape[0]):
sft = shift_idxs[i]
skp = int(overlap)+1
idg = idx_grid[sft[0]::skp, sft[1]::skp, sft[2]::skp]
pc, ci, cpidx = np_shifted_crop(v, idg, sft, crop_size=crop_size,
ntarget=ntarget)
point_crops.append(pc)
crop_indices.append(ci)
crop_point_idxs += cpidx
point_crops = np.concatenate(point_crops, axis=0) # [ncrops, nsurface, 6]
crop_indices = np.concatenate(crop_indices, axis=0) # [ncrops, 3]
if normalize_crops:
# normalize each crop
crop_corners = crop_indices * 0.5 * crop_size
crop_centers = crop_corners + 0.5 * crop_size # [ncrops, 3]
crop_centers = crop_centers[:, np.newaxis, :] # [ncrops, 1, 3]
point_crops[..., :3] = point_crops[..., :3] -crop_centers
point_crops[..., :3] = point_crops[..., :3] / crop_size * 2
outputs = [point_crops, crop_indices]
if return_shape: outputs += [idx_grid.shape[:3]]
if return_crop_point_idxs:
outputs += [crop_point_idxs]
return tuple(outputs)
| # 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
"""Additional data utilities for point preprocessing.
"""
import numpy as np
from plyfile import PlyData
from plyfile import PlyElement
def read_point_ply(filename):
"""Load point cloud from ply file.
Args:
filename: str, filename for ply file to load.
Returns:
v: np.array of shape [#v, 3], vertex coordinates
n: np.array of shape [#v, 3], vertex normals
"""
pd = PlyData.read(filename)['vertex']
v = np.array(np.stack([pd[i] for i in ['x', 'y', 'z']], axis=-1))
n = np.array(np.stack([pd[i] for i in ['nx', 'ny', 'nz']], axis=-1))
return v, n
def write_point_ply(filename, v, n):
"""Write point cloud to ply file.
Args:
filename: str, filename for ply file to load.
v: np.array of shape [#v, 3], vertex coordinates
n: np.array of shape [#v, 3], vertex normals
"""
vn = np.concatenate([v, n], axis=1)
vn = [tuple(vn[i]) for i in range(vn.shape[0])]
vn = np.array(vn, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4')])
el = PlyElement.describe(vn, 'vertex')
PlyData([el]).write(filename)
def np_pad_points(points, ntarget):
"""Pad point cloud to required size.
If number of points is larger than ntarget, take ntarget random samples.
If number of points is smaller than ntarget, pad by repeating last point.
Args:
points: `[npoints, nchannel]` np array, where first 3 channels are xyz.
ntarget: int, number of target channels.
Returns:
result: `[ntarget, nchannel]` np array, padded points to ntarget numbers.
"""
if points.shape[0] < ntarget:
mult = np.ceil(float(ntarget)/float(points.shape[0])) - 1
rand_pool = np.tile(points, [int(mult), 1])
nextra = ntarget-points.shape[0]
extra_idx = np.random.choice(rand_pool.shape[0], nextra, replace=False)
extra_pts = rand_pool[extra_idx]
points_out = np.concatenate([points, extra_pts], axis=0)
else:
idx_choice = np.random.choice(points.shape[0],
size=ntarget,
replace=False)
points_out = points[idx_choice]
return points_out
def np_gather_ijk_index(arr, index):
arr_flat = arr.reshape(-1, arr.shape[-1])
_, j, k, _ = arr.shape
index_transform = index[:, 0]*j*k+index[:, 1]*k+index[:, 2]
return arr_flat[index_transform]
def np_shifted_crop(v, idx_grid, shift, crop_size, ntarget):
"""Create a shifted crop."""
nchannel = v.shape[1]
vxyz = v[:, :3] - shift * crop_size * 0.5
vall = v.copy()
point_idxs = np.arange(v.shape[0])
point_grid_idx = np.floor(vxyz / crop_size).astype(np.int32)
valid_mask = np.ones(point_grid_idx.shape[0]).astype(np.bool)
for i in range(3):
valid_mask = np.logical_and(valid_mask, point_grid_idx[:, i] >= 0)
valid_mask = np.logical_and(valid_mask,
point_grid_idx[:, i] < idx_grid.shape[i])
point_grid_idx = point_grid_idx[valid_mask]
# translate to global grid index
point_grid_idx = np_gather_ijk_index(idx_grid, point_grid_idx)
vall = vall[valid_mask]
point_idxs = point_idxs[valid_mask]
crop_indices, revidx = np.unique(point_grid_idx, axis=0,
return_inverse=True)
ncrops = crop_indices.shape[0]
sortarr = np.argsort(revidx)
revidx_sorted = revidx[sortarr]
vall_sorted = vall[sortarr]
point_idxs_sorted = point_idxs[sortarr]
bins = np.searchsorted(revidx_sorted, np.arange(ncrops))
bins = list(bins) + [v.shape[0]]
sid = bins[0:-1]
eid = bins[1:]
# initialize outputs
point_crops = np.zeros([ncrops, ntarget, nchannel])
crop_point_idxs = []
# extract crops and pad
for i, (s, e) in enumerate(zip(sid, eid)):
cropped_points = vall_sorted[s:e]
crop_point_idx = point_idxs_sorted[s:e]
crop_point_idxs.append(crop_point_idx)
if cropped_points.shape[0] < ntarget:
padded_points = np_pad_points(cropped_points, ntarget=ntarget)
else:
choice_idx = np.random.choice(cropped_points.shape[0],
ntarget, replace=False)
padded_points = cropped_points[choice_idx]
point_crops[i] = padded_points
return point_crops, crop_indices, crop_point_idxs
def np_get_occupied_idx(v,
xmin=(0., 0., 0.),
xmax=(1., 1., 1.),
crop_size=.125, ntarget=2048,
overlap=True, normalize_crops=False,
return_shape=False, return_crop_point_idxs=False):
"""Get crop indices for point clouds."""
v = v.copy()-xmin
xmin = np.array(xmin)
xmax = np.array(xmax)
r = (xmax-xmin)/crop_size
r = np.ceil(r)
rr = r.astype(np.int32) if not overlap else (2*r-1).astype(np.int32)
# create index grid
idx_grid = np.stack(np.meshgrid(np.arange(rr[0]),
np.arange(rr[1]),
np.arange(rr[2]), indexing='ij'), axis=-1)
# [rr[0], rr[1], rr[2], 3]
shift_idxs = np.stack(
np.meshgrid(np.arange(int(overlap)+1),
np.arange(int(overlap)+1),
np.arange(int(overlap)+1), indexing='ij'), axis=-1)
shift_idxs = np.reshape(shift_idxs, [-1, 3])
point_crops = []
crop_indices = []
crop_point_idxs = []
for i in range(shift_idxs.shape[0]):
sft = shift_idxs[i]
skp = int(overlap)+1
idg = idx_grid[sft[0]::skp, sft[1]::skp, sft[2]::skp]
pc, ci, cpidx = np_shifted_crop(v, idg, sft, crop_size=crop_size,
ntarget=ntarget)
point_crops.append(pc)
crop_indices.append(ci)
crop_point_idxs += cpidx
point_crops = np.concatenate(point_crops, axis=0) # [ncrops, nsurface, 6]
crop_indices = np.concatenate(crop_indices, axis=0) # [ncrops, 3]
if normalize_crops:
# normalize each crop
crop_corners = crop_indices * 0.5 * crop_size
crop_centers = crop_corners + 0.5 * crop_size # [ncrops, 3]
crop_centers = crop_centers[:, np.newaxis, :] # [ncrops, 1, 3]
point_crops[..., :3] = point_crops[..., :3] -crop_centers
point_crops[..., :3] = point_crops[..., :3] / crop_size * 2
outputs = [point_crops, crop_indices]
if return_shape: outputs += [idx_grid.shape[:3]]
if return_crop_point_idxs:
outputs += [crop_point_idxs]
return tuple(outputs)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/look_at.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 OpenGL lookAt functionalities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow_graphics.math import vector
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
def right_handed(camera_position, look_at, up_vector, name=None):
"""Builds a right handed look at view matrix.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
camera_position: A tensor of shape `[A1, ..., An, 3]`, where the last
dimension represents the 3D position of the camera.
look_at: 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 'right_handed'.
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, 4, 4]`, containing right handed look at
matrices.
"""
with tf.compat.v1.name_scope(name, "right_handed",
[camera_position, look_at, up_vector]):
camera_position = tf.convert_to_tensor(value=camera_position)
look_at = tf.convert_to_tensor(value=look_at)
up_vector = tf.convert_to_tensor(value=up_vector)
shape.check_static(
tensor=camera_position,
tensor_name="camera_position",
has_dim_equals=(-1, 3))
shape.check_static(
tensor=look_at, tensor_name="look_at", has_dim_equals=(-1, 3))
shape.check_static(
tensor=up_vector, tensor_name="up_vector", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(camera_position, look_at, up_vector),
last_axes=-2,
tensor_names=("camera_position", "look_at", "up_vector"),
broadcast_compatible=False)
z_axis = tf.linalg.l2_normalize(look_at - camera_position, axis=-1)
horizontal_axis = tf.linalg.l2_normalize(
vector.cross(z_axis, up_vector), axis=-1)
vertical_axis = vector.cross(horizontal_axis, z_axis)
batch_shape = tf.shape(input=horizontal_axis)[:-1]
zeros = tf.zeros(
shape=tf.concat((batch_shape, (3,)), axis=-1),
dtype=horizontal_axis.dtype)
one = tf.ones(
shape=tf.concat((batch_shape, (1,)), axis=-1),
dtype=horizontal_axis.dtype)
matrix = tf.concat(
(horizontal_axis, -vector.dot(horizontal_axis, camera_position),
vertical_axis, -vector.dot(vertical_axis, camera_position), -z_axis,
vector.dot(z_axis, camera_position), zeros, one),
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)
# 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 OpenGL lookAt functionalities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow_graphics.math import vector
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
def right_handed(camera_position, look_at, up_vector, name=None):
"""Builds a right handed look at view matrix.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
camera_position: A tensor of shape `[A1, ..., An, 3]`, where the last
dimension represents the 3D position of the camera.
look_at: 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 'right_handed'.
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, 4, 4]`, containing right handed look at
matrices.
"""
with tf.compat.v1.name_scope(name, "right_handed",
[camera_position, look_at, up_vector]):
camera_position = tf.convert_to_tensor(value=camera_position)
look_at = tf.convert_to_tensor(value=look_at)
up_vector = tf.convert_to_tensor(value=up_vector)
shape.check_static(
tensor=camera_position,
tensor_name="camera_position",
has_dim_equals=(-1, 3))
shape.check_static(
tensor=look_at, tensor_name="look_at", has_dim_equals=(-1, 3))
shape.check_static(
tensor=up_vector, tensor_name="up_vector", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(camera_position, look_at, up_vector),
last_axes=-2,
tensor_names=("camera_position", "look_at", "up_vector"),
broadcast_compatible=False)
z_axis = tf.linalg.l2_normalize(look_at - camera_position, axis=-1)
horizontal_axis = tf.linalg.l2_normalize(
vector.cross(z_axis, up_vector), axis=-1)
vertical_axis = vector.cross(horizontal_axis, z_axis)
batch_shape = tf.shape(input=horizontal_axis)[:-1]
zeros = tf.zeros(
shape=tf.concat((batch_shape, (3,)), axis=-1),
dtype=horizontal_axis.dtype)
one = tf.ones(
shape=tf.concat((batch_shape, (1,)), axis=-1),
dtype=horizontal_axis.dtype)
matrix = tf.concat(
(horizontal_axis, -vector.dot(horizontal_axis, camera_position),
vertical_axis, -vector.dot(vertical_axis, camera_position), -z_axis,
vector.dot(z_axis, camera_position), zeros, one),
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)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/image/tests/matting_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 matting."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.image import matting
from tensorflow_graphics.util import asserts
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import test_case
def _laplacian_matrix(image, size=3, eps=1e-5, name=None):
"""Generates the closed form matting Laplacian matrices.
Generates the closed form matting Laplacian as proposed by Levin et
al. in "A Closed Form Solution to Natural Image Matting".
Args:
image: A tensor of shape `[B, H, W, C]`.
size: An `int` representing the size of the patches used to enforce
smoothness.
eps: A small number of type `float` to regularize the problem.
name: A name for this op. Defaults to "matting_laplacian_matrix".
Returns:
A tensor of shape `[B, H, W, size^2, size^2]` containing the
matting Laplacian matrices.
Raises:
ValueError: If `image` is not of rank 4.
"""
with tf.compat.v1.name_scope(name, "matting_laplacian_matrix", [image]):
image = tf.convert_to_tensor(value=image)
shape.check_static(image, has_rank=4)
if size % 2 == 0:
raise ValueError("The patch size is expected to be an odd value.")
pixels = size**2
channels = tf.shape(input=image)[-1]
dtype = image.dtype
patches = tf.image.extract_patches(
image,
sizes=(1, size, size, 1),
strides=(1, 1, 1, 1),
rates=(1, 1, 1, 1),
padding="VALID")
batches = tf.shape(input=patches)[:-1]
new_shape = tf.concat((batches, (pixels, channels)), axis=-1)
patches = tf.reshape(patches, shape=new_shape)
mean = tf.reduce_mean(input_tensor=patches, axis=-2, keepdims=True)
demean = patches - mean
covariance = tf.matmul(demean, demean, transpose_a=True) / pixels
regularizer = (eps / pixels) * tf.eye(channels, dtype=dtype)
covariance_inv = tf.linalg.inv(covariance + regularizer)
covariance_inv = asserts.assert_no_infs_or_nans(covariance_inv)
mat = tf.matmul(tf.matmul(demean, covariance_inv), demean, transpose_b=True)
return tf.eye(pixels, dtype=dtype) - (1.0 + mat) / pixels
class MattingTest(test_case.TestCase):
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_jacobian_random(self, size, channels):
"""Tests the Jacobian of the build_matrices function."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
with self.subTest(name="laplacian"):
self.assert_jacobian_is_correct_fn(
lambda image: matting.build_matrices(image, size=size)[0],
[image_init])
with self.subTest(name="pseudo_inverse"):
self.assert_jacobian_is_correct_fn(
lambda image: matting.build_matrices(image, size=size)[1],
[image_init])
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_laplacian_zero_rows_and_columns(self, size, channels):
"""Tests that the laplacian matrix rows and columns sum to zero."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
image = tf.convert_to_tensor(value=image_init)
laplacian, _ = matting.build_matrices(image, size=size)
rows = tf.reduce_sum(input_tensor=laplacian, axis=-2)
columns = tf.reduce_sum(input_tensor=laplacian, axis=-1)
with self.subTest(name="rows"):
self.assertAllClose(rows, tf.zeros_like(rows))
with self.subTest(name="columns"):
self.assertAllClose(columns, tf.zeros_like(columns))
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_laplacian_versions(self, size, channels):
"""Compares two ways of computing the laplacian matrix."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
image = tf.convert_to_tensor(value=image_init)
laplacian_v1, _ = matting.build_matrices(image, size=size)
laplacian_v2 = _laplacian_matrix(image, size=size)
self.assertAllClose(laplacian_v1, laplacian_v2)
@parameterized.parameters(
(3, (None, None, None, 1)),
(3, (None, None, None, 3)),
(5, (None, None, None, 1)),
(5, (None, None, None, 3)),
(3, (1, 3, 3, 1)),
(3, (1, 3, 3, 3)),
(5, (1, 5, 5, 1)),
(5, (1, 5, 5, 3)),
)
def test_build_matrices_not_raised(self, size, *shapes):
"""Tests that the shape exceptions are not raised."""
build_matrices = lambda image: matting.build_matrices(image, size=size)
self.assert_exception_is_not_raised(build_matrices, shapes)
@parameterized.parameters(
("tensor must have a rank of 4, but it has rank", 3, (1,)),
("tensor must have a rank of 4, but it has rank", 3, (1, 1, 1, 1, 1)),
("The patch size is expected to be an odd value.", 2, (1, 1, 1, 1)),
)
def test_build_matrices_raised(self, error_msg, size, *shapes):
"""Tests that the shape exceptions are properly raised."""
build_matrices = lambda image: matting.build_matrices(image, size=size)
self.assert_exception_is_raised(build_matrices, error_msg, shapes)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_jacobian_random(self, size):
"""Tests the Jacobian of the linear_coefficients function."""
tensor_shape = np.random.randint(size, 6, size=3)
matte_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist() + [1])
tensor_shape[1:3] -= (size - 1)
num_coeffs = np.random.randint(2, 4)
pseudo_inverse_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [num_coeffs, size**2])
def a_fn(matte, pseudo_inverse):
a, _ = matting.linear_coefficients(matte, pseudo_inverse)
return a
def b_fn(matte, pseudo_inverse):
_, b = matting.linear_coefficients(matte, pseudo_inverse)
return b
with self.subTest(name="a"):
self.assert_jacobian_is_correct_fn(a_fn,
[matte_init, pseudo_inverse_init])
with self.subTest(name="b"):
self.assert_jacobian_is_correct_fn(b_fn,
[matte_init, pseudo_inverse_init])
@parameterized.parameters(
((None, None, None, 1), (None, None, None, 4, 9)),
((None, None, None, 1), (None, None, None, 2, 25)),
((1, 6, 6, 1), (1, 4, 4, 2, 9)),
((1, 10, 10, 1), (1, 6, 6, 2, 25)),
)
def test_linear_coefficients_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.linear_coefficients, shapes)
@parameterized.parameters(
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2),
(1, 4, 4, 2, 9)),
("Not all batch dimensions are identical.", (1, 6, 6, 1),
(2, 4, 4, 2, 9)),
)
def test_linear_coefficients_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.linear_coefficients, error_msg,
shapes)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_reconstruction_same_images(self, size):
"""Tests that the matte can be reconstructed by using the coefficients ."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
_, pseudo_inverse = matting.build_matrices(image, size=size)
a, b = matting.linear_coefficients(image, pseudo_inverse)
reconstructed = matting.reconstruct(image, a, b)
self.assertAllClose(image, reconstructed, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_reconstruction_opposite_images(self, size):
"""Tests that the matte can be reconstructed by using the coefficients ."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
_, pseudo_inverse = matting.build_matrices(image, size=size)
a, b = matting.linear_coefficients(1.0 - image, pseudo_inverse)
reconstructed = matting.reconstruct(image, a, b)
self.assertAllClose(1.0 - image, reconstructed, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_jacobian_random(self, size):
"""Tests the Jacobian of the matting loss function."""
tensor_shape = np.random.randint(size, 6, size=3)
matte_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist() + [1])
tensor_shape[1:3] -= (size - 1)
laplacian_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [size**2, size**2])
with self.subTest(name="matte"):
self.assert_jacobian_is_correct_fn(matting.loss,
[matte_init, laplacian_init])
@parameterized.parameters(
((None, None, None, 1), (None, None, None, 9, 9)),
((None, None, None, 1), (None, None, None, 25, 25)),
((1, 6, 6, 1), (1, 4, 4, 9, 9)),
((1, 10, 10, 1), (1, 6, 6, 25, 25)),
)
def test_loss_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.loss, shapes)
@parameterized.parameters(
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2),
(1, 4, 4, 9, 9)),
("must have exactly 9 dimensions in axis -2", (1, 6, 6, 1),
(1, 4, 4, 1, 9)),
("Not all batch dimensions are identical.", (1, 6, 6, 1),
(2, 4, 4, 9, 9)),
)
def test_loss_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.loss, error_msg, shapes)
@parameterized.parameters((3,), (5,))
def test_loss_opposite_images(self, size):
"""Tests that passing opposite images results in a loss close to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(1.0 - image, laplacian)
self.assertAllClose(loss, 0.0, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_same_images(self, size):
"""Tests that passing same images results in a loss close to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(image, laplacian)
self.assertAllClose(loss, 0.0, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_positive(self, size):
"""Tests that the loss is always greater or equal to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = tf.random.uniform(minval=0.0, maxval=1.0, shape=tensor_shape + [3])
matte = tf.random.uniform(minval=0.0, maxval=1.0, shape=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(matte, laplacian)
self.assertAllGreaterEqual(loss, 0.0)
@parameterized.parameters((1,), (3,))
def test_reconstruct_jacobian_random(self, channels):
"""Tests the Jacobian of the reconstruct function."""
tensor_shape = np.random.randint(1, 5, size=3).tolist()
image_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
mul_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
add_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
self.assert_jacobian_is_correct_fn(matting.reconstruct,
[image_init, mul_init, add_init])
@parameterized.parameters(
((None, None, None, 3), (None, None, None, 3), (None, None, None, 1)),
((1, 6, 6, 3), (1, 6, 6, 3), (1, 6, 6, 1)),
)
def test_reconstruct_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.reconstruct, shapes)
@parameterized.parameters(
("tensor must have a rank of 4, but it has rank", (1, 6, 6), (1, 6, 6, 2),
(1, 6, 6, 1)),
("tensor must have a rank of 4, but it has rank", (1, 6, 6, 2), (1, 6, 6),
(1, 6, 6, 1)),
("tensor must have a rank of 4, but it has rank", (1, 6, 6, 2),
(1, 6, 6, 2), (1, 6, 6)),
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2), (1, 6, 6, 2),
(1, 6, 6, 2)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 4),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 4, 6, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(1, 4, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 4, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(1, 6, 4, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (4, 6, 6, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(4, 6, 6, 1)),
)
def test_reconstruct_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.reconstruct, error_msg, 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 matting."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.image import matting
from tensorflow_graphics.util import asserts
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import test_case
def _laplacian_matrix(image, size=3, eps=1e-5, name=None):
"""Generates the closed form matting Laplacian matrices.
Generates the closed form matting Laplacian as proposed by Levin et
al. in "A Closed Form Solution to Natural Image Matting".
Args:
image: A tensor of shape `[B, H, W, C]`.
size: An `int` representing the size of the patches used to enforce
smoothness.
eps: A small number of type `float` to regularize the problem.
name: A name for this op. Defaults to "matting_laplacian_matrix".
Returns:
A tensor of shape `[B, H, W, size^2, size^2]` containing the
matting Laplacian matrices.
Raises:
ValueError: If `image` is not of rank 4.
"""
with tf.compat.v1.name_scope(name, "matting_laplacian_matrix", [image]):
image = tf.convert_to_tensor(value=image)
shape.check_static(image, has_rank=4)
if size % 2 == 0:
raise ValueError("The patch size is expected to be an odd value.")
pixels = size**2
channels = tf.shape(input=image)[-1]
dtype = image.dtype
patches = tf.image.extract_patches(
image,
sizes=(1, size, size, 1),
strides=(1, 1, 1, 1),
rates=(1, 1, 1, 1),
padding="VALID")
batches = tf.shape(input=patches)[:-1]
new_shape = tf.concat((batches, (pixels, channels)), axis=-1)
patches = tf.reshape(patches, shape=new_shape)
mean = tf.reduce_mean(input_tensor=patches, axis=-2, keepdims=True)
demean = patches - mean
covariance = tf.matmul(demean, demean, transpose_a=True) / pixels
regularizer = (eps / pixels) * tf.eye(channels, dtype=dtype)
covariance_inv = tf.linalg.inv(covariance + regularizer)
covariance_inv = asserts.assert_no_infs_or_nans(covariance_inv)
mat = tf.matmul(tf.matmul(demean, covariance_inv), demean, transpose_b=True)
return tf.eye(pixels, dtype=dtype) - (1.0 + mat) / pixels
class MattingTest(test_case.TestCase):
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_jacobian_random(self, size, channels):
"""Tests the Jacobian of the build_matrices function."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
with self.subTest(name="laplacian"):
self.assert_jacobian_is_correct_fn(
lambda image: matting.build_matrices(image, size=size)[0],
[image_init])
with self.subTest(name="pseudo_inverse"):
self.assert_jacobian_is_correct_fn(
lambda image: matting.build_matrices(image, size=size)[1],
[image_init])
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_laplacian_zero_rows_and_columns(self, size, channels):
"""Tests that the laplacian matrix rows and columns sum to zero."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
image = tf.convert_to_tensor(value=image_init)
laplacian, _ = matting.build_matrices(image, size=size)
rows = tf.reduce_sum(input_tensor=laplacian, axis=-2)
columns = tf.reduce_sum(input_tensor=laplacian, axis=-1)
with self.subTest(name="rows"):
self.assertAllClose(rows, tf.zeros_like(rows))
with self.subTest(name="columns"):
self.assertAllClose(columns, tf.zeros_like(columns))
@parameterized.parameters((3, 1), (3, 3), (5, 3), (5, 1))
def test_build_matrices_laplacian_versions(self, size, channels):
"""Compares two ways of computing the laplacian matrix."""
tensor_shape = np.random.randint(size, 6, size=3)
image_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [channels])
image = tf.convert_to_tensor(value=image_init)
laplacian_v1, _ = matting.build_matrices(image, size=size)
laplacian_v2 = _laplacian_matrix(image, size=size)
self.assertAllClose(laplacian_v1, laplacian_v2)
@parameterized.parameters(
(3, (None, None, None, 1)),
(3, (None, None, None, 3)),
(5, (None, None, None, 1)),
(5, (None, None, None, 3)),
(3, (1, 3, 3, 1)),
(3, (1, 3, 3, 3)),
(5, (1, 5, 5, 1)),
(5, (1, 5, 5, 3)),
)
def test_build_matrices_not_raised(self, size, *shapes):
"""Tests that the shape exceptions are not raised."""
build_matrices = lambda image: matting.build_matrices(image, size=size)
self.assert_exception_is_not_raised(build_matrices, shapes)
@parameterized.parameters(
("tensor must have a rank of 4, but it has rank", 3, (1,)),
("tensor must have a rank of 4, but it has rank", 3, (1, 1, 1, 1, 1)),
("The patch size is expected to be an odd value.", 2, (1, 1, 1, 1)),
)
def test_build_matrices_raised(self, error_msg, size, *shapes):
"""Tests that the shape exceptions are properly raised."""
build_matrices = lambda image: matting.build_matrices(image, size=size)
self.assert_exception_is_raised(build_matrices, error_msg, shapes)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_jacobian_random(self, size):
"""Tests the Jacobian of the linear_coefficients function."""
tensor_shape = np.random.randint(size, 6, size=3)
matte_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist() + [1])
tensor_shape[1:3] -= (size - 1)
num_coeffs = np.random.randint(2, 4)
pseudo_inverse_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [num_coeffs, size**2])
def a_fn(matte, pseudo_inverse):
a, _ = matting.linear_coefficients(matte, pseudo_inverse)
return a
def b_fn(matte, pseudo_inverse):
_, b = matting.linear_coefficients(matte, pseudo_inverse)
return b
with self.subTest(name="a"):
self.assert_jacobian_is_correct_fn(a_fn,
[matte_init, pseudo_inverse_init])
with self.subTest(name="b"):
self.assert_jacobian_is_correct_fn(b_fn,
[matte_init, pseudo_inverse_init])
@parameterized.parameters(
((None, None, None, 1), (None, None, None, 4, 9)),
((None, None, None, 1), (None, None, None, 2, 25)),
((1, 6, 6, 1), (1, 4, 4, 2, 9)),
((1, 10, 10, 1), (1, 6, 6, 2, 25)),
)
def test_linear_coefficients_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.linear_coefficients, shapes)
@parameterized.parameters(
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2),
(1, 4, 4, 2, 9)),
("Not all batch dimensions are identical.", (1, 6, 6, 1),
(2, 4, 4, 2, 9)),
)
def test_linear_coefficients_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.linear_coefficients, error_msg,
shapes)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_reconstruction_same_images(self, size):
"""Tests that the matte can be reconstructed by using the coefficients ."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
_, pseudo_inverse = matting.build_matrices(image, size=size)
a, b = matting.linear_coefficients(image, pseudo_inverse)
reconstructed = matting.reconstruct(image, a, b)
self.assertAllClose(image, reconstructed, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_linear_coefficients_reconstruction_opposite_images(self, size):
"""Tests that the matte can be reconstructed by using the coefficients ."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
_, pseudo_inverse = matting.build_matrices(image, size=size)
a, b = matting.linear_coefficients(1.0 - image, pseudo_inverse)
reconstructed = matting.reconstruct(image, a, b)
self.assertAllClose(1.0 - image, reconstructed, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_jacobian_random(self, size):
"""Tests the Jacobian of the matting loss function."""
tensor_shape = np.random.randint(size, 6, size=3)
matte_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist() + [1])
tensor_shape[1:3] -= (size - 1)
laplacian_init = np.random.uniform(
0.0, 1.0, size=tensor_shape.tolist() + [size**2, size**2])
with self.subTest(name="matte"):
self.assert_jacobian_is_correct_fn(matting.loss,
[matte_init, laplacian_init])
@parameterized.parameters(
((None, None, None, 1), (None, None, None, 9, 9)),
((None, None, None, 1), (None, None, None, 25, 25)),
((1, 6, 6, 1), (1, 4, 4, 9, 9)),
((1, 10, 10, 1), (1, 6, 6, 25, 25)),
)
def test_loss_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.loss, shapes)
@parameterized.parameters(
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2),
(1, 4, 4, 9, 9)),
("must have exactly 9 dimensions in axis -2", (1, 6, 6, 1),
(1, 4, 4, 1, 9)),
("Not all batch dimensions are identical.", (1, 6, 6, 1),
(2, 4, 4, 9, 9)),
)
def test_loss_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.loss, error_msg, shapes)
@parameterized.parameters((3,), (5,))
def test_loss_opposite_images(self, size):
"""Tests that passing opposite images results in a loss close to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(1.0 - image, laplacian)
self.assertAllClose(loss, 0.0, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_same_images(self, size):
"""Tests that passing same images results in a loss close to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(image, laplacian)
self.assertAllClose(loss, 0.0, atol=1e-4)
@parameterized.parameters((3,), (5,))
def test_loss_positive(self, size):
"""Tests that the loss is always greater or equal to 0.0."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = tf.random.uniform(minval=0.0, maxval=1.0, shape=tensor_shape + [3])
matte = tf.random.uniform(minval=0.0, maxval=1.0, shape=tensor_shape + [1])
laplacian, _ = matting.build_matrices(image, size=size)
loss = matting.loss(matte, laplacian)
self.assertAllGreaterEqual(loss, 0.0)
@parameterized.parameters((1,), (3,))
def test_reconstruct_jacobian_random(self, channels):
"""Tests the Jacobian of the reconstruct function."""
tensor_shape = np.random.randint(1, 5, size=3).tolist()
image_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
mul_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
add_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
self.assert_jacobian_is_correct_fn(matting.reconstruct,
[image_init, mul_init, add_init])
@parameterized.parameters(
((None, None, None, 3), (None, None, None, 3), (None, None, None, 1)),
((1, 6, 6, 3), (1, 6, 6, 3), (1, 6, 6, 1)),
)
def test_reconstruct_not_raised(self, *shapes):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(matting.reconstruct, shapes)
@parameterized.parameters(
("tensor must have a rank of 4, but it has rank", (1, 6, 6), (1, 6, 6, 2),
(1, 6, 6, 1)),
("tensor must have a rank of 4, but it has rank", (1, 6, 6, 2), (1, 6, 6),
(1, 6, 6, 1)),
("tensor must have a rank of 4, but it has rank", (1, 6, 6, 2),
(1, 6, 6, 2), (1, 6, 6)),
("must have exactly 1 dimensions in axis -1", (1, 6, 6, 2), (1, 6, 6, 2),
(1, 6, 6, 2)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 4),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 4, 6, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(1, 4, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 4, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(1, 6, 4, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (4, 6, 6, 1),
(1, 6, 6, 1)),
("Not all batch dimensions are identical.", (1, 6, 6, 1), (1, 6, 6, 1),
(4, 6, 6, 1)),
)
def test_reconstruct_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised."""
self.assert_exception_is_raised(matting.reconstruct, error_msg, shapes)
if __name__ == "__main__":
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/rotation_matrix_3d.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 3d rotation matrix utility functions.
More details rotation matrices can be found on [this page.]
(https://en.wikipedia.org/wiki/Rotation_matrix)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
from tensorflow_graphics.geometry.transformation import rotation_matrix_common
from tensorflow_graphics.util import asserts
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import tfg_flags
FLAGS = flags.FLAGS
def _build_matrix_from_sines_and_cosines(sin_angles, cos_angles):
"""Builds a rotation matrix from sines and cosines of Euler angles.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
sin_angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the sine of the Euler angles.
cos_angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the cosine of the Euler angles.
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
"""
sin_angles.shape.assert_is_compatible_with(cos_angles.shape)
sx, sy, sz = tf.unstack(sin_angles, axis=-1)
cx, cy, cz = tf.unstack(cos_angles, axis=-1)
m00 = cy * cz
m01 = (sx * sy * cz) - (cx * sz)
m02 = (cx * sy * cz) + (sx * sz)
m10 = cy * sz
m11 = (sx * sy * sz) + (cx * cz)
m12 = (cx * sy * sz) - (sx * cz)
m20 = -sy
m21 = sx * cy
m22 = cx * cy
matrix = tf.stack((m00, m01, m02,
m10, m11, m12,
m20, m21, m22),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=sin_angles)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def assert_rotation_matrix_normalized(matrix, eps=1e-3, name=None):
"""Checks whether a matrix is a rotation matrix.
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 3d rotation matrix.
eps: The absolute tolerance parameter.
name: A name for this op that defaults to
'assert_rotation_matrix_normalized'.
Returns:
The input matrix, with dependence on the assertion operator in the graph.
Raises:
tf.errors.InvalidArgumentError: If rotation_matrix_3d is not normalized.
"""
if not FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value:
return matrix
with tf.compat.v1.name_scope(name, "assert_rotation_matrix_normalized",
[matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
is_matrix_normalized = is_valid(matrix, atol=eps)
with tf.control_dependencies([
tf.compat.v1.assert_equal(
is_matrix_normalized,
tf.ones_like(is_matrix_normalized, dtype=tf.bool))
]):
return tf.identity(matrix)
def from_axis_angle(axis, angle, name=None):
"""Convert an axis-angle representation to a rotation matrix.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
axis: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a normalized axis.
angle: A tensor of shape `[A1, ..., An, 1]`, where the last dimension
represents a normalized axis.
name: A name for this op that defaults to
"rotation_matrix_3d_from_axis_angle".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represents a 3d rotation matrix.
Raises:
ValueError: If the shape of `axis` or `angle` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_axis_angle",
[axis, angle]):
axis = tf.convert_to_tensor(value=axis)
angle = tf.convert_to_tensor(value=angle)
shape.check_static(tensor=axis, tensor_name="axis", has_dim_equals=(-1, 3))
shape.check_static(
tensor=angle, tensor_name="angle", has_dim_equals=(-1, 1))
shape.compare_batch_dimensions(
tensors=(axis, angle),
tensor_names=("axis", "angle"),
last_axes=-2,
broadcast_compatible=True)
axis = asserts.assert_normalized(axis)
sin_axis = tf.sin(angle) * axis
cos_angle = tf.cos(angle)
cos1_axis = (1.0 - cos_angle) * axis
_, axis_y, axis_z = tf.unstack(axis, axis=-1)
cos1_axis_x, cos1_axis_y, _ = tf.unstack(cos1_axis, axis=-1)
sin_axis_x, sin_axis_y, sin_axis_z = tf.unstack(sin_axis, axis=-1)
tmp = cos1_axis_x * axis_y
m01 = tmp - sin_axis_z
m10 = tmp + sin_axis_z
tmp = cos1_axis_x * axis_z
m02 = tmp + sin_axis_y
m20 = tmp - sin_axis_y
tmp = cos1_axis_y * axis_z
m12 = tmp - sin_axis_x
m21 = tmp + sin_axis_x
diag = cos1_axis * axis + cos_angle
diag_x, diag_y, diag_z = tf.unstack(diag, axis=-1)
matrix = tf.stack((diag_x, m01, m02,
m10, diag_y, m12,
m20, m21, diag_z),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=axis)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def from_euler(angles, name=None):
r"""Convert an Euler angle representation to a rotation matrix.
The resulting matrix is $$\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$$.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three Euler angles. `[A1, ..., An, 0]` is the angle about
`x` in radians `[A1, ..., An, 1]` is the angle about `y` in radians and
`[A1, ..., An, 2]` is the angle about `z` in radians.
name: A name for this op that defaults to "rotation_matrix_3d_from_euler".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `angles` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_euler", [angles]):
angles = tf.convert_to_tensor(value=angles)
shape.check_static(
tensor=angles, tensor_name="angles", has_dim_equals=(-1, 3))
sin_angles = tf.sin(angles)
cos_angles = tf.cos(angles)
return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles)
def from_euler_with_small_angles_approximation(angles, name=None):
r"""Convert an Euler angle representation to a rotation matrix.
The resulting matrix is $$\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$$.
Under the small angle assumption, $$\sin(x)$$ and $$\cos(x)$$ can be
approximated by their second order Taylor expansions, where
$$\sin(x) \approx x$$ and $$\cos(x) \approx 1 - \frac{x^2}{2}$$.
In the current implementation, the smallness of the angles is not verified.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three small Euler angles. `[A1, ..., An, 0]` is the angle
about `x` in radians, `[A1, ..., An, 1]` is the angle about `y` in radians
and `[A1, ..., An, 2]` is the angle about `z` in radians.
name: A name for this op that defaults to "rotation_matrix_3d_from_euler".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `angles` is not supported.
"""
with tf.compat.v1.name_scope(
name, "rotation_matrix_3d_from_euler_with_small_angles", [angles]):
angles = tf.convert_to_tensor(value=angles)
shape.check_static(
tensor=angles, tensor_name="angles", has_dim_equals=(-1, 3))
sin_angles = angles
cos_angles = 1.0 - 0.5 * tf.square(angles)
return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles)
def from_quaternion(quaternion, name=None):
"""Convert a quaternion to a rotation matrix.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
quaternion: A tensor of shape `[A1, ..., An, 4]`, where the last dimension
represents a normalized quaternion.
name: A name for this op that defaults to
"rotation_matrix_3d_from_quaternion".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `quaternion` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_quaternion",
[quaternion]):
quaternion = tf.convert_to_tensor(value=quaternion)
shape.check_static(
tensor=quaternion, tensor_name="quaternion", has_dim_equals=(-1, 4))
quaternion = asserts.assert_normalized(quaternion)
x, y, z, w = tf.unstack(quaternion, axis=-1)
tx = 2.0 * x
ty = 2.0 * y
tz = 2.0 * z
twx = tx * w
twy = ty * w
twz = tz * w
txx = tx * x
txy = ty * x
txz = tz * x
tyy = ty * y
tyz = tz * y
tzz = tz * z
matrix = tf.stack((1.0 - (tyy + tzz), txy - twz, txz + twy,
txy + twz, 1.0 - (txx + tzz), tyz - twx,
txz - twy, tyz + twx, 1.0 - (txx + tyy)),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=quaternion)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def inverse(matrix, name=None):
"""Computes the inverse of a 3D rotation matrix.
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 3d rotation matrix.
name: A name for this op that defaults to "rotation_matrix_3d_inverse".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `matrix` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_inverse", [matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
matrix = assert_rotation_matrix_normalized(matrix)
ndims = matrix.shape.ndims
perm = list(range(ndims - 2)) + [ndims - 1, ndims - 2]
return tf.transpose(a=matrix, perm=perm)
def is_valid(matrix, atol=1e-3, name=None):
"""Determines if a matrix is a valid rotation matrix.
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 matrix.
atol: Absolute tolerance parameter.
name: A name for this op that defaults to "rotation_matrix_3d_is_valid".
Returns:
A tensor of type `bool` and shape `[A1, ..., An, 1]` where False indicates
that the input is not a valid rotation matrix.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_is_valid", [matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
return rotation_matrix_common.is_valid(matrix, atol)
def rotate(point, matrix, name=None):
"""Rotate a point using a rotation matrix 3d.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a 3d point.
matrix: A tensor of shape `[A1, ..., An, 3,3]`, where the last dimension
represents a 3d rotation matrix.
name: A name for this op that defaults to "rotation_matrix_3d_rotate".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
a 3d point.
Raises:
ValueError: If the shape of `point` or `rotation_matrix_3d` is not
supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_rotate",
[point, matrix]):
point = tf.convert_to_tensor(value=point)
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=point, tensor_name="point", has_dim_equals=(-1, 3))
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
shape.compare_batch_dimensions(
tensors=(point, matrix),
tensor_names=("point", "matrix"),
last_axes=(-2, -3),
broadcast_compatible=True)
matrix = assert_rotation_matrix_normalized(matrix)
point = tf.expand_dims(point, axis=-1)
common_batch_shape = shape.get_broadcasted_shape(
point.shape[:-2], matrix.shape[:-2])
def dim_value(dim):
return 1 if dim is None else tf.compat.v1.dimension_value(dim)
common_batch_shape = [dim_value(dim) for dim in common_batch_shape]
point = tf.broadcast_to(point, common_batch_shape + [3, 1])
matrix = tf.broadcast_to(matrix, common_batch_shape + [3, 3])
rotated_point = tf.matmul(matrix, point)
return tf.squeeze(rotated_point, 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 3d rotation matrix utility functions.
More details rotation matrices can be found on [this page.]
(https://en.wikipedia.org/wiki/Rotation_matrix)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
from tensorflow_graphics.geometry.transformation import rotation_matrix_common
from tensorflow_graphics.util import asserts
from tensorflow_graphics.util import export_api
from tensorflow_graphics.util import shape
from tensorflow_graphics.util import tfg_flags
FLAGS = flags.FLAGS
def _build_matrix_from_sines_and_cosines(sin_angles, cos_angles):
"""Builds a rotation matrix from sines and cosines of Euler angles.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
sin_angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the sine of the Euler angles.
cos_angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the cosine of the Euler angles.
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
"""
sin_angles.shape.assert_is_compatible_with(cos_angles.shape)
sx, sy, sz = tf.unstack(sin_angles, axis=-1)
cx, cy, cz = tf.unstack(cos_angles, axis=-1)
m00 = cy * cz
m01 = (sx * sy * cz) - (cx * sz)
m02 = (cx * sy * cz) + (sx * sz)
m10 = cy * sz
m11 = (sx * sy * sz) + (cx * cz)
m12 = (cx * sy * sz) - (sx * cz)
m20 = -sy
m21 = sx * cy
m22 = cx * cy
matrix = tf.stack((m00, m01, m02,
m10, m11, m12,
m20, m21, m22),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=sin_angles)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def assert_rotation_matrix_normalized(matrix, eps=1e-3, name=None):
"""Checks whether a matrix is a rotation matrix.
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 3d rotation matrix.
eps: The absolute tolerance parameter.
name: A name for this op that defaults to
'assert_rotation_matrix_normalized'.
Returns:
The input matrix, with dependence on the assertion operator in the graph.
Raises:
tf.errors.InvalidArgumentError: If rotation_matrix_3d is not normalized.
"""
if not FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value:
return matrix
with tf.compat.v1.name_scope(name, "assert_rotation_matrix_normalized",
[matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
is_matrix_normalized = is_valid(matrix, atol=eps)
with tf.control_dependencies([
tf.compat.v1.assert_equal(
is_matrix_normalized,
tf.ones_like(is_matrix_normalized, dtype=tf.bool))
]):
return tf.identity(matrix)
def from_axis_angle(axis, angle, name=None):
"""Convert an axis-angle representation to a rotation matrix.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
axis: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a normalized axis.
angle: A tensor of shape `[A1, ..., An, 1]`, where the last dimension
represents a normalized axis.
name: A name for this op that defaults to
"rotation_matrix_3d_from_axis_angle".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represents a 3d rotation matrix.
Raises:
ValueError: If the shape of `axis` or `angle` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_axis_angle",
[axis, angle]):
axis = tf.convert_to_tensor(value=axis)
angle = tf.convert_to_tensor(value=angle)
shape.check_static(tensor=axis, tensor_name="axis", has_dim_equals=(-1, 3))
shape.check_static(
tensor=angle, tensor_name="angle", has_dim_equals=(-1, 1))
shape.compare_batch_dimensions(
tensors=(axis, angle),
tensor_names=("axis", "angle"),
last_axes=-2,
broadcast_compatible=True)
axis = asserts.assert_normalized(axis)
sin_axis = tf.sin(angle) * axis
cos_angle = tf.cos(angle)
cos1_axis = (1.0 - cos_angle) * axis
_, axis_y, axis_z = tf.unstack(axis, axis=-1)
cos1_axis_x, cos1_axis_y, _ = tf.unstack(cos1_axis, axis=-1)
sin_axis_x, sin_axis_y, sin_axis_z = tf.unstack(sin_axis, axis=-1)
tmp = cos1_axis_x * axis_y
m01 = tmp - sin_axis_z
m10 = tmp + sin_axis_z
tmp = cos1_axis_x * axis_z
m02 = tmp + sin_axis_y
m20 = tmp - sin_axis_y
tmp = cos1_axis_y * axis_z
m12 = tmp - sin_axis_x
m21 = tmp + sin_axis_x
diag = cos1_axis * axis + cos_angle
diag_x, diag_y, diag_z = tf.unstack(diag, axis=-1)
matrix = tf.stack((diag_x, m01, m02,
m10, diag_y, m12,
m20, m21, diag_z),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=axis)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def from_euler(angles, name=None):
r"""Convert an Euler angle representation to a rotation matrix.
The resulting matrix is $$\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$$.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three Euler angles. `[A1, ..., An, 0]` is the angle about
`x` in radians `[A1, ..., An, 1]` is the angle about `y` in radians and
`[A1, ..., An, 2]` is the angle about `z` in radians.
name: A name for this op that defaults to "rotation_matrix_3d_from_euler".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `angles` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_euler", [angles]):
angles = tf.convert_to_tensor(value=angles)
shape.check_static(
tensor=angles, tensor_name="angles", has_dim_equals=(-1, 3))
sin_angles = tf.sin(angles)
cos_angles = tf.cos(angles)
return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles)
def from_euler_with_small_angles_approximation(angles, name=None):
r"""Convert an Euler angle representation to a rotation matrix.
The resulting matrix is $$\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$$.
Under the small angle assumption, $$\sin(x)$$ and $$\cos(x)$$ can be
approximated by their second order Taylor expansions, where
$$\sin(x) \approx x$$ and $$\cos(x) \approx 1 - \frac{x^2}{2}$$.
In the current implementation, the smallness of the angles is not verified.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
angles: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the three small Euler angles. `[A1, ..., An, 0]` is the angle
about `x` in radians, `[A1, ..., An, 1]` is the angle about `y` in radians
and `[A1, ..., An, 2]` is the angle about `z` in radians.
name: A name for this op that defaults to "rotation_matrix_3d_from_euler".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `angles` is not supported.
"""
with tf.compat.v1.name_scope(
name, "rotation_matrix_3d_from_euler_with_small_angles", [angles]):
angles = tf.convert_to_tensor(value=angles)
shape.check_static(
tensor=angles, tensor_name="angles", has_dim_equals=(-1, 3))
sin_angles = angles
cos_angles = 1.0 - 0.5 * tf.square(angles)
return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles)
def from_quaternion(quaternion, name=None):
"""Convert a quaternion to a rotation matrix.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
quaternion: A tensor of shape `[A1, ..., An, 4]`, where the last dimension
represents a normalized quaternion.
name: A name for this op that defaults to
"rotation_matrix_3d_from_quaternion".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `quaternion` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_from_quaternion",
[quaternion]):
quaternion = tf.convert_to_tensor(value=quaternion)
shape.check_static(
tensor=quaternion, tensor_name="quaternion", has_dim_equals=(-1, 4))
quaternion = asserts.assert_normalized(quaternion)
x, y, z, w = tf.unstack(quaternion, axis=-1)
tx = 2.0 * x
ty = 2.0 * y
tz = 2.0 * z
twx = tx * w
twy = ty * w
twz = tz * w
txx = tx * x
txy = ty * x
txz = tz * x
tyy = ty * y
tyz = tz * y
tzz = tz * z
matrix = tf.stack((1.0 - (tyy + tzz), txy - twz, txz + twy,
txy + twz, 1.0 - (txx + tzz), tyz - twx,
txz - twy, tyz + twx, 1.0 - (txx + tyy)),
axis=-1) # pyformat: disable
output_shape = tf.concat((tf.shape(input=quaternion)[:-1], (3, 3)), axis=-1)
return tf.reshape(matrix, shape=output_shape)
def inverse(matrix, name=None):
"""Computes the inverse of a 3D rotation matrix.
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 3d rotation matrix.
name: A name for this op that defaults to "rotation_matrix_3d_inverse".
Returns:
A tensor of shape `[A1, ..., An, 3, 3]`, where the last two dimensions
represent a 3d rotation matrix.
Raises:
ValueError: If the shape of `matrix` is not supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_inverse", [matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
matrix = assert_rotation_matrix_normalized(matrix)
ndims = matrix.shape.ndims
perm = list(range(ndims - 2)) + [ndims - 1, ndims - 2]
return tf.transpose(a=matrix, perm=perm)
def is_valid(matrix, atol=1e-3, name=None):
"""Determines if a matrix is a valid rotation matrix.
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 matrix.
atol: Absolute tolerance parameter.
name: A name for this op that defaults to "rotation_matrix_3d_is_valid".
Returns:
A tensor of type `bool` and shape `[A1, ..., An, 1]` where False indicates
that the input is not a valid rotation matrix.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_is_valid", [matrix]):
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
return rotation_matrix_common.is_valid(matrix, atol)
def rotate(point, matrix, name=None):
"""Rotate a point using a rotation matrix 3d.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents a 3d point.
matrix: A tensor of shape `[A1, ..., An, 3,3]`, where the last dimension
represents a 3d rotation matrix.
name: A name for this op that defaults to "rotation_matrix_3d_rotate".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
a 3d point.
Raises:
ValueError: If the shape of `point` or `rotation_matrix_3d` is not
supported.
"""
with tf.compat.v1.name_scope(name, "rotation_matrix_3d_rotate",
[point, matrix]):
point = tf.convert_to_tensor(value=point)
matrix = tf.convert_to_tensor(value=matrix)
shape.check_static(
tensor=point, tensor_name="point", has_dim_equals=(-1, 3))
shape.check_static(
tensor=matrix,
tensor_name="matrix",
has_rank_greater_than=1,
has_dim_equals=((-2, 3), (-1, 3)))
shape.compare_batch_dimensions(
tensors=(point, matrix),
tensor_names=("point", "matrix"),
last_axes=(-2, -3),
broadcast_compatible=True)
matrix = assert_rotation_matrix_normalized(matrix)
point = tf.expand_dims(point, axis=-1)
common_batch_shape = shape.get_broadcasted_shape(
point.shape[:-2], matrix.shape[:-2])
def dim_value(dim):
return 1 if dim is None else tf.compat.v1.dimension_value(dim)
common_batch_shape = [dim_value(dim) for dim in common_batch_shape]
point = tf.broadcast_to(point, common_batch_shape + [3, 1])
matrix = tf.broadcast_to(matrix, common_batch_shape + [3, 3])
rotated_point = tf.matmul(matrix, point)
return tf.squeeze(rotated_point, axis=-1)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/nn/metric/__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.
"""Metrics module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_graphics.nn.metric import fscore
from tensorflow_graphics.nn.metric import intersection_over_union
from tensorflow_graphics.nn.metric import precision
from tensorflow_graphics.nn.metric import recall
from tensorflow_graphics.util import export_api as _export_api
# API contains submodules of tensorflow_graphics.nn.metric.
__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.
"""Metrics module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_graphics.nn.metric import fscore
from tensorflow_graphics.nn.metric import intersection_over_union
from tensorflow_graphics.nn.metric import precision
from tensorflow_graphics.nn.metric import recall
from tensorflow_graphics.util import export_api as _export_api
# API contains submodules of tensorflow_graphics.nn.metric.
__all__ = _export_api.get_modules()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/transformation/tests/dual_quaternion_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 dual quaternion."""
from absl.testing import flagsaver
from absl.testing import parameterized
import tensorflow.compat.v2 as tf
from tensorflow_graphics.geometry.transformation import dual_quaternion
from tensorflow_graphics.geometry.transformation.tests import test_helpers
from tensorflow_graphics.util import test_case
class DualQuaternionTest(test_case.TestCase):
@parameterized.parameters(
((8,),),
((None, 8),),
)
def test_conjugate_exception_not_raised(self, *shape):
"""Tests that the shape exceptions of conjugate are not raised."""
self.assert_exception_is_not_raised(dual_quaternion.conjugate, shape)
@parameterized.parameters(
("must have exactly 8 dimensions", (3,)),)
def test_conjugate_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(dual_quaternion.conjugate, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_jacobian_preset(self):
"""Tests the Jacobian of the conjugate function."""
x_init = test_helpers.generate_preset_test_dual_quaternions()
self.assert_jacobian_is_correct_fn(dual_quaternion.conjugate, [x_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_jacobian_random(self):
"""Tests the Jacobian of the conjugate function."""
x_init = test_helpers.generate_random_test_dual_quaternions()
self.assert_jacobian_is_correct_fn(dual_quaternion.conjugate, [x_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_preset(self):
"""Tests if the conjugate function is providing correct results."""
x_init = test_helpers.generate_preset_test_dual_quaternions()
x = tf.convert_to_tensor(value=x_init)
y = tf.convert_to_tensor(value=x_init)
x = dual_quaternion.conjugate(x)
x_real, x_dual = tf.split(x, (4, 4), axis=-1)
y_real, y_dual = tf.split(y, (4, 4), axis=-1)
xyz_y_real, w_y_real = tf.split(y_real, (3, 1), axis=-1)
xyz_y_dual, w_y_dual = tf.split(y_dual, (3, 1), axis=-1)
y_real = tf.concat((-xyz_y_real, w_y_real), axis=-1)
y_dual = tf.concat((-xyz_y_dual, w_y_dual), axis=-1)
self.assertAllEqual(x_real, y_real)
self.assertAllEqual(x_dual, y_dual)
| # 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 dual quaternion."""
from absl.testing import flagsaver
from absl.testing import parameterized
import tensorflow.compat.v2 as tf
from tensorflow_graphics.geometry.transformation import dual_quaternion
from tensorflow_graphics.geometry.transformation.tests import test_helpers
from tensorflow_graphics.util import test_case
class DualQuaternionTest(test_case.TestCase):
@parameterized.parameters(
((8,),),
((None, 8),),
)
def test_conjugate_exception_not_raised(self, *shape):
"""Tests that the shape exceptions of conjugate are not raised."""
self.assert_exception_is_not_raised(dual_quaternion.conjugate, shape)
@parameterized.parameters(
("must have exactly 8 dimensions", (3,)),)
def test_conjugate_exception_raised(self, error_msg, *shape):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(dual_quaternion.conjugate, error_msg, shape)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_jacobian_preset(self):
"""Tests the Jacobian of the conjugate function."""
x_init = test_helpers.generate_preset_test_dual_quaternions()
self.assert_jacobian_is_correct_fn(dual_quaternion.conjugate, [x_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_jacobian_random(self):
"""Tests the Jacobian of the conjugate function."""
x_init = test_helpers.generate_random_test_dual_quaternions()
self.assert_jacobian_is_correct_fn(dual_quaternion.conjugate, [x_init])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_conjugate_preset(self):
"""Tests if the conjugate function is providing correct results."""
x_init = test_helpers.generate_preset_test_dual_quaternions()
x = tf.convert_to_tensor(value=x_init)
y = tf.convert_to_tensor(value=x_init)
x = dual_quaternion.conjugate(x)
x_real, x_dual = tf.split(x, (4, 4), axis=-1)
y_real, y_dual = tf.split(y, (4, 4), axis=-1)
xyz_y_real, w_y_real = tf.split(y_real, (3, 1), axis=-1)
xyz_y_dual, w_y_dual = tf.split(y_dual, (3, 1), axis=-1)
y_real = tf.concat((-xyz_y_real, w_y_real), axis=-1)
y_dual = tf.concat((-xyz_y_dual, w_y_dual), axis=-1)
self.assertAllEqual(x_real, y_real)
self.assertAllEqual(x_dual, y_dual)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/light/tests/point_light_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 point light."""
import math
from absl.testing import flagsaver
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.rendering.light import point_light
from tensorflow_graphics.util import test_case
def fake_brdf(incoming_light_direction, outgoing_light_direction,
surface_point_normal):
del incoming_light_direction, surface_point_normal # Unused.
return outgoing_light_direction
def returning_zeros_brdf(incoming_light_direction, outgoing_light_direction,
surface_point_normal):
del incoming_light_direction, outgoing_light_direction # Unused.
return tf.zeros_like(surface_point_normal)
def random_tensor(tensor_shape):
return np.random.uniform(low=-100.0, high=100.0, size=tensor_shape)
class PointLightTest(test_case.TestCase):
@parameterized.parameters(
# Light direction is parallel to the surface normal.
([1.], [[0., 0., 1.]], [2., 0., 0.], [1.0 / (4. * math.pi), 0., 0.]),
# Light direction is parallel to the surface normal and the reflected
# light fall off is included in the calculation.
([1.], [[0., 0., 1.]], [2., 0., 0.], \
[0.25 / (4. * math.pi), 0., 0.], True),
# Light direction is perpendicular to the surface normal.
([1.], [[3., 0., 0.]], [1., 2., 3.], [0., 0., 0.]),
# Angle between surface normal and the incoming light direction is pi/3.
([1.], [[math.sqrt(3), 0., 1.]], \
[0., 1., 0.], [0., 0.125 / (4. * math.pi), 0.]),
# Angle between surface normal and the incoming light direction is pi/4.
([1.], [[0., 1., 1.]], [1., 1., 0.],
[0.25 / (4. * math.pi), 0.25 / (4. * math.pi), 0.]),
# Light has 3 radiances.
([2., 4., 1.], [[0., 1., 1.]], [1., 1., 0.],
[0.5 / (4. * math.pi), 1. / (4. * math.pi), 0.]),
# Light is behind the surface.
([1.], [[0., 0., -2.]], [7., 0., 0.], [0., 0., 0.]),
# Observation point is behind the surface.
([1.], [[0., 0., 2.]], [5., 0., -2.], [0., 0., 0.]),
# Light and observation point are behind the surface.
([1.], [[0., 0., -2.]], [5., 0., -2.], [0., 0., 0.]),
)
def test_estimate_radiance_preset(self,
light_radiance,
light_pos,
observation_pos,
expected_result,
reflected_light_fall_off=False):
"""Tests the output of estimate radiance function with various parameters.
In this test the point on the surface is always [0, 0, 0] ,the surface
normal is [0, 0, 1] and the fake brdf function returns the (normalized)
direction of the outgoing light as its output.
Args:
light_radiance: An array of size K representing the point light radiances.
light_pos: An array of size [3,] representing the point light positions.
observation_pos: An array of size [3,] representing the observation point.
expected_result: An array of size [3,] representing the expected result of
the estimated reflected radiance function.
reflected_light_fall_off: A boolean specifying whether or not to include
the fall off of the reflected light in the calculation. Defaults to
False.
"""
tensor_size = np.random.randint(1, 3) + 1
tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist()
lights_tensor_size = np.random.randint(1, 3) + 1
lights_tensor_shape = np.random.randint(
1, 10, size=lights_tensor_size).tolist()
point_light_radiance = np.tile(light_radiance, lights_tensor_shape + [1])
point_light_position = np.tile(light_pos, lights_tensor_shape + [1])
surface_point_normal = np.tile([0.0, 0.0, 1.0], tensor_shape + [1])
surface_point_position = np.tile([0.0, 0.0, 0.0], tensor_shape + [1])
observation_point = np.tile(observation_pos, tensor_shape + [1])
expected = np.tile(expected_result,
tensor_shape + lights_tensor_shape + [1])
pred = point_light.estimate_radiance(
point_light_radiance,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point,
fake_brdf,
reflected_light_fall_off=reflected_light_fall_off)
self.assertAllClose(expected, pred)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_estimate_radiance_jacobian_random(self):
"""Tests the Jacobian of the point lighting equation."""
tensor_size = np.random.randint(1, 3)
tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist()
light_tensor_size = np.random.randint(1, 3)
lights_tensor_shape = np.random.randint(
1, 10, size=light_tensor_size).tolist()
point_light_radiance_init = random_tensor(lights_tensor_shape + [1])
point_light_position_init = random_tensor(lights_tensor_shape + [3])
surface_point_position_init = random_tensor(tensor_shape + [3])
surface_point_normal_init = random_tensor(tensor_shape + [3])
observation_point_init = random_tensor(tensor_shape + [3])
def estimate_radiance_fn(point_light_position, surface_point_position,
surface_point_normal, observation_point):
return point_light.estimate_radiance(point_light_radiance_init,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point, fake_brdf)
self.assert_jacobian_is_correct_fn(estimate_radiance_fn, [
point_light_position_init, surface_point_position_init,
surface_point_normal_init, observation_point_init
])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_estimate_radiance_jacobian_preset(self):
"""Tests the Jacobian of the point lighting equation.
Verifies that the Jacobian of the point lighting equation is correct when
the light direction is orthogonal to the surface normal.
"""
delta = 1e-5
point_light_radiance_init = np.array(1.0).reshape((1, 1))
point_light_position_init = np.array((delta, 1.0, 0.0)).reshape((1, 3))
surface_point_position_init = np.array((0.0, 0.0, 0.0))
surface_point_normal_init = np.array((1.0, 0.0, 0.0))
observation_point_init = np.array((delta, 3.0, 0.0))
def estimate_radiance_fn(point_light_position, surface_point_position,
surface_point_normal, observation_point):
return point_light.estimate_radiance(point_light_radiance_init,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point, fake_brdf)
self.assert_jacobian_is_correct_fn(estimate_radiance_fn, [
point_light_position_init, surface_point_position_init,
surface_point_normal_init, observation_point_init
])
@parameterized.parameters(
((1, 1), (1, 3), (3,), (3,), (3,)),
((4, 1, 1), (4, 1, 3), (1, 3), (1, 3), (1, 3)),
((3, 2, 1), (3, 2, 3), (2, 3), (2, 3), (2, 3)),
((1, 1), (3,), (1, 3), (1, 2, 3), (1, 3)),
((4, 5, 1), (3, 4, 5, 3), (1, 3), (1, 2, 2, 3), (1, 2, 3)),
((1,), (1, 2, 2, 3), (1, 2, 3), (1, 3), (3,)),
)
def test_estimate_radiance_shape_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(
point_light.estimate_radiance, shape, brdf=returning_zeros_brdf)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 1), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (5, 1), (5, 2), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 4), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (1,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (2,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (4,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (1,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (2,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (4,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(4,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(2,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3, 1),
(1, 3, 3), (2, 3), (4, 3), (3,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3, 1),
(1, 4, 3), (2, 3), (3,), (3,)),
)
def test_estimate_radiance_shape_exception_raised(self, error_msg, *shape):
"""Tests that the shape exception is raised."""
self.assert_exception_is_raised(
point_light.estimate_radiance,
error_msg,
shape,
brdf=returning_zeros_brdf)
def test_estimate_radiance_value_exceptions_raised(self):
"""Tests that the value exceptions are raised correctly."""
point_light_radiance = random_tensor(tensor_shape=(1, 1))
point_light_position = random_tensor(tensor_shape=(1, 3))
surface_point_position = random_tensor(tensor_shape=(3,))
surface_point_normal = random_tensor(tensor_shape=(3,))
observation_point = random_tensor(tensor_shape=(3,))
# Verify that an InvalidArgumentError is raised as the given
# surface_point_normal is not normalized.
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(
point_light.estimate_radiance(point_light_radiance,
point_light_position,
surface_point_position,
surface_point_normal, observation_point,
returning_zeros_brdf))
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 point light."""
import math
from absl.testing import flagsaver
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.rendering.light import point_light
from tensorflow_graphics.util import test_case
def fake_brdf(incoming_light_direction, outgoing_light_direction,
surface_point_normal):
del incoming_light_direction, surface_point_normal # Unused.
return outgoing_light_direction
def returning_zeros_brdf(incoming_light_direction, outgoing_light_direction,
surface_point_normal):
del incoming_light_direction, outgoing_light_direction # Unused.
return tf.zeros_like(surface_point_normal)
def random_tensor(tensor_shape):
return np.random.uniform(low=-100.0, high=100.0, size=tensor_shape)
class PointLightTest(test_case.TestCase):
@parameterized.parameters(
# Light direction is parallel to the surface normal.
([1.], [[0., 0., 1.]], [2., 0., 0.], [1.0 / (4. * math.pi), 0., 0.]),
# Light direction is parallel to the surface normal and the reflected
# light fall off is included in the calculation.
([1.], [[0., 0., 1.]], [2., 0., 0.], \
[0.25 / (4. * math.pi), 0., 0.], True),
# Light direction is perpendicular to the surface normal.
([1.], [[3., 0., 0.]], [1., 2., 3.], [0., 0., 0.]),
# Angle between surface normal and the incoming light direction is pi/3.
([1.], [[math.sqrt(3), 0., 1.]], \
[0., 1., 0.], [0., 0.125 / (4. * math.pi), 0.]),
# Angle between surface normal and the incoming light direction is pi/4.
([1.], [[0., 1., 1.]], [1., 1., 0.],
[0.25 / (4. * math.pi), 0.25 / (4. * math.pi), 0.]),
# Light has 3 radiances.
([2., 4., 1.], [[0., 1., 1.]], [1., 1., 0.],
[0.5 / (4. * math.pi), 1. / (4. * math.pi), 0.]),
# Light is behind the surface.
([1.], [[0., 0., -2.]], [7., 0., 0.], [0., 0., 0.]),
# Observation point is behind the surface.
([1.], [[0., 0., 2.]], [5., 0., -2.], [0., 0., 0.]),
# Light and observation point are behind the surface.
([1.], [[0., 0., -2.]], [5., 0., -2.], [0., 0., 0.]),
)
def test_estimate_radiance_preset(self,
light_radiance,
light_pos,
observation_pos,
expected_result,
reflected_light_fall_off=False):
"""Tests the output of estimate radiance function with various parameters.
In this test the point on the surface is always [0, 0, 0] ,the surface
normal is [0, 0, 1] and the fake brdf function returns the (normalized)
direction of the outgoing light as its output.
Args:
light_radiance: An array of size K representing the point light radiances.
light_pos: An array of size [3,] representing the point light positions.
observation_pos: An array of size [3,] representing the observation point.
expected_result: An array of size [3,] representing the expected result of
the estimated reflected radiance function.
reflected_light_fall_off: A boolean specifying whether or not to include
the fall off of the reflected light in the calculation. Defaults to
False.
"""
tensor_size = np.random.randint(1, 3) + 1
tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist()
lights_tensor_size = np.random.randint(1, 3) + 1
lights_tensor_shape = np.random.randint(
1, 10, size=lights_tensor_size).tolist()
point_light_radiance = np.tile(light_radiance, lights_tensor_shape + [1])
point_light_position = np.tile(light_pos, lights_tensor_shape + [1])
surface_point_normal = np.tile([0.0, 0.0, 1.0], tensor_shape + [1])
surface_point_position = np.tile([0.0, 0.0, 0.0], tensor_shape + [1])
observation_point = np.tile(observation_pos, tensor_shape + [1])
expected = np.tile(expected_result,
tensor_shape + lights_tensor_shape + [1])
pred = point_light.estimate_radiance(
point_light_radiance,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point,
fake_brdf,
reflected_light_fall_off=reflected_light_fall_off)
self.assertAllClose(expected, pred)
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_estimate_radiance_jacobian_random(self):
"""Tests the Jacobian of the point lighting equation."""
tensor_size = np.random.randint(1, 3)
tensor_shape = np.random.randint(1, 10, size=tensor_size).tolist()
light_tensor_size = np.random.randint(1, 3)
lights_tensor_shape = np.random.randint(
1, 10, size=light_tensor_size).tolist()
point_light_radiance_init = random_tensor(lights_tensor_shape + [1])
point_light_position_init = random_tensor(lights_tensor_shape + [3])
surface_point_position_init = random_tensor(tensor_shape + [3])
surface_point_normal_init = random_tensor(tensor_shape + [3])
observation_point_init = random_tensor(tensor_shape + [3])
def estimate_radiance_fn(point_light_position, surface_point_position,
surface_point_normal, observation_point):
return point_light.estimate_radiance(point_light_radiance_init,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point, fake_brdf)
self.assert_jacobian_is_correct_fn(estimate_radiance_fn, [
point_light_position_init, surface_point_position_init,
surface_point_normal_init, observation_point_init
])
@flagsaver.flagsaver(tfg_add_asserts_to_graph=False)
def test_estimate_radiance_jacobian_preset(self):
"""Tests the Jacobian of the point lighting equation.
Verifies that the Jacobian of the point lighting equation is correct when
the light direction is orthogonal to the surface normal.
"""
delta = 1e-5
point_light_radiance_init = np.array(1.0).reshape((1, 1))
point_light_position_init = np.array((delta, 1.0, 0.0)).reshape((1, 3))
surface_point_position_init = np.array((0.0, 0.0, 0.0))
surface_point_normal_init = np.array((1.0, 0.0, 0.0))
observation_point_init = np.array((delta, 3.0, 0.0))
def estimate_radiance_fn(point_light_position, surface_point_position,
surface_point_normal, observation_point):
return point_light.estimate_radiance(point_light_radiance_init,
point_light_position,
surface_point_position,
surface_point_normal,
observation_point, fake_brdf)
self.assert_jacobian_is_correct_fn(estimate_radiance_fn, [
point_light_position_init, surface_point_position_init,
surface_point_normal_init, observation_point_init
])
@parameterized.parameters(
((1, 1), (1, 3), (3,), (3,), (3,)),
((4, 1, 1), (4, 1, 3), (1, 3), (1, 3), (1, 3)),
((3, 2, 1), (3, 2, 3), (2, 3), (2, 3), (2, 3)),
((1, 1), (3,), (1, 3), (1, 2, 3), (1, 3)),
((4, 5, 1), (3, 4, 5, 3), (1, 3), (1, 2, 2, 3), (1, 2, 3)),
((1,), (1, 2, 2, 3), (1, 2, 3), (1, 3), (3,)),
)
def test_estimate_radiance_shape_exception_not_raised(self, *shape):
"""Tests that the shape exceptions are not raised."""
self.assert_exception_is_not_raised(
point_light.estimate_radiance, shape, brdf=returning_zeros_brdf)
@parameterized.parameters(
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 1), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (5, 1), (5, 2), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 4), (3,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (1,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (2,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (4,), (3,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (1,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (2,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (4,),
(3,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(4,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(2,)),
("must have exactly 3 dimensions in axis -1", (1, 1), (1, 3), (3,), (3,),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3, 1),
(1, 3, 3), (2, 3), (4, 3), (3,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3, 1),
(1, 4, 3), (2, 3), (3,), (3,)),
)
def test_estimate_radiance_shape_exception_raised(self, error_msg, *shape):
"""Tests that the shape exception is raised."""
self.assert_exception_is_raised(
point_light.estimate_radiance,
error_msg,
shape,
brdf=returning_zeros_brdf)
def test_estimate_radiance_value_exceptions_raised(self):
"""Tests that the value exceptions are raised correctly."""
point_light_radiance = random_tensor(tensor_shape=(1, 1))
point_light_position = random_tensor(tensor_shape=(1, 3))
surface_point_position = random_tensor(tensor_shape=(3,))
surface_point_normal = random_tensor(tensor_shape=(3,))
observation_point = random_tensor(tensor_shape=(3,))
# Verify that an InvalidArgumentError is raised as the given
# surface_point_normal is not normalized.
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(
point_light.estimate_radiance(point_light_radiance,
point_light_position,
surface_point_position,
surface_point_normal, observation_point,
returning_zeros_brdf))
if __name__ == "__main__":
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/datasets/pix3d/pix3d.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
"""pix3d dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import numpy as np
import tensorflow as tf
from tensorflow_datasets import features as tfds_features
import tensorflow_datasets.public_api as tfds
from tensorflow_graphics.datasets import features as tfg_features
_CITATION = '''
@inproceedings{pix3d,
title={Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling},
author={Sun, Xingyuan and Wu, Jiajun and Zhang, Xiuming and Zhang, Zhoutong
and Zhang, Chengkai and Xue, Tianfan and Tenenbaum, Joshua B and
Freeman, William T},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
'''
_DESCRIPTION = '''
Pix3D is a large-scale dataset of diverse image-shape pairs
with pixel-level 2D-3D alignment. It has wide applications in shape-related
tasks including reconstruction, retrieval, viewpoint estimation, etc.
Pix3D contains 10,069 2D-3D pairs of 395 distinct 3D shapes, categorised into
nine object categories. Each sample comprises of an image, 3D shape represented
as (non-watertight) triangle mesh and voxel grid, bounding-box,
segmentation mask, intrinsic and extrinsic camera parameters and 2D and 3D key
points.
Notes:
* The object and camera poses are provided with respect to the scene, whereas
the camera is placed at the origin. Pix3D also provides the features
`camera/position_with_respect_to_object` and `camera/inplane_rotation`.
Those values are defined in object coordinates and will reproduce an image
that is equivalent to the original image under a homography transformation.
They are defined for viewer-centered algorithms whose predictions need to be
rotated back to the canonical view for evaluations against ground truth
shapes. This is necessary as most algorithms assume that the camera is
looking at the object's center, the raw input images are usually cropped or
transformed before sending into their pipeline.
* There are two wrong segmentation masks in the annotations of the original
Pix3D dataset (See https://github.com/xingyuansun/pix3d/issues/18 for
details). We ignore those samples in this version of the dataset. However,
if you want to use them, we provide own rendered segmentation masks in
`tensorflow_graphics/datasets/pix3d/fixed_masks/`. Feel free to copy those
two masks to your local Pix3D directory in `<PIX3D_HOME>/mask/table/`.
Additionally, you need to add the indices of these samples to the split
files located at
`<TF Graphics Repository>/tensorflow_graphics/datasets/pix3d/splits`.
The index `7953` needs to be appended to the train index and `9657` belongs
to the test index.
Train/Test split:
Pix3D does not provide a standard train/test split. Therefore, this
implementation adopts the S2 split from Mesh R-CNN
(https://arxiv.org/abs/1906.02739, Sec. 4.2). This split ensures that the 3D
models appearing in the train and test sets are disjoint.
'''
class Pix3d(tfds.core.GeneratorBasedBuilder):
"""Pix3D is a large-scale dataset of diverse image-shape pairs with pixel-level 2D-3D alignment."""
VERSION = tfds.core.Version('0.1.0')
TRAIN_SPLIT_IDX = os.path.join(os.path.dirname(__file__),
'splits/pix3d_train.npy')
TEST_SPLIT_IDX = os.path.join(os.path.dirname(__file__),
'splits/pix3d_test.npy')
CLASS_INDEX = ['background', 'bed', 'bookcase', 'chair', 'desk', 'misc',
'sofa', 'table', 'tool', 'wardrobe']
def _info(self):
return tfds.core.DatasetInfo(
builder=self,
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# tfds.features.FeatureConnectors
features=tfds.features.FeaturesDict({
'image': tfds_features.Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename': tfds_features.Text(),
'image/source': tfds_features.Text(),
'2d_keypoints': tfds_features.FeaturesDict({
'num_annotators': tf.uint8,
'num_keypoints': tf.uint8,
'keypoints': tfds_features.Tensor(shape=(None,),
dtype=tf.float32),
}),
'mask': tfds_features.Image(shape=(None, None, 1), dtype=tf.uint8),
'model': tfg_features.TriangleMesh(),
'model/source': tfds_features.Text(),
'3d_keypoints': tfds_features.Tensor(shape=(None, 3),
dtype=tf.float32),
'voxel': tfg_features.VoxelGrid(shape=(128, 128, 128)),
'pose': tfg_features.Pose(), # pose of object w.r.t to world.
'camera': tfds_features.FeaturesDict({
'parameters': tfg_features.Camera(),
'position_with_respect_to_object': tfds_features.Tensor(
shape=(3,), dtype=tf.float32
),
'inplane_rotation': tf.float32,
}),
'category': tfds_features.ClassLabel(
num_classes=len(self.CLASS_INDEX)
),
'bbox': tfds_features.BBoxFeature(),
'truncated': tf.bool,
'occluded': tf.bool,
'slightly_occluded': tf.bool,
}),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='http://pix3d.csail.mit.edu/',
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
pix3d_dir = dl_manager.download_and_extract(
'http://pix3d.csail.mit.edu/data/pix3d.zip')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
'samples_directory': pix3d_dir,
'split_file': self.TRAIN_SPLIT_IDX
},
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
gen_kwargs={
'samples_directory': pix3d_dir,
'split_file': self.TEST_SPLIT_IDX
},
),
]
def _generate_examples(self, samples_directory, split_file):
"""Yields examples.
As Pix3D does not come with a predefined train/test split, we adopt one from
Mesh R-CNN. The split ensures
that the 3D models appearing in the train and test sets are disjoint.
Args:
samples_directory: `str`, path to the directory where Pix3D is stored.
split_file: `str`, path to .npy file containing the indices of the current
split.
"""
with tf.io.gfile.GFile(os.path.join(samples_directory, 'pix3d.json'),
mode='r') as pix3d_index:
pix3d = json.load(pix3d_index)
split_samples = map(pix3d.__getitem__, np.load(split_file))
def _build_bbox(box, img_size):
"""Create a BBox with correct order of coordinates.
Args:
box: Bounding box of the object as provided by Pix3d
img_size: size of the image, in the format of [width, height]
Returns:
tfds.features.BBox.
"""
xmin, ymin, xmax, ymax = box
width, height = img_size
return tfds_features.BBox(ymin=ymin / height, xmin=xmin / width,
ymax=ymax / height, xmax=xmax / width)
def _build_camera(f, img_size):
"""Prepare features for `Camera` FeatureConnector.
The pose originates from the official Pix3D GitHub repository and
describes the cameras position with respect to the scene.
The focal length is originally provided in mm, but will be converted to
pixel here using the fixed sensor with of 32 mm, which also originates
from the Pix3D GitHub repository.
Link to the official Pix3D repository:
https://github.com/xingyuansun/pix3d.
Args:
f: float, denoting the focal length in mm.
img_size: tuple of two floats, denoting the image height and width.
Returns:
Dictionary with all Camera Parameters.
"""
sensor_width = 32.
return {
'pose': {
'R': np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]],
dtype=np.float32),
't': np.zeros(3, dtype=np.float32)
},
'optical_center': (img_size[0] / 2, img_size[1] / 2),
'f': (f / sensor_width * img_size[0])
}
def _build_2d_keypoints(keypoints):
"""Wraps keypoint feature in dict, because TFDS does not allow more than sone unknown dimensions.
Args:
keypoints: Array of dimension `[N, M, 2]`, where N denotes the number of
annotators and M is the number of 2D keypoints. Keypoints are stored
as (origin: top left corner; +x: rightward; +y: downward);
[-1.0, -1.0] if an annotator marked this keypoint hard to label.
Returns:
Dictionary containing shape and flattened keypoints.
"""
if keypoints.ndim != 3 or keypoints.shape[-1] != 2:
raise ValueError('2D keypoints should be in shape (N, M, 2).')
return {
'num_annotators': keypoints.shape[0],
'num_keypoints': keypoints.shape[1],
'keypoints': keypoints.ravel()
}
for sample in split_samples:
example = {
'image': os.path.join(samples_directory, sample['img']),
'image/filename': sample['img'],
'image/source': sample['img_source'],
'2d_keypoints': _build_2d_keypoints(
np.asarray(sample['2d_keypoints'],
dtype=np.float32)),
'mask': os.path.join(samples_directory, sample['mask']),
'model': os.path.join(samples_directory, sample['model']),
'model/source': sample['model_source'],
'3d_keypoints': np.loadtxt(
os.path.join(samples_directory, sample['3d_keypoints']),
dtype=np.float32),
'voxel': {
'path': os.path.join(samples_directory, sample['voxel']),
'key': 'voxel'
},
'pose': {
'R': sample['rot_mat'],
't': sample['trans_mat']
},
'camera': {
'parameters': _build_camera(
sample['focal_length'],
sample['img_size'],
),
'position_with_respect_to_object': sample['cam_position'],
'inplane_rotation': sample['inplane_rotation'],
},
'category': self.CLASS_INDEX.index(sample['category']),
'bbox': _build_bbox(sample['bbox'], sample['img_size']),
'truncated': sample['truncated'],
'occluded': sample['occluded'],
'slightly_occluded': sample['slightly_occluded']
}
yield sample['img'], example
| # 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
"""pix3d dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import numpy as np
import tensorflow as tf
from tensorflow_datasets import features as tfds_features
import tensorflow_datasets.public_api as tfds
from tensorflow_graphics.datasets import features as tfg_features
_CITATION = '''
@inproceedings{pix3d,
title={Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling},
author={Sun, Xingyuan and Wu, Jiajun and Zhang, Xiuming and Zhang, Zhoutong
and Zhang, Chengkai and Xue, Tianfan and Tenenbaum, Joshua B and
Freeman, William T},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
'''
_DESCRIPTION = '''
Pix3D is a large-scale dataset of diverse image-shape pairs
with pixel-level 2D-3D alignment. It has wide applications in shape-related
tasks including reconstruction, retrieval, viewpoint estimation, etc.
Pix3D contains 10,069 2D-3D pairs of 395 distinct 3D shapes, categorised into
nine object categories. Each sample comprises of an image, 3D shape represented
as (non-watertight) triangle mesh and voxel grid, bounding-box,
segmentation mask, intrinsic and extrinsic camera parameters and 2D and 3D key
points.
Notes:
* The object and camera poses are provided with respect to the scene, whereas
the camera is placed at the origin. Pix3D also provides the features
`camera/position_with_respect_to_object` and `camera/inplane_rotation`.
Those values are defined in object coordinates and will reproduce an image
that is equivalent to the original image under a homography transformation.
They are defined for viewer-centered algorithms whose predictions need to be
rotated back to the canonical view for evaluations against ground truth
shapes. This is necessary as most algorithms assume that the camera is
looking at the object's center, the raw input images are usually cropped or
transformed before sending into their pipeline.
* There are two wrong segmentation masks in the annotations of the original
Pix3D dataset (See https://github.com/xingyuansun/pix3d/issues/18 for
details). We ignore those samples in this version of the dataset. However,
if you want to use them, we provide own rendered segmentation masks in
`tensorflow_graphics/datasets/pix3d/fixed_masks/`. Feel free to copy those
two masks to your local Pix3D directory in `<PIX3D_HOME>/mask/table/`.
Additionally, you need to add the indices of these samples to the split
files located at
`<TF Graphics Repository>/tensorflow_graphics/datasets/pix3d/splits`.
The index `7953` needs to be appended to the train index and `9657` belongs
to the test index.
Train/Test split:
Pix3D does not provide a standard train/test split. Therefore, this
implementation adopts the S2 split from Mesh R-CNN
(https://arxiv.org/abs/1906.02739, Sec. 4.2). This split ensures that the 3D
models appearing in the train and test sets are disjoint.
'''
class Pix3d(tfds.core.GeneratorBasedBuilder):
"""Pix3D is a large-scale dataset of diverse image-shape pairs with pixel-level 2D-3D alignment."""
VERSION = tfds.core.Version('0.1.0')
TRAIN_SPLIT_IDX = os.path.join(os.path.dirname(__file__),
'splits/pix3d_train.npy')
TEST_SPLIT_IDX = os.path.join(os.path.dirname(__file__),
'splits/pix3d_test.npy')
CLASS_INDEX = ['background', 'bed', 'bookcase', 'chair', 'desk', 'misc',
'sofa', 'table', 'tool', 'wardrobe']
def _info(self):
return tfds.core.DatasetInfo(
builder=self,
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# tfds.features.FeatureConnectors
features=tfds.features.FeaturesDict({
'image': tfds_features.Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename': tfds_features.Text(),
'image/source': tfds_features.Text(),
'2d_keypoints': tfds_features.FeaturesDict({
'num_annotators': tf.uint8,
'num_keypoints': tf.uint8,
'keypoints': tfds_features.Tensor(shape=(None,),
dtype=tf.float32),
}),
'mask': tfds_features.Image(shape=(None, None, 1), dtype=tf.uint8),
'model': tfg_features.TriangleMesh(),
'model/source': tfds_features.Text(),
'3d_keypoints': tfds_features.Tensor(shape=(None, 3),
dtype=tf.float32),
'voxel': tfg_features.VoxelGrid(shape=(128, 128, 128)),
'pose': tfg_features.Pose(), # pose of object w.r.t to world.
'camera': tfds_features.FeaturesDict({
'parameters': tfg_features.Camera(),
'position_with_respect_to_object': tfds_features.Tensor(
shape=(3,), dtype=tf.float32
),
'inplane_rotation': tf.float32,
}),
'category': tfds_features.ClassLabel(
num_classes=len(self.CLASS_INDEX)
),
'bbox': tfds_features.BBoxFeature(),
'truncated': tf.bool,
'occluded': tf.bool,
'slightly_occluded': tf.bool,
}),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='http://pix3d.csail.mit.edu/',
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
pix3d_dir = dl_manager.download_and_extract(
'http://pix3d.csail.mit.edu/data/pix3d.zip')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
'samples_directory': pix3d_dir,
'split_file': self.TRAIN_SPLIT_IDX
},
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
gen_kwargs={
'samples_directory': pix3d_dir,
'split_file': self.TEST_SPLIT_IDX
},
),
]
def _generate_examples(self, samples_directory, split_file):
"""Yields examples.
As Pix3D does not come with a predefined train/test split, we adopt one from
Mesh R-CNN. The split ensures
that the 3D models appearing in the train and test sets are disjoint.
Args:
samples_directory: `str`, path to the directory where Pix3D is stored.
split_file: `str`, path to .npy file containing the indices of the current
split.
"""
with tf.io.gfile.GFile(os.path.join(samples_directory, 'pix3d.json'),
mode='r') as pix3d_index:
pix3d = json.load(pix3d_index)
split_samples = map(pix3d.__getitem__, np.load(split_file))
def _build_bbox(box, img_size):
"""Create a BBox with correct order of coordinates.
Args:
box: Bounding box of the object as provided by Pix3d
img_size: size of the image, in the format of [width, height]
Returns:
tfds.features.BBox.
"""
xmin, ymin, xmax, ymax = box
width, height = img_size
return tfds_features.BBox(ymin=ymin / height, xmin=xmin / width,
ymax=ymax / height, xmax=xmax / width)
def _build_camera(f, img_size):
"""Prepare features for `Camera` FeatureConnector.
The pose originates from the official Pix3D GitHub repository and
describes the cameras position with respect to the scene.
The focal length is originally provided in mm, but will be converted to
pixel here using the fixed sensor with of 32 mm, which also originates
from the Pix3D GitHub repository.
Link to the official Pix3D repository:
https://github.com/xingyuansun/pix3d.
Args:
f: float, denoting the focal length in mm.
img_size: tuple of two floats, denoting the image height and width.
Returns:
Dictionary with all Camera Parameters.
"""
sensor_width = 32.
return {
'pose': {
'R': np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]],
dtype=np.float32),
't': np.zeros(3, dtype=np.float32)
},
'optical_center': (img_size[0] / 2, img_size[1] / 2),
'f': (f / sensor_width * img_size[0])
}
def _build_2d_keypoints(keypoints):
"""Wraps keypoint feature in dict, because TFDS does not allow more than sone unknown dimensions.
Args:
keypoints: Array of dimension `[N, M, 2]`, where N denotes the number of
annotators and M is the number of 2D keypoints. Keypoints are stored
as (origin: top left corner; +x: rightward; +y: downward);
[-1.0, -1.0] if an annotator marked this keypoint hard to label.
Returns:
Dictionary containing shape and flattened keypoints.
"""
if keypoints.ndim != 3 or keypoints.shape[-1] != 2:
raise ValueError('2D keypoints should be in shape (N, M, 2).')
return {
'num_annotators': keypoints.shape[0],
'num_keypoints': keypoints.shape[1],
'keypoints': keypoints.ravel()
}
for sample in split_samples:
example = {
'image': os.path.join(samples_directory, sample['img']),
'image/filename': sample['img'],
'image/source': sample['img_source'],
'2d_keypoints': _build_2d_keypoints(
np.asarray(sample['2d_keypoints'],
dtype=np.float32)),
'mask': os.path.join(samples_directory, sample['mask']),
'model': os.path.join(samples_directory, sample['model']),
'model/source': sample['model_source'],
'3d_keypoints': np.loadtxt(
os.path.join(samples_directory, sample['3d_keypoints']),
dtype=np.float32),
'voxel': {
'path': os.path.join(samples_directory, sample['voxel']),
'key': 'voxel'
},
'pose': {
'R': sample['rot_mat'],
't': sample['trans_mat']
},
'camera': {
'parameters': _build_camera(
sample['focal_length'],
sample['img_size'],
),
'position_with_respect_to_object': sample['cam_position'],
'inplane_rotation': sample['inplane_rotation'],
},
'category': self.CLASS_INDEX.index(sample['category']),
'bbox': _build_bbox(sample['bbox'], sample['img_size']),
'truncated': sample['truncated'],
'occluded': sample['occluded'],
'slightly_occluded': sample['slightly_occluded']
}
yield sample['img'], example
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/opengl/BUILD | # OpenGL functionalities for tf-graphics.
licenses(["notice"]) # Apache 2.0
package(default_visibility = ["//visibility:public"])
genrule(
name = "rasterizer_op",
srcs = [
"cleanup.h",
"egl_offscreen_context.cc",
"egl_offscreen_context.h",
"egl_util.cc",
"egl_util.h",
"gl_program.cc",
"gl_program.h",
"gl_render_targets.cc",
"gl_render_targets.h",
"gl_shader_storage_buffer.cc",
"gl_shader_storage_buffer.h",
"macros.h",
"rasterizer.cc",
"rasterizer.h",
"rasterizer_op.cc",
"rasterizer_with_context.cc",
"rasterizer_with_context.h",
"thread_safe_resource_pool.h",
],
outs = ["rasterizer_op.so"],
cmd = "TF_CFLAGS=$$(python -c 'import tensorflow as tf; print(\" \".join(tf.sysconfig.get_compile_flags()))');\
TF_LFLAGS=$$(python -c 'import tensorflow as tf; print(\" \".join(tf.sysconfig.get_link_flags()))');\
g++ -std=c++11 -shared $(SRCS) -o $(OUTS) -fPIC $${TF_CFLAGS[@]} $${TF_LFLAGS[@]}\
-DUSE_OZONE -Wl,-L/usr/lib/x86_64-linux-gnu/mesa-egl -Wl,-L/usr/lib/x86_64-linux-gnu -Wl,-lEGL -Wl,-lGLESv2 -O2;\
VAR_OUTS=$(OUTS);\
VAR_GENDIR=$(GENDIR);\
cp $(OUTS) $(BASEDIR)/$${VAR_OUTS#$$VAR_GENDIR}",
)
| # OpenGL functionalities for tf-graphics.
licenses(["notice"]) # Apache 2.0
package(default_visibility = ["//visibility:public"])
genrule(
name = "rasterizer_op",
srcs = [
"cleanup.h",
"egl_offscreen_context.cc",
"egl_offscreen_context.h",
"egl_util.cc",
"egl_util.h",
"gl_program.cc",
"gl_program.h",
"gl_render_targets.cc",
"gl_render_targets.h",
"gl_shader_storage_buffer.cc",
"gl_shader_storage_buffer.h",
"macros.h",
"rasterizer.cc",
"rasterizer.h",
"rasterizer_op.cc",
"rasterizer_with_context.cc",
"rasterizer_with_context.h",
"thread_safe_resource_pool.h",
],
outs = ["rasterizer_op.so"],
cmd = "TF_CFLAGS=$$(python -c 'import tensorflow as tf; print(\" \".join(tf.sysconfig.get_compile_flags()))');\
TF_LFLAGS=$$(python -c 'import tensorflow as tf; print(\" \".join(tf.sysconfig.get_link_flags()))');\
g++ -std=c++11 -shared $(SRCS) -o $(OUTS) -fPIC $${TF_CFLAGS[@]} $${TF_LFLAGS[@]}\
-DUSE_OZONE -Wl,-L/usr/lib/x86_64-linux-gnu/mesa-egl -Wl,-L/usr/lib/x86_64-linux-gnu -Wl,-lEGL -Wl,-lGLESv2 -O2;\
VAR_OUTS=$(OUTS);\
VAR_GENDIR=$(GENDIR);\
cp $(OUTS) $(BASEDIR)/$${VAR_OUTS#$$VAR_GENDIR}",
)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/representation/triangle.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 triangle utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 normal(v0, v1, v2, clockwise=False, normalize=True, name=None):
"""Computes face normals (triangles).
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
clockwise: Winding order to determine front-facing triangles.
normalize: A `bool` indicating whether output normals should be normalized
by the function.
name: A name for this op. Defaults to "triangle_normal".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
a normalized vector.
Raises:
ValueError: If the shape of `v0`, `v1`, or `v2` is not supported.
"""
with tf.compat.v1.name_scope(name, "triangle_normal", [v0, v1, v2]):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normal_vector = vector.cross(v1 - v0, v2 - v0, axis=-1)
normal_vector = asserts.assert_nonzero_norm(normal_vector)
if not clockwise:
normal_vector *= -1.0
if normalize:
return tf.nn.l2_normalize(normal_vector, axis=-1)
return normal_vector
def area(v0, v1, v2, name=None):
"""Computes triangle areas.
Note: Computed triangle area = 0.5 * | e1 x e2 | where e1 and e2 are edges
of triangle. A degenerate triangle will return 0 area, whereas the normal
for a degenerate triangle is not defined.
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
name: A name for this op. Defaults to "triangle_area".
Returns:
A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents
a normalized vector.
"""
with tf.compat.v1.name_scope(name, "triangle_area", [v0, v1, v2]):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normals = vector.cross(v1 - v0, v2 - v0, axis=-1)
return 0.5 * tf.linalg.norm(tensor=normals, axis=-1, keepdims=True)
# 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 triangle utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 normal(v0, v1, v2, clockwise=False, normalize=True, name=None):
"""Computes face normals (triangles).
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
clockwise: Winding order to determine front-facing triangles.
normalize: A `bool` indicating whether output normals should be normalized
by the function.
name: A name for this op. Defaults to "triangle_normal".
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
a normalized vector.
Raises:
ValueError: If the shape of `v0`, `v1`, or `v2` is not supported.
"""
with tf.compat.v1.name_scope(name, "triangle_normal", [v0, v1, v2]):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normal_vector = vector.cross(v1 - v0, v2 - v0, axis=-1)
normal_vector = asserts.assert_nonzero_norm(normal_vector)
if not clockwise:
normal_vector *= -1.0
if normalize:
return tf.nn.l2_normalize(normal_vector, axis=-1)
return normal_vector
def area(v0, v1, v2, name=None):
"""Computes triangle areas.
Note: Computed triangle area = 0.5 * | e1 x e2 | where e1 and e2 are edges
of triangle. A degenerate triangle will return 0 area, whereas the normal
for a degenerate triangle is not defined.
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
v0: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the first vertex of a triangle.
v1: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the second vertex of a triangle.
v2: A tensor of shape `[A1, ..., An, 3]`, where the last dimension
represents the third vertex of a triangle.
name: A name for this op. Defaults to "triangle_area".
Returns:
A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents
a normalized vector.
"""
with tf.compat.v1.name_scope(name, "triangle_area", [v0, v1, v2]):
v0 = tf.convert_to_tensor(value=v0)
v1 = tf.convert_to_tensor(value=v1)
v2 = tf.convert_to_tensor(value=v2)
shape.check_static(tensor=v0, tensor_name="v0", has_dim_equals=(-1, 3))
shape.check_static(tensor=v1, tensor_name="v1", has_dim_equals=(-1, 3))
shape.check_static(tensor=v2, tensor_name="v2", has_dim_equals=(-1, 3))
shape.compare_batch_dimensions(
tensors=(v0, v1, v2), last_axes=-2, broadcast_compatible=True)
normals = vector.cross(v1 - v0, v2 - v0, axis=-1)
return 0.5 * tf.linalg.norm(tensor=normals, axis=-1, keepdims=True)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/math/math_helpers.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 contains math routines that are shared by across different modules."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
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 cartesian_to_spherical_coordinates(point_cartesian, eps=None, name=None):
"""Function to transform Cartesian coordinates to spherical coordinates.
This function assumes a right handed coordinate system with `z` pointing up.
When `x` and `y` are both `0`, the function outputs `0` for `phi`. Note that
the function is not smooth when `x = y = 0`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_cartesian: A tensor of shape `[A1, ..., An, 3]`. In the last
dimension, the data follows the `x`, `y`, `z` order.
eps: A small `float`, to be added to the denominator. If left as `None`,
its value is automatically selected using `point_cartesian.dtype`.
name: A name for this op. Defaults to `cartesian_to_spherical_coordinates`.
Returns:
A tensor of shape `[A1, ..., An, 3]`. The last dimensions contains
(`r`,`theta`,`phi`), where `r` is the sphere radius, `theta` is the polar
angle and `phi` is the azimuthal angle. Returns `NaN` gradient if x = y = 0.
"""
with tf.compat.v1.name_scope(name, "cartesian_to_spherical_coordinates",
[point_cartesian]):
point_cartesian = tf.convert_to_tensor(value=point_cartesian)
shape.check_static(
tensor=point_cartesian,
tensor_name="point_cartesian",
has_dim_equals=(-1, 3))
x, y, z = tf.unstack(point_cartesian, axis=-1)
radius = tf.norm(tensor=point_cartesian, axis=-1)
theta = tf.acos(
tf.clip_by_value(safe_ops.safe_unsigned_div(z, radius, eps), -1., 1.))
phi = tf.atan2(y, x)
return tf.stack((radius, theta, phi), axis=-1)
def _double_factorial_loop_body(n, result, two):
result = tf.compat.v1.where(tf.greater_equal(n, two), result * n, result)
return n - two, result, two
def _double_factorial_loop_condition(n, result, two):
del result # Unused
return tf.cast(tf.math.count_nonzero(tf.greater_equal(n, two)), tf.bool)
def double_factorial(n):
"""Computes the double factorial of `n`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
n: A tensor of shape `[A1, ..., An]` containing positive integer values.
Returns:
A tensor of shape `[A1, ..., An]` containing the double factorial of `n`.
"""
n = tf.convert_to_tensor(value=n)
two = tf.ones_like(n) * 2
result = tf.ones_like(n)
_, result, _ = tf.while_loop(
cond=_double_factorial_loop_condition,
body=_double_factorial_loop_body,
loop_vars=[n, result, two])
return result
def factorial(n):
"""Computes the factorial of `n`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
n: A tensor of shape `[A1, ..., An]`.
Returns:
A tensor of shape `[A1, ..., An]`.
"""
n = tf.convert_to_tensor(value=n)
return tf.exp(tf.math.lgamma(n + 1))
def spherical_to_cartesian_coordinates(point_spherical, name=None):
"""Function to transform Cartesian coordinates to spherical coordinates.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_spherical: A tensor of shape `[A1, ..., An, 3]`. The last dimension
contains r, theta, and phi that respectively correspond to the radius,
polar angle and azimuthal angle; r must be non-negative.
name: A name for this op. Defaults to 'spherical_to_cartesian_coordinates'.
Raises:
tf.errors.InvalidArgumentError: If r, theta or phi contains out of range
data.
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension contains the
cartesian coordinates in x,y,z order.
"""
with tf.compat.v1.name_scope(name, "spherical_to_cartesian_coordinates",
[point_spherical]):
point_spherical = tf.convert_to_tensor(value=point_spherical)
shape.check_static(
tensor=point_spherical,
tensor_name="point_spherical",
has_dim_equals=(-1, 3))
r, theta, phi = tf.unstack(point_spherical, axis=-1)
r = asserts.assert_all_above(r, 0)
tmp = r * tf.sin(theta)
x = tmp * tf.cos(phi)
y = tmp * tf.sin(phi)
z = r * tf.cos(theta)
return tf.stack((x, y, z), axis=-1)
def square_to_spherical_coordinates(point_2d, name=None):
"""Maps points from a unit square to a unit sphere.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_2d: A tensor of shape `[A1, ..., An, 2]` with values in [0,1].
name: A name for this op. Defaults to
"math_square_to_spherical_coordinates".
Returns:
A tensor of shape `[A1, ..., An, 2]` with [..., 0] having values in
[0.0, pi] and [..., 1] with values in [0.0, 2pi].
Raises:
ValueError: if the shape of `point_2d` is not supported.
InvalidArgumentError: if at least an element of `point_2d` is outside of
[0,1].
"""
with tf.compat.v1.name_scope(name, "math_square_to_spherical_coordinates",
[point_2d]):
point_2d = tf.convert_to_tensor(value=point_2d)
shape.check_static(
tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2))
point_2d = asserts.assert_all_in_range(
point_2d, 0.0, 1.0, open_bounds=False)
x, y = tf.unstack(point_2d, axis=-1)
theta = 2.0 * tf.acos(tf.sqrt(1.0 - x))
phi = 2.0 * np.pi * y
return tf.stack((tf.ones_like(theta), theta, phi), 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 contains math routines that are shared by across different modules."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
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 cartesian_to_spherical_coordinates(point_cartesian, eps=None, name=None):
"""Function to transform Cartesian coordinates to spherical coordinates.
This function assumes a right handed coordinate system with `z` pointing up.
When `x` and `y` are both `0`, the function outputs `0` for `phi`. Note that
the function is not smooth when `x = y = 0`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_cartesian: A tensor of shape `[A1, ..., An, 3]`. In the last
dimension, the data follows the `x`, `y`, `z` order.
eps: A small `float`, to be added to the denominator. If left as `None`,
its value is automatically selected using `point_cartesian.dtype`.
name: A name for this op. Defaults to `cartesian_to_spherical_coordinates`.
Returns:
A tensor of shape `[A1, ..., An, 3]`. The last dimensions contains
(`r`,`theta`,`phi`), where `r` is the sphere radius, `theta` is the polar
angle and `phi` is the azimuthal angle. Returns `NaN` gradient if x = y = 0.
"""
with tf.compat.v1.name_scope(name, "cartesian_to_spherical_coordinates",
[point_cartesian]):
point_cartesian = tf.convert_to_tensor(value=point_cartesian)
shape.check_static(
tensor=point_cartesian,
tensor_name="point_cartesian",
has_dim_equals=(-1, 3))
x, y, z = tf.unstack(point_cartesian, axis=-1)
radius = tf.norm(tensor=point_cartesian, axis=-1)
theta = tf.acos(
tf.clip_by_value(safe_ops.safe_unsigned_div(z, radius, eps), -1., 1.))
phi = tf.atan2(y, x)
return tf.stack((radius, theta, phi), axis=-1)
def _double_factorial_loop_body(n, result, two):
result = tf.compat.v1.where(tf.greater_equal(n, two), result * n, result)
return n - two, result, two
def _double_factorial_loop_condition(n, result, two):
del result # Unused
return tf.cast(tf.math.count_nonzero(tf.greater_equal(n, two)), tf.bool)
def double_factorial(n):
"""Computes the double factorial of `n`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
n: A tensor of shape `[A1, ..., An]` containing positive integer values.
Returns:
A tensor of shape `[A1, ..., An]` containing the double factorial of `n`.
"""
n = tf.convert_to_tensor(value=n)
two = tf.ones_like(n) * 2
result = tf.ones_like(n)
_, result, _ = tf.while_loop(
cond=_double_factorial_loop_condition,
body=_double_factorial_loop_body,
loop_vars=[n, result, two])
return result
def factorial(n):
"""Computes the factorial of `n`.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
n: A tensor of shape `[A1, ..., An]`.
Returns:
A tensor of shape `[A1, ..., An]`.
"""
n = tf.convert_to_tensor(value=n)
return tf.exp(tf.math.lgamma(n + 1))
def spherical_to_cartesian_coordinates(point_spherical, name=None):
"""Function to transform Cartesian coordinates to spherical coordinates.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_spherical: A tensor of shape `[A1, ..., An, 3]`. The last dimension
contains r, theta, and phi that respectively correspond to the radius,
polar angle and azimuthal angle; r must be non-negative.
name: A name for this op. Defaults to 'spherical_to_cartesian_coordinates'.
Raises:
tf.errors.InvalidArgumentError: If r, theta or phi contains out of range
data.
Returns:
A tensor of shape `[A1, ..., An, 3]`, where the last dimension contains the
cartesian coordinates in x,y,z order.
"""
with tf.compat.v1.name_scope(name, "spherical_to_cartesian_coordinates",
[point_spherical]):
point_spherical = tf.convert_to_tensor(value=point_spherical)
shape.check_static(
tensor=point_spherical,
tensor_name="point_spherical",
has_dim_equals=(-1, 3))
r, theta, phi = tf.unstack(point_spherical, axis=-1)
r = asserts.assert_all_above(r, 0)
tmp = r * tf.sin(theta)
x = tmp * tf.cos(phi)
y = tmp * tf.sin(phi)
z = r * tf.cos(theta)
return tf.stack((x, y, z), axis=-1)
def square_to_spherical_coordinates(point_2d, name=None):
"""Maps points from a unit square to a unit sphere.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
point_2d: A tensor of shape `[A1, ..., An, 2]` with values in [0,1].
name: A name for this op. Defaults to
"math_square_to_spherical_coordinates".
Returns:
A tensor of shape `[A1, ..., An, 2]` with [..., 0] having values in
[0.0, pi] and [..., 1] with values in [0.0, 2pi].
Raises:
ValueError: if the shape of `point_2d` is not supported.
InvalidArgumentError: if at least an element of `point_2d` is outside of
[0,1].
"""
with tf.compat.v1.name_scope(name, "math_square_to_spherical_coordinates",
[point_2d]):
point_2d = tf.convert_to_tensor(value=point_2d)
shape.check_static(
tensor=point_2d, tensor_name="point_2d", has_dim_equals=(-1, 2))
point_2d = asserts.assert_all_in_range(
point_2d, 0.0, 1.0, open_bounds=False)
x, y = tf.unstack(point_2d, axis=-1)
theta = 2.0 * tf.acos(tf.sqrt(1.0 - x))
phi = 2.0 * np.pi * y
return tf.stack((tf.ones_like(theta), theta, phi), axis=-1)
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/pointnet/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 for PointNet v1 on modelnet40."""
# pylint: disable=missing-function-docstring
import tensorflow as tf
from tensorflow_graphics.datasets import modelnet40
from tensorflow_graphics.nn.layer import pointnet
import tqdm # pylint: disable=g-bad-import-order
from . import augment # pylint: disable=g-bad-import-order
from . import helpers # pylint: disable=g-bad-import-order
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
parser = helpers.ArgumentParser()
parser.add("--batch_size", 32)
parser.add("--num_epochs", 250)
parser.add("--num_points", 2048, help="subsampled (max 2048)")
parser.add("--learning_rate", 1e-3, help="initial Adam learning rate")
parser.add("--lr_decay", True, help="enable learning rate decay")
parser.add("--bn_decay", .5, help="batch norm decay momentum")
parser.add("--tb_every", 100, help="tensorboard frequency (iterations)")
parser.add("--ev_every", 308, help="evaluation frequency (iterations)")
parser.add("--augment", True, help="use augmentations")
parser.add("--tqdm", True, help="enable the progress bar")
FLAGS = parser.parse_args()
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
if FLAGS.lr_decay:
lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
FLAGS.learning_rate,
decay_steps=6250, #< 200.000 / 32 (batch size) (from original pointnet)
decay_rate=0.7,
staircase=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_scheduler)
else:
optimizer = tf.keras.optimizers.Adam(learning_rate=FLAGS.learning_rate)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
model = pointnet.PointNetVanillaClassifier(
num_classes=40, momentum=FLAGS.bn_decay)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
@tf.function
def wrapped_tf_function(points, label):
"""Performs one step of minimization of the loss."""
# --- subsampling (order DO matter)
points = points[0:FLAGS.num_points, ...]
# --- augmentation
if FLAGS.augment:
points = tf.map_fn(augment.rotate, points)
points = augment.jitter(points)
# --- training
with tf.GradientTape() as tape:
logits = model(points, training=True)
loss = model.loss(label, logits)
variables = model.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return loss
def train(example):
"""Performs one step of minimization of the loss and populates the summary."""
points = example["points"]
label = example["label"]
step = optimizer.iterations.numpy()
# --- optimize
loss = wrapped_tf_function(points, label)
if step % FLAGS.tb_every == 0:
tf.summary.scalar(name="loss", data=loss, step=step)
# --- report rate in summaries
if FLAGS.lr_decay and step % FLAGS.tb_every == 0:
tf.summary.scalar(name="learning_rate", data=lr_scheduler(step), step=step)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def evaluate():
"""Identify the best accuracy reached during training."""
step = optimizer.iterations.numpy()
if "best_accuracy" not in evaluate.__dict__:
evaluate.best_accuracy = 0
if step % FLAGS.ev_every != 0:
return evaluate.best_accuracy
aggregator = tf.keras.metrics.SparseCategoricalAccuracy()
for example in ds_test:
points, labels = example["points"], example["label"]
logits = model(points, training=False)
aggregator.update_state(labels, logits)
accuracy = aggregator.result()
evaluate.best_accuracy = max(accuracy, evaluate.best_accuracy)
tf.summary.scalar(name="accuracy_test", data=accuracy, step=step)
return evaluate.best_accuracy
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
ds_train, info = modelnet40.ModelNet40.load(split="train", with_info=True)
num_examples = info.splits["train"].num_examples
ds_train = ds_train.shuffle(num_examples, reshuffle_each_iteration=True)
ds_train = ds_train.repeat(FLAGS.num_epochs)
ds_train = ds_train.batch(FLAGS.batch_size)
ds_test = modelnet40.ModelNet40.load(split="test").batch(FLAGS.batch_size)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
try:
helpers.setup_tensorboard(FLAGS)
helpers.summary_command(parser, FLAGS)
total = tf.data.experimental.cardinality(ds_train).numpy()
pbar = tqdm.tqdm(ds_train, leave=False, total=total, disable=not FLAGS.tqdm)
for train_example in pbar:
train(train_example)
best_accuracy = evaluate()
pbar.set_postfix_str("best accuracy: {:.3f}".format(best_accuracy))
except KeyboardInterrupt:
helpers.handle_keyboard_interrupt(FLAGS)
| # 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 for PointNet v1 on modelnet40."""
# pylint: disable=missing-function-docstring
import tensorflow as tf
from tensorflow_graphics.datasets import modelnet40
from tensorflow_graphics.nn.layer import pointnet
import tqdm # pylint: disable=g-bad-import-order
from . import augment # pylint: disable=g-bad-import-order
from . import helpers # pylint: disable=g-bad-import-order
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
parser = helpers.ArgumentParser()
parser.add("--batch_size", 32)
parser.add("--num_epochs", 250)
parser.add("--num_points", 2048, help="subsampled (max 2048)")
parser.add("--learning_rate", 1e-3, help="initial Adam learning rate")
parser.add("--lr_decay", True, help="enable learning rate decay")
parser.add("--bn_decay", .5, help="batch norm decay momentum")
parser.add("--tb_every", 100, help="tensorboard frequency (iterations)")
parser.add("--ev_every", 308, help="evaluation frequency (iterations)")
parser.add("--augment", True, help="use augmentations")
parser.add("--tqdm", True, help="enable the progress bar")
FLAGS = parser.parse_args()
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
if FLAGS.lr_decay:
lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
FLAGS.learning_rate,
decay_steps=6250, #< 200.000 / 32 (batch size) (from original pointnet)
decay_rate=0.7,
staircase=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_scheduler)
else:
optimizer = tf.keras.optimizers.Adam(learning_rate=FLAGS.learning_rate)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
model = pointnet.PointNetVanillaClassifier(
num_classes=40, momentum=FLAGS.bn_decay)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
@tf.function
def wrapped_tf_function(points, label):
"""Performs one step of minimization of the loss."""
# --- subsampling (order DO matter)
points = points[0:FLAGS.num_points, ...]
# --- augmentation
if FLAGS.augment:
points = tf.map_fn(augment.rotate, points)
points = augment.jitter(points)
# --- training
with tf.GradientTape() as tape:
logits = model(points, training=True)
loss = model.loss(label, logits)
variables = model.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return loss
def train(example):
"""Performs one step of minimization of the loss and populates the summary."""
points = example["points"]
label = example["label"]
step = optimizer.iterations.numpy()
# --- optimize
loss = wrapped_tf_function(points, label)
if step % FLAGS.tb_every == 0:
tf.summary.scalar(name="loss", data=loss, step=step)
# --- report rate in summaries
if FLAGS.lr_decay and step % FLAGS.tb_every == 0:
tf.summary.scalar(name="learning_rate", data=lr_scheduler(step), step=step)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def evaluate():
"""Identify the best accuracy reached during training."""
step = optimizer.iterations.numpy()
if "best_accuracy" not in evaluate.__dict__:
evaluate.best_accuracy = 0
if step % FLAGS.ev_every != 0:
return evaluate.best_accuracy
aggregator = tf.keras.metrics.SparseCategoricalAccuracy()
for example in ds_test:
points, labels = example["points"], example["label"]
logits = model(points, training=False)
aggregator.update_state(labels, logits)
accuracy = aggregator.result()
evaluate.best_accuracy = max(accuracy, evaluate.best_accuracy)
tf.summary.scalar(name="accuracy_test", data=accuracy, step=step)
return evaluate.best_accuracy
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
ds_train, info = modelnet40.ModelNet40.load(split="train", with_info=True)
num_examples = info.splits["train"].num_examples
ds_train = ds_train.shuffle(num_examples, reshuffle_each_iteration=True)
ds_train = ds_train.repeat(FLAGS.num_epochs)
ds_train = ds_train.batch(FLAGS.batch_size)
ds_test = modelnet40.ModelNet40.load(split="test").batch(FLAGS.batch_size)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
try:
helpers.setup_tensorboard(FLAGS)
helpers.summary_command(parser, FLAGS)
total = tf.data.experimental.cardinality(ds_train).numpy()
pbar = tqdm.tqdm(ds_train, leave=False, total=total, disable=not FLAGS.tqdm)
for train_example in pbar:
train(train_example)
best_accuracy = evaluate()
pbar.set_postfix_str("best accuracy: {:.3f}".format(best_accuracy))
except KeyboardInterrupt:
helpers.handle_keyboard_interrupt(FLAGS)
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/math/interpolation/tests/slerp_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 slerp."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.math.interpolation import slerp
from tensorflow_graphics.util import test_case
_SQRT2_DIV2 = np.sqrt(2.0).astype(np.float32) * 0.5
class SlerpTest(test_case.TestCase):
def _pick_random_quaternion(self):
"""Creates a random quaternion with 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])
def _quaternion_slerp_helper(self, q1, q2, p):
"""Calls interpolate function for quaternions."""
return slerp.interpolate(q1, q2, p, slerp.InterpolationType.QUATERNION)
def _vector_slerp_helper(self, q1, q2, p):
"""Calls interpolate function for vectors."""
return slerp.interpolate(q1, q2, p, slerp.InterpolationType.VECTOR)
def test_interpolate_raises_exceptions(self):
"""Tests if unknown methods raise exceptions."""
vector1 = self._pick_random_quaternion()
self.assert_exception_is_raised(
slerp.interpolate,
error_msg="Unknown interpolation type supplied.",
shapes=[],
vector1=vector1,
vector2=-vector1,
percent=0.1,
method=2)
def test_interpolate_with_weights_quaternion_preset(self):
"""Compares interpolate to quaternion_weights + interpolate_with_weights."""
q1 = self._pick_random_quaternion()
q2 = q1 + tf.ones_like(q1)
q1 = tf.nn.l2_normalize(q1, axis=-1)
q2 = tf.nn.l2_normalize(q2, axis=-1)
weight1, weight2 = slerp.quaternion_weights(q1, q2, 0.25)
qf = slerp.interpolate_with_weights(q1, q2, weight1, weight2)
qi = slerp.interpolate(
q1, q2, 0.25, method=slerp.InterpolationType.QUATERNION)
self.assertAllClose(qf, qi, atol=1e-9)
def test_interpolate_with_weights_vector_preset(self):
"""Compares interpolate to vector_weights + interpolate_with_weights."""
# Any quaternion is a valid vector
q1 = self._pick_random_quaternion()
q2 = q1 + tf.ones_like(q1)
weight1, weight2 = slerp.vector_weights(q1, q2, 0.75)
qf = slerp.interpolate_with_weights(q1, q2, weight1, weight2)
qi = slerp.interpolate(q1, q2, 0.75, method=slerp.InterpolationType.VECTOR)
self.assertAllClose(qf, qi, atol=1e-9)
@parameterized.parameters(
# Orthogonal, same hemisphere
(((1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.5,)),
((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),)),
(((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)), ((0.5, 0.5, 0.5, 0.5),)),
# Same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)),
((0.408248290463863, 0.0, 0.816496580927726, 0.408248290463863),)),
# Same quaternions
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)),
((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.2,)),
((-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0),)),
# Anti-polar - large percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.8,)),
((-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0),)),
# Extrapolation - same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-0.5,)),
((0.408248290463863, -0.408248290463863, 0.816496580927726, 0.0),)),
# Extrapolation - opposite hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-0.5,)),
((-0.408248290463863, -0.408248290463863, -0.816496580927726, 0.0),)),
)
def test_quaternion_slerp_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of qslerp against numpy-quaternion values."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(self._quaternion_slerp_helper, test_inputs,
test_outputs, tile=False)
def test_unnormalized_quaternion_weights_exception_raised(self):
"""Tests if quaternion_weights raise exceptions for unnormalized input."""
q1 = self._pick_random_quaternion()
q2 = tf.nn.l2_normalize(q1, axis=-1)
p = tf.constant((0.5), dtype=q1.dtype)
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(slerp.quaternion_weights(q1, q2, p))
@parameterized.parameters(
((4,), (4,), (1,)),
((None, 4), (None, 4), (None, 1)),
((None, 4), (None, 4), (None, 4)),
)
def test_quaternion_weights_exception_not_raised(self, *shapes):
"""Tests that valid input shapes do not raise exceptions for qslerp."""
self.assert_exception_is_not_raised(slerp.quaternion_weights, shapes)
@parameterized.parameters(
("must have exactly 4 dimensions in axis -1", (3,), (4,), (1,)),
("must have exactly 4 dimensions in axis -1", (4,), (3,), (1,)),
("Not all batch dimensions are broadcast-compatible.", (2, 4), (3, 4),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 4), (3, 4),
(2,)),
)
def test_quaternion_weights_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised for qslerp."""
self.assert_exception_is_raised(slerp.quaternion_weights, error_msg, shapes)
@parameterized.parameters(
# Same quaternions
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)), (
(0.25,),
(0.75,),
)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.2,)), (
(-0.8,),
(0.2,),
)),
# Anti-polar - large percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.8,)), (
(-0.2,),
(0.8,),
)),
)
def test_quaternion_weights_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of quaternion_weights for problem cases."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(slerp.quaternion_weights, test_inputs,
test_outputs, tile=False)
@parameterized.parameters(
((3,), (3,), (1,)),
((None, 4), (None, 4), (None, 1)),
)
def test_vector_weights_exception_not_raised(self, *shapes):
"""Tests that valid inputs do not raise exceptions for vector_weights."""
self.assert_exception_is_not_raised(slerp.vector_weights, shapes)
@parameterized.parameters(
("must have the same number of dimensions in axes", (None, 3), (None, 4),
(1,)),
("must have the same number of dimensions in axes", (2, 3), (2, 4), (1,)),
("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3), (3, 3),
(2,)),
)
def test_vector_weights_exception_raised(self, error_msg, *shapes):
"""Tests that shape exceptions are properly raised for vector_weights."""
self.assert_exception_is_raised(slerp.vector_weights, error_msg, shapes)
@parameterized.parameters(
# Orthogonal, same hemisphere
(((1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.5,)),
((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),)),
(((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)), ((0.5, 0.5, 0.5, 0.5),)),
# Same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)),
((0.408248290463863, 0.0, 0.816496580927726, 0.408248290463863),)),
# Same vectors
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)),
((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),)),
# Anti-polar - equal weights
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.5,)),
((0.0, 0.0, 0.0, 0.0),)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.25,)),
((0.5, 0.0, 0.5, 0.0),)),
# Extrapolation - same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-1.0,)),
((0.0, -_SQRT2_DIV2, _SQRT2_DIV2, 0.0),)),
# Extrapolation - opposite hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (1.5,)),
((-_SQRT2_DIV2, -0.0, -_SQRT2_DIV2, 0.0),)),
# Unnormalized vectors
(((4.0, 0.0), (0.0, 1.0), (0.5,)), ((2.82842712, _SQRT2_DIV2),)),
)
def test_vector_slerp_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of vector slerp results."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(self._vector_slerp_helper, test_inputs,
test_outputs, tile=False)
def test_vector_weights_reduce_to_lerp_preset(self):
"""Tests if vector slerp reduces to lerp for identical vectors as input."""
q1 = tf.constant((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0))
q2 = tf.constant((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0))
p = tf.constant((0.75,), dtype=q1.dtype)
w1, w2 = slerp.vector_weights(q1, q2, p)
self.assertAllClose(w1, (0.25,), rtol=1e-6)
self.assertAllClose(w2, (0.75,), 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 slerp."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.math.interpolation import slerp
from tensorflow_graphics.util import test_case
_SQRT2_DIV2 = np.sqrt(2.0).astype(np.float32) * 0.5
class SlerpTest(test_case.TestCase):
def _pick_random_quaternion(self):
"""Creates a random quaternion with 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])
def _quaternion_slerp_helper(self, q1, q2, p):
"""Calls interpolate function for quaternions."""
return slerp.interpolate(q1, q2, p, slerp.InterpolationType.QUATERNION)
def _vector_slerp_helper(self, q1, q2, p):
"""Calls interpolate function for vectors."""
return slerp.interpolate(q1, q2, p, slerp.InterpolationType.VECTOR)
def test_interpolate_raises_exceptions(self):
"""Tests if unknown methods raise exceptions."""
vector1 = self._pick_random_quaternion()
self.assert_exception_is_raised(
slerp.interpolate,
error_msg="Unknown interpolation type supplied.",
shapes=[],
vector1=vector1,
vector2=-vector1,
percent=0.1,
method=2)
def test_interpolate_with_weights_quaternion_preset(self):
"""Compares interpolate to quaternion_weights + interpolate_with_weights."""
q1 = self._pick_random_quaternion()
q2 = q1 + tf.ones_like(q1)
q1 = tf.nn.l2_normalize(q1, axis=-1)
q2 = tf.nn.l2_normalize(q2, axis=-1)
weight1, weight2 = slerp.quaternion_weights(q1, q2, 0.25)
qf = slerp.interpolate_with_weights(q1, q2, weight1, weight2)
qi = slerp.interpolate(
q1, q2, 0.25, method=slerp.InterpolationType.QUATERNION)
self.assertAllClose(qf, qi, atol=1e-9)
def test_interpolate_with_weights_vector_preset(self):
"""Compares interpolate to vector_weights + interpolate_with_weights."""
# Any quaternion is a valid vector
q1 = self._pick_random_quaternion()
q2 = q1 + tf.ones_like(q1)
weight1, weight2 = slerp.vector_weights(q1, q2, 0.75)
qf = slerp.interpolate_with_weights(q1, q2, weight1, weight2)
qi = slerp.interpolate(q1, q2, 0.75, method=slerp.InterpolationType.VECTOR)
self.assertAllClose(qf, qi, atol=1e-9)
@parameterized.parameters(
# Orthogonal, same hemisphere
(((1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.5,)),
((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),)),
(((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)), ((0.5, 0.5, 0.5, 0.5),)),
# Same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)),
((0.408248290463863, 0.0, 0.816496580927726, 0.408248290463863),)),
# Same quaternions
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)),
((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.2,)),
((-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0),)),
# Anti-polar - large percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.8,)),
((-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0),)),
# Extrapolation - same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-0.5,)),
((0.408248290463863, -0.408248290463863, 0.816496580927726, 0.0),)),
# Extrapolation - opposite hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-0.5,)),
((-0.408248290463863, -0.408248290463863, -0.816496580927726, 0.0),)),
)
def test_quaternion_slerp_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of qslerp against numpy-quaternion values."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(self._quaternion_slerp_helper, test_inputs,
test_outputs, tile=False)
def test_unnormalized_quaternion_weights_exception_raised(self):
"""Tests if quaternion_weights raise exceptions for unnormalized input."""
q1 = self._pick_random_quaternion()
q2 = tf.nn.l2_normalize(q1, axis=-1)
p = tf.constant((0.5), dtype=q1.dtype)
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(slerp.quaternion_weights(q1, q2, p))
@parameterized.parameters(
((4,), (4,), (1,)),
((None, 4), (None, 4), (None, 1)),
((None, 4), (None, 4), (None, 4)),
)
def test_quaternion_weights_exception_not_raised(self, *shapes):
"""Tests that valid input shapes do not raise exceptions for qslerp."""
self.assert_exception_is_not_raised(slerp.quaternion_weights, shapes)
@parameterized.parameters(
("must have exactly 4 dimensions in axis -1", (3,), (4,), (1,)),
("must have exactly 4 dimensions in axis -1", (4,), (3,), (1,)),
("Not all batch dimensions are broadcast-compatible.", (2, 4), (3, 4),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 4), (3, 4),
(2,)),
)
def test_quaternion_weights_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are properly raised for qslerp."""
self.assert_exception_is_raised(slerp.quaternion_weights, error_msg, shapes)
@parameterized.parameters(
# Same quaternions
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)), (
(0.25,),
(0.75,),
)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.2,)), (
(-0.8,),
(0.2,),
)),
# Anti-polar - large percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.8,)), (
(-0.2,),
(0.8,),
)),
)
def test_quaternion_weights_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of quaternion_weights for problem cases."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(slerp.quaternion_weights, test_inputs,
test_outputs, tile=False)
@parameterized.parameters(
((3,), (3,), (1,)),
((None, 4), (None, 4), (None, 1)),
)
def test_vector_weights_exception_not_raised(self, *shapes):
"""Tests that valid inputs do not raise exceptions for vector_weights."""
self.assert_exception_is_not_raised(slerp.vector_weights, shapes)
@parameterized.parameters(
("must have the same number of dimensions in axes", (None, 3), (None, 4),
(1,)),
("must have the same number of dimensions in axes", (2, 3), (2, 4), (1,)),
("Not all batch dimensions are broadcast-compatible.", (2, 3), (3, 3),
(1,)),
("Not all batch dimensions are broadcast-compatible.", (1, 3), (3, 3),
(2,)),
)
def test_vector_weights_exception_raised(self, error_msg, *shapes):
"""Tests that shape exceptions are properly raised for vector_weights."""
self.assert_exception_is_raised(slerp.vector_weights, error_msg, shapes)
@parameterized.parameters(
# Orthogonal, same hemisphere
(((1.0, 0.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.5,)),
((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),)),
(((_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)), ((0.5, 0.5, 0.5, 0.5),)),
# Same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(0.0, 0.0, _SQRT2_DIV2, _SQRT2_DIV2), (0.5,)),
((0.408248290463863, 0.0, 0.816496580927726, 0.408248290463863),)),
# Same vectors
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0), (0.75,)),
((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),)),
# Anti-polar - equal weights
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.5,)),
((0.0, 0.0, 0.0, 0.0),)),
# Anti-polar - small percent
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, 0.0, -_SQRT2_DIV2, 0.0), (0.25,)),
((0.5, 0.0, 0.5, 0.0),)),
# Extrapolation - same hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (-1.0,)),
((0.0, -_SQRT2_DIV2, _SQRT2_DIV2, 0.0),)),
# Extrapolation - opposite hemisphere
(((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0),
(-_SQRT2_DIV2, _SQRT2_DIV2, 0.0, 0.0), (1.5,)),
((-_SQRT2_DIV2, -0.0, -_SQRT2_DIV2, 0.0),)),
# Unnormalized vectors
(((4.0, 0.0), (0.0, 1.0), (0.5,)), ((2.82842712, _SQRT2_DIV2),)),
)
def test_vector_slerp_preset(self, test_inputs, test_outputs):
"""Tests the accuracy of vector slerp results."""
test_inputs = [np.array(test_input).astype(np.float32)
for test_input in test_inputs]
self.assert_output_is_correct(self._vector_slerp_helper, test_inputs,
test_outputs, tile=False)
def test_vector_weights_reduce_to_lerp_preset(self):
"""Tests if vector slerp reduces to lerp for identical vectors as input."""
q1 = tf.constant((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0))
q2 = tf.constant((_SQRT2_DIV2, 0.0, _SQRT2_DIV2, 0.0))
p = tf.constant((0.75,), dtype=q1.dtype)
w1, w2 = slerp.vector_weights(q1, q2, p)
self.assertAllClose(w1, (0.25,), rtol=1e-6)
self.assertAllClose(w2, (0.75,), rtol=1e-6)
if __name__ == "__main__":
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/datasets/shapenet/fakes/taxonomy.json | [{
"synsetId": "02691156",
"name": "airplane,aeroplane,plane",
"children": [
"02690373",
"02842573",
"02867715",
"03174079",
"03335030",
"03595860",
"04012084",
"04160586",
"20000000",
"20000001",
"20000002"
],
"numInstances": 4045
},
{
"synsetId": "02690373",
"name": "airliner",
"children": [
"03809312",
"04583620"
],
"numInstances": 1490
},
{
"synsetId": "03809312",
"name": "narrowbody aircraft,narrow-body aircraft,narrow-body",
"children": [],
"numInstances": 14
},
{"synsetId": "04583620"},
{"synsetId": "02842573"},
{"synsetId": "02867715"},
{"synsetId": "03174079"},
{"synsetId": "03335030"},
{"synsetId": "03595860"},
{"synsetId": "03321419"},
{"synsetId": "03604311"},
{"synsetId": "04012084"},
{"synsetId": "04160586"},
{"synsetId": "03373611"},
{"synsetId": "20000000"},
{"synsetId": "20000001"},
{"synsetId": "20000002"},
{"synsetId": "02747177"},
{"synsetId": "02773838"},
{"synsetId": "03986949"},
{"synsetId": "02801938"},
{"synsetId": "03482405"},
{"synsetId": "03050864"},
{"synsetId": "04582349"},
{"synsetId": "02808440"},
{"synsetId": "02818832"},
{"synsetId": "02831724"},
{"synsetId": "02920083"},
{"synsetId": "02920259"},
{"synsetId": "03114504"},
{"synsetId": "03115762"},
{"synsetId": "03388549"},
{"synsetId": "03482252"},
{"synsetId": "03962852"},
{"synsetId": "04222210"},
{"synsetId": "03540914"},
{"synsetId": "20000004"},
{"synsetId": "20000005"},
{"synsetId": "20000006"},
{"synsetId": "20000007"},
{"synsetId": "02828884"},
{"synsetId": "03360622"},
{"synsetId": "03891251"},
{"synsetId": "03920867"},
{"synsetId": "04177820"},
{"synsetId": "04590021"},
{"synsetId": "02834778"},
{"synsetId": "02843684"},
{"synsetId": "02871439"},
{"synsetId": "02876657"},
{"synsetId": "02823428"},
{"synsetId": "03359566"},
{"synsetId": "02952374"},
{"synsetId": "03603722"},
{"synsetId": "03923379"},
{"synsetId": "03983396"},
{"synsetId": "04591713"},
{"synsetId": "02880940"},
{"synsetId": "04263257"},
{"synsetId": "02924116"},
{"synsetId": "04146614"},
{"synsetId": "04487081"},
{"synsetId": "02933112"},
{"synsetId": "03018349"},
{"synsetId": "03237340"},
{"synsetId": "03742115"},
{"synsetId": "20000008"},
{"synsetId": "20000009"},
{"synsetId": "20000010"},
{"synsetId": "20000011"},
{"synsetId": "20000012"},
{"synsetId": "20000013"},
{"synsetId": "02942699"},
{"synsetId": "02884994"},
{"synsetId": "03196062"},
{"synsetId": "04569063"},
{"synsetId": "03358726"},
{"synsetId": "03789171"},
{"synsetId": "02946921"},
{"synsetId": "02823510"},
{"synsetId": "04255586"},
{"synsetId": "02954340"},
{"synsetId": "02799323"},
{"synsetId": "03049924"},
{"synsetId": "03610682"},
{"synsetId": "04387095"},
{"synsetId": "02958343"},
{"synsetId": "02701002"},
{"synsetId": "02814533"},
{"synsetId": "02930766"},
{"synsetId": "03100240"},
{"synsetId": "03119396"},
{"synsetId": "03141065"},
{"synsetId": "03881534"},
{"synsetId": "03498781"},
{"synsetId": "03543394"},
{"synsetId": "03594945"},
{"synsetId": "03670208"},
{"synsetId": "03770679"},
{"synsetId": "03870105"},
{"synsetId": "04037443"},
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| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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"],
))
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/geometry/representation/point.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 point utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 distance_to_ray(point, origin, direction, keepdims=True, name=None):
"""Computes the distance from a M-d point to a M-d ray.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, M]`.
origin: A tensor of shape `[A1, ..., An, M]`.
direction: A tensor of shape `[A1, ..., An, M]`. The last dimension must be
normalized.
keepdims: A `bool`, whether to keep the last dimension with length 1 or to
remove it.
name: A name for this op. Defaults to "point_distance_to_ray".
Returns:
A tensor of shape `[A1, ..., An, 1]` containing the distance from each point
to the corresponding ray.
Raises:
ValueError: If the shape of `point`, `origin`, or 'direction' is not
supported.
"""
with tf.compat.v1.name_scope(name, "point_distance_to_ray",
[point, origin, direction]):
point = tf.convert_to_tensor(value=point)
origin = tf.convert_to_tensor(value=origin)
direction = tf.convert_to_tensor(value=direction)
shape.compare_dimensions((point, origin, direction), -1,
("point", "origin", "direction"))
shape.compare_batch_dimensions(
tensors=(point, origin, direction),
last_axes=-2,
broadcast_compatible=True)
direction = asserts.assert_normalized(direction)
vec = point - origin
dot = vector.dot(vec, direction)
vec -= dot * direction
return tf.norm(tensor=vec, axis=-1, keepdims=keepdims)
def project_to_ray(point, origin, direction, name=None):
"""Computes the projection of a M-d point on a M-d ray.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, M]`.
origin: A tensor of shape `[A1, ..., An, M]`.
direction: A tensor of shape `[A1, ..., An, M]`. The last dimension must be
normalized.
name: A name for this op. Defaults to "point_project_to_ray".
Returns:
A tensor of shape `[A1, ..., An, M]` containing the projected point.
Raises:
ValueError: If the shape of `point`, `origin`, or 'direction' is not
supported.
"""
with tf.compat.v1.name_scope(name, "point_project_to_ray",
[point, origin, direction]):
point = tf.convert_to_tensor(value=point)
origin = tf.convert_to_tensor(value=origin)
direction = tf.convert_to_tensor(value=direction)
shape.compare_dimensions((point, origin, direction), -1,
("point", "origin", "direction"))
shape.compare_batch_dimensions(
tensors=(point, origin, direction),
last_axes=-2,
broadcast_compatible=True)
direction = asserts.assert_normalized(direction)
vec = point - origin
dot = vector.dot(vec, direction)
return origin + dot * direction
# 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 point utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 distance_to_ray(point, origin, direction, keepdims=True, name=None):
"""Computes the distance from a M-d point to a M-d ray.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, M]`.
origin: A tensor of shape `[A1, ..., An, M]`.
direction: A tensor of shape `[A1, ..., An, M]`. The last dimension must be
normalized.
keepdims: A `bool`, whether to keep the last dimension with length 1 or to
remove it.
name: A name for this op. Defaults to "point_distance_to_ray".
Returns:
A tensor of shape `[A1, ..., An, 1]` containing the distance from each point
to the corresponding ray.
Raises:
ValueError: If the shape of `point`, `origin`, or 'direction' is not
supported.
"""
with tf.compat.v1.name_scope(name, "point_distance_to_ray",
[point, origin, direction]):
point = tf.convert_to_tensor(value=point)
origin = tf.convert_to_tensor(value=origin)
direction = tf.convert_to_tensor(value=direction)
shape.compare_dimensions((point, origin, direction), -1,
("point", "origin", "direction"))
shape.compare_batch_dimensions(
tensors=(point, origin, direction),
last_axes=-2,
broadcast_compatible=True)
direction = asserts.assert_normalized(direction)
vec = point - origin
dot = vector.dot(vec, direction)
vec -= dot * direction
return tf.norm(tensor=vec, axis=-1, keepdims=keepdims)
def project_to_ray(point, origin, direction, name=None):
"""Computes the projection of a M-d point on a M-d ray.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible.
Args:
point: A tensor of shape `[A1, ..., An, M]`.
origin: A tensor of shape `[A1, ..., An, M]`.
direction: A tensor of shape `[A1, ..., An, M]`. The last dimension must be
normalized.
name: A name for this op. Defaults to "point_project_to_ray".
Returns:
A tensor of shape `[A1, ..., An, M]` containing the projected point.
Raises:
ValueError: If the shape of `point`, `origin`, or 'direction' is not
supported.
"""
with tf.compat.v1.name_scope(name, "point_project_to_ray",
[point, origin, direction]):
point = tf.convert_to_tensor(value=point)
origin = tf.convert_to_tensor(value=origin)
direction = tf.convert_to_tensor(value=direction)
shape.compare_dimensions((point, origin, direction), -1,
("point", "origin", "direction"))
shape.compare_batch_dimensions(
tensors=(point, origin, direction),
last_axes=-2,
broadcast_compatible=True)
direction = asserts.assert_normalized(direction)
vec = point - origin
dot = vector.dot(vec, direction)
return origin + dot * direction
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/rendering/camera/tests/quadratic_radial_distortion_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 quadratic_radial_distortion."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.rendering.camera import quadratic_radial_distortion
from tensorflow_graphics.util import test_case
RANDOM_TESTS_NUM_IMAGES = 10
RANDOM_TESTS_HEIGHT = 8
RANDOM_TESTS_WIDTH = 8
RADII_SHAPE = (RANDOM_TESTS_NUM_IMAGES, RANDOM_TESTS_HEIGHT, RANDOM_TESTS_WIDTH)
COEFFICIENT_SHAPE = (RANDOM_TESTS_NUM_IMAGES,)
def _get_random_radii():
return np.random.rand(*RADII_SHAPE).astype('float32')
def _get_zeros_radii():
return np.zeros(shape=RADII_SHAPE).astype('float32')
def _get_ones_radii():
return np.ones(shape=RADII_SHAPE).astype('float32')
def _get_random_coefficient():
return np.random.rand(*COEFFICIENT_SHAPE).astype('float32')
def _get_zeros_coefficient():
return np.zeros(shape=COEFFICIENT_SHAPE).astype('float32')
def _get_ones_coefficient():
return np.ones(shape=COEFFICIENT_SHAPE).astype('float32')
def _make_shape_compatible(coefficients):
return np.expand_dims(np.expand_dims(coefficients, axis=-1), axis=-1)
class QuadraticRadialDistortionTest(test_case.TestCase):
def test_distortion_factor_random_positive_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(1.0 + distortion_coefficient * squared_radii,
distortion)
# No overflow when distortion_coefficient >= 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
def test_distortion_factor_preset_zero_distortion_coefficient(self):
"""Tests distortion_factor at zero distortion coefficient."""
squared_radii = _get_random_radii() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, 0.0)
with self.subTest(name='distortion'):
self.assertAllClose(tf.ones_like(squared_radii), distortion)
# No overflow when distortion_coefficient = 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
def test_distortion_factor_random_negative_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * -0.2
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
max_squared_radii = -1.0 / 3.0 / distortion_coefficient
expected_overflow_mask = squared_radii > max_squared_radii
valid_mask = np.logical_not(expected_overflow_mask)
# We assert correctness of the mask, and of all the pixels that are not in
# overflow.
actual_distortion_when_valid = self.evaluate(distortion)[valid_mask]
expected_distortion_when_valid = (
1.0 + distortion_coefficient * squared_radii)[valid_mask]
with self.subTest(name='distortion'):
self.assertAllClose(expected_distortion_when_valid,
actual_distortion_when_valid)
with self.subTest(name='mask'):
self.assertAllEqual(expected_overflow_mask, mask)
def test_distortion_factor_preset_zero_radius(self):
"""Tests distortion_factor at the corner case of zero radius."""
squared_radii = _get_zeros_radii()
distortion_coefficient = _get_random_coefficient() - 0.5
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(np.ones_like(squared_radii), distortion)
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_negative_radius_exception_raised(self, distortion_function):
"""Tests that an exception is raised when the squared radius is negative."""
squared_radii = _get_zeros_radii() - 0.5
distortion_coefficient = _get_random_coefficient() - 0.5
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(distortion_function(squared_radii, distortion_coefficient))
@parameterized.parameters((2, 2e-3), (3, 1e-8))
def test_undistortion_factor_random_positive_distortion_coefficient(
self, num_iterations, tolerance):
"""Tests that undistortion_factor produces the expected outputs."""
distorted_squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * 0.2
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
distorted_squared_radii, distortion_coefficient, num_iterations)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
undistorted_squared_radii = tf.square(
undistortion) * distorted_squared_radii
# We distort again the undistorted radii and compare to the original
# distorted_squared_radii.
redistorted_squared_radii = tf.square(
1.0 + distortion_coefficient *
undistorted_squared_radii) * undistorted_squared_radii
with self.subTest(name='distortion'):
self.assertAllClose(
distorted_squared_radii, redistorted_squared_radii, atol=tolerance)
# Positive distortion_coefficients never overflow.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
@parameterized.parameters((2, 2e-2), (3, 6e-3), (4, 6e-4))
def test_undistortion_factor_random_negative_distortion_coefficient(
self, num_iterations, tolerance):
"""Tests that undistortion_factor produces the expected outputs."""
distorted_squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * -0.2
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
distorted_squared_radii, distortion_coefficient, num_iterations)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
undistorted_squared_radii = tf.square(
undistortion) * distorted_squared_radii
# See explanation in the implementation comments for this formula.
expected_overflow_mask = (
distorted_squared_radii * distortion_coefficient + 4.0 / 27.0 < 0)
redistorted_squared_radii = tf.square(
1.0 + distortion_coefficient *
undistorted_squared_radii) * undistorted_squared_radii
valid_mask = np.logical_not(expected_overflow_mask)
redistorted_squared_radii_when_valid = self.evaluate(
redistorted_squared_radii)[valid_mask]
distorted_squared_radii_when_valid = distorted_squared_radii[valid_mask]
with self.subTest(name='distortion'):
self.assertAllClose(
distorted_squared_radii_when_valid,
redistorted_squared_radii_when_valid,
rtol=tolerance,
atol=tolerance)
# We assert correctness of the mask, and of all the pixels that are not in
# overflow, distorting again the undistorted radii and comparing to the
# original distorted_squared_radii.
with self.subTest(name='mask'):
self.assertAllEqual(expected_overflow_mask, mask)
def test_undistortion_factor_zero_distortion_coefficient(self):
"""Tests undistortion_factor at zero distortion coefficient."""
squared_radii = _get_random_radii() * 2.0
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
squared_radii, 0.0)
with self.subTest(name='distortion'):
self.assertAllClose(tf.ones_like(squared_radii), undistortion)
# No overflow when distortion_coefficient = 0.0.
with self.subTest(name='mask'):
self.assertAllEqual(np.zeros_like(squared_radii), mask)
@parameterized.parameters(
('must have a rank greater than 1', (2,), (2, 1)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3,)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3, 3)),
)
def test_distortion_factor_shape_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(
func=quadratic_radial_distortion.distortion_factor,
error_msg=error_msg,
shapes=shapes)
@parameterized.parameters(
((2, 2), ()),
((1, 2, 2), (2,)),
((2, 2, 2), (2,)),
((2, 2), (2, 2)),
((2, 2, 2), (1, 2)),
((2, 3, 4), (1,)),
((2, 3, 4), (1, 1)),
((2, 3, 4), (2,)),
)
def test_distortion_factor_shape_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_not_raised(
func=quadratic_radial_distortion.distortion_factor, shapes=shapes)
@parameterized.parameters(
('must have a rank greater than 1', (2,), (2, 1)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3,)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3, 3)),
)
def test_undistortion_factor_shape_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(
func=quadratic_radial_distortion.undistortion_factor,
error_msg=error_msg,
shapes=shapes)
@parameterized.parameters(
((2, 2), ()),
((1, 2, 2), (2,)),
((2, 2, 2), (2,)),
((2, 2), (2, 2)),
((2, 2, 2), (1, 2)),
((2, 3, 4), (1,)),
((2, 3, 4), (1, 1)),
((2, 3, 4), (2,)),
)
def test_undistortion_factor_shape_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_not_raised(
func=quadratic_radial_distortion.undistortion_factor, shapes=shapes)
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_radial_jacobian(self, distortion_function):
"""Test the Jacobians with respect to squared radii."""
squared_radii = _get_random_radii().astype(np.float64) * 0.5
distortion_coefficients = _get_random_coefficient().astype(np.float64) * 0.5
distortion_coefficients -= 0.25
def distortion_fn(squared_radii):
distortion, _ = distortion_function(squared_radii,
distortion_coefficients)
return distortion
self.assert_jacobian_is_correct_fn(
distortion_fn, [squared_radii], delta=1e-7, atol=1e-3)
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_distortion_coefficient_jacobian(self, distortion_function):
"""Test the Jacobians with respect to distortion coefficients."""
squared_radii = _get_random_radii().astype(np.float64) * 0.5
distortion_coefficients = _get_random_coefficient().astype(np.float64) * 0.5
distortion_coefficients -= 0.25
def distortion_fn(distortion_coefficients):
distortion, _ = distortion_function(squared_radii,
distortion_coefficients)
return distortion
self.assert_jacobian_is_correct_fn(
distortion_fn, [distortion_coefficients], delta=1e-7, atol=1e-3)
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 quadratic_radial_distortion."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow_graphics.rendering.camera import quadratic_radial_distortion
from tensorflow_graphics.util import test_case
RANDOM_TESTS_NUM_IMAGES = 10
RANDOM_TESTS_HEIGHT = 8
RANDOM_TESTS_WIDTH = 8
RADII_SHAPE = (RANDOM_TESTS_NUM_IMAGES, RANDOM_TESTS_HEIGHT, RANDOM_TESTS_WIDTH)
COEFFICIENT_SHAPE = (RANDOM_TESTS_NUM_IMAGES,)
def _get_random_radii():
return np.random.rand(*RADII_SHAPE).astype('float32')
def _get_zeros_radii():
return np.zeros(shape=RADII_SHAPE).astype('float32')
def _get_ones_radii():
return np.ones(shape=RADII_SHAPE).astype('float32')
def _get_random_coefficient():
return np.random.rand(*COEFFICIENT_SHAPE).astype('float32')
def _get_zeros_coefficient():
return np.zeros(shape=COEFFICIENT_SHAPE).astype('float32')
def _get_ones_coefficient():
return np.ones(shape=COEFFICIENT_SHAPE).astype('float32')
def _make_shape_compatible(coefficients):
return np.expand_dims(np.expand_dims(coefficients, axis=-1), axis=-1)
class QuadraticRadialDistortionTest(test_case.TestCase):
def test_distortion_factor_random_positive_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(1.0 + distortion_coefficient * squared_radii,
distortion)
# No overflow when distortion_coefficient >= 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
def test_distortion_factor_preset_zero_distortion_coefficient(self):
"""Tests distortion_factor at zero distortion coefficient."""
squared_radii = _get_random_radii() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, 0.0)
with self.subTest(name='distortion'):
self.assertAllClose(tf.ones_like(squared_radii), distortion)
# No overflow when distortion_coefficient = 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
def test_distortion_factor_random_negative_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * -0.2
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
max_squared_radii = -1.0 / 3.0 / distortion_coefficient
expected_overflow_mask = squared_radii > max_squared_radii
valid_mask = np.logical_not(expected_overflow_mask)
# We assert correctness of the mask, and of all the pixels that are not in
# overflow.
actual_distortion_when_valid = self.evaluate(distortion)[valid_mask]
expected_distortion_when_valid = (
1.0 + distortion_coefficient * squared_radii)[valid_mask]
with self.subTest(name='distortion'):
self.assertAllClose(expected_distortion_when_valid,
actual_distortion_when_valid)
with self.subTest(name='mask'):
self.assertAllEqual(expected_overflow_mask, mask)
def test_distortion_factor_preset_zero_radius(self):
"""Tests distortion_factor at the corner case of zero radius."""
squared_radii = _get_zeros_radii()
distortion_coefficient = _get_random_coefficient() - 0.5
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(np.ones_like(squared_radii), distortion)
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_negative_radius_exception_raised(self, distortion_function):
"""Tests that an exception is raised when the squared radius is negative."""
squared_radii = _get_zeros_radii() - 0.5
distortion_coefficient = _get_random_coefficient() - 0.5
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(distortion_function(squared_radii, distortion_coefficient))
@parameterized.parameters((2, 2e-3), (3, 1e-8))
def test_undistortion_factor_random_positive_distortion_coefficient(
self, num_iterations, tolerance):
"""Tests that undistortion_factor produces the expected outputs."""
distorted_squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * 0.2
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
distorted_squared_radii, distortion_coefficient, num_iterations)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
undistorted_squared_radii = tf.square(
undistortion) * distorted_squared_radii
# We distort again the undistorted radii and compare to the original
# distorted_squared_radii.
redistorted_squared_radii = tf.square(
1.0 + distortion_coefficient *
undistorted_squared_radii) * undistorted_squared_radii
with self.subTest(name='distortion'):
self.assertAllClose(
distorted_squared_radii, redistorted_squared_radii, atol=tolerance)
# Positive distortion_coefficients never overflow.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False,))
@parameterized.parameters((2, 2e-2), (3, 6e-3), (4, 6e-4))
def test_undistortion_factor_random_negative_distortion_coefficient(
self, num_iterations, tolerance):
"""Tests that undistortion_factor produces the expected outputs."""
distorted_squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * -0.2
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
distorted_squared_radii, distortion_coefficient, num_iterations)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
undistorted_squared_radii = tf.square(
undistortion) * distorted_squared_radii
# See explanation in the implementation comments for this formula.
expected_overflow_mask = (
distorted_squared_radii * distortion_coefficient + 4.0 / 27.0 < 0)
redistorted_squared_radii = tf.square(
1.0 + distortion_coefficient *
undistorted_squared_radii) * undistorted_squared_radii
valid_mask = np.logical_not(expected_overflow_mask)
redistorted_squared_radii_when_valid = self.evaluate(
redistorted_squared_radii)[valid_mask]
distorted_squared_radii_when_valid = distorted_squared_radii[valid_mask]
with self.subTest(name='distortion'):
self.assertAllClose(
distorted_squared_radii_when_valid,
redistorted_squared_radii_when_valid,
rtol=tolerance,
atol=tolerance)
# We assert correctness of the mask, and of all the pixels that are not in
# overflow, distorting again the undistorted radii and comparing to the
# original distorted_squared_radii.
with self.subTest(name='mask'):
self.assertAllEqual(expected_overflow_mask, mask)
def test_undistortion_factor_zero_distortion_coefficient(self):
"""Tests undistortion_factor at zero distortion coefficient."""
squared_radii = _get_random_radii() * 2.0
undistortion, mask = quadratic_radial_distortion.undistortion_factor(
squared_radii, 0.0)
with self.subTest(name='distortion'):
self.assertAllClose(tf.ones_like(squared_radii), undistortion)
# No overflow when distortion_coefficient = 0.0.
with self.subTest(name='mask'):
self.assertAllEqual(np.zeros_like(squared_radii), mask)
@parameterized.parameters(
('must have a rank greater than 1', (2,), (2, 1)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3,)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3, 3)),
)
def test_distortion_factor_shape_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(
func=quadratic_radial_distortion.distortion_factor,
error_msg=error_msg,
shapes=shapes)
@parameterized.parameters(
((2, 2), ()),
((1, 2, 2), (2,)),
((2, 2, 2), (2,)),
((2, 2), (2, 2)),
((2, 2, 2), (1, 2)),
((2, 3, 4), (1,)),
((2, 3, 4), (1, 1)),
((2, 3, 4), (2,)),
)
def test_distortion_factor_shape_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_not_raised(
func=quadratic_radial_distortion.distortion_factor, shapes=shapes)
@parameterized.parameters(
('must have a rank greater than 1', (2,), (2, 1)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3,)),
('Not all batch dimensions are broadcast-compatible', (2, 2, 2), (3, 3)),
)
def test_undistortion_factor_shape_exception_raised(self, error_msg, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_raised(
func=quadratic_radial_distortion.undistortion_factor,
error_msg=error_msg,
shapes=shapes)
@parameterized.parameters(
((2, 2), ()),
((1, 2, 2), (2,)),
((2, 2, 2), (2,)),
((2, 2), (2, 2)),
((2, 2, 2), (1, 2)),
((2, 3, 4), (1,)),
((2, 3, 4), (1, 1)),
((2, 3, 4), (2,)),
)
def test_undistortion_factor_shape_exception_not_raised(self, *shapes):
"""Tests that the shape exceptions are raised."""
self.assert_exception_is_not_raised(
func=quadratic_radial_distortion.undistortion_factor, shapes=shapes)
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_radial_jacobian(self, distortion_function):
"""Test the Jacobians with respect to squared radii."""
squared_radii = _get_random_radii().astype(np.float64) * 0.5
distortion_coefficients = _get_random_coefficient().astype(np.float64) * 0.5
distortion_coefficients -= 0.25
def distortion_fn(squared_radii):
distortion, _ = distortion_function(squared_radii,
distortion_coefficients)
return distortion
self.assert_jacobian_is_correct_fn(
distortion_fn, [squared_radii], delta=1e-7, atol=1e-3)
@parameterized.parameters(quadratic_radial_distortion.distortion_factor,
quadratic_radial_distortion.undistortion_factor)
def test_both_distortion_coefficient_jacobian(self, distortion_function):
"""Test the Jacobians with respect to distortion coefficients."""
squared_radii = _get_random_radii().astype(np.float64) * 0.5
distortion_coefficients = _get_random_coefficient().astype(np.float64) * 0.5
distortion_coefficients -= 0.25
def distortion_fn(distortion_coefficients):
distortion, _ = distortion_function(squared_radii,
distortion_coefficients)
return distortion
self.assert_jacobian_is_correct_fn(
distortion_fn, [distortion_coefficients], delta=1e-7, atol=1e-3)
if __name__ == '__main__':
test_case.main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/datasets/features/trimesh_feature_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 for tensorflow_graphics.datasets.features.trimesh_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import trimesh_feature
import trimesh
_TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), 'test_data')
class TrimeshFeatureTest(tfds.testing.FeatureExpectationsTestCase):
def test_trimesh(self):
obj_file_path = os.path.join(_TEST_DATA_DIR, 'cube.obj')
obj_file = tf.io.gfile.GFile(obj_file_path)
obj_mesh = trimesh.load(obj_file_path)
expected_vertices = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 0.0], [0.0, 1.0, 1.0],
[1.0, 0.0, 0.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 0.0], [1.0, 1.0, 1.0]])
expected_faces = np.array(
[[0, 6, 4], [0, 2, 6], [0, 3, 2], [0, 1, 3], [2, 7, 6], [2, 3, 7],
[4, 6, 7], [4, 7, 5], [0, 4, 5], [0, 5, 1], [1, 5, 7], [1, 7, 3]],
dtype=np.uint64)
expected_trimesh = {'vertices': expected_vertices, 'faces': expected_faces}
# Create a scene with two cubes.
scene = trimesh.Scene()
scene.add_geometry(obj_mesh)
scene.add_geometry(obj_mesh)
# The expected TriangleFeature for the scene.
expected_scene_feature = {
'vertices':
np.tile(expected_vertices, [2, 1]).astype(np.float32),
'faces':
np.concatenate(
[expected_faces, expected_faces + len(expected_vertices)],
axis=0)
}
self.assertFeature(
feature=trimesh_feature.TriangleMesh(),
shape={
'vertices': (None, 3),
'faces': (None, 3)
},
dtype={
'vertices': tf.float32,
'faces': tf.uint64
},
tests=[
# File path
tfds.testing.FeatureExpectationItem(
value=obj_file_path,
expected=expected_trimesh,
),
# File object
tfds.testing.FeatureExpectationItem(
value=obj_file,
expected=expected_trimesh,
),
# Trimesh
tfds.testing.FeatureExpectationItem(
value=obj_mesh,
expected=expected_trimesh,
),
# Scene
tfds.testing.FeatureExpectationItem(
value=scene,
expected=expected_scene_feature,
),
# FeaturesDict
tfds.testing.FeatureExpectationItem(
value=expected_scene_feature,
expected=expected_scene_feature,
),
# Invalid type
tfds.testing.FeatureExpectationItem(
value=np.random.rand(80, 3),
raise_cls=ValueError,
raise_msg='obj should be either a Trimesh or a Scene',
),
],
)
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 for tensorflow_graphics.datasets.features.trimesh_feature."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
from tensorflow_graphics.datasets.features import trimesh_feature
import trimesh
_TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), 'test_data')
class TrimeshFeatureTest(tfds.testing.FeatureExpectationsTestCase):
def test_trimesh(self):
obj_file_path = os.path.join(_TEST_DATA_DIR, 'cube.obj')
obj_file = tf.io.gfile.GFile(obj_file_path)
obj_mesh = trimesh.load(obj_file_path)
expected_vertices = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 0.0], [0.0, 1.0, 1.0],
[1.0, 0.0, 0.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 0.0], [1.0, 1.0, 1.0]])
expected_faces = np.array(
[[0, 6, 4], [0, 2, 6], [0, 3, 2], [0, 1, 3], [2, 7, 6], [2, 3, 7],
[4, 6, 7], [4, 7, 5], [0, 4, 5], [0, 5, 1], [1, 5, 7], [1, 7, 3]],
dtype=np.uint64)
expected_trimesh = {'vertices': expected_vertices, 'faces': expected_faces}
# Create a scene with two cubes.
scene = trimesh.Scene()
scene.add_geometry(obj_mesh)
scene.add_geometry(obj_mesh)
# The expected TriangleFeature for the scene.
expected_scene_feature = {
'vertices':
np.tile(expected_vertices, [2, 1]).astype(np.float32),
'faces':
np.concatenate(
[expected_faces, expected_faces + len(expected_vertices)],
axis=0)
}
self.assertFeature(
feature=trimesh_feature.TriangleMesh(),
shape={
'vertices': (None, 3),
'faces': (None, 3)
},
dtype={
'vertices': tf.float32,
'faces': tf.uint64
},
tests=[
# File path
tfds.testing.FeatureExpectationItem(
value=obj_file_path,
expected=expected_trimesh,
),
# File object
tfds.testing.FeatureExpectationItem(
value=obj_file,
expected=expected_trimesh,
),
# Trimesh
tfds.testing.FeatureExpectationItem(
value=obj_mesh,
expected=expected_trimesh,
),
# Scene
tfds.testing.FeatureExpectationItem(
value=scene,
expected=expected_scene_feature,
),
# FeaturesDict
tfds.testing.FeatureExpectationItem(
value=expected_scene_feature,
expected=expected_scene_feature,
),
# Invalid type
tfds.testing.FeatureExpectationItem(
value=np.random.rand(80, 3),
raise_cls=ValueError,
raise_msg='obj should be either a Trimesh or a Scene',
),
],
)
if __name__ == '__main__':
tfds.testing.test_main()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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.
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/math/vector.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 vector utility functions."""
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 cross(vector1, vector2, axis=-1, name=None):
"""Computes the cross product between two tensors along an axis.
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector1: A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension
i = axis represents a 3d vector.
vector2: A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension
i = axis represents a 3d vector.
axis: The dimension along which to compute the cross product.
name: A name for this op which defaults to "vector_cross".
Returns:
A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension i = axis
represents the result of the cross product.
"""
with tf.compat.v1.name_scope(name, "vector_cross", [vector1, vector2]):
vector1 = tf.convert_to_tensor(value=vector1)
vector2 = tf.convert_to_tensor(value=vector2)
shape.check_static(
tensor=vector1, tensor_name="vector1", has_dim_equals=(axis, 3))
shape.check_static(
tensor=vector2, tensor_name="vector2", has_dim_equals=(axis, 3))
shape.compare_batch_dimensions(
tensors=(vector1, vector2), last_axes=-1, broadcast_compatible=True)
vector1_x, vector1_y, vector1_z = tf.unstack(vector1, axis=axis)
vector2_x, vector2_y, vector2_z = tf.unstack(vector2, axis=axis)
n_x = vector1_y * vector2_z - vector1_z * vector2_y
n_y = vector1_z * vector2_x - vector1_x * vector2_z
n_z = vector1_x * vector2_y - vector1_y * vector2_x
return tf.stack((n_x, n_y, n_z), axis=axis)
def dot(vector1, vector2, axis=-1, keepdims=True, name=None):
"""Computes the dot product between two tensors along an axis.
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector1: Tensor of rank R and shape `[A1, ..., Ai, ..., An]`, where the
dimension i = axis represents a vector.
vector2: Tensor of rank R and shape `[A1, ..., Ai, ..., An]`, where the
dimension i = axis represents a vector.
axis: The dimension along which to compute the dot product.
keepdims: If True, retains reduced dimensions with length 1.
name: A name for this op which defaults to "vector_dot".
Returns:
A tensor of shape `[A1, ..., Ai = 1, ..., An]`, where the dimension i = axis
represents the result of the dot product.
"""
with tf.compat.v1.name_scope(name, "vector_dot", [vector1, vector2]):
vector1 = tf.convert_to_tensor(value=vector1)
vector2 = tf.convert_to_tensor(value=vector2)
shape.compare_batch_dimensions(
tensors=(vector1, vector2), last_axes=-1, broadcast_compatible=True)
shape.compare_dimensions(
tensors=(vector1, vector2),
axes=axis,
tensor_names=("vector1", "vector2"))
return tf.reduce_sum(
input_tensor=vector1 * vector2, axis=axis, keepdims=keepdims)
def reflect(vector, normal, axis=-1, name=None):
r"""Computes the reflection direction for an incident vector.
For an incident vector \\(\mathbf{v}\\) and normal $$\mathbf{n}$$ this
function computes the reflected vector as
\\(\mathbf{r} = \mathbf{v} - 2(\mathbf{n}^T\mathbf{v})\mathbf{n}\\).
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector: A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i =
axis represents a vector.
normal: A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i =
axis represents a normal around which the vector needs to be reflected.
The normal vector needs to be normalized.
axis: The dimension along which to compute the reflection.
name: A name for this op which defaults to "vector_reflect".
Returns:
A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i = axis
represents a reflected vector.
"""
with tf.compat.v1.name_scope(name, "vector_reflect", [vector, normal]):
vector = tf.convert_to_tensor(value=vector)
normal = tf.convert_to_tensor(value=normal)
shape.compare_dimensions(
tensors=(vector, normal), axes=axis, tensor_names=("vector", "normal"))
shape.compare_batch_dimensions(
tensors=(vector, normal), last_axes=-1, broadcast_compatible=True)
normal = asserts.assert_normalized(normal, axis=axis)
dot_product = dot(vector, normal, axis=axis)
return vector - 2.0 * dot_product * normal
# 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 vector utility functions."""
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 cross(vector1, vector2, axis=-1, name=None):
"""Computes the cross product between two tensors along an axis.
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector1: A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension
i = axis represents a 3d vector.
vector2: A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension
i = axis represents a 3d vector.
axis: The dimension along which to compute the cross product.
name: A name for this op which defaults to "vector_cross".
Returns:
A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension i = axis
represents the result of the cross product.
"""
with tf.compat.v1.name_scope(name, "vector_cross", [vector1, vector2]):
vector1 = tf.convert_to_tensor(value=vector1)
vector2 = tf.convert_to_tensor(value=vector2)
shape.check_static(
tensor=vector1, tensor_name="vector1", has_dim_equals=(axis, 3))
shape.check_static(
tensor=vector2, tensor_name="vector2", has_dim_equals=(axis, 3))
shape.compare_batch_dimensions(
tensors=(vector1, vector2), last_axes=-1, broadcast_compatible=True)
vector1_x, vector1_y, vector1_z = tf.unstack(vector1, axis=axis)
vector2_x, vector2_y, vector2_z = tf.unstack(vector2, axis=axis)
n_x = vector1_y * vector2_z - vector1_z * vector2_y
n_y = vector1_z * vector2_x - vector1_x * vector2_z
n_z = vector1_x * vector2_y - vector1_y * vector2_x
return tf.stack((n_x, n_y, n_z), axis=axis)
def dot(vector1, vector2, axis=-1, keepdims=True, name=None):
"""Computes the dot product between two tensors along an axis.
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector1: Tensor of rank R and shape `[A1, ..., Ai, ..., An]`, where the
dimension i = axis represents a vector.
vector2: Tensor of rank R and shape `[A1, ..., Ai, ..., An]`, where the
dimension i = axis represents a vector.
axis: The dimension along which to compute the dot product.
keepdims: If True, retains reduced dimensions with length 1.
name: A name for this op which defaults to "vector_dot".
Returns:
A tensor of shape `[A1, ..., Ai = 1, ..., An]`, where the dimension i = axis
represents the result of the dot product.
"""
with tf.compat.v1.name_scope(name, "vector_dot", [vector1, vector2]):
vector1 = tf.convert_to_tensor(value=vector1)
vector2 = tf.convert_to_tensor(value=vector2)
shape.compare_batch_dimensions(
tensors=(vector1, vector2), last_axes=-1, broadcast_compatible=True)
shape.compare_dimensions(
tensors=(vector1, vector2),
axes=axis,
tensor_names=("vector1", "vector2"))
return tf.reduce_sum(
input_tensor=vector1 * vector2, axis=axis, keepdims=keepdims)
def reflect(vector, normal, axis=-1, name=None):
r"""Computes the reflection direction for an incident vector.
For an incident vector \\(\mathbf{v}\\) and normal $$\mathbf{n}$$ this
function computes the reflected vector as
\\(\mathbf{r} = \mathbf{v} - 2(\mathbf{n}^T\mathbf{v})\mathbf{n}\\).
Note:
In the following, A1 to An are optional batch dimensions, which should be
broadcast compatible.
Args:
vector: A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i =
axis represents a vector.
normal: A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i =
axis represents a normal around which the vector needs to be reflected.
The normal vector needs to be normalized.
axis: The dimension along which to compute the reflection.
name: A name for this op which defaults to "vector_reflect".
Returns:
A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i = axis
represents a reflected vector.
"""
with tf.compat.v1.name_scope(name, "vector_reflect", [vector, normal]):
vector = tf.convert_to_tensor(value=vector)
normal = tf.convert_to_tensor(value=normal)
shape.compare_dimensions(
tensors=(vector, normal), axes=axis, tensor_names=("vector", "normal"))
shape.compare_batch_dimensions(
tensors=(vector, normal), last_axes=-1, broadcast_compatible=True)
normal = asserts.assert_normalized(normal, axis=axis)
dot_product = dot(vector, normal, axis=axis)
return vector - 2.0 * dot_product * normal
# API contains all public functions and classes.
__all__ = export_api.get_functions_and_classes()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./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()
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./MANIFEST.in | recursive-include tensorflow_graphics *.so | recursive-include tensorflow_graphics *.so | -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| ./tensorflow_graphics/projects/neural_voxel_renderer/prepare_tfrecords/README.md | # Dataset generation for Neural Voxel Renderer
___
The [training](https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/projects/neural_voxel_renderer/train.ipynb)
and [inference](https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/projects/neural_voxel_renderer/demo.ipynb)
examples use demo data to illustrate the functionality of Neural Voxel Renderer (NVR).
In this document, we describe how to generate the the full dataset to train NVR from scratch.
**Warning:** the generated TFRecords will take ~350GB of disk space.
___
## Download the colored voxels
This dataset contains the colored voxels of 2040 chairs. The size of the dataset is
**~16GB**. Each shape is represented as 128<sup>3</sup> x 4 voxel grid, where
each voxel contains an RGB and occupancy value. The color was obtained from a single image
aligned with the voxels.
``` bash
PATH_TO_COLOR_VOXELS=/tmp/colored_voxels/
mkdir $PATH_TO_COLOR_VOXELS
bash download_colored_voxels.sh $PATH_TO_COLOR_VOXELS
```
## Download the synthetic images
The dataset contains the target images (rendered using Blender) and all the necessary
information that was used to set-up the scene in 3D (object rotation, translation, camera parameters, etc.).
The size of the dataset is **~400MB**
``` bash
PATH_TO_SYNTHETIC_DATASET=/tmp/synthetic_dataset/
mkdir $PATH_TO_SYNTHETIC_DATASET
wget -P $PATH_TO_SYNTHETIC_DATASET https://storage.googleapis.com/tensorflow-graphics/notebooks/neural_voxel_renderer/blender_dataset/default_chairs_test.tfrecord
wget -P $PATH_TO_SYNTHETIC_DATASET https://storage.googleapis.com/tensorflow-graphics/notebooks/neural_voxel_renderer/blender_dataset/default_chairs_train.tfrecord
```
## Run the script
The script iterates over all the synthetic images and pairs them with the corresponding
colored voxels, placed according to the scene set-up. Additionally, it estimates
the rendered image directly from the voxels which is used as additional input in
NVR plus.
``` python
PATH_TO_TFRECORDS=/tmp/tfrecords/
mkdir $PATH_TO_TFRECORDS
python generate_tfrecords_nvr_plus.py -- --mode test --voxels_dir $PATH_TO_COLOR_VOXELS --images_dir $PATH_TO_SYNTHETIC_DATASET --output_dir $PATH_TO_TFRECORDS
python generate_tfrecords_nvr_plus.py -- --mode train --voxels_dir $PATH_TO_COLOR_VOXELS --images_dir $PATH_TO_SYNTHETIC_DATASET --output_dir $PATH_TO_TFRECORDS
```
| # Dataset generation for Neural Voxel Renderer
___
The [training](https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/projects/neural_voxel_renderer/train.ipynb)
and [inference](https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/projects/neural_voxel_renderer/demo.ipynb)
examples use demo data to illustrate the functionality of Neural Voxel Renderer (NVR).
In this document, we describe how to generate the the full dataset to train NVR from scratch.
**Warning:** the generated TFRecords will take ~350GB of disk space.
___
## Download the colored voxels
This dataset contains the colored voxels of 2040 chairs. The size of the dataset is
**~16GB**. Each shape is represented as 128<sup>3</sup> x 4 voxel grid, where
each voxel contains an RGB and occupancy value. The color was obtained from a single image
aligned with the voxels.
``` bash
PATH_TO_COLOR_VOXELS=/tmp/colored_voxels/
mkdir $PATH_TO_COLOR_VOXELS
bash download_colored_voxels.sh $PATH_TO_COLOR_VOXELS
```
## Download the synthetic images
The dataset contains the target images (rendered using Blender) and all the necessary
information that was used to set-up the scene in 3D (object rotation, translation, camera parameters, etc.).
The size of the dataset is **~400MB**
``` bash
PATH_TO_SYNTHETIC_DATASET=/tmp/synthetic_dataset/
mkdir $PATH_TO_SYNTHETIC_DATASET
wget -P $PATH_TO_SYNTHETIC_DATASET https://storage.googleapis.com/tensorflow-graphics/notebooks/neural_voxel_renderer/blender_dataset/default_chairs_test.tfrecord
wget -P $PATH_TO_SYNTHETIC_DATASET https://storage.googleapis.com/tensorflow-graphics/notebooks/neural_voxel_renderer/blender_dataset/default_chairs_train.tfrecord
```
## Run the script
The script iterates over all the synthetic images and pairs them with the corresponding
colored voxels, placed according to the scene set-up. Additionally, it estimates
the rendered image directly from the voxels which is used as additional input in
NVR plus.
``` python
PATH_TO_TFRECORDS=/tmp/tfrecords/
mkdir $PATH_TO_TFRECORDS
python generate_tfrecords_nvr_plus.py -- --mode test --voxels_dir $PATH_TO_COLOR_VOXELS --images_dir $PATH_TO_SYNTHETIC_DATASET --output_dir $PATH_TO_TFRECORDS
python generate_tfrecords_nvr_plus.py -- --mode train --voxels_dir $PATH_TO_COLOR_VOXELS --images_dir $PATH_TO_SYNTHETIC_DATASET --output_dir $PATH_TO_TFRECORDS
```
| -1 |
tensorflow/graphics | 480 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions. | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
| copybara-service[bot] | "2021-01-19T21:31:22Z" | "2021-02-01T16:01:31Z" | d047500d9b6cb9b716e4b02859d5cc9efb004156 | e539c142799936d76d84d0861951ed883a9b4673 | - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.. - Fix use case when `screen_dimensions` or `lower_left_corner` don't have batch dimensions, while other tensors do have batch dimensions.
- Unify rasterization backend output to produce channel dimension for `mask` and `triangle_index`
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| -1 |