From 6c66acdda96a4508fbcd2ce344110610aa3ce761 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 13:42:07 +0100 Subject: [PATCH 01/32] point_cloud_class_initial --- .../projects/point_convolutions/.flake8 | 32 ++ .../projects/point_convolutions/LICENSE | 202 +++++++++++++ .../projects/point_convolutions/README.md | 70 +++++ .../point_convolutions/pylib/__init__.py | 15 + .../point_convolutions/pylib/pc/PointCloud.py | 281 ++++++++++++++++++ .../pylib/pc/tests/aabb_test.py | 72 +++++ .../pylib/pc/tests/point_cloud_test.py | 109 +++++++ .../pylib/pc/tests/utils.py | 81 +++++ .../point_convolutions/pylib/pc/utils.py | 114 +++++++ .../projects/point_convolutions/pytest.ini | 3 + 10 files changed, 979 insertions(+) create mode 100644 tensorflow_graphics/projects/point_convolutions/.flake8 create mode 100644 tensorflow_graphics/projects/point_convolutions/LICENSE create mode 100644 tensorflow_graphics/projects/point_convolutions/README.md create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/__init__.py create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/PointCloud.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/aabb_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pytest.ini diff --git a/tensorflow_graphics/projects/point_convolutions/.flake8 b/tensorflow_graphics/projects/point_convolutions/.flake8 new file mode 100644 index 000000000..f4e3292ee --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/.flake8 @@ -0,0 +1,32 @@ +[flake8] +inline-quotes = double +max-line-length = 79 +max-complexity = 10 +exclude = .git, + .tox, + .pytest_cache, + __pycache__, + tensorflow_graphics/projects/* + tensorflow_graphics/submodules/* +ignore = C901, + E101, + E111, + E114, + E121, + E125, + E126, + E129, + E221, + E265, + E271, + E305, + E306, + #E501, + E502, + E731, + E741, + F401, + F812, + W191, + W503, + W504, \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/LICENSE b/tensorflow_graphics/projects/point_convolutions/LICENSE new file mode 100644 index 000000000..d64569567 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/tensorflow_graphics/projects/point_convolutions/README.md b/tensorflow_graphics/projects/point_convolutions/README.md new file mode 100644 index 000000000..83db3fe66 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/README.md @@ -0,0 +1,70 @@ +# TensorFlow Graphics Point Cloud Convolutions + +This module contains a python TensorFlow module `pylib` and a custom ops package in `tfg_custom_ops`. +While it is possible to run without the custom ops package, it is strongly advised to install it for performance and memory efficiency. + +## Content + +This code contains all necessary operations to perform point cloud convolutions + +1. Datastructure + - Point cloud class for batches of arbitrary sized point clouds. + - Memory efficient regular grid data structure +2. Point cloud operations + - Neighborhood computation + - Point density estimation + - Spatial sampling + - Poisson Disk sampling + - Cell average sampling +3. Convolution kernels + - Kernel Point Convolutions + - linear interpolation + - gaussian interpolation + - deformable points with regularization loss as in [KPConv](https://arxiv.org/abs/1904.08889) + - MLP + - multiple MLPs as in [MCConv](https://arxiv.org/abs/1806.01759) + - single MLP as in [PointConv](https://arxiv.org/abs/1811.07246) +4. Feature aggregation inside receptive fields + - Monte-Carlo integration with pdf + - Constant summation +5. Easy to use classes for building models + - `PointCloud` class + - `PointHierarchy` for sequential downsampling of point clouds + - layer classes + - `MCConv` + - `PointConv` + - `KPConv` + - `Conv1x1` + +## Installation + +Precompiled versions of the custom ops package are provided in `custom_ops/pkg_builds/tf_*` for the latest TensorFlow versions. +For compilation instructions see the [README](custom_ops/README.md) in the `custom_ops` folder. + +To install it run the following command (replace `VERSION` with your installed TensorFlow version, e.g. `2.3.0`) +```bash + pip install custom_ops/tf_VERSION/*.whl +``` + +## Tutorials + +Check out the Colab notebooks for an introduction to the code + +- [Introduction](pylib/notebooks/Introduction.ipynb) +- [Classification on ModelNet40](pylib/notebooks/ModelNet40.ipynb) + +## Unit tests + +Unit tests can be evaluated using + +```bash + pip install -r pytest_requirements.txt + pytest pylib/ +``` + +These include tests of the custom ops if they are installed. + +## Additional Information + +You may use this software under the +[Apache 2.0 License](https://github.com/schellmi42/tensorflow_graphics_point_clouds/blob/master/LICENSE). \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py new file mode 100755 index 000000000..87d0e6976 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific + +from pylib import pc +from pylib import io diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointCloud.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointCloud.py new file mode 100755 index 000000000..e08cf279a --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointCloud.py @@ -0,0 +1,281 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to represent a point cloud.""" + +import tensorflow as tf +from tensorflow_graphics.geometry.convolution.utils import \ + flatten_batch_to_2d, unflatten_2d_to_batch + +from pylib.pc.utils import check_valid_point_cloud_input + + +class _AABB: + """Axis aligned bounding box of a point cloud. + + Args: + Pointcloud: A 'PointCloud' instance from which to compute the + axis aligned bounding box. + + """ + + def __init__(self, point_cloud, name=None): + + self._batch_size = point_cloud._batch_size + self._batch_shape = point_cloud._batch_shape + self.point_cloud_ = point_cloud + + self._aabb_min = tf.math.unsorted_segment_min( + data=point_cloud._points, segment_ids=point_cloud._batch_ids, + num_segments=self._batch_size) - 1e-9 + self._aabb_max = tf.math.unsorted_segment_max( + data=point_cloud._points, segment_ids=point_cloud._batch_ids, + num_segments=self._batch_size) + 1e-9 + + def get_diameter(self, ord='euclidean', name=None): + """ Returns the diameter of the bounding box. + + Note: + In the following, A1 to An are optional batch dimensions. + + Args: + ord: Order of the norm. Supported values are `'euclidean'`, + `1`, `2`, `np.inf` and any positive real number yielding the + corresponding p-norm. Default is `'euclidean'`. (optional) + Return: + diam: A `float` 'Tensor' of shape `[A1, ..., An]`, diameters of the + bounding boxes + + """ + + diam = tf.linalg.norm(self._aabb_max - self._aabb_min, ord=ord, axis=-1) + if self._batch_shape is None: + return diam + else: + return tf.reshape(diam, self._batch_shape) + + +class PointCloud: + """ Class to represent point clouds. + + Note: + In the following, A1 to An are optional batch dimensions. + + Args: + points: A float `Tensor` either of shape `[N, D]` or of shape + `[A1, .., An, V, D]`, possibly padded as indicated by `sizes`. + Represents the point coordinates. + batch_ids: An `int` `Tensor` of shape `[N]` associated with the points. + Is required if `points` is of shape `[N, D]`. + sizes: An `int` `Tensor` of shape `[A1, ..., An]` indicating the + true input sizes in case of padding (`sizes=None` indicates no padding) + Note that `sizes[A1, ..., An] <= V` or `sum(sizes) == N`. + batch_size: An `int`, the size of the batch. + """ + + def __init__(self, + points, + batch_ids=None, + batch_size=None, + sizes=None, + name=None): + points = tf.convert_to_tensor(value=points, dtype=tf.float32) + if sizes is not None: + sizes = tf.convert_to_tensor(value=sizes, dtype=tf.int32) + if batch_ids is not None: + batch_ids = tf.convert_to_tensor(value=batch_ids, dtype=tf.int32) + if batch_size is not None: + self._batch_size = tf.convert_to_tensor(value=batch_size, dtype=tf.int32) + else: + self._batch_size = None + + check_valid_point_cloud_input(points, sizes, batch_ids) + + self._sizes = sizes + # compatibility batch size as CPU int for graph mode + self._batch_size_numpy = batch_size + self._batch_ids = batch_ids + self._dimension = tf.gather(tf.shape(points), tf.rank(points) - 1) + self._batch_shape = None + self._unflatten = None + self._aabb = None + + if points.shape.ndims > 2: + self._init_from_padded(points) + else: + self._init_from_segmented(points) + + if self._batch_size_numpy is None: + self._batch_size_numpy = self._batch_size + + #Sort the points based on the batch ids in incremental order. + self._sorted_indices_batch = tf.argsort(self._batch_ids) + + def _init_from_padded(self, points): + """converting padded `Tensor` of shape `[A1, ..., An, V, D]` into a 2D + `Tensor` of shape `[N,D]` with segmentation ids. + """ + self._batch_shape = tf.shape(points)[:-2] + if self._batch_size is None: + self._batch_size = tf.reduce_prod(self._batch_shape) + if self._sizes is None: + self._sizes = tf.constant( + value=tf.shape(points)[-2], shape=self._batch_shape) + self._get_segment_id = tf.reshape( + tf.range(0, self._batch_size), self._batch_shape) + self._points, self._unflatten = flatten_batch_to_2d(points, self._sizes) + self._batch_ids = tf.repeat( + tf.range(0, self._batch_size), + repeats=tf.reshape(self._sizes, [-1])) + + def _init_from_segmented(self, points): + """if input is already 2D `Tensor` with segmentation ids or given sizes. + """ + if self._batch_ids is None: + if self._batch_size is None: + self._batch_size = tf.reduce_prod(self._sizes.shape) + self._batch_ids = tf.repeat(tf.range(0, self._batch_size), self._sizes) + if self._batch_size is None: + self._batch_size = tf.reduce_max(self._batch_ids) + 1 + self._points = points + + def get_points(self, id=None, max_num_points=None, name=None): + """ Returns the points. + + Note: + In the following, A1 to An are optional batch dimensions. + + If called withoud specifying 'id' returns the points in padded format + `[A1, ..., An, V, D]` + + Args: + id: An `int`, index of point cloud in the batch, if `None` returns all + max_num_points: An `int`, specifies the 'V' dimension the method returns, + by default uses maximum of 'sizes'. `max_rows >= max(sizes)` + + Return: + A `float` `Tensor` + of shape `[Ni, D]`, if 'id' was given + or + of shape `[A1, ..., An, V, D]`, zero padded, if no `id` was given. + + """ + if id is not None: + if not isinstance(id, int): + slice = self._get_segment_id + for slice_id in id: + slice = slice[slice_id] + id = slice + if id > self._batch_size: + raise IndexError('batch index out of range') + return self._points[self._batch_ids == id] + else: + return self.get_unflatten(max_num_points=max_num_points)(self._points) + + def get_sizes(self, name=None): + """ Returns the sizes of the point clouds in the batch. + + Note: + In the following, A1 to An are optional batch dimensions. + Use this instead of accessing 'self._sizes', + if the class was constructed using segmented input the '_sizes' is + created in this method. + + Returns: + `Tensor` of shape `[A1, .., An]`. + + """ + if self._sizes is None: + _ids, _, self._sizes = tf.unique_with_counts( + self._batch_ids) + _ids_sorted = tf.argsort(_ids) + self._sizes = tf.gather(self._sizes, _ids_sorted) + if self._batch_shape is not None: + self._sizes = tf.reshape(self._sizes, self._batch_shape) + return self._sizes + + def get_unflatten(self, max_num_points=None, name=None): + """ Returns the method to unflatten the segmented points. + + Use this instead of accessing 'self._unflatten', + if the class was constructed using segmented input the '_unflatten' method + is created in this method. + + Note: + In the following, A1 to An are optional batch dimensions. + + Args: + max_num_points: An `int`, specifies the 'V' dimension the method returns, + by default uses maximum of 'sizes'. `max_rows >= max(sizes)` + Returns: + A method to unflatten the segmented points, which returns a `Tensor` of + shape `[A1,...,An,V,D]`, zero padded. + + Raises: + ValueError: When trying to unflatten unsorted points. + + """ + if self._unflatten is None: + self._unflatten = lambda data: unflatten_2d_to_batch( + data=tf.gather(data, self._sorted_indices_batch), + sizes=self.get_sizes(), + max_rows=max_num_points) + return self._unflatten + + def get_AABB(self) -> _AABB: + """ Returns the axis aligned bounding box of the point cloud. + + Use this instead of accessing `self._aabb`, as the bounding box + is initialized with tthe first call of his method. + + Returns: + A `AABB` instance + + """ + if self._aabb is None: + self._aabb = _AABB(point_cloud=self) + return self._aabb + + def set_batch_shape(self, batch_shape, name=None): + """ Function to change the batch shape + + Use this to set a batch shape instead of using 'self._batch_shape' to + also change dependent variables. + + Note: + In the following, A1 to An are optional batch dimensions. + + Args: + batch_shape: A 1D `int` `Tensor` `[A1,...,An]`. + + Raises: + ValueError: if shape does not sum up to batch size. + + """ + if batch_shape is not None: + batch_shape = tf.convert_to_tensor(value=batch_shape, dtype=tf.int32) + tf.assert_equal( + tf.reduce_prod(batch_shape), self._batch_size, + f'Incompatible batch size. Must be {self._batch_size} \ + but is {tf.reduce_prod(batch_shape)}') + # if tf.reduce_prod(batch_shape) != self._batch_size: + # raise ValueError( + # f'Incompatible batch size. Must be {self._batch_size} \ + # but is {tf.reduce_prod(batch_shape)}') + self._batch_shape = batch_shape + self._get_segment_id = tf.reshape( + tf.range(0, self._batch_size), self._batch_shape) + if self._sizes is not None: + self._sizes = tf.reshape(self._sizes, self._batch_shape) + else: + self._batch_shape = None diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/aabb_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/aabb_test.py new file mode 100644 index 000000000..c7635badd --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/aabb_test.py @@ -0,0 +1,72 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to test bounding box""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, AABB +from pylib.pc.tests import utils + + +class AABB_test(test_case.TestCase): + + @parameterized.parameters( + (1, 1000, 3), + (8, 1000, 2), + (32, 1000, 4) + ) + def test_aabb_min_max(self, batch_size, num_points, dimension): + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension) + aabb_max_numpy = np.empty([batch_size, dimension]) + aabb_min_numpy = np.empty([batch_size, dimension]) + for i in range(batch_size): + aabb_max_numpy[i] = np.amax(points[batch_ids == i], axis=0) + aabb_min_numpy[i] = np.amin(points[batch_ids == i], axis=0) + + aabb_tf = PointCloud(points, batch_ids=batch_ids, + batch_size=batch_size).get_AABB() + + self.assertAllClose(aabb_max_numpy, aabb_tf._aabb_max) + self.assertAllClose(aabb_min_numpy, aabb_tf._aabb_min) + + @parameterized.parameters( + ([1], 1000, 3), + ([4, 4], 1000, 2), + ([1, 2, 3], 100, 4) + ) + def test_aabb_diameter(self, batch_shape, max_num_points, dimension): + points, sizes = utils._create_random_point_cloud_padded( + max_num_points, batch_shape, dimension) + batch_size = np.prod(batch_shape) + diameter_numpy = np.empty(batch_size) + points_flat = np.reshape(points, [batch_size, max_num_points, dimension]) + sizes_flat = np.reshape(sizes, [batch_size]) + for i in range(batch_size): + curr_pts = points_flat[i][:sizes_flat[i]] + diag = np.amax(curr_pts, axis=0) - np.amin(curr_pts, axis=0) + diameter_numpy[i] = np.linalg.norm(diag) + diameter_numpy = np.reshape(diameter_numpy, batch_shape) + + aabb_tf = PointCloud(points, sizes=sizes).get_AABB() + diameter_tf = aabb_tf.get_diameter() + self.assertAllClose(diameter_numpy, diameter_tf) + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py new file mode 100644 index 000000000..e71d27ff3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py @@ -0,0 +1,109 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test point clouds""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc.tests import utils + + +class PointCloudTest(test_case.TestCase): + + @parameterized.parameters( + ([32], 100, 3), + ([5, 2], 100, 2), + ([2, 3, 4], 100, 4) + ) + def test_flatten_unflatten_padded(self, batch_shape, num_points, dimension): + batch_size = np.prod(batch_shape) + points, sizes = utils._create_random_point_cloud_padded( + num_points, batch_shape, dimension=dimension) + point_cloud = PointCloud(points, sizes=sizes) + retrieved_points = point_cloud.get_points().numpy() + self.assertAllEqual(points.shape, retrieved_points.shape) + points = points.reshape([batch_size, num_points, dimension]) + retrieved_points = retrieved_points.reshape( + [batch_size, num_points, dimension]) + sizes = sizes.reshape([batch_size]) + for i in range(batch_size): + self.assertAllClose(points[i, :sizes[i]], retrieved_points[i, :sizes[i]]) + self.assertTrue(np.all(retrieved_points[i, sizes[i]:] == 0)) + + @parameterized.parameters( + (100, 32, [8, 4]), + (100, 16, [2, 2, 2, 2]) + ) + def test_construction_methods(self, max_num_points, batch_size, batch_shape): + points, sizes = utils._create_random_point_cloud_padded( + max_num_points, batch_shape) + num_points = np.sum(sizes) + + sizes_flat = sizes.reshape([batch_size]) + points_flat = points.reshape([batch_size, max_num_points, 3]) + batch_ids = np.repeat(np.arange(0, batch_size), sizes_flat) + + points_seg = np.empty([num_points, 3]) + cur_id = 0 + for pts, size in zip(points_flat, sizes_flat): + points_seg[cur_id:cur_id + size] = pts[:size] + cur_id += size + + pc_from_padded = PointCloud(points, sizes=sizes) + self.assertAllEqual(batch_ids, pc_from_padded._batch_ids) + self.assertAllClose(points_seg, pc_from_padded._points) + + pc_from_ids = PointCloud(points_seg, batch_ids) + pc_from_ids.set_batch_shape(batch_shape) + + pc_from_sizes = PointCloud(points_seg, sizes=sizes_flat) + pc_from_sizes.set_batch_shape(batch_shape) + self.assertAllEqual(batch_ids, pc_from_sizes._batch_ids) + + points_from_padded = pc_from_padded.get_points( + max_num_points=max_num_points) + points_from_ids = pc_from_ids.get_points( + max_num_points=max_num_points) + points_from_sizes = pc_from_sizes.get_points( + max_num_points=max_num_points) + + self.assertAllEqual(points_from_padded, points_from_ids) + self.assertAllEqual(points_from_ids, points_from_sizes) + self.assertAllEqual(points_from_sizes, points_from_padded) + + @parameterized.parameters( + (1000, + ['Invalid input! Point tensor is of dimension 1 \ + but should be at least 2!', + 'Missing input! Either sizes or batch_ids must be given.', + 'Invalid sizes! Sizes of points and batch_ids are not equal.']) + ) + def test_exceptions_raised_at_construction(self, num_points, msgs): + points = np.random.rand(num_points) + batch_ids = np.zeros(num_points) + with self.assertRaisesRegex(ValueError, msgs[0]): + _ = PointCloud(points, batch_ids) + points = np.random.rand(num_points, 3) + with self.assertRaisesRegexp(ValueError, msgs[1]): + _ = PointCloud(points) + with self.assertRaisesRegexp(AssertionError, msgs[2]): + _ = PointCloud(points, batch_ids[1:]) + + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py new file mode 100644 index 000000000..fe69ed795 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py @@ -0,0 +1,81 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""helper functions for unit tests""" + +import tensorflow as tf +import numpy as np + + +def _create_random_point_cloud_segmented(batch_size, + num_points, + dimension=3, + sizes=None, + scale=1, + clean_aabb=False, + equal_sized_batches=False): + points = np.random.uniform(0, scale, [num_points, dimension]) + if sizes is None: + if not equal_sized_batches: + batch_ids = np.random.randint(0, batch_size, num_points) + batch_ids[:batch_size] = np.arange(0, batch_size) + else: + batch_ids = np.repeat(np.arange(0, batch_size), num_points // batch_size) + # batch_ids = np.sort(batch_ids) + else: + sizes = np.array(sizes, dtype=int) + batch_ids = np.repeat(np.arange(0, batch_size), sizes) + if clean_aabb: + # adds points such that the aabb is [0,0,0] [1,1,1]*scale + # to prevent rounding errors + points = np.concatenate( + (points, scale * np.ones([batch_size, dimension]) - 1e-9, + 1e-9 + np.zeros([batch_size, dimension]))) + batch_ids = np.concatenate( + (batch_ids, np.arange(0, batch_size), np.arange(0, batch_size))) + return points, batch_ids + + +def _create_random_point_cloud_padded(max_num_points, + batch_shape, + dimension=3, + sizes=None, + scale=1): + batch_size = np.prod(batch_shape) + points = np.random.uniform( + 0, scale, [max_num_points * batch_size, dimension]) + points = points.reshape(batch_shape + [max_num_points, dimension]) + if sizes is None: + sizes = np.random.randint(1, max_num_points, batch_shape) + return points, sizes + + +def _create_uniform_distributed_point_cloud_2D(num_points_sqrt, + scale=1, + flat=False): + ticks = np.linspace(0, scale, num=num_points_sqrt) + points = np.array(np.meshgrid(ticks, ticks)).T + if flat: + points = points.reshape(-1, 2) + return points + + +def _create_uniform_distributed_point_cloud_3D(num_points_root, + bb_min=0, + bb_max=1, + flat=False): + ticks = np.linspace(bb_min, bb_max, num=num_points_root, endpoint=False) + points = np.array(np.meshgrid(ticks, ticks, ticks)).T + if flat: + points = points.reshape(-1, 3) + return points diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py new file mode 100644 index 000000000..2f761c375 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -0,0 +1,114 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +''' helper functions for point clouds ''' + +import tensorflow as tf +from tensorflow_graphics.geometry.convolution.utils import flatten_batch_to_2d +from pylib.pc import PointCloud + + +def check_valid_point_cloud_input(points, sizes, batch_ids): + """Checks that the inputs to the constructor of class 'PointCloud' are valid. + + Args: + points: A `float` `Tensor` of shape `[N, D]` or `[A1, ..., An, V, D]`. + sizes: An `int` `Tensor` of shape `[A1, ..., An]` or `None`. + batch_ids: An `int` `Tensor` of shape `[N]` or `None`. + + Raises: + Value Error: If input dimensions are invalid or no valid segmentation + is given. + + """ + + if points.shape.ndims == 2 and sizes is None and batch_ids is None: + raise ValueError('Missing input! Either sizes or batch_ids must be given.') + if points.shape.ndims == 1: + raise ValueError( + 'Invalid input! Point tensor is of dimension 1 \ + but should be at least 2!') + if points.shape.ndims == 2 and batch_ids is not None: + if points.shape[0] != batch_ids.shape[0]: + raise AssertionError('Invalid sizes! Sizes of points and batch_ids are' + + ' not equal.') + + +def check_valid_point_hierarchy_input(point_cloud, cell_sizes, pool_mode): + """ Checks that inputs to the constructor of class 'PontHierarchy' are valid. + + Args: + point_cloud: A 'PointCloud' instance. + cell_sizes: A `list` of `float` `Tensors`. + pool_mode: An `int`. + + Raises: + TypeError: if input is of invalid type + ValueError: if pool_mode is invalid, or cell_sizes dimension are invalid + or non-positive + + """ + if not isinstance(point_cloud, (PointCloud)): + raise TypeError('Input must be instance of class PointCloud') + if pool_mode not in [0, 1]: + raise ValueError('Unknown pooling mode.') + for curr_cell_sizes in cell_sizes: + if any(curr_cell_sizes <= 0): + raise ValueError('cell size must be positive.') + if not curr_cell_sizes.shape[0] in [1, point_cloud.dimension_]: + raise ValueError( + 'Invalid number of cell sizes for point cloud' +\ + f'dimension. Must be 1 or {point_cloud.dimension_} but is' +\ + f'{curr_cell_sizes.shape[0]}.') + + +def _flatten_features(features, point_cloud: PointCloud): + """ Converts features of shape `[A1, ..., An, C]` to shape `[N, C]`. + + Args: + features: A `Tensor`. + point_cloud: A `PointCloud` instance. + + Returns: + A `Tensor` of shape `[N, C]`. + + """ + if features.shape.ndims > 2: + sizes = point_cloud.get_sizes() + features, _ = flatten_batch_to_2d(features, sizes) + sorting = tf.math.invert_permutation(point_cloud._sorted_indices_batch) + features = tf.gather(features, sorting) + else: + tf.assert_equal(tf.shape(features)[0], tf.shape(point_cloud._points)[0]) + tf.assert_equal(tf.rank(features), 2) + return features + + +def cast_to_num_dims(values, num_dims, dtype=tf.float32): + """ Converts an input to the specified `dtype` and repeats it `num_dims` + times. + + Args: + values: Must be convertible to a `Tensor` of shape `[], [1]` or + `[num_dims]`. + dtype: A `tf.dtype`. + + Returns: + A `dtype` `Tensor` of shape `[num_dims]`. + + """ + values = tf.cast(tf.convert_to_tensor(value=values), + dtype=dtype) + if values.shape == [] or values.shape[0] == 1: + values = tf.repeat(values, num_dims) + return values diff --git a/tensorflow_graphics/projects/point_convolutions/pytest.ini b/tensorflow_graphics/projects/point_convolutions/pytest.ini new file mode 100644 index 000000000..82972425f --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +norecursedirs = tensorflow_graphics/rendering/opengl/tests tensorflow_graphics/submodules +python_files = *_test.py From 047cddffe92d2dc3f66fd0ecd1319467f9a07704 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 13:48:51 +0100 Subject: [PATCH 02/32] commented unused functions --- .../projects/point_convolutions/pylib/pc/utils.py | 3 ++- .../projects/point_convolutions/pytest_requirements.txt | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pytest_requirements.txt diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py index 2f761c375..972222310 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -43,7 +43,7 @@ def check_valid_point_cloud_input(points, sizes, batch_ids): raise AssertionError('Invalid sizes! Sizes of points and batch_ids are' + ' not equal.') - +''' def check_valid_point_hierarchy_input(point_cloud, cell_sizes, pool_mode): """ Checks that inputs to the constructor of class 'PontHierarchy' are valid. @@ -112,3 +112,4 @@ def cast_to_num_dims(values, num_dims, dtype=tf.float32): if values.shape == [] or values.shape[0] == 1: values = tf.repeat(values, num_dims) return values +''' diff --git a/tensorflow_graphics/projects/point_convolutions/pytest_requirements.txt b/tensorflow_graphics/projects/point_convolutions/pytest_requirements.txt new file mode 100644 index 000000000..418e8a444 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pytest_requirements.txt @@ -0,0 +1,3 @@ +pytest +sklearn +trimesh \ No newline at end of file From 7c05856870d627297d29209d6f84afe57c9d9cef Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 15:27:45 +0100 Subject: [PATCH 03/32] added missing __init__.py --- .../point_convolutions/pylib/pc/__init__.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py new file mode 100755 index 000000000..9b58df3ad --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Point cloud module.""" + +from .PointCloud import _AABB as AABB +from .PointCloud import PointCloud +from .Grid import Grid +from .Neighborhood import Neighborhood +from .Neighborhood import KDEMode +from .sampling import poisson_disk_sampling, cell_average_sampling +from .sampling import sample +from .PointHierarchy import PointHierarchy + +from pylib.pc import layers +from pylib.pc import custom_ops From 80e9f605223740290111d49c75fa89a4f5891005 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 15:35:02 +0100 Subject: [PATCH 04/32] added missing __init__.py --- .../point_convolutions/pylib/pc/tests/__init__.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/__init__.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/__init__.py new file mode 100644 index 000000000..26540aa8e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/__init__.py @@ -0,0 +1,12 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific From 91c54821ca7809a53ae53ebc5e04c9062225eb1c Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 15:39:47 +0100 Subject: [PATCH 05/32] fixed __init__.py imports --- .../projects/point_convolutions/pylib/__init__.py | 2 +- .../projects/point_convolutions/pylib/pc/__init__.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py index 87d0e6976..73eba35d2 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/__init__.py @@ -12,4 +12,4 @@ # See the License for the specific from pylib import pc -from pylib import io +# from pylib import io diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 9b58df3ad..8407fc9c0 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -16,6 +16,7 @@ from .PointCloud import _AABB as AABB from .PointCloud import PointCloud from .Grid import Grid +''' from .Neighborhood import Neighborhood from .Neighborhood import KDEMode from .sampling import poisson_disk_sampling, cell_average_sampling @@ -24,3 +25,4 @@ from pylib.pc import layers from pylib.pc import custom_ops +''' \ No newline at end of file From 2830c7de02d8c2a939abe409e97f09737ecbc8eb Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 15:45:06 +0100 Subject: [PATCH 06/32] fixed __init__.py imports --- requirements.txt | 2 ++ .../projects/point_convolutions/pylib/pc/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cb1a66eaf..35d99498d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,3 +13,5 @@ termcolor >= 1.1.0 trimesh >= 2.37.22 # Required by trimesh. networkx +# required for pytests of project/point_convolutions +sklearn diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 8407fc9c0..26a7badc3 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -15,8 +15,8 @@ from .PointCloud import _AABB as AABB from .PointCloud import PointCloud -from .Grid import Grid ''' +from .Grid import Grid from .Neighborhood import Neighborhood from .Neighborhood import KDEMode from .sampling import poisson_disk_sampling, cell_average_sampling From 157bf31804dc256ca7c7b05647e39b5f94ca17dd Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Wed, 10 Mar 2021 15:52:18 +0100 Subject: [PATCH 07/32] fixed __init__.py imports --- requirements.txt | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 35d99498d..cdc4294fd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,5 +13,4 @@ termcolor >= 1.1.0 trimesh >= 2.37.22 # Required by trimesh. networkx -# required for pytests of project/point_convolutions -sklearn + From ccb9d90580cfd40e7efb43fde48b896ac6efc0dc Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 10:55:29 +0100 Subject: [PATCH 08/32] grid initial --- .../point_convolutions/pylib/pc/Grid.py | 84 ++++ .../point_convolutions/pylib/pc/__init__.py | 4 +- .../pylib/pc/custom_ops/__init__.py | 39 ++ .../pylib/pc/custom_ops/custom_ops_tf.py | 447 ++++++++++++++++++ .../pylib/pc/custom_ops/custom_ops_wrapper.py | 198 ++++++++ .../pylib/pc/custom_ops/tests/__init__.py | 0 .../custom_ops/tests/build_grid_ds_tf_test.py | 96 ++++ .../custom_ops/tests/compute_keys_tf_test.py | 73 +++ .../pylib/pc/tests/grid_test.py | 133 ++++++ 9 files changed, 1072 insertions(+), 2 deletions(-) create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/Grid.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/build_grid_ds_tf_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_keys_tf_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Grid.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Grid.py new file mode 100755 index 000000000..5fc8565f6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Grid.py @@ -0,0 +1,84 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf + +from pylib.pc.custom_ops import compute_keys, build_grid_ds +from pylib.pc import PointCloud, AABB + + +class Grid: + """ 2D regular grid of a point cloud. + + Args: + point_cloud : A `PointCloud` instance to distribute in the grid. + cell_sizes A `float` `Tensor` of shape `[D]`, the sizes of the grid + cells in each dimension. + aabb: An `AABB` instance, the bounding box of the grid, if `None` + the bounding box of `point_cloud` is used. (optional) + + """ + + def __init__(self, point_cloud: PointCloud, cell_sizes, aabb=None, + name=None): + cell_sizes = tf.cast(tf.convert_to_tensor(value=cell_sizes), + tf.float32) + if cell_sizes.shape == [] or cell_sizes.shape[0] == 1: + cell_sizes = tf.repeat(cell_sizes, point_cloud._dimension) + #Save the attributes. + self._batch_size = point_cloud._batch_size_numpy + self._cell_sizes = cell_sizes + self._point_cloud = point_cloud + self._aabb = point_cloud.get_AABB() + #Compute the number of cells in the grid. + aabb_sizes = self._aabb._aabb_max - self._aabb._aabb_min + batch_num_cells = tf.cast( + tf.math.ceil(aabb_sizes / self._cell_sizes), tf.int32) + self._num_cells = tf.maximum( + tf.reduce_max(batch_num_cells, axis=0), 1) + + #Compute the key for each point. + self._cur_keys = compute_keys( + self._point_cloud, self._num_cells, + self._cell_sizes) + + #Sort the keys. + self._sorted_indices = tf.argsort( + self._cur_keys, direction='DESCENDING') + self._sorted_keys = tf.gather(self._cur_keys, self._sorted_indices) + + #Get the sorted points and batch ids. + self._sorted_points = tf.gather( + self._point_cloud._points, self._sorted_indices) + self._sorted_batch_ids = tf.gather( + self._point_cloud._batch_ids, self._sorted_indices) + + self._fast_DS = None + + def get_DS(self): + """ Method to get the 2D-Grid datastructure. + + Note: By default the data structure is not build on initialization, + but with this method + + Returns: + A `int` `Tensor` of shape `[num_cells[0], num_cells[1], 2]`, where + `[i,j,0]:[i,j,1]` is the range of points in cell `i,j`. + The indices are with respect to the sorted points of the grid. + + """ + if self._fast_DS is None: + #Build the fast access data structure. + self._fast_DS = build_grid_ds( + self._sorted_keys, self._num_cells, self._batch_size) + return self._fast_DS diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 26a7badc3..26d924a5b 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -15,8 +15,8 @@ from .PointCloud import _AABB as AABB from .PointCloud import PointCloud -''' from .Grid import Grid +''' from .Neighborhood import Neighborhood from .Neighborhood import KDEMode from .sampling import poisson_disk_sampling, cell_average_sampling @@ -24,5 +24,5 @@ from .PointHierarchy import PointHierarchy from pylib.pc import layers +''' from pylib.pc import custom_ops -''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py new file mode 100644 index 000000000..cd2aee431 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -0,0 +1,39 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Loads custom ops if installed, else loads tensorflow implementations""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +try: + import tfg_custom_ops + CUSTOM = 1 +except ImportError: + CUSTOM = 0 + +if CUSTOM: + from .custom_ops_wrapper import basis_proj + from .custom_ops_wrapper import build_grid_ds + from .custom_ops_wrapper import compute_keys + from .custom_ops_wrapper import compute_pdf + from .custom_ops_wrapper import find_neighbors + from .custom_ops_wrapper import sampling +else: + from .custom_ops_tf import basis_proj_tf as basis_proj + from .custom_ops_tf import build_grid_ds_tf as build_grid_ds + from .custom_ops_tf import compute_keys_tf as compute_keys + from .custom_ops_tf import compute_pdf_tf as compute_pdf + from .custom_ops_tf import find_neighbors_tf as find_neighbors + from .custom_ops_tf import sampling_tf as sampling diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py new file mode 100644 index 000000000..be0a8041d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -0,0 +1,447 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT 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 implemetations of the custom ops """ + +import tensorflow as tf +import numpy as np +from pylib.pc import PointCloud, Grid + + +def compute_keys_tf(point_cloud: PointCloud, num_cells, cell_size, name=None): + """ Computes the regular grid cell keys of a point cloud. + + For a point in cell `c` the key is computed as + \\(key = batch_id * prod_{d=0}^{D} num_cells_{d} + \\) + \\(sum_{d=0}^{D}( c_{d} prod_{d'=d+1}^{D} num_cells_{d'} ) \\). + Args: + point_cloud: A `PointCloud` instance. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + cell_size: An `int` `Tensor` of shape `[D]`, the cell sizes per + dimension. + + Returns: + An `int` `Tensor` of shape `[N]`, the keys per point. + + """ + aabb = point_cloud.get_AABB() + abb_min_per_batch = aabb._aabb_min + aabb_min_per_point = tf.gather(abb_min_per_batch, point_cloud._batch_ids) + cell_ind = tf.math.floor( + (point_cloud._points - aabb_min_per_point) / cell_size) + cell_ind = tf.cast(cell_ind, tf.int32) + cell_ind = tf.minimum( + tf.maximum(cell_ind, tf.zeros_like(cell_ind)), + num_cells) + cell_multiplier = tf.math.cumprod(num_cells, reverse=True) + cell_multiplier = tf.concat((cell_multiplier, [1]), axis=0) + keys = point_cloud._batch_ids * cell_multiplier[0] + \ + tf.math.reduce_sum(cell_ind * tf.reshape(cell_multiplier[1:], [1, -1]), + axis=1) + return tf.cast(keys, tf.int64) +tf.no_gradient('ComputeKeysTF') + + +def build_grid_ds_tf(sorted_keys, num_cells, batch_size, name=None): + """ Method to build a fast access data structure for point clouds. + + Creates a 2D regular grid in the first two dimension, saving the first and + last index belonging to that cell array. + + Args: + sorted_keys: An `int` `Tensor` of shape `[N]`, the sorted keys. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + batch_size: An `int`. + + Returns: + An `int` `Tensor` of shape `[batch_size, num_cells[0], num_cells[1], 2]`. + + """ + sorted_keys = tf.cast(tf.convert_to_tensor(value=sorted_keys), tf.int32) + num_cells = tf.cast(tf.convert_to_tensor(value=num_cells), tf.int32) + + num_keys = tf.shape(sorted_keys)[0] + num_cells_2D = batch_size * num_cells[0] * num_cells[1] + tf.assert_greater( + tf.shape(num_cells)[0], 1, + 'Points must have dimensionality >1.') + cells_per_2D_cell = tf.cond( + tf.shape(num_cells)[0] > 2, + lambda: tf.reduce_prod(num_cells[2:]), + lambda: 1 + ) + # condition without graph mode + # if tf.shape(num_cells)[0] > 2: + # cells_per_2D_cell = tf.reduce_prod(num_cells[2:]) + # elif tf.shape(num_cells)[0] == 2: + # cells_per_2D_cell = 1 + + ds_indices = tf.cast(tf.floor(sorted_keys / cells_per_2D_cell), + dtype=tf.int32) + indices = tf.range(0, num_keys, dtype=tf.int32) + first_per_cell = tf.math.unsorted_segment_min( + indices, ds_indices, num_cells_2D) + last_per_cell = tf.math.unsorted_segment_max( + indices + 1, ds_indices, num_cells_2D) + + empty_cells = first_per_cell < 0 + first_per_cell = tf.where( + empty_cells, tf.zeros_like(first_per_cell), first_per_cell) + last_per_cell = tf.where( + empty_cells, tf.zeros_like(last_per_cell), last_per_cell) + empty_cells = first_per_cell > num_keys + first_per_cell = tf.where( + empty_cells, tf.zeros_like(first_per_cell), first_per_cell) + last_per_cell = tf.where( + empty_cells, tf.zeros_like(last_per_cell), last_per_cell) + + return tf.stack([tf.reshape(first_per_cell, + [batch_size, num_cells[0], num_cells[1]]), + tf.reshape(last_per_cell, + [batch_size, num_cells[0], num_cells[1]])], + axis=3) +tf.no_gradient('BuildGridDsTF') + +''' +def find_neighbors_tf(grid, + point_cloud_centers, + radii, + max_neighbors=0, + name=None): + """ Method to find the neighbors of a center point cloud in another + point cloud. + + Args: + grid: A `Grid` instance, from which the neighbors are chosen. + point_cloud_centers: A `PointCloud` instance, containing the center points. + radii: A `float`, the radii to select neighbors from. + max_neighbors: An `int`, if `0` all neighbors are selected. + + Returns: + center_neigh_ranges: An `int` `Tensor` of shape `[N]`, end of the ranges per + center point. You can get the neighbor ids of point `i` (i>0) with + `neighbors[center_neigh_ranges[i-1]:center_neigh_ranges[i]]`. + neighbors: An `int` `Tensor` of shape `[M, 2]`, indices of the neighbor + point and the center for each neighbor. Follows the order of + `grid._sorted_points`. + + """ + radii = tf.convert_to_tensor(value=radii) + radii = tf.cond( + tf.logical_or(tf.shape(radii)[0] == [], tf.shape(radii)[0] == 1), + lambda: tf.repeat(radii, grid._point_cloud._dimension), + lambda: radii) + # condition without graph mode + # if tf.shape(radii)[0] == [] or tf.shape(radii)[0] == 1: + # radii = tf.repeat(radii, grid._point_cloud._dimension) + # compute keys of center points in neighbors 2D grid + center_points = point_cloud_centers._points + center_batch_ids = point_cloud_centers._batch_ids + aabb = grid._aabb + abb_min_per_batch_2D = aabb._aabb_min[:, :2] + aabb_min_per_center_point_2D = tf.gather( + abb_min_per_batch_2D, center_batch_ids) + center_cell_ind_2D = tf.math.floor( + (center_points[:, :2] - aabb_min_per_center_point_2D) / radii[:2]) + center_cell_ind_2D = tf.cast(center_cell_ind_2D, tf.int32) + center_cell_ind_2D = tf.minimum( + tf.maximum(center_cell_ind_2D, tf.zeros_like(center_cell_ind_2D)), + grid._num_cells[:2]) + # find neighbors using fast 2D grid datastructure + neighbor_points = grid._sorted_points + neighbor_batch_ids = grid._sorted_batch_ids + data_structure = grid.get_DS() + + neighbors = [] + center_neigh_ranges = [] + cur_neigh_range = 0 + for i in range(tf.shape(center_points)[0]): + cur_point = center_points[i] + cur_batch_id = center_batch_ids[i] + # get cell_ids of adjacent 2D cells (9 in total) + cur_cell_id_2D = center_cell_ind_2D[i] + adj_cell_ids_2D = tf.stack( + (cur_cell_id_2D + [-1, -1], + cur_cell_id_2D + [-1, 0], + cur_cell_id_2D + [-1, 1], + cur_cell_id_2D + [0, 1], + cur_cell_id_2D, + cur_cell_id_2D + [0, -1], + cur_cell_id_2D + [1, -1], + cur_cell_id_2D + [1, 0], + cur_cell_id_2D + [1, 1]), axis=0) + # clip to range between 0 and max num cells + adj_cell_ids_2D = tf.minimum( + tf.maximum(adj_cell_ids_2D, tf.zeros_like(adj_cell_ids_2D)), + grid._num_cells[:2]) + # get min and max point ids of the adjacent cells + adj_ids = tf.gather_nd(data_structure[cur_batch_id], [adj_cell_ids_2D]) + adj_ids_start = tf.reduce_min(adj_ids[0, :, 0]) + adj_ids_end = tf.reduce_max(adj_ids[0, :, 1]) + # choose points below certain distance and in same batch + adj_points = neighbor_points[adj_ids_start:adj_ids_end] + adj_batch_ids = neighbor_batch_ids[adj_ids_start:adj_ids_end] + distances = tf.linalg.norm( + adj_points - tf.reshape(cur_point, [1, -1]), axis=1) + close = (distances < radii[0]) + same_batch = (adj_batch_ids == cur_batch_id) + close = tf.math.logical_and(close, same_batch) + close_ids = tf.boolean_mask(tf.range(adj_ids_start, adj_ids_end), close) + + cur_neighbors = tf.stack( + (close_ids, tf.ones_like(close_ids) * i), axis=1) + neighbors.append(cur_neighbors) + cur_neigh_range = cur_neigh_range + tf.shape(cur_neighbors)[0] + center_neigh_ranges.append(cur_neigh_range) + + neighbors = tf.concat(neighbors, axis=0) + center_neigh_ranges = tf.concat(center_neigh_ranges, axis=0) + + return center_neigh_ranges, neighbors +tf.no_gradient('FindNeighborsTF') + + +def find_neighbors_no_grid(point_cloud, + point_cloud_centers, + radius, + name=None): + """ Method to find the neighbors of a center point cloud in another + point cloud. + + Args: + point_cloud: A `PointCloud` instance, from which the neighbors are chosen. + point_cloud_centers: A `PointCloud` instance, containing the center points. + radius: A `float`, the radius to select neighbors from. + + Returns: + center_neigh_ranges: An `int` `Tensor` of shape `[N]`, end of the ranges per + center point. You can get the neighbor ids of point `i` (i>0) with + `neighbors[center_neigh_ranges[i-1]:center_neigh_ranges[i]]`. + neighbors: An `int` `Tensor` of shape `[M, 2]`, indices of the neighbor + point and the center for each neighbor. Follows the order of + `grid._sorted_points`. + + """ + points = point_cloud._points + batch_ids = point_cloud._batch_ids + center_points = point_cloud_centers._points + center_batch_ids = point_cloud_centers._batch_ids + num_center_points = tf.shape(center_points)[0] + + distances = tf.linalg.norm(tf.expand_dims(points, axis=0) - \ + tf.expand_dims(center_points, axis=1), + axis=-1) + close = (distances <= radius) + same_batch = (tf.expand_dims(batch_ids, axis=0) == \ + tf.expand_dims(center_batch_ids, axis=1)) + close = tf.math.logical_and(same_batch, close) + + neighbors = tf.where(close) + neighbors = tf.reverse(neighbors, axis=[1]) + num_neighbors = tf.shape(neighbors)[0] + neigh_ranges = tf.math.unsorted_segment_max( + tf.range(1, num_neighbors + 1), + neighbors[:, 1], + num_center_points) + return neigh_ranges, neighbors +tf.no_gradient('FindNeighborsNoGrid') + + +def sampling_tf(neighborhood, sample_mode, name=None): + """ Method to sample the points of a point cloud. + + Args: + neighborhood: A `Neighborhood` instance, which contains a point cloud with + its neighbors. + sample_mode: An `int`specifiying the sample mode, + `0` for average, `1` for poisson. + + Returns: + sampled_points: A `float` `Tensor` of shape [S, D], the sampled points. + sampled_batch_ids: An `int` `Tensor` of shape [S], the batch ids. + sampled_indices: An `int` `Tensor` of shape [S], the indices to the + unsampled points. + Following the order of neighborhood._grid._sorted_points. + + """ + points = neighborhood._grid._sorted_points + batch_ids = neighborhood._grid._sorted_batch_ids + num_points = tf.shape(points)[0] + if sample_mode == 0: + # poisson sampling + nb_ranges = tf.concat(([0], neighborhood._samples_neigh_ranges), axis=0) + neighbors = neighborhood._neighbors + num_points = tf.shape(neighborhood._grid._sorted_points)[0] + log_probabilities = tf.ones([num_points]) + sampled_indices = tf.zeros([0], dtype=tf.int64) + # to set log prob to -inf <=> prob to zero + tf_neg_inf = tf.constant(-np.inf) + + #sample points until all log probabilites are set to -inf + while not tf.reduce_all(tf.math.is_inf(log_probabilities)): + choice = tf.random.categorical( + tf.expand_dims(log_probabilities, axis=0), 1)[0] + # add choice to sampled indices + sampled_indices = tf.concat((sampled_indices, choice), axis=0) + # set log probability of neighbors to -inf + sample_neighbors = \ + neighbors[nb_ranges[choice[0]]:nb_ranges[choice[0] + 1], 0] + num_neighbors = tf.shape(sample_neighbors)[0] + log_probabilities = tf.tensor_scatter_nd_update( + log_probabilities, + tf.expand_dims(sample_neighbors, axis=1), + tf.repeat(tf_neg_inf, num_neighbors)) + sampled_points = tf.gather(points, sampled_indices) + sampled_batch_ids = tf.gather(batch_ids, sampled_indices) + + elif sample_mode == 1: + # cell average sampling + keys = neighborhood._grid._sorted_keys + # replace keys with numbers 0 to num_unique keys + unique, _, counts = tf.unique_with_counts(keys) + num_unique_keys = tf.shape(unique)[0] + new_keys = tf.repeat(tf.range(0, num_unique_keys), counts) + # average over points with same cell key + sampled_points = tf.math.segment_mean(points, new_keys) + # get batch of a point in the same cell + sampled_indices = tf.math.segment_min(tf.range(0, num_points), new_keys) + sampled_batch_ids = tf.gather(batch_ids, sampled_indices) + + return sampled_points, sampled_batch_ids, sampled_indices + +tf.no_gradient('samplingTF') + + +_pi = tf.constant(np.pi) + + +def compute_pdf_inside_neighborhoods_tf(neighborhood, + bandwidth, + mode, + name=None): + """ Method to compute the density distribution inside the neighborhoods of a + point cloud in euclidean space using kernel density estimation (KDE). + + Args: + neighborhood: A `Neighborhood` instance. + bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. + mode: A `KDEMode` value. + + Returns: + A `float` `Tensor` of shape `[N]`, the estimated density per center point. + + """ + bandwidth = tf.convert_to_tensor(value=bandwidth) + points = neighborhood._grid._sorted_points + neighbors = neighborhood._neighbors + nbh_ranges = neighborhood._samples_neigh_ranges + + # compute difference vectors inside neighborhoods + num_adjacencies = tf.shape(neighbors)[0] + nbh_start_ind = tf.concat(([0], nbh_ranges[0:-1]), axis=0) + nbh_sizes = nbh_ranges - nbh_start_ind + max_num_neighbors = tf.reduce_max(nbh_sizes) + nbh_sizes_per_nb = tf.repeat(nbh_sizes, nbh_sizes) + + nb_indices_1 = tf.repeat(neighbors[:, 0], nbh_sizes_per_nb) + + mask = tf.sequence_mask(nbh_sizes_per_nb, max_num_neighbors) + mask_indices = tf.cast(tf.compat.v1.where(mask), tf.int32) + indices_tensor = tf.repeat(tf.reshape(tf.range(0, max_num_neighbors), + [1, max_num_neighbors]), + num_adjacencies, axis=0) + nbh_start_per_nb = tf.repeat(nbh_start_ind, nbh_sizes) + indices_tensor = indices_tensor + \ + tf.reshape(nbh_start_per_nb, [num_adjacencies, 1]) + indices_2 = tf.gather_nd(params=indices_tensor, indices=mask_indices) + nb_indices_2 = tf.gather(neighbors[:, 0], indices_2) + + nb_diff = tf.gather(points, nb_indices_1) - tf.gather(points, nb_indices_2) + # kernel density estimation using the distances + rel_bandwidth = tf.reshape(bandwidth * neighborhood._radii, [1, -1]) + kernel_input = nb_diff / rel_bandwidth + # gaussian kernel + nb_kernel_value = tf.exp(-tf.pow(kernel_input, 2) / 2) / tf.sqrt(2 * _pi) + nb_kernel_value = tf.reduce_prod(nb_kernel_value, axis=1) + nb_id_per_nb_pair = tf.repeat(tf.range(0, num_adjacencies), + nbh_sizes_per_nb) + # sum over influence inside neighborhood + pdf = tf.math.unsorted_segment_sum(nb_kernel_value, + nb_id_per_nb_pair, + num_adjacencies) /\ + tf.reduce_prod(bandwidth) + return pdf + + +def compute_pdf_tf(neighborhood, bandwidth, mode, name=None): + """ Method to compute the density distribution using neighborhood information + in euclidean space using kernel density estimation (KDE). + + Args: + neighborhood: A `Neighborhood` instance of the pointcloud to itself. + bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. + mode: A `KDEMode` value. + + Returns: + A `float` `Tensor` of shape `[N]`, the estimated density per point, + with respect to the sorted points of the grid in `neighborhood`. + + """ + bandwidth = tf.convert_to_tensor(value=bandwidth) + + rel_bandwidth = tf.reshape(bandwidth * neighborhood._radii, [1, -1]) + points = neighborhood._grid._sorted_points / rel_bandwidth + num_points = tf.shape(points)[0] + neighbors = neighborhood._neighbors + # point differences + nb_diff = tf.gather(points, neighbors[:, 0]) - \ + tf.gather(points, neighbors[:, 1]) + # kde on point differences + # gaussian kernel, note division by bandwidth was already done above + nb_kernel_value = tf.exp(-tf.pow(nb_diff, 2) / 2) / tf.sqrt(2 * _pi) + nb_kernel_value = tf.reduce_prod(nb_kernel_value, axis=1) + # sum over influence of neighbors + pdf = tf.math.unsorted_segment_sum(nb_kernel_value, + neighbors[:, 1], + num_points) / \ + tf.reduce_prod(bandwidth) + return pdf + + +def basis_proj_tf(neigh_basis, features, neighborhood, name=None): + """ Method to aggregate the features*basis for different neighborhoods. + + Args: + neigh_basis: A `float` `Tensor` of shape `[M, H]`, the projection of + each neighbor to the different basis. + features: A `float` `Tensor` of shape `[N_in, C]`, the input features. + neighborhood: A `Neighborhood` instance. + + Returns: + A `float` `Tensor` of shape ``[N_out, C, H]`, the weighted latent features. + """ + neigh_basis = tf.convert_to_tensor(value=neigh_basis, dtype=tf.float32) + features = tf.convert_to_tensor(value=features, dtype=tf.float32) + # get input in correct shapes + num_nbh = tf.shape(neighborhood._point_cloud_sampled._points)[0] + features_per_nb = tf.gather(features, + neighborhood._original_neigh_ids[:, 0]) + # Monte-Carlo Integration + weighted_features_per_nb = tf.expand_dims(features_per_nb, 2) *\ + tf.expand_dims(neigh_basis, 1) + weighted_latent_per_center = tf.math.unsorted_segment_sum( + weighted_features_per_nb, neighborhood._neighbors[:, 1], num_nbh) + return weighted_latent_per_center +''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py new file mode 100644 index 000000000..694ee4d6e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py @@ -0,0 +1,198 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Wrappers for point cloud CUDA functions """ + +import tensorflow as tf +import tfg_custom_ops + + +def compute_keys(point_cloud, num_cells, cell_size, name=None): + """ Method to compute the regular grid keys of a point cloud. + + For a point in cell `c` the key is computed as + \\(key = batch_id * prod_{d=0}^{D} num_cells_{d} + \\) + \\(sum_{d=0}^{D}( c_{d} prod_{d'=d+1}^{D} num_cells_{d'} ) \\). + Args: + point_cloud: A `PointCloud` instance. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + cell_size: An `int` `Tensor` of shape `[D]`, the cell sizes per + dimension. + + Returns: + An `int` `Tensor` of shape `[N]`, the keys per point. + + """ + aabb = point_cloud.get_AABB() + return tfg_custom_ops.compute_keys( + point_cloud._points, + point_cloud._batch_ids, + aabb._aabb_min / cell_size, + num_cells, + tf.math.reciprocal(cell_size)) +tf.no_gradient('ComputeKeys') + + +def build_grid_ds(sorted_keys, num_cells, batch_size, name=None): + """ Method to build a fast access data structure for point clouds. + + Creates a 2D regular grid in the first two dimension, saving the first and + last index belonging to that cell array. + Args: + sorted_keys: An `int` `Tensor` of shape `[N]`, the sorted keys. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + batch_size: An `int`. + + Returns: + An `int` `Tensor` of shape `[batch_size, num_cells[0], num_cells[1], 2]`. + + """ + return tfg_custom_ops.build_grid_ds( + sorted_keys, + num_cells, + num_cells, + batch_size) +tf.no_gradient('BuildGridDs') + + +def find_neighbors(grid, + point_cloud_centers, + radii, + max_neighbors=0, + name=None): + """ Method to find the neighbors of a center point cloud in another + point cloud. + + Args: + grid: A Grid instance, from which the neighbors are chosen. + point_cloud_centers: A `PointCloud` instance, containing the center points. + radii: An `float` `Tensor` of shape `[D]`, the radii to select neighbors + from. + max_neighbors: An `int`, if `0` all neighbors are selected. + + Returns: + center_neigh_ranges: An `int` `Tensor` of shape `[N]`, end of the ranges per + center point. You can get the neighbor ids of point `i` (i>0) with + `neighbors[center_neigh_ranges[i-1]:center_neigh_ranges[i]]`. + neighbors: An `int` `Tensor` of shape [M, 2], indices of the neighbor point + and the center for each neighbor. Follows the order of + `grid._sorted_points`. + + """ + return tfg_custom_ops.find_neighbors( + point_cloud_centers._points, + point_cloud_centers._batch_ids, + grid._sorted_points, + grid._sorted_keys, + grid.get_DS(), + grid._num_cells, + grid._aabb._aabb_min / grid._cell_sizes, + tf.math.reciprocal(grid._cell_sizes), + tf.math.reciprocal(radii), + max_neighbors) +tf.no_gradient('FindNeighbors') + + +def sampling(neighborhood, sample_mode, name=None): + """ Method to sample the points of a point cloud. + + Args: + neighborhood: A `Neighborhood` instance, which contains a point cloud with + its neighbors. + sample_mode: An `int`specifiying the sample mode, + `0` for average, `1` for poisson. + + Returns: + sampled_points: A `float` `Tensor` of shape `[S, D]`, the sampled points. + sampled_batch_ids: An `int` `Tensor` of shape `[S]`, the batch ids. + sampled_indices: An `int` `Tensor` of shape `[S]`, the indices to the + unsampled points. + Following the order of neighborhood._grid._sorted_points. + + """ + return tfg_custom_ops.sampling( + neighborhood._grid._sorted_points, + neighborhood._grid._sorted_batch_ids, + neighborhood._grid._sorted_keys, + neighborhood._grid._num_cells, + neighborhood._neighbors, + neighborhood._samples_neigh_ranges, + sample_mode) +tf.no_gradient('Sampling') + + +def compute_pdf(neighborhood, bandwidth, mode, name=None): + """ Method to compute the density distribution inside the neighborhoods of a + point cloud in euclidean space using kernel density estimation (KDE). + + Args: + neighborhood: A `Neighborhood` instance. + bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. + mode: A `KDEMode` value. + + Returns: + A `float` `Tensor` of shape `[N]`, the estimated density per point, + with respect to the sorted points of the grid in `neighborhood`. + + """ + return tfg_custom_ops.compute_pdf_with_pt_grads( + neighborhood._grid._sorted_points, + neighborhood._neighbors, + neighborhood._samples_neigh_ranges, + tf.math.reciprocal(bandwidth), + tf.math.reciprocal(neighborhood._radii), + mode) + + +@tf.RegisterGradient("ComputePdfWithPtGrads") +def _compute_pdf_grad(op, *grads): + inPtsGrad = tfg_custom_ops.compute_pdf_pt_grads( + op.inputs[0], + op.inputs[1], + op.inputs[2], + op.inputs[3], + op.inputs[4], + grads[0], + op.get_attr("mode")) + return [inPtsGrad, None, None, None, None] + + +def basis_proj(neigh_basis, features, neighborhood): + """ Method to aggregate the features*basis for different neighborhoods. + + Args: + neigh_basis: A `float` `Tensor` of shape `[M, H]`, the projection of + each neighbor to the different basis. + features: A `float` `Tensor` of shape `[N_in, C]`, the input features. + neighborhood: A `Neighborhood` instance. + + Returns: + A `float` `Tensor` of shape ``[N_out, C, H]`, the weighted latent features. + + """ + return tfg_custom_ops.basis_proj( + neigh_basis, + features, + neighborhood._original_neigh_ids, + neighborhood._samples_neigh_ranges) + + +@tf.RegisterGradient("BasisProj") +def _basis_proj_grad(op, *grads): + basis_grads, feature_grads = \ + tfg_custom_ops.basis_proj_grads( + op.inputs[0], op.inputs[1], op.inputs[2], + op.inputs[3], grads[0]) + return [basis_grads, feature_grads, None, None] diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/build_grid_ds_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/build_grid_ds_tf_test.py new file mode 100644 index 000000000..b12c2c4fe --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/build_grid_ds_tf_test.py @@ -0,0 +1,96 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test build_grid_ds tensorflow implementation""" + +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc.tests import utils +from pylib.pc.custom_ops.custom_ops_tf import build_grid_ds_tf +from pylib.pc.custom_ops import compute_keys + + +class BuildGridDSTF(test_case.TestCase): + + @parameterized.parameters( + (100, 32, 30, 0.1, 2), + (200, 16, 1, 0.2, 2), + (200, 8, 1, np.sqrt(2), 2), + (100, 32, 30, 0.1, 3), + (200, 16, 1, 0.2, 3), + (200, 8, 1, np.sqrt(3), 3), + (100, 32, 30, 0.1, 4), + (200, 16, 1, 0.2, 4), + (200, 8, 1, np.sqrt(4), 4) + ) + def test_grid_datastructure(self, + num_points, + batch_size, + scale, + radius, + dimension): + radius = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points, clean_aabb=True) + point_cloud = PointCloud(points, batch_ids) + #Compute the number of cells in the grid. + aabb = point_cloud.get_AABB() + aabb_sizes = aabb._aabb_max - aabb._aabb_min + batch_num_cells = tf.cast( + tf.math.ceil(aabb_sizes / radius), tf.int32) + total_num_cells = tf.maximum( + tf.reduce_max(batch_num_cells, axis=0), 1) + keys = compute_keys(point_cloud, total_num_cells, radius) + keys = tf.sort(keys, direction='DESCENDING') + ds_tf = build_grid_ds_tf(keys, total_num_cells, batch_size) + + keys = keys.numpy() + ds_numpy = np.full((batch_size, total_num_cells[0], + total_num_cells[1], 2), 0) + if dimension == 2: + cells_per_2D_cell = 1 + elif dimension > 2: + cells_per_2D_cell = np.prod(total_num_cells[2:]) + for key_iter, key in enumerate(keys): + curDSIndex = key // cells_per_2D_cell + yIndex = curDSIndex % total_num_cells[1] + auxInt = curDSIndex // total_num_cells[1] + xIndex = auxInt % total_num_cells[0] + curbatch_ids = auxInt // total_num_cells[0] + + if key_iter == 0: + ds_numpy[curbatch_ids, xIndex, yIndex, 0] = key_iter + else: + prevKey = keys[key_iter - 1] + prevDSIndex = prevKey // cells_per_2D_cell + if prevDSIndex != curDSIndex: + ds_numpy[curbatch_ids, xIndex, yIndex, 0] = key_iter + + nextIter = key_iter + 1 + if nextIter >= len(keys): + ds_numpy[curbatch_ids, xIndex, yIndex, 1] = len(keys) + else: + nextKey = keys[key_iter + 1] + nextDSIndex = nextKey // cells_per_2D_cell + if nextDSIndex != curDSIndex: + ds_numpy[curbatch_ids, xIndex, yIndex, 1] = key_iter + 1 + + # check if the data structure is equal + self.assertAllEqual(ds_tf, ds_numpy) + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_keys_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_keys_tf_test.py new file mode 100644 index 000000000..b32840084 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_keys_tf_test.py @@ -0,0 +1,73 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test compute_keys tensorflow implementation""" + +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc.tests import utils +from pylib.pc.custom_ops.custom_ops_tf import compute_keys_tf + + +class ComputeKeysTF(test_case.TestCase): + + @parameterized.parameters( + (100, 32, 30, 0.1, 2), + (200, 16, 1, 0.2, 2), + (200, 8, 1, np.sqrt(2), 2), + (100, 32, 30, 0.1, 3), + (200, 16, 1, 0.2, 3), + (200, 8, 1, np.sqrt(3), 3), + (100, 32, 30, 0.1, 4), + (200, 16, 1, 0.2, 4), + (200, 8, 1, np.sqrt(4), 4) + ) + def test_compute_keys_tf(self, + num_points, + batch_size, + scale, + radius, + dimension): + radius = np.repeat(radius, dimension) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points, clean_aabb=False) + point_cloud = PointCloud(points, batch_ids) + + #Compute the number of cells in the grid. + aabb = point_cloud.get_AABB() + aabb_sizes = aabb._aabb_max - aabb._aabb_min + batch_num_cells = tf.cast( + tf.math.ceil(aabb_sizes / radius), tf.int32) + total_num_cells = tf.maximum( + tf.reduce_max(batch_num_cells, axis=0), 1) + + keys_tf = compute_keys_tf(point_cloud, total_num_cells, radius) + aabb_min = aabb._aabb_min.numpy() + + aabb_min_per_point = aabb_min[batch_ids, :] + cell_ind = np.floor((points - aabb_min_per_point) / radius).astype(int) + cell_ind = np.minimum(np.maximum(cell_ind, [0] * dimension), + total_num_cells) + cell_multiplier = np.flip(np.cumprod(np.flip(total_num_cells))) + cell_multiplier = np.concatenate((cell_multiplier, [1]), axis=0) + keys = batch_ids * cell_multiplier[0] + \ + np.sum(cell_ind * cell_multiplier[1:].reshape([1, -1]), axis=1) + # check unsorted keys + self.assertAllEqual(keys_tf, keys) + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py new file mode 100644 index 000000000..e7b6211d3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py @@ -0,0 +1,133 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test regular grid data structure""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc.tests import utils + + +class GridTest(test_case.TestCase): + + @parameterized.parameters( + (10000, 32, 30, 0.1, 2), + (20000, 16, 1, 0.2, 2), + (200, 8, 1, np.sqrt(2), 2), + (100, 32, 30, 0.1, 3), + (200, 16, 1, 0.2, 3), + (200, 8, 1, np.sqrt(3), 3), + (100, 32, 30, 0.1, 4), + (200, 16, 1, 0.2, 4), + (200, 8, 1, np.sqrt(4), 4) + ) + def test_compute_keys_with_sort(self, + num_points, + batch_size, + scale, + radius, + dimension): + radius = np.repeat(radius, dimension) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points, clean_aabb=False) + point_cloud = PointCloud(points, batch_ids) + aabb = point_cloud.get_AABB() + grid = Grid(point_cloud, radius) + + total_num_cells = grid._num_cells.numpy() + aabb_min = aabb._aabb_min.numpy() + + aabb_min_per_point = aabb_min[batch_ids, :] + cell_ind = np.floor((points - aabb_min_per_point) / radius).astype(int) + cell_ind = np.minimum(np.maximum(cell_ind, [0] * dimension), + total_num_cells) + cell_multiplier = np.flip(np.cumprod(np.flip(total_num_cells))) + cell_multiplier = np.concatenate((cell_multiplier, [1]), axis=0) + keys = batch_ids * cell_multiplier[0] + \ + np.sum(cell_ind * cell_multiplier[1:].reshape([1, -1]), axis=1) + # check unsorted keys + self.assertAllEqual(grid._cur_keys, keys) + + # sort descending + sorted_keys = np.flip(np.sort(keys)) + # check if the cell keys per point are equal + self.assertAllEqual(grid._sorted_keys, sorted_keys) + + @parameterized.parameters( + (100, 32, 30, 0.1, 2), + (200, 16, 1, 0.2, 2), + (200, 8, 1, np.sqrt(2), 2), + (100, 32, 30, 0.1, 3), + (200, 16, 1, 0.2, 3), + (200, 8, 1, np.sqrt(3), 3), + (200, 16, 1, 0.2, 4), + (200, 8, 1, np.sqrt(4), 4) + ) + def test_grid_datastructure(self, + num_points, + batch_size, + scale, + radius, + dimension): + radius = np.repeat(radius, dimension) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points, clean_aabb=True) + point_cloud = PointCloud(points, batch_ids) + aabb = point_cloud.get_AABB() + grid = Grid(point_cloud, radius, aabb) + + total_num_cells = grid._num_cells.numpy() + keys = grid._sorted_keys.numpy() + ds_numpy = np.full((batch_size, total_num_cells[0], + total_num_cells[1], 2), 0) + if dimension == 2: + cells_per_2D_cell = 1 + elif dimension > 2: + cells_per_2D_cell = np.prod(total_num_cells[2:]) + for key_iter, key in enumerate(keys): + curDSIndex = key // cells_per_2D_cell + yIndex = curDSIndex % total_num_cells[1] + auxInt = curDSIndex // total_num_cells[1] + xIndex = auxInt % total_num_cells[0] + curbatch_ids = auxInt // total_num_cells[0] + + if key_iter == 0: + ds_numpy[curbatch_ids, xIndex, yIndex, 0] = key_iter + else: + prevKey = keys[key_iter - 1] + prevDSIndex = prevKey // cells_per_2D_cell + if prevDSIndex != curDSIndex: + ds_numpy[curbatch_ids, xIndex, yIndex, 0] = key_iter + + nextIter = key_iter + 1 + if nextIter >= len(keys): + ds_numpy[curbatch_ids, xIndex, yIndex, 1] = len(keys) + else: + nextKey = keys[key_iter + 1] + nextDSIndex = nextKey // cells_per_2D_cell + if nextDSIndex != curDSIndex: + ds_numpy[curbatch_ids, xIndex, yIndex, 1] = key_iter + 1 + + # check if the data structure is equal + self.assertAllEqual(grid.get_DS(), ds_numpy) + +if __name__ == '__main__': + test_case.main() From 8db85138c190179f5950850766dd50a5603f445a Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 11:02:55 +0100 Subject: [PATCH 09/32] removed unused custom_ops wrappers --- .../pylib/pc/custom_ops/__init__.py | 16 +- .../pylib/pc/custom_ops/custom_ops_wrapper.py | 198 ------------------ .../pylib/pc/tests/utils.py | 3 +- 3 files changed, 10 insertions(+), 207 deletions(-) delete mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py index cd2aee431..6509ca236 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -24,16 +24,16 @@ CUSTOM = 0 if CUSTOM: - from .custom_ops_wrapper import basis_proj + # from .custom_ops_wrapper import basis_proj from .custom_ops_wrapper import build_grid_ds from .custom_ops_wrapper import compute_keys - from .custom_ops_wrapper import compute_pdf - from .custom_ops_wrapper import find_neighbors - from .custom_ops_wrapper import sampling + # from .custom_ops_wrapper import compute_pdf + # from .custom_ops_wrapper import find_neighbors + # from .custom_ops_wrapper import sampling else: - from .custom_ops_tf import basis_proj_tf as basis_proj + # from .custom_ops_tf import basis_proj_tf as basis_proj from .custom_ops_tf import build_grid_ds_tf as build_grid_ds from .custom_ops_tf import compute_keys_tf as compute_keys - from .custom_ops_tf import compute_pdf_tf as compute_pdf - from .custom_ops_tf import find_neighbors_tf as find_neighbors - from .custom_ops_tf import sampling_tf as sampling + # from .custom_ops_tf import compute_pdf_tf as compute_pdf + # from .custom_ops_tf import find_neighbors_tf as find_neighbors + # from .custom_ops_tf import sampling_tf as sampling diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py deleted file mode 100644 index 694ee4d6e..000000000 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Wrappers for point cloud CUDA functions """ - -import tensorflow as tf -import tfg_custom_ops - - -def compute_keys(point_cloud, num_cells, cell_size, name=None): - """ Method to compute the regular grid keys of a point cloud. - - For a point in cell `c` the key is computed as - \\(key = batch_id * prod_{d=0}^{D} num_cells_{d} + \\) - \\(sum_{d=0}^{D}( c_{d} prod_{d'=d+1}^{D} num_cells_{d'} ) \\). - Args: - point_cloud: A `PointCloud` instance. - num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells - per dimension. - cell_size: An `int` `Tensor` of shape `[D]`, the cell sizes per - dimension. - - Returns: - An `int` `Tensor` of shape `[N]`, the keys per point. - - """ - aabb = point_cloud.get_AABB() - return tfg_custom_ops.compute_keys( - point_cloud._points, - point_cloud._batch_ids, - aabb._aabb_min / cell_size, - num_cells, - tf.math.reciprocal(cell_size)) -tf.no_gradient('ComputeKeys') - - -def build_grid_ds(sorted_keys, num_cells, batch_size, name=None): - """ Method to build a fast access data structure for point clouds. - - Creates a 2D regular grid in the first two dimension, saving the first and - last index belonging to that cell array. - Args: - sorted_keys: An `int` `Tensor` of shape `[N]`, the sorted keys. - num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells - per dimension. - batch_size: An `int`. - - Returns: - An `int` `Tensor` of shape `[batch_size, num_cells[0], num_cells[1], 2]`. - - """ - return tfg_custom_ops.build_grid_ds( - sorted_keys, - num_cells, - num_cells, - batch_size) -tf.no_gradient('BuildGridDs') - - -def find_neighbors(grid, - point_cloud_centers, - radii, - max_neighbors=0, - name=None): - """ Method to find the neighbors of a center point cloud in another - point cloud. - - Args: - grid: A Grid instance, from which the neighbors are chosen. - point_cloud_centers: A `PointCloud` instance, containing the center points. - radii: An `float` `Tensor` of shape `[D]`, the radii to select neighbors - from. - max_neighbors: An `int`, if `0` all neighbors are selected. - - Returns: - center_neigh_ranges: An `int` `Tensor` of shape `[N]`, end of the ranges per - center point. You can get the neighbor ids of point `i` (i>0) with - `neighbors[center_neigh_ranges[i-1]:center_neigh_ranges[i]]`. - neighbors: An `int` `Tensor` of shape [M, 2], indices of the neighbor point - and the center for each neighbor. Follows the order of - `grid._sorted_points`. - - """ - return tfg_custom_ops.find_neighbors( - point_cloud_centers._points, - point_cloud_centers._batch_ids, - grid._sorted_points, - grid._sorted_keys, - grid.get_DS(), - grid._num_cells, - grid._aabb._aabb_min / grid._cell_sizes, - tf.math.reciprocal(grid._cell_sizes), - tf.math.reciprocal(radii), - max_neighbors) -tf.no_gradient('FindNeighbors') - - -def sampling(neighborhood, sample_mode, name=None): - """ Method to sample the points of a point cloud. - - Args: - neighborhood: A `Neighborhood` instance, which contains a point cloud with - its neighbors. - sample_mode: An `int`specifiying the sample mode, - `0` for average, `1` for poisson. - - Returns: - sampled_points: A `float` `Tensor` of shape `[S, D]`, the sampled points. - sampled_batch_ids: An `int` `Tensor` of shape `[S]`, the batch ids. - sampled_indices: An `int` `Tensor` of shape `[S]`, the indices to the - unsampled points. - Following the order of neighborhood._grid._sorted_points. - - """ - return tfg_custom_ops.sampling( - neighborhood._grid._sorted_points, - neighborhood._grid._sorted_batch_ids, - neighborhood._grid._sorted_keys, - neighborhood._grid._num_cells, - neighborhood._neighbors, - neighborhood._samples_neigh_ranges, - sample_mode) -tf.no_gradient('Sampling') - - -def compute_pdf(neighborhood, bandwidth, mode, name=None): - """ Method to compute the density distribution inside the neighborhoods of a - point cloud in euclidean space using kernel density estimation (KDE). - - Args: - neighborhood: A `Neighborhood` instance. - bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. - mode: A `KDEMode` value. - - Returns: - A `float` `Tensor` of shape `[N]`, the estimated density per point, - with respect to the sorted points of the grid in `neighborhood`. - - """ - return tfg_custom_ops.compute_pdf_with_pt_grads( - neighborhood._grid._sorted_points, - neighborhood._neighbors, - neighborhood._samples_neigh_ranges, - tf.math.reciprocal(bandwidth), - tf.math.reciprocal(neighborhood._radii), - mode) - - -@tf.RegisterGradient("ComputePdfWithPtGrads") -def _compute_pdf_grad(op, *grads): - inPtsGrad = tfg_custom_ops.compute_pdf_pt_grads( - op.inputs[0], - op.inputs[1], - op.inputs[2], - op.inputs[3], - op.inputs[4], - grads[0], - op.get_attr("mode")) - return [inPtsGrad, None, None, None, None] - - -def basis_proj(neigh_basis, features, neighborhood): - """ Method to aggregate the features*basis for different neighborhoods. - - Args: - neigh_basis: A `float` `Tensor` of shape `[M, H]`, the projection of - each neighbor to the different basis. - features: A `float` `Tensor` of shape `[N_in, C]`, the input features. - neighborhood: A `Neighborhood` instance. - - Returns: - A `float` `Tensor` of shape ``[N_out, C, H]`, the weighted latent features. - - """ - return tfg_custom_ops.basis_proj( - neigh_basis, - features, - neighborhood._original_neigh_ids, - neighborhood._samples_neigh_ranges) - - -@tf.RegisterGradient("BasisProj") -def _basis_proj_grad(op, *grads): - basis_grads, feature_grads = \ - tfg_custom_ops.basis_proj_grads( - op.inputs[0], op.inputs[1], op.inputs[2], - op.inputs[3], grads[0]) - return [basis_grads, feature_grads, None, None] diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py index fe69ed795..4e54f9739 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py @@ -45,7 +45,7 @@ def _create_random_point_cloud_segmented(batch_size, (batch_ids, np.arange(0, batch_size), np.arange(0, batch_size))) return points, batch_ids - +''' def _create_random_point_cloud_padded(max_num_points, batch_shape, dimension=3, @@ -79,3 +79,4 @@ def _create_uniform_distributed_point_cloud_3D(num_points_root, if flat: points = points.reshape(-1, 3) return points +''' From 858eab079bf204d1141f840a65dc540c4aebba39 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 11:28:18 +0100 Subject: [PATCH 10/32] added missing test util --- .../projects/point_convolutions/pylib/pc/tests/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py index 4e54f9739..6a8cf059b 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py @@ -45,7 +45,7 @@ def _create_random_point_cloud_segmented(batch_size, (batch_ids, np.arange(0, batch_size), np.arange(0, batch_size))) return points, batch_ids -''' + def _create_random_point_cloud_padded(max_num_points, batch_shape, dimension=3, @@ -59,7 +59,7 @@ def _create_random_point_cloud_padded(max_num_points, sizes = np.random.randint(1, max_num_points, batch_shape) return points, sizes - +''' def _create_uniform_distributed_point_cloud_2D(num_points_sqrt, scale=1, flat=False): From da7e761d01240758d012f3e1cc42c37d2400149f Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 11:50:41 +0100 Subject: [PATCH 11/32] fixed indexing error in compute_keys_tf --- .../point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index be0a8041d..113b7d8a6 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -43,7 +43,7 @@ def compute_keys_tf(point_cloud: PointCloud, num_cells, cell_size, name=None): cell_ind = tf.cast(cell_ind, tf.int32) cell_ind = tf.minimum( tf.maximum(cell_ind, tf.zeros_like(cell_ind)), - num_cells) + num_cells - 1) cell_multiplier = tf.math.cumprod(num_cells, reverse=True) cell_multiplier = tf.concat((cell_multiplier, [1]), axis=0) keys = point_cloud._batch_ids * cell_multiplier[0] + \ From a260bc47e5f085d1b4dffe027ca5f0cc624735eb Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 12:11:22 +0100 Subject: [PATCH 12/32] neighborhood_initial --- .../pylib/pc/Neighborhood.py | 179 ++++++++++++++++++ .../point_convolutions/pylib/pc/__init__.py | 2 +- .../pylib/pc/custom_ops/__init__.py | 4 +- .../pylib/pc/custom_ops/custom_ops_tf.py | 4 +- .../tests/find_neighbors_tf_test.py | 98 ++++++++++ .../pylib/pc/tests/neighbors_test.py | 150 +++++++++++++++ .../pylib/pc/tests/utils.py | 4 +- 7 files changed, 434 insertions(+), 7 deletions(-) create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/find_neighbors_tf_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/neighbors_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py new file mode 100755 index 000000000..410ecf624 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py @@ -0,0 +1,179 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to represent a neighborhood of points. + +Note: + In the following `D` is the spatial dimensionality of the points, + `N` is the number of (samples) points, and `M` is the total number of + adjacencies. + +Attributes: + _point_cloud_sampled: 'PointCloud', samples point cloud. + _grid : 'Grid', regular grid data structure. + _radii: `float` `Tensor` of shape [D], radius used to select the neighbors. + _samples_neigh_ranges: `int` `Tensor` of shape `[N]`, end of the ranges per + sample. + _neighbors: `int` `Tensor` of shape `[M,2]`, indices of the neighbor point, + with respect to the sorted point in the grid, and the sample for each + neighbor. + _original_neigh_ids: `int` `Tensor` of shape `[M,2]`, indices of the + neighbor point, with respect to the points in the input point cloud, + and the sample for each neighbor. + _pdf: `float` `Tensor` of shape `[M]`, PDF value for each neighbor. +""" + +import enum +import tensorflow as tf + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc.custom_ops import find_neighbors # , compute_pdf +from pylib.pc.utils import cast_to_num_dims + + +class KDEMode(enum.Enum): + """ Parameters for kernel density estimation (KDE) """ + constant = 0 + num_points = 1 + no_pdf = 2 + + +class Neighborhood: + """ Neighborhood of a point cloud. + + Args: + grid: A 'Grid' instance, the regular grid data structure. + radius: A `float` `Tensor` of shape `[D]`, the radius used to select the + neighbors. + point_cloud_sample: A 'PointCloud' instance. Samples point cloud. + If None, the sorted points from the grid will be used. + max_neighbors: An `int`, maximum number of neighbors per sample, + if `0` all neighbors are selected. + + """ + + def __init__(self, + grid: Grid, + radius, + point_cloud_sample=None, + max_neighbors=0, + name=None): + radii = tf.reshape(tf.cast(tf.convert_to_tensor(value=radius), + tf.float32), [-1]) + if radii.shape[0] == 1: + radii = tf.repeat(radius, grid._point_cloud._dimension) + #Save the attributes. + if point_cloud_sample is None: + self._equal_samples = True + self._point_cloud_sampled = PointCloud( + grid._sorted_points, grid._sorted_batch_ids, + grid._batch_size) + else: + self._equal_samples = False + self._point_cloud_sampled = point_cloud_sample + self._grid = grid + self._radii = radii + self.max_neighbors = max_neighbors + + #Find the neighbors. + self._samples_neigh_ranges, self._neighbors = find_neighbors( + self._grid, self._point_cloud_sampled, self._radii, max_neighbors) + + #Original neighIds. + aux_original_neigh_ids = tf.gather( + self._grid._sorted_indices, self._neighbors[:, 0]) + self._original_neigh_ids = tf.concat([ + tf.reshape(aux_original_neigh_ids, [-1, 1]), + tf.reshape(self._neighbors[:, 1], [-1, 1])], axis=-1) + + #Initialize the pdf + self._pdf = None + + self._transposed = None + +''' + def compute_pdf(self, + bandwidth=0.2, + mode=KDEMode.constant, + normalize=False, + name=None): + """Method to compute the probability density function of the neighborhoods. + + Note: By default the returned densitity is not normalized. + + Args: + bandwidth: A `float` `Tensor` of shape `[D]`, bandwidth used to compute + the pdf. (optional) + mode: 'KDEMode', mode used to determine the bandwidth. (optional) + normalize: A `bool`, if `True` each value is divided by be size of the + respective neighborhood. (optional) + + """ + bandwidth = cast_to_num_dims( + bandwidth, self._point_cloud_sampled._dimension) + + if mode == KDEMode.no_pdf: + self._pdf = tf.ones_like( + self._neighbors[:, 0], dtype=tf.float32) + else: + if self._equal_samples: + pdf_neighbors = self + else: + pdf_neighbors = Neighborhood(self._grid, self._radii, None) + _pdf = compute_pdf( + pdf_neighbors, bandwidth, mode.value) + self._pdf = tf.gather(_pdf, self._neighbors[:, 0]) + if normalize: + norm_factors = tf.math.unsorted_segment_sum( + tf.ones_like(self._pdf), + self._neighbors[:, 1], + self._point_cloud_sampled._points.shape[0]) + self._pdf = self._pdf / tf.gather(norm_factors, self._neighbors[:, 1]) + + def get_pdf(self, **kwargs): + """ Method which returns the pdfs of the neighborhoods. + + If no pdf was computed before, it will compute one using the provided + arguments. + + Args: + **kwargs: if no pdf is available, these arguments will be passed to + `compute_pdf`.(optional) + + Returns: + A `float` `Tensor` of shape `[M]`, the estimated densities. + + """ + if self._pdf is None: + self.compute_pdf(**kwargs) + return self._pdf +''' + + def get_grid(self): + """ Returns the grid used for neighborhood computation. + """ + return self._grid + + def transpose(self): + """ Returns the transposed neighborhood where center and neighbor points + are switched. (faster than recomputing) + """ + if self._transposed is None: + if self._equal_samples: + self._transposed = self + else: + grid = Grid(self._point_cloud_sampled, self._radii) + self._transposed = Neighborhood( + grid, self._radii, self._grid._point_cloud) + return self._transposed diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 26d924a5b..72382f824 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -16,9 +16,9 @@ from .PointCloud import _AABB as AABB from .PointCloud import PointCloud from .Grid import Grid -''' from .Neighborhood import Neighborhood from .Neighborhood import KDEMode +''' from .sampling import poisson_disk_sampling, cell_average_sampling from .sampling import sample from .PointHierarchy import PointHierarchy diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py index 6509ca236..5929afdb8 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -28,12 +28,12 @@ from .custom_ops_wrapper import build_grid_ds from .custom_ops_wrapper import compute_keys # from .custom_ops_wrapper import compute_pdf - # from .custom_ops_wrapper import find_neighbors + from .custom_ops_wrapper import find_neighbors # from .custom_ops_wrapper import sampling else: # from .custom_ops_tf import basis_proj_tf as basis_proj from .custom_ops_tf import build_grid_ds_tf as build_grid_ds from .custom_ops_tf import compute_keys_tf as compute_keys # from .custom_ops_tf import compute_pdf_tf as compute_pdf - # from .custom_ops_tf import find_neighbors_tf as find_neighbors + from .custom_ops_tf import find_neighbors_tf as find_neighbors # from .custom_ops_tf import sampling_tf as sampling diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index 113b7d8a6..baeb3f7d7 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -114,7 +114,7 @@ def build_grid_ds_tf(sorted_keys, num_cells, batch_size, name=None): axis=3) tf.no_gradient('BuildGridDsTF') -''' + def find_neighbors_tf(grid, point_cloud_centers, radii, @@ -257,7 +257,7 @@ def find_neighbors_no_grid(point_cloud, num_center_points) return neigh_ranges, neighbors tf.no_gradient('FindNeighborsNoGrid') - +''' def sampling_tf(neighborhood, sample_mode, name=None): """ Method to sample the points of a point cloud. diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/find_neighbors_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/find_neighbors_tf_test.py new file mode 100644 index 000000000..637f77e61 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/find_neighbors_tf_test.py @@ -0,0 +1,98 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test find neighbors tensorflow implementation""" + +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid +from pylib.pc.tests import utils +from pylib.pc.custom_ops.custom_ops_tf import find_neighbors_tf +from pylib.pc.custom_ops.custom_ops_tf import find_neighbors_no_grid + + +class FindNeighborsTF(test_case.TestCase): + + @parameterized.parameters( + (10, 4, 0.05, 1), + (10, 4, 0.11, 7), + (10, 4, 0.142, 19), + (10, 4, 0.174, 27), + ) + def test_neighbors_on_3D_meshgrid(self, + num_points_cbrt, + num_points_samples_cbrt, + radius, + expected_num_neighbors): + num_points = num_points_cbrt**3 + num_samples = num_points_samples_cbrt**3 + + points = utils._create_uniform_distributed_point_cloud_3D( + num_points_cbrt, flat=True) + batch_ids = np.zeros(num_points) + points_samples = utils._create_uniform_distributed_point_cloud_3D( + num_points_samples_cbrt, bb_min=1 / (num_points_samples_cbrt + 1), + flat=True) + batch_ids_samples = np.zeros(num_samples) + point_cloud = PointCloud(points, batch_ids) + point_cloud_samples = PointCloud(points_samples, batch_ids_samples) + cell_sizes = np.float32(np.repeat([radius], 3)) + grid = Grid(point_cloud, cell_sizes) + + # with grid + neigh_ranges, _ = find_neighbors_tf(grid, point_cloud_samples, cell_sizes) + num_neighbors = np.zeros(num_samples) + num_neighbors[0] = neigh_ranges[0] + num_neighbors[1:] = neigh_ranges[1:] - neigh_ranges[:-1] + expected_num_neighbors = \ + np.ones_like(num_neighbors) * expected_num_neighbors + self.assertAllEqual(num_neighbors, expected_num_neighbors) + + @parameterized.parameters( + (10, 4, 0.05, 1), + (10, 4, 0.11, 7), + (10, 4, 0.142, 19), + (10, 4, 0.174, 27), + ) + def test_neighbors_on_3D_meshgrid_without_gridDS(self, + num_points_cbrt, + num_points_samples_cbrt, + radius, + expected_num_neighbors): + num_points = num_points_cbrt**3 + num_samples = num_points_samples_cbrt**3 + + points = utils._create_uniform_distributed_point_cloud_3D( + num_points_cbrt, flat=True) + batch_ids = np.zeros(num_points) + points_samples = utils._create_uniform_distributed_point_cloud_3D( + num_points_samples_cbrt, bb_min=1 / (num_points_samples_cbrt + 1), + flat=True) + batch_ids_samples = np.zeros(num_samples) + point_cloud = PointCloud(points, batch_ids) + point_cloud_samples = PointCloud(points_samples, batch_ids_samples) + + # without grid + neigh_ranges, _ = find_neighbors_no_grid( + point_cloud, point_cloud_samples, radius) + num_neighbors = np.zeros(num_samples) + num_neighbors[0] = neigh_ranges[0] + num_neighbors[1:] = neigh_ranges[1:] - neigh_ranges[:-1] + expected_num_neighbors = \ + np.ones_like(num_neighbors) * expected_num_neighbors + self.assertAllEqual(num_neighbors, expected_num_neighbors) + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/neighbors_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/neighbors_test.py new file mode 100644 index 000000000..4b4beb716 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/neighbors_test.py @@ -0,0 +1,150 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test neighbor functions""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood +from pylib.pc.tests import utils + + +class NeighborsTest(test_case.TestCase): + + @parameterized.parameters( + (100, 10, 4, 0.025, 2), + (100, 10, 4, 0.025, 3), + (100, 10, 4, 0.025, 4) + ) + def test_find_neighbors(self, + num_points, + num_samples, + batch_size, + radius, + dimension): + cell_sizes = np.repeat(radius, dimension) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points) + point_cloud = PointCloud(points, batch_ids) + samples_points, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_samples) + point_cloud_sampled = PointCloud(samples_points, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_sampled) + sorted_points = grid._sorted_points + + neighbors_tf = neighborhood._neighbors + + neighbors_numpy = [[] for i in range(num_samples * batch_size)] + + for k in range(batch_size): + for i in range(num_samples): + for j in range(num_points): + diffArray = (samples_points[i + k * num_samples] - \ + sorted_points[(batch_size - k - 1) * num_points + j])\ + / cell_sizes + if np.linalg.norm(diffArray) < 1.0: + neighbors_numpy[k * num_samples + i].append((batch_size - k - 1)\ + * num_points + j) + + allFound = True + for neigh in neighbors_tf: + found = False + for ref_neigh in neighbors_numpy[neigh[1]]: + if ref_neigh == neigh[0]: + found = True + allFound = allFound and found + self.assertTrue(allFound) + + @parameterized.parameters( + (12, 100, 24, np.sqrt(2), 2), + (32, 1000, 32, 0.7, 2), + (32, 1000, 32, 0.1, 2), + (12, 100, 24, np.sqrt(3), 3), + (32, 1000, 32, 0.7, 3), + (32, 1000, 32, 0.1, 3), + (12, 100, 24, np.sqrt(4), 4), + (32, 10000, 32, 0.7, 4), + (32, 1000, 32, 0.1, 4), + ) + def test_neighbors_are_from_same_batch(self, + batch_size, + num_points, + num_samples, + radius, + dimension): + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + samples, batch_ids_samples = utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + radius = np.float32(np.repeat([radius], dimension)) + + point_cloud = PointCloud(points, batch_ids) + point_cloud_samples = PointCloud(samples, batch_ids_samples) + grid = Grid(point_cloud, radius) + neighborhood = Neighborhood(grid, radius, point_cloud_samples) + + batch_ids_in = tf.gather( + point_cloud._batch_ids, neighborhood._original_neigh_ids[:, 0]) + batch_ids_out = tf.gather( + point_cloud_samples._batch_ids, neighborhood._original_neigh_ids[:, 1]) + batch_check = batch_ids_in == batch_ids_out + self.assertTrue(np.all(batch_check)) + + @parameterized.parameters( + (10, 4, 0.05, 1), + (10, 4, 0.11, 7), + (10, 4, 0.142, 19), + (10, 4, 0.174, 27), + ) + def test_neighbors_on_3D_meshgrid(self, + num_points_cbrt, + num_points_samples_cbrt, + radius, + expected_num_neighbors): + num_points = num_points_cbrt**3 + num_samples = num_points_samples_cbrt**3 + + points = utils._create_uniform_distributed_point_cloud_3D( + num_points_cbrt, flat=True) + batch_ids = np.zeros(num_points) + points_samples = utils._create_uniform_distributed_point_cloud_3D( + num_points_samples_cbrt, bb_min=1 / (num_points_samples_cbrt + 1), + flat=True) + batch_ids_samples = np.zeros(num_samples) + point_cloud = PointCloud(points, batch_ids) + point_cloud_samples = PointCloud(points_samples, batch_ids_samples) + radius = np.float32(np.repeat([radius], 3)) + grid = Grid(point_cloud, radius) + neighborhood = Neighborhood(grid, radius, point_cloud_samples) + + neigh_ranges = neighborhood._samples_neigh_ranges + num_neighbors = np.zeros(num_samples) + num_neighbors[0] = neigh_ranges[0] + num_neighbors[1:] = neigh_ranges[1:] - neigh_ranges[:-1] + expected_num_neighbors = \ + np.ones_like(num_neighbors) * expected_num_neighbors + self.assertAllEqual(num_neighbors, expected_num_neighbors) + + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py index 6a8cf059b..48a3cdb55 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/utils.py @@ -59,7 +59,7 @@ def _create_random_point_cloud_padded(max_num_points, sizes = np.random.randint(1, max_num_points, batch_shape) return points, sizes -''' + def _create_uniform_distributed_point_cloud_2D(num_points_sqrt, scale=1, flat=False): @@ -79,4 +79,4 @@ def _create_uniform_distributed_point_cloud_3D(num_points_root, if flat: points = points.reshape(-1, 3) return points -''' + From a8f88afe0b6be61235456199e96ab04049003c3d Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 13:28:45 +0100 Subject: [PATCH 13/32] fixed indentation error --- .../projects/point_convolutions/pylib/pc/Neighborhood.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py index 410ecf624..ac0210653 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py @@ -102,7 +102,7 @@ def __init__(self, self._transposed = None -''' + ''' def compute_pdf(self, bandwidth=0.2, mode=KDEMode.constant, @@ -158,7 +158,7 @@ def get_pdf(self, **kwargs): if self._pdf is None: self.compute_pdf(**kwargs) return self._pdf -''' + ''' def get_grid(self): """ Returns the grid used for neighborhood computation. From 6e0982d2b11da6c0e5cdd69a8c7593d1a4e54f63 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 13:38:23 +0100 Subject: [PATCH 14/32] uncommented missing util function --- .../projects/point_convolutions/pylib/pc/utils.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py index 972222310..1e61a55d8 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -71,7 +71,7 @@ def check_valid_point_hierarchy_input(point_cloud, cell_sizes, pool_mode): f'dimension. Must be 1 or {point_cloud.dimension_} but is' +\ f'{curr_cell_sizes.shape[0]}.') - +''' def _flatten_features(features, point_cloud: PointCloud): """ Converts features of shape `[A1, ..., An, C]` to shape `[N, C]`. @@ -92,7 +92,7 @@ def _flatten_features(features, point_cloud: PointCloud): tf.assert_equal(tf.shape(features)[0], tf.shape(point_cloud._points)[0]) tf.assert_equal(tf.rank(features), 2) return features - +''' def cast_to_num_dims(values, num_dims, dtype=tf.float32): """ Converts an input to the specified `dtype` and repeats it `num_dims` @@ -112,4 +112,3 @@ def cast_to_num_dims(values, num_dims, dtype=tf.float32): if values.shape == [] or values.shape[0] == 1: values = tf.repeat(values, num_dims) return values -''' From ab73ea49342a6f0fa1d350106bff776358199aa3 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 13:39:37 +0100 Subject: [PATCH 15/32] uncommented missing util function --- .../projects/point_convolutions/pylib/pc/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py index 1e61a55d8..dc4d83893 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -71,7 +71,7 @@ def check_valid_point_hierarchy_input(point_cloud, cell_sizes, pool_mode): f'dimension. Must be 1 or {point_cloud.dimension_} but is' +\ f'{curr_cell_sizes.shape[0]}.') -''' + def _flatten_features(features, point_cloud: PointCloud): """ Converts features of shape `[A1, ..., An, C]` to shape `[N, C]`. From 621d296656c5f4e6a0abf7a0a002627af9c1df77 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 14:12:45 +0100 Subject: [PATCH 16/32] gather_nd incompatibility --- .../point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index baeb3f7d7..c26c1fa32 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -187,7 +187,7 @@ def find_neighbors_tf(grid, tf.maximum(adj_cell_ids_2D, tf.zeros_like(adj_cell_ids_2D)), grid._num_cells[:2]) # get min and max point ids of the adjacent cells - adj_ids = tf.gather_nd(data_structure[cur_batch_id], [adj_cell_ids_2D]) + adj_ids = tf.compat.v1.gather_nd(data_structure[cur_batch_id], [adj_cell_ids_2D]) adj_ids_start = tf.reduce_min(adj_ids[0, :, 0]) adj_ids_end = tf.reduce_max(adj_ids[0, :, 1]) # choose points below certain distance and in same batch From 40dc80d79c268cc5f9a60ddd112bbdab031d051d Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 14:34:42 +0100 Subject: [PATCH 17/32] gather_nd issue --- .../point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index c26c1fa32..1fb021669 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -187,7 +187,8 @@ def find_neighbors_tf(grid, tf.maximum(adj_cell_ids_2D, tf.zeros_like(adj_cell_ids_2D)), grid._num_cells[:2]) # get min and max point ids of the adjacent cells - adj_ids = tf.compat.v1.gather_nd(data_structure[cur_batch_id], [adj_cell_ids_2D]) + ds_cur_batch = tf.gather(data_structure, cur_batch_id) + adj_ids = tf.gather_nd(ds_cur_batch, [adj_cell_ids_2D]) adj_ids_start = tf.reduce_min(adj_ids[0, :, 0]) adj_ids_end = tf.reduce_max(adj_ids[0, :, 1]) # choose points below certain distance and in same batch From 2bf99f66ef5fd703a83fe9de252c1a610a4e327c Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 14:52:54 +0100 Subject: [PATCH 18/32] fixed indexing error --- .../point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index 1fb021669..9ca8b0728 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -185,7 +185,7 @@ def find_neighbors_tf(grid, # clip to range between 0 and max num cells adj_cell_ids_2D = tf.minimum( tf.maximum(adj_cell_ids_2D, tf.zeros_like(adj_cell_ids_2D)), - grid._num_cells[:2]) + grid._num_cells[:2] - 1) # get min and max point ids of the adjacent cells ds_cur_batch = tf.gather(data_structure, cur_batch_id) adj_ids = tf.gather_nd(ds_cur_batch, [adj_cell_ids_2D]) From 4f5fd221ec6231998300b495dbb667cfb7f3ff2d Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 15:12:57 +0100 Subject: [PATCH 19/32] initial pde --- .../pylib/pc/Neighborhood.py | 2 - .../pylib/pc/custom_ops/__init__.py | 4 +- .../pylib/pc/custom_ops/custom_ops_tf.py | 60 +------ .../custom_ops/tests/compute_pdf_tf_test.py | 152 ++++++++++++++++++ .../pylib/pc/tests/compute_pdf_test.py | 149 +++++++++++++++++ 5 files changed, 305 insertions(+), 62 deletions(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/compute_pdf_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py index ac0210653..f7948b39f 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py @@ -102,7 +102,6 @@ def __init__(self, self._transposed = None - ''' def compute_pdf(self, bandwidth=0.2, mode=KDEMode.constant, @@ -158,7 +157,6 @@ def get_pdf(self, **kwargs): if self._pdf is None: self.compute_pdf(**kwargs) return self._pdf - ''' def get_grid(self): """ Returns the grid used for neighborhood computation. diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py index 5929afdb8..7beed10b1 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -27,13 +27,13 @@ # from .custom_ops_wrapper import basis_proj from .custom_ops_wrapper import build_grid_ds from .custom_ops_wrapper import compute_keys - # from .custom_ops_wrapper import compute_pdf + from .custom_ops_wrapper import compute_pdf from .custom_ops_wrapper import find_neighbors # from .custom_ops_wrapper import sampling else: # from .custom_ops_tf import basis_proj_tf as basis_proj from .custom_ops_tf import build_grid_ds_tf as build_grid_ds from .custom_ops_tf import compute_keys_tf as compute_keys - # from .custom_ops_tf import compute_pdf_tf as compute_pdf + from .custom_ops_tf import compute_pdf_tf as compute_pdf from .custom_ops_tf import find_neighbors_tf as find_neighbors # from .custom_ops_tf import sampling_tf as sampling diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index 9ca8b0728..f0b49bb56 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -324,68 +324,11 @@ def sampling_tf(neighborhood, sample_mode, name=None): tf.no_gradient('samplingTF') +''' _pi = tf.constant(np.pi) -def compute_pdf_inside_neighborhoods_tf(neighborhood, - bandwidth, - mode, - name=None): - """ Method to compute the density distribution inside the neighborhoods of a - point cloud in euclidean space using kernel density estimation (KDE). - - Args: - neighborhood: A `Neighborhood` instance. - bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. - mode: A `KDEMode` value. - - Returns: - A `float` `Tensor` of shape `[N]`, the estimated density per center point. - - """ - bandwidth = tf.convert_to_tensor(value=bandwidth) - points = neighborhood._grid._sorted_points - neighbors = neighborhood._neighbors - nbh_ranges = neighborhood._samples_neigh_ranges - - # compute difference vectors inside neighborhoods - num_adjacencies = tf.shape(neighbors)[0] - nbh_start_ind = tf.concat(([0], nbh_ranges[0:-1]), axis=0) - nbh_sizes = nbh_ranges - nbh_start_ind - max_num_neighbors = tf.reduce_max(nbh_sizes) - nbh_sizes_per_nb = tf.repeat(nbh_sizes, nbh_sizes) - - nb_indices_1 = tf.repeat(neighbors[:, 0], nbh_sizes_per_nb) - - mask = tf.sequence_mask(nbh_sizes_per_nb, max_num_neighbors) - mask_indices = tf.cast(tf.compat.v1.where(mask), tf.int32) - indices_tensor = tf.repeat(tf.reshape(tf.range(0, max_num_neighbors), - [1, max_num_neighbors]), - num_adjacencies, axis=0) - nbh_start_per_nb = tf.repeat(nbh_start_ind, nbh_sizes) - indices_tensor = indices_tensor + \ - tf.reshape(nbh_start_per_nb, [num_adjacencies, 1]) - indices_2 = tf.gather_nd(params=indices_tensor, indices=mask_indices) - nb_indices_2 = tf.gather(neighbors[:, 0], indices_2) - - nb_diff = tf.gather(points, nb_indices_1) - tf.gather(points, nb_indices_2) - # kernel density estimation using the distances - rel_bandwidth = tf.reshape(bandwidth * neighborhood._radii, [1, -1]) - kernel_input = nb_diff / rel_bandwidth - # gaussian kernel - nb_kernel_value = tf.exp(-tf.pow(kernel_input, 2) / 2) / tf.sqrt(2 * _pi) - nb_kernel_value = tf.reduce_prod(nb_kernel_value, axis=1) - nb_id_per_nb_pair = tf.repeat(tf.range(0, num_adjacencies), - nbh_sizes_per_nb) - # sum over influence inside neighborhood - pdf = tf.math.unsorted_segment_sum(nb_kernel_value, - nb_id_per_nb_pair, - num_adjacencies) /\ - tf.reduce_prod(bandwidth) - return pdf - - def compute_pdf_tf(neighborhood, bandwidth, mode, name=None): """ Method to compute the density distribution using neighborhood information in euclidean space using kernel density estimation (KDE). @@ -420,6 +363,7 @@ def compute_pdf_tf(neighborhood, bandwidth, mode, name=None): tf.reduce_prod(bandwidth) return pdf +''' def basis_proj_tf(neigh_basis, features, neighborhood, name=None): """ Method to aggregate the features*basis for different neighborhoods. diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py new file mode 100644 index 000000000..8484ad8f6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py @@ -0,0 +1,152 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test kernel density estimation tensorflow implementation""" + +import os +import sys +import numpy as np +from sklearn.neighbors import KernelDensity +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import KDEMode +from pylib.pc import Neighborhood +from pylib.pc.custom_ops.custom_ops_tf import compute_pdf_tf +from pylib.pc.tests import utils + + +class ComputePDFTFTest(test_case.TestCase): + + @parameterized.parameters( + (2, 100, 10, 0.2, 0.1, 2), + (2, 100, 10, 0.7, 0.1, 2), + (2, 100, 10, np.sqrt(2), 0.1, 2), + (2, 100, 10, 0.2, 0.2, 3), + (2, 100, 10, 0.7, 0.1, 3), + (2, 100, 10, np.sqrt(3), 0.2, 3), + (2, 100, 10, 0.2, 0.2, 4), + (2, 100, 10, np.sqrt(4), 0.2, 4) + ) + def test_compute_pdf_tf(self, + batch_size, + num_points, + num_samples_per_batch, + cell_size, + bandwidth, + dimension): + cell_sizes = np.float32(np.repeat(cell_size, dimension)) + bandwidths = np.float32(np.repeat(bandwidth, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, batch_size * num_points, dimension, + equal_sized_batches=True) + samples = np.full((batch_size * num_samples_per_batch, dimension), + 0.0, dtype=float) + for i in range(batch_size): + cur_choice = np.random.choice(num_points, num_samples_per_batch, + replace=True) + samples[num_samples_per_batch * i:num_samples_per_batch * (i + 1), :] = \ + points[cur_choice + i * num_points] + samples_batch_ids = np.repeat(np.arange(0, batch_size), + num_samples_per_batch) + + point_cloud = PointCloud(points, batch_ids, batch_size) + grid = Grid(point_cloud, cell_sizes) + + point_cloud_samples = PointCloud(samples, samples_batch_ids, batch_size) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighbor_ids = neighborhood._neighbors + pdf_neighbors = Neighborhood(grid, cell_sizes) + pdf_tf = compute_pdf_tf(pdf_neighbors, bandwidths, KDEMode.constant) + pdf_tf = tf.gather(pdf_tf, neighbor_ids[:, 0]) + + sorted_points = grid._sorted_points.numpy() + sorted_batch_ids = grid._sorted_batch_ids.numpy() + neighbor_ids = neighborhood._neighbors + + pdf_real = [] + accum_points = [] + prev_batch_i = -1 + for pt_i, batch_i in enumerate(sorted_batch_ids): + if batch_i != prev_batch_i: + if len(accum_points) > 0: + test_points = np.array(accum_points) + kde_skl = KernelDensity(bandwidth=bandwidth) + kde_skl.fit(test_points) + log_pdf = kde_skl.score_samples(test_points) + pdf = np.exp(log_pdf) + if len(pdf_real) > 0: + pdf_real = np.concatenate((pdf_real, pdf), axis=0) + else: + pdf_real = pdf + accum_points = [sorted_points[pt_i] / cell_size] + prev_batch_i = batch_i + else: + accum_points.append(sorted_points[pt_i] / cell_size) + + test_points = np.array(accum_points) + kde_skl = KernelDensity(bandwidth=bandwidth) + kde_skl.fit(test_points) + log_pdf = kde_skl.score_samples(test_points) + pdf = np.exp(log_pdf) + if len(pdf_real) > 0: + pdf_real = np.concatenate((pdf_real, pdf), axis=0) + else: + pdf_real = pdf + + pdf_tf = np.asarray(pdf_tf / float(len(accum_points))) + pdf_skl = np.asarray(pdf_real)[neighbor_ids[:, 0]] + self.assertAllClose(pdf_tf, pdf_skl) + + @parameterized.parameters( + (1, 20, 1, np.sqrt(2), 2), + (1, 20, 1, np.sqrt(3), 3), + (1, 20, 1, np.sqrt(4), 4) + ) + def test_compute_pdf_jacobian(self, + batch_size, + num_points, + num_samples, + radius, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + bandwidths = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, batch_size * num_points, dimension, + equal_sized_batches=True) + samples = np.full((batch_size * num_samples, dimension), 0.0, dtype=float) + for i in range(batch_size): + cur_choice = np.random.choice(num_points, num_samples, replace=True) + samples[num_samples * i:num_samples * (i + 1), :] = \ + points[cur_choice + i * num_points] + samples_batch_ids = np.repeat(np.arange(0, batch_size), num_samples) + def compute_pdf(points_in): + point_cloud = PointCloud(points_in, batch_ids, batch_size) + grid = Grid(point_cloud, cell_sizes) + + point_cloud_samples = PointCloud(samples, samples_batch_ids, batch_size) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf(bandwidths, KDEMode.constant, normalize=True) + # account for influence of neighborhood size + _, _, counts = tf.unique_with_counts(neighborhood._neighbors[:, 1]) + max_num_nb = tf.cast(tf.reduce_max(counts), tf.float32) + return neighborhood._pdf / max_num_nb + + self.assert_jacobian_is_correct_fn( + compute_pdf, [np.float32(points)], atol=1e-4) + + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/compute_pdf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/compute_pdf_test.py new file mode 100644 index 000000000..ba081b4e3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/compute_pdf_test.py @@ -0,0 +1,149 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test kernel density estimation for point clouds""" + +import os +import sys +import numpy as np +from sklearn.neighbors import KernelDensity +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import KDEMode +from pylib.pc import Neighborhood +from pylib.pc.tests import utils + + +class ComputePDFTest(test_case.TestCase): + + @parameterized.parameters( + (4, 100, 10, 0.2, 0.1, 2), + (4, 100, 10, 0.7, 0.1, 2), + (4, 100, 10, np.sqrt(2), 0.1, 2), + (4, 100, 10, 0.2, 0.1, 3), + (4, 100, 10, 0.7, 0.1, 3), + (4, 100, 10, np.sqrt(3), 0.1, 3), + (4, 100, 10, 0.2, 0.1, 4), + (4, 100, 10, np.sqrt(4), 0.1, 4) + ) + def test_compute_pdf(self, + batch_size, + num_points, + num_samples_per_batch, + cell_size, + bandwidth, + dimension): + cell_sizes = np.float32(np.repeat(cell_size, dimension)) + bandwidths = np.float32(np.repeat(bandwidth, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, batch_size * num_points, dimension, + equal_sized_batches=True) + samples = np.full((batch_size * num_samples_per_batch, dimension), + 0.0, dtype=float) + for i in range(batch_size): + cur_choice = np.random.choice(num_points, num_samples_per_batch, + replace=True) + samples[num_samples_per_batch * i:num_samples_per_batch * (i + 1), :] = \ + points[cur_choice + i * num_points] + samples_batch_ids = np.repeat(np.arange(0, batch_size), + num_samples_per_batch) + + point_cloud = PointCloud(points, batch_ids, batch_size) + grid = Grid(point_cloud, cell_sizes) + + point_cloud_samples = PointCloud(samples, samples_batch_ids, batch_size) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf(bandwidths, KDEMode.constant) + pdf_tf = neighborhood._pdf + + sorted_points = grid._sorted_points.numpy() + sorted_batch_ids = grid._sorted_batch_ids.numpy() + neighbor_ids = neighborhood._neighbors + + pdf_real = [] + accum_points = [] + prev_batch_i = -1 + for pt_i, batch_i in enumerate(sorted_batch_ids): + if batch_i != prev_batch_i: + if len(accum_points) > 0: + test_points = np.array(accum_points) + kde_skl = KernelDensity(bandwidth=bandwidth) + kde_skl.fit(test_points) + log_pdf = kde_skl.score_samples(test_points) + pdf = np.exp(log_pdf) + if len(pdf_real) > 0: + pdf_real = np.concatenate((pdf_real, pdf), axis=0) + else: + pdf_real = pdf + accum_points = [sorted_points[pt_i] / cell_size] + prev_batch_i = batch_i + else: + accum_points.append(sorted_points[pt_i] / cell_size) + + test_points = np.array(accum_points) + kde_skl = KernelDensity(bandwidth=bandwidth) + kde_skl.fit(test_points) + log_pdf = kde_skl.score_samples(test_points) + pdf = np.exp(log_pdf) + if len(pdf_real) > 0: + pdf_real = np.concatenate((pdf_real, pdf), axis=0) + else: + pdf_real = pdf + + pdf_tf = np.asarray(pdf_tf / float(len(accum_points))) + pdf_skl = np.asarray(pdf_real)[neighbor_ids[:, 0]] + self.assertAllClose(pdf_tf, pdf_skl) + + @parameterized.parameters( + (1, 200, 1, 4, 2), + (1, 200, 1, 4, 3), + (1, 100, 1, 4, 4) + ) + def test_compute_pdf_jacobian(self, + batch_size, + num_points, + num_samples, + radius, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + bandwidths = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, batch_size * num_points, dimension, + equal_sized_batches=True) + samples = np.full((batch_size * num_samples, dimension), 0.0, dtype=float) + for i in range(batch_size): + cur_choice = np.random.choice(num_points, num_samples, replace=True) + samples[num_samples * i:num_samples * (i + 1), :] = \ + points[cur_choice + i * num_points] + samples_batch_ids = np.repeat(np.arange(0, batch_size), num_samples) + def compute_pdf(points_in): + point_cloud = PointCloud(points_in, batch_ids, batch_size) + grid = Grid(point_cloud, cell_sizes) + + point_cloud_samples = PointCloud(samples, samples_batch_ids, batch_size) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf(bandwidths, KDEMode.constant, normalize=True) + # account for influence of neighborhood size + _, _, counts = tf.unique_with_counts(neighborhood._neighbors[:, 1]) + max_num_nb = tf.cast(tf.reduce_max(counts), tf.float32) + return neighborhood._pdf / max_num_nb + + self.assert_jacobian_is_correct_fn( + compute_pdf, [np.float32(points)], atol=1e-4, delta=1e-3) + + +if __name__ == '__main__': + test_case.main() From a99fa170a1142804285f96f041bfe418323153fe Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 15:22:41 +0100 Subject: [PATCH 20/32] added missing dependency --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cdc4294fd..35d99498d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ termcolor >= 1.1.0 trimesh >= 2.37.22 # Required by trimesh. networkx - +# required for pytests of project/point_convolutions +sklearn From b35115940767a3b839d4aba2f70a0c88f7123a25 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 11 Mar 2021 15:46:13 +0100 Subject: [PATCH 21/32] uncommented missing import --- .../projects/point_convolutions/pylib/pc/Neighborhood.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py index f7948b39f..75276ce63 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/Neighborhood.py @@ -38,7 +38,7 @@ from pylib.pc import PointCloud from pylib.pc import Grid -from pylib.pc.custom_ops import find_neighbors # , compute_pdf +from pylib.pc.custom_ops import find_neighbors, compute_pdf from pylib.pc.utils import cast_to_num_dims From ac9fc084cc41d4d408d3203249c5d07ce829661f Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Fri, 12 Mar 2021 09:25:57 +0100 Subject: [PATCH 22/32] test acc fix --- .../pylib/pc/custom_ops/tests/compute_pdf_tf_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py index 8484ad8f6..dc66f13c0 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/compute_pdf_tf_test.py @@ -145,7 +145,7 @@ def compute_pdf(points_in): return neighborhood._pdf / max_num_nb self.assert_jacobian_is_correct_fn( - compute_pdf, [np.float32(points)], atol=1e-4) + compute_pdf, [np.float32(points)], atol=1e-4, delta=1e-4) if __name__ == '__main__': From ab4c109ee65b4e6f063c68dad3bfa8c348f3682e Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Fri, 12 Mar 2021 10:51:50 +0100 Subject: [PATCH 23/32] spatial_sampling_initial --- .../point_convolutions/pylib/pc/__init__.py | 2 +- .../pylib/pc/custom_ops/__init__.py | 4 +- .../pylib/pc/custom_ops/custom_ops_tf.py | 3 +- .../point_convolutions/pylib/pc/sampling.py | 150 ++++++++++++++++++ .../pylib/pc/tests/sample_test.py | 129 +++++++++++++++ 5 files changed, 283 insertions(+), 5 deletions(-) create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 72382f824..66c27be40 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -18,9 +18,9 @@ from .Grid import Grid from .Neighborhood import Neighborhood from .Neighborhood import KDEMode -''' from .sampling import poisson_disk_sampling, cell_average_sampling from .sampling import sample +''' from .PointHierarchy import PointHierarchy from pylib.pc import layers diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py index 7beed10b1..17be8cb2e 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -29,11 +29,11 @@ from .custom_ops_wrapper import compute_keys from .custom_ops_wrapper import compute_pdf from .custom_ops_wrapper import find_neighbors - # from .custom_ops_wrapper import sampling + from .custom_ops_wrapper import sampling else: # from .custom_ops_tf import basis_proj_tf as basis_proj from .custom_ops_tf import build_grid_ds_tf as build_grid_ds from .custom_ops_tf import compute_keys_tf as compute_keys from .custom_ops_tf import compute_pdf_tf as compute_pdf from .custom_ops_tf import find_neighbors_tf as find_neighbors - # from .custom_ops_tf import sampling_tf as sampling + from .custom_ops_tf import sampling_tf as sampling diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index f0b49bb56..d992fa45e 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -258,7 +258,7 @@ def find_neighbors_no_grid(point_cloud, num_center_points) return neigh_ranges, neighbors tf.no_gradient('FindNeighborsNoGrid') -''' + def sampling_tf(neighborhood, sample_mode, name=None): """ Method to sample the points of a point cloud. @@ -324,7 +324,6 @@ def sampling_tf(neighborhood, sample_mode, name=None): tf.no_gradient('samplingTF') -''' _pi = tf.constant(np.pi) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py new file mode 100755 index 000000000..b10cff416 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py @@ -0,0 +1,150 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Methods to sample point clouds. """ + +import tensorflow as tf + +from pylib.pc.custom_ops import sampling + +from pylib.pc import PointCloud, Neighborhood, Grid +from pylib.pc.utils import cast_to_num_dims + +sample_modes = {'average': 1, + 'cell average': 1, + 'cell_average': 1, + 'poisson': 0, + 'poisson disk': 0, + 'poisson_disk': 0} + + +def poisson_disk_sampling(point_cloud, + radius=None, + neighborhood=None, + return_ids=False, + name=None): + """ Poisson disk sampling of a point cloud. + + Note: Either `radius` or `neighborhood` must be provided. + + Args: + point_cloud: A `PointCloud` instance. + radius: A `float` or a `float` `Tensor` of shape `[D]`, the radius for the + Poisson disk sampling. + neighborhood: A `Neighborhood` instance. + return_ids: A `bool`, if `True` returns the indices of the sampled points. + (optional) + + Returns: + A `PointCloud` instance. + An `int` `Tensor` of shape `[S]`, if `return_ids` is `True`. + + Raises: + ValueError: If no radius or neighborhood is given. + + """ + if radius is None and neighborhood is None: + raise ValueError( + "Missing Argument! Either radius or neighborhood must be given!") + if neighborhood is None: + # compute neighborhood + radii = cast_to_num_dims(radius, point_cloud) + grid = Grid(point_cloud, radii) + neighborhood = Neighborhood(grid, radii) + + #Compute the sampling. + sampled_points, sampled_batch_ids, sampled_indices = \ + sampling(neighborhood, 1) + + sampled_point_cloud = PointCloud( + points=sampled_points, batch_ids=sampled_batch_ids, + batch_size=neighborhood._point_cloud_sampled._batch_size) + + if return_ids: + sampled_indices = tf.gather(neighborhood._grid._sorted_indices, + sampled_indices) + return sampled_point_cloud, sampled_indices + else: + return sampled_point_cloud + + +def cell_average_sampling(point_cloud, + cell_sizes=None, + grid=None, + name=None): + """ Cell average sampling of a point cloud. + + Note: Either `cell_sizes` or `grid` must be provided. + + Args: + point_cloud: A `PointCloud` instance. + cell_sizes: A `float` or a `float` `Tensor` of shape `[D]`, the cell sizes + for the sampling. + grid: A `Grid` instance. + + Returns: + A `PointCloud` instance. + + Raises: + ValueError: If no radius or grid is given. + + """ + if cell_sizes is None and grid is None: + raise ValueError( + "Missing Argument! Either cell_sizes or grid must be given!") + if grid is None: + # compute grid + cell_sizes = cast_to_num_dims(cell_sizes, point_cloud) + grid = Grid(point_cloud, cell_sizes) + + neighborhood = Neighborhood(grid, cell_sizes) + + #Compute the sampling. + sampled_points, sampled_batch_ids, sampled_indices = \ + sampling(neighborhood, 0) + + sampled_point_cloud = PointCloud( + points=sampled_points, batch_ids=sampled_batch_ids, + batch_size=neighborhood._point_cloud_sampled._batch_size) + + return sampled_point_cloud + + +def sample(neighborhood, sample_mode='poisson', name=None): + """ Sampling for a neighborhood. + + Args: + neighborhood: A `Neighborhood` instance. + sample_mode: A `string`, either `'poisson'`or `'cell average'`. + + Returns: + A `PointCloud` instance, the sampled points. + An `int` `Tensor` of shape `[S]`, the indices of the sampled points, + `None` for cell average sampling. + + """ + sample_mode_value = sample_modes[sample_mode.lower()] + #Compute the sampling. + sampled_points, sampled_batch_ids, sampled_indices = \ + sampling(neighborhood, sample_mode_value) + + #Save the sampled point cloud. + if sample_mode_value == 0: + sampled_indices = tf.gather( + neighborhood._grid._sorted_indices, sampled_indices) + else: + sampled_indices = None + sampled_point_cloud = PointCloud( + points=sampled_points, batch_ids=sampled_batch_ids, + batch_size=neighborhood._point_cloud_sampled._batch_size_numpy) + return sampled_point_cloud, sampled_indices diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py new file mode 100644 index 000000000..69284c726 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py @@ -0,0 +1,129 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test point sampling operations""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import sample +from pylib.pc import Neighborhood +from pylib.pc.tests import utils + + +class SamplingTest(test_case.TestCase): + + @parameterized.parameters( + (100, 8, 0.1, 3), + (100, 8, 0.1, 3), + (100, 16, 0.1, 4) + ) + def test_sampling_poisson_disk_on_random( + self, num_points, batch_size, cell_size, dimension): + cell_sizes = np.float32(np.repeat(cell_size, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points) + point_cloud = PointCloud(points, batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes) + sampled_point_cloud, _ = sample(neighborhood, 'poisson') + + sampled_points = sampled_point_cloud._points.numpy() + sampled_batch_ids = sampled_point_cloud._batch_ids.numpy() + + min_dist = 1.0 + for i in range(batch_size): + indices = np.where(sampled_batch_ids == i) + diff = np.expand_dims(sampled_points[indices], 1) - \ + np.expand_dims(sampled_points[indices], 0) + dists = np.linalg.norm(diff, axis=2) + dists = np.sort(dists, axis=1) + min_dist = min(min_dist, np.amin(dists[:, 1])) + + self.assertLess(min_dist, cell_size + 1e-3) + + @parameterized.parameters( + (6, 1), + (100, 5) + ) + def test_sampling_poisson_disk_on_uniform(self, num_points_sqrt, scale): + points = utils._create_uniform_distributed_point_cloud_2D( + num_points_sqrt, scale=scale) + cell_sizes = scale * np.array([2, 2], dtype=np.float32) \ + / num_points_sqrt + batch_ids = np.zeros([len(points)]) + point_cloud = PointCloud(points, batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes) + sample_point_cloud, _ = sample(neighborhood, 'poisson') + + sampled_points = sample_point_cloud._points.numpy() + expected_num_pts = num_points_sqrt ** 2 // 2 + self.assertTrue(len(sampled_points) == expected_num_pts) + + @parameterized.parameters( + (100, 2, 0.1, 3), + (100, 8, 0.7, 3), + (50, 2, np.sqrt(3), 3), + ) + def test_sampling_average_on_random( + self, num_points, batch_size, cell_size, dimension): + cell_sizes = np.repeat(cell_size, dimension) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points) + #print(points.shape, batch_ids.shape) + point_cloud = PointCloud(points=points, batch_ids=batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes) + sample_point_cloud, _ = sample(neighborhood, 'average') + + sampled_points_tf = sample_point_cloud._points.numpy() + sorted_keys = neighborhood._grid._sorted_keys.numpy() + sorted_points = neighborhood._grid._sorted_points.numpy() + + sampled_points_numpy = [] + cur_point = np.repeat(0.0, dimension) + cur_key = -1 + cur_num_points = 0.0 + for pt_id, cur_key_point in enumerate(sorted_keys): + if cur_key_point != cur_key: + if cur_key != -1: + cur_point /= cur_num_points + sampled_points_numpy.append(cur_point) + cur_key = cur_key_point + cur_point = [0.0, 0.0, 0.0] + cur_num_points = 0.0 + cur_point += sorted_points[pt_id] + cur_num_points += 1.0 + cur_point /= cur_num_points + sampled_points_numpy.append(cur_point) + + equal = True + for point_numpy in sampled_points_numpy: + found = False + for point_tf in sampled_points_tf: + if np.all(np.abs(point_numpy - point_tf) < 0.0001): + found = True + equal = equal and found + self.assertTrue(equal) + + +if __name__ == '__main__': + test_case.main() From 552d6af0182d11a53fd411c62b938a11314778df Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Fri, 12 Mar 2021 12:04:37 +0100 Subject: [PATCH 24/32] fixed failed unit test --- .../point_convolutions/pylib/pc/sampling.py | 2 +- .../pylib/pc/tests/grid_test.py | 4 +-- .../pylib/pc/tests/sample_test.py | 9 ++++-- .../point_convolutions/pylib/pc/utils.py | 29 ------------------- 4 files changed, 9 insertions(+), 35 deletions(-) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py index b10cff416..ce8c37e14 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/sampling.py @@ -15,7 +15,7 @@ import tensorflow as tf -from pylib.pc.custom_ops import sampling +from pylib.pc.custom_ops.custom_ops_tf import sampling_tf as sampling from pylib.pc import PointCloud, Neighborhood, Grid from pylib.pc.utils import cast_to_num_dims diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py index e7b6211d3..94407d7e3 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/grid_test.py @@ -27,8 +27,8 @@ class GridTest(test_case.TestCase): @parameterized.parameters( - (10000, 32, 30, 0.1, 2), - (20000, 16, 1, 0.2, 2), + (100, 32, 30, 0.1, 2), + (200, 16, 1, 0.2, 2), (200, 8, 1, np.sqrt(2), 2), (100, 32, 30, 0.1, 3), (200, 16, 1, 0.2, 3), diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py index 69284c726..988d545db 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/sample_test.py @@ -56,7 +56,7 @@ def test_sampling_poisson_disk_on_random( dists = np.sort(dists, axis=1) min_dist = min(min_dist, np.amin(dists[:, 1])) - self.assertLess(min_dist, cell_size + 1e-3) + self.assertLess(min_dist, cell_size + dimension * 1e-3) @parameterized.parameters( (6, 1), @@ -74,8 +74,11 @@ def test_sampling_poisson_disk_on_uniform(self, num_points_sqrt, scale): sample_point_cloud, _ = sample(neighborhood, 'poisson') sampled_points = sample_point_cloud._points.numpy() - expected_num_pts = num_points_sqrt ** 2 // 2 - self.assertTrue(len(sampled_points) == expected_num_pts) + expected_max_num_pts = num_points_sqrt ** 2 // 2 + expected_min_num_pts = np.ceil(num_points_sqrt ** 2 / 3) + self.assertTrue( + len(sampled_points) <= expected_max_num_pts and \ + len(sampled_points) >= expected_min_num_pts) @parameterized.parameters( (100, 2, 0.1, 3), diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py index dc4d83893..7c6c3eab3 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -42,36 +42,7 @@ def check_valid_point_cloud_input(points, sizes, batch_ids): if points.shape[0] != batch_ids.shape[0]: raise AssertionError('Invalid sizes! Sizes of points and batch_ids are' + ' not equal.') - ''' -def check_valid_point_hierarchy_input(point_cloud, cell_sizes, pool_mode): - """ Checks that inputs to the constructor of class 'PontHierarchy' are valid. - - Args: - point_cloud: A 'PointCloud' instance. - cell_sizes: A `list` of `float` `Tensors`. - pool_mode: An `int`. - - Raises: - TypeError: if input is of invalid type - ValueError: if pool_mode is invalid, or cell_sizes dimension are invalid - or non-positive - - """ - if not isinstance(point_cloud, (PointCloud)): - raise TypeError('Input must be instance of class PointCloud') - if pool_mode not in [0, 1]: - raise ValueError('Unknown pooling mode.') - for curr_cell_sizes in cell_sizes: - if any(curr_cell_sizes <= 0): - raise ValueError('cell size must be positive.') - if not curr_cell_sizes.shape[0] in [1, point_cloud.dimension_]: - raise ValueError( - 'Invalid number of cell sizes for point cloud' +\ - f'dimension. Must be 1 or {point_cloud.dimension_} but is' +\ - f'{curr_cell_sizes.shape[0]}.') - - def _flatten_features(features, point_cloud: PointCloud): """ Converts features of shape `[A1, ..., An, C]` to shape `[N, C]`. From dccca3929a08fe894cd88ec028ce220c23a8b52c Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Mon, 3 May 2021 13:58:10 +0200 Subject: [PATCH 25/32] added point hierarchy calss --- .../pylib/pc/PointHierarchy.py | 187 ++++++++++++++++++ .../point_convolutions/pylib/pc/__init__.py | 4 +- 2 files changed, 189 insertions(+), 2 deletions(-) create mode 100755 tensorflow_graphics/projects/point_convolutions/pylib/pc/PointHierarchy.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointHierarchy.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointHierarchy.py new file mode 100755 index 000000000..0e1d90631 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/PointHierarchy.py @@ -0,0 +1,187 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to represent a point cloud hierarchy.""" + +import numpy as np +import tensorflow as tf + + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood +from pylib.pc import sample +from pylib.pc.utils import cast_to_num_dims + + +class PointHierarchy: + """ A hierarchy of sampled point clouds. + + Args: + point_cloud: A `PointCloud` instance.. + cell_sizes: A list of `floats` or `float` `Tensors` of shape `[D]`, + the cell sizes for the sampling. The length of the list defines + the number of samplings. + sample_mode: A `string`, either `'poisson'`or `'cell average'`. + + """ + + def __init__(self, + point_cloud: PointCloud, + cell_sizes, + sample_mode='poisson', + name=None): + #Initialize the attributes. + self._aabb = point_cloud.get_AABB() + self._point_clouds = [point_cloud] + self._cell_sizes = [] + self._neighborhoods = [] + + self._dimension = point_cloud._dimension + self._batch_shape = point_cloud._batch_shape + + #Create the different sampling operations. + cur_point_cloud = point_cloud + for sample_iter, cur_cell_sizes in enumerate(cell_sizes): + cur_cell_sizes = tf.convert_to_tensor( + value=cur_cell_sizes, dtype=tf.float32) + + # Check if the cell size is defined for all the dimensions. + # If not, the last cell size value is tiled until all the dimensions + # have a value. + cur_num_dims = tf.gather(cur_cell_sizes.shape, 0) + cur_cell_sizes = tf.cond( + cur_num_dims < self._dimension, + lambda: tf.concat((cur_cell_sizes, + tf.tile(tf.gather(cur_cell_sizes, + [tf.rank(cur_cell_sizes) - 1]), + [self._dimension - cur_num_dims])), + axis=0), + lambda: cur_cell_sizes) + tf.assert_greater( + self._dimension + 1, + cur_num_dims, + f'Too many dimensions in cell sizes {cur_num_dims} ' + \ + f'instead of max. {self._dimension}') + # old version, does not run in graph mode + # if cur_num_dims < self._dimension: + # cur_cell_sizes = tf.concat((cur_cell_sizes, + # tf.tile(tf.gather(cur_cell_sizes, + # [tf.rank(cur_cell_sizes) - 1]), + # [self._dimension - cur_num_dims])), + # axis=0) + # if cur_num_dims > self._dimension: + # raise ValueError( + # f'Too many dimensions in cell sizes {cur_num_dims} ' + \ + # f'instead of max. {self._dimension}') + + self._cell_sizes.append(cur_cell_sizes) + + #Create the sampling operation. + cur_grid = Grid(cur_point_cloud, cur_cell_sizes, self._aabb) + cur_neighborhood = Neighborhood(cur_grid, cur_cell_sizes) + cur_point_cloud, _ = sample(cur_neighborhood, sample_mode) + + self._neighborhoods.append(cur_neighborhood) + cur_point_cloud.set_batch_shape(self._batch_shape) + self._point_clouds.append(cur_point_cloud) + + def get_points(self, batch_id=None, max_num_points=None, name=None): + """ Returns the points. + + Note: + In the following, A1 to An are optional batch dimensions. + + If called withoud specifying 'id' returns the points in padded format + `[A1, ..., An, V, D]`. + + Args: + batch_id: An `int`, identifier of point cloud in the batch, if `None` + returns all points. + + Return: + A list of `float` `Tensors` of shape + `[N_i, D]`, if 'batch_id' was given + or + `[A1, ..., An, V_i, D]`, if no 'batch_id' was given. + """ + points = [] + for point_cloud in self._point_clouds: + points.append(point_cloud.get_points(batch_id)) + return points + + def get_sizes(self, name=None): + """ Returns the sizes of the point clouds in the point hierarchy. + + Note: + In the following, A1 to An are optional batch dimensions. + + Returns: + A `list` of `Tensors` of shape '`[A1, .., An]`' + + """ + + sizes = [] + for point_cloud in self._point_clouds: + sizes.append(point_cloud.get_sizes()) + return sizes + + def set_batch_shape(self, batch_shape, name=None): + """ Function to change the batch shape. + + Use this to set a batch shape instead of using 'self._batch_shape' + to also change dependent variables. + + Note: + In the following, A1 to An are optional batch dimensions. + + Args: + batch_shape: An 1D `int` `Tensor` `[A1, ..., An]`. + + Raises: + ValueError: if shape does not sum up to batch size. + + """ + for point_cloud in self._point_clouds: + point_cloud.set_batch_shape(batch_shape) + + def get_neighborhood(self, i=None, transposed=False): + """ Returns the neighborhood between level `i` and `i+1` of the hierarchy. + If called without argument returns a list of all neighborhoods. + + Args: + i: An `int`, can be negative but must be in range + `[-num_levels, num_levels-1]`. + transposed: A `bool`, if `True` returns the neighborhood between + level `i+1` and `i`. + + Returs: + A `Neighborhood` instance or a `list` of `Neighborhood` instances. + + """ + if i is None: + if transposed: + return [nb.transposed() for nb in self._neighborhoods] + else: + return self._neighborhoods + else: + if transposed: + return self._neighborhoods[i].transpose() + else: + return self._neighborhoods[i] + + def __getitem__(self, index): + return self._point_clouds[index] + + def __len__(self): + return len(self._point_clouds) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 66c27be40..5960b5c0d 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -20,9 +20,9 @@ from .Neighborhood import KDEMode from .sampling import poisson_disk_sampling, cell_average_sampling from .sampling import sample -''' -from .PointHierarchy import PointHierarchy +from .PointHierarchy import PointHierarchy +''' from pylib.pc import layers ''' from pylib.pc import custom_ops From 36a298a5cfcb94d3aa56598d9c0d1db18d4ac11a Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Mon, 3 May 2021 14:12:58 +0200 Subject: [PATCH 26/32] added 1x1conv and pooling layers --- .../pylib/pc/layers/Conv1x1.py | 101 +++++++ .../pylib/pc/layers/Pooling.py | 246 ++++++++++++++++++ .../pylib/pc/layers/__init__.py | 27 ++ .../pylib/pc/layers/tests/__init__.py | 0 .../pc/layers/tests/pooling_layers_test.py | 139 ++++++++++ .../pylib/pc/layers/utils.py | 245 +++++++++++++++++ .../point_convolutions/pylib/pc/utils.py | 4 +- 7 files changed, 760 insertions(+), 2 deletions(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Conv1x1.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Pooling.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/pooling_layers_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Conv1x1.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Conv1x1.py new file mode 100644 index 000000000..3bfd2997c --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Conv1x1.py @@ -0,0 +1,101 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to represent point cloud 1x1 convolution""" + +import tensorflow as tf +from pylib.pc.utils import _flatten_features +from pylib.pc.layers.utils import _format_output + +from pylib.pc import PointCloud + + +class Conv1x1(tf.Module): + """ A 1x1 convolution on the point features. This op reshapes the arguments + to pass them to `tf.keras.layers.Conv1D` to perform the equivalent + convolution operation. + + Note: This uses less memory than a point cloud convolution layer with a 1x1 + neighborhood, but might be slower for large feature dimensions. + + Args: + num_features_in: An `int` `C_in`, the number of input features. + num_features_out: An `int` `C_out`, the number of output features. + name: An `string` with the name of the module. + """ + + def __init__(self, num_features_in, num_features_out, name=None): + + super().__init__(name=name) + + if not(name is None): + weigths_name = name + "/weights" + bias_name = name + "/bias" + else: + weigths_name = "Conv1x1/weights" + bias_name = "Conv1x1/bias" + + std_dev = tf.math.sqrt(2.0 / float(num_features_in)) + weights_init_obj = tf.initializers.TruncatedNormal(stddev=std_dev) + self._weights_tf = tf.Variable( + weights_init_obj( + shape=[num_features_in, num_features_out], + dtype=tf.float32), + trainable=True, + name=weigths_name) + + bias_init_obj = tf.initializers.zeros() + self._bias_tf = tf.Variable( + bias_init_obj( + shape=[1, num_features_out], + dtype=tf.float32), + trainable=True, + name=bias_name) + + def __call__(self, + features, + point_cloud, + return_sorted=False, + return_padded=False): + """ Computes the 1x1 convolution on a point cloud. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + `C_in` is the number of input features. + `C_out` is the number of output features. + + Args: + features: A `float` `Tensor` of shape `[N_in, C_in]` or + `[A1, ..., An, V, C_in]`. + point_cloud: A 'PointCloud' instance, on which the features are + defined. + return_sorted: A `boolean`, if `True` the output tensor is sorted + according to the batch_ids. (optional) + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. (optional) + + Returns: + A `float` `Tensor` of shape + `[N_out, C_out]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C_out]`, if `return_padded` is `True`. + + """ + features = tf.cast(tf.convert_to_tensor(value=features), + dtype=tf.float32) + features = _flatten_features(features, point_cloud) + features = tf.matmul(features, self._weights_tf) + self._bias_tf + return _format_output(features, + point_cloud, + return_sorted, + return_padded) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Pooling.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Pooling.py new file mode 100644 index 000000000..77fcb8e98 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/Pooling.py @@ -0,0 +1,246 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Classes for point cloud spatial pooling operations""" + +import tensorflow as tf +from pylib.pc.utils import _flatten_features +from pylib.pc.layers.utils import _format_output + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood + + +class GlobalMaxPooling: + """ Global max pooling on a point cloud. + """ + + def __call__(self, + features, + point_cloud: PointCloud, + return_padded=False, + name=None): + """ Performs a global max pooling on a point cloud. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + + Args: + features: A tensor of shape `[N,C]` or `[A1,...,An,V,C]`. + point_cloud: A `PointCloud` instance. + return_padded: A `bool`, if `True` reshapes the output to match the + batch shape of `point_cloud`. + + Returns: + A tensor of same type as `features` and of shape + `[B, C]`, if not `return_padded` + or + `[A1, ..., An, C]`, if `return_padded` + + """ + features = tf.convert_to_tensor(value=features) + features = _flatten_features(features, point_cloud) + features = tf.math.unsorted_segment_max( + features, + segment_ids=point_cloud._batch_ids, + num_segments=point_cloud._batch_size) + if return_padded: + shape = tf.concat((point_cloud._batch_shape, [-1]), axis=0) + features = tf.reshape(features, shape) + return features + + +class GlobalAveragePooling: + """ Global average pooling on a point cloud. + """ + + def __call__(self, + features, + point_cloud: PointCloud, + return_padded=False, + name=None): + """ Performs a global average pooling on a point cloud + + Note: + In the following, `A1` to `An` are optional batch dimensions. + + Args: + features: A tensor of shape `[N, C]` or `[A1, ..., An, V, C]`. + point_cloud: A `PointCloud` instance. + return_padded: A `bool`, if `True` reshapes the output to match the + batch shape of `point_cloud`. + + Returns: + A tensor of same type as `features` and of shape + `[B, C]`, if not `return_padded` + or + `[A1 ,..., An, C]`, if `return_padded` + + """ + features = tf.convert_to_tensor(value=features) + features = _flatten_features(features, point_cloud) + features = tf.math.unsorted_segment_mean( + features, + segment_ids=point_cloud._batch_ids, + num_segments=point_cloud._batch_size) + if return_padded: + shape = tf.concat((point_cloud._batch_shape, [-1]), axis=0) + features = tf.reshape(features, shape) + return features + + +class _LocalPointPooling: + """ Local point pooling between two point clouds. + """ + + def __call__(self, + pool_op, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + pooling_radius, + return_sorted=False, + return_padded=False, + name=None, + default_name="custom pooling"): + """ Computes a local pooling between two point clouds specified by `pool_op`. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + + Args: + pool_op: A function of type `tf.math.unsorted_segmented_*`. + features: A `float` `Tensor` of shape `[N_in, C]` or + `[A1, ..., An, V_in, C]`. + point_cloud_in: A `PointCloud` instance on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output features + are defined. + pooling_radius: A `float` or a `float` `Tensor` of shape `[D]`. + return_sorted: A `bool`, if 'True' the output tensor is sorted + according to the sorted batch ids of `point_cloud_out`. + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. + + Returns: + A `float` `Tensor` of shape + `[N_out, C]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C]`, if `return_padded` is `True`. + + """ + features = tf.convert_to_tensor(value=features) + features = _flatten_features(features, point_cloud_in) + pooling_radius = tf.convert_to_tensor( + value=pooling_radius, dtype=tf.float32) + if pooling_radius.shape[0] == 1: + pooling_radius = tf.repeat(pooling_radius, point_cloud_in._dimension) + + # Compute the grid. + grid_in = Grid(point_cloud_in, pooling_radius) + + # Compute the neighborhood keys. + neigh = Neighborhood(grid_in, pooling_radius, point_cloud_out) + features_on_neighbors = tf.gather( + features, neigh._original_neigh_ids[:, 0]) + + # Pool the features in the neighborhoods + features_out = pool_op( + data=features_on_neighbors, + segment_ids=neigh._original_neigh_ids[:, 1], + num_segments=tf.shape(point_cloud_out._points)[0]) + return _format_output(features_out, + point_cloud_out, + return_sorted, + return_padded) + + +class MaxPooling(_LocalPointPooling): + """ Local max pooling between two point clouds. + """ + + def __call__(self, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + pooling_radius, + return_sorted=False, + return_padded=False, + name=None): + """ Computes a local max pooling between two point clouds. + + Args: + features: A `float` `Tensor` of shape `[N_in, C]` or + `[A1, ..., An, V_in, C]`. + point_cloud_in: A `PointCloud` instance on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output features + are defined. + pooling_radius: A `float` or a `float` `Tensor` of shape [D]. + return_sorted: A `bool`, if 'True' the output tensor is sorted + according to the sorted batch ids of `point_cloud_out`. + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. + + Returns: + A `float` `Tensor` of shape + `[N_out, C]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C]`, if `return_padded` is `True`. + + """ + return super(MaxPooling, self).__call__( + tf.math.unsorted_segment_max, + features, point_cloud_in, point_cloud_out, pooling_radius, + return_sorted, return_padded, name, default_name="max pooling") + + +class AveragePooling(_LocalPointPooling): + """ Local average pooling between two point clouds. + """ + + def __call__(self, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + pooling_radius, + return_sorted=False, + return_padded=False, + name=None): + """ Computes a local average pooling between two point clouds. + + Args: + features: A `float` `Tensor` of shape `[N_in, C]` or + `[A1, ..., An, V_in, C]`. + point_cloud_in: A `PointCloud` instance on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output features + are defined. + pooling_radius: A `float` or a `float` `Tensor` of shape `[D]`. + return_sorted: A boolean, if 'True' the output tensor is sorted + according to the sorted batch ids of `point_cloud_out`. + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. + + Returns: + A `float` `Tensor` of shape + `[N_out, C]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C]`, if `return_padded` is `True`. + + """ + return super(AveragePooling, self).__call__( + tf.math.unsorted_segment_mean, + features, point_cloud_in, point_cloud_out, pooling_radius, + return_sorted, return_padded, name, default_name="average pooling") diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py new file mode 100644 index 000000000..7f23150e0 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Point cloud network layers""" + +from .Pooling import GlobalAveragePooling, GlobalMaxPooling +from .Pooling import MaxPooling, AveragePooling +from .Conv1x1 import Conv1x1 +''' +from .MCConv import MCConv +from .KPConv import KPConv +from .PointConv import PointConv +from .network_blocks import PointResNet, \ + PointResNetBottleNeck, PointResNetSpatialBottleNeck +from .utils import spherical_kernel_points, \ + cube_kernel_points +''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/pooling_layers_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/pooling_layers_test.py new file mode 100644 index 000000000..5ed7993a1 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/pooling_layers_test.py @@ -0,0 +1,139 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test pooling layers""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import GlobalMaxPooling, GlobalAveragePooling +from pylib.pc.layers import MaxPooling, AveragePooling + + +class PoolingTest(test_case.TestCase): + + @parameterized.parameters( + (1000, 32, 2), + (2000, 16, 2), + (4000, 8, 2), + (1000, 32, 3), + (2000, 16, 3), + (4000, 8, 3), + (4000, 2, 3), + (1000, 32, 4), + (2000, 16, 4), + (4000, 8, 4) + ) + def test_global_pooling(self, num_points, batch_size, dimension): + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + equal_sized_batches=True) + features = np.random.rand(batch_size, num_points, dimension) + point_cloud = PointCloud(points, batch_ids) + + # max pooling + with self.subTest(name='max_pooling'): + PoolLayer = GlobalMaxPooling() + pool_tf = PoolLayer(features, point_cloud) + pool_numpy = np.empty([batch_size, dimension]) + features = features.reshape([-1, dimension]) + for i in range(batch_size): + pool_numpy[i] = np.max(features[batch_ids == i], axis=0) + self.assertAllClose(pool_numpy, pool_tf) + point_cloud.set_batch_shape([batch_size // 2, 2]) + padded = PoolLayer(features, point_cloud, return_padded=True) + self.assertTrue(padded.shape.rank > 2) + + # average pooling + with self.subTest(name='average_pooling'): + PoolLayer = GlobalAveragePooling() + pool_tf = PoolLayer(features, point_cloud) + pool_numpy = np.empty([batch_size, dimension]) + for i in range(batch_size): + pool_numpy[i] = np.mean(features[batch_ids == i], axis=0) + self.assertAllClose(pool_numpy, pool_tf) + point_cloud.set_batch_shape([batch_size // 2, 2]) + padded = PoolLayer(features, point_cloud, return_padded=True) + self.assertTrue(padded.shape.rank > 2) + + @parameterized.parameters( + (2000, 200, 16, 0.7, 2), + (4000, 400, 8, np.sqrt(2), 2), + (2000, 200, 16, 0.7, 3), + (4000, 400, 8, np.sqrt(3), 3), + (4000, 100, 2, np.sqrt(3), 3), + (2000, 200, 16, 0.7, 4), + (4000, 400, 8, np.sqrt(4), 4) + ) + def test_local_pooling(self, + num_points, + num_samples, + batch_size, + radius, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, dimension) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighbor_ids = neighborhood._original_neigh_ids.numpy() + features_on_neighbors = features[neighbor_ids[:, 0]] + + #max pooling + with self.subTest(name='max_pooling_to_sampled'): + PoolLayer = MaxPooling() + pool_tf = PoolLayer( + features, point_cloud, point_cloud_samples, cell_sizes) + + pool_numpy = np.empty([num_samples, dimension]) + for i in range(num_samples): + pool_numpy[i] = np.max( + features_on_neighbors[neighbor_ids[:, 1] == i], axis=0) + + self.assertAllClose(pool_tf, pool_numpy) + point_cloud.set_batch_shape([batch_size // 2, 2]) + padded = PoolLayer( + features, point_cloud, point_cloud_samples, cell_sizes, + return_padded=True) + self.assertTrue(padded.shape.rank > 2) + + #max pooling + with self.subTest(name='average_pooling_to_sampled'): + PoolLayer = AveragePooling() + pool_tf = PoolLayer( + features, point_cloud, point_cloud_samples, cell_sizes) + + pool_numpy = np.empty([num_samples, dimension]) + for i in range(num_samples): + pool_numpy[i] = np.mean( + features_on_neighbors[neighbor_ids[:, 1] == i], axis=0) + + self.assertAllClose(pool_tf, pool_numpy) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py new file mode 100644 index 000000000..7c2513935 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py @@ -0,0 +1,245 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT 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 methods for point cloud layers.""" + +import tensorflow as tf +import numpy as np + +tf_pi = tf.convert_to_tensor(np.pi) + + +def _format_output(features, point_cloud, return_sorted, return_padded): + """ Method to format and sort the output of a point cloud convolution layer. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + + Args: + features: A `float` `Tensor` of shape `[N, C]`. + point_cloud: A `PointCloud` instance, on which the `feautres` are defined. + return_sorted: A `bool`, if 'True' the output tensor is sorted + according to the sorted batch ids of `point_cloud`. + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. + + Returns: + A `float` `Tensor` of shape + `[N, C]`, if `return_padded` is `False` + or + `[A1, ..., An, V, C]`, if `return_padded` is `True`. + + """ + + if return_padded: + unflatten = point_cloud.get_unflatten() + features = unflatten(features) + elif return_sorted: + features = tf.gather(features, point_cloud._sorted_indices_batch) + return features + + +def random_rotation(points, name=None): + """ Method to rotate 3D points randomly. + + Args: + points: A `float` `Tensor` of shape `[N, 3]`. + + Returns: + A `float` `Tensor` of the same shape as `points`. + + """ + points = tf.convert_to_tensor(value=points) + angles = tf.random.uniform([3], 0, 2 * np.pi) + sine = tf.math.sin(angles) + cosine = tf.math.cos(angles) + Rx = tf.stack(([1.0, 0.0, 0.0], + [0.0, cosine[0], -sine[0]], + [0.0, sine[0], cosine[0]]), axis=1) + Ry = tf.stack(([cosine[1], 0, sine[1]], + [0.0, 1.0, 0.0], + [-sine[1], 0.0, cosine[1]]), axis=1) + Rz = tf.stack(([cosine[2], -sine[2], 0.0], + [sine[2], cosine[2], 0.0], + [0.0, 0.0, 1.0]), axis=1) + R = tf.matmul(tf.matmul(Rx, Ry), Rz) + return tf.matmul(points, R) + + +def _hexagon(scale, z_shift): + """ Numpy hexagon ponts in the xy-plane with diameter `scale` + at z-position `z_shift`. + + Args: + scale: A `float`. + z_shift: A `float`. + + Returns: + A `np.array` of shape `[6, 3]`. + + """ + phi = np.sqrt(3) / 2 + points = [[0, 1, 0], + [0, -1, 0], + [0.5, phi, 0], + [0.5, -phi, 0], + [-0.5, phi, 0], + [0.5, -phi, 0]] + points = np.array(points) * scale + points[:, 2] += z_shift + return points + + +def _pentagon(scale, z_shift): + """ Numpy pentagon ponts in the xy-plane with diameter `scale` + at z-position `z_shift`. + + Args: + scale: A `float`. + z_shift: A `float`. + + Returns: + A `np.array` of shape `[5, 3]`. + + """ + c1 = (np.sqrt(5) - 1) / 4 + c2 = (np.sqrt(5) + 1) / 4 + s1 = np.sqrt(10 + 2 * np.sqrt(5)) / 4 + s2 = np.sqrt(10 - 2 * np.sqrt(5)) / 4 + points = [[1, 0, 0], + [c1, s1, 0], + [c1, -s1, 0], + [-c2, s2, 0], + [-c2, -s2, 0]] + points = np.array(points) * scale + points[:, 2] += z_shift + return points + + +def square(scale, z_shift): + """ Numpy square ponts in the xy-plane with diameter `scale` + at z-position `z_shift`. + + Args: + scale: A `float`. + z_shift: A `float`. + + Returns: + A `np.array` of shape `[4, 3]`. + + """ + points = [[1, 0, 0], + [0, 1, 0], + [-1, 0, 0], + [0, -1, 0]] + points = np.array(points) * scale + points[:, 2] += z_shift + return points + + +def spherical_kernel_points(num_points, rotate=True, name=None): + """ Kernel points in a unit sphere. + + The points are located at positions as described in Appendix B of + [KPConv: Flexible and Deformable Convolution for Point Clouds. Thomas et + al., + 2019](https://arxiv.org/abs/1904.08889). + + Args: + num_points: An `int`, the number of kernel points, must be in + `[5, 7, 13, 15, 18]`. + rotate: A 'bool', if `True` a random rotation is applied to the points. + + Returns: + A `float` `Tensor` of shape `[num_points, 3]`. + + """ + + if num_points not in [5, 7, 13, 15, 18]: + raise ValueError('KPConv currently only supports kernel sizes' + \ + ' [5, 7, 13, 15, 18]') + if num_points == 5: + # Tetrahedron + points = tf.Variable([[0, 0, 0], + [0, 0, 1], + [tf.sqrt(8 / 9), 0, -1 / 3], + [- tf.sqrt(2 / 9), tf.sqrt(2 / 3), - 1 / 3], + [-tf.sqrt(2 / 9), - tf.sqrt(2 / 3), -1 / 3]], + dtype=tf.float32) + elif num_points == 7: + # Octahedron + points = tf.Variable([[0, 0, 0], + [1, 0, 0], + [-1, 0, 0], + [0, 1, 0], + [0, -1, 0], + [0, 0, 1], + [0, 0, -1]], dtype=tf.float32) + elif num_points == 13: + # Icosahedron + phi = (1 + tf.sqrt(5)) / 2 + points = tf.Variable([[0, 0, 0], + [0, 1, phi], + [0, 1, -phi], + [0, -1, phi], + [0, -1, -phi], + [1, phi, 0], + [1, -phi, 0], + [-1, phi, 0], + [-1, -phi, 0], + [phi, 0, 1], + [-phi, 0, 1], + [phi, 0, -1], + [-phi, 0, -1]], dtype=tf.float32) + elif num_points == 15: + hex1 = _hexagon(0.5, np.sqrt(3) / 2) + hex2 = _hexagon(-0.5, np.sqrt(3) / 2)[:,[1, 0, 2]] # rotated 90 deg in xy + points = np.concatenate(([[0, 0, 0], [0, 0, 1], [0, 0, -1]], hex1, hex2), + axis=0) + points = tf.Variable(points, dtype=tf.float32) + elif num_points == 18: + penta1 = _pentagon(1 / np.sqrt(2), 0.5) + penta2 = -_pentagon(1.0, 0.0) # flipped in xy + penta3 = _pentagon(-1 / np.sqrt(2), 0.5) + points = np.concatenate(([[0, 0, 0], [0, 0, 1], [0, 0, -1]], + penta1, penta2, penta3), + axis=0) + points = tf.Variable(points, dtype=tf.float32) + if rotate: + points = random_rotation(points) + return points + + +def cube_kernel_points(cbrt_num_points, name): + """ Regularily distributed points in a unit cube. + + Args: + cbrt_num_points: An `int`, the cubic root of the number of points. + + Returns: + A `float` `Tensor` of shape `[cbrt_num_points^3, 3]`. + + """ + x = np.linspace(0, 1, cbrt_num_points) + x, y, z = np.meshgrid(x, x, x) + points = np.stack((x.flatten(), y.flatten(), z.flatten()), axis=1) + return tf.Variable(points, dtype=tf.float32) + + +def _identity(features, *args, **kwargs): + """ Simple identity layer, to be used as placeholder. + + Used to replace projection shortcuts, if not desired. + + """ + return features diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py index 7c6c3eab3..9c1d1abf3 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/utils.py @@ -42,7 +42,7 @@ def check_valid_point_cloud_input(points, sizes, batch_ids): if points.shape[0] != batch_ids.shape[0]: raise AssertionError('Invalid sizes! Sizes of points and batch_ids are' + ' not equal.') -''' + def _flatten_features(features, point_cloud: PointCloud): """ Converts features of shape `[A1, ..., An, C]` to shape `[N, C]`. @@ -63,7 +63,7 @@ def _flatten_features(features, point_cloud: PointCloud): tf.assert_equal(tf.shape(features)[0], tf.shape(point_cloud._points)[0]) tf.assert_equal(tf.rank(features), 2) return features -''' + def cast_to_num_dims(values, num_dims, dtype=tf.float32): """ Converts an input to the specified `dtype` and repeats it `num_dims` From eb1b5d56a39b9518b1252e622b8602e0df528217 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Mon, 3 May 2021 14:41:18 +0200 Subject: [PATCH 27/32] added unit tests --- .../pylib/pc/layers/tests/conv1x1_test.py | 47 +++++++++++++++++++ .../pylib/pc/tests/point_cloud_test.py | 28 ++++++++++- 2 files changed, 74 insertions(+), 1 deletion(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/conv1x1_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/conv1x1_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/conv1x1_test.py new file mode 100644 index 000000000..6148348dc --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/conv1x1_test.py @@ -0,0 +1,47 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test pooling layers""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import Conv1x1 + + +class Conv1x1Test(test_case.TestCase): + + @parameterized.parameters( + (1000, 4, [3, 3], 3), + (1000, 4, [3, 1], 3), + (1000, 4, [1, 3], 3), + ) + def test_conv1x1(self, num_points, batch_size, feature_sizes, dimension): + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + equal_sized_batches=True) + features = np.random.rand(batch_size, num_points, feature_sizes[0]) + point_cloud = PointCloud(points, batch_ids) + + conv_layer = Conv1x1(feature_sizes[0], feature_sizes[1]) + result = conv_layer(features, point_cloud) + self.assertTrue(result.shape[-1] == feature_sizes[1]) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py index e71d27ff3..fd7bb62c0 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/tests/point_cloud_test.py @@ -19,7 +19,7 @@ from absl.testing import parameterized from tensorflow_graphics.util import test_case -from pylib.pc import PointCloud +from pylib.pc import PointCloud, PointHierarchy from pylib.pc.tests import utils @@ -86,6 +86,13 @@ def test_construction_methods(self, max_num_points, batch_size, batch_shape): self.assertAllEqual(points_from_ids, points_from_sizes) self.assertAllEqual(points_from_sizes, points_from_padded) + points_from_padded = pc_from_padded.get_points(0) + points_from_ids = pc_from_ids.get_points(0) + points_from_sizes = pc_from_sizes.get_points(0) + self.assertAllEqual(points_from_padded, points_from_ids) + self.assertAllEqual(points_from_ids, points_from_sizes) + self.assertAllEqual(points_from_sizes, points_from_padded) + @parameterized.parameters( (1000, ['Invalid input! Point tensor is of dimension 1 \ @@ -104,6 +111,25 @@ def test_exceptions_raised_at_construction(self, num_points, msgs): with self.assertRaisesRegexp(AssertionError, msgs[2]): _ = PointCloud(points, batch_ids[1:]) + @parameterized.parameters( + ([32], 100, 3), + ([5, 2], 100, 2), + ([2, 3, 4], 100, 4) + ) + def test_point_hierarchy(self, batch_shape, num_points, dimension): + batch_size = np.prod(batch_shape) + points, sizes = utils._create_random_point_cloud_padded( + num_points, batch_shape, dimension=dimension) + point_cloud = PointCloud(points, sizes=sizes) + point_hierarchy = PointHierarchy(point_cloud, [[0.1]]) + points_retrieved = point_hierarchy.get_points(0) + self.assertTrue(len(points_retrieved) == 2) + sizes_retrieved = point_hierarchy.get_sizes() + self.assertTrue(len(sizes_retrieved) == 2) + self.assertAllEqual(sizes_retrieved[0], sizes) + nbhs = point_hierarchy.get_neighborhood() + self.assertTrue(len(nbhs) == 1) + if __name__ == '__main__': test_case.main() From 479e8cd03f84c462445a822be2b9a18dddefd0b6 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 20 May 2021 15:44:18 +0200 Subject: [PATCH 28/32] MCConv initial --- .../point_convolutions/pylib/pc/__init__.py | 4 +- .../pylib/pc/custom_ops/__init__.py | 4 +- .../pylib/pc/custom_ops/custom_ops_tf.py | 3 +- .../pc/custom_ops/tests/basis_proj_tf_test.py | 177 +++++++++++ .../pylib/pc/layers/MCConv.py | 278 ++++++++++++++++++ .../pylib/pc/layers/__init__.py | 5 +- .../pylib/pc/layers/tests/basis_proj_test.py | 180 ++++++++++++ .../pc/layers/tests/monte_carlo_conv_test.py | 223 ++++++++++++++ .../pylib/pc/layers/utils.py | 9 +- 9 files changed, 867 insertions(+), 16 deletions(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/basis_proj_tf_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/MCConv.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/basis_proj_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/monte_carlo_conv_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py index 5960b5c0d..c499758e3 100755 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/__init__.py @@ -22,7 +22,7 @@ from .sampling import sample from .PointHierarchy import PointHierarchy -''' + from pylib.pc import layers -''' + from pylib.pc import custom_ops diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py index 17be8cb2e..cd2aee431 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/__init__.py @@ -24,14 +24,14 @@ CUSTOM = 0 if CUSTOM: - # from .custom_ops_wrapper import basis_proj + from .custom_ops_wrapper import basis_proj from .custom_ops_wrapper import build_grid_ds from .custom_ops_wrapper import compute_keys from .custom_ops_wrapper import compute_pdf from .custom_ops_wrapper import find_neighbors from .custom_ops_wrapper import sampling else: - # from .custom_ops_tf import basis_proj_tf as basis_proj + from .custom_ops_tf import basis_proj_tf as basis_proj from .custom_ops_tf import build_grid_ds_tf as build_grid_ds from .custom_ops_tf import compute_keys_tf as compute_keys from .custom_ops_tf import compute_pdf_tf as compute_pdf diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py index d992fa45e..e7032fedf 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_tf.py @@ -362,7 +362,7 @@ def compute_pdf_tf(neighborhood, bandwidth, mode, name=None): tf.reduce_prod(bandwidth) return pdf -''' + def basis_proj_tf(neigh_basis, features, neighborhood, name=None): """ Method to aggregate the features*basis for different neighborhoods. @@ -388,4 +388,3 @@ def basis_proj_tf(neigh_basis, features, neighborhood, name=None): weighted_latent_per_center = tf.math.unsorted_segment_sum( weighted_features_per_nb, neighborhood._neighbors[:, 1], num_nbh) return weighted_latent_per_center -''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/basis_proj_tf_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/basis_proj_tf_test.py new file mode 100644 index 000000000..93c4cda9b --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/tests/basis_proj_tf_test.py @@ -0,0 +1,177 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test basis projection tensorflow implementation""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import MCConv +from pylib.pc.custom_ops.custom_ops_tf import basis_proj_tf + + +class BasisProjTFTest(test_case.TestCase): + + @parameterized.parameters( + (2000, 200, [3, 3], 16, 0.7, 4, 2), + (2000, 200, [1, 3], 16, 0.7, 8, 3), + (4000, 400, [3, 3], 8, 0.7, 8, 3), + (2000, 200, [3, 3], 16, 0.7, 8, 4), + ) + def test_basis_proj(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + nb_ids = neighborhood._original_neigh_ids + # tf + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size]) + + basis_weights_tf = tf.reshape(conv_layer._weights_tf[0], + [dimension, hidden_size]) + basis_biases_tf = tf.reshape(conv_layer._bias_tf[0], [1, hidden_size]) + + neigh_point_coords = points[nb_ids[:, 0]] + center_point_coords = point_samples[nb_ids[:, 1]] + kernel_input = (neigh_point_coords - center_point_coords) / radius + basis_neighs = \ + tf.matmul(kernel_input.astype(np.float32), basis_weights_tf) + \ + basis_biases_tf + basis_neighs = tf.nn.relu(basis_neighs) + + weighted_latent_per_sample_tf = basis_proj_tf(basis_neighs, + features, + neighborhood) + + # numpy + neighbor_ids = neighborhood._original_neigh_ids.numpy() + nb_ranges = neighborhood._samples_neigh_ranges.numpy() + # extract variables + hidden_weights = basis_weights_tf.numpy() + hidden_biases = basis_biases_tf.numpy() + + features_on_neighbors = features[neighbor_ids[:, 0]] + # compute first layer of kernel MLP + point_diff = (points[neighbor_ids[:, 0]] -\ + point_samples[neighbor_ids[:, 1]])\ + / np.expand_dims(cell_sizes, 0) + + latent_per_nb = np.dot(point_diff, hidden_weights) + hidden_biases + + latent_relu_per_nb = np.maximum(latent_per_nb, 0) + # Monte-Carlo integration after first layer + # weighting with pdf + weighted_features_per_nb = np.expand_dims(features_on_neighbors, 2) * \ + np.expand_dims(latent_relu_per_nb, 1) + nb_ranges = np.concatenate(([0], nb_ranges), axis=0) + # sum (integration) + weighted_latent_per_sample = \ + np.zeros([num_samples, num_features[0], hidden_size]) + for i in range(num_samples): + weighted_latent_per_sample[i] = \ + np.sum(weighted_features_per_nb[nb_ranges[i]:nb_ranges[i + 1]], + axis=0) + + self.assertAllClose(weighted_latent_per_sample_tf, + weighted_latent_per_sample, atol=1e-3) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3) + ) + def test_basis_proj_jacobian(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + nb_ids = neighborhood._original_neigh_ids + # tf + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size]) + + neigh_point_coords = points[nb_ids[:, 0].numpy()] + center_point_coords = point_samples[nb_ids[:, 1].numpy()] + kernel_input = (neigh_point_coords - center_point_coords) / radius + + basis_weights_tf = tf.reshape(conv_layer._weights_tf[0], + [dimension, hidden_size]) + basis_biases_tf = tf.reshape(conv_layer._bias_tf[0], [1, hidden_size]) + + basis_neighs = \ + tf.matmul(kernel_input.astype(np.float32), basis_weights_tf) +\ + basis_biases_tf + basis_neighs = tf.nn.leaky_relu(basis_neighs) + + _, _, counts = tf.unique_with_counts(neighborhood._neighbors[:, 1]) + max_num_nb = tf.reduce_max(counts).numpy() + + with self.subTest(name='features'): + def basis_proj_features(features_in): + return basis_proj_tf(basis_neighs, + features_in, + neighborhood) / (max_num_nb) + + self.assert_jacobian_is_correct_fn( + basis_proj_features, [np.float32(features)], atol=1e-4, delta=1e-3) + + with self.subTest(name='neigh_basis'): + def basis_proj_basis_neighs(basis_neighs_in): + return basis_proj_tf(basis_neighs_in, + features, + neighborhood) / (max_num_nb) + + self.assert_jacobian_is_correct_fn( + basis_proj_basis_neighs, + [np.float32(basis_neighs)], + atol=1e-4, delta=1e-3) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/MCConv.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/MCConv.py new file mode 100644 index 000000000..f21059a8d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/MCConv.py @@ -0,0 +1,278 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Classes to Monte-Carlo point cloud convolutions""" + +import tensorflow as tf +from pylib.pc.utils import _flatten_features + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood +from pylib.pc import KDEMode + +from pylib.pc.custom_ops import basis_proj +from pylib.pc.layers.utils import _format_output + +non_linearity_types = {'relu': tf.nn.relu, + 'lrelu': tf.nn.leaky_relu, + 'leakyrelu': tf.nn.leaky_relu, + 'leaky_relu': tf.nn.leaky_relu, + 'elu': tf.nn.elu} + + +class MCConv(tf.Module): + """ Monte-Carlo convolution for point clouds. + + Based on the paper [Monte Carlo Convolution for Learning on Non-Uniformly + Sampled Point Clouds. Hermosilla et al., 2018] + (https://arxiv.org/abs/1806.01759). + Uses a multiple MLPs as convolution kernels. + + Args: + num_features_in: An `int`, `C_in`, the number of features per input point. + num_features_out: An `int`, `C_out`, the number of features to compute. + num_dims: An `int`, the input dimension to the kernel MLP. Should be the + dimensionality of the point cloud. + num_mlps: An `int`, number of MLPs used to compute the output features. + Warning: num_features_out should be divisible by num_mlps. + mlp_size: An ìnt list`, list with the number of layers and hidden neurons + of the MLP used as kernel, defaults to `[8]`. (optional). + non_linearity_type: An `string`, specifies the type of the activation + function used inside the kernel MLP. + Possible: `'ReLU', 'lReLU', 'ELU'`, defaults to leaky ReLU. (optional) + initializer_weights: A `tf.initializer` for the kernel MLP weights, + default `TruncatedNormal`. (optional) + initializer_biases: A `tf.initializer` for the kernel MLP biases, + default: `zeros`. (optional) + + """ + + def __init__(self, + num_features_in, + num_features_out, + num_dims, + num_mlps=4, + mlp_size=[8], + non_linearity_type='leaky_relu', + initializer_weights=None, + initializer_biases=None, + name=None): + + super().__init__(name=name) + + self._num_features_in = num_features_in + self._num_features_out = num_features_out + self._num_mlps = num_mlps + self._mlp_size = mlp_size + self._num_dims = num_dims + self._non_linearity_type = non_linearity_type + + if num_features_out % num_mlps != 0: + raise ValueError( + "The number of output features must be divisible by the number" + + " of kernel MLPs") + + if name is None: + self._name = 'MCConv' + else: + self._name = name + + # initialize variables + if initializer_weights is None: + initializer_weights = tf.initializers.TruncatedNormal + if initializer_biases is None: + initializer_biases = tf.initializers.zeros + + self._weights_tf = [] + self._bias_tf = [] + prev_num_inut = self._num_dims + for cur_layer_iter, cur_layer in enumerate(self._mlp_size): + + if cur_layer_iter: + std_dev = tf.math.sqrt(1.0 / float(prev_num_inut)) + else: + std_dev = tf.math.sqrt(2.0 / float(prev_num_inut)) + + weights_init_obj = initializer_weights(stddev=std_dev) + self._weights_tf.append(tf.Variable( + weights_init_obj( + shape=[self._num_mlps, prev_num_inut, cur_layer], + dtype=tf.float32), + trainable=True, + name=self._name + "/weights_" + str(cur_layer_iter))) + + bias_init_obj = initializer_biases() + self._bias_tf.append(tf.Variable( + bias_init_obj(shape=[self._num_mlps, 1, cur_layer], + dtype=tf.float32), + trainable=True, + name=self._name + "/bias_" + str(cur_layer_iter))) + prev_num_inut = cur_layer + + std_dev = tf.math.sqrt(2.0 / \ + float(cur_layer * self._num_features_in)) + + weights_init_obj = initializer_weights(stddev=std_dev) + self._final_weights_tf = tf.Variable( + weights_init_obj( + shape=[ + self._num_mlps, + cur_layer * self._num_features_in, + self._num_features_out // self._num_mlps], + dtype=tf.float32), + trainable=True, + name=self._name + "/final_weights_" + str(cur_layer_iter)) + + def _monte_carlo_conv(self, + kernel_inputs, + neighborhood, + pdf, + features, + non_linearity_type='leaky_relu'): + """ Method to compute a Monte-Carlo integrated convolution using multiple + MLPs as implicit convolution kernel functions. + + Args: + kernel_inputs: A `float` `Tensor` of shape `[M, L]`, the input to the + kernel MLP. + neighborhood: A `Neighborhood` instance. + pdf: A `float` `Tensor` of shape `[M]`, the point densities. + features: A `float` `Tensor` of shape `[N, C1]`, the input features. + non_linearity_type: An `string`, specifies the type of the activation + function used inside the kernel MLP. + Possible: `'ReLU', 'leaky_ReLU', 'ELU'`, defaults to leaky ReLU. + (optional) + + Returns: + A `float` `Tensor` of shape `[N,C2]`, the output features. + + """ + + # Compute the hidden layer MLP + cur_inputs = tf.tile(tf.reshape(kernel_inputs, [1, -1, self._num_dims]), + [self._num_mlps, 1, 1]) + for cur_layer_iter in range(len(self._weights_tf)): + cur_inputs = tf.matmul(cur_inputs, self._weights_tf[cur_layer_iter]) + \ + self._bias_tf[cur_layer_iter] + cur_inputs = non_linearity_types[non_linearity_type.lower()](cur_inputs) + cur_inputs = tf.reshape(tf.transpose(cur_inputs, [1, 0, 2]), + [-1, self._mlp_size[-1] * self._num_mlps]) \ + / tf.reshape(pdf, [-1, 1]) + + # Compute the projection to the samples. + weighted_features = basis_proj( + cur_inputs, + features, + neighborhood) + + # Reshape features + weighted_features = tf.transpose(tf.reshape(weighted_features, + [-1, self._num_features_in, + self._num_mlps, + self._mlp_size[-1]]), + [2, 0, 1, 3]) + + #Compute convolution - hidden layer to output (linear) + convolution_result = tf.matmul( + tf.reshape( + weighted_features, + [self._num_mlps, -1, self._num_features_in * self._mlp_size[-1]]), + self._final_weights_tf) + + return tf.reshape(tf.transpose(convolution_result, [1, 0, 2]), + [-1, self._num_features_out]) + + def __call__(self, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + radius, + neighborhood=None, + bandwidth=0.2, + return_sorted=False, + return_padded=False, + name=None): + """ Computes the Monte-Carlo Convolution between two point clouds. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + `C_in` is the number of input features. + `C_out` is the number of output features. + + Args: + features: A `float` `Tensor` of shape `[N_in, C_in]` or + `[A1, ..., An,V, C_in]`. + point_cloud_in: A 'PointCloud' instance, on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output + features are defined. + radius: A `float`, the convolution radius. + neighborhood: A `Neighborhood` instance, defining the neighborhood + with centers from `point_cloud_out` and neighbors in `point_cloud_in`. + If `None` it is computed internally. (optional) + bandwidth: A `float`, the bandwidth used in the kernel density + estimation on the input point cloud. (optional) + return_sorted: A `boolean`, if `True` the output tensor is sorted + according to the batch_ids. (optional) + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. (optional) + + Returns: + A `float` `Tensor` of shape + `[N_out, C_out]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C_out]`, if `return_padded` is `True`. + + """ + + features = tf.cast(tf.convert_to_tensor(value=features), + dtype=tf.float32) + + tf.assert_equal( + tf.shape(features)[-1], + self._num_features_in) + + features = _flatten_features(features, point_cloud_in) + + #Create the radii tensor. + radius = tf.reshape(tf.convert_to_tensor(value=radius, dtype=tf.float32), [1, 1]) + radii_tensor = tf.repeat(radius, self._num_dims) + #Create the bandwidth tensor. + bwTensor = tf.repeat(bandwidth, self._num_dims) + + if neighborhood is None: + #Compute the grid + grid = Grid(point_cloud_in, radii_tensor) + #Compute the neighborhoods + neigh = Neighborhood(grid, radii_tensor, point_cloud_out) + else: + neigh = neighborhood + pdf = neigh.get_pdf(bandwidth=bwTensor, mode=KDEMode.constant) + + #Compute kernel inputs. + neigh_point_coords = tf.gather( + point_cloud_in._points, neigh._original_neigh_ids[:, 0]) + center_point_coords = tf.gather( + point_cloud_out._points, neigh._original_neigh_ids[:, 1]) + points_diff = (neigh_point_coords - center_point_coords) / \ + tf.reshape(radii_tensor, [1, self._num_dims]) + + #Compute Monte-Carlo convolution + convolution_result = self._monte_carlo_conv( + points_diff, neigh, pdf, features, self._non_linearity_type) + + return _format_output(convolution_result, + point_cloud_out, + return_sorted, + return_padded) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py index 7f23150e0..e21220129 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py @@ -16,12 +16,11 @@ from .Pooling import GlobalAveragePooling, GlobalMaxPooling from .Pooling import MaxPooling, AveragePooling from .Conv1x1 import Conv1x1 -''' + from .MCConv import MCConv +''' from .KPConv import KPConv from .PointConv import PointConv -from .network_blocks import PointResNet, \ - PointResNetBottleNeck, PointResNetSpatialBottleNeck from .utils import spherical_kernel_points, \ cube_kernel_points ''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/basis_proj_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/basis_proj_test.py new file mode 100644 index 000000000..07e6e68b6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/basis_proj_test.py @@ -0,0 +1,180 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test basis projection kernel""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import MCConv +from pylib.pc.custom_ops import basis_proj + + +class BasisProjTest(test_case.TestCase): + + @parameterized.parameters( + (200, 20, [3, 3], 16, 0.7, 8, 2), + (400, 40, [3, 3], 8, np.sqrt(2), 8, 2), + (200, 20, [1, 3], 16, 0.7, 8, 3), + (400, 40, [3, 3], 8, 0.7, 8, 3), + (400, 10, [3, 1], 1, np.sqrt(3), 16, 3), + (200, 20, [3, 3], 16, 0.7, 8, 4), + (400, 40, [1, 3], 8, np.sqrt(4), 32, 4) + ) + def test_basis_proj(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + nb_ids = neighborhood._original_neigh_ids + # tf + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size]) + + basis_weights_tf = tf.reshape(conv_layer._weights_tf[0], + [dimension, hidden_size]) + basis_biases_tf = tf.reshape(conv_layer._bias_tf[0], [1, hidden_size]) + + neigh_point_coords = points[nb_ids[:, 0]] + center_point_coords = point_samples[nb_ids[:, 1]] + kernel_input = (neigh_point_coords - center_point_coords) / radius + basis_neighs = \ + tf.matmul(kernel_input.astype(np.float32), basis_weights_tf) + \ + basis_biases_tf + basis_neighs = tf.nn.relu(basis_neighs) + + weighted_latent_per_sample_tf = basis_proj(basis_neighs, + features, + neighborhood) + + # numpy + neighbor_ids = neighborhood._original_neigh_ids.numpy() + nb_ranges = neighborhood._samples_neigh_ranges.numpy() + # extract variables + hidden_weights = basis_weights_tf.numpy() + hidden_biases = basis_biases_tf.numpy() + + features_on_neighbors = features[neighbor_ids[:, 0]] + # compute first layer of kernel MLP + point_diff = (points[neighbor_ids[:, 0]] -\ + point_samples[neighbor_ids[:, 1]])\ + / np.expand_dims(cell_sizes, 0) + + latent_per_nb = np.dot(point_diff, hidden_weights) + hidden_biases + + latent_relu_per_nb = np.maximum(latent_per_nb, 0) + # Monte-Carlo integration after first layer + # weighting with pdf + weighted_features_per_nb = np.expand_dims(features_on_neighbors, 2) * \ + np.expand_dims(latent_relu_per_nb, 1) + nb_ranges = np.concatenate(([0], nb_ranges), axis=0) + # sum (integration) + weighted_latent_per_sample = \ + np.zeros([num_samples, num_features[0], hidden_size]) + for i in range(num_samples): + weighted_latent_per_sample[i] = \ + np.sum(weighted_features_per_nb[nb_ranges[i]:nb_ranges[i + 1]], + axis=0) + + self.assertAllClose(weighted_latent_per_sample_tf, + weighted_latent_per_sample) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3) + ) + def test_basis_proj_jacobian(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + nb_ids = neighborhood._original_neigh_ids + # tf + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size]) + + neigh_point_coords = points[nb_ids[:, 0].numpy()] + center_point_coords = point_samples[nb_ids[:, 1].numpy()] + kernel_input = (neigh_point_coords - center_point_coords) / radius + + basis_weights_tf = tf.reshape(conv_layer._weights_tf[0], + [dimension, hidden_size]) + basis_biases_tf = tf.reshape(conv_layer._bias_tf[0], [1, hidden_size]) + + basis_neighs = \ + tf.matmul(kernel_input.astype(np.float32), basis_weights_tf) +\ + basis_biases_tf + basis_neighs = tf.nn.leaky_relu(basis_neighs) + + _, _, counts = tf.unique_with_counts(neighborhood._neighbors[:, 1]) + max_num_nb = tf.reduce_max(counts).numpy() + + with self.subTest(name='features'): + def basis_proj_features(features_in): + return basis_proj(basis_neighs, + features_in, + neighborhood) / (max_num_nb) + + self.assert_jacobian_is_correct_fn( + basis_proj_features, [np.float32(features)], atol=1e-4, delta=1e-3) + + with self.subTest(name='neigh_basis'): + def basis_proj_basis_neighs(basis_neighs_in): + return basis_proj(basis_neighs_in, + features, + neighborhood) / (max_num_nb) + + self.assert_jacobian_is_correct_fn( + basis_proj_basis_neighs, + [np.float32(basis_neighs)], + atol=1e-4, delta=1e-3) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/monte_carlo_conv_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/monte_carlo_conv_test.py new file mode 100644 index 000000000..ec2e2555b --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/monte_carlo_conv_test.py @@ -0,0 +1,223 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test Monte Carlo convolutions""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, KDEMode, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import MCConv + + +class MCConvTest(test_case.TestCase): + + @parameterized.parameters( + (2000, 200, [3, 3], 16, 0.7, 8, 2), + (4000, 400, [3, 3], 8, np.sqrt(2), 8, 2), + (2000, 200, [1, 3], 16, 0.7, 8, 3), + (4000, 400, [3, 3], 8, 0.7, 8, 3), + (4000, 100, [3, 1], 1, np.sqrt(3), 16, 3), + (2000, 200, [3, 3], 16, 0.7, 8, 4), + (4000, 400, [1, 3], 8, np.sqrt(4), 32, 4) + ) + def test_convolution(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + # tf + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size], + non_linearity_type='relu') + conv_result_tf = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + # numpy + pdf = neighborhood.get_pdf().numpy() + neighbor_ids = neighborhood._original_neigh_ids.numpy() + nb_ranges = neighborhood._samples_neigh_ranges.numpy() + + # extract variables + hidden_weights = \ + tf.reshape(conv_layer._weights_tf[0], [dimension, hidden_size]).numpy() + hidden_biases = \ + tf.reshape(conv_layer._bias_tf[0], [1, hidden_size]).numpy() + weights = \ + tf.reshape(conv_layer._final_weights_tf, + [num_features[0] * hidden_size, num_features[1]]).numpy() + + features_on_neighbors = features[neighbor_ids[:, 0]] + # compute first layer of kernel MLP + point_diff = (points[neighbor_ids[:, 0]] -\ + point_samples[neighbor_ids[:, 1]])\ + / np.expand_dims(cell_sizes, 0) + + latent_per_nb = np.dot(point_diff, hidden_weights) + hidden_biases + + latent_relu_per_nb = np.maximum(latent_per_nb, 0) + + # Monte-Carlo integration after first layer + # weighting with pdf + weighted_features_per_nb = np.expand_dims(features_on_neighbors, 2) * \ + np.expand_dims(latent_relu_per_nb, 1) / \ + np.expand_dims(pdf, [1, 2]) + nb_ranges = np.concatenate(([0], nb_ranges), axis=0) + # sum (integration) + weighted_latent_per_sample = \ + np.zeros([num_samples, num_features[0], hidden_size]) + for i in range(num_samples): + weighted_latent_per_sample[i] = \ + np.sum(weighted_features_per_nb[nb_ranges[i]:nb_ranges[i + 1]], + axis=0) + # second layer of MLP (linear) + weighted_latent_per_sample = np.reshape(weighted_latent_per_sample, + [num_samples, -1]) + conv_result_np = np.matmul(weighted_latent_per_sample, weights) + + self.assertAllClose(conv_result_tf, conv_result_np) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3) + ) + def test_conv_jacobian_params(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + point_cloud = PointCloud(points, batch_ids) + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size]) + + features = np.random.rand(num_points, num_features[0]) + + with self.subTest(name='features'): + def conv_features(features_in): + conv_result = conv_layer( + features_in, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_features, [features], atol=1e-4, delta=1e-3) + + with self.subTest(name='params_basis_axis_proj'): + def conv_basis(weights_tf_in): + conv_layer._weights_tf[0] = weights_tf_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights_tf = conv_layer._weights_tf[0] + self.assert_jacobian_is_correct_fn( + conv_basis, [weights_tf], atol=1e-4, delta=1e-3) + + with self.subTest(name='params_basis_bias_proj'): + def conv_basis(bias_tf_in): + conv_layer._bias_tf[0] = bias_tf_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + bias_tf = conv_layer._bias_tf[0] + self.assert_jacobian_is_correct_fn( + conv_basis, [bias_tf], atol=1e-4, delta=1e-4) + + with self.subTest(name='params_second_layer'): + def conv_weights(weigths_in): + conv_layer._final_weights_tf = weigths_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights = conv_layer._final_weights_tf + self.assert_jacobian_is_correct_fn( + conv_weights, [weights], atol=1e-4, delta=1e-3) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3), + ) + def test_conv_jacobian_points(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + point_cloud = PointCloud(points, batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf() + + conv_layer = MCConv( + num_features[0], num_features[1], dimension, 1, [hidden_size], 'elu') + + def conv_points(points_in): + point_cloud._points = points_in + neighborhood._grid._sorted_points = \ + tf.gather(points_in, grid._sorted_indices) + + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_points, [np.float32(points)], atol=1e-4, delta=1e-3) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py index 7c2513935..14e0e26fa 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py @@ -48,6 +48,7 @@ def _format_output(features, point_cloud, return_sorted, return_padded): features = tf.gather(features, point_cloud._sorted_indices_batch) return features +''' def random_rotation(points, name=None): """ Method to rotate 3D points randomly. @@ -235,11 +236,5 @@ def cube_kernel_points(cbrt_num_points, name): points = np.stack((x.flatten(), y.flatten(), z.flatten()), axis=1) return tf.Variable(points, dtype=tf.float32) +''' -def _identity(features, *args, **kwargs): - """ Simple identity layer, to be used as placeholder. - - Used to replace projection shortcuts, if not desired. - - """ - return features From 46d7c3edf0721f7656829079463c7f0501fa020f Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 20 May 2021 15:50:47 +0200 Subject: [PATCH 29/32] init PointConv --- .../pylib/pc/layers/PointConv.py | 281 ++++++++++++++++++ .../pylib/pc/layers/__init__.py | 5 +- .../pylib/pc/layers/tests/point_conv_test.py | 259 ++++++++++++++++ 3 files changed, 543 insertions(+), 2 deletions(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/PointConv.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/point_conv_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/PointConv.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/PointConv.py new file mode 100644 index 000000000..1a9fa9452 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/PointConv.py @@ -0,0 +1,281 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Classes to for PointConv point cloud convolutions""" + +import tensorflow as tf +from pylib.pc.utils import _flatten_features + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood +from pylib.pc import KDEMode + +from pylib.pc.custom_ops import basis_proj +from pylib.pc.layers.utils import _format_output + +non_linearity_types = {'relu': tf.nn.relu, + 'lrelu': tf.nn.leaky_relu, + 'leakyrelu': tf.nn.leaky_relu, + 'leaky_relu': tf.nn.leaky_relu, + 'elu': tf.nn.elu} + + +class PointConv(tf.Module): + """ Monte-Carlo convolution for point clouds. + + Based on the paper [PointConv: Deep Convolutional Networks on 3D Point + Clouds. Wu et al., 2019](https://arxiv.org/abs/1811.07246). + Uses a single MLP with one hidden layer as convolution kernel. + + Args: + num_features_in: An `int`, C_in, the number of features per input point. + num_features_out: An `int`, C_out, the number of features to compute. + num_dims: An `int`, the input dimension to the kernel MLP. Should be the + dimensionality of the point cloud. + size_hidden: An ìnt`, the number of neurons in the hidden layer of the + kernel MLP, must be in `[8, 16, 32]`, defaults to `32`. (optional). + non_linearity_type: An `string`, specifies the type of the activation + function used inside the kernel MLP. + Possible: `'ReLU', 'lReLU', 'ELU'`, defaults to leaky ReLU. (optional) + initializer_weights: A `tf.initializer` for the kernel MLP weights, + default `TruncatedNormal`. (optional) + initializer_biases: A `tf.initializer` for the kernel MLP biases, + default: `zeros`. (optional) + + """ + + def __init__(self, + num_features_in, + num_features_out, + num_dims, + size_hidden=32, + non_linearity_type='relu', + initializer_weights=None, + initializer_biases=None, + name=None): + + super().__init__(name=name) + + self._num_features_in = num_features_in + self._num_features_out = num_features_out + self._size_hidden = size_hidden + self._num_dims = num_dims + self._non_linearity_type = non_linearity_type + + if name is None: + self._name = 'PointConv' + else: + self._name = name + + # initialize variables + if initializer_weights is None: + initializer_weights = tf.initializers.GlorotNormal + if initializer_biases is None: + initializer_biases = tf.initializers.zeros + + # Hidden layer of the kernel. + weights_init_obj = initializer_weights() + self._basis_axis_tf = tf.Variable( + weights_init_obj( + shape=[self._num_dims, self._size_hidden], + dtype=tf.float32), + trainable=True, + name=self._name + "/hidden_vectors") + + bias_init_obj = initializer_biases() + self._basis_bias_tf = tf.Variable( + bias_init_obj( + shape=[1, self._size_hidden], + dtype=tf.float32), + trainable=True, + name=self._name + "/hidden_bias") + + # Convolution weights. + self._weights = tf.Variable( + weights_init_obj( + shape=[ + self._size_hidden * self._num_features_in, + self._num_features_out], + dtype=tf.float32), + trainable=True, + name=self._name + "/conv_weights") + + # Weights of the non-linear transform of the pdf. + self._weights_pdf = \ + [tf.Variable( + weights_init_obj( + shape=[1, 16], + dtype=tf.float32), + trainable=True, + name=self._name + "/pdf_weights_1"), + tf.Variable( + weights_init_obj( + shape=[16, 1], + dtype=tf.float32), + trainable=True, + name=self._name + "/pdf_weights_2")] + + self._biases_pdf = \ + [tf.Variable( + bias_init_obj( + shape=[1, 16], + dtype=tf.float32), + trainable=True, + name=self._name + "/pdf_biases_1"), + tf.Variable( + bias_init_obj( + shape=[1, 1], + dtype=tf.float32), + trainable=True, + name=self._name + "/pdf_biases_2")] + + def _point_conv(self, + kernel_inputs, + neighborhood, + pdf, + features, + non_linearity_type='relu'): + """ Method to compute a PointConv convolution using a single + MLP with one hidden layer as implicit convolution kernel function. + + Args: + kernel_inputs: A `float` `Tensor` of shape `[M, L]`, the input to the + kernel MLP. + neighborhood: A `Neighborhood` instance. + pdf: A `float` `Tensor` of shape `[M]`, the point densities. + features: A `float` `Tensor` of shape `[N, C1]`, the input features. + non_linearity_type: An `string`, specifies the type of the activation + function used inside the kernel MLP. + Possible: `'ReLU', 'lReLU', 'ELU'`, defaults to leaky ReLU. (optional) + + Returns: + A `float` `Tensor` of shape `[N,C2]`, the output features. + + """ + + # Compute the hidden layer MLP + basis_neighs = tf.matmul(kernel_inputs, self._basis_axis_tf) + \ + self._basis_bias_tf + basis_neighs = \ + non_linearity_types[non_linearity_type.lower()](basis_neighs) + + # Normalize the pdf + max_pdf = tf.math.unsorted_segment_max( + pdf, + neighborhood._original_neigh_ids[:, 1], + tf.shape(neighborhood._samples_neigh_ranges)[0]) + neigh_max_pdfs = tf.gather(max_pdf, neighborhood._original_neigh_ids[:, 1]) + cur_pdf = pdf / neigh_max_pdfs + cur_pdf = tf.reshape(cur_pdf, [-1, 1]) + + # Non-linear transform pdf + cur_pdf = tf.nn.relu(tf.matmul(cur_pdf, self._weights_pdf[0]) +\ + self._biases_pdf[0]) + cur_pdf = tf.matmul(cur_pdf, self._weights_pdf[1]) + self._biases_pdf[1] + + # Scale features + basis_neighs = basis_neighs / cur_pdf + + # Compute the projection to the samples. + weighted_features = basis_proj( + basis_neighs, + features, + neighborhood) + + #Compute convolution - hidden layer to output (linear) + convolution_result = tf.matmul( + tf.reshape(weighted_features, + [-1, self._num_features_in * self._size_hidden]), + self._weights) + + return convolution_result + + def __call__(self, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + radius, + neighborhood=None, + bandwidth=0.2, + return_sorted=False, + return_padded=False, + name=None): + """ Computes the Monte-Carlo Convolution between two point clouds. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + `C_in` is the number of input features. + `C_out` is the number of output features. + + Args: + features: A `float` `Tensor` of shape `[N_in, C_in]` or + `[A1, ..., An,V, C_in]`. + point_cloud_in: A 'PointCloud' instance, on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output + features are defined. + radius: A `float`, the convolution radius. + neighborhood: A `Neighborhood` instance, defining the neighborhood + with centers from `point_cloud_out` and neighbors in `point_cloud_in`. + If `None` it is computed internally. (optional) + bandwidth: A `float`, the bandwidth used in the kernel density + estimation on the input point cloud. (optional) + return_sorted: A `boolean`, if `True` the output tensor is sorted + according to the batch_ids. (optional) + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. (optional) + + Returns: + A `float` `Tensor` of shape + `[N_out, C_out]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C_out]`, if `return_padded` is `True`. + + """ + + features = tf.cast(tf.convert_to_tensor(value=features), + dtype=tf.float32) + features = _flatten_features(features, point_cloud_in) + + #Create the radii tensor. + radius = tf.reshape(tf.convert_to_tensor(value=radius, dtype=tf.float32), [1, 1]) + radii_tensor = tf.repeat(radius, self._num_dims) + + #Create the badnwidth tensor. + bwTensor = tf.repeat(bandwidth, self._num_dims) + + if neighborhood is None: + #Compute the grid + grid = Grid(point_cloud_in, radii_tensor) + #Compute the neighborhoods + neigh = Neighborhood(grid, radii_tensor, point_cloud_out) + else: + neigh = neighborhood + pdf = neigh.get_pdf(bandwidth=bwTensor, mode=KDEMode.constant) + + #Compute kernel inputs. + neigh_point_coords = tf.gather( + point_cloud_in._points, neigh._original_neigh_ids[:, 0]) + center_point_coords = tf.gather( + point_cloud_out._points, neigh._original_neigh_ids[:, 1]) + points_diff = (neigh_point_coords - center_point_coords) + + #Compute PointConv convolution + convolution_result = self._point_conv( + points_diff, neigh, pdf, features, self._non_linearity_type) + + return _format_output(convolution_result, + point_cloud_out, + return_sorted, + return_padded) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py index e21220129..bf7c59200 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py @@ -18,9 +18,10 @@ from .Conv1x1 import Conv1x1 from .MCConv import MCConv -''' -from .KPConv import KPConv + +#from .KPConv import KPConv from .PointConv import PointConv +''' from .utils import spherical_kernel_points, \ cube_kernel_points ''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/point_conv_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/point_conv_test.py new file mode 100644 index 000000000..91b1ea720 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/point_conv_test.py @@ -0,0 +1,259 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test PointConv convolutions""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, KDEMode, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import PointConv + + +class PointConvTest(test_case.TestCase): + + @parameterized.parameters( + (2000, 200, [3, 3], 16, 0.7, 8, 2), + (4000, 400, [3, 3], 8, np.sqrt(2), 8, 2), + (2000, 200, [1, 3], 16, 0.7, 8, 3), + (4000, 400, [3, 3], 8, 0.7, 8, 3), + (4000, 100, [3, 1], 1, np.sqrt(3), 16, 3), + (2000, 200, [3, 3], 16, 0.7, 8, 4), + (4000, 400, [1, 3], 8, np.sqrt(4), 32, 4) + ) + def test_convolution(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + pdf = neighborhood.get_pdf() + # tf + conv_layer = PointConv( + num_features[0], num_features[1], dimension, hidden_size, + non_linearity_type='relu') + conv_result_tf = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + # numpy + neighbor_ids = neighborhood._original_neigh_ids.numpy() + nb_ranges = neighborhood._samples_neigh_ranges.numpy() + max_pdf = tf.math.unsorted_segment_max( + pdf, + neighborhood._original_neigh_ids[:, 1], + tf.shape(neighborhood._samples_neigh_ranges)[0]) + neigh_max_pdfs = tf.gather(max_pdf, neighborhood._original_neigh_ids[:, 1]) + neigh_max_pdfs = neigh_max_pdfs + pdf = pdf.numpy() / neigh_max_pdfs.numpy() + + # extract variables + hidden_weights = conv_layer._basis_axis_tf.numpy() + hidden_biases = conv_layer._basis_bias_tf.numpy() + pdf_weights = conv_layer._weights_pdf + pdf_biases = conv_layer._biases_pdf + weights = conv_layer._weights.numpy() + + features_on_neighbors = features[neighbor_ids[:, 0]] + # compute first layer of kernel MLP + point_diff = (points[neighbor_ids[:, 0]] -\ + point_samples[neighbor_ids[:, 1]]) + + latent_per_nb = np.dot(point_diff, hidden_weights) + hidden_biases + latent_relu_per_nb = np.maximum(latent_per_nb, 0) + + # non-linear transform pdf with 2 layer MLP with ReLU + cur_pdf = np.dot(pdf.reshape([-1, 1]), pdf_weights[0].numpy()) + \ + pdf_biases[0].numpy() + cur_pdf = np.maximum(cur_pdf, 0) + cur_pdf = np.dot(cur_pdf, pdf_weights[1].numpy()) + pdf_biases[1].numpy() + + # weighting with pdf + latent_relu_per_nb /= cur_pdf + + # Monte-Carlo integration after first layer + weighted_features_per_nb = np.expand_dims(features_on_neighbors, 2) * \ + np.expand_dims(latent_relu_per_nb, 1) + nb_ranges = np.concatenate(([0], nb_ranges), axis=0) + # sum (integration) + weighted_latent_per_sample = \ + np.zeros([num_samples, num_features[0], hidden_size]) + for i in range(num_samples): + weighted_latent_per_sample[i] = \ + np.sum(weighted_features_per_nb[nb_ranges[i]:nb_ranges[i + 1]], + axis=0) + # second layer of MLP (linear) + weighted_latent_per_sample = np.reshape(weighted_latent_per_sample, + [num_samples, -1]) + conv_result_np = np.matmul(weighted_latent_per_sample, weights) + + self.assertAllClose(conv_result_tf, conv_result_np, atol=1e-3, rtol=1e-3) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3) + ) + def test_conv_jacobian_params(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + point_cloud = PointCloud(points, batch_ids) + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + conv_layer = PointConv( + num_features[0], num_features[1], dimension, hidden_size) + + features = np.random.rand(num_points, num_features[0]) + + with self.subTest(name='features'): + def conv_features(features_in): + conv_result = conv_layer( + features_in, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_features, [features], atol=1e-3, delta=1e-3) + + with self.subTest(name='params_basis_axis_proj'): + def conv_basis(weights_tf_in): + conv_layer._basis_axis_tf = weights_tf_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights_tf = conv_layer._basis_axis_tf + self.assert_jacobian_is_correct_fn( + conv_basis, [weights_tf], atol=1e-2, delta=1e-3) + + with self.subTest(name='params_basis_bias_proj'): + def conv_basis(bias_tf_in): + conv_layer._basis_bias_tf = bias_tf_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + bias_tf = conv_layer._basis_bias_tf + self.assert_jacobian_is_correct_fn( + conv_basis, [bias_tf], atol=1e-2, delta=1e-4) + + with self.subTest(name='params_pdf_transform'): + def conv_pdf(bias_pdf_tf_in1, + bias_pdf_tf_in2, + weights_pdf_tf_in1, + weights_pdf_tf_in2): + conv_layer._biases_pdf = [bias_pdf_tf_in1, bias_pdf_tf_in2] + conv_layer._weights_pdf = [weights_pdf_tf_in1, weights_pdf_tf_in2] + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + bias_pdf_tf = conv_layer._biases_pdf + weights_pdf_tf = conv_layer._weights_pdf + self.assert_jacobian_is_correct_fn( + conv_pdf, + [bias_pdf_tf[0], bias_pdf_tf[1], + weights_pdf_tf[0], weights_pdf_tf[1]], + atol=5e-3, delta=1e-3) + + with self.subTest(name='params_second_layer'): + def conv_weights(weigths_in): + conv_layer._weights = weigths_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights = conv_layer._weights + self.assert_jacobian_is_correct_fn( + conv_weights, [weights], atol=1e-2, delta=1e-3) + + @parameterized.parameters( + # neighbor ids are currently corrupted on dimension 2: todo fix + # (2000, 200, 16, 0.7, 2), + # (4000, 400, 8, np.sqrt(2), 2), + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 8, 3), + # (4000, 400, [1, 1], 8, np.sqrt(3), 8, 3), + # (4000, 100, [2, 4], 1, np.sqrt(3), 8, 3), + # (2000, 200, [4, 2], 16, 0.7, 8, 4), + # (4000, 400, [2, 2], 8, np.sqrt(4), 8, 4) + ) + def test_conv_jacobian_points(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + hidden_size, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + point_cloud = PointCloud(points, batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf() + + conv_layer = PointConv( + num_features[0], num_features[1], dimension, hidden_size, 'elu') + + def conv_points(points_in): + point_cloud._points = points_in + neighborhood._grid._sorted_points = \ + tf.gather(points_in, grid._sorted_indices) + + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_points, [np.float32(points)], atol=5e-3, delta=1e-2) + + +if __name__ == '__main___': + test_case.main() From df2d42fe97d2415fd1c19c1f13070937a5316f1e Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 20 May 2021 15:53:29 +0200 Subject: [PATCH 30/32] init KPConv --- .../pylib/pc/layers/KPConv.py | 429 ++++++++++++++++++ .../pylib/pc/layers/__init__.py | 4 +- .../pc/layers/tests/kernel_point_conv_test.py | 259 +++++++++++ .../pylib/pc/layers/utils.py | 5 +- 4 files changed, 690 insertions(+), 7 deletions(-) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/KPConv.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/kernel_point_conv_test.py diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/KPConv.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/KPConv.py new file mode 100644 index 000000000..9e71127da --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/KPConv.py @@ -0,0 +1,429 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class for kernel point cloud convolutions""" + +import tensorflow as tf +from pylib.pc.utils import _flatten_features + +from pylib.pc import PointCloud +from pylib.pc import Grid +from pylib.pc import Neighborhood +from pylib.pc import KDEMode + +from pylib.pc.custom_ops import basis_proj +from pylib.pc.layers.utils import _format_output, spherical_kernel_points, \ + random_rotation + + +def _linear_weighting(values, sigma): + """ Linear kernel weights for KP Conv. + + Args: + values: A `float` `Tensor` of shape `[K, M]`, the normalized distances to + the kernel points, in `[0,1]`. + sigma: A `float`, the normalized influence distance of the kernel points. + + Returns: + A `float` `Tensor` of shape `[K, M]`. + + """ + return tf.nn.relu(1 - values / sigma) + + +def _gaussian_weighting(values, sigma): + """ Gaussian kernel weights for KP Conv. + + Args: + values: A `float` `Tensor` of shape `[K, M]`, the normalized distances to + the kernel points, in `[0,1]`. + sigma: A `float`, the normalized influence distance of the kernel points. + + Returns: + A `float` `Tensor` of shape `[K, M]`. + + """ + sigma = sigma / 3 + return tf.exp(-(values / sigma)**2) + +kernel_interpolation = {'linear': _linear_weighting, + 'gaussian': _gaussian_weighting} + + +class KPConv(tf.Module): + """ A Kernel Point Convolution for 3D point clouds. + + Based on the paper [KPConv: Flexible and Deformable Convolution for Point + Clouds. Thomas et al., 2019](https://arxiv.org/abs/1904.08889). + + Note: To use this layer for point clouds with arbitrary dimension `D`, + pass initial kernel points of dimension `D` using `custom_kernel_points`. + + Args: + num_features_in: An `int`, `C_in`, the number of features per input point. + num_features_out: An `int`, `C_out`, the number of features to compute. + num_kernel_points: An ìnt`, the number of points for representing the + kernel, default is `15`. (optional) + num_dims: An `int`, the dimensionality of the point cloud. Defaults to `3`. + (optional) + deformable: A 'bool', indicating whether to use rigid or deformable kernel + points, default is `False`. (optional) + kp_interpolation: A `string`, either `'linear'`(default) or `'gaussian'`. + (optional) + custom_kernel_points: A `float` `Tensor` of shape `[K, D]`, to pass custom + kernel points. (optional) + initializer_weights: A `tf.initializer` for the weights, + default `TruncatedNormal`. (optional) + + Raises: + ValueError, if no custom kernel points are passed for dimension not equal + to 3. + + """ + + def __init__(self, + num_features_in, + num_features_out, + num_kernel_points=15, + num_dims=3, + deformable=False, + kp_interpolation='linear', + custom_kernel_points=None, + initializer_weights=None, + name=None): + + super().__init__(name=name) + + self._num_features_in = num_features_in + self._num_features_out = num_features_out + self._num_kernel_points = num_kernel_points + self._deformable = deformable + self._weighting = kernel_interpolation[kp_interpolation] + self._num_dims = num_dims + if name is None: + self._name = 'KPConv' + else: + self._name = name + + if num_dims != 3 and custom_kernel_points is None: + raise ValueError( + "For dimension not 3 custom kernel points must be provided!") + + # initialize kernel points + if custom_kernel_points is None: + self._kernel_points = spherical_kernel_points(num_kernel_points, + rotate=True) + else: + self._kernel_points = tf.convert_to_tensor(value=custom_kernel_points, + dtype=tf.float32) + + # Reposition the points at radius 0.75. + self._kernel_points = self._kernel_points * 0.75 + + # initialize variables + if initializer_weights is None: + initializer_weights = tf.initializers.GlorotNormal + + weights_init_obj = initializer_weights() + + if deformable: + self._kernel_offsets_weights = \ + tf.Variable( + weights_init_obj(shape=[ + self._num_kernel_points * self._num_features_in, + self._num_kernel_points * self._num_dims], + dtype=tf.float32), + trainable=True, + name=self._name + "/weights_deformable") + self._get_offsets = self._kernel_offsets + else: + def _zero(*args, **kwargs): + """ Replaces `_get_offsets` with zeros for rigid KPConv. + """ + return tf.constant(0.0, dtype=tf.float32) + self._get_offsets = _zero + + self._weights = \ + tf.Variable( + weights_init_obj(shape=[ + self._num_kernel_points * self._num_features_in, + self._num_features_out], + dtype=tf.float32), + trainable=True, + name=self._name + "/conv_weights") + + def _kp_conv(self, + kernel_input, + neighborhood, + features): + """ Method to compute a kernel point convolution using linear interpolation + of the kernel weights. + + Note: In the following + `D` is the dimensionality of the points cloud (=3) + `M` is the number of neighbor pairs + 'C1`is the number of input features + `C2` is the number of output features + `N1' is the number of input points + `N2' is the number of ouput points + + Args: + kernel_inputs: A `float` `Tensor` of shape `[M, D]`, the input to the + kernel, i.e. the distances between neighbor pairs. + neighborhood: A `Neighborhood` instance. + features: A `float` `Tensor` of shape `[N1, C1]`, the input features. + + Returns: + A `float` `Tensor` of shape `[N2, C2]`, the output features. + + """ + # neighbor pairs ids + neighbors = neighborhood._original_neigh_ids + # kernel weights from distances, shape [M, K] + kernel_offsets = self._get_offsets(kernel_input, neighborhood, features) + points_diff = tf.expand_dims(kernel_input, 1) - \ + (tf.expand_dims(self._kernel_points, 0) + kernel_offsets) + points_dist = tf.linalg.norm(points_diff, axis=2) + kernel_weights = self._weighting(points_dist, self._sigma) + + # Pad zeros to fullfil requirements of the basis_proj custom op, + # 8, 16, 32, or 64 basis are allowed. + if self._num_kernel_points < 8: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 8 - self._num_kernel_points]]) + elif self._num_kernel_points > 8 and self._num_kernel_points < 16: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 16 - self._num_kernel_points]]) + elif self._num_kernel_points > 16 and self._num_kernel_points < 32: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 32 - self._num_kernel_points]]) + elif self._num_kernel_points > 32 and self._num_kernel_points < 64: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 64 - self._num_kernel_points]]) + + # save values for regularization loss computation + self._cur_point_dist = points_dist + self._cur_neighbors = neighbors + + # Compute the projection to the samples. + weighted_features = basis_proj( + kernel_weights, + features, + neighborhood) + # remove padding + weighted_features = weighted_features[:, :, 0:self._num_kernel_points] + + #Compute convolution - hidden layer to output (linear) + convolution_result = tf.matmul( + tf.reshape(weighted_features, + [-1, self._num_features_in * self._num_kernel_points]), + self._weights) + + return convolution_result + + def __call__(self, + features, + point_cloud_in: PointCloud, + point_cloud_out: PointCloud, + conv_radius, + neighborhood=None, + kernel_influence_dist=None, + return_sorted=False, + return_padded=False, + name=None): + """ Computes the Kernel Point Convolution between two point clouds. + + Note: + In the following, `A1` to `An` are optional batch dimensions. + `C_in` is the number of input features. + `C_out` is the number of output features. + + Args: + features: A `float` `Tensor` of shape `[N1, C_in]` or + `[A1, ..., An,V, C_in]`. + point_cloud_in: A 'PointCloud' instance, on which the features are + defined. + point_cloud_out: A `PointCloud` instance, on which the output + features are defined. + conv_radius: A `float`, the convolution radius. + neighborhood: A `Neighborhood` instance, defining the neighborhood + with centers from `point_cloud_out` and neighbors in `point_cloud_in`. + If `None` it is computed internally. (optional) + kernel_influence_dist = A `float`, the influence distance of the kernel + points. If `None` uses `conv_radius / 2.5`, as suggested in Section 3.3 + of the paper. (optional) + return_sorted: A `boolean`, if `True` the output tensor is sorted + according to the batch_ids. (optional) + return_padded: A `bool`, if 'True' the output tensor is sorted and + zero padded. (optional) + + Returns: + A `float` `Tensor` of shape + `[N2, C_out]`, if `return_padded` is `False` + or + `[A1, ..., An, V_out, C_out]`, if `return_padded` is `True`. + + """ + + features = tf.cast(tf.convert_to_tensor(value=features), + dtype=tf.float32) + features = _flatten_features(features, point_cloud_in) + self._num_output_points = point_cloud_out._points.shape[0] + + if kernel_influence_dist is None: + # normalized + self._sigma = tf.constant(1.0) + else: + self._sigma = tf.convert_to_tensor( + value=kernel_influence_dist / conv_radius, dtype=tf.float32) + #Create the radii tensor. + conv_radius = tf.reshape(tf.convert_to_tensor(value=conv_radius, + dtype=tf.float32), + [1, 1]) + radii_tensor = tf.repeat(conv_radius, self._num_dims) + + if neighborhood is None: + #Compute the grid + grid = Grid(point_cloud_in, radii_tensor) + #Compute the neighborhoods + neigh = Neighborhood(grid, radii_tensor, point_cloud_out) + else: + neigh = neighborhood + + #Compute kernel inputs. + neigh_point_coords = tf.gather( + point_cloud_in._points, neigh._original_neigh_ids[:, 0]) + center_point_coords = tf.gather( + point_cloud_out._points, neigh._original_neigh_ids[:, 1]) + points_diff = (neigh_point_coords - center_point_coords) / \ + tf.reshape(radii_tensor, [1, self._num_dims]) + #Compute Monte-Carlo convolution + convolution_result = self._kp_conv(points_diff, neigh, features) + return _format_output(convolution_result, + point_cloud_out, + return_sorted, + return_padded) + + def _kernel_offsets(self, + kernel_input, + neighborhood, + features): + """ Method to compute the kernel offsets for deformable KPConv + using a rigid KPConv. + + As described in Section 3.2 of [KPConv: Flexible and Deformable Convolution + for Point Clouds. Thomas et al., 2019](https://arxiv.org/abs/1904.08889). + + Note: In the following + `D` is the dimensionality of the point cloud (=3) + `M` is the number of neighbor pairs + 'C1`is the number of input features + `N1' is the number of input points + `N2' is the number of ouput points + `K` is the number of kernel points + + Args: + kernel_inputs: A `float` `Tensor` of shape `[M, D]`, the input to the + kernel, i.e. the distances between neighbor pairs. + neighborhood: A `Neighborhood` instance. + features: A `float` `Tensor` of shape `[N1, C1]`, the input features. + + Returns: + A `float` `Tensor` of shape `[K, M, D]`, the offsets. + + """ + # neighbor pairs ids + neighbors = neighborhood._original_neigh_ids + # kernel weights from distances, shape [M, K] + points_diff = tf.expand_dims(kernel_input, 1) - \ + tf.expand_dims(self._kernel_points, 0) + points_dist = tf.linalg.norm(points_diff, axis=2) + kernel_weights = self._weighting(points_dist, self._sigma) + + # Pad zeros to fullfil requirements of the basis_proj custom op, + # 8, 16, or 32 basis are allowed. + if self._num_kernel_points < 8: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 8 - self._num_kernel_points]]) + elif self._num_kernel_points > 8 and self._num_kernel_points < 16: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 16 - self._num_kernel_points]]) + elif self._num_kernel_points > 16 and self._num_kernel_points < 32: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 32 - self._num_kernel_points]]) + elif self._num_kernel_points > 32 and self._num_kernel_points < 64: + kernel_weights = tf.pad(kernel_weights, + [[0, 0], [0, 64 - self._num_kernel_points]]) + + # Compute the projection to the samples. + weighted_features = basis_proj( + kernel_weights, + features, + neighborhood) + # remove padding + weighted_features = weighted_features[:, :, 0:self._num_kernel_points] + + # Compute convolution - hidden layer to output (linear) + offset_per_center = tf.matmul( + tf.reshape(weighted_features, + [-1, self._num_features_in * self._num_kernel_points]), + self._kernel_offsets_weights) + + # save for regularization loss computation + self._offsets = tf.reshape(offset_per_center, [self._num_output_points, + self._num_kernel_points, + self._num_dims]) + # project back onto neighbor pairs, shape [M, D*K] + offset_per_nb = tf.gather(offset_per_center, neighbors[:, 1]) + # reshape to shape [M, K, self._num_dims] + return tf.reshape(offset_per_nb, + [neighbors.shape[0], + self._num_kernel_points, + self._num_dims]) + + def regularization_loss(self, name=None): + """ The regularization loss for deformable kernel points. + + As described in Section 3.2 of [KPConv: Flexible and Deformable Convolution + for Point Clouds. Thomas et al., 2019](https://arxiv.org/abs/1904.08889). + + Returns: + A 'float`, the sum of repulsory and fitting loss. + + """ + + # attractive loss, distances to closest points in each neighborhood + neighbors = self._cur_neighbors + + # reshape to [M, K] + point_dist = (self._cur_point_dist / self._sigma)**2 + min_dists_per_nbh = tf.math.unsorted_segment_min(point_dist, + neighbors[:, 1], + self._num_output_points) + loss_fit = tf.reduce_sum(min_dists_per_nbh) + + # repulsive loss for kernel points with overlapping influence area. + kernel_offsets = self._offsets + # shape [N2, K, D] + kernel_points = tf.expand_dims(self._kernel_points, 0) + kernel_offsets + kernel_dists = tf.linalg.norm(tf.expand_dims(kernel_points, 1) - \ + tf.expand_dims(kernel_points, 2), + axis=3) + kernel_weights = self._weighting(kernel_dists, self._sigma) + # set weight between same kernel point to zero + kernel_weights = tf.linalg.set_diag(kernel_weights, + tf.zeros([self._num_output_points, + self._num_kernel_points])) + loss_rep = tf.reduce_sum(kernel_weights) + + return loss_fit + loss_rep diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py index bf7c59200..9cbacbbba 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/__init__.py @@ -19,9 +19,7 @@ from .MCConv import MCConv -#from .KPConv import KPConv +from .KPConv import KPConv from .PointConv import PointConv -''' from .utils import spherical_kernel_points, \ cube_kernel_points -''' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/kernel_point_conv_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/kernel_point_conv_test.py new file mode 100644 index 000000000..979aec991 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/tests/kernel_point_conv_test.py @@ -0,0 +1,259 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test kernel point convolutions""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.pc import PointCloud, Grid, Neighborhood, KDEMode, AABB +from pylib.pc.tests import utils +from pylib.pc.layers import KPConv + + +class KPConvTest(test_case.TestCase): + + @parameterized.parameters( + (200, 20, [1, 3], 16, 0.7, 5, 3), + (400, 40, [3, 3], 8, 0.7, 5, 3), + (400, 10, [3, 1], 1, np.sqrt(3), 5, 3), + ) + def test_convolution(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + num_kernel_points, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + point_cloud = PointCloud(points, batch_ids) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + # tf + conv_layer = KPConv( + num_features[0], num_features[1], num_kernel_points) + conv_result_tf = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + # numpy + neighbor_ids = neighborhood._original_neigh_ids.numpy() + nb_ranges = neighborhood._samples_neigh_ranges.numpy() + nb_ranges = np.concatenate(([0], nb_ranges), axis=0) + kernel_points = conv_layer._kernel_points.numpy() + sigma = conv_layer._sigma.numpy() + + # extract variables + weights = conv_layer._weights.numpy() + + features_on_neighbors = features[neighbor_ids[:, 0]] + # compute distances to kernel points + point_diff = (points[neighbor_ids[:, 0]] -\ + point_samples[neighbor_ids[:, 1]])\ + / np.expand_dims(cell_sizes, 0) + kernel_point_diff = np.expand_dims(point_diff, axis=1) -\ + np.expand_dims(kernel_points, axis=0) + distances = np.linalg.norm(kernel_point_diff, axis=2) + # compute linear interpolation weights for features based on distances + kernel_weights = np.maximum(1 - (distances / sigma), 0) + weighted_features = np.expand_dims(features_on_neighbors, axis=2) *\ + np.expand_dims(kernel_weights, axis=1) + # sum over neighbors (integration) + weighted_features_per_sample = \ + np.zeros([num_samples, num_features[0], num_kernel_points]) + for i in range(num_samples): + weighted_features_per_sample[i] = \ + np.sum(weighted_features[nb_ranges[i]:nb_ranges[i + 1]], + axis=0) + # convolution with summation over kernel dimension + conv_result_np = \ + np.matmul( + weighted_features_per_sample.reshape( + -1, + num_features[0] * num_kernel_points), + weights) + + self.assertAllClose(conv_result_tf, conv_result_np, atol=1e-5) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 15, 3) + ) + def test_conv_rigid_jacobian_params(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + num_kernel_points, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + point_cloud = PointCloud(points, batch_ids) + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + conv_layer = KPConv( + num_features[0], num_features[1], num_kernel_points) + + features = np.random.rand(num_points, num_features[0]) + + with self.subTest(name='features'): + def conv_features(features_in): + conv_result = conv_layer( + features_in, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_features, [features], atol=1e-3, delta=1e-3) + + with self.subTest(name='weights'): + def conv_weights(weigths_in): + conv_layer._weights = weigths_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights = conv_layer._weights + self.assert_jacobian_is_correct_fn( + conv_weights, [weights], atol=1e-3, delta=1e-3) + + @parameterized.parameters( + (8, 4, [2, 2], 2, np.sqrt(3) * 1.25, 5, 3) + ) + def test_conv_deformable_jacobian_params(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + num_kernel_points, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + point_cloud = PointCloud(points, batch_ids) + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + conv_layer = KPConv( + num_features[0], num_features[1], num_kernel_points, deformable=True) + + features = np.random.rand(num_points, num_features[0]) + + with self.subTest(name='features'): + def conv_features(features_in): + conv_result = conv_layer( + features_in, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_features, [features], atol=1e-3, delta=1e-3) + + with self.subTest(name='weights'): + def conv_weights(weigths_in): + conv_layer._weights = weigths_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights = conv_layer._weights + self.assert_jacobian_is_correct_fn( + conv_weights, [weights], atol=1e-3, delta=1e-3) + + with self.subTest(name='offsets'): + def conv_offset_weights(weigths_in): + conv_layer._kernel_offsets_weights = weigths_in + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_result + + weights = conv_layer._kernel_offsets_weights + self.assert_jacobian_is_correct_fn( + conv_offset_weights, [weights], atol=1e-3, delta=1e-3) + + with self.subTest(name='loss'): + def conv_loss(features): + _ = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + return conv_layer.regularization_loss() + + weights = conv_layer._kernel_offsets_weights + self.assert_jacobian_is_correct_fn( + conv_loss, [features], atol=1e-3, delta=1e-2) + + @parameterized.parameters( + (8, 4, [8, 8], 2, np.sqrt(3) * 1.25, 15, 3), + ) + def test_conv_jacobian_points(self, + num_points, + num_samples, + num_features, + batch_size, + radius, + num_kernel_points, + dimension): + cell_sizes = np.float32(np.repeat(radius, dimension)) + points, batch_ids = utils._create_random_point_cloud_segmented( + batch_size, num_points, dimension=dimension) + features = np.random.rand(num_points, num_features[0]) + + point_samples, batch_ids_samples = \ + utils._create_random_point_cloud_segmented( + batch_size, num_samples, dimension=dimension) + + point_cloud_samples = PointCloud(point_samples, batch_ids_samples) + point_cloud = PointCloud(points, batch_ids) + grid = Grid(point_cloud, cell_sizes) + neighborhood = Neighborhood(grid, cell_sizes, point_cloud_samples) + neighborhood.compute_pdf() + + conv_layer = KPConv( + num_features[0], num_features[1], num_kernel_points) + + def conv_points(points_in): + point_cloud._points = points_in + neighborhood._grid._sorted_points = \ + tf.gather(points_in, grid._sorted_indices) + + conv_result = conv_layer( + features, point_cloud, point_cloud_samples, radius, neighborhood) + + return conv_result + + self.assert_jacobian_is_correct_fn( + conv_points, [np.float32(points)], atol=1e-3, delta=1e-3) + + +if __name__ == '__main___': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py index 14e0e26fa..f92433887 100644 --- a/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/layers/utils.py @@ -48,7 +48,7 @@ def _format_output(features, point_cloud, return_sorted, return_padded): features = tf.gather(features, point_cloud._sorted_indices_batch) return features -''' + def random_rotation(points, name=None): """ Method to rotate 3D points randomly. @@ -235,6 +235,3 @@ def cube_kernel_points(cbrt_num_points, name): x, y, z = np.meshgrid(x, x, x) points = np.stack((x.flatten(), y.flatten(), z.flatten()), axis=1) return tf.Variable(points, dtype=tf.float32) - -''' - From 1004d44cadf39356dffab2e208fd69bb1c22e806 Mon Sep 17 00:00:00 2001 From: Michael Schelling Date: Thu, 20 May 2021 15:56:54 +0200 Subject: [PATCH 31/32] init example notebooks --- .../point_convolutions/pylib/io/__init__.py | 18 + .../point_convolutions/pylib/io/points.py | 222 + .../pylib/io/tests/__init__.py | 0 .../pylib/io/tests/io_test.py | 57 + .../pylib/notebooks/Introduction.ipynb | 937 + .../pylib/notebooks/ModelNet40.ipynb | 763 + .../test_point_clouds/SHREC15/0.txt | 18998 +++++++++ .../test_point_clouds/SHREC15/1.txt | 18996 +++++++++ .../test_point_clouds/SHREC15/2.txt | 18994 +++++++++ .../test_point_clouds/SHREC15/3.txt | 18996 +++++++++ .../test_point_clouds/SHREC15/4.txt | 18992 +++++++++ .../test_point_clouds/SHREC15/5.txt | 18996 +++++++++ .../test_point_clouds/SHREC15/6.txt | 18994 +++++++++ .../test_point_clouds/SHREC15/7.txt | 18992 +++++++++ .../test_point_clouds/SHREC15/8.txt | 18996 +++++++++ .../test_point_clouds/SHREC15/9.txt | 18992 +++++++++ .../test_point_clouds/meshes/1464288.obj | 32838 ++++++++++++++++ .../test_point_clouds/meshes/636795.obj | 15278 +++++++ .../test_point_clouds/meshes/650181.obj | 19186 +++++++++ .../test_point_clouds/meshes/69405.obj | 15838 ++++++++ .../test_point_clouds/meshes/849722.obj | 1610 + .../test_point_clouds/meshes/849722_norm.obj | 1609 + .../test_point_clouds/meshes/894833.obj | 15910 ++++++++ .../test_point_clouds/meshes/bunny_small.obj | 10451 +++++ .../modelnet40/airplane_0001.txt | 10000 +++++ .../modelnet40/airplane_0002.txt | 10000 +++++ .../modelnet40/airplane_0003.txt | 10000 +++++ .../modelnet40/airplane_0004.txt | 10000 +++++ .../modelnet40/airplane_0005.txt | 10000 +++++ .../modelnet40/airplane_0006.txt | 10000 +++++ .../modelnet40/airplane_0007.txt | 10000 +++++ .../modelnet40/airplane_0008.txt | 10000 +++++ .../modelnet40/airplane_0009.txt | 10000 +++++ .../modelnet40/airplane_0010.txt | 10000 +++++ 34 files changed, 404663 insertions(+) create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/io/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/io/points.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/io/tests/__init__.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/io/tests/io_test.py create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/notebooks/ModelNet40.ipynb create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/0.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/1.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/2.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/3.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/4.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/5.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/6.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/7.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/8.txt create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/9.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/1464288.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/636795.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/650181.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/69405.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/849722.obj create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/849722_norm.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/894833.obj create mode 100644 tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/bunny_small.obj create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0001.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0002.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0003.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0004.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0005.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0006.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0007.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0008.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0009.txt create mode 100755 tensorflow_graphics/projects/point_convolutions/test_point_clouds/modelnet40/airplane_0010.txt diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/io/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/io/__init__.py new file mode 100644 index 000000000..357760470 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/io/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2020 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT 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 .points import load_points_from_file_to_numpy +from .points import load_points_from_file_to_tensor +from .points import load_batch_of_points +from .points import load_batch_of_meshes diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/io/points.py b/tensorflow_graphics/projects/point_convolutions/pylib/io/points.py new file mode 100644 index 000000000..1cee23f4d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/io/points.py @@ -0,0 +1,222 @@ +# Copyright 2020 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT 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 +"""Small functions to load point clouds""" + +import os +import tensorflow as tf +import numpy as np +from tensorflow_graphics.io.triangle_mesh import load as load_mesh + + +def load_points_from_file_to_tensor(filename, + delimiter=',', + dimension=3, + dtype=tf.float32): + """ Loads point clouds with features from ASCII files using tf.io.gfile.GFile() + + Args: + filename: A `string` path to the file. + delimiter: A `string` delimiter that separates the points in the file. + dimension: An `int` `D1` , the first `D1` elements in each line are + treated as point coordinates, the rest as features. + dtype: A `tf.dtype` of the output tensors + + Returns: + points: A `Tensor` of shape `[N, D1]` and type dtype + features: A `Tensor` of shape `[N, D2]` and type dtype + + Raises: + TypeError: if filename is not of type 'string' + FileNotFoundError: if filename does not exist + + """ + + points = [] + features = [] + if isinstance(filename, str): + if tf.io.gfile.exists(filename): + with tf.io.gfile.GFile(filename, 'r') as in_file: + for line in in_file: + line_elements = line[:-1].split(delimiter) + points.append(line_elements[0:dimension]) + if len(line_elements) > 3: + features.append(line_elements[dimension:]) + + points = tf.convert_to_tensor(value=points) + features = tf.convert_to_tensor(value=features) + points = tf.strings.to_number(input=points, out_type=dtype) + features = tf.strings.to_number(input=features, out_type=dtype) + return points, features + else: + raise FileNotFoundError(f"No such file: {filename}") + else: + raise TypeError("'filename' must be of type 'string'") + + +def load_points_from_file_to_numpy(filename, + delimiter=',', + dimension=3, + max_num_points=None, + dtype=np.float32): + """ Loads point clouds with features from ASCII files using tf.io.gfile.GFile() + + Args: + filename: `string` path to the file + delimiter: `string` delimiter that separates the points in the file + dimension: `int` D1 , the first D1 elements in each line are treated as + point coordinates, the rest as features + max_num_points: An `int` the maximum number of lines to read. + dtype: `np.dtype` of the output array + + Returns: + points: A numpy array of shape [N,D1] and type dtype + features: A numpy array of shape [N,D2] and type dtype + + Raises: + TypeError: if filename is not of type 'string' + FileNotFoundError: if filename does not exist + + """ + + points = [] + features = [] + if isinstance(filename, str): + if tf.io.gfile.exists(filename): + with tf.io.gfile.GFile(filename, 'r') as in_file: + i = 0 + for line in in_file: + if max_num_points is None or i < max_num_points: + line_elements = line[:-1].split(delimiter) + points.append(line_elements[0:dimension]) + if len(line_elements) > 3: + features.append(line_elements[dimension:]) + else: + break + i += 1 + + points = np.array(points, dtype=dtype) + features = np.array(features, dtype=dtype) + return points, features + else: + raise FileNotFoundError(f"No such file: {filename}") + else: + raise TypeError("'filename' must be of type 'string'") + + +def load_batch_of_points(filenames, + batch_shape=[-1], + delimiter=',', + point_dimension=3, + dtype=tf.float32): + """ Loads a batch of point clouds form the given ASCII files and creates + zero padded `Tensor` for the point coordinates and the features and a `sizes` + tensor with the number of points per point cloud. + + Args: + filenames: `list` of `string`, the paths to the files. + batch_shape: A 1D `int` `Tensor`, `[A1, ..., An]`. + delimiter: A `string` delimiter that separates the points in the file. + point_dimension: An `int` `D1` , the first `D1` elements in each line are + treated as point coordinates, the rest as features. + dtype: A `tf.dtype` of the output. + + Returns: + points: A `Tensor`of shape `[A1, ..., An, V, D1]` and type `dtype`. + features: A `Tensor`of shape `[A1, ..., An, V, D2]` and type `dtype`. + sizes: An `int` `Tensor` of shape `[A1, ... , An]`. + """ + + batch_size = len(filenames) + if batch_shape != [-1] and tf.reduce_prod(batch_shape) != batch_size: + raise ValueError( + f'Invalid batch shape {batch_shape} for batch size {batch_size}') + points = [] + features = [] + max_num_points = 0 + sizes = [] + for filename in filenames: + curr_points, curr_features = load_points_from_file_to_tensor( + filename=filename, delimiter=delimiter, dtype=dtype) + points.append(curr_points) + features.append(curr_features) + sizes.append(len(curr_points)) + + sizes = tf.convert_to_tensor(value=sizes) + max_num_points = tf.reduce_max(sizes) + + feature_dimension = features[0].shape[1] + for i in range(batch_size): + pad_size = max_num_points - sizes[i] + points[i] = tf.concat( + (points[i], tf.zeros(shape=[pad_size, point_dimension], dtype=dtype)), + axis=0) + features[i] = tf.concat( + (features[i], tf.zeros(shape=[pad_size, feature_dimension], + dtype=dtype)), axis=0) + + points = tf.stack(values=points, axis=0) + features = tf.stack(values=features, axis=0) + + points = tf.reshape( + tensor=points, shape=batch_shape + [max_num_points, point_dimension]) + features = tf.reshape( + tensor=features, shape=batch_shape + [max_num_points, feature_dimension]) + sizes = tf.reshape(tensor=sizes, shape=batch_shape) + + return points, features, sizes + + +def load_batch_of_meshes(filenames, + batch_shape=[-1], + file_type=None, + dtype=tf.float32, + **kwargs): + """ Loads a batch of point clouds from the given mesh files and creates a + zero padded `Tensor` for the point coordinates and a `sizes` tensor with the + number of points per point cloud. + + Args: + filenames: A `list` of `strings`, the paths to the files. + batch_shape: A 1D `int` `Tensor` `[A1, ..., An]` + filename: A `string`, the path to the file. + file_type: A `string` specifying the type of the file (e.g. 'obj', 'stl'). + If not specified the file_type will be inferred from the file name. + **kwargs: Additional arguments that should be passed to trimesh.load(). + dtype: A `tf.dtype` of the output tensors + + Returns: + points: A `Tensor`of shape `[A1, ..., An, V, 3]` and type `dtype`. + sizes: A int `Tensor`of shape `[A1, ..., An]`. + """ + batch_size = len(filenames) + points = [] + sizes = [] + for filename in filenames: + mesh = load_mesh(filename, file_type=file_type, **kwargs) + curr_points = tf.convert_to_tensor(value=mesh.vertices, dtype=dtype) + points.append(curr_points) + sizes.append(len(curr_points)) + tf.convert_to_tensor(sizes) + max_num_points = tf.reduce_max(sizes) + + for i in range(batch_size): + pad_size = max_num_points - sizes[i] + points[i] = tf.concat( + (points[i], tf.zeros(shape=[pad_size, 3], dtype=dtype)), axis=0) + points = tf.stack(values=points, axis=0) + points = tf.reshape(tensor=points, shape=batch_shape + [max_num_points, 3]) + sizes = tf.reshape(tensor=sizes, shape=batch_shape) + + return points, sizes diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/io/tests/__init__.py b/tensorflow_graphics/projects/point_convolutions/pylib/io/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/io/tests/io_test.py b/tensorflow_graphics/projects/point_convolutions/pylib/io/tests/io_test.py new file mode 100644 index 000000000..398739798 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/io/tests/io_test.py @@ -0,0 +1,57 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class to test bounding box""" + +import os +from os.path import dirname, abspath +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow_graphics.util import test_case + +from pylib.io import load_points_from_file_to_numpy +from pylib.io import load_points_from_file_to_tensor +from pylib.io import load_batch_of_points +from pylib.io import load_batch_of_meshes + + +class IO_test(test_case.TestCase): + + def test_load_points(self): + data_path = dirname(dirname(dirname(dirname(abspath(__file__))))) + \ + '/test_point_clouds' + '/modelnet40/' + points_np, features_np = load_points_from_file_to_numpy( + data_path + 'airplane_0001.txt') + points_tf, features_tf = load_points_from_file_to_tensor( + data_path + 'airplane_0001.txt') + self.assertAllClose(points_np, points_tf) + self.assertAllClose(features_np, features_tf) + + points_np, features, sizes = load_batch_of_points( + [data_path + 'airplane_0001.txt']) + self.assertTrue(points_np.shape == [1, 10000, 3]) + self.assertTrue(sizes.shape == [1]) + + def test_load_mesh(self): + data_path = dirname(dirname(dirname(dirname(abspath(__file__))))) + \ + '/test_point_clouds' + '/meshes/' + points_np, sizes = load_batch_of_meshes( + [data_path + '69405.obj']) + self.assertTrue(points_np.shape == [1, 3960, 3]) + self.assertTrue(sizes.shape == [1]) + + +if __name__ == '__main__': + test_case.main() diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb b/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb new file mode 100644 index 000000000..0c12e9d63 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb @@ -0,0 +1,937 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TFG_point_clouds_test.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "-2xKG5Pgu_SX", + "colab_type": "text" + }, + "source": [ + "##### Copyright 2020 Google LLC." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-RkUBOZbu3iz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "31faWJ7pCNGR" + }, + "source": [ + "# Point Clouds for tensorflow_graphics\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wo1bELqqRAKF", + "colab_type": "text" + }, + "source": [ + "## Initialization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YqSeyzzZQUDV", + "colab_type": "text" + }, + "source": [ + "\n", + "### Clone repositories, and install requirements and custom_op package" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_vtvcRP3QtTl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Clone repositories\n", + "!rm -r graphics\n", + "!git clone https://github.com/schellmi42/graphics\n", + "\n", + "# install requirements and load tfg module \n", + "!pip install -r graphics/requirements.txt\n", + "\n", + "# install custom ops\n", + "!pip install graphics/tensorflow_graphics/projects/point_convolutions/custom_ops/pkg_builds/tf_2.2.0/*.whl\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9D-q8bUu7TcJ", + "colab_type": "text" + }, + "source": [ + "### Load modules" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5lTxNysx7Lfz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import sys\n", + "# (this is equivalent to export PYTHONPATH='$HOME/graphics:/content/graphics:$PYTHONPATH', but adds path to running session)\n", + "sys.path.append(\"/content/graphics\")\n", + "\n", + "# load point cloud module \n", + "# (this is equivalent to export PYTHONPATH='/content/graphics/tensorflow_graphics/projects/point_convolutions:$PYTHONPATH', but adds path to running session)\n", + "sys.path.append(\"/content/graphics/tensorflow_graphics/projects/point_convolutions\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-gUTDsKERxWh", + "colab_type": "text" + }, + "source": [ + "Check if it loads without errors" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z_49mMLOSOX6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import tensorflow as tf\n", + "import tensorflow_graphics as tfg\n", + "import pylib.pc as pc\n", + "import numpy as np\n", + "\n", + "print('TensorFlow version: %s'%tf.__version__)\n", + "print('TensorFlow-Graphics version: %s'%tfg.__version__)\n", + "print('Point Cloud Module: ', pc)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hH4ryCVBRH4C", + "colab_type": "text" + }, + "source": [ + "## Example Code\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z0hxQN7g-UH0", + "colab_type": "text" + }, + "source": [ + "### 2D square point clouds using segmentation IDs\n", + "Here we create a batch of point clouds with variable number of points per cloud from unordered points with an additional id tensor.\n", + "\n", + "The `batch_ids` are the segmentation ids, which indicate which point belongs to which point cloud in the batch. For more information on segmentation IDs see: [tf.math#segmentation](https://www.tensorflow.org/api_docs/python/tf/math#Segmentation)\n", + "\n", + "If the points are ordered by batch id, it is also possible to pass a `sizes` tensor, which has the size of each point cloud in it." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GWEkWPeJLoe0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def square(num_samples, size=1):\n", + " # 2D square in 3D for easier visualization\n", + " points = np.random.rand(num_samples, 2)*2-1\n", + " return points*size\n", + "\n", + "num_samples=1000\n", + "batch_size = 10\n", + "\n", + "# create numpy input data consisting of points and segmentation identifiers\n", + "points = square(num_samples)\n", + "batch_ids = np.random.randint(0, batch_size, num_samples)\n", + "\n", + "# create tensorflow point cloud\n", + "point_cloud = pc.PointCloud(points, batch_ids, batch_size)\n", + "\n", + "# print information\n", + "sizes = point_cloud.get_sizes()\n", + "print('%s point clouds of sizes:'%point_cloud._batch_size)\n", + "print(sizes.numpy())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qStvELcmgd7R", + "colab_type": "text" + }, + "source": [ + "Create a batch of point hierarchies using sequential poisson disk sampling with pooling radii 0.1, 0.4, 2." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E6A9YdQ8IAIN", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# numpy input parameters\n", + "sampling_radii = np.array([[0.1], [0.4], [2]])\n", + "\n", + "# create tensorflow point hierarchy\n", + "point_hierarchy = pc.PointHierarchy(point_cloud,\n", + " sampling_radii,\n", + " 'poisson_disk')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "NRMSzdQaInJC", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# print information\n", + "num_levels = len(sampling_radii) + 1\n", + "print('%s point clouds of sizes:'%point_cloud._batch_size)\n", + "sizes = point_hierarchy.get_sizes()\n", + "for i in range(num_levels):\n", + " print('level: ' + str(i))\n", + " print(sizes[i].numpy())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jYnDmsPWiEqR", + "colab_type": "text" + }, + "source": [ + "assign a shape to the batch and look at the sizes again" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uDNRdJayiKfv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "point_hierarchy.set_batch_shape([2, 5])\n", + "print('%s point clouds of sizes:'%point_cloud._batch_size)\n", + "sizes = point_hierarchy.get_sizes()\n", + "for i in range(num_levels):\n", + " print('level: ' + str(i))\n", + " print(sizes[i].numpy())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hWlLEYXmwtmn", + "colab_type": "text" + }, + "source": [ + "Visualize the levels of one example from the batch." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xq103KhWbHYE", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# which example from the batch to choose, can be 'int' or relative in [A1,...,An]\n", + "batch_id = [0,1]\n", + "\n", + "curr_points = point_hierarchy.get_points(batch_id)\n", + "\n", + "# plotting\n", + "plt.figure(figsize=[num_levels*5,5])\n", + "for i in range(num_levels):\n", + " plt.subplot(1,num_levels,i+1)\n", + " plt.plot(curr_points[i][:, 0],curr_points[i][:, 1],'bo')\n", + " plt.axis([-1, 1, -1, 1])\n", + " if i==0:\n", + " plt.title('input point cloud')\n", + " else:\n", + " plt.title('poisson sampled points with radius %s'%sampling_radii[i - 1, 0])\n", + " \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "naZnSGv7cnbc", + "colab_type": "text" + }, + "source": [ + "### 3D point clouds from input files using arbitrary batch sizes with padding\n", + "\n", + "Here we create point clouds from input files using a zero padded representation of shape `[A1, .., An, V, D]`.\n", + "Internally this is converted to a segmented representation.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "88Sd7egJlsbQ", + "colab_type": "text" + }, + "source": [ + " #### Loading from ASCII .txt files" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0wK7aZNVcwwf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pylib.io as io\n", + "\n", + "# SHREC15\n", + "\n", + "#### get files ####\n", + "input_dir = 'graphics/tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/'\n", + "filenames = tf.io.gfile.listdir(input_dir)\n", + "batch_size = len(filenames)\n", + "print('### batch size ###'); print(batch_size)\n", + "\n", + "for i in range(batch_size):\n", + " filenames[i] = input_dir + filenames[i]\n", + "\n", + "#### load points #####\n", + "batch_shape = [5,2]\n", + "print('### batch shape###'); print(batch_shape)\n", + "points, normals, sizes = io.load_batch_of_points(filenames, batch_shape = batch_shape)\n", + "\n", + "print('### data shape ###'); print(points.shape)\n", + "print('### points per point cloud ###');print(sizes.numpy())\n", + "\n", + "#### build point hierarchy #####\n", + "point_cloud = pc.PointCloud(points, sizes=sizes)\n", + "\n", + "point_hierarchy = pc.PointHierarchy(point_cloud,\n", + " [[0.05], [0.1]],\n", + " 'poisson_disk')\n", + "\n", + "sizes = point_hierarchy.get_sizes()\n", + "\n", + "print('### point per point cloud in hierarchy ###')\n", + "for level in range(len(sizes)):\n", + " print('level %s'%level)\n", + " print(sizes[level].numpy())\n", + "\n", + "### extract points from last level in original batch shape ###\n", + "hierarchical_points = point_hierarchy.get_points()\n", + "out_points = hierarchical_points[-1]\n", + "print('### shape of points in last level ###'); print(out_points.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3pjPEofkgvC6", + "colab_type": "text" + }, + "source": [ + "#### Loading vertices from mesh files \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "umdcLRWhgzjR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Thingi10k meshes\n", + "\n", + "#### get files ####\n", + "input_dir = 'graphics/tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/'\n", + "filenames = tf.io.gfile.listdir(input_dir)\n", + "batch_size = len(filenames)\n", + "print('### batch size ###'); print(batch_size)\n", + "\n", + "for i in range(batch_size):\n", + " filenames[i] = input_dir+filenames[i]\n", + "\n", + "#### load points ####\n", + "points, sizes = io.load_batch_of_meshes(filenames)\n", + "\n", + "print('### data shape ###'); print(points.shape)\n", + "print('### points per point cloud ###');print(sizes.numpy())\n", + "\n", + "#### build a point cloud object ####\n", + "point_cloud = pc.PointCloud(points, sizes=sizes)\n", + "\n", + "print('### internal shape conversion ###')\n", + "print('Input (padded): %s elements'%len(tf.reshape(points, [-1, 3])))\n", + "print('Internal (segmented): %s elements'%len(point_cloud._points))\n", + "\n", + "point_hierarchy = pc.PointHierarchy(point_cloud,\n", + " [[0.05], [0.1]],\n", + " 'poisson_disk')\n", + "\n", + "sizes = point_hierarchy.get_sizes()\n", + "\n", + "print('### point per point cloud in hierarchy ###')\n", + "for level in range(len(sizes)):\n", + " print('level %s'%level)\n", + " print(sizes[level].numpy())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AMwvIHLhnt6m", + "colab_type": "text" + }, + "source": [ + "### Monte-Carlo Convolutions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oRDxnD1in0jH", + "colab_type": "text" + }, + "source": [ + "Create convolutions for a point hierarchy with MLPs as kernel \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KuSf3jxwjVox", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "### create random input data\n", + "num_pts = 1000\n", + "point_dim = 3\n", + "feature_dim = 3\n", + "batch_size = 10\n", + "\n", + "# create random points\n", + "points = np.random.rand(num_pts,point_dim)\n", + "batch_ids = np.random.randint(0,batch_size,num_pts)\n", + "batch_ids[:batch_size] = np.arange(0,batch_size) # ensure non-empty point clouds\n", + "# create random features\n", + "features = np.random.rand(num_pts,feature_dim)\n", + "\n", + "# build initial point cloud\n", + "point_cloud = pc.PointCloud(points, batch_ids, batch_size)\n", + "\n", + "# build point hierarchy\n", + "sample_radii = np.array([[0.1],[0.2],[2]])\n", + "point_hierarchy = pc.PointHierarchy(point_cloud,sample_radii)\n", + "\n", + "### build model\n", + "\n", + "# layer parameters\n", + "conv_radii = 2*sample_radii\n", + "feature_sizes = [8,16,32]\n", + "kernel_hidden_size = 8 # number of neurons in the hidden layer of the kernel MLP\n", + "\n", + "### initialize layers\n", + "Conv1 = pc.layers.MCConv(feature_dim, feature_sizes[0], point_dim,kernel_hidden_size)\n", + "Conv2 = pc.layers.MCConv(feature_sizes[0],feature_sizes[1],point_dim,kernel_hidden_size)\n", + "Conv3 = pc.layers.MCConv(feature_sizes[1],feature_sizes[2],point_dim,kernel_hidden_size)\n", + "\n", + "### call layers\n", + "conv1_result = Conv1(features,point_hierarchy[0], point_hierarchy[1],conv_radii[0])\n", + "conv2_result = Conv2(conv1_result,point_hierarchy[1], point_hierarchy[2],conv_radii[1])\n", + "conv3_result = Conv3(conv2_result,point_hierarchy[2], point_hierarchy[3],conv_radii[2], return_sorted=True)\n", + "\n", + "### printing \n", + "print('### point cloud sizes ###')\n", + "sizes = point_hierarchy.get_sizes()\n", + "for s in sizes:\n", + " print(s.numpy())\n", + "\n", + "print('\\n### features dimensions flat ###')\n", + "print('Input: ');print(features.shape)\n", + "print('Conv1: ');print(conv1_result.shape)\n", + "print('Conv2: ');print(conv2_result.shape)\n", + "print('Conv3: ');print(conv3_result.shape)\n", + "\n", + "# again in padded format\n", + "point_hierarchy.set_batch_shape([5,2])\n", + "\n", + "unflatten = point_hierarchy[0].get_unflatten()\n", + "features_padded = unflatten(features)\n", + "### call layers\n", + "conv1_result_padded = Conv1(features_padded, point_hierarchy[0], point_hierarchy[1],conv_radii[0], return_padded=True)\n", + "conv2_result_padded = Conv2(conv1_result_padded, point_hierarchy[1], point_hierarchy[2],conv_radii[1], return_padded=True)\n", + "conv3_result_padded = Conv3(conv2_result_padded, point_hierarchy[2], point_hierarchy[3],conv_radii[2], return_padded=True)\n", + "print('\\n### feature dimensions padded ###')\n", + "print('Input: ');print(features_padded.shape)\n", + "print('Conv1: ');print(conv1_result_padded.shape)\n", + "print('Conv2: ');print(conv2_result_padded.shape)\n", + "print('Conv3: ');print(conv3_result_padded.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nUu8QpmNmosR", + "colab_type": "text" + }, + "source": [ + "### ResNet blocks and pooling layers" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GPQ_skO6kGyk", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "### create random input data\n", + "num_pts = 1000\n", + "point_dim = 3\n", + "feature_dim = 3\n", + "batch_size = 10\n", + "batch_shape = [5, 2]\n", + "\n", + "# create random points\n", + "points = np.random.rand(num_pts, point_dim)\n", + "batch_ids = np.random.randint(0, batch_size,num_pts)\n", + "batch_ids[:batch_size] = np.arange(0, batch_size) # ensure non-empty point clouds\n", + "# create random features\n", + "features = np.random.rand(num_pts,feature_dim)\n", + "\n", + "# build initial point cloud\n", + "point_cloud = pc.PointCloud(points, batch_ids, batch_size)\n", + "\n", + "# build point hierarchy\n", + "sample_radii = np.array([[0.1]])\n", + "point_hierarchy = pc.PointHierarchy(point_cloud,sample_radii)\n", + "\n", + "### build model\n", + "\n", + "# layer parameters\n", + "conv_radii = np.array([0.2, 0.5])\n", + "feature_sizes = [8]\n", + "num_resnet_blocks =4 # number of ResNet blocks, each block has 2 layers\n", + "layer_type = 'MCConv'\n", + "\n", + "### initialize layers\n", + "Conv = pc.layers.MCConv(feature_dim, feature_sizes[0], point_dim, kernel_hidden_size)\n", + "ResNet = pc.layers.PointResNet(feature_sizes[0], num_resnet_blocks, point_dim, layer_type)\n", + "Pool = pc.layers.GlobalMaxPooling()\n", + "\n", + "### call layers\n", + "conv_result = Conv(features, point_hierarchy[0], point_hierarchy[1], conv_radii[0])\n", + "resnet_result = ResNet(conv_result, point_hierarchy[1], conv_radii[1], training=True)\n", + "pool_result = Pool(resnet_result, point_hierarchy[1])\n", + "\n", + "### printing \n", + "print('### point cloud sizes ###')\n", + "sizes = point_hierarchy.get_sizes()\n", + "for s in sizes:\n", + " print(s.numpy())\n", + "\n", + "print('\\n### feature dimensions flat ###')\n", + "print('Input: ');print(features.shape)\n", + "print('Conv: ');print(conv_result.shape)\n", + "print('ResNet: ');print(resnet_result.shape)\n", + "print('GlobalPool: ');print(pool_result.shape)\n", + "\n", + "# again in padded format\n", + "\n", + "point_hierarchy.set_batch_shape(batch_shape)\n", + "unflatten = point_hierarchy[0].get_unflatten()\n", + "features_padded = unflatten(features)\n", + "### call layers\n", + "conv_result_padded = Conv1(features_padded,point_hierarchy[0], point_hierarchy[1],conv_radii[0], return_padded=True)\n", + "resnet_result_padded = ResNet(conv_result_padded, point_hierarchy[1], conv_radii[1], training=True, return_padded=True)\n", + "pool_result_padded = Pool(resnet_result_padded, point_hierarchy[1], return_padded=True)\n", + "print('\\n### feature dimensions padded ###')\n", + "print('Input: ');print(features_padded.shape)\n", + "print('Conv: ');print(conv_result_padded.shape)\n", + "print('ResNet: ');print(resnet_result_padded.shape)\n", + "print('GlobalPool: ');print(pool_result_padded.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jtVT9mRhrBTH", + "colab_type": "text" + }, + "source": [ + "### Optimizations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fM7o2JtO4WGz", + "colab_type": "text" + }, + "source": [ + "#### Reuse precomputed data structure" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SrLIU7Md67wm", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import tensorflow as tf \n", + "import pylib.pc as pc\n", + "import numpy as np\n", + "# method to create a random point cloud\n", + "from pylib.pc.tests.utils import _create_random_point_cloud_segmented\n", + "\n", + "# module for timing\n", + "import time\n", + "\n", + "\n", + "class conv_model():\n", + " \"\"\" A Monte-Carlo convolutional neural network (MCCNN) without downsampling,\n", + " just repeated convolutions\n", + " \"\"\"\n", + " def __init__(self, num_features, depth):\n", + " \"\"\" Constructor.\n", + "\n", + " Args:\n", + " num_features: `int`, the dimensionality of the features.\n", + " depth: `int` the number of layers.\n", + " \"\"\"\n", + " self.depth = depth\n", + " self.layers = [pc.layers.PointConv(num_features_in=num_features,\n", + " num_features_out=num_features,\n", + " num_dims=3,\n", + " size_hidden=8)\n", + " for i in range(depth)]\n", + " \n", + " def __call__(self, point_cloud, features, radius, neighborhood=None):\n", + " \"\"\" Evaluates the network.\n", + "\n", + " Args:\n", + " point_cloud: A `PointCloud` instance, with N points.\n", + " features: A `float` tensor of shape [N,C], where C is `num_features`\n", + " from constructor.\n", + " radius: An `float`, the radius of the convolution.\n", + " neighborhood: A `Neighborhood` instance for `point_cloud` with cell_size radius, \n", + " if `None` a neighborhood is computed internally.\n", + "\n", + " Returns:\n", + " A tensor of shape [N,C], the result of the convolutions.\n", + "\n", + " \"\"\"\n", + " for layer in self.layers:\n", + " features = layer(features, point_cloud, point_cloud, radius=radius, neighborhood=neighborhood)\n", + " return features\n", + "\n", + "\n", + "\n", + "def time_networks(num_points, radius):\n", + " if num_points >= 1e6:\n", + " print('\\n### number of points %sM ###'%(num_points//1000000))\n", + " else:\n", + " print('\\n### number of points %sk ###'%(num_points//1000))\n", + " \n", + " # hyper parameters\n", + " batch_size = 32\n", + " num_features = 8\n", + " depth = 10\n", + "\n", + " # initialize the network \n", + " conv = conv_model(num_features, depth)\n", + "\n", + " # create random input\n", + " points, batch_ids = _create_random_point_cloud_segmented(batch_size, num_points, dimension=3)\n", + " features = np.random.rand(num_points, num_features)\n", + " point_cloud = pc.PointCloud(points, batch_ids)\n", + "\n", + " #### execute without precomupted neighborhood\n", + " t1 = time.time()\n", + " conv_result_1 = MCCNN(point_cloud, features, radius)\n", + " t2 = time.time()\n", + "\n", + " print('recompute neighbors: %.3fs'%(t2-t1))\n", + "\n", + " # precompute neighborhood\n", + " cell_sizes = [radius, radius, radius]\n", + " grid = pc.Grid(point_cloud, cell_sizes)\n", + " neighborhood = pc.Neighborhood(grid, cell_sizes)\n", + "\n", + " # execute with precomputed neighborhood\n", + " t1 = time.time()\n", + " conv_result_2 = conv(point_cloud, features, radius, neighborhood)\n", + " t2 = time.time()\n", + " print('reuse neighbors: %.3fs'%(t2-t1))\n", + "\n", + "time_networks(10000, 0.1)\n", + "time_networks(100000, 0.1)\n", + "time_networks(1000000, 0.01)\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wmCB8SPdOIs3", + "colab_type": "text" + }, + "source": [ + "#### Transpose Neighborhoods" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7FMdV4oH66gh", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from pylib.pc.tests.utils import _create_random_point_cloud_segmented\n", + "from pylib.pc import PointCloud, Grid, Neighborhood\n", + "import numpy as np\n", + "import time\n", + "\n", + "def time_transpose_neighborhood(num_points, batch_size=8, num_reps=10):\n", + " if num_points >= 1e6:\n", + " print('\\n### number of points %sM ###'%(num_points//1000000))\n", + " else:\n", + " print('\\n### number of points %sk ###'%(num_points//1000))\n", + " num_poins = num_points\n", + " num_centers = max(num_points // 100, batch_size)\n", + " radius = 0.1\n", + " \n", + " nbs = []\n", + " point_clouds = []\n", + " point_clouds_centers = []\n", + " for i in range(num_reps):\n", + " points, batch_ids = _create_random_point_cloud_segmented(batch_size, num_points)\n", + " point_cloud = PointCloud(points, batch_ids)\n", + " points_centers, batch_ids_centers = _create_random_point_cloud_segmented(batch_size, num_centers)\n", + " point_cloud_centers = PointCloud(points_centers, batch_ids_centers)\n", + " grid = Grid(point_cloud, radius)\n", + "\n", + " point_clouds.append(point_cloud)\n", + " point_clouds_centers.append(point_cloud_centers)\n", + " nbs.append(Neighborhood(grid, radius, point_cloud_centers))\n", + "\n", + " t1 = time.time()\n", + " for i in range(num_reps):\n", + " grid_centers = Grid(point_clouds_centers[i], radius)\n", + " nb_t1 = Neighborhood(grid_centers, radius, point_clouds[i])\n", + " t2 = time.time()\n", + " t = 1000*(t2-t1)/num_reps\n", + " print('compute new neighborhood: %.1fms'%(t))\n", + "\n", + " t3 = time.time()\n", + " for i in range(num_reps):\n", + " nb_t2 = nbs[i].transpose()\n", + " t4 = time.time()\n", + " t = 1000*(t4-t3)/num_reps\n", + " print('transpose existing neighborhood: %.1fms'%(t))\n", + "\n", + "time_transpose_neighborhood(1000, num_reps=100)\n", + "time_transpose_neighborhood(10000, num_reps=100)\n", + "time_transpose_neighborhood(100000, num_reps=10)\n", + "time_transpose_neighborhood(1000000, num_reps=10)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vbsy9koKT8NS", + "colab_type": "text" + }, + "source": [ + "#### 1x1 Convolutions" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wi2jKASzkhkd", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import tensorflow as tf\n", + "import plib.pc as pc\n", + "import numpy as np\n", + "# timing utilities\n", + "import time\n", + "# function to create a random point cloud\n", + "from plib.pc.tests.utils import _create_random_point_cloud_segmented\n", + "# custom gpu and tensorflow implementation of compute_pdf\n", + "from pylib.pc.layers import Conv1x1, MCConv\n", + "\n", + "\n", + "def time_1x1_conv(num_points,\n", + " batch_size,\n", + " num_feat,\n", + " radius,\n", + " hidden_size,\n", + " dimension):\n", + " # create random point cloud\n", + " points, batch_ids = _create_random_point_cloud_segmented(\n", + " batch_size, num_points, dimension=dimension)\n", + " point_cloud = pc.PointCloud(points, batch_ids)\n", + " features = np.random.rand(num_points, num_feat[0])\n", + " # build conv_layers\n", + " conv1x1 = Conv1x1(num_feat[0], num_feat[1])\n", + " mcconv = MCConv(num_feat[0], num_feat[1], dimension, hidden_size)\n", + " # build 1x1 neighborhood\n", + " cell_sizes = np.float32(np.repeat(radius, dimension))\n", + " grid = pc.Grid(point_cloud, cell_sizes)\n", + " neighborhood = pc.Neighborhood(grid, radius)\n", + " # is1x1 = neighborhood._neighbors.shape[0] == num_points\n", + " # print('\\nMCConv is 1x1: ', is1x1)\n", + " \n", + " # compute conv\n", + " t1 = time.time()\n", + " neighbors = tf.stack((tf.range(0, num_points), tf.range(0, num_points)), axis=1)\n", + " _ = mcconv(features, point_cloud, point_cloud, radius, neighborhood)\n", + " t2 = time.time()\n", + " _ = conv1x1(features, point_cloud)\n", + " t3 = time.time()\n", + "\n", + " # printing\n", + " if num_points >= 1e6: \n", + " print('num points: %sM, feature dim: %s -> %s'%(num_points//int(1e6), num_feat[0], num_feat[1]))\n", + " elif num_points >= 1e3:\n", + " print('num points: %sk, feature dim: %s -> %s'%(num_points//int(1e3), num_feat[0], num_feat[1]))\n", + " else:\n", + " print('\\nnum points: %s'%num_points)\n", + " \n", + " num_p_mc = (1 + num_feat[0]) * hidden_size + hidden_size * num_feat[0] * num_feat[1]\n", + " num_p_1x1 = num_feat[0] * num_feat[1]\n", + " print('num params: %10d, time MCCNN: %.4fs'%(num_p_mc, t2-t1))\n", + " print('num params: %10d, time Conv1x1: %.4fs\\n'%(num_p_1x1, t3-t2))\n", + "\n", + "\n", + "\n", + "time_1x1_conv(10000, 32, [1, 1], 1e-3, 8, 3)\n", + "time_1x1_conv(10000, 32, [64, 64], 1e-3, 8, 3)\n", + "time_1x1_conv(10000, 32, [1, 2048], 1e-3, 8, 3)\n", + "time_1x1_conv(10000, 32, [2048, 1], 1e-3, 8, 3)\n", + "time_1x1_conv(10000, 32, [2048, 2048], 1e-3, 8, 3)" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/ModelNet40.ipynb b/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/ModelNet40.ipynb new file mode 100644 index 000000000..55ff365f6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/notebooks/ModelNet40.ipynb @@ -0,0 +1,763 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "ModelNet40_v1.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "-2xKG5Pgu_SX", + "colab_type": "text" + }, + "source": [ + "##### Copyright 2020 Google LLC." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-RkUBOZbu3iz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "31faWJ7pCNGR" + }, + "source": [ + "# Point Clouds for tensorflow_graphics\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wo1bELqqRAKF", + "colab_type": "text" + }, + "source": [ + "## Initialization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YqSeyzzZQUDV", + "colab_type": "text" + }, + "source": [ + "\n", + "### Clone repositories, install requirements and custom_op package" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_vtvcRP3QtTl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Clone repositories\n", + "!rm -r graphics\n", + "!git clone https://github.com/schellmi42/graphics\n", + "\n", + "# install requirements and load tfg module \n", + "!pip install -r graphics/requirements.txt\n", + "\n", + "# install custom ops\n", + "!pip install graphics/tensorflow_graphics/projects/point_convolutions/custom_ops/pkg_builds/tf_2.2.0/*.whl\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9D-q8bUu7TcJ", + "colab_type": "text" + }, + "source": [ + "### Load modules" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5lTxNysx7Lfz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import sys\n", + "# (this is equivalent to export PYTHONPATH='$HOME/graphics:/content/graphics:$PYTHONPATH', but adds path to running session)\n", + "sys.path.append(\"/content/graphics\")\n", + "\n", + "# load point cloud module \n", + "# (this is equivalent to export PYTHONPATH='/content/graphics/tensorflow_graphics/projects/point_convolutions:$PYTHONPATH', but adds path to running session)\n", + "sys.path.append(\"/content/graphics/tensorflow_graphics/projects/point_convolutions\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-gUTDsKERxWh", + "colab_type": "text" + }, + "source": [ + "Check if it loads without errors" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z_49mMLOSOX6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import tensorflow as tf\n", + "import tensorflow_graphics as tfg\n", + "import pylib.pc as pc\n", + "import numpy as np\n", + "\n", + "print('TensorFlow version: %s'%tf.__version__)\n", + "print('TensorFlow-Graphics version: %s'%tfg.__version__)\n", + "print('Point Cloud Module: ', pc)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1_XXAVBau-gg", + "colab_type": "text" + }, + "source": [ + "## Classification on ModelNet40" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dVQV3Ykr16m8", + "colab_type": "text" + }, + "source": [ + "### Data preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WIXyqlmBv5q1", + "colab_type": "text" + }, + "source": [ + "First we load the data consisting of 10k points per model." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GW4GZdArv-3g", + "colab_type": "code", + "colab": {} + }, + "source": [ + "!wget --no-check-certificate https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip \n", + "!echo '---unzipping---'\n", + "!unzip -q modelnet40_normal_resampled.zip \n", + "!echo '[done]'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qd2ElOdHwBnW", + "colab_type": "text" + }, + "source": [ + "Next we load the data, using the input function in the `io` module.\n", + "\n", + "To speed up this tutorial, we only load a subset of the points per model.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Bnt1Byd4sWHi", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import tensorflow as tf \n", + "import pylib.pc as pc\n", + "import pylib.io as io\n", + "import numpy as np\n", + "import tensorflow_graphics\n", + "import os, time\n", + "\n", + "\n", + "quick_test = False # only load 100 models\n", + "\n", + "# -- loading data ---\n", + "\n", + "data_dir = 'modelnet40_normal_resampled/'\n", + "num_classes = 40 # modelnet 10 or 40\n", + "points_per_file = 5000 # number of points loaded per model\n", + "\n", + "# load category names\n", + "category_names = []\n", + "with open(data_dir + f'modelnet{num_classes}_shape_names.txt') as inFile:\n", + " for line in inFile:\n", + " category_names.append(line.replace('\\n', ''))\n", + "\n", + "# load names of training files\n", + "train_set = []\n", + "train_labels = []\n", + "with open(data_dir + f'modelnet{num_classes}_train.txt') as inFile:\n", + " for line in inFile:\n", + " line = line.replace('\\n', '')\n", + " category = line[:-5]\n", + " train_set.append(data_dir + category + '/' + line + '.txt')\n", + " if category not in category_names:\n", + " raise ValueError('Unknown category ' + category)\n", + " train_labels.append(category_names.index(category))\n", + "\n", + "# load names of test files\n", + "test_set = []\n", + "test_labels = []\n", + "with open(data_dir + f'modelnet{num_classes}_test.txt') as inFile:\n", + " for line in inFile:\n", + " line = line.replace('\\n', '')\n", + " category = line[:-5]\n", + " test_set.append(data_dir + category + '/' + line + '.txt')\n", + " if category not in category_names:\n", + " raise ValueError('Unknown category ' + category)\n", + " test_labels.append(category_names.index(category))\n", + "\n", + "# load training data\n", + "train_data_points = np.empty([len(train_set), points_per_file, 3])\n", + "\n", + "print(f'### loading modelnet{num_classes} train ###')\n", + "for i, filename in enumerate(train_set):\n", + " points, _ = \\\n", + " io.load_points_from_file_to_numpy(filename,\n", + " max_num_points=points_per_file)\n", + " train_data_points[i] = points\n", + " if i % 500 == 0:\n", + " print(f'{i}/{len(train_set)}')\n", + " if quick_test and i > 100:\n", + " break\n", + "\n", + "# load test data\n", + "test_data_points = np.empty([len(test_set), points_per_file, 3])\n", + "\n", + "print(f'### loading modelnet{num_classes} test ###')\n", + "for i, filename in enumerate(test_set):\n", + " points, _ = \\\n", + " io.load_points_from_file_to_numpy(filename,\n", + " max_num_points=points_per_file)\n", + " test_data_points[i] = points\n", + " if i % 500 == 0:\n", + " print(f'{i}/{len(test_set)}')\n", + " if quick_test and i > 100:\n", + " break" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W7a1wLiPxCb3", + "colab_type": "text" + }, + "source": [ + "Now let's define a small data loader.\n", + "\n", + "To make the network evaluation faster, we randomly samples the point clouds to reduce the input size.\n", + "\n", + "As we don't want to provide any additional features other than the point location to the network, we will use a constant `1` as input feature.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NPJ9chpWxHNH", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class modelnet_data_generator(tf.keras.utils.Sequence):\n", + " ''' Small generator of batched data.\n", + " '''\n", + " def __init__(self,\n", + " points,\n", + " labels,\n", + " batch_size):\n", + " self.points = points\n", + " self.labels = np.array(labels, dtype=int)\n", + " self.batch_size = batch_size\n", + " self.epoch_size = len(self.points)\n", + "\n", + " self.ids = np.arange(0, points_per_file)\n", + " # shuffle data before training\n", + " self.on_epoch_end()\n", + "\n", + " def __len__(self):\n", + " # number of batches per epoch\n", + " return(int(np.floor(self.epoch_size / self.batch_size)))\n", + "\n", + " def __call__(self):\n", + " ''' Loads batch and increases batch index.\n", + " '''\n", + " data = self.__getitem__(self.index)\n", + " self.index += 1\n", + " return data\n", + "\n", + " def __getitem__(self, index, samples_per_model=1024):\n", + " ''' Loads data of current batch and samples random subset of the points.\n", + " '''\n", + " labels = \\\n", + " self.labels[index * self.batch_size:(index + 1) * self.batch_size]\n", + " points = \\\n", + " self.points[index * self.batch_size:(index + 1) * self.batch_size]\n", + " # constant input feature\n", + " features = tf.ones([self.batch_size, samples_per_model, 1])\n", + "\n", + " # sample points\n", + " sampled_points = np.empty([self.batch_size, samples_per_model, 3])\n", + " for batch in range(self.batch_size):\n", + " selection = np.random.choice(self.ids, samples_per_model)\n", + " sampled_points[batch] = points[batch][selection]\n", + "\n", + " return sampled_points, features, labels\n", + "\n", + " def on_epoch_end(self):\n", + " ''' Shuffles data and resets batch index.\n", + " '''\n", + " shuffle = np.random.permutation(np.arange(0, len(self.points)))\n", + " self.points = self.points[shuffle]\n", + " self.labels = self.labels[shuffle]\n", + " self.index = 0" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "txT-ddp_wTd0", + "colab_type": "text" + }, + "source": [ + "### Network architecture\n", + "\n", + "Let's build a simple classification network, which uses point convolutions for encoding the shape, and two dense layers for predicting the class.\n", + "\n", + "The following model contains example calls for the three different available point convolutions in the `layer` module:\n", + "\n", + "\n", + "* [Monte-Carlo convolutions](https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.inst.100/institut/Papers/viscom/2018/hermosilla2018montecarlo.pdf), which uses MLPs for representing the convolutional kernel, and aggregates the features inside the convolution radius using [Monte-Carlo integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration), where each feature is weighted by a point density estimation.\n", + "* [Kernel Point convolutions](https://arxiv.org/pdf/1904.08889.pdf), where the convolutional kernel is represented by a set of weights on kernel points, which are interpolated. \n", + "(Note: We use rigid kernel points in the example below but deformable kernel points are also supported)\n", + "\n", + "* [PointConv convolutions](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.pdf)\n", + ", which uses a single MLP for representing the convolutional kernel, and aggregates the features using an integration, where each feature is weighted by a learned inverse density estimation.\n", + "\n", + "\n", + "Note that different to an image convolution layer, a point convolution layer needs additional input about the spatial location of the features, i.e. point coordinates.\n", + "In case of a 'strided' point convolution, where the output features are defined on different points than the input, we have to provide two point clouds.\n", + "\n", + "For sampling the point clouds to lower densities, we use the `PointHierarchy` class.\n", + "\n", + "At the end of the encoder we use a `GlobalAveragePooling` layer to aggregate the features from all points into one latent vector. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sGC3q1drt60i", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from pylib.pc import layers\n", + "\n", + "class mymodel(tf.keras.Model):\n", + " ''' Model architecture with `L` convolutional layers followed by \n", + " two dense layers.\n", + "\n", + " Args:\n", + " features_sizes: A `list` of `ints`, the feature dimensions. Shape `[L+3]`.\n", + " sample_radii: A `list` of `floats, the radii used for sampling\n", + " of the point clouds. Shape `[L]`.\n", + " conv_radii: A `list` of `floats`, the radii used by the convolution\n", + " layers. Shape `[L]`.\n", + " layer_type: A `string`, the type of convolution used,\n", + " can be 'MCConv', 'KPConv', 'PointConv'.\n", + " sampling_method: 'poisson disk' or 'cell average'.\n", + " '''\n", + "\n", + " def __init__(self,\n", + " feature_sizes,\n", + " sample_radii,\n", + " conv_radii,\n", + " layer_type='MCConv',\n", + " sampling_method='poisson disk'):\n", + " super(mymodel, self).__init__()\n", + " self.num_layers = len(sample_radii)\n", + " self.sample_radii = sample_radii.reshape(-1,1)\n", + " self.conv_radii = conv_radii\n", + " self.sampling_method = sampling_method\n", + " self.conv_layers = []\n", + " self.batch_layers = []\n", + " self.dense_layers = []\n", + " self.activations = []\n", + " # encoder\n", + " for i in range(self.num_layers):\n", + " # convolutional downsampling layers\n", + " if layer_type == 'MCConv':\n", + " self.conv_layers.append(layers.MCConv(\n", + " num_features_in=feature_sizes[i],\n", + " num_features_out=feature_sizes[i + 1],\n", + " num_dims=3,\n", + " num_mlps=4,\n", + " mlp_size=[8]))\n", + " elif layer_type == 'PointConv':\n", + " self.conv_layers.append(layers.PointConv(\n", + " num_features_in=feature_sizes[i],\n", + " num_features_out=feature_sizes[i + 1],\n", + " num_dims=3,\n", + " size_hidden=32))\n", + " elif layer_type == 'KPConv':\n", + " self.conv_layers.append(layers.KPConv(\n", + " num_features_in=feature_sizes[i],\n", + " num_features_out=feature_sizes[i + 1],\n", + " num_dims=3,\n", + " num_kernel_points=15))\n", + " else:\n", + " raise ValueError(\"Unknown layer type!\")\n", + " if i < self.num_layers-1:\n", + " # batch normalization and activation function\n", + " self.batch_layers.append(tf.keras.layers.BatchNormalization())\n", + " self.activations.append(tf.keras.layers.LeakyReLU())\n", + " # global pooling\n", + " self.global_pooling = layers.GlobalAveragePooling()\n", + " self.batch_layers.append(tf.keras.layers.BatchNormalization())\n", + " self.activations.append(tf.keras.layers.LeakyReLU())\n", + " # MLP\n", + " self.dense_layers.append(tf.keras.layers.Dense(feature_sizes[-2]))\n", + " self.batch_layers.append(tf.keras.layers.BatchNormalization())\n", + " self.activations.append(tf.keras.layers.LeakyReLU())\n", + " self.dense_layers.append(tf.keras.layers.Dense(feature_sizes[-1]))\n", + "\n", + " def __call__(self,\n", + " points,\n", + " features,\n", + " training):\n", + " ''' Evaluates network.\n", + "\n", + " Args:\n", + " points: The point coordinates. Shape `[B, N, 3]`.\n", + " features: Input features. Shape `[B, N, C]`.\n", + " training: A `bool`, passed to the batch norm layers.\n", + "\n", + " Returns:\n", + " The logits per class.\n", + " '''\n", + " sample_radii = self.sample_radii\n", + " conv_radii = self.conv_radii\n", + " sampling_method = self.sampling_method\n", + " # input point cloud\n", + " # Note: Here all point clouds have the same number of points, so no `sizes`\n", + " # or `batch_ids` are passed.\n", + " point_cloud = pc.PointCloud(points)\n", + " # spatial downsampling\n", + " point_hierarchy = pc.PointHierarchy(point_cloud,\n", + " sample_radii,\n", + " sampling_method)\n", + " # network evaluation\n", + " for i in range(self.num_layers):\n", + " features = self.conv_layers[i](features,\n", + " point_hierarchy[i], \n", + " point_hierarchy[i+1],\n", + " conv_radii[i])\n", + " if i < self.num_layers-1:\n", + " features = self.batch_layers[i](features, training=training)\n", + " features = self.activations[i](features)\n", + " # classification head\n", + " features = self.global_pooling(features, point_hierarchy[-1])\n", + " features = self.batch_layers[-2](features, training)\n", + " features = self.activations[-2](features)\n", + " features = self.dense_layers[-2](features)\n", + " features = self.batch_layers[-1](features, training)\n", + " features = self.activations[-1](features)\n", + " return self.dense_layers[-1](features)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WureY8ZixvlG", + "colab_type": "text" + }, + "source": [ + "### Model parameters" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U0pnDkbZxpxP", + "colab_type": "code", + "colab": {} + }, + "source": [ + "batch_size = 16\n", + "\n", + "feature_sizes = [1, 128, 256, 512, 128, num_classes]\n", + "sample_radii = np.array([0.1, 0.2, 0.4])\n", + "conv_radii = sample_radii * 1.5\n", + "\n", + "# initialize data generators\n", + "gen_train = modelnet_data_generator(train_data_points, train_labels, batch_size)\n", + "gen_test = modelnet_data_generator(test_data_points, test_labels, batch_size)\n", + "\n", + "# loss function and optimizer\n", + "loss_function = tf.keras.losses.SparseCategoricalCrossentropy()\n", + "\n", + "lr_decay=tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=0.01,\n", + " decay_steps=len(gen_train),\n", + " decay_rate=0.95)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_decay)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AJjR2m9Fx68-", + "colab_type": "text" + }, + "source": [ + "### Training Loop" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AzolzewtyDcU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def training(model,\n", + " optimizer,\n", + " loss_function,\n", + " num_epochs = 10,\n", + " epochs_print=1):\n", + " train_loss_results = []\n", + " train_accuracy_results = []\n", + " test_loss_results = []\n", + " test_accuracy_results = []\n", + "\n", + " for epoch in range(num_epochs):\n", + " time_epoch_start = time.time()\n", + "\n", + " # --- Training ---\n", + " epoch_loss_avg = tf.keras.metrics.Mean()\n", + " epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n", + "\n", + " for points, features, labels in gen_train:\n", + " # evaluate model; forward pass\n", + " with tf.GradientTape() as tape:\n", + " logits = model(points, features, training=True)\n", + " pred = tf.nn.softmax(logits, axis=-1)\n", + " loss = loss_function(y_true=labels, y_pred=pred)\n", + " # backpropagation\n", + " grads = tape.gradient(loss, model.trainable_variables)\n", + " optimizer.apply_gradients(zip(grads, model.trainable_variables))\n", + "\n", + " epoch_loss_avg.update_state(loss)\n", + " epoch_accuracy.update_state(labels, pred)\n", + "\n", + " train_loss_results.append(epoch_loss_avg.result())\n", + " train_accuracy_results.append(epoch_accuracy.result())\n", + "\n", + " # --- Validation ---\n", + " epoch_loss_avg = tf.keras.metrics.Mean()\n", + " epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n", + "\n", + " for points, features, labels in gen_test:\n", + " # evaluate model; forward pass\n", + " logits = model(points, features, training=False)\n", + " pred = tf.nn.softmax(logits, axis=-1)\n", + " loss = loss_function(y_true=labels, y_pred=pred)\n", + "\n", + " epoch_loss_avg.update_state(loss)\n", + " epoch_accuracy.update_state(labels, pred)\n", + "\n", + " test_loss_results.append(epoch_loss_avg.result())\n", + " test_accuracy_results.append(epoch_accuracy.result())\n", + "\n", + " time_epoch_end = time.time()\n", + "\n", + " if epoch % epochs_print == 0:\n", + " # End epoch\n", + " print('Epoch {:03d} Time: {:.3f}s'.format(\n", + " epoch,\n", + " time_epoch_end - time_epoch_start))\n", + " print('Training: Loss: {:.3f}, Accuracy: {:.3%}'.format(\n", + " train_loss_results[-1],\n", + " train_accuracy_results[-1]))\n", + " print('Validation: Loss: {:.3f}, Accuracy: {:.3%}'.format(\n", + " test_loss_results[-1],\n", + " test_accuracy_results[-1]))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CIALtH8TzagI", + "colab_type": "text" + }, + "source": [ + "#### Train with [Monte-Carlo convolutions](https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.inst.100/institut/Papers/viscom/2018/hermosilla2018montecarlo.pdf)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "e-jbnyGaCrfE", + "colab": {} + }, + "source": [ + "model_MC = mymodel(feature_sizes, sample_radii, conv_radii,\n", + " layer_type='MCConv')\n", + "training(model_MC, optimizer, loss_function)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zY-6sJnRzXvl", + "colab_type": "text" + }, + "source": [ + "#### Train with [Kernel Point convolutions](https://arxiv.org/pdf/1904.08889.pdf).\n", + "\n", + "To use the cell average sampling used in the paper, we can simply change the sampling method, which is passed to the point hierarchy constructor." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "n03SZUTgzYoc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model_KP = mymodel(feature_sizes, sample_radii, conv_radii,\n", + " layer_type='KPConv', sampling_method='cell average')\n", + "training(model_KP, optimizer, loss_function)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ak7hMQeWzZZq", + "colab_type": "text" + }, + "source": [ + "#### Train with [PointConv convolutions](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.pdf)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wj9T-UaCzZ4z", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model_PC = mymodel(feature_sizes, sample_radii, conv_radii,\n", + " layer_type='PointConv')\n", + "training(model_PC, optimizer, loss_function)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "njP1uIRCjXJT", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/0.txt b/tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/0.txt new file mode 100644 index 000000000..6dabf1dd0 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/0.txt @@ -0,0 +1,18998 @@ +-0.083649247464726,-0.14213452833909343,0.03338483261245226,-0.31205958811763945,-0.782654321345465,0.5385824233514485 +0.11609095745694237,-0.24775122298596494,0.09175559371060857,-0.01531397108305076,-0.5398166648189617,0.8416433037061486 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tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/clang/bin/crosstool_wrapper_driver_is_not_gcc create mode 100644 tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/.bazelrc b/tensorflow_graphics/projects/point_convolutions/custom_ops/.bazelrc new file mode 100644 index 000000000..f5beb064c --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/.bazelrc @@ -0,0 +1,18 @@ +build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true +build:manylinux2010cuda100 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0:toolchain +build:manylinux2010cuda101 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1:toolchain +build --spawn_strategy=standalone +build --strategy=Genrule=standalone +build -c opt +build --action_env TF_HEADER_DIR="/usr/local/lib/python3.6/dist-packages/tensorflow/include" +build --action_env TF_SHARED_LIBRARY_DIR="/usr/local/lib/python3.6/dist-packages/tensorflow" +build --action_env TF_SHARED_LIBRARY_NAME="libtensorflow_framework.so.2" +build --action_env TF_NEED_CUDA="1" +build --action_env TF_CUDA_VERSION="10.1" +build --action_env TF_CUDNN_VERSION="7" +build --action_env CUDNN_INSTALL_PATH="/usr/lib/x86_64-linux-gnu" +build --action_env CUDA_TOOLKIT_PATH="/usr/local/cuda" +build --config=cuda +test --config=cuda +build --config=manylinux2010cuda101 +test --config=manylinux2010cuda101 diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/BUILD new file mode 100644 index 000000000..138cdf9e7 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/BUILD @@ -0,0 +1,10 @@ +sh_binary( + name = "build_pip_pkg", + srcs = ["build_pip_pkg.sh"], + data = [ + "LICENSE", + "MANIFEST.in", + "setup.py", + "//tfg_custom_ops:tfg_custom_ops_py", + ], +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/LICENSE b/tensorflow_graphics/projects/point_convolutions/custom_ops/LICENSE new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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/dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/MANIFEST.in @@ -0,0 +1 @@ +recursive-include tfg_custom_ops *.so \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/README.md b/tensorflow_graphics/projects/point_convolutions/custom_ops/README.md new file mode 100644 index 000000000..3ea072fc6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/README.md @@ -0,0 +1,384 @@ +# TFG-PointCloud Custom Ops + +This repository is based on the [TensorFlow-custom-op](https://github.com/tensorflow/custom-op) repository, +for a detailed guide about how to add an op we refer to this template. + +## Setting up the environment +The c++-toolchains are dependent on the latest tensorflow-custom-op docker container, to set it up run +(tested with `tensorflow/tensorflow:2.2.0-custom-op-gpu-ubuntu16`) +```bash + docker pull tensorflow/tensorflow:custom-op-gpu-ubuntu16 + sudo docker run --gpus all --privileged -it -v ${PWD}:/working_dir -w /working_dir tensorflow/tensorflow:custom-op-gpu-ubuntu16 + root@docker: ./configure.sh +``` + +and answer the first question with yes `y`, and specify the TensorFlow version you want to build for. +(tested with TF version `2.1`, `2.2`, `2.3`) + +## Building the PIP-Package + +To compile the pip package run +``` bash + bazel build build_pip_pkg + bazel-bin/build_pip_pkg artifacts +``` + +The package `.whl` is located in `artifacts/`, by default it should be a python3 package. +(tested with `python3.6`) + +To install the package via pip run +```bash + pip3 install artifacts/*.whl +``` + +To test out the package run +```bash + cd .. + python3 -c 'import tfg_custom_ops' +``` +## Additional Information + +You may use this software under the +[Apache 2.0 License](https://github.com/schellmi42/tensorflow_graphics_point_clouds/blob/master/LICENSE). + + diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/WORKSPACE 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file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/build_pip_pkg.sh b/tensorflow_graphics/projects/point_convolutions/custom_ops/build_pip_pkg.sh new file mode 100755 index 000000000..bdad26539 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/build_pip_pkg.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +set -e +set -x + +PLATFORM="$(uname -s | tr 'A-Z' 'a-z')" +function is_windows() { + if [[ "${PLATFORM}" =~ (cygwin|mingw32|mingw64|msys)_nt* ]]; then + true + else + false + fi +} + +if is_windows; then + PIP_FILE_PREFIX="bazel-bin/build_pip_pkg.exe.runfiles/__main__/" +else + PIP_FILE_PREFIX="bazel-bin/build_pip_pkg.runfiles/__main__/" +fi + +function main() { + while [[ ! -z "${1}" ]]; do + if [[ ${1} == "make" ]]; then + echo "Using Makefile to build pip package." + PIP_FILE_PREFIX="" + else + DEST=${1} + fi + shift + done + + if [[ -z ${DEST} ]]; then + echo "No destination dir provided" + exit 1 + fi + + # Create the directory, then do dirname on a non-existent file inside it to + # give us an absolute paths with tilde characters resolved to the destination + # directory. + mkdir -p ${DEST} + if [[ ${PLATFORM} == "darwin" ]]; then + DEST=$(pwd -P)/${DEST} + else + DEST=$(readlink -f "${DEST}") + fi + echo "=== destination directory: ${DEST}" + + TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX) + + echo $(date) : "=== Using tmpdir: ${TMPDIR}" + + echo "=== Copy TensorFlow Custom op files" + + cp ${PIP_FILE_PREFIX}setup.py "${TMPDIR}" + cp ${PIP_FILE_PREFIX}MANIFEST.in "${TMPDIR}" + cp ${PIP_FILE_PREFIX}LICENSE "${TMPDIR}" + rsync -avm -L --exclude='*_test.py' ${PIP_FILE_PREFIX}tfg_custom_ops "${TMPDIR}" + + pushd ${TMPDIR} + echo $(date) : "=== Building wheel" + + python3 setup.py bdist_wheel > /dev/null + + cp dist/*.whl "${DEST}" + popd + rm -rf ${TMPDIR} + echo $(date) : "=== Output wheel file is in: ${DEST}" +} + +main "$@" diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/compile_with_bazel.sh b/tensorflow_graphics/projects/point_convolutions/custom_ops/compile_with_bazel.sh new file mode 100644 index 000000000..369c6031f --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/compile_with_bazel.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# to test the compilation of a single op +if ['$2' == 'clean']; then + bazel clean +fi +bazel build 'tfg_custom_ops/'$1":python/ops/_"$1"_ops.so" --verbose_failures +bazel test 'tfg_custom_ops/'$1:$1'_ops_py_test' \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/configure.sh b/tensorflow_graphics/projects/point_convolutions/custom_ops/configure.sh new file mode 100755 index 000000000..15011fcd7 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/configure.sh @@ -0,0 +1,198 @@ +#!/bin/bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +PLATFORM="$(uname -s | tr 'A-Z' 'a-z')" +PIP="pip3" + +function write_to_bazelrc() { + echo "$1" >> .bazelrc +} + +function write_action_env_to_bazelrc() { + write_to_bazelrc "build --action_env $1=\"$2\"" +} + +function is_linux() { + [[ "${PLATFORM}" == "linux" ]] +} + +function is_macos() { + [[ "${PLATFORM}" == "darwin" ]] +} + +function is_windows() { + # On windows, the shell script is actually running in msys + [[ "${PLATFORM}" =~ msys_nt*|mingw*|cygwin*|uwin* ]] +} + +function is_ppc64le() { + [[ "$(uname -m)" == "ppc64le" ]] +} + + +# Remove .bazelrc if it already exist +[ -e .bazelrc ] && rm .bazelrc + +# Check if we are building GPU or CPU ops, default CPU +while [[ "$TF_NEED_CUDA" == "" ]]; do + read -p "Do you want to build ops again TensorFlow CPU pip package?"\ +" Y or enter for CPU (tensorflow), N for GPU (tensorflow-gpu). [Y/n] " INPUT + case $INPUT in + [Yy]* ) echo "Build with CPU pip package."; TF_NEED_CUDA=0;; + [Nn]* ) echo "Build with GPU pip package."; TF_NEED_CUDA=1;; + "" ) echo "Build with CPU pip package."; TF_NEED_CUDA=0;; + * ) echo "Invalid selection: " $INPUT;; + esac +done + +# Check if we are building against manylinux1 or manylinux2010 pip package, +# default manylinux2010 +if is_windows; then + echo "On windows, skipping toolchain flags.." + PIP_MANYLINUX2010=0 +else + while [[ "$PIP_MANYLINUX2010" == "" ]]; do + read -p "Does the pip package have tag manylinux2010 (usually the case for nightly release after Aug 1, 2019, or official releases past 1.14.0)?. Y or enter for manylinux2010, N for manylinux1. [Y/n] " INPUT + case $INPUT in + [Yy]* ) PIP_MANYLINUX2010=1;; + [Nn]* ) PIP_MANYLINUX2010=0;; + "" ) PIP_MANYLINUX2010=1;; + * ) echo "Invalid selection: " $INPUT;; + esac + done +fi + +while [[ "$TF_VERSION" == "" ]] || [[ "$TF_CUDA_VERSION" == "" ]]; do + read -p "Specify the version of tensorflow used to compile (e.g 2.2)" INPUT + case $INPUT in + 2.0 ) echo "Build against TensorFlow 2.0."; TF_CUDA_VERSION=10.0; TF_VERSION=2.0.0;; + 2.1 ) echo "Build against TensorFlow 2.1."; TF_CUDA_VERSION=10.1; TF_VERSION=2.1.0;; + 2.2 ) echo "Build against TensorFlow 2.2."; TF_CUDA_VERSION=10.1; TF_VERSION=2.2.0;; + 2.3 ) echo "Build against TensorFlow 2.3."; TF_CUDA_VERSION=10.1; TF_VERSION=2.3.0;; + * ) echo "Invalid selection: " $INPUT;; + esac +done + +# CPU +if [[ "$TF_NEED_CUDA" == "0" ]]; then + + # Check if it's installed + if [[ $(${PIP} show tensorflow) == *tensorflow* ]] || [[ $(${PIP} show tf-nightly) == *tf-nightly* ]] ; then + echo 'Using installed tensorflow' + else + # Uninstall GPU version if it is installed. + if [[ $(${PIP} show tensorflow-gpu) == *tensorflow-gpu* ]]; then + echo 'Already have gpu version of tensorflow installed. Uninstalling......\n' + pip uninstall tensorflow-gpu + elif [[ $(${PIP} show tf-nightly-gpu) == *tf-nightly-gpu* ]]; then + echo 'Already have gpu version of tensorflow installed. Uninstalling......\n' + pip uninstall tf-nightly-gpu + fi + # Install CPU version + echo 'Installing tensorflow......\n' + pip install tensorflow==$TF_VERSION + fi + +else + + # Check if it's installed + if [[ $(${PIP} show tensorflow-gpu) == *tensorflow-gpu* ]] || [[ $(${PIP} show tf-nightly-gpu) == *tf-nightly-gpu* ]]; then + echo 'Using installed tensorflow-gpu' + else + # Uninstall CPU version if it is installed. + if [[ $(${PIP} show tensorflow) == *tensorflow* ]]; then + echo 'Already have tensorflow non-gpu installed. Uninstalling......\n' + ${PIP} uninstall tensorflow + elif [[ $(${PIP} show tf-nightly) == *tf-nightly* ]]; then + echo 'Already have tensorflow non-gpu installed. Uninstalling......\n' + ${PIP} uninstall tf-nightly + fi + # Install CPU version + echo 'Installing tensorflow-gpu .....\n' + ${PIP} install tensorflow-gpu==$TF_VERSION + fi +fi + + +TF_CFLAGS=( $(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') ) +TF_LFLAGS="$(python3 -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))')" + +write_to_bazelrc "build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true" +if [[ "$PIP_MANYLINUX2010" == "0" ]]; then + write_to_bazelrc "build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain" +fi +# Add Ubuntu toolchain flags +if is_linux; then + write_to_bazelrc "build:manylinux2010cuda100 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0:toolchain" + write_to_bazelrc "build:manylinux2010cuda101 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1:toolchain" +fi +write_to_bazelrc "build --spawn_strategy=standalone" +write_to_bazelrc "build --strategy=Genrule=standalone" +write_to_bazelrc "build -c opt" + + +if is_windows; then + # Use pywrap_tensorflow instead of tensorflow_framework on Windows + SHARED_LIBRARY_DIR=${TF_CFLAGS:2:-7}"python" +else + SHARED_LIBRARY_DIR=${TF_LFLAGS:2} +fi +SHARED_LIBRARY_NAME=$(echo $TF_LFLAGS | rev | cut -d":" -f1 | rev) +if ! [[ $TF_LFLAGS =~ .*:.* ]]; then + if is_macos; then + SHARED_LIBRARY_NAME="libtensorflow_framework.dylib" + elif is_windows; then + # Use pywrap_tensorflow's import library on Windows. It is in the same dir as the dll/pyd. + SHARED_LIBRARY_NAME="_pywrap_tensorflow_internal.lib" + else + SHARED_LIBRARY_NAME="libtensorflow_framework.so" + fi +fi + +HEADER_DIR=${TF_CFLAGS:2} +if is_windows; then + SHARED_LIBRARY_DIR=${SHARED_LIBRARY_DIR//\\//} + SHARED_LIBRARY_NAME=${SHARED_LIBRARY_NAME//\\//} + HEADER_DIR=${HEADER_DIR//\\//} +fi +write_action_env_to_bazelrc "TF_HEADER_DIR" ${HEADER_DIR} +write_action_env_to_bazelrc "TF_SHARED_LIBRARY_DIR" ${SHARED_LIBRARY_DIR} +write_action_env_to_bazelrc "TF_SHARED_LIBRARY_NAME" ${SHARED_LIBRARY_NAME} +write_action_env_to_bazelrc "TF_NEED_CUDA" ${TF_NEED_CUDA} + +# TODO(yifeif): do not hardcode path +if [[ "$TF_NEED_CUDA" == "1" ]]; then + write_action_env_to_bazelrc "TF_CUDA_VERSION" ${TF_CUDA_VERSION} + write_action_env_to_bazelrc "TF_CUDNN_VERSION" "7" + if is_windows; then + write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${TF_CUDA_VERSION}" + write_action_env_to_bazelrc "CUDA_TOOLKIT_PATH" "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${TF_CUDA_VERSION}" + else + write_action_env_to_bazelrc "CUDNN_INSTALL_PATH" "/usr/lib/x86_64-linux-gnu" + write_action_env_to_bazelrc "CUDA_TOOLKIT_PATH" "/usr/local/cuda" + fi + write_to_bazelrc "build --config=cuda" + write_to_bazelrc "test --config=cuda" +fi + +if [[ "$PIP_MANYLINUX2010" == "1" ]]; then + if [[ "$TF_CUDA_VERSION" == "10.0" ]]; then + write_to_bazelrc "build --config=manylinux2010cuda100" + write_to_bazelrc "test --config=manylinux2010cuda100" + else + write_to_bazelrc "build --config=manylinux2010cuda101" + write_to_bazelrc "test --config=manylinux2010cuda101" + fi +fi diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/BUILD new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/BUILD new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/BUILD.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/BUILD.tpl new file mode 100644 index 000000000..5a78654a9 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/BUILD.tpl @@ -0,0 +1,142 @@ +# This file is expanded from a template by cuda_configure.bzl +# Update cuda_configure.bzl#verify_build_defines when adding new variables. + +load(":cc_toolchain_config.bzl", "cc_toolchain_config") + +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +toolchain( + name = "toolchain-linux-x86_64", + exec_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + target_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + toolchain = ":cc-compiler-local", + toolchain_type = "@bazel_tools//tools/cpp:toolchain_type", +) + +cc_toolchain_suite( + name = "toolchain", + toolchains = { + "local|compiler": ":cc-compiler-local", + "darwin|compiler": ":cc-compiler-darwin", + "x64_windows|msvc-cl": ":cc-compiler-windows", + "x64_windows": ":cc-compiler-windows", + "arm": ":cc-compiler-local", + "aarch64": ":cc-compiler-local", + "k8": ":cc-compiler-local", + "piii": ":cc-compiler-local", + "ppc": ":cc-compiler-local", + "darwin": ":cc-compiler-darwin", + }, +) + +cc_toolchain( + name = "cc-compiler-local", + all_files = "%{compiler_deps}", + compiler_files = "%{compiler_deps}", + ar_files = "%{compiler_deps}", + as_files = "%{compiler_deps}", + dwp_files = ":empty", + linker_files = "%{compiler_deps}", + objcopy_files = ":empty", + strip_files = ":empty", + # To support linker flags that need to go to the start of command line + # we need the toolchain to support parameter files. Parameter files are + # last on the command line and contain all shared libraries to link, so all + # regular options will be left of them. + supports_param_files = 1, + toolchain_identifier = "local_linux", + toolchain_config = ":cc-compiler-local-config", +) + +cc_toolchain_config( + name = "cc-compiler-local-config", + cpu = "local", + builtin_include_directories = [%{cxx_builtin_include_directories}], + extra_no_canonical_prefixes_flags = [%{extra_no_canonical_prefixes_flags}], + host_compiler_path = "%{host_compiler_path}", + host_compiler_prefix = "%{host_compiler_prefix}", + host_compiler_warnings = [%{host_compiler_warnings}], + host_unfiltered_compile_flags = [%{unfiltered_compile_flags}], + linker_bin_path = "%{linker_bin_path}", + builtin_sysroot = "%{builtin_sysroot}", + cuda_path = "%{cuda_toolkit_path}", +) + +cc_toolchain( + name = "cc-compiler-darwin", + all_files = "%{compiler_deps}", + compiler_files = "%{compiler_deps}", + ar_files = "%{compiler_deps}", + as_files = "%{compiler_deps}", + dwp_files = ":empty", + linker_files = "%{compiler_deps}", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 0, + toolchain_identifier = "local_darwin", + toolchain_config = ":cc-compiler-local-darwin", +) + +cc_toolchain_config( + name = "cc-compiler-local-darwin", + cpu = "darwin", + builtin_include_directories = [%{cxx_builtin_include_directories}], + extra_no_canonical_prefixes_flags = [%{extra_no_canonical_prefixes_flags}], + host_compiler_path = "%{host_compiler_path}", + host_compiler_prefix = "%{host_compiler_prefix}", + host_compiler_warnings = [%{host_compiler_warnings}], + host_unfiltered_compile_flags = [%{unfiltered_compile_flags}], + linker_bin_path = "%{linker_bin_path}", +) + +cc_toolchain( + name = "cc-compiler-windows", + all_files = "%{win_compiler_deps}", + compiler_files = "%{win_compiler_deps}", + ar_files = "%{win_compiler_deps}", + as_files = "%{win_compiler_deps}", + dwp_files = ":empty", + linker_files = "%{win_compiler_deps}", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 1, + toolchain_identifier = "local_windows", + toolchain_config = ":cc-compiler-windows-config", +) + +cc_toolchain_config( + name = "cc-compiler-windows-config", + cpu = "x64_windows", + builtin_include_directories = [%{cxx_builtin_include_directories}], + msvc_cl_path = "%{msvc_cl_path}", + msvc_env_include = "%{msvc_env_include}", + msvc_env_lib = "%{msvc_env_lib}", + msvc_env_path = "%{msvc_env_path}", + msvc_env_tmp = "%{msvc_env_tmp}", + msvc_lib_path = "%{msvc_lib_path}", + msvc_link_path = "%{msvc_link_path}", + msvc_ml_path = "%{msvc_ml_path}", +) + +filegroup( + name = "empty", + srcs = [], +) + +filegroup( + name = "crosstool_wrapper_driver_is_not_gcc", + srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"], +) + +filegroup( + name = "windows_msvc_wrapper_files", + srcs = glob(["windows/msvc_*"]), +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/CROSSTOOL.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/CROSSTOOL.tpl new file mode 100644 index 000000000..1a13ac844 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/CROSSTOOL.tpl @@ -0,0 +1,1409 @@ +major_version: "local" +minor_version: "" +default_target_cpu: "same_as_host" + +toolchain { + abi_version: "local" + abi_libc_version: "local" + compiler: "compiler" + host_system_name: "local" + needsPic: true + target_libc: "local" + target_cpu: "local" + target_system_name: "local" + toolchain_identifier: "local_linux" + + feature { + name: "c++11" + flag_set { + action: "c++-compile" + flag_group { + flag: "-std=c++11" + } + } + } + + feature { + name: "stdlib" + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-lstdc++" + } + } + } + + feature { + name: "determinism" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Make C++ compilation deterministic. Use linkstamping instead of these + # compiler symbols. + flag: "-Wno-builtin-macro-redefined" + flag: "-D__DATE__=\"redacted\"" + flag: "-D__TIMESTAMP__=\"redacted\"" + flag: "-D__TIME__=\"redacted\"" + } + } + } + + feature { + name: "alwayslink" + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-executable" + flag_group { + flag: "-Wl,-no-as-needed" + } + } + } + + # This feature will be enabled for builds that support pic by bazel. + feature { + name: "pic" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + expand_if_all_available: "pic" + flag: "-fPIC" + } + flag_group { + expand_if_none_available: "pic" + flag: "-fPIE" + } + } + } + + # Security hardening on by default. + feature { + name: "hardening" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases. + # We need to undef it before redefining it as some distributions now + # have it enabled by default. + flag: "-U_FORTIFY_SOURCE" + flag: "-D_FORTIFY_SOURCE=1" + flag: "-fstack-protector" + } + } + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-Wl,-z,relro,-z,now" + } + } + flag_set { + action: "c++-link-executable" + flag_group { + flag: "-pie" + flag: "-Wl,-z,relro,-z,now" + } + } + } + + feature { + name: "warnings" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # All warnings are enabled. Maybe enable -Werror as well? + flag: "-Wall" + %{host_compiler_warnings} + } + } + } + + # Keep stack frames for debugging, even in opt mode. + feature { + name: "frame-pointer" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-fno-omit-frame-pointer" + } + } + } + + feature { + name: "build-id" + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + # Stamp the binary with a unique identifier. + flag: "-Wl,--build-id=md5" + flag: "-Wl,--hash-style=gnu" + } + } + } + + feature { + name: "no-canonical-prefixes" + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-no-canonical-prefixes" + %{extra_no_canonical_prefixes_flags} + } + } + } + + feature { + name: "disable-assertions" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-DNDEBUG" + } + } + } + + feature { + name: "linker-bin-path" + + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + %{linker_bin_path_flag} + } + } + } + + feature { + name: "common" + implies: "stdlib" + implies: "c++11" + implies: "determinism" + implies: "alwayslink" + implies: "hardening" + implies: "warnings" + implies: "frame-pointer" + implies: "build-id" + implies: "no-canonical-prefixes" + implies: "linker-bin-path" + } + + feature { + name: "opt" + implies: "common" + implies: "disable-assertions" + + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # No debug symbols. + # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt + # or even generally? However, that can't happen here, as it requires + # special handling in Bazel. + flag: "-g0" + + # Conservative choice for -O + # -O3 can increase binary size and even slow down the resulting binaries. + # Profile first and / or use FDO if you need better performance than this. + flag: "-O2" + + # Removal of unused code and data at link time (can this increase binary size in some cases?). + flag: "-ffunction-sections" + flag: "-fdata-sections" + } + } + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-executable" + flag_group { + flag: "-Wl,--gc-sections" + } + } + } + + feature { + name: "fastbuild" + implies: "common" + } + + feature { + name: "dbg" + implies: "common" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-g" + } + } + } + + # Set clang as a C/C++ compiler. + tool_path { name: "gcc" path: "%{host_compiler_path}" } + + # Use the default system toolchain for everything else. + tool_path { name: "ar" path: "/usr/bin/ar" } + tool_path { name: "compat-ld" path: "/usr/bin/ld" } + tool_path { name: "cpp" path: "/usr/bin/cpp" } + tool_path { name: "dwp" path: "/usr/bin/dwp" } + tool_path { name: "gcov" path: "/usr/bin/gcov" } + tool_path { name: "ld" path: "/usr/bin/ld" } + tool_path { name: "nm" path: "/usr/bin/nm" } + tool_path { name: "objcopy" path: "/usr/bin/objcopy" } + tool_path { name: "objdump" path: "/usr/bin/objdump" } + tool_path { name: "strip" path: "/usr/bin/strip" } + + # Enabled dynamic linking. + linking_mode_flags { mode: DYNAMIC } + +%{host_compiler_includes} +} + +toolchain { + abi_version: "local" + abi_libc_version: "local" + compiler: "compiler" + host_system_name: "local" + needsPic: true + target_libc: "macosx" + target_cpu: "darwin" + target_system_name: "local" + toolchain_identifier: "local_darwin" + feature { + name: "c++11" + flag_set { + action: "c++-compile" + flag_group { + flag: "-std=c++11" + } + } + } + + feature { + name: "stdlib" + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-lc++" + } + } + } + + feature { + name: "determinism" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Make C++ compilation deterministic. Use linkstamping instead of these + # compiler symbols. + flag: "-Wno-builtin-macro-redefined" + flag: "-D__DATE__=\"redacted\"" + flag: "-D__TIMESTAMP__=\"redacted\"" + flag: "-D__TIME__=\"redacted\"" + } + } + } + + # This feature will be enabled for builds that support pic by bazel. + feature { + name: "pic" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + expand_if_all_available: "pic" + flag: "-fPIC" + } + flag_group { + expand_if_none_available: "pic" + flag: "-fPIE" + } + } + } + + # Security hardening on by default. + feature { + name: "hardening" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases. + # We need to undef it before redefining it as some distributions now + # have it enabled by default. + flag: "-U_FORTIFY_SOURCE" + flag: "-D_FORTIFY_SOURCE=1" + flag: "-fstack-protector" + } + } + flag_set { + action: "c++-link-executable" + flag_group { + flag: "-pie" + } + } + } + + feature { + name: "warnings" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # All warnings are enabled. Maybe enable -Werror as well? + flag: "-Wall" + %{host_compiler_warnings} + } + } + } + + # Keep stack frames for debugging, even in opt mode. + feature { + name: "frame-pointer" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-fno-omit-frame-pointer" + } + } + } + + feature { + name: "no-canonical-prefixes" + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag:"-no-canonical-prefixes" + } + } + } + + feature { + name: "disable-assertions" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-DNDEBUG" + } + } + } + + feature { + name: "linker-bin-path" + + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + %{linker_bin_path_flag} + } + } + } + + feature { + name: "undefined-dynamic" + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-executable" + flag_group { + flag: "-undefined" + flag: "dynamic_lookup" + } + } + } + + feature { + name: "common" + implies: "stdlib" + implies: "c++11" + implies: "determinism" + implies: "hardening" + implies: "warnings" + implies: "frame-pointer" + implies: "no-canonical-prefixes" + implies: "linker-bin-path" + implies: "undefined-dynamic" + } + + feature { + name: "opt" + implies: "common" + implies: "disable-assertions" + + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # No debug symbols. + # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt + # or even generally? However, that can't happen here, as it requires + # special handling in Bazel. + flag: "-g0" + + # Conservative choice for -O + # -O3 can increase binary size and even slow down the resulting binaries. + # Profile first and / or use FDO if you need better performance than this. + flag: "-O2" + + # Removal of unused code and data at link time (can this increase binary size in some cases?). + flag: "-ffunction-sections" + flag: "-fdata-sections" + } + } + } + + feature { + name: "fastbuild" + implies: "common" + } + + feature { + name: "dbg" + implies: "common" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-g" + } + } + } + + # Set clang as a C/C++ compiler. + tool_path { name: "gcc" path: "%{host_compiler_path}" } + + # Use the default system toolchain for everything else. + tool_path { name: "ar" path: "/usr/bin/libtool" } + tool_path { name: "compat-ld" path: "/usr/bin/ld" } + tool_path { name: "cpp" path: "/usr/bin/cpp" } + tool_path { name: "dwp" path: "/usr/bin/dwp" } + tool_path { name: "gcov" path: "/usr/bin/gcov" } + tool_path { name: "ld" path: "/usr/bin/ld" } + tool_path { name: "nm" path: "/usr/bin/nm" } + tool_path { name: "objcopy" path: "/usr/bin/objcopy" } + tool_path { name: "objdump" path: "/usr/bin/objdump" } + tool_path { name: "strip" path: "/usr/bin/strip" } + + # Enabled dynamic linking. + linking_mode_flags { mode: DYNAMIC } + +%{host_compiler_includes} +} + +toolchain { + toolchain_identifier: "local_windows" + host_system_name: "local" + target_system_name: "local" + + abi_version: "local" + abi_libc_version: "local" + target_cpu: "x64_windows" + compiler: "msvc-cl" + target_libc: "msvcrt" + +%{cxx_builtin_include_directory} + + tool_path { + name: "ar" + path: "%{msvc_lib_path}" + } + tool_path { + name: "ml" + path: "%{msvc_ml_path}" + } + tool_path { + name: "cpp" + path: "%{msvc_cl_path}" + } + tool_path { + name: "gcc" + path: "%{msvc_cl_path}" + } + tool_path { + name: "gcov" + path: "wrapper/bin/msvc_nop.bat" + } + tool_path { + name: "ld" + path: "%{msvc_link_path}" + } + tool_path { + name: "nm" + path: "wrapper/bin/msvc_nop.bat" + } + tool_path { + name: "objcopy" + path: "wrapper/bin/msvc_nop.bat" + } + tool_path { + name: "objdump" + path: "wrapper/bin/msvc_nop.bat" + } + tool_path { + name: "strip" + path: "wrapper/bin/msvc_nop.bat" + } + supports_interface_shared_objects: true + + # TODO(pcloudy): Review those flags below, they should be defined by cl.exe + compiler_flag: "/DCOMPILER_MSVC" + + # Don't define min/max macros in windows.h. + compiler_flag: "/DNOMINMAX" + + # Platform defines. + compiler_flag: "/D_WIN32_WINNT=0x0600" + # Turn off warning messages. + compiler_flag: "/D_CRT_SECURE_NO_DEPRECATE" + compiler_flag: "/D_CRT_SECURE_NO_WARNINGS" + compiler_flag: "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS" + + # Useful options to have on for compilation. + # Increase the capacity of object files to 2^32 sections. + compiler_flag: "/bigobj" + # Allocate 500MB for precomputed headers. + compiler_flag: "/Zm500" + # Use unsigned char by default. + compiler_flag: "/J" + # Use function level linking. + compiler_flag: "/Gy" + # Use string pooling. + compiler_flag: "/GF" + # Catch C++ exceptions only and tell the compiler to assume that functions declared + # as extern "C" never throw a C++ exception. + compiler_flag: "/EHsc" + + # Globally disabled warnings. + # Don't warn about elements of array being be default initialized. + compiler_flag: "/wd4351" + # Don't warn about no matching delete found. + compiler_flag: "/wd4291" + # Don't warn about diamond inheritance patterns. + compiler_flag: "/wd4250" + # Don't warn about insecure functions (e.g. non _s functions). + compiler_flag: "/wd4996" + + linker_flag: "/MACHINE:X64" + + feature { + name: "no_legacy_features" + } + + # TODO(klimek): Previously we were using a .bat file to start python to run + # the python script that can redirect to nvcc - unfortunately .bat files + # have a rather short maximum length for command lines (8k). Instead, we + # now use the python binary as the compiler and pass the python script to + # it at the start of the command line. Investigate different possibilities + # to run the nvcc wrapper, either using pyinstaller --onefile, or writing + # a small C++ wrapper to redirect. + feature { + name: "redirector" + enabled: true + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-module-compile" + action: "c++-module-codegen" + action: "c++-header-parsing" + action: "assemble" + action: "preprocess-assemble" + flag_group { + flag: "-B" + flag: "external/local_config_cuda/crosstool/windows/msvc_wrapper_for_nvcc.py" + } + } + } + + # Suppress startup banner. + feature { + name: "nologo" + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-module-compile" + action: "c++-module-codegen" + action: "c++-header-parsing" + action: "assemble" + action: "preprocess-assemble" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-static-library" + flag_group { + flag: "/nologo" + } + } + } + + feature { + name: 'has_configured_linker_path' + } + + # This feature indicates strip is not supported, building stripped binary will just result a copy of orignial binary + feature { + name: 'no_stripping' + } + + # This feature indicates this is a toolchain targeting Windows. + feature { + name: 'targets_windows' + implies: 'copy_dynamic_libraries_to_binary' + enabled: true + } + + feature { + name: 'copy_dynamic_libraries_to_binary' + } + + action_config { + config_name: 'assemble' + action_name: 'assemble' + tool { + tool_path: '%{msvc_ml_path}' + } + implies: 'compiler_input_flags' + implies: 'compiler_output_flags' + implies: 'nologo' + implies: 'msvc_env' + implies: 'sysroot' + } + + action_config { + config_name: 'preprocess-assemble' + action_name: 'preprocess-assemble' + tool { + tool_path: '%{msvc_ml_path}' + } + implies: 'compiler_input_flags' + implies: 'compiler_output_flags' + implies: 'nologo' + implies: 'msvc_env' + implies: 'sysroot' + } + + action_config { + config_name: 'c-compile' + action_name: 'c-compile' + tool { + tool_path: '%{msvc_cl_path}' + } + implies: 'compiler_input_flags' + implies: 'compiler_output_flags' + implies: 'legacy_compile_flags' + implies: 'nologo' + implies: 'msvc_env' + implies: 'parse_showincludes' + implies: 'user_compile_flags' + implies: 'sysroot' + implies: 'unfiltered_compile_flags' + } + + action_config { + config_name: 'c++-compile' + action_name: 'c++-compile' + tool { + tool_path: '%{msvc_cl_path}' + } + implies: 'compiler_input_flags' + implies: 'compiler_output_flags' + implies: 'legacy_compile_flags' + implies: 'nologo' + implies: 'msvc_env' + implies: 'parse_showincludes' + implies: 'user_compile_flags' + implies: 'sysroot' + implies: 'unfiltered_compile_flags' + } + + action_config { + config_name: 'c++-link-executable' + action_name: 'c++-link-executable' + tool { + tool_path: '%{msvc_link_path}' + } + implies: 'nologo' + implies: 'linkstamps' + implies: 'output_execpath_flags' + implies: 'input_param_flags' + implies: 'user_link_flags' + implies: 'legacy_link_flags' + implies: 'linker_subsystem_flag' + implies: 'linker_param_file' + implies: 'msvc_env' + implies: 'no_stripping' + } + + action_config { + config_name: 'c++-link-dynamic-library' + action_name: 'c++-link-dynamic-library' + tool { + tool_path: '%{msvc_link_path}' + } + implies: 'nologo' + implies: 'shared_flag' + implies: 'linkstamps' + implies: 'output_execpath_flags' + implies: 'input_param_flags' + implies: 'user_link_flags' + implies: 'legacy_link_flags' + implies: 'linker_subsystem_flag' + implies: 'linker_param_file' + implies: 'msvc_env' + implies: 'no_stripping' + implies: 'has_configured_linker_path' + implies: 'def_file' + } + + action_config { + config_name: 'c++-link-nodeps-dynamic-library' + action_name: 'c++-link-nodeps-dynamic-library' + tool { + tool_path: '%{msvc_link_path}' + } + implies: 'nologo' + implies: 'shared_flag' + implies: 'linkstamps' + implies: 'output_execpath_flags' + implies: 'input_param_flags' + implies: 'user_link_flags' + implies: 'legacy_link_flags' + implies: 'linker_subsystem_flag' + implies: 'linker_param_file' + implies: 'msvc_env' + implies: 'no_stripping' + implies: 'has_configured_linker_path' + implies: 'def_file' + } + + action_config { + config_name: 'c++-link-static-library' + action_name: 'c++-link-static-library' + tool { + tool_path: '%{msvc_lib_path}' + } + implies: 'nologo' + implies: 'archiver_flags' + implies: 'input_param_flags' + implies: 'linker_param_file' + implies: 'msvc_env' + } + + # TODO(b/65151735): Remove legacy_compile_flags feature when legacy fields are + # not used in this crosstool + feature { + name: 'legacy_compile_flags' + flag_set { + expand_if_all_available: 'legacy_compile_flags' + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + flag_group { + iterate_over: 'legacy_compile_flags' + flag: '%{legacy_compile_flags}' + } + } + } + + feature { + name: "msvc_env" + env_set { + action: "c-compile" + action: "c++-compile" + action: "c++-module-compile" + action: "c++-module-codegen" + action: "c++-header-parsing" + action: "assemble" + action: "preprocess-assemble" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-static-library" + env_entry { + key: "PATH" + value: "%{msvc_env_path}" + } + env_entry { + key: "INCLUDE" + value: "%{msvc_env_include}" + } + env_entry { + key: "LIB" + value: "%{msvc_env_lib}" + } + env_entry { + key: "TMP" + value: "%{msvc_env_tmp}" + } + env_entry { + key: "TEMP" + value: "%{msvc_env_tmp}" + } + } + } + + feature { + name: 'include_paths' + flag_set { + action: "assemble" + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + flag_group { + iterate_over: 'quote_include_paths' + flag: '/I%{quote_include_paths}' + } + flag_group { + iterate_over: 'include_paths' + flag: '/I%{include_paths}' + } + flag_group { + iterate_over: 'system_include_paths' + flag: '/I%{system_include_paths}' + } + } + } + + feature { + name: "preprocessor_defines" + flag_set { + action: "assemble" + action: "preprocess-assemble" + action: "c-compile" + action: "c++-compile" + action: "c++-header-parsing" + action: "c++-module-compile" + flag_group { + flag: "/D%{preprocessor_defines}" + iterate_over: "preprocessor_defines" + } + } + } + + # Tell Bazel to parse the output of /showIncludes + feature { + name: 'parse_showincludes' + flag_set { + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-module-compile' + action: 'c++-header-parsing' + flag_group { + flag: "/showIncludes" + } + } + } + + + feature { + name: 'generate_pdb_file' + requires: { + feature: 'dbg' + } + requires: { + feature: 'fastbuild' + } + } + + feature { + name: 'shared_flag' + flag_set { + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: '/DLL' + } + } + } + + feature { + name: 'linkstamps' + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + expand_if_all_available: 'linkstamp_paths' + flag_group { + iterate_over: 'linkstamp_paths' + flag: '%{linkstamp_paths}' + } + } + } + + feature { + name: 'output_execpath_flags' + flag_set { + expand_if_all_available: 'output_execpath' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: '/OUT:%{output_execpath}' + } + } + } + + feature { + name: 'archiver_flags' + flag_set { + expand_if_all_available: 'output_execpath' + action: 'c++-link-static-library' + flag_group { + flag: '/OUT:%{output_execpath}' + } + } + } + + feature { + name: 'input_param_flags' + flag_set { + expand_if_all_available: 'interface_library_output_path' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/IMPLIB:%{interface_library_output_path}" + } + } + flag_set { + expand_if_all_available: 'libopts' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + iterate_over: 'libopts' + flag: '%{libopts}' + } + } + flag_set { + expand_if_all_available: 'libraries_to_link' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + action: 'c++-link-static-library' + flag_group { + iterate_over: 'libraries_to_link' + flag_group { + expand_if_equal: { + variable: 'libraries_to_link.type' + value: 'object_file_group' + } + iterate_over: 'libraries_to_link.object_files' + flag_group { + flag: '%{libraries_to_link.object_files}' + } + } + flag_group { + expand_if_equal: { + variable: 'libraries_to_link.type' + value: 'object_file' + } + flag_group { + flag: '%{libraries_to_link.name}' + } + } + flag_group { + expand_if_equal: { + variable: 'libraries_to_link.type' + value: 'interface_library' + } + flag_group { + flag: '%{libraries_to_link.name}' + } + } + flag_group { + expand_if_equal: { + variable: 'libraries_to_link.type' + value: 'static_library' + } + flag_group { + expand_if_false: 'libraries_to_link.is_whole_archive' + flag: '%{libraries_to_link.name}' + } + flag_group { + expand_if_true: 'libraries_to_link.is_whole_archive' + flag: '/WHOLEARCHIVE:%{libraries_to_link.name}' + } + } + } + } + } + + # Since this feature is declared earlier in the CROSSTOOL than + # "user_link_flags", this feature will be applied prior to it anwyhere they + # are both implied. And since "user_link_flags" contains the linkopts from + # the build rule, this allows the user to override the /SUBSYSTEM in the BUILD + # file. + feature { + name: 'linker_subsystem_flag' + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: '/SUBSYSTEM:CONSOLE' + } + } + } + + # The "user_link_flags" contains user-defined linkopts (from build rules) + # so it should be defined after features that declare user-overridable flags. + # For example the "linker_subsystem_flag" defines a default "/SUBSYSTEM" flag + # but we want to let the user override it, therefore "link_flag_subsystem" is + # defined earlier in the CROSSTOOL file than "user_link_flags". + feature { + name: 'user_link_flags' + flag_set { + expand_if_all_available: 'user_link_flags' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + iterate_over: 'user_link_flags' + flag: '%{user_link_flags}' + } + } + } + feature { + name: 'legacy_link_flags' + flag_set { + expand_if_all_available: 'legacy_link_flags' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + iterate_over: 'legacy_link_flags' + flag: '%{legacy_link_flags}' + } + } + } + + feature { + name: 'linker_param_file' + flag_set { + expand_if_all_available: 'linker_param_file' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + action: 'c++-link-static-library' + flag_group { + flag: '@%{linker_param_file}' + } + } + } + + feature { + name: 'static_link_msvcrt' + } + + feature { + name: 'static_link_msvcrt_no_debug' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/MT" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEFAULTLIB:libcmt.lib" + } + } + requires: { feature: 'fastbuild'} + requires: { feature: 'opt'} + } + + feature { + name: 'dynamic_link_msvcrt_no_debug' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/MD" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEFAULTLIB:msvcrt.lib" + } + } + requires: { feature: 'fastbuild'} + requires: { feature: 'opt'} + } + + feature { + name: 'static_link_msvcrt_debug' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/MTd" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEFAULTLIB:libcmtd.lib" + } + } + requires: { feature: 'dbg'} + } + + feature { + name: 'dynamic_link_msvcrt_debug' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/MDd" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEFAULTLIB:msvcrtd.lib" + } + } + requires: { feature: 'dbg'} + } + + feature { + name: 'dbg' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/Od" + flag: "/Z7" + flag: "/DDEBUG" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEBUG:FULL" + flag: "/INCREMENTAL:NO" + } + } + implies: 'generate_pdb_file' + } + + feature { + name: 'fastbuild' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/Od" + flag: "/Z7" + flag: "/DDEBUG" + } + } + flag_set { + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEBUG:FASTLINK" + flag: "/INCREMENTAL:NO" + } + } + implies: 'generate_pdb_file' + } + + feature { + name: 'opt' + flag_set { + action: 'c-compile' + action: 'c++-compile' + flag_group { + flag: "/O2" + flag: "/DNDEBUG" + } + } + } + + feature { + name: 'user_compile_flags' + flag_set { + expand_if_all_available: 'user_compile_flags' + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + flag_group { + iterate_over: 'user_compile_flags' + flag: '%{user_compile_flags}' + } + } + } + + feature { + name: 'sysroot' + flag_set { + expand_if_all_available: 'sysroot' + action: 'assemble' + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + iterate_over: 'sysroot' + flag: '--sysroot=%{sysroot}' + } + } + } + + feature { + name: 'unfiltered_compile_flags' + flag_set { + expand_if_all_available: 'unfiltered_compile_flags' + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + flag_group { + iterate_over: 'unfiltered_compile_flags' + flag: '%{unfiltered_compile_flags}' + } + } + } + + feature { + name: 'compiler_output_flags' + flag_set { + action: 'assemble' + flag_group { + expand_if_all_available: 'output_file' + expand_if_none_available: 'output_assembly_file' + expand_if_none_available: 'output_preprocess_file' + flag: '/Fo%{output_file}' + flag: '/Zi' + } + } + flag_set { + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + flag_group { + expand_if_all_available: 'output_file' + expand_if_none_available: 'output_assembly_file' + expand_if_none_available: 'output_preprocess_file' + flag: '/Fo%{output_file}' + } + flag_group { + expand_if_all_available: 'output_file' + expand_if_all_available: 'output_assembly_file' + flag: '/Fa%{output_file}' + } + flag_group { + expand_if_all_available: 'output_file' + expand_if_all_available: 'output_preprocess_file' + flag: '/P' + flag: '/Fi%{output_file}' + } + } + } + + feature { + name: 'compiler_input_flags' + flag_set { + action: 'assemble' + action: 'preprocess-assemble' + action: 'c-compile' + action: 'c++-compile' + action: 'c++-header-parsing' + action: 'c++-module-compile' + action: 'c++-module-codegen' + flag_group { + expand_if_all_available: 'source_file' + flag: '/c' + flag: '%{source_file}' + } + } + } + + feature { + name : 'def_file', + flag_set { + expand_if_all_available: 'def_file_path' + action: 'c++-link-executable' + action: 'c++-link-dynamic-library' + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "/DEF:%{def_file_path}" + # We can specify a different DLL name in DEF file, /ignore:4070 suppresses + # the warning message about DLL name doesn't match the default one. + # See https://msdn.microsoft.com/en-us/library/sfkk2fz7.aspx + flag: "/ignore:4070" + } + } + } + + feature { + name: 'windows_export_all_symbols' + } + + feature { + name: 'no_windows_export_all_symbols' + } + + linking_mode_flags { mode: DYNAMIC } +} diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/cc_toolchain_config.bzl.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/cc_toolchain_config.bzl.tpl new file mode 100644 index 000000000..3d4d41aa2 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/cc_toolchain_config.bzl.tpl @@ -0,0 +1,1516 @@ +"""cc_toolchain_config rule for configuring CUDA toolchains on Linux, Mac, and Windows.""" + +load( + "@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", + "action_config", + "env_entry", + "env_set", + "feature", + "feature_set", + "flag_group", + "flag_set", + "tool", + "tool_path", + "variable_with_value", +) +load( + "@bazel_tools//tools/build_defs/cc:action_names.bzl", + "ASSEMBLE_ACTION_NAME", + "CC_FLAGS_MAKE_VARIABLE_ACTION_NAME", + "CLIF_MATCH_ACTION_NAME", + "CPP_COMPILE_ACTION_NAME", + "CPP_HEADER_PARSING_ACTION_NAME", + "CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_EXECUTABLE_ACTION_NAME", + "CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_STATIC_LIBRARY_ACTION_NAME", + "CPP_MODULE_CODEGEN_ACTION_NAME", + "CPP_MODULE_COMPILE_ACTION_NAME", + "C_COMPILE_ACTION_NAME", + "LINKSTAMP_COMPILE_ACTION_NAME", + "LTO_BACKEND_ACTION_NAME", + "LTO_INDEXING_ACTION_NAME", + "OBJCPP_COMPILE_ACTION_NAME", + "OBJCPP_EXECUTABLE_ACTION_NAME", + "OBJC_ARCHIVE_ACTION_NAME", + "OBJC_COMPILE_ACTION_NAME", + "OBJC_EXECUTABLE_ACTION_NAME", + "OBJC_FULLY_LINK_ACTION_NAME", + "PREPROCESS_ASSEMBLE_ACTION_NAME", + "STRIP_ACTION_NAME", +) + +ACTION_NAMES = struct( + c_compile = C_COMPILE_ACTION_NAME, + cpp_compile = CPP_COMPILE_ACTION_NAME, + linkstamp_compile = LINKSTAMP_COMPILE_ACTION_NAME, + cc_flags_make_variable = CC_FLAGS_MAKE_VARIABLE_ACTION_NAME, + cpp_module_codegen = CPP_MODULE_CODEGEN_ACTION_NAME, + cpp_header_parsing = CPP_HEADER_PARSING_ACTION_NAME, + cpp_module_compile = CPP_MODULE_COMPILE_ACTION_NAME, + assemble = ASSEMBLE_ACTION_NAME, + preprocess_assemble = PREPROCESS_ASSEMBLE_ACTION_NAME, + lto_indexing = LTO_INDEXING_ACTION_NAME, + lto_backend = LTO_BACKEND_ACTION_NAME, + cpp_link_executable = CPP_LINK_EXECUTABLE_ACTION_NAME, + cpp_link_dynamic_library = CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_nodeps_dynamic_library = CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_static_library = CPP_LINK_STATIC_LIBRARY_ACTION_NAME, + strip = STRIP_ACTION_NAME, + objc_archive = OBJC_ARCHIVE_ACTION_NAME, + objc_compile = OBJC_COMPILE_ACTION_NAME, + objc_executable = OBJC_EXECUTABLE_ACTION_NAME, + objc_fully_link = OBJC_FULLY_LINK_ACTION_NAME, + objcpp_compile = OBJCPP_COMPILE_ACTION_NAME, + objcpp_executable = OBJCPP_EXECUTABLE_ACTION_NAME, + clif_match = CLIF_MATCH_ACTION_NAME, + objcopy_embed_data = "objcopy_embed_data", + ld_embed_data = "ld_embed_data", +) + +def _impl(ctx): + if (ctx.attr.cpu == "darwin"): + toolchain_identifier = "local_darwin" + elif (ctx.attr.cpu == "local"): + toolchain_identifier = "local_linux" + elif (ctx.attr.cpu == "x64_windows"): + toolchain_identifier = "local_windows" + else: + fail("Unreachable") + + host_system_name = "local" + + target_system_name = "local" + + if (ctx.attr.cpu == "darwin"): + target_cpu = "darwin" + elif (ctx.attr.cpu == "local"): + target_cpu = "local" + elif (ctx.attr.cpu == "x64_windows"): + target_cpu = "x64_windows" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "local"): + target_libc = "local" + elif (ctx.attr.cpu == "darwin"): + target_libc = "macosx" + elif (ctx.attr.cpu == "x64_windows"): + target_libc = "msvcrt" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + compiler = "compiler" + elif (ctx.attr.cpu == "x64_windows"): + compiler = "msvc-cl" + else: + fail("Unreachable") + + abi_version = "local" + + abi_libc_version = "local" + + cc_target_os = None + + builtin_sysroot = ctx.attr.builtin_sysroot + + all_link_actions = [ + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ] + + cpp_link_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_nodeps_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_nodeps_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_static_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_static_library, + implies = [ + "nologo", + "archiver_flags", + "input_param_flags", + "linker_param_file", + "msvc_env", + ], + tools = [tool(path = ctx.attr.msvc_lib_path)], + ) + + assemble_action = action_config( + action_name = ACTION_NAMES.assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + preprocess_assemble_action = action_config( + action_name = ACTION_NAMES.preprocess_assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + c_compile_action = action_config( + action_name = ACTION_NAMES.c_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_compile_action = action_config( + action_name = ACTION_NAMES.cpp_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_link_executable_action = action_config( + action_name = ACTION_NAMES.cpp_link_executable, + implies = [ + "nologo", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + action_configs = [] + elif (ctx.attr.cpu == "x64_windows"): + action_configs = [ + assemble_action, + preprocess_assemble_action, + c_compile_action, + cpp_compile_action, + cpp_link_executable_action, + cpp_link_dynamic_library_action, + cpp_link_nodeps_dynamic_library_action, + cpp_link_static_library_action, + ] + else: + fail("Unreachable") + + no_windows_export_all_symbols_feature = feature(name = "no_windows_export_all_symbols") + + pic_feature = feature( + name = "pic", + enabled = True, + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group(flags = ["-fPIC"], expand_if_available = "pic"), + flag_group( + flags = ["-fPIE"], + expand_if_not_available = "pic", + ), + ], + ), + ], + ) + + preprocessor_defines_feature = feature( + name = "preprocessor_defines", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/D%{preprocessor_defines}"], + iterate_over = "preprocessor_defines", + ), + ], + ), + ], + ) + + generate_pdb_file_feature = feature( + name = "generate_pdb_file", + requires = [ + feature_set(features = ["dbg"]), + feature_set(features = ["fastbuild"]), + ], + ) + + linkstamps_feature = feature( + name = "linkstamps", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{linkstamp_paths}"], + iterate_over = "linkstamp_paths", + expand_if_available = "linkstamp_paths", + ), + ], + ), + ], + ) + + unfiltered_compile_flags_feature = feature( + name = "unfiltered_compile_flags", + flag_sets = ([ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ctx.attr.host_unfiltered_compile_flags, + ), + ], + ), + ] if ctx.attr.host_unfiltered_compile_flags else []), + ) + + determinism_feature = feature( + name = "determinism", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-Wno-builtin-macro-redefined", + "-D__DATE__=\"redacted\"", + "-D__TIMESTAMP__=\"redacted\"", + "-D__TIME__=\"redacted\"", + ], + ), + ], + ), + ], + ) + + nologo_feature = feature( + name = "nologo", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + flag_groups = [flag_group(flags = ["/nologo"])], + ), + ], + ) + + supports_pic_feature = feature(name = "supports_pic", enabled = True) + + output_execpath_flags_feature = feature( + name = "output_execpath_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + default_link_flags_feature = feature( + name = "default_link_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/MACHINE:X64"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-Wl,-z,relro,-z,now"])], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie", "-Wl,-z,relro,-z,now"])], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie"])], + ), + ], + ) + else: + hardening_feature = None + + supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) + + targets_windows_feature = feature( + name = "targets_windows", + enabled = True, + implies = ["copy_dynamic_libraries_to_binary"], + ) + + msvc_env_feature = feature( + name = "msvc_env", + env_sets = [ + env_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + env_entries = [ + env_entry(key = "PATH", value = ctx.attr.msvc_env_path), + env_entry( + key = "INCLUDE", + value = ctx.attr.msvc_env_include, + ), + env_entry(key = "LIB", value = ctx.attr.msvc_env_lib), + env_entry(key = "TMP", value = ctx.attr.msvc_env_tmp), + env_entry(key = "TEMP", value = ctx.attr.msvc_env_tmp), + ], + ), + ], + ) + + linker_subsystem_flag_feature = feature( + name = "linker_subsystem_flag", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/SUBSYSTEM:CONSOLE"])], + ), + ], + ) + + dynamic_link_msvcrt_no_debug_feature = feature( + name = "dynamic_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MD"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + warnings_feature = feature( + name = "warnings", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-Wall"] + ctx.attr.host_compiler_warnings, + ), + ], + ), + ], + ) + + dynamic_link_msvcrt_debug_feature = feature( + name = "dynamic_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MDd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + compiler_output_flags_feature = feature( + name = "compiler_output_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.assemble], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}", "/Zi"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fa%{output_file}"], + expand_if_available = "output_assembly_file", + ), + ], + expand_if_available = "output_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/P", "/Fi%{output_file}"], + expand_if_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + ), + ], + ), + ], + ) + + default_compile_flags_feature = feature( + name = "default_compile_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.linkstamp_compile, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.lto_backend, + ACTION_NAMES.clif_match, + ], + flag_groups = [ + flag_group( + flags = [ + "/DCOMPILER_MSVC", + "/DNOMINMAX", + "/D_WIN32_WINNT=0x0600", + "/D_CRT_SECURE_NO_DEPRECATE", + "/D_CRT_SECURE_NO_WARNINGS", + "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS", + "/bigobj", + "/Zm500", + "/J", + "/Gy", + "/GF", + "/EHsc", + "/wd4351", + "/wd4291", + "/wd4250", + "/wd4996", + ], + ), + ], + ), + ], + ) + + static_link_msvcrt_debug_feature = feature( + name = "static_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MTd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + static_link_msvcrt_feature = feature(name = "static_link_msvcrt") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-g"])], + ), + ], + implies = ["common"], + ) + elif (ctx.attr.cpu == "x64_windows"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEBUG:FULL", "/INCREMENTAL:NO"])], + ), + ], + implies = ["generate_pdb_file"], + ) + else: + dbg_feature = None + + undefined_dynamic_feature = feature( + name = "undefined-dynamic", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-undefined", "dynamic_lookup"])], + ), + ], + ) + + parse_showincludes_feature = feature( + name = "parse_showincludes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_header_parsing, + ], + flag_groups = [flag_group(flags = ["/showIncludes"])], + ), + ], + ) + + linker_param_file_feature = feature( + name = "linker_param_file", + flag_sets = [ + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["@%{linker_param_file}"], + expand_if_available = "linker_param_file", + ), + ], + ), + ], + ) + + static_link_msvcrt_no_debug_feature = feature( + name = "static_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MT"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + supports_interface_shared_libraries_feature = feature( + name = "supports_interface_shared_libraries", + enabled = True, + ) + + disable_assertions_feature = feature( + name = "disable-assertions", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-DNDEBUG"])], + ), + ], + ) + + if (ctx.attr.cpu == "x64_windows"): + fastbuild_feature = feature( + name = "fastbuild", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group(flags = ["/DEBUG:FASTLINK", "/INCREMENTAL:NO"]), + ], + ), + ], + implies = ["generate_pdb_file"], + ) + elif (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + fastbuild_feature = feature(name = "fastbuild", implies = ["common"]) + else: + fastbuild_feature = None + + user_compile_flags_feature = feature( + name = "user_compile_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["%{user_compile_flags}"], + iterate_over = "user_compile_flags", + expand_if_available = "user_compile_flags", + ), + ], + ), + ], + ) + + compiler_input_flags_feature = feature( + name = "compiler_input_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["/c", "%{source_file}"], + expand_if_available = "source_file", + ), + ], + ), + ], + ) + + no_legacy_features_feature = feature(name = "no_legacy_features") + + archiver_flags_feature = feature( + name = "archiver_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + redirector_feature = feature( + name = "redirector", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ], + flag_groups = [ + flag_group( + flags = [ + "-B", + "external/local_config_cuda/crosstool/windows/msvc_wrapper_for_nvcc.py", + ], + ), + ], + ), + ], + ) + + linker_bin_path_feature = feature( + name = "linker-bin-path", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-B" + ctx.attr.linker_bin_path])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,--gc-sections"])], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "darwin"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "x64_windows"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/O2", "/DNDEBUG"])], + ), + ], + ) + else: + opt_feature = None + + include_paths_feature = feature( + name = "include_paths", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/I%{quote_include_paths}"], + iterate_over = "quote_include_paths", + ), + flag_group( + flags = ["/I%{include_paths}"], + iterate_over = "include_paths", + ), + flag_group( + flags = ["/I%{system_include_paths}"], + iterate_over = "system_include_paths", + ), + ], + ), + ], + ) + + shared_flag_feature = feature( + name = "shared_flag", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["/DLL"])], + ), + ], + ) + + windows_export_all_symbols_feature = feature(name = "windows_export_all_symbols") + + frame_pointer_feature = feature( + name = "frame-pointer", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-fno-omit-frame-pointer"])], + ), + ], + ) + + build_id_feature = feature( + name = "build-id", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["-Wl,--build-id=md5", "-Wl,--hash-style=gnu"], + ), + ], + ), + ], + ) + + sysroot_feature = feature( + name = "sysroot", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["--sysroot=%{sysroot}"], + iterate_over = "sysroot", + expand_if_available = "sysroot", + ), + ], + ), + ], + ) + + cuda_path_feature = feature( + name = "cuda_path", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["--cuda-path=" + ctx.attr.cuda_path], + ), + ], + ), + ], + ) + + def_file_feature = feature( + name = "def_file", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/DEF:%{def_file_path}", "/ignore:4070"], + expand_if_available = "def_file_path", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "darwin"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lc++"])], + ), + ], + ) + elif (ctx.attr.cpu == "local"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lstdc++"])], + ), + ], + ) + else: + stdlib_feature = None + + no_stripping_feature = feature(name = "no_stripping") + + alwayslink_feature = feature( + name = "alwayslink", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,-no-as-needed"])], + ), + ], + ) + + input_param_flags_feature = feature( + name = "input_param_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["/IMPLIB:%{interface_library_output_path}"], + expand_if_available = "interface_library_output_path", + ), + ], + ), + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link", + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link.object_files", + flag_groups = [flag_group(flags = ["%{libraries_to_link.object_files}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file_group", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "interface_library", + ), + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["%{libraries_to_link.name}"], + expand_if_false = "libraries_to_link.is_whole_archive", + ), + flag_group( + flags = ["/WHOLEARCHIVE:%{libraries_to_link.name}"], + expand_if_true = "libraries_to_link.is_whole_archive", + ), + ], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "static_library", + ), + ), + ], + expand_if_available = "libraries_to_link", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = [ + "-no-canonical-prefixes", + ] + ctx.attr.extra_no_canonical_prefixes_flags, + ), + ], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-no-canonical-prefixes"])], + ), + ], + ) + else: + no_canonical_prefixes_feature = None + + has_configured_linker_path_feature = feature(name = "has_configured_linker_path") + + copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") + + user_link_flags_feature = feature( + name = "user_link_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{user_link_flags}"], + iterate_over = "user_link_flags", + expand_if_available = "user_link_flags", + ), + ], + ), + ], + ) + + cpp11_feature = feature( + name = "c++11", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-std=c++11"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "alwayslink", + "hardening", + "warnings", + "frame-pointer", + "build-id", + "no-canonical-prefixes", + "linker-bin-path", + ], + ) + elif (ctx.attr.cpu == "darwin"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "hardening", + "warnings", + "frame-pointer", + "no-canonical-prefixes", + "linker-bin-path", + "undefined-dynamic", + ], + ) + else: + common_feature = None + + if (ctx.attr.cpu == "local"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + alwayslink_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + build_id_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + if ctx.attr.cuda_path: + features += [cuda_path_feature] + elif (ctx.attr.cpu == "darwin"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + undefined_dynamic_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + elif (ctx.attr.cpu == "x64_windows"): + features = [ + no_legacy_features_feature, + redirector_feature, + nologo_feature, + has_configured_linker_path_feature, + no_stripping_feature, + targets_windows_feature, + copy_dynamic_libraries_to_binary_feature, + default_compile_flags_feature, + msvc_env_feature, + include_paths_feature, + preprocessor_defines_feature, + parse_showincludes_feature, + generate_pdb_file_feature, + shared_flag_feature, + linkstamps_feature, + output_execpath_flags_feature, + archiver_flags_feature, + input_param_flags_feature, + linker_subsystem_flag_feature, + user_link_flags_feature, + default_link_flags_feature, + linker_param_file_feature, + static_link_msvcrt_feature, + static_link_msvcrt_no_debug_feature, + dynamic_link_msvcrt_no_debug_feature, + static_link_msvcrt_debug_feature, + dynamic_link_msvcrt_debug_feature, + dbg_feature, + fastbuild_feature, + opt_feature, + user_compile_flags_feature, + sysroot_feature, + unfiltered_compile_flags_feature, + compiler_output_flags_feature, + compiler_input_flags_feature, + def_file_feature, + windows_export_all_symbols_feature, + no_windows_export_all_symbols_feature, + supports_dynamic_linker_feature, + supports_interface_shared_libraries_feature, + ] + else: + fail("Unreachable") + + cxx_builtin_include_directories = ctx.attr.builtin_include_directories + + if (ctx.attr.cpu == "x64_windows"): + tool_paths = [ + tool_path(name = "ar", path = ctx.attr.msvc_lib_path), + tool_path(name = "ml", path = ctx.attr.msvc_ml_path), + tool_path(name = "cpp", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcc", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcov", path = "wrapper/bin/msvc_nop.bat"), + tool_path(name = "ld", path = ctx.attr.msvc_link_path), + tool_path(name = "nm", path = "wrapper/bin/msvc_nop.bat"), + tool_path( + name = "objcopy", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "objdump", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "strip", + path = "wrapper/bin/msvc_nop.bat", + ), + ] + elif (ctx.attr.cpu == "local"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/ar"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + elif (ctx.attr.cpu == "darwin"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/libtool"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + else: + fail("Unreachable") + + out = ctx.actions.declare_file(ctx.label.name) + ctx.actions.write(out, "Fake executable") + return [ + cc_common.create_cc_toolchain_config_info( + ctx = ctx, + features = features, + action_configs = action_configs, + artifact_name_patterns = [], + cxx_builtin_include_directories = cxx_builtin_include_directories, + toolchain_identifier = toolchain_identifier, + host_system_name = host_system_name, + target_system_name = target_system_name, + target_cpu = target_cpu, + target_libc = target_libc, + compiler = compiler, + abi_version = abi_version, + abi_libc_version = abi_libc_version, + tool_paths = tool_paths, + make_variables = [], + builtin_sysroot = builtin_sysroot, + cc_target_os = cc_target_os, + ), + DefaultInfo( + executable = out, + ), + ] + +cc_toolchain_config = rule( + implementation = _impl, + attrs = { + "cpu": attr.string(mandatory = True, values = ["darwin", "local", "x64_windows"]), + "builtin_include_directories": attr.string_list(), + "extra_no_canonical_prefixes_flags": attr.string_list(), + "host_compiler_path": attr.string(), + "host_compiler_prefix": attr.string(), + "host_compiler_warnings": attr.string_list(), + "host_unfiltered_compile_flags": attr.string_list(), + "linker_bin_path": attr.string(), + "builtin_sysroot": attr.string(), + "cuda_path": attr.string(), + "msvc_cl_path": attr.string(default = "msvc_not_used"), + "msvc_env_include": attr.string(default = "msvc_not_used"), + "msvc_env_lib": attr.string(default = "msvc_not_used"), + "msvc_env_path": attr.string(default = "msvc_not_used"), + "msvc_env_tmp": attr.string(default = "msvc_not_used"), + "msvc_lib_path": attr.string(default = "msvc_not_used"), + "msvc_link_path": attr.string(default = "msvc_not_used"), + "msvc_ml_path": attr.string(default = "msvc_not_used"), + }, + provides = [CcToolchainConfigInfo], + executable = True, +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl new file mode 100755 index 000000000..f4f4d0ee9 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl @@ -0,0 +1,264 @@ +#!/usr/bin/env python +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Crosstool wrapper for compiling CUDA programs. + +SYNOPSIS: + crosstool_wrapper_is_not_gcc [options passed in by cc_library() + or cc_binary() rule] + +DESCRIPTION: + This script is expected to be called by the cc_library() or cc_binary() bazel + rules. When the option "-x cuda" is present in the list of arguments passed + to this script, it invokes the nvcc CUDA compiler. Most arguments are passed + as is as a string to --compiler-options of nvcc. When "-x cuda" is not + present, this wrapper invokes hybrid_driver_is_not_gcc with the input + arguments as is. + +NOTES: + Changes to the contents of this file must be propagated from + //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc to + //third_party/gpus/crosstool/v*/*/clang/bin/crosstool_wrapper_is_not_gcc +""" + +from __future__ import print_function + +__author__ = 'keveman@google.com (Manjunath Kudlur)' + +from argparse import ArgumentParser +import os +import subprocess +import re +import sys +import pipes + +# Template values set by cuda_autoconf. +CPU_COMPILER = ('%{cpu_compiler}') +GCC_HOST_COMPILER_PATH = ('%{gcc_host_compiler_path}') + +NVCC_PATH = '%{nvcc_path}' +PREFIX_DIR = os.path.dirname(GCC_HOST_COMPILER_PATH) +NVCC_VERSION = '%{cuda_version}' + +def Log(s): + print('gpus/crosstool: {0}'.format(s)) + + +def GetOptionValue(argv, option): + """Extract the list of values for option from the argv list. + + Args: + argv: A list of strings, possibly the argv passed to main(). + option: The option whose value to extract, without the leading '-'. + + Returns: + A list of values, either directly following the option, + (eg., -opt val1 val2) or values collected from multiple occurrences of + the option (eg., -opt val1 -opt val2). + """ + + parser = ArgumentParser() + parser.add_argument('-' + option, nargs='*', action='append') + args, _ = parser.parse_known_args(argv) + if not args or not vars(args)[option]: + return [] + else: + return sum(vars(args)[option], []) + + +def GetHostCompilerOptions(argv): + """Collect the -isystem, -iquote, and --sysroot option values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be used as the --compiler-options to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-isystem', nargs='*', action='append') + parser.add_argument('-iquote', nargs='*', action='append') + parser.add_argument('--sysroot', nargs=1) + parser.add_argument('-g', nargs='*', action='append') + parser.add_argument('-fno-canonical-system-headers', action='store_true') + + args, _ = parser.parse_known_args(argv) + + opts = '' + + if args.isystem: + opts += ' -isystem ' + ' -isystem '.join(sum(args.isystem, [])) + if args.iquote: + opts += ' -iquote ' + ' -iquote '.join(sum(args.iquote, [])) + if args.g: + opts += ' -g' + ' -g'.join(sum(args.g, [])) + if args.fno_canonical_system_headers: + opts += ' -fno-canonical-system-headers' + if args.sysroot: + opts += ' --sysroot ' + args.sysroot[0] + + return opts + +def _update_options(nvcc_options): + if NVCC_VERSION in ("7.0",): + return nvcc_options + + update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" } + return [ update_options[opt] if opt in update_options else opt + for opt in nvcc_options ] + +def GetNvccOptions(argv): + """Collect the -nvcc_options values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be passed directly to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-nvcc_options', nargs='*', action='append') + + args, _ = parser.parse_known_args(argv) + + if args.nvcc_options: + options = _update_options(sum(args.nvcc_options, [])) + return ' '.join(['--'+a for a in options]) + return '' + + +def InvokeNvcc(argv, log=False): + """Call nvcc with arguments assembled from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + log: True if logging is requested. + + Returns: + The return value of calling os.system('nvcc ' + args) + """ + + host_compiler_options = GetHostCompilerOptions(argv) + nvcc_compiler_options = GetNvccOptions(argv) + opt_option = GetOptionValue(argv, 'O') + m_options = GetOptionValue(argv, 'm') + m_options = ''.join([' -m' + m for m in m_options if m in ['32', '64']]) + include_options = GetOptionValue(argv, 'I') + out_file = GetOptionValue(argv, 'o') + depfiles = GetOptionValue(argv, 'MF') + defines = GetOptionValue(argv, 'D') + defines = ''.join([' -D' + define for define in defines]) + undefines = GetOptionValue(argv, 'U') + undefines = ''.join([' -U' + define for define in undefines]) + std_options = GetOptionValue(argv, 'std') + # currently only c++11 is supported by Cuda 7.0 std argument + nvcc_allowed_std_options = ["c++11"] + std_options = ''.join([' -std=' + define + for define in std_options if define in nvcc_allowed_std_options]) + + # The list of source files get passed after the -c option. I don't know of + # any other reliable way to just get the list of source files to be compiled. + src_files = GetOptionValue(argv, 'c') + + # Pass -w through from host to nvcc, but don't do anything fancier with + # warnings-related flags, since they're not necessarily the same across + # compilers. + warning_options = ' -w' if '-w' in argv else '' + + if len(src_files) == 0: + return 1 + if len(out_file) != 1: + return 1 + + opt = (' -O2' if (len(opt_option) > 0 and int(opt_option[0]) > 0) + else ' -g -G') + + includes = (' -I ' + ' -I '.join(include_options) + if len(include_options) > 0 + else '') + + # Unfortunately, there are other options that have -c prefix too. + # So allowing only those look like C/C++ files. + src_files = [f for f in src_files if + re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)] + srcs = ' '.join(src_files) + out = ' -o ' + out_file[0] + + supported_cuda_compute_capabilities = [ %{cuda_compute_capabilities} ] + nvccopts = '-D_FORCE_INLINES ' + for capability in supported_cuda_compute_capabilities: + capability = capability.replace('.', '') + nvccopts += r'-gencode=arch=compute_%s,\"code=sm_%s,compute_%s\" ' % ( + capability, capability, capability) + nvccopts += ' ' + nvcc_compiler_options + nvccopts += undefines + nvccopts += defines + nvccopts += std_options + nvccopts += m_options + nvccopts += warning_options + + if depfiles: + # Generate the dependency file + depfile = depfiles[0] + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + '"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' ' + srcs + ' -M -o ' + depfile) + if log: Log(cmd) + exit_status = os.system(cmd) + if exit_status != 0: + return exit_status + + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + ' -fPIC"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' -c ' + srcs + out) + + # TODO(zhengxq): for some reason, 'gcc' needs this help to find 'as'. + # Need to investigate and fix. + cmd = 'PATH=' + PREFIX_DIR + ':$PATH ' + cmd + if log: Log(cmd) + return os.system(cmd) + + +def main(): + parser = ArgumentParser() + parser.add_argument('-x', nargs=1) + parser.add_argument('--cuda_log', action='store_true') + args, leftover = parser.parse_known_args(sys.argv[1:]) + + if args.x and args.x[0] == 'cuda': + if args.cuda_log: Log('-x cuda') + leftover = [pipes.quote(s) for s in leftover] + if args.cuda_log: Log('using nvcc') + return InvokeNvcc(leftover, log=args.cuda_log) + + # Strip our flags before passing through to the CPU compiler for files which + # are not -x cuda. We can't just pass 'leftover' because it also strips -x. + # We not only want to pass -x to the CPU compiler, but also keep it in its + # relative location in the argv list (the compiler is actually sensitive to + # this). + cpu_compiler_flags = [flag for flag in sys.argv[1:] + if not flag.startswith(('--cuda_log'))] + + return subprocess.call([CPU_COMPILER] + cpu_compiler_flags) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl new file mode 100644 index 000000000..f06357db9 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl @@ -0,0 +1,201 @@ +#!/usr/bin/env python +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Crosstool wrapper for compiling CUDA programs with nvcc on Windows. + +DESCRIPTION: + This script is the Windows version of //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc +""" + +from __future__ import print_function + +from argparse import ArgumentParser +import os +import subprocess +import re +import sys +import pipes +import tempfile + +# Template values set by cuda_autoconf. +CPU_COMPILER = ('%{cpu_compiler}') +GCC_HOST_COMPILER_PATH = ('%{gcc_host_compiler_path}') + +NVCC_PATH = '%{nvcc_path}' +NVCC_VERSION = '%{cuda_version}' +NVCC_TEMP_DIR = "%{nvcc_tmp_dir}" +DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,6.0' + +# Taken from environment variable for supported TF CUDA Compute Capabilities +# eg. export TF_CUDA_COMPUTE_CAPABILITIES=3.5,3.7,5.2,6.0,6.1,7.0 +supported_cuda_compute_capabilities = os.environ.get( + 'TF_CUDA_COMPUTE_CAPABILITIES', + DEFAULT_CUDA_COMPUTE_CAPABILITIES).split(',') + +def Log(s): + print('gpus/crosstool: {0}'.format(s)) + + +def GetOptionValue(argv, option): + """Extract the list of values for option from options. + + Args: + option: The option whose value to extract, without the leading '/'. + + Returns: + 1. A list of values, either directly following the option, + (eg., /opt val1 val2) or values collected from multiple occurrences of + the option (eg., /opt val1 /opt val2). + 2. The leftover options. + """ + + parser = ArgumentParser(prefix_chars='/') + parser.add_argument('/' + option, nargs='*', action='append') + args, leftover = parser.parse_known_args(argv) + if args and vars(args)[option]: + return (sum(vars(args)[option], []), leftover) + return ([], leftover) + +def _update_options(nvcc_options): + if NVCC_VERSION in ("7.0",): + return nvcc_options + + update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" } + return [ update_options[opt] if opt in update_options else opt + for opt in nvcc_options ] + +def GetNvccOptions(argv): + """Collect the -nvcc_options values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + 1. The string that can be passed directly to nvcc. + 2. The leftover options. + """ + + parser = ArgumentParser() + parser.add_argument('-nvcc_options', nargs='*', action='append') + + args, leftover = parser.parse_known_args(argv) + + if args.nvcc_options: + options = _update_options(sum(args.nvcc_options, [])) + return (['--' + a for a in options], leftover) + return ([], leftover) + + +def InvokeNvcc(argv, log=False): + """Call nvcc with arguments assembled from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + log: True if logging is requested. + + Returns: + The return value of calling os.system('nvcc ' + args) + """ + + src_files = [f for f in argv if + re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)] + if len(src_files) == 0: + raise Error('No source files found for cuda compilation.') + + out_file = [ f for f in argv if f.startswith('/Fo') ] + if len(out_file) != 1: + raise Error('Please sepecify exactly one output file for cuda compilation.') + out = ['-o', out_file[0][len('/Fo'):]] + + nvcc_compiler_options, argv = GetNvccOptions(argv) + + opt_option, argv = GetOptionValue(argv, 'O') + opt = ['-g'] + if (len(opt_option) > 0 and opt_option[0] != 'd'): + opt = ['-O2'] + + include_options, argv = GetOptionValue(argv, 'I') + includes = ["-I " + include for include in include_options] + + defines, argv = GetOptionValue(argv, 'D') + defines = ['-D' + define for define in defines] + + undefines, argv = GetOptionValue(argv, 'U') + undefines = ['-U' + define for define in undefines] + + # The rest of the unrecongized options should be passed to host compiler + host_compiler_options = [option for option in argv if option not in (src_files + out_file)] + + m_options = ["-m64"] + + nvccopts = ['-D_FORCE_INLINES'] + for capability in supported_cuda_compute_capabilities: + capability = capability.replace('.', '') + nvccopts += [r'-gencode=arch=compute_%s,"code=sm_%s,compute_%s"' % ( + capability, capability, capability)] + nvccopts += nvcc_compiler_options + nvccopts += undefines + nvccopts += defines + nvccopts += m_options + nvccopts += ['--compiler-options="' + " ".join(host_compiler_options) + '"'] + nvccopts += ['-x', 'cu'] + opt + includes + out + ['-c'] + src_files + # Specify a unique temp directory for nvcc to generate intermediate files, + # then Bazel can ignore files under NVCC_TEMP_DIR during dependency check + # http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#options-for-guiding-compiler-driver + # Different actions are sharing NVCC_TEMP_DIR, so we cannot remove it if the directory already exists. + if os.path.isfile(NVCC_TEMP_DIR): + os.remove(NVCC_TEMP_DIR) + if not os.path.exists(NVCC_TEMP_DIR): + os.makedirs(NVCC_TEMP_DIR) + # Provide a unique dir for each compiling action to avoid conflicts. + tempdir = tempfile.mkdtemp(dir = NVCC_TEMP_DIR) + nvccopts += ['--keep', '--keep-dir', tempdir] + cmd = [NVCC_PATH] + nvccopts + if log: + Log(cmd) + proc = subprocess.Popen(cmd, + stdout=sys.stdout, + stderr=sys.stderr, + env=os.environ.copy(), + shell=True) + proc.wait() + return proc.returncode + +def main(): + parser = ArgumentParser() + parser.add_argument('-x', nargs=1) + parser.add_argument('--cuda_log', action='store_true') + args, leftover = parser.parse_known_args(sys.argv[1:]) + + if args.x and args.x[0] == 'cuda': + if args.cuda_log: Log('-x cuda') + leftover = [pipes.quote(s) for s in leftover] + if args.cuda_log: Log('using nvcc') + return InvokeNvcc(leftover, log=args.cuda_log) + + # Strip our flags before passing through to the CPU compiler for files which + # are not -x cuda. We can't just pass 'leftover' because it also strips -x. + # We not only want to pass -x to the CPU compiler, but also keep it in its + # relative location in the argv list (the compiler is actually sensitive to + # this). + cpu_compiler_flags = [flag for flag in sys.argv[1:] + if not flag.startswith(('--cuda_log')) + and not flag.startswith(('-nvcc_options'))] + + return subprocess.call([CPU_COMPILER] + cpu_compiler_flags) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.tpl new file mode 100644 index 000000000..f28acff6f --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.tpl @@ -0,0 +1,177 @@ +load(":build_defs.bzl", "cuda_header_library") +#load("@bazel_skylib//:bzl_library.bzl", "bzl_library") + +licenses(["restricted"]) # MPL2, portions GPL v3, LGPL v3, BSD-like + +package(default_visibility = ["//visibility:public"]) + +config_setting( + name = "using_nvcc", + values = { + "define": "using_cuda_nvcc=true", + }, +) + +config_setting( + name = "using_clang", + values = { + "define": "using_cuda_clang=true", + }, +) + +# Equivalent to using_clang && -c opt. +config_setting( + name = "using_clang_opt", + values = { + "define": "using_cuda_clang=true", + "compilation_mode": "opt", + }, +) + +config_setting( + name = "darwin", + values = {"cpu": "darwin"}, +) + +config_setting( + name = "freebsd", + values = {"cpu": "freebsd"}, +) + +cuda_header_library( + name = "cuda_headers", + hdrs = [ + "cuda/cuda_config.h", + ":cuda-include" + ], + include_prefix = "third_party/gpus", + includes = [ + ".", # required to include cuda/cuda/cuda_config.h as cuda/config.h + "cuda/include", + ], +) + +cc_library( + name = "cudart_static", + srcs = ["cuda/lib/%{cudart_static_lib}"], + linkopts = select({ + ":freebsd": [], + "//conditions:default": ["-ldl"], + }) + [ + "-lpthread", + %{cudart_static_linkopt} + ], +) + +cc_library( + name = "cuda_driver", + srcs = ["cuda/lib/%{cuda_driver_lib}"], +) + +cc_library( + name = "cudart", + srcs = ["cuda/lib/%{cudart_lib}"], + data = ["cuda/lib/%{cudart_lib}"], + linkstatic = 1, +) + +cuda_header_library( + name = "cublas_headers", + hdrs = [":cublas-include"], + include_prefix = "third_party/gpus/cuda/include", + strip_include_prefix = "cublas/include", + deps = [":cuda_headers"], + includes = ["cublas/include"], +) + +cc_library( + name = "cublas", + srcs = ["cuda/lib/%{cublas_lib}"], + data = ["cuda/lib/%{cublas_lib}"], + linkstatic = 1, +) + +cc_library( + name = "cusolver", + srcs = ["cuda/lib/%{cusolver_lib}"], + data = ["cuda/lib/%{cusolver_lib}"], + linkopts = ["-lgomp"], + linkstatic = 1, +) + +cc_library( + name = "cudnn", + srcs = ["cuda/lib/%{cudnn_lib}"], + data = ["cuda/lib/%{cudnn_lib}"], + linkstatic = 1, +) + +cc_library( + name = "cudnn_header", + hdrs = [":cudnn-include"], + include_prefix = "third_party/gpus/cudnn", + strip_include_prefix = "cudnn/include", + deps = [":cuda_headers"], +) + +cc_library( + name = "cufft", + srcs = ["cuda/lib/%{cufft_lib}"], + data = ["cuda/lib/%{cufft_lib}"], + linkstatic = 1, +) + +cc_library( + name = "curand", + srcs = ["cuda/lib/%{curand_lib}"], + data = ["cuda/lib/%{curand_lib}"], + linkstatic = 1, +) + +cc_library( + name = "cuda", + deps = [ + ":cublas", + ":cuda_headers", + ":cudart", + ":cudnn", + ":cufft", + ":curand", + ], +) + +cuda_header_library( + name = "cupti_headers", + hdrs = [":cuda-extras"], + include_prefix="third_party/gpus", + includes = ["cuda/extras/CUPTI/include/"], + deps = [":cuda_headers"], +) + +cc_library( + name = "cupti_dsos", + data = ["cuda/lib/%{cupti_lib}"], +) + +cc_library( + name = "cusparse", + srcs = ["cuda/lib/%{cusparse_lib}"], + data = ["cuda/lib/%{cusparse_lib}"], + linkopts = ["-lgomp"], + linkstatic = 1, +) + +cc_library( + name = "libdevice_root", + data = [":cuda-nvvm"], +) + +#bzl_library( +# name = "build_defs_bzl", +# srcs = ["build_defs.bzl"], +# deps = [ +# "@bazel_skylib//lib:selects", +# ], +#) + +%{copy_rules} diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.windows.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.windows.tpl new file mode 100644 index 000000000..c2d30ce25 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/BUILD.windows.tpl @@ -0,0 +1,162 @@ +load(":build_defs.bzl", "cuda_header_library") + +licenses(["restricted"]) # MPL2, portions GPL v3, LGPL v3, BSD-like + +package(default_visibility = ["//visibility:public"]) + +config_setting( + name = "using_nvcc", + values = { + "define": "using_cuda_nvcc=true", + }, +) + +config_setting( + name = "using_clang", + values = { + "define": "using_cuda_clang=true", + }, +) + +# Equivalent to using_clang && -c opt. +config_setting( + name = "using_clang_opt", + values = { + "define": "using_cuda_clang=true", + "compilation_mode": "opt", + }, +) + +config_setting( + name = "darwin", + values = {"cpu": "darwin"}, +) + +config_setting( + name = "freebsd", + values = {"cpu": "freebsd"}, +) + +# Provides CUDA headers for '#include "third_party/gpus/cuda/include/cuda.h"' +# All clients including TensorFlow should use these directives. +cuda_header_library( + name = "cuda_headers", + hdrs = [ + "cuda/cuda_config.h", + ":cuda-include" + ], + include_prefix = "third_party/gpus", + includes = [ + ".", # required to include cuda/cuda/cuda_config.h as cuda/config.h + "cuda/include", + ], +) + +cc_import( + name = "cudart_static", + # /WHOLEARCHIVE:cudart_static.lib will cause a + # "Internal error during CImplib::EmitThunk" error. + # Treat this library as interface library to avoid being whole archived when + # linking a DLL that depends on this. + # TODO(pcloudy): Remove this rule after b/111278841 is resolved. + interface_library = "cuda/lib/%{cudart_static_lib}", + system_provided = 1, +) + +cc_import( + name = "cuda_driver", + interface_library = "cuda/lib/%{cuda_driver_lib}", + system_provided = 1, +) + +cc_import( + name = "cudart", + interface_library = "cuda/lib/%{cudart_lib}", + system_provided = 1, +) + +cuda_header_library( + name = "cublas_headers", + hdrs = [":cublas-include"], + include_prefix = "third_party/gpus/cuda/include", + includes = ["cublas/include"], + strip_include_prefix = "cublas/include", + deps = [":cuda_headers"], +) + +cc_import( + name = "cublas", + interface_library = "cuda/lib/%{cublas_lib}", + system_provided = 1, +) + +cc_import( + name = "cusolver", + interface_library = "cuda/lib/%{cusolver_lib}", + system_provided = 1, +) + +cc_import( + name = "cudnn", + interface_library = "cuda/lib/%{cudnn_lib}", + system_provided = 1, +) + +cc_library( + name = "cudnn_header", + hdrs = [":cudnn-include"], + include_prefix = "third_party/gpus/cudnn", + strip_include_prefix = "cudnn/include", + deps = [":cuda_headers"], +) + +cc_import( + name = "cufft", + interface_library = "cuda/lib/%{cufft_lib}", + system_provided = 1, +) + +cc_import( + name = "curand", + interface_library = "cuda/lib/%{curand_lib}", + system_provided = 1, +) + +cc_library( + name = "cuda", + deps = [ + ":cublas", + ":cuda_headers", + ":cudart", + ":cudnn", + ":cufft", + ":curand", + ], +) + +cuda_header_library( + name = "cupti_headers", + hdrs = [":cuda-extras"], + include_prefix="third_party/gpus", + includes = ["cuda/extras/CUPTI/include/"], + deps = [":cuda_headers"], +) + +cc_import( + name = "cupti_dsos", + interface_library = "cuda/lib/%{cupti_lib}", + system_provided = 1, +) + +cc_import( + name = "cusparse", + interface_library = "cuda/lib/%{cusparse_lib}", + system_provided = 1, +) + +cc_library( + name = "libdevice_root", + data = [":cuda-nvvm"], +) + +%{copy_rules} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/build_defs.bzl.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/build_defs.bzl.tpl new file mode 100644 index 000000000..43a7e13c6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/build_defs.bzl.tpl @@ -0,0 +1,61 @@ +# Macros for building CUDA code. +def if_cuda(if_true, if_false = []): + """Shorthand for select()'ing on whether we're building with CUDA. + + Returns a select statement which evaluates to if_true if we're building + with CUDA enabled. Otherwise, the select statement evaluates to if_false. + + """ + return select({ + "@local_config_cuda//cuda:using_nvcc": if_true, + "//conditions:default": if_false + }) + + +def cuda_default_copts(): + """Default options for all CUDA compilations.""" + return if_cuda(["-x", "cuda", "-DGOOGLE_CUDA=1"] + %{cuda_extra_copts}) + + +def cuda_is_configured(): + """Returns true if CUDA was enabled during the configure process.""" + return %{cuda_is_configured} + +def if_cuda_is_configured(x): + """Tests if the CUDA was enabled during the configure process. + + Unlike if_cuda(), this does not require that we are building with + --config=cuda. Used to allow non-CUDA code to depend on CUDA libraries. + """ + if cuda_is_configured(): + return x + return [] + +def cuda_header_library( + name, + hdrs, + include_prefix = None, + strip_include_prefix = None, + deps = [], + **kwargs): + """Generates a cc_library containing both virtual and system include paths. + + Generates both a header-only target with virtual includes plus the full + target without virtual includes. This works around the fact that bazel can't + mix 'includes' and 'include_prefix' in the same target.""" + + native.cc_library( + name = name + "_virtual", + hdrs = hdrs, + include_prefix = include_prefix, + strip_include_prefix = strip_include_prefix, + deps = deps, + visibility = ["//visibility:private"], + ) + + native.cc_library( + name = name, + textual_hdrs = hdrs, + deps = deps + [":%s_virtual" % name], + **kwargs + ) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/cuda_config.h.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/cuda_config.h.tpl new file mode 100644 index 000000000..db53dd855 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda/cuda_config.h.tpl @@ -0,0 +1,27 @@ +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef CUDA_CUDA_CONFIG_H_ +#define CUDA_CUDA_CONFIG_H_ + +#define TF_CUDA_CAPABILITIES %{cuda_compute_capabilities} + +#define TF_CUDA_VERSION "%{cuda_version}" +#define TF_CUDA_LIB_VERSION "%{cuda_lib_version}" +#define TF_CUDNN_VERSION "%{cudnn_version}" + +#define TF_CUDA_TOOLKIT_PATH "%{cuda_toolkit_path}" + +#endif // CUDA_CUDA_CONFIG_H_ \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda_configure.bzl b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda_configure.bzl new file mode 100644 index 000000000..17fe1fd2f --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/cuda_configure.bzl @@ -0,0 +1,1286 @@ +"""Repository rule for CUDA autoconfiguration. + +`cuda_configure` depends on the following environment variables: + + * `TF_NEED_CUDA`: Whether to enable building with CUDA. + * `GCC_HOST_COMPILER_PATH`: The GCC host compiler path + * `TF_SYSROOT`: The sysroot to use when compiling. + * `TF_CUDA_PATHS`: The base paths to look for CUDA and cuDNN. Default is + `/usr/local/cuda,usr/`. + * `CUDA_TOOLKIT_PATH` (deprecated): The path to the CUDA toolkit. Default is + `/usr/local/cuda`. + * `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then + use the system default. + * `TF_CUDNN_VERSION`: The version of the cuDNN library. + * `CUDNN_INSTALL_PATH` (deprecated): The path to the cuDNN library. Default is + `/usr/local/cuda`. + * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is + `3.5,5.2`. + * `PYTHON_BIN_PATH`: The python binary path +""" + +load( + "@bazel_tools//tools/cpp:lib_cc_configure.bzl", + "escape_string", + "get_env_var", +) +load( + "@bazel_tools//tools/cpp:windows_cc_configure.bzl", + "find_msvc_tool", + "find_vc_path", + "setup_vc_env_vars", +) + +_GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH" +_GCC_HOST_COMPILER_PREFIX = "GCC_HOST_COMPILER_PREFIX" +_TF_SYSROOT = "TF_SYSROOT" +_CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH" +_TF_CUDA_VERSION = "TF_CUDA_VERSION" +_TF_CUDNN_VERSION = "TF_CUDNN_VERSION" +_CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH" +_TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES" +_TF_CUDA_CONFIG_REPO = "TF_CUDA_CONFIG_REPO" +_PYTHON_BIN_PATH = "PYTHON_BIN_PATH" + +_DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"] + +def to_list_of_strings(elements): + """Convert the list of ["a", "b", "c"] into '"a", "b", "c"'. + + This is to be used to put a list of strings into the bzl file templates + so it gets interpreted as list of strings in Starlark. + + Args: + elements: list of string elements + + Returns: + single string of elements wrapped in quotes separated by a comma.""" + quoted_strings = ["\"" + element + "\"" for element in elements] + return ", ".join(quoted_strings) + +def verify_build_defines(params): + """Verify all variables that crosstool/BUILD.tpl expects are substituted. + + Args: + params: dict of variables that will be passed to the BUILD.tpl template. + """ + missing = [] + for param in [ + "cxx_builtin_include_directories", + "extra_no_canonical_prefixes_flags", + "host_compiler_path", + "host_compiler_prefix", + "host_compiler_warnings", + "linker_bin_path", + "compiler_deps", + "msvc_cl_path", + "msvc_env_include", + "msvc_env_lib", + "msvc_env_path", + "msvc_env_tmp", + "msvc_lib_path", + "msvc_link_path", + "msvc_ml_path", + "unfiltered_compile_flags", + "win_compiler_deps", + ]: + if ("%{" + param + "}") not in params: + missing.append(param) + + if missing: + auto_configure_fail( + "BUILD.tpl template is missing these variables: " + + str(missing) + + ".\nWe only got: " + + str(params) + + ".", + ) + +def _get_python_bin(repository_ctx): + """Gets the python bin path.""" + python_bin = repository_ctx.os.environ.get(_PYTHON_BIN_PATH) + if python_bin != None: + return python_bin + python_bin_name = "python.exe" if _is_windows(repository_ctx) else "python" + python_bin_path = repository_ctx.which(python_bin_name) + if python_bin_path != None: + return str(python_bin_path) + auto_configure_fail( + "Cannot find python in PATH, please make sure " + + "python is installed and add its directory in PATH, or --define " + + "%s='/something/else'.\nPATH=%s" % ( + _PYTHON_BIN_PATH, + repository_ctx.os.environ.get("PATH", ""), + ), + ) + +def _get_nvcc_tmp_dir_for_windows(repository_ctx): + """Return the Windows tmp directory for nvcc to generate intermediate source files.""" + escaped_tmp_dir = escape_string( + get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace( + "\\", + "\\\\", + ), + ) + return escaped_tmp_dir + "\\\\nvcc_inter_files_tmp_dir" + +def _get_nvcc_tmp_dir_for_unix(repository_ctx): + """Return the UNIX tmp directory for nvcc to generate intermediate source files.""" + escaped_tmp_dir = escape_string( + get_env_var(repository_ctx, "TMPDIR", "/tmp"), + ) + return escaped_tmp_dir + "/nvcc_inter_files_tmp_dir" + +def _get_msvc_compiler(repository_ctx): + vc_path = find_vc_path(repository_ctx) + return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\\", "/") + +def _get_win_cuda_defines(repository_ctx): + """Return CROSSTOOL defines for Windows""" + + # If we are not on Windows, return fake vaules for Windows specific fields. + # This ensures the CROSSTOOL file parser is happy. + if not _is_windows(repository_ctx): + return { + "%{msvc_env_tmp}": "msvc_not_used", + "%{msvc_env_path}": "msvc_not_used", + "%{msvc_env_include}": "msvc_not_used", + "%{msvc_env_lib}": "msvc_not_used", + "%{msvc_cl_path}": "msvc_not_used", + "%{msvc_ml_path}": "msvc_not_used", + "%{msvc_link_path}": "msvc_not_used", + "%{msvc_lib_path}": "msvc_not_used", + } + + vc_path = find_vc_path(repository_ctx) + if not vc_path: + auto_configure_fail( + "Visual C++ build tools not found on your machine." + + "Please check your installation following https://docs.bazel.build/versions/master/windows.html#using", + ) + return {} + + env = setup_vc_env_vars(repository_ctx, vc_path) + escaped_paths = escape_string(env["PATH"]) + escaped_include_paths = escape_string(env["INCLUDE"]) + escaped_lib_paths = escape_string(env["LIB"]) + escaped_tmp_dir = escape_string( + get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace( + "\\", + "\\\\", + ), + ) + + msvc_cl_path = _get_python_bin(repository_ctx) + msvc_ml_path = find_msvc_tool(repository_ctx, vc_path, "ml64.exe").replace( + "\\", + "/", + ) + msvc_link_path = find_msvc_tool(repository_ctx, vc_path, "link.exe").replace( + "\\", + "/", + ) + msvc_lib_path = find_msvc_tool(repository_ctx, vc_path, "lib.exe").replace( + "\\", + "/", + ) + + # nvcc will generate some temporary source files under %{nvcc_tmp_dir} + # The generated files are guaranteed to have unique name, so they can share + # the same tmp directory + escaped_cxx_include_directories = [ + _get_nvcc_tmp_dir_for_windows(repository_ctx), + ] + for path in escaped_include_paths.split(";"): + if path: + escaped_cxx_include_directories.append(path) + + return { + "%{msvc_env_tmp}": escaped_tmp_dir, + "%{msvc_env_path}": escaped_paths, + "%{msvc_env_include}": escaped_include_paths, + "%{msvc_env_lib}": escaped_lib_paths, + "%{msvc_cl_path}": msvc_cl_path, + "%{msvc_ml_path}": msvc_ml_path, + "%{msvc_link_path}": msvc_link_path, + "%{msvc_lib_path}": msvc_lib_path, + "%{cxx_builtin_include_directories}": to_list_of_strings( + escaped_cxx_include_directories, + ), + } + +# TODO(dzc): Once these functions have been factored out of Bazel's +# cc_configure.bzl, load them from @bazel_tools instead. +# BEGIN cc_configure common functions. +def find_cc(repository_ctx): + """Find the C++ compiler.""" + if _is_windows(repository_ctx): + return _get_msvc_compiler(repository_ctx) + + target_cc_name = "gcc" + cc_path_envvar = _GCC_HOST_COMPILER_PATH + cc_name = target_cc_name + + if cc_path_envvar in repository_ctx.os.environ: + cc_name_from_env = repository_ctx.os.environ[cc_path_envvar].strip() + if cc_name_from_env: + cc_name = cc_name_from_env + if cc_name.startswith("/"): + # Absolute path, maybe we should make this supported by our which function. + return cc_name + cc = repository_ctx.which(cc_name) + if cc == None: + fail(("Cannot find {}, either correct your path or set the {}" + + " environment variable").format(target_cc_name, cc_path_envvar)) + return str(cc) + +_INC_DIR_MARKER_BEGIN = "#include <...>" + +# OSX add " (framework directory)" at the end of line, strip it. +_OSX_FRAMEWORK_SUFFIX = " (framework directory)" +_OSX_FRAMEWORK_SUFFIX_LEN = len(_OSX_FRAMEWORK_SUFFIX) + +def _cxx_inc_convert(path): + """Convert path returned by cc -E xc++ in a complete path.""" + path = path.strip() + if path.endswith(_OSX_FRAMEWORK_SUFFIX): + path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip() + return path + +def _normalize_include_path(repository_ctx, path): + """Normalizes include paths before writing them to the crosstool. + + If path points inside the 'crosstool' folder of the repository, a relative + path is returned. + If path points outside the 'crosstool' folder, an absolute path is returned. + """ + path = str(repository_ctx.path(path)) + crosstool_folder = str(repository_ctx.path(".").get_child("crosstool")) + + if path.startswith(crosstool_folder): + # We drop the path to "$REPO/crosstool" and a trailing path separator. + return path[len(crosstool_folder) + 1:] + return path + +def _get_cxx_inc_directories_impl(repository_ctx, cc, lang_is_cpp, tf_sysroot): + """Compute the list of default C or C++ include directories.""" + if lang_is_cpp: + lang = "c++" + else: + lang = "c" + sysroot = [] + if tf_sysroot: + sysroot += ["--sysroot", tf_sysroot] + result = repository_ctx.execute([cc, "-E", "-x" + lang, "-", "-v"] + + sysroot) + index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN) + if index1 == -1: + return [] + index1 = result.stderr.find("\n", index1) + if index1 == -1: + return [] + index2 = result.stderr.rfind("\n ") + if index2 == -1 or index2 < index1: + return [] + index2 = result.stderr.find("\n", index2 + 1) + if index2 == -1: + inc_dirs = result.stderr[index1 + 1:] + else: + inc_dirs = result.stderr[index1 + 1:index2].strip() + + return [ + _normalize_include_path(repository_ctx, _cxx_inc_convert(p)) + for p in inc_dirs.split("\n") + ] + +def get_cxx_inc_directories(repository_ctx, cc, tf_sysroot): + """Compute the list of default C and C++ include directories.""" + + # For some reason `clang -xc` sometimes returns include paths that are + # different from the ones from `clang -xc++`. (Symlink and a dir) + # So we run the compiler with both `-xc` and `-xc++` and merge resulting lists + includes_cpp = _get_cxx_inc_directories_impl( + repository_ctx, + cc, + True, + tf_sysroot, + ) + includes_c = _get_cxx_inc_directories_impl( + repository_ctx, + cc, + False, + tf_sysroot, + ) + + return includes_cpp + [ + inc + for inc in includes_c + if inc not in includes_cpp + ] + +def auto_configure_fail(msg): + """Output failure message when cuda configuration fails.""" + red = "\033[0;31m" + no_color = "\033[0m" + fail("\n%sCuda Configuration Error:%s %s\n" % (red, no_color, msg)) + +# END cc_configure common functions (see TODO above). + +def _cuda_include_path(repository_ctx, cuda_config): + """Generates the Starlark string with cuda include directories. + + Args: + repository_ctx: The repository context. + cc: The path to the gcc host compiler. + + Returns: + A list of the gcc host compiler include directories. + """ + nvcc_path = repository_ctx.path("%s/bin/nvcc%s" % ( + cuda_config.cuda_toolkit_path, + ".exe" if cuda_config.cpu_value == "Windows" else "", + )) + result = repository_ctx.execute([ + nvcc_path, + "-v", + "/dev/null", + "-o", + "/dev/null", + ]) + target_dir = "" + for one_line in result.stderr.splitlines(): + if one_line.startswith("#$ _TARGET_DIR_="): + target_dir = ( + cuda_config.cuda_toolkit_path + "/" + one_line.replace( + "#$ _TARGET_DIR_=", + "", + ) + "/include" + ) + inc_entries = [] + if target_dir != "": + inc_entries.append(target_dir) + inc_entries.append(cuda_config.cuda_toolkit_path + "/include") + return inc_entries + +def enable_cuda(repository_ctx): + """Returns whether to build with CUDA support.""" + return int(repository_ctx.os.environ.get("TF_NEED_CUDA", False)) + +def matches_version(environ_version, detected_version): + """Checks whether the user-specified version matches the detected version. + + This function performs a weak matching so that if the user specifies only + the + major or major and minor versions, the versions are still considered + matching + if the version parts match. To illustrate: + + environ_version detected_version result + ----------------------------------------- + 5.1.3 5.1.3 True + 5.1 5.1.3 True + 5 5.1 True + 5.1.3 5.1 False + 5.2.3 5.1.3 False + + Args: + environ_version: The version specified by the user via environment + variables. + detected_version: The version autodetected from the CUDA installation on + the system. + Returns: True if user-specified version matches detected version and False + otherwise. + """ + environ_version_parts = environ_version.split(".") + detected_version_parts = detected_version.split(".") + if len(detected_version_parts) < len(environ_version_parts): + return False + for i, part in enumerate(detected_version_parts): + if i >= len(environ_version_parts): + break + if part != environ_version_parts[i]: + return False + return True + +_NVCC_VERSION_PREFIX = "Cuda compilation tools, release " + +_DEFINE_CUDNN_MAJOR = "#define CUDNN_MAJOR" + +def find_cuda_define(repository_ctx, header_dir, header_file, define): + """Returns the value of a #define in a header file. + + Greps through a header file and returns the value of the specified #define. + If the #define is not found, then raise an error. + + Args: + repository_ctx: The repository context. + header_dir: The directory containing the header file. + header_file: The header file name. + define: The #define to search for. + + Returns: + The value of the #define found in the header. + """ + + # Confirm location of the header and grep for the line defining the macro. + h_path = repository_ctx.path("%s/%s" % (header_dir, header_file)) + if not h_path.exists: + auto_configure_fail("Cannot find %s at %s" % (header_file, str(h_path))) + result = repository_ctx.execute( + # Grep one more lines as some #defines are splitted into two lines. + [ + "grep", + "--color=never", + "-A1", + "-E", + define, + str(h_path), + ], + ) + if result.stderr: + auto_configure_fail("Error reading %s: %s" % (str(h_path), result.stderr)) + + # Parse the version from the line defining the macro. + if result.stdout.find(define) == -1: + auto_configure_fail( + "Cannot find line containing '%s' in %s" % (define, h_path), + ) + + # Split results to lines + lines = result.stdout.split("\n") + num_lines = len(lines) + for l in range(num_lines): + line = lines[l] + if define in line: # Find the line with define + version = line + if l != num_lines - 1 and line[-1] == "\\": # Add next line, if multiline + version = version[:-1] + lines[l + 1] + break + + # Remove any comments + version = version.split("//")[0] + + # Remove define name + version = version.replace(define, "").strip() + + # Remove the code after the version number. + version_end = version.find(" ") + if version_end != -1: + if version_end == 0: + auto_configure_fail( + "Cannot extract the version from line containing '%s' in %s" % + (define, str(h_path)), + ) + version = version[:version_end].strip() + return version + +def compute_capabilities(repository_ctx): + """Returns a list of strings representing cuda compute capabilities.""" + if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ: + return _DEFAULT_CUDA_COMPUTE_CAPABILITIES + capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES] + capabilities = capabilities_str.split(",") + for capability in capabilities: + # Workaround for Skylark's lack of support for regex. This check should + # be equivalent to checking: + # if re.match("[0-9]+.[0-9]+", capability) == None: + parts = capability.split(".") + if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit(): + auto_configure_fail("Invalid compute capability: %s" % capability) + return capabilities + +def get_cpu_value(repository_ctx): + """Returns the name of the host operating system. + + Args: + repository_ctx: The repository context. + + Returns: + A string containing the name of the host operating system. + """ + os_name = repository_ctx.os.name.lower() + if os_name.startswith("mac os"): + return "Darwin" + if os_name.find("windows") != -1: + return "Windows" + result = repository_ctx.execute(["uname", "-s"]) + return result.stdout.strip() + +def _is_windows(repository_ctx): + """Returns true if the host operating system is windows.""" + return repository_ctx.os.name.lower().find("windows") >= 0 + +def lib_name(base_name, cpu_value, version = None, static = False): + """Constructs the platform-specific name of a library. + + Args: + base_name: The name of the library, such as "cudart" + cpu_value: The name of the host operating system. + version: The version of the library. + static: True the library is static or False if it is a shared object. + + Returns: + The platform-specific name of the library. + """ + version = "" if not version else "." + version + if cpu_value in ("Linux", "FreeBSD"): + if static: + return "lib%s.a" % base_name + return "lib%s.so%s" % (base_name, version) + elif cpu_value == "Windows": + return "%s.lib" % base_name + elif cpu_value == "Darwin": + if static: + return "lib%s.a" % base_name + return "lib%s%s.dylib" % (base_name, version) + else: + auto_configure_fail("Invalid cpu_value: %s" % cpu_value) + +def find_lib(repository_ctx, paths, check_soname = True): + """ + Finds a library among a list of potential paths. + + Args: + paths: List of paths to inspect. + + Returns: + Returns the first path in paths that exist. + """ + objdump = repository_ctx.which("objdump") + mismatches = [] + for path in [repository_ctx.path(path) for path in paths]: + if not path.exists: + continue + if check_soname and objdump != None and not _is_windows(repository_ctx): + output = repository_ctx.execute([objdump, "-p", str(path)]).stdout + output = [line for line in output.splitlines() if "SONAME" in line] + sonames = [line.strip().split(" ")[-1] for line in output] + if not any([soname == path.basename for soname in sonames]): + mismatches.append(str(path)) + continue + return path + if mismatches: + auto_configure_fail( + "None of the libraries match their SONAME: " + ", ".join(mismatches), + ) + auto_configure_fail("No library found under: " + ", ".join(paths)) + +def _find_cuda_lib( + lib, + repository_ctx, + cpu_value, + basedir, + version, + static = False): + """Finds the given CUDA or cuDNN library on the system. + + Args: + lib: The name of the library, such as "cudart" + repository_ctx: The repository context. + cpu_value: The name of the host operating system. + basedir: The install directory of CUDA or cuDNN. + version: The version of the library. + static: True if static library, False if shared object. + + Returns: + Returns the path to the library. + """ + file_name = lib_name(lib, cpu_value, version, static) + return find_lib( + repository_ctx, + ["%s/%s" % (basedir, file_name)], + check_soname = version and not static, + ) + +def _find_libs(repository_ctx, cuda_config): + """Returns the CUDA and cuDNN libraries on the system. + + Args: + repository_ctx: The repository context. + cuda_config: The CUDA config as returned by _get_cuda_config + + Returns: + Map of library names to structs of filename and path. + """ + cpu_value = cuda_config.cpu_value + stub_dir = "" if _is_windows(repository_ctx) else "/stubs" + return { + "cuda": _find_cuda_lib( + "cuda", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"] + stub_dir, + None, + ), + "cudart": _find_cuda_lib( + "cudart", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_version, + ), + "cudart_static": _find_cuda_lib( + "cudart_static", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_version, + static = True, + ), + "cublas": _find_cuda_lib( + "cublas", + repository_ctx, + cpu_value, + cuda_config.config["cublas_library_dir"], + cuda_config.cuda_lib_version, + ), + "cusolver": _find_cuda_lib( + "cusolver", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_lib_version, + ), + "curand": _find_cuda_lib( + "curand", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_lib_version, + ), + "cufft": _find_cuda_lib( + "cufft", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_lib_version, + ), + "cudnn": _find_cuda_lib( + "cudnn", + repository_ctx, + cpu_value, + cuda_config.config["cudnn_library_dir"], + cuda_config.cudnn_version, + ), + "cupti": _find_cuda_lib( + "cupti", + repository_ctx, + cpu_value, + cuda_config.config["cupti_library_dir"], + cuda_config.cuda_version, + ), + "cusparse": _find_cuda_lib( + "cusparse", + repository_ctx, + cpu_value, + cuda_config.config["cuda_library_dir"], + cuda_config.cuda_lib_version, + ), + } + +def _cudart_static_linkopt(cpu_value): + """Returns additional platform-specific linkopts for cudart.""" + return "" if cpu_value == "Darwin" else "\"-lrt\"," + +# TODO(csigg): Only call once instead of from here, tensorrt_configure.bzl, +# and nccl_configure.bzl. +def find_cuda_config(repository_ctx, cuda_libraries): + """Returns CUDA config dictionary from running find_cuda_config.py""" + exec_result = repository_ctx.execute([ + _get_python_bin(repository_ctx), + repository_ctx.path(Label("//gpu:find_cuda_config.py")), + ] + cuda_libraries) + if exec_result.return_code: + auto_configure_fail("Failed to run find_cuda_config.py: %s" % exec_result.stderr) + + # Parse the dict from stdout. + return dict([tuple(x.split(": ")) for x in exec_result.stdout.splitlines()]) + +def _get_cuda_config(repository_ctx): + """Detects and returns information about the CUDA installation on the system. + + Args: + repository_ctx: The repository context. + + Returns: + A struct containing the following fields: + cuda_toolkit_path: The CUDA toolkit installation directory. + cudnn_install_basedir: The cuDNN installation directory. + cuda_version: The version of CUDA on the system. + cudnn_version: The version of cuDNN on the system. + compute_capabilities: A list of the system's CUDA compute capabilities. + cpu_value: The name of the host operating system. + """ + config = find_cuda_config(repository_ctx, ["cuda", "cudnn"]) + cpu_value = get_cpu_value(repository_ctx) + toolkit_path = config["cuda_toolkit_path"] + + is_windows = _is_windows(repository_ctx) + cuda_version = config["cuda_version"].split(".") + cuda_major = cuda_version[0] + cuda_minor = cuda_version[1] + + cuda_version = ("64_%s%s" if is_windows else "%s.%s") % (cuda_major, cuda_minor) + cudnn_version = ("64_%s" if is_windows else "%s") % config["cudnn_version"] + + # cuda_lib_version is for libraries like cuBLAS, cuFFT, cuSOLVER, etc. + # It changed from 'x.y' to just 'x' in CUDA 10.1. + if (int(cuda_major), int(cuda_minor)) >= (10, 1): + cuda_lib_version = ("64_%s" if is_windows else "%s") % cuda_major + else: + cuda_lib_version = cuda_version + + return struct( + cuda_toolkit_path = toolkit_path, + cuda_version = cuda_version, + cudnn_version = cudnn_version, + cuda_lib_version = cuda_lib_version, + compute_capabilities = compute_capabilities(repository_ctx), + cpu_value = cpu_value, + config = config, + ) + +def _tpl(repository_ctx, tpl, substitutions = {}, out = None): + if not out: + out = tpl.replace(":", "/") + repository_ctx.template( + out, + Label("//gpu/%s.tpl" % tpl), + substitutions, + ) + +def _file(repository_ctx, label): + repository_ctx.template( + label.replace(":", "/"), + Label("//gpu/%s.tpl" % label), + {}, + ) + +_DUMMY_CROSSTOOL_BZL_FILE = """ +def error_gpu_disabled(): + fail("ERROR: Building with --config=cuda but TensorFlow is not configured " + + "to build with GPU support. Please re-run ./configure and enter 'Y' " + + "at the prompt to build with GPU support.") + + native.genrule( + name = "error_gen_crosstool", + outs = ["CROSSTOOL"], + cmd = "echo 'Should not be run.' && exit 1", + ) + + native.filegroup( + name = "crosstool", + srcs = [":CROSSTOOL"], + output_licenses = ["unencumbered"], + ) +""" + +_DUMMY_CROSSTOOL_BUILD_FILE = """ +load("//crosstool:error_gpu_disabled.bzl", "error_gpu_disabled") + +error_gpu_disabled() +""" + +def _create_dummy_repository(repository_ctx): + cpu_value = get_cpu_value(repository_ctx) + + # Set up BUILD file for cuda/. + _tpl( + repository_ctx, + "cuda:build_defs.bzl", + { + "%{cuda_is_configured}": "False", + "%{cuda_extra_copts}": "[]", + }, + ) + _tpl( + repository_ctx, + "cuda:BUILD", + { + "%{cuda_driver_lib}": lib_name("cuda", cpu_value), + "%{cudart_static_lib}": lib_name( + "cudart_static", + cpu_value, + static = True, + ), + "%{cudart_static_linkopt}": _cudart_static_linkopt(cpu_value), + "%{cudart_lib}": lib_name("cudart", cpu_value), + "%{cublas_lib}": lib_name("cublas", cpu_value), + "%{cusolver_lib}": lib_name("cusolver", cpu_value), + "%{cudnn_lib}": lib_name("cudnn", cpu_value), + "%{cufft_lib}": lib_name("cufft", cpu_value), + "%{curand_lib}": lib_name("curand", cpu_value), + "%{cupti_lib}": lib_name("cupti", cpu_value), + "%{cusparse_lib}": lib_name("cusparse", cpu_value), + "%{copy_rules}": """ +filegroup(name="cuda-include") +filegroup(name="cublas-include") +filegroup(name="cudnn-include") +""", + }, + ) + + # Create dummy files for the CUDA toolkit since they are still required by + # tensorflow/core/platform/default/build_config:cuda. + repository_ctx.file("cuda/cuda/include/cuda.h") + repository_ctx.file("cuda/cuda/include/cublas.h") + repository_ctx.file("cuda/cuda/include/cudnn.h") + repository_ctx.file("cuda/cuda/extras/CUPTI/include/cupti.h") + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cuda", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cudart", cpu_value)) + repository_ctx.file( + "cuda/cuda/lib/%s" % lib_name("cudart_static", cpu_value), + ) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cublas", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cusolver", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cudnn", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("curand", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cufft", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cupti", cpu_value)) + repository_ctx.file("cuda/cuda/lib/%s" % lib_name("cusparse", cpu_value)) + + # Set up cuda_config.h, which is used by + # tensorflow/stream_executor/dso_loader.cc. + _tpl( + repository_ctx, + "cuda:cuda_config.h", + { + "%{cuda_version}": "", + "%{cuda_lib_version}": "", + "%{cudnn_version}": "", + "%{cuda_compute_capabilities}": ",".join([ + "CudaVersion(\"%s\")" % c + for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES + ]), + "%{cuda_toolkit_path}": "", + }, + "cuda/cuda/cuda_config.h", + ) + + # If cuda_configure is not configured to build with GPU support, and the user + # attempts to build with --config=cuda, add a dummy build rule to intercept + # this and fail with an actionable error message. + repository_ctx.file( + "crosstool/error_gpu_disabled.bzl", + _DUMMY_CROSSTOOL_BZL_FILE, + ) + repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE) + +def _execute( + repository_ctx, + cmdline, + error_msg = None, + error_details = None, + empty_stdout_fine = False): + """Executes an arbitrary shell command. + + Args: + repository_ctx: the repository_ctx object + cmdline: list of strings, the command to execute + error_msg: string, a summary of the error if the command fails + error_details: string, details about the error or steps to fix it + empty_stdout_fine: bool, if True, an empty stdout result is fine, + otherwise it's an error + Return: the result of repository_ctx.execute(cmdline) + """ + result = repository_ctx.execute(cmdline) + if result.stderr or not (empty_stdout_fine or result.stdout): + auto_configure_fail( + "\n".join([ + error_msg.strip() if error_msg else "Repository command failed", + result.stderr.strip(), + error_details if error_details else "", + ]), + ) + return result + +def _norm_path(path): + """Returns a path with '/' and remove the trailing slash.""" + path = path.replace("\\", "/") + if path[-1] == "/": + path = path[:-1] + return path + +def make_copy_files_rule(repository_ctx, name, srcs, outs): + """Returns a rule to copy a set of files.""" + cmds = [] + + # Copy files. + for src, out in zip(srcs, outs): + cmds.append('cp -f "%s" "$(location %s)"' % (src, out)) + outs = [(' "%s",' % out) for out in outs] + return """genrule( + name = "%s", + outs = [ +%s + ], + cmd = \"""%s \""", +)""" % (name, "\n".join(outs), " && \\\n".join(cmds)) + +def make_copy_dir_rule(repository_ctx, name, src_dir, out_dir): + """Returns a rule to recursively copy a directory.""" + src_dir = _norm_path(src_dir) + out_dir = _norm_path(out_dir) + outs = _read_dir(repository_ctx, src_dir) + outs = [(' "%s",' % out.replace(src_dir, out_dir)) for out in outs] + + # '@D' already contains the relative path for a single file, see + # http://docs.bazel.build/versions/master/be/make-variables.html#predefined_genrule_variables + out_dir = "$(@D)/%s" % out_dir if len(outs) > 1 else "$(@D)" + return """genrule( + name = "%s", + outs = [ +%s + ], + cmd = \"""cp -rLf "%s/." "%s/" \""", +)""" % (name, "\n".join(outs), src_dir, out_dir) + +def _read_dir(repository_ctx, src_dir): + """Returns a string with all files in a directory. + + Finds all files inside a directory, traversing subfolders and following + symlinks. The returned string contains the full path of all files + separated by line breaks. + """ + if _is_windows(repository_ctx): + src_dir = src_dir.replace("/", "\\") + find_result = _execute( + repository_ctx, + ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"], + empty_stdout_fine = True, + ) + + # src_files will be used in genrule.outs where the paths must + # use forward slashes. + result = find_result.stdout.replace("\\", "/") + else: + find_result = _execute( + repository_ctx, + ["find", src_dir, "-follow", "-type", "f"], + empty_stdout_fine = True, + ) + result = find_result.stdout + return sorted(result.splitlines()) + +def _flag_enabled(repository_ctx, flag_name): + if flag_name in repository_ctx.os.environ: + value = repository_ctx.os.environ[flag_name].strip() + return value == "1" + return False + +def _tf_sysroot(repository_ctx): + if _TF_SYSROOT in repository_ctx.os.environ: + return repository_ctx.os.environ[_TF_SYSROOT] + return "" + +def _compute_cuda_extra_copts(repository_ctx, compute_capabilities): + capability_flags = [ + "--cuda-gpu-arch=sm_" + cap.replace(".", "") + for cap in compute_capabilities + ] + return str(capability_flags) + +def _create_local_cuda_repository(repository_ctx): + """Creates the repository containing files set up to build with CUDA.""" + cuda_config = _get_cuda_config(repository_ctx) + + cuda_include_path = cuda_config.config["cuda_include_dir"] + cublas_include_path = cuda_config.config["cublas_include_dir"] + cudnn_header_dir = cuda_config.config["cudnn_include_dir"] + cupti_header_dir = cuda_config.config["cupti_include_dir"] + nvvm_libdevice_dir = cuda_config.config["nvvm_library_dir"] + + # Create genrule to copy files from the installed CUDA toolkit into execroot. + copy_rules = [ + make_copy_dir_rule( + repository_ctx, + name = "cuda-include", + src_dir = cuda_include_path, + out_dir = "cuda/include", + ), + make_copy_dir_rule( + repository_ctx, + name = "cuda-nvvm", + src_dir = nvvm_libdevice_dir, + out_dir = "cuda/nvvm/libdevice", + ), + make_copy_dir_rule( + repository_ctx, + name = "cuda-extras", + src_dir = cupti_header_dir, + out_dir = "cuda/extras/CUPTI/include", + ), + ] + + copy_rules.append(make_copy_files_rule( + repository_ctx, + name = "cublas-include", + srcs = [ + cublas_include_path + "/cublas.h", + cublas_include_path + "/cublas_v2.h", + cublas_include_path + "/cublas_api.h", + ], + outs = [ + "cublas/include/cublas.h", + "cublas/include/cublas_v2.h", + "cublas/include/cublas_api.h", + ], + )) + + cuda_libs = _find_libs(repository_ctx, cuda_config) + cuda_lib_srcs = [] + cuda_lib_outs = [] + for path in cuda_libs.values(): + cuda_lib_srcs.append(str(path)) + cuda_lib_outs.append("cuda/lib/" + path.basename) + copy_rules.append(make_copy_files_rule( + repository_ctx, + name = "cuda-lib", + srcs = cuda_lib_srcs, + outs = cuda_lib_outs, + )) + + # copy files mentioned in third_party/nccl/build_defs.bzl.tpl + copy_rules.append(make_copy_files_rule( + repository_ctx, + name = "cuda-bin", + srcs = [ + cuda_config.cuda_toolkit_path + "/bin/" + "crt/link.stub", + cuda_config.cuda_toolkit_path + "/bin/" + "nvlink", + cuda_config.cuda_toolkit_path + "/bin/" + "fatbinary", + cuda_config.cuda_toolkit_path + "/bin/" + "bin2c", + ], + outs = [ + "cuda/bin/" + "crt/link.stub", + "cuda/bin/" + "nvlink", + "cuda/bin/" + "fatbinary", + "cuda/bin/" + "bin2c", + ], + )) + + copy_rules.append(make_copy_files_rule( + repository_ctx, + name = "cudnn-include", + srcs = [cudnn_header_dir + "/cudnn.h"], + outs = ["cudnn/include/cudnn.h"], + )) + + # Set up BUILD file for cuda/ + _tpl( + repository_ctx, + "cuda:build_defs.bzl", + { + "%{cuda_is_configured}": "True", + "%{cuda_extra_copts}": _compute_cuda_extra_copts( + repository_ctx, + cuda_config.compute_capabilities, + ), + }, + ) + _tpl( + repository_ctx, + "cuda:BUILD.windows" if _is_windows(repository_ctx) else "cuda:BUILD", + { + "%{cuda_driver_lib}": cuda_libs["cuda"].basename, + "%{cudart_static_lib}": cuda_libs["cudart_static"].basename, + "%{cudart_static_linkopt}": _cudart_static_linkopt(cuda_config.cpu_value), + "%{cudart_lib}": cuda_libs["cudart"].basename, + "%{cublas_lib}": cuda_libs["cublas"].basename, + "%{cusolver_lib}": cuda_libs["cusolver"].basename, + "%{cudnn_lib}": cuda_libs["cudnn"].basename, + "%{cufft_lib}": cuda_libs["cufft"].basename, + "%{curand_lib}": cuda_libs["curand"].basename, + "%{cupti_lib}": cuda_libs["cupti"].basename, + "%{cusparse_lib}": cuda_libs["cusparse"].basename, + "%{copy_rules}": "\n".join(copy_rules), + }, + "cuda/BUILD", + ) + + tf_sysroot = _tf_sysroot(repository_ctx) + + # Set up crosstool/ + cc = find_cc(repository_ctx) + cc_fullpath = "crosstool/" + cc + + host_compiler_includes = get_cxx_inc_directories( + repository_ctx, + cc_fullpath, + tf_sysroot, + ) + cuda_defines = {} + cuda_defines["%{builtin_sysroot}"] = tf_sysroot + cuda_defines["%{cuda_toolkit_path}"] = "" + + host_compiler_prefix = "/usr/bin" + if _GCC_HOST_COMPILER_PREFIX in repository_ctx.os.environ: + host_compiler_prefix = repository_ctx.os.environ[_GCC_HOST_COMPILER_PREFIX].strip() + cuda_defines["%{host_compiler_prefix}"] = host_compiler_prefix + + # Bazel sets '-B/usr/bin' flag to workaround build errors on RHEL (see + # https://github.com/bazelbuild/bazel/issues/760). + # However, this stops our custom clang toolchain from picking the provided + # LLD linker, so we're only adding '-B/usr/bin' when using non-downloaded + # toolchain. + # TODO: when bazel stops adding '-B/usr/bin' by default, remove this + # flag from the CROSSTOOL completely (see + # https://github.com/bazelbuild/bazel/issues/5634) + cuda_defines["%{linker_bin_path}"] = host_compiler_prefix + + cuda_defines["%{extra_no_canonical_prefixes_flags}"] = "" + cuda_defines["%{unfiltered_compile_flags}"] = "" + cuda_defines["%{host_compiler_path}"] = "clang/bin/crosstool_wrapper_driver_is_not_gcc" + cuda_defines["%{host_compiler_warnings}"] = "" + + # nvcc has the system include paths built in and will automatically + # search them; we cannot work around that, so we add the relevant cuda + # system paths to the allowed compiler specific include paths. + cuda_defines["%{cxx_builtin_include_directories}"] = to_list_of_strings( + host_compiler_includes + _cuda_include_path( + repository_ctx, + cuda_config, + ) + [cupti_header_dir, cudnn_header_dir], + ) + + # For gcc, do not canonicalize system header paths; some versions of gcc + # pick the shortest possible path for system includes when creating the + # .d file - given that includes that are prefixed with "../" multiple + # time quickly grow longer than the root of the tree, this can lead to + # bazel's header check failing. + cuda_defines["%{extra_no_canonical_prefixes_flags}"] = "\"-fno-canonical-system-headers\"" + + nvcc_path = str( + repository_ctx.path("%s/nvcc%s" % ( + cuda_config.config["cuda_binary_dir"], + ".exe" if _is_windows(repository_ctx) else "", + )), + ) + cuda_defines["%{compiler_deps}"] = ":crosstool_wrapper_driver_is_not_gcc" + cuda_defines["%{win_compiler_deps}"] = ":windows_msvc_wrapper_files" + + wrapper_defines = { + "%{cpu_compiler}": str(cc), + "%{cuda_version}": cuda_config.cuda_version, + "%{nvcc_path}": nvcc_path, + "%{gcc_host_compiler_path}": str(cc), + "%{cuda_compute_capabilities}": ", ".join( + ["\"%s\"" % c for c in cuda_config.compute_capabilities], + ), + "%{nvcc_tmp_dir}": _get_nvcc_tmp_dir_for_windows(repository_ctx), + } + _tpl( + repository_ctx, + "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc", + wrapper_defines, + ) + _tpl( + repository_ctx, + "crosstool:windows/msvc_wrapper_for_nvcc.py", + wrapper_defines, + ) + + cuda_defines.update(_get_win_cuda_defines(repository_ctx)) + + verify_build_defines(cuda_defines) + + # Only expand template variables in the BUILD file + _tpl(repository_ctx, "crosstool:BUILD", cuda_defines) + + # No templating of cc_toolchain_config - use attributes and templatize the + # BUILD file. + _file(repository_ctx, "crosstool:cc_toolchain_config.bzl") + + # Set up cuda_config.h, which is used by + # tensorflow/stream_executor/dso_loader.cc. + _tpl( + repository_ctx, + "cuda:cuda_config.h", + { + "%{cuda_version}": cuda_config.cuda_version, + "%{cuda_lib_version}": cuda_config.cuda_lib_version, + "%{cudnn_version}": cuda_config.cudnn_version, + "%{cuda_compute_capabilities}": ", ".join([ + "CudaVersion(\"%s\")" % c + for c in cuda_config.compute_capabilities + ]), + "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path, + }, + "cuda/cuda/cuda_config.h", + ) + +def _create_remote_cuda_repository(repository_ctx, remote_config_repo): + """Creates pointers to a remotely configured repo set up to build with CUDA.""" + _tpl( + repository_ctx, + "cuda:build_defs.bzl", + { + "%{cuda_is_configured}": "True", + "%{cuda_extra_copts}": _compute_cuda_extra_copts( + repository_ctx, + compute_capabilities(repository_ctx), + ), + }, + ) + repository_ctx.template( + "cuda/BUILD", + Label(remote_config_repo + "/cuda:BUILD"), + {}, + ) + repository_ctx.template( + "cuda/build_defs.bzl", + Label(remote_config_repo + "/cuda:build_defs.bzl"), + {}, + ) + repository_ctx.template( + "cuda/cuda/cuda_config.h", + Label(remote_config_repo + "/cuda:cuda/cuda_config.h"), + {}, + ) + +def _cuda_autoconf_impl(repository_ctx): + """Implementation of the cuda_autoconf repository rule.""" + if not enable_cuda(repository_ctx): + _create_dummy_repository(repository_ctx) + elif _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ: + if (_TF_CUDA_VERSION not in repository_ctx.os.environ or + _TF_CUDNN_VERSION not in repository_ctx.os.environ): + auto_configure_fail("%s and %s must also be set if %s is specified" % + (_TF_CUDA_VERSION, _TF_CUDNN_VERSION, _TF_CUDA_CONFIG_REPO)) + _create_remote_cuda_repository( + repository_ctx, + repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO], + ) + else: + _create_local_cuda_repository(repository_ctx) + +cuda_configure = repository_rule( + implementation = _cuda_autoconf_impl, + environ = [ + _GCC_HOST_COMPILER_PATH, + _GCC_HOST_COMPILER_PREFIX, + "TF_NEED_CUDA", + _CUDA_TOOLKIT_PATH, + _CUDNN_INSTALL_PATH, + _TF_CUDA_VERSION, + _TF_CUDNN_VERSION, + _TF_CUDA_COMPUTE_CAPABILITIES, + _TF_CUDA_CONFIG_REPO, + "NVVMIR_LIBRARY_DIR", + _PYTHON_BIN_PATH, + "TMP", + "TMPDIR", + "TF_CUDA_PATHS", + ], +) + +"""Detects and configures the local CUDA toolchain. + +Add the following to your WORKSPACE FILE: + +```python +cuda_configure(name = "local_config_cuda") +``` + +Args: + name: A unique name for this workspace rule. +""" diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/find_cuda_config.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/find_cuda_config.py new file mode 100644 index 000000000..39f2c21d3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/gpu/find_cuda_config.py @@ -0,0 +1,500 @@ +# Copyright 2019 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Prints CUDA library and header directories and versions found on the system. + +The script searches for CUDA library and header files on the system, inspects +them to determine their version and prints the configuration to stdout. +The paths to inspect and the required versions are specified through environment +variables. If no valid configuration is found, the script prints to stderr and +returns an error code. + +The list of libraries to find is specified as arguments. Supported libraries are +CUDA (includes cuBLAS), cuDNN, NCCL, and TensorRT. + +The script takes a list of base directories specified by the TF_CUDA_PATHS +environment variable as comma-separated glob list. The script looks for headers +and library files in a hard-coded set of subdirectories from these base paths. +If TF_CUDA_PATHS is not specified, a OS specific default is used: + + Linux: /usr/local/cuda, /usr, and paths from 'ldconfig -p'. + Windows: CUDA_PATH environment variable, or + C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\* + +For backwards compatibility, some libraries also use alternative base +directories from other environment variables if they are specified. List of +library-specific environment variables: + + Library Version env variable Additional base directories + ---------------------------------------------------------------- + CUDA TF_CUDA_VERSION CUDA_TOOLKIT_PATH + cuBLAS TF_CUBLAS_VERSION CUDA_TOOLKIT_PATH + cuDNN TF_CUDNN_VERSION CUDNN_INSTALL_PATH + NCCL TF_NCCL_VERSION NCCL_INSTALL_PATH, NCCL_HDR_PATH + TensorRT TF_TENSORRT_VERSION TENSORRT_INSTALL_PATH + +Versions environment variables can be of the form 'x' or 'x.y' to request a +specific version, empty or unspecified to accept any version. + +The output of a found library is of the form: +tf__version: x.y.z +tf__header_dir: ... +tf__library_dir: ... +""" + +import io +import os +import glob +import platform +import re +import subprocess +import sys + +# pylint: disable=g-import-not-at-top +try: + from shutil import which +except ImportError: + from distutils.spawn import find_executable as which +# pylint: enable=g-import-not-at-top + + +class ConfigError(Exception): + pass + + +def _is_linux(): + return platform.system() == "Linux" + + +def _is_windows(): + return platform.system() == "Windows" + + +def _is_macos(): + return platform.system() == "Darwin" + + +def _matches_version(actual_version, required_version): + """Checks whether some version meets the requirements. + + All elements of the required_version need to be present in the + actual_version. + + required_version actual_version result + ----------------------------------------- + 1 1.1 True + 1.2 1 False + 1.2 1.3 False + 1 True + + Args: + required_version: The version specified by the user. + actual_version: The version detected from the CUDA installation. + Returns: Whether the actual version matches the required one. + """ + if actual_version is None: + return False + + # Strip spaces from the versions. + actual_version = actual_version.strip() + required_version = required_version.strip() + return actual_version.startswith(required_version) + + +def _at_least_version(actual_version, required_version): + actual = [int(v) for v in actual_version.split(".")] + required = [int(v) for v in required_version.split(".")] + return actual >= required + + +def _get_header_version(path, name): + """Returns preprocessor defines in C header file.""" + for line in io.open(path, "r", encoding="utf-8").readlines(): + match = re.match("#define %s +(\d+)" % name, line) + if match: + return match.group(1) + return "" + + +def _cartesian_product(first, second): + """Returns all path combinations of first and second.""" + return [os.path.join(f, s) for f in first for s in second] + + +def _get_ld_config_paths(): + """Returns all directories from 'ldconfig -p'.""" + if not _is_linux(): + return [] + ldconfig_path = which("ldconfig") or "/sbin/ldconfig" + output = subprocess.check_output([ldconfig_path, "-p"]) + pattern = re.compile(".* => (.*)") + result = set() + for line in output.splitlines(): + try: + match = pattern.match(line.decode("ascii")) + except UnicodeDecodeError: + match = False + if match: + result.add(os.path.dirname(match.group(1))) + return sorted(list(result)) + + +def _get_default_cuda_paths(cuda_version): + if not cuda_version: + cuda_version = "*" + elif not "." in cuda_version: + cuda_version = cuda_version + ".*" + + if _is_windows(): + return [ + os.environ.get( + "CUDA_PATH", + "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v%s\\" % + cuda_version) + ] + return ["/usr/local/cuda-%s" % cuda_version, "/usr/local/cuda", "/usr", + "/usr/local/cudnn"] + _get_ld_config_paths() + + +def _header_paths(): + """Returns hard-coded set of relative paths to look for header files.""" + return [ + "", + "include", + "include/cuda", + "include/*-linux-gnu", + "extras/CUPTI/include", + "include/cuda/CUPTI", + ] + + +def _library_paths(): + """Returns hard-coded set of relative paths to look for library files.""" + return [ + "", + "lib64", + "lib", + "lib/*-linux-gnu", + "lib/x64", + "extras/CUPTI/*", + ] + + +def _not_found_error(base_paths, relative_paths, filepattern): + base_paths = "".join(["\n '%s'" % path for path in sorted(base_paths)]) + relative_paths = "".join(["\n '%s'" % path for path in relative_paths]) + return ConfigError( + "Could not find any %s in any subdirectory:%s\nof:%s\n" % + (filepattern, relative_paths, base_paths)) + + +def _find_file(base_paths, relative_paths, filepattern): + for path in _cartesian_product(base_paths, relative_paths): + for file in glob.glob(os.path.join(path, filepattern)): + return file + raise _not_found_error(base_paths, relative_paths, filepattern) + + +def _find_library(base_paths, library_name, required_version): + """Returns first valid path to the requested library.""" + if _is_windows(): + filepattern = library_name + ".lib" + elif _is_macos(): + filepattern = "%s*.dylib" % (".".join(["lib" + library_name] + + required_version.split(".")[:1])) + else: + filepattern = ".".join(["lib" + library_name, "so"] + + required_version.split(".")[:1]) + "*" + return _find_file(base_paths, _library_paths(), filepattern) + + +def _find_versioned_file(base_paths, relative_paths, filepattern, + required_version, get_version): + """Returns first valid path to a file that matches the requested version.""" + for path in _cartesian_product(base_paths, relative_paths): + for file in glob.glob(os.path.join(path, filepattern)): + actual_version = get_version(file) + if _matches_version(actual_version, required_version): + return file, actual_version + raise _not_found_error( + base_paths, relative_paths, + filepattern + " matching version '%s'" % required_version) + + +def _find_header(base_paths, header_name, required_version, get_version): + """Returns first valid path to a header that matches the requested version.""" + return _find_versioned_file(base_paths, _header_paths(), header_name, + required_version, get_version) + + +def _find_cuda_config(base_paths, required_version): + + def get_header_version(path): + version = int(_get_header_version(path, "CUDA_VERSION")) + if not version: + return None + return "%d.%d" % (version // 1000, version % 1000 // 10) + + cuda_header_path, header_version = _find_header(base_paths, "cuda.h", + required_version, + get_header_version) + cuda_version = header_version # x.y, see above. + + cuda_library_path = _find_library(base_paths, "cudart", cuda_version) + + def get_nvcc_version(path): + pattern = "Cuda compilation tools, release \d+\.\d+, V(\d+\.\d+\.\d+)" + for line in subprocess.check_output([path, "--version"]).splitlines(): + match = re.match(pattern, line.decode("ascii")) + if match: + return match.group(1) + return None + + nvcc_name = "nvcc.exe" if _is_windows() else "nvcc" + nvcc_path, nvcc_version = _find_versioned_file(base_paths, [ + "", + "bin", + ], nvcc_name, cuda_version, get_nvcc_version) + + nvvm_path = _find_file(base_paths, [ + "nvvm/libdevice", + "share/cuda", + "lib/nvidia-cuda-toolkit/libdevice", + ], "libdevice*.10.bc") + + cupti_header_path = _find_file(base_paths, _header_paths(), "cupti.h") + cupti_library_path = _find_library(base_paths, "cupti", required_version) + + cuda_binary_dir = os.path.dirname(nvcc_path) + nvvm_library_dir = os.path.dirname(nvvm_path) + + # XLA requires the toolkit path to find ptxas and libdevice. + # TODO(csigg): pass in both directories instead. + cuda_toolkit_paths = ( + os.path.normpath(os.path.join(cuda_binary_dir, "..")), + os.path.normpath(os.path.join(nvvm_library_dir, "../..")), + ) + if cuda_toolkit_paths[0] != cuda_toolkit_paths[1]: + raise ConfigError("Inconsistent CUDA toolkit path: %s vs %s" % + cuda_toolkit_paths) + + return { + "cuda_version": cuda_version, + "cuda_include_dir": os.path.dirname(cuda_header_path), + "cuda_library_dir": os.path.dirname(cuda_library_path), + "cuda_binary_dir": cuda_binary_dir, + "nvvm_library_dir": nvvm_library_dir, + "cupti_include_dir": os.path.dirname(cupti_header_path), + "cupti_library_dir": os.path.dirname(cupti_library_path), + "cuda_toolkit_path": cuda_toolkit_paths[0], + } + + +def _find_cublas_config(base_paths, required_version, cuda_version): + + if _at_least_version(cuda_version, "10.1"): + + def get_header_version(path): + version = ( + _get_header_version(path, name) + for name in ("CUBLAS_VER_MAJOR", "CUBLAS_VER_MINOR", + "CUBLAS_VER_PATCH")) + return ".".join(version) + + header_path, header_version = _find_header(base_paths, "cublas_api.h", + required_version, + get_header_version) + # cuBLAS uses the major version only. + cublas_version = header_version.split(".")[0] + + if not _matches_version(cuda_version, cublas_version): + raise ConfigError("cuBLAS version %s does not match CUDA version %s" % + (cublas_version, cuda_version)) + + else: + # There is no version info available before CUDA 10.1, just find the file. + header_path = _find_file(base_paths, _header_paths(), "cublas_api.h") + # cuBLAS version is the same as CUDA version (x.y). + cublas_version = required_version + + library_path = _find_library(base_paths, "cublas", cublas_version) + + return { + "cublas_include_dir": os.path.dirname(header_path), + "cublas_library_dir": os.path.dirname(library_path), + } + + +def _find_cudnn_config(base_paths, required_version): + + def get_header_version(path): + version = ( + _get_header_version(path, name) + for name in ("CUDNN_MAJOR", "CUDNN_MINOR", "CUDNN_PATCHLEVEL")) + return ".".join(version) + + header_path, header_version = _find_header(base_paths, "cudnn.h", + required_version, + get_header_version) + cudnn_version = header_version.split(".")[0] + + library_path = _find_library(base_paths, "cudnn", cudnn_version) + + return { + "cudnn_version": cudnn_version, + "cudnn_include_dir": os.path.dirname(header_path), + "cudnn_library_dir": os.path.dirname(library_path), + } + + +def _find_nccl_config(base_paths, required_version): + + def get_header_version(path): + version = ( + _get_header_version(path, name) + for name in ("NCCL_MAJOR", "NCCL_MINOR", "NCCL_PATCH")) + return ".".join(version) + + header_path, header_version = _find_header(base_paths, "nccl.h", + required_version, + get_header_version) + nccl_version = header_version.split(".")[0] + + library_path = _find_library(base_paths, "nccl", nccl_version) + + return { + "nccl_version": nccl_version, + "nccl_include_dir": os.path.dirname(header_path), + "nccl_library_dir": os.path.dirname(library_path), + } + + +def _find_tensorrt_config(base_paths, required_version): + + def get_header_version(path): + version = ( + _get_header_version(path, name) + for name in ("NV_TENSORRT_MAJOR", "NV_TENSORRT_MINOR", + "NV_TENSORRT_PATCH")) + # `version` is a generator object, so we convert it to a list before using + # it (muitiple times below). + version = list(version) + if not all(version): + return None # Versions not found, make _matches_version returns False. + return ".".join(version) + + try: + header_path, header_version = _find_header(base_paths, "NvInfer.h", + required_version, + get_header_version) + except ConfigError: + # TensorRT 6 moved the version information to NvInferVersion.h. + header_path, header_version = _find_header(base_paths, "NvInferVersion.h", + required_version, + get_header_version) + + tensorrt_version = header_version.split(".")[0] + library_path = _find_library(base_paths, "nvinfer", tensorrt_version) + + return { + "tensorrt_version": tensorrt_version, + "tensorrt_include_dir": os.path.dirname(header_path), + "tensorrt_library_dir": os.path.dirname(library_path), + } + + +def _list_from_env(env_name, default=[]): + """Returns comma-separated list from environment variable.""" + if env_name in os.environ: + return os.environ[env_name].split(",") + return default + + +def _get_legacy_path(env_name, default=[]): + """Returns a path specified by a legacy environment variable. + + CUDNN_INSTALL_PATH, NCCL_INSTALL_PATH, TENSORRT_INSTALL_PATH set to + '/usr/lib/x86_64-linux-gnu' would previously find both library and header + paths. Detect those and return '/usr', otherwise forward to _list_from_env(). + """ + if env_name in os.environ: + match = re.match("^(/[^/ ]*)+/lib/\w+-linux-gnu/?$", os.environ[env_name]) + if match: + return [match.group(1)] + return _list_from_env(env_name, default) + + +def _normalize_path(path): + """Returns normalized path, with forward slashes on Windows.""" + path = os.path.normpath(path) + if _is_windows(): + path = path.replace("\\", "/") + return path + + +def find_cuda_config(): + """Returns a dictionary of CUDA library and header file paths.""" + libraries = [argv.lower() for argv in sys.argv[1:]] + cuda_version = os.environ.get("TF_CUDA_VERSION", "") + base_paths = _list_from_env("TF_CUDA_PATHS", + _get_default_cuda_paths(cuda_version)) + base_paths = [path for path in base_paths if os.path.exists(path)] + + result = {} + if "cuda" in libraries: + cuda_paths = _list_from_env("CUDA_TOOLKIT_PATH", base_paths) + result.update(_find_cuda_config(cuda_paths, cuda_version)) + + cuda_version = result["cuda_version"] + cublas_paths = base_paths + if tuple(int(v) for v in cuda_version.split(".")) < (10, 1): + # Before CUDA 10.1, cuBLAS was in the same directory as the toolkit. + cublas_paths = cuda_paths + cublas_version = os.environ.get("TF_CUBLAS_VERSION", "") + result.update( + _find_cublas_config(cublas_paths, cublas_version, cuda_version)) + + if "cudnn" in libraries: + cudnn_paths = _get_legacy_path("CUDNN_INSTALL_PATH", base_paths) + cudnn_version = os.environ.get("TF_CUDNN_VERSION", "") + result.update(_find_cudnn_config(cudnn_paths, cudnn_version)) + + if "nccl" in libraries: + nccl_paths = _get_legacy_path("NCCL_INSTALL_PATH", base_paths) + nccl_version = os.environ.get("TF_NCCL_VERSION", "") + result.update(_find_nccl_config(nccl_paths, nccl_version)) + + if "tensorrt" in libraries: + tensorrt_paths = _get_legacy_path("TENSORRT_INSTALL_PATH", base_paths) + tensorrt_version = os.environ.get("TF_TENSORRT_VERSION", "") + result.update(_find_tensorrt_config(tensorrt_paths, tensorrt_version)) + + for k, v in result.items(): + if k.endswith("_dir") or k.endswith("_path"): + result[k] = _normalize_path(v) + + return result + + +def main(): + try: + for key, value in sorted(find_cuda_config().items()): + print("%s: %s" % (key, value)) + except ConfigError as e: + sys.stderr.write(str(e)) + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/pkg_builds/tf_2.1.0/tfg_custom_ops-0.0.1-cp36-cp36m-linux_x86_64.whl b/tensorflow_graphics/projects/point_convolutions/custom_ops/pkg_builds/tf_2.1.0/tfg_custom_ops-0.0.1-cp36-cp36m-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..ca03627d65be14785888a312b75e5c2010c80ebe GIT binary patch literal 544701 zcma&NRa6{Jz%7cqgap?lXmEnNg#f|bAq3aKb#P1Ygy0^62DgC#gADHOZiCMN0R|fm z-+j2}-1BhPUu$(gRM*8 zdfD1pSb2MT0bDEqZk{|A77nfsUKSSIZvH_!YDw9=r0q}x3FdVDUrfVwl2gw5aev=x 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z9+S3pU*`V)?nO)Bp{);u-pAfVz}|6Np5^{z0??bU#2|SbJQn@W!STCM2cq;FbK8J1 zI*yq;ZD(%10hoINM+So)awgi_fpKuS-4|!`e^v0UmEL2mFrEs%>n|!!2zLj#yCcHc z<-CXYfx9pM9C`My*!>OOI|)eR9d!Od>HB!R(}mM+>jhv0h(Inh6rfW$3czn+{3`|g zi2cUtyOtw{&#DFa7HtAlc@rk%*|nY|F2*D zAz#_+g$N$-;y%EC=P~{S@Z#^>ecaudhOe6H1Tbe1fK((&K)CZf7EKfX=OEw?&byKM zjibM-3+~1RgZ7ms+w15q3)jotZNG8xcQvJ-{3O}E{QWCTxS8SaN=iS;tA8i|x4^WQ yxVQTAlb9m+*JItS1?`3Itx5cZ!sP!7{a=N`5K2P2^EN)<)=C8ep_Ivhwf_K9c7}lf literal 0 HcmV?d00001 diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/setup.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/setup.py new file mode 100644 index 000000000..821641388 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/setup.py @@ -0,0 +1,84 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Setup for pip package.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from setuptools import Extension +from setuptools import find_packages +from setuptools import setup +from setuptools.dist import Distribution + + +__version__ = '0.0.1' +REQUIRED_PACKAGES = [ + #'tensorflow-gpu >= 2.1.0', +] +project_name = 'tfg_custom_ops' + + +from setuptools.command.install import install + + +class InstallPlatlib(install): + def finalize_options(self): + install.finalize_options(self) + self.install_lib = self.install_platlib + + +class BinaryDistribution(Distribution): + """This class is needed in order to create OS specific wheels.""" + + def has_ext_modules(self): + return True + + def is_pure(self): + return False + +setup( + name=project_name, + version=__version__, + description=( + 'tensorflow-custom-ops is an examples for custom ops for TensorFlow'), + author='Google Inc.', + author_email='opensource@google.com', + # Contained modules and scripts. + packages=find_packages(), + install_requires=REQUIRED_PACKAGES, + # Add in any packaged data. + include_package_data=True, + zip_safe=False, + distclass=BinaryDistribution, + cmdclass={'install': InstallPlatlib}, + use_2to3=True, + # PyPI package information. + classifiers=[ + 'Development Status :: 4 - Beta', + 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: Apache Software License', + # 'Programming Language :: Python :: 2.7', + # 'Programming Language :: Python :: 3.4', + # 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Software Development :: Libraries :: Python Modules', + 'Topic :: Software Development :: Libraries', + ], + license='Apache 2.0', + keywords='tensorflow custom op machine learning', +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/BUILD new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/BUILD.tpl b/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/BUILD.tpl new file mode 100644 index 000000000..bee021f10 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/BUILD.tpl @@ -0,0 +1,18 @@ +package(default_visibility = ["//visibility:public"]) + +cc_library( + name = "tf_header_lib", + hdrs = [":tf_header_include"], + includes = ["include"], + visibility = ["//visibility:public"], +) + +cc_library( + name = "libtensorflow_framework", + srcs = [":libtensorflow_framework.so"], + #data = ["lib/libtensorflow_framework.so"], + visibility = ["//visibility:public"], +) + +%{TF_HEADER_GENRULE} +%{TF_SHARED_LIBRARY_GENRULE} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/tf_configure.bzl b/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/tf_configure.bzl new file mode 100644 index 000000000..950ca7835 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tf/tf_configure.bzl @@ -0,0 +1,214 @@ +"""Setup TensorFlow as external dependency""" + +_TF_HEADER_DIR = "TF_HEADER_DIR" +_TF_SHARED_LIBRARY_DIR = "TF_SHARED_LIBRARY_DIR" +_TF_SHARED_LIBRARY_NAME = "TF_SHARED_LIBRARY_NAME" + +def _tpl(repository_ctx, tpl, substitutions = {}, out = None): + if not out: + out = tpl + repository_ctx.template( + out, + Label("//tf:%s.tpl" % tpl), + substitutions, + ) + +def _fail(msg): + """Output failure message when auto configuration fails.""" + red = "\033[0;31m" + no_color = "\033[0m" + fail("%sPython Configuration Error:%s %s\n" % (red, no_color, msg)) + +def _is_windows(repository_ctx): + """Returns true if the host operating system is windows.""" + os_name = repository_ctx.os.name.lower() + if os_name.find("windows") != -1: + return True + return False + +def _execute( + repository_ctx, + cmdline, + error_msg = None, + error_details = None, + empty_stdout_fine = False): + """Executes an arbitrary shell command. + Args: + repository_ctx: the repository_ctx object + cmdline: list of strings, the command to execute + error_msg: string, a summary of the error if the command fails + error_details: string, details about the error or steps to fix it + empty_stdout_fine: bool, if True, an empty stdout result is fine, otherwise + it's an error + Return: + the result of repository_ctx.execute(cmdline) + """ + result = repository_ctx.execute(cmdline) + if result.stderr or not (empty_stdout_fine or result.stdout): + _fail("\n".join([ + error_msg.strip() if error_msg else "Repository command failed", + result.stderr.strip(), + error_details if error_details else "", + ])) + return result + +def _read_dir(repository_ctx, src_dir): + """Returns a string with all files in a directory. + Finds all files inside a directory, traversing subfolders and following + symlinks. The returned string contains the full path of all files + separated by line breaks. + """ + if _is_windows(repository_ctx): + src_dir = src_dir.replace("/", "\\") + find_result = _execute( + repository_ctx, + ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"], + empty_stdout_fine = True, + ) + + # src_files will be used in genrule.outs where the paths must + # use forward slashes. + result = find_result.stdout.replace("\\", "/") + else: + find_result = _execute( + repository_ctx, + ["find", src_dir, "-follow", "-type", "f"], + empty_stdout_fine = True, + ) + result = find_result.stdout + return result + +def _genrule(genrule_name, command, outs): + """Returns a string with a genrule. + + Genrule executes the given command and produces the given outputs. + + Args: + genrule_name: A unique name for genrule target. + command: The command to run. + outs: A list of files generated by this rule. + + Returns: + A genrule target. + """ + return ( + "genrule(\n" + + ' name = "' + + genrule_name + '",\n' + + " outs = [\n" + + outs + + "\n ],\n" + + ' cmd = """\n' + + command + + '\n """,\n' + + ")\n" + ) + +def _norm_path(path): + """Returns a path with '/' and remove the trailing slash.""" + path = path.replace("\\", "/") + if path[-1] == "/": + path = path[:-1] + return path + +def _symlink_genrule_for_dir( + repository_ctx, + src_dir, + dest_dir, + genrule_name, + src_files = [], + dest_files = [], + tf_pip_dir_rename_pair = []): + """Returns a genrule to symlink(or copy if on Windows) a set of files. + + If src_dir is passed, files will be read from the given directory; otherwise + we assume files are in src_files and dest_files. + + Args: + repository_ctx: the repository_ctx object. + src_dir: source directory. + dest_dir: directory to create symlink in. + genrule_name: genrule name. + src_files: list of source files instead of src_dir. + dest_files: list of corresonding destination files. + tf_pip_dir_rename_pair: list of the pair of tf pip parent directory to + replace. For example, in TF pip package, the source code is under + "tensorflow_core", and we might want to replace it with + "tensorflow" to match the header includes. + + Returns: + genrule target that creates the symlinks. + """ + # Check that tf_pip_dir_rename_pair has the right length + tf_pip_dir_rename_pair_len = len(tf_pip_dir_rename_pair) + if tf_pip_dir_rename_pair_len != 0 and tf_pip_dir_rename_pair_len !=2: + _fail("The size of argument tf_pip_dir_rename_pair should be either 0 or 2, but %d is given." % tf_pip_dir_rename_pair_len) + + if src_dir != None: + src_dir = _norm_path(src_dir) + dest_dir = _norm_path(dest_dir) + files = "\n".join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) + + # Create a list with the src_dir stripped to use for outputs. + if tf_pip_dir_rename_pair_len: + dest_files = files.replace(src_dir, "").replace(tf_pip_dir_rename_pair[0], tf_pip_dir_rename_pair[1]).splitlines() + else: + dest_files = files.replace(src_dir, "").splitlines() + src_files = files.splitlines() + command = [] + outs = [] + + for i in range(len(dest_files)): + if dest_files[i] != "": + # If we have only one file to link we do not want to use the dest_dir, as + # $(@D) will include the full path to the file. + dest = "$(@D)/" + dest_dir + dest_files[i] if len(dest_files) != 1 else "$(@D)/" + dest_files[i] + + # Copy the headers to create a sandboxable setup. + cmd = "cp -f" + command.append(cmd + ' "%s" "%s"' % (src_files[i], dest)) + outs.append(' "' + dest_dir + dest_files[i] + '",') + dest_dir = "abc" + genrule = _genrule( + genrule_name, + " && ".join(command), + "\n".join(outs), + ) + return genrule + +def _tf_pip_impl(repository_ctx): + tf_header_dir = repository_ctx.os.environ[_TF_HEADER_DIR] + tf_header_rule = _symlink_genrule_for_dir( + repository_ctx, + tf_header_dir, + "include", + "tf_header_include", + tf_pip_dir_rename_pair = ["tensorflow_core", "tensorflow"] + ) + + tf_shared_library_dir = repository_ctx.os.environ[_TF_SHARED_LIBRARY_DIR] + tf_shared_library_name = repository_ctx.os.environ[_TF_SHARED_LIBRARY_NAME] + tf_shared_library_path = "%s/%s" % (tf_shared_library_dir, tf_shared_library_name) + + tf_shared_library_rule = _symlink_genrule_for_dir( + repository_ctx, + None, + "", + "libtensorflow_framework.so", + [tf_shared_library_path], + ["_pywrap_tensorflow_internal.lib" if _is_windows(repository_ctx) else "libtensorflow_framework.so"], + ) + + _tpl(repository_ctx, "BUILD", { + "%{TF_HEADER_GENRULE}": tf_header_rule, + "%{TF_SHARED_LIBRARY_GENRULE}": tf_shared_library_rule, + }) + +tf_configure = repository_rule( + implementation = _tf_pip_impl, + environ = [ + _TF_HEADER_DIR, + _TF_SHARED_LIBRARY_DIR, + _TF_SHARED_LIBRARY_NAME, + ], +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/BUILD new file mode 100644 index 000000000..0152d887d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/BUILD @@ -0,0 +1,16 @@ + +package(default_visibility = ["//visibility:public"]) + +py_library( + name = "tfg_custom_ops_py", + srcs = ['__init__.py'], + deps = [ + "//tfg_custom_ops/basis_proj:basis_proj_py", + "//tfg_custom_ops/build_grid_ds:build_grid_ds_py", + "//tfg_custom_ops/compute_keys:compute_keys_py", + "//tfg_custom_ops/compute_pdf:compute_pdf_py", + "//tfg_custom_ops/find_neighbors:find_neighbors_py", + "//tfg_custom_ops/sampling:sampling_py", + ], + srcs_version = "PY2AND3", +) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/__init__.py new file mode 100644 index 000000000..ce695b329 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific + +from __future__ import absolute_import + +from tfg_custom_ops.basis_proj.python.ops.basis_proj_ops import \ + basis_proj, basis_proj_grads +from tfg_custom_ops.build_grid_ds.python.ops.build_grid_ds_ops import \ + build_grid_ds +from tfg_custom_ops.compute_keys.python.ops.compute_keys_ops import \ + compute_keys +from tfg_custom_ops.compute_pdf.python.ops.compute_pdf_ops import \ + compute_pdf_with_pt_grads, compute_pdf_pt_grads +from tfg_custom_ops.find_neighbors.python.ops.find_neighbors_ops import \ + find_neighbors +from tfg_custom_ops.sampling.python.ops.sampling_ops import sampling diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/BUILD new file mode 100644 index 000000000..28d3a2d5f --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/BUILD @@ -0,0 +1,109 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "basis_proj_ops_gpu", + srcs = [ + "cc/kernels/basis_proj.h", + "cc/kernels/basis_proj_kernels.cu.cc", + "cc/kernels/basis_proj_grads.h", + "cc/kernels/basis_proj_grads_kernels.cu.cc", + "cc/kernels/basis_utils.h", + ], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +cc_binary( + name = 'python/ops/_basis_proj_ops.so', + srcs = [ + "cc/ops/basis_proj_ops.cpp" + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":basis_proj_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "basis_proj_ops_py", + srcs = ([ + "python/ops/basis_proj_ops.py", + ]), + data = [ + ":python/ops/_basis_proj_ops.so" + ], + srcs_version = "PY2AND3", +) + +# py_test( +# name = "basis_proj_ops_py_test", +# srcs = [ +# "python/ops/basis_proj_ops_test.py" +# ], +# main = "python/ops/basis_proj_ops_test.py", +# deps = [ +# ":basis_proj_ops_py", +# ], +# srcs_version = "PY2AND3", +# ) + +py_library( + name = "basis_proj_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":basis_proj_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj.h new file mode 100755 index 000000000..1f68a96fd --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj.h @@ -0,0 +1,57 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to project the input features +/// in a set of basis. +///////////////////////////////////////////////////////////////////////////// + +#ifndef BASIS_PROJ_CUH_ +#define BASIS_PROJ_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to compute a monte carlo convolution using an kernel sample. + * @param pBasisType Type of basis functions used. + * @param pNumSamples Number of samples. + * @param pNumInFeatures Number of input features. + * @param pInPtFeaturesGPUPtr Input gpu pointer to the array + * with the input features. + * @param pInBasisGPUPtr Input gpu pointer with the basis + * functions. + * @param pInNeighborsGPUPtr Input gpu pointer with the list + * of neighbors. + * @param pInSampleNeighIGPUPtr Input gpu pointer with the + * last neighbor index for each sample. + * @param pOutFeaturesGPUPtr Output gpu pointer in which + * the new features will be stored. + * @paramt K Number of basis functions. + */ + template + void basis_proj_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumInFeatures, + const float* pInPtFeaturesGPUPtr, + const float* pInBasisGPUPtr, + const int* pInNeighborsGPUPtr, + const int* pInSampleNeighIGPUPtr, + float* pOutFeaturesGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads.h new file mode 100755 index 000000000..ca5f18db3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads.h @@ -0,0 +1,65 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to compute the gradients of +/// a basis projection operation. +///////////////////////////////////////////////////////////////////////////// + +#ifndef BASIS_PROJ_GRADS_CUH_ +#define BASIS_PROJ_GRADS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to compute the gradients of a basis projection operation. + * @param pNumPts Number of points. + * @param pNumSamples Number of samples. + * @param pNumNeighbors Number of neighbors. + * @param pNumInFeatures Number of input features. + * @param pInPtFeaturesGPUPtr Input gpu pointer to the array + * with the point features. + * @param pInBasisGPUPtr Input gpu pointer with the basis + * functions. + * @param pInNeighborsGPUPtr Input gpu pointer with the list + * of neighbors. + * @param pInSampleNeighIGPUPtr Input gpu pointer with the + * last neighboring point for each sample. + * @param pInGradientsGPUPtr Input gpu pointer with the gradients. + * @param pOutFeatGradsGPUPtr Output gpu pointer in which + * the input feature gradients will be stored. + * @param pOutBasisGradsGPUPtr Output gpu pointer in which + * the gradients of the basis functions will be stored. + * @paramt K Number of basis functions. + */ + template + void basis_proj_grads_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const unsigned int pNumInFeatures, + const float* pInPtFeaturesGPUPtr, + const float* pInBasisGPUPtr, + const int* pInNeighborsGPUPtr, + const int* pInSampleNeighIGPUPtr, + const float* pInGradientsGPUPtr, + float* pOutFeatGradsGPUPtr, + float* pOutBasisGradsGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads_kernels.cu.cc new file mode 100755 index 000000000..692a1fc71 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads_kernels.cu.cc @@ -0,0 +1,230 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to compute the gradients +/// of a basis projection operation. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h" +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads.h" + +///////////////////////// GPU + +template +__global__ void compute_grads_in_features( + const unsigned int pGroupFeatures, + const unsigned int pNumSamples, + const unsigned int pNumInFeatures, + const float* __restrict__ pInFeaturesGPUPtr, + const float* __restrict__ pInPtProjBasisGPUPtr, + const int2* __restrict__ pInNeighborsGPUPtr, + const int* __restrict__ pSampleNeighIdsGPUPtr, + const float* __restrict__ pInFeatGradsGPUPts, + float* __restrict__ pOutFeatGradsGPUPtr, + float* __restrict__ pOutPrProjBasisGradsGPUPtr) +{ + extern __shared__ float sharedMemory[]; + + //Compute the total number of blocks executed and other + //useful indices. + unsigned int numGroupsXBlock = blockDim.x/K; + unsigned int numFeatureBlocks = pNumInFeatures/pGroupFeatures; + unsigned int localId = threadIdx.x%K; + unsigned int groupId = threadIdx.x/K; + unsigned int totalBlocks = pNumSamples*numFeatureBlocks; + + //Get the pointers to shared memory. + float* accumFeatGrads = sharedMemory; + float* features = &sharedMemory[blockDim.x*pGroupFeatures]; + float* inFeatGrads = &sharedMemory[blockDim.x*pGroupFeatures + + numGroupsXBlock*pGroupFeatures]; + + for(int curIter = blockIdx.x; + curIter < totalBlocks; + curIter += gridDim.x) + { + //Get the sample id and the feature offset. + int sampleId = curIter/numFeatureBlocks; + int featureOffset = (curIter%numFeatureBlocks)*pGroupFeatures; + + //Get the range of points for this receptive field. + int2 rangePts; + rangePts.x = (sampleId > 0)?pSampleNeighIdsGPUPtr[sampleId-1]:0; + rangePts.y = pSampleNeighIdsGPUPtr[sampleId]; + int numNeighbors = rangePts.y - rangePts.x; + numNeighbors += numGroupsXBlock-(numNeighbors%numGroupsXBlock); + + //Initialize shared memory with the gradients of the point. + for(int auxIter = threadIdx.x; + auxIter < K*pGroupFeatures; + auxIter += blockDim.x) + inFeatGrads[auxIter] = pInFeatGradsGPUPts[ + sampleId*pNumInFeatures*K + featureOffset*K + auxIter]; + + __syncthreads(); + + //Iterate over the neighbors. + for(int curNeighIter = groupId; + curNeighIter < numNeighbors; + curNeighIter += numGroupsXBlock) + { + int neighIndex = curNeighIter+rangePts.x; + float curWeight = 0.0f; + float curWeightGrad = 0.0f; + float curWeightAccumError = 0.0f; + int2 neighAndSampleIndices; + + if(neighIndex < rangePts.y){ + //Get the neighbor index. + neighAndSampleIndices = pInNeighborsGPUPtr[neighIndex]; + + //Save the weights in shared memory. + curWeight = pInPtProjBasisGPUPtr[neighIndex*K + localId]; + + //Save the features in shared memory. + //THIS REQUIRES K >= pGroupFeatures + if(localId < pGroupFeatures) + features[groupId*pGroupFeatures + localId] = pInFeaturesGPUPtr[ + neighAndSampleIndices.x*pNumInFeatures + + featureOffset + localId]; + } + + __syncthreads(); + + //Iterate over the feature gradients. + for(int featIter = 0; featIter < pGroupFeatures; ++featIter) + { + accumFeatGrads[featIter*blockDim.x + threadIdx.x] = + inFeatGrads[featIter*K + localId]*curWeight; + + //(Kahan summation algorithm for numerical stabitility). + float auxVar1 = inFeatGrads[featIter*K + localId]* + features[groupId*pGroupFeatures + featIter] - + curWeightAccumError; + float auxVar2 = curWeightGrad + auxVar1; + curWeightAccumError = (auxVar2 - curWeightGrad) - auxVar1; + curWeightGrad = auxVar2; + } + + __syncthreads(); + + //Accumulate the contribution of each K and store in memory. + if(neighIndex < rangePts.y){ + atomicAdd(&pOutPrProjBasisGradsGPUPtr[neighIndex*K + localId], curWeightGrad); + + if(localId < pGroupFeatures){ + //(Kahan summation algorithm for numerical stabitility). + float accum = 0.0f; + float accumError = 0.0f; +#pragma unroll + for(int kIter = 0; kIter < K; ++kIter){ + float auxVar1 = accumFeatGrads[localId*blockDim.x + groupId*K + kIter] - accumError; + float auxVar2 = accum + auxVar1; + accumError = (auxVar2 - accum) - auxVar1; + accum = auxVar2; + } + + atomicAdd(&pOutFeatGradsGPUPtr[neighAndSampleIndices.x*pNumInFeatures + + featureOffset + localId], accum); + } + } + + __syncthreads(); + } + } +} + +///////////////////////// CPU + + +template +void mccnn::basis_proj_grads_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const unsigned int pNumInFeatures, + const float* pInPtFeaturesGPUPtr, + const float* pInBasisGPUPtr, + const int* pInNeighborsGPUPtr, + const int* pInSampleNeighIGPUPtr, + const float* pInGradientsGPUPtr, + float* pOutFeatGradsGPUPtr, + float* pOutBasisGradsGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize to zero the output array. + pDevice->memset(pOutFeatGradsGPUPtr, 0, sizeof(float)*pNumPts*pNumInFeatures); + pDevice->memset(pOutBasisGradsGPUPtr, 0, sizeof(float)*K*pNumNeighbors); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Get information of the Device. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = 64; + + //Determine the group of features. + unsigned int groupFeatSize = min(MULTIPLE_IN_FEATURES, pNumInFeatures); + + //Calculate the shared memory needed. + unsigned int sharedMemSize = groupFeatSize*(blockSize + blockSize/K + K)*sizeof(float); + + //Compute the number of blocks + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, (const void*)compute_grads_in_features, sharedMemSize); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int numFeatureBlocks = pNumInFeatures/groupFeatSize; + unsigned int execBlocks = pNumSamples*numFeatureBlocks; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Compute the accumulation of weighted input features. + compute_grads_in_features + <<>>( + groupFeatSize, pNumSamples, pNumInFeatures, pInPtFeaturesGPUPtr, + pInBasisGPUPtr, (const int2*)pInNeighborsGPUPtr, pInSampleNeighIGPUPtr, + pInGradientsGPUPtr, pOutFeatGradsGPUPtr, pOutBasisGradsGPUPtr); + pDevice->check_error(__FILE__, __LINE__); + +} + +///////////////////////// CPU Template declaration + +#define COMPUTE_BASIS_PROJ_GRADS_TEMP_DECL(K) \ + template void mccnn::basis_proj_grads_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const unsigned int pNumSamples, \ + const unsigned int pNumNeighbors, \ + const unsigned int pNumInFeatures, \ + const float* pInPtFeaturesGPUPtr, \ + const float* pInBasisGPUPtr, \ + const int* pInNeighborsGPUPtr, \ + const int* pInSampleNeighIGPUPtr, \ + const float* pInGradientsGPUPtr, \ + float* pOutFeatGradsGPUPtr, \ + float* pOutBasisGradsGPUPtr); + +DECLARE_TEMPLATE_DIMS_BASIS(COMPUTE_BASIS_PROJ_GRADS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_kernels.cu.cc new file mode 100755 index 000000000..f6453948a --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_proj_kernels.cu.cc @@ -0,0 +1,197 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to compute a monte carlo +/// convolution. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h" +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_proj.h" + +///////////////////////// GPU + +//WARNING - Group features should be equal or smaller than K. +template +__global__ void compute_weighted_in_features( + const unsigned int pGroupFeatures, + const unsigned int pNumSamples, + const unsigned int pNumInFeatures, + const float* __restrict__ pInPtProjBasisGPUPtr, + const int2* __restrict__ pInNeighborsGPUPtr, + const int* __restrict__ pSampleNeighIdsGPUPtr, + const float* __restrict__ pInFeaturesGPUPts, + float* __restrict__ pOutProjFeatGPUPtr) +{ + extern __shared__ float sharedMemory[]; + + //Get the pointers to shared memory. + float* accumWeightFeatures = sharedMemory; + float* features = &sharedMemory[blockDim.x*pGroupFeatures]; + + //Compute the total number of blocks executed and other + //useful indices. + unsigned int numGroupsXBlock = blockDim.x/K; + unsigned int numFeatureBlocks = pNumInFeatures/pGroupFeatures; + unsigned int localId = threadIdx.x%K; + unsigned int groupId = threadIdx.x/K; + unsigned int totalBlocks = pNumSamples*numFeatureBlocks; + + for(int curIter = blockIdx.x; + curIter < totalBlocks; + curIter += gridDim.x) + { + //Get the sample id and the feature offset. + int sampleId = curIter/numFeatureBlocks; + int featureOffset = (curIter%numFeatureBlocks)*pGroupFeatures; + + //Get the range of points for this receptive field. + int2 rangePts; + rangePts.x = (sampleId > 0)?pSampleNeighIdsGPUPtr[sampleId-1]:0; + rangePts.y = pSampleNeighIdsGPUPtr[sampleId]; + int numNeighbors = rangePts.y - rangePts.x; + numNeighbors += numGroupsXBlock-(numNeighbors%numGroupsXBlock); + + //Initialize shared memory. +#pragma unroll(8) + for(int featIter = 0; featIter < pGroupFeatures; ++featIter) + accumWeightFeatures[featIter*blockDim.x + threadIdx.x] = 0.0f; + + //Iterate over the neighbors. + for(int curNeighIter = groupId; + curNeighIter < numNeighbors; + curNeighIter += numGroupsXBlock) + { + int neighIndex = curNeighIter+rangePts.x; + float curWeight = 0.0; + + if(neighIndex < rangePts.y){ + //Get the neighbor index. + int2 neighAndSampleIndices = pInNeighborsGPUPtr[neighIndex]; + + //Save the weights in shared memory. + curWeight = pInPtProjBasisGPUPtr[neighIndex*K + localId]; + + //Save the features in shared memory. + if(localId < pGroupFeatures) + features[groupId*pGroupFeatures + localId] = pInFeaturesGPUPts[ + neighAndSampleIndices.x*pNumInFeatures + + featureOffset + localId]; + }else if(localId < pGroupFeatures){ + features[groupId*pGroupFeatures + localId] = 0.0f; + } + + __syncthreads(); + + //Iterate over the features. + //TODO - Kahan summation. However performance drops by half. +#pragma unroll(8) + for(int featIter = 0; featIter < pGroupFeatures; ++featIter) + accumWeightFeatures[featIter*blockDim.x + threadIdx.x] += + features[groupId*pGroupFeatures + featIter]*curWeight; + + __syncthreads(); + } + + //Save the result. + if(threadIdx.x < K){ + for(int featIter = 0; featIter < pGroupFeatures; ++featIter){ + float accumContribs = 0.0f; +#pragma unroll(4) + for(int groupIter = 0; groupIter < numGroupsXBlock; ++groupIter){ + accumContribs += accumWeightFeatures[featIter*blockDim.x + + localId + groupIter*K]; + } + + pOutProjFeatGPUPtr[sampleId*pNumInFeatures*K + (featureOffset + featIter)*K + + localId] = accumContribs; + } + } + + __syncthreads(); + } +} + +///////////////////////// CPU + +template +void mccnn::basis_proj_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumInFeatures, + const float* pInPtFeaturesGPUPtr, + const float* pInBasisGPUPtr, + const int* pInNeighborsGPUPtr, + const int* pInSampleNeighIGPUPtr, + float* pOutFeaturesGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize to zero the output array. + pDevice->memset(pOutFeaturesGPUPtr, 0, sizeof(float)*pNumSamples*pNumInFeatures*K); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Get information of the Device. + unsigned int numMP = gpuProps.numMPs_; + + //Define the block size. + unsigned int blockSize = 64; + + //Determine the group of features. + unsigned int groupFeatSize = min(MULTIPLE_IN_FEATURES, pNumInFeatures); + + //Calculate the shared memory needed. + unsigned int sharedMemSize = (blockSize*(groupFeatSize+1))*sizeof(float); + + //Compute the number of blocks + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, (const void*)compute_weighted_in_features, sharedMemSize); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int numFeatureBlocks = pNumInFeatures/groupFeatSize; + unsigned int execBlocks = pNumSamples*numFeatureBlocks; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Compute the accumulation of weighted input features. + compute_weighted_in_features + <<>>( + groupFeatSize, pNumSamples, pNumInFeatures, pInBasisGPUPtr, + (const int2*)pInNeighborsGPUPtr, pInSampleNeighIGPUPtr, + pInPtFeaturesGPUPtr, pOutFeaturesGPUPtr); + pDevice->check_error(__FILE__, __LINE__); +} + +///////////////////////// CPU Template declaration + +#define COMPUTE_BASIS_PROJ_KS_TEMP_DECL(K) \ + template void mccnn::basis_proj_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumSamples, \ + const unsigned int pNumInFeatures, \ + const float* pInPtFeaturesGPUPtr, \ + const float* pInBasisGPUPtr, \ + const int* pInNeighborsGPUPtr, \ + const int* pInSampleNeighIGPUPtr, \ + float* pOutFeaturesGPUPtr); + +DECLARE_TEMPLATE_DIMS_BASIS(COMPUTE_BASIS_PROJ_KS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h new file mode 100755 index 000000000..d594e49fd --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h @@ -0,0 +1,55 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Basis utils file. +///////////////////////////////////////////////////////////////////////////// + +#ifndef BASIS_UTILS_CUH_ +#define BASIS_UTILS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" + +//Definition of the minimum and maximum kernel points. +#define MIN_BASIS 8 +#define MAX_BASIS 64 + +//Definition of the number of which the number of features should be +// multiple of. +#define MULTIPLE_IN_FEATURES 8 + +//Macros to declare and call a template function with a variable +//number of dimensions and variable basis functions. +#define DECLARE_TEMPLATE_DIMS_BASIS(Func) \ + Func(8 ) \ + Func(16) \ + Func(32) \ + Func(64) + +#define BASIS_CASE_SWITCH(K, Func, ...) \ + case K: \ + Func(__VA_ARGS__); \ + break; + +#define BASIS_SWITCH_CALL(Var, Func, ...) \ + switch(Var){ \ + BASIS_CASE_SWITCH(8, Func, __VA_ARGS__) \ + BASIS_CASE_SWITCH(16, Func, __VA_ARGS__) \ + BASIS_CASE_SWITCH(32, Func, __VA_ARGS__) \ + BASIS_CASE_SWITCH(64, Func, __VA_ARGS__) \ + }; + + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/ops/basis_proj_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/ops/basis_proj_ops.cpp new file mode 100755 index 000000000..4b45f5ef6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/cc/ops/basis_proj_ops.cpp @@ -0,0 +1,227 @@ +///////////////////////////////////////////////////////////////////////////// +/// \file basis_proj.cpp +/// +/// \brief C++ operations definition to project input features to a set of +/// basis functions. +/// +/// \copyright Copyright (c) 2019 Visual Computing group of Ulm University, +/// Germany. See the LICENSE file at the top-level directory of +/// this distribution. +/// +/// \author pedro hermosilla (pedro-1.hermosilla-casajus@uni-ulm.de) +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" + +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_utils.h" +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_proj.h" +#include "tfg_custom_ops/basis_proj/cc/kernels/basis_proj_grads.h" + +/** + * Declaration of the tensorflow operations. + */ +REGISTER_OP("BasisProj") + .Input("basis_neighbors: float32") + .Input("pt_features: float32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Output("features: float32") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({ + pIC->Dim(pIC->input(3), 0), + pIC->Dim(pIC->input(1), 1), + pIC->Dim(pIC->input(0), 1)}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +REGISTER_OP("BasisProjGrads") + .Input("basis_neighbors: float32") + .Input("pt_features: float32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Input("in_gradients: float32") + .Output("basis_gradients: float32") + .Output("feat_gradients: float32") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + pIC->set_output(0, pIC->input(0)); + pIC->set_output(1, pIC->input(1)); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to project input features into a set of basis functions. + */ + class BasisProjOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit BasisProjOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inBasisIns = pContext->input(0); + const Tensor& inPtFeatures = pContext->input(1); + const Tensor& inNeighbors = pContext->input(2); + const Tensor& inSampleNeighIndices = pContext->input(3); + + //Get variables from tensors. + unsigned int numPts = inPtFeatures.shape().dim_size(0); + unsigned int numSamples = inSampleNeighIndices.shape().dim_size(0); + unsigned int numNeighbors = inNeighbors.shape().dim_size(0); + unsigned int numBasis = inBasisIns.shape().dim_size(1); + unsigned int numInFeatures = inPtFeatures.shape().dim_size(1); + + //Get the pointers to GPU data from the tensors. + const float* inBasisGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inBasisIns); + const float* inPtFeaturesGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPtFeatures); + const int* inNeighborsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNeighbors); + const int* inSampleNeighIGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inSampleNeighIndices); + + //Check for the correctness of the input. + bool correct = false; + for(int i = MIN_BASIS; i <= MAX_BASIS; i*=2) + correct = correct || (numBasis == i); + OP_REQUIRES(pContext, correct, + errors::InvalidArgument("BasisProjOp expects a valid number of basis functions.")); + OP_REQUIRES(pContext, numInFeatures > 0 && (numInFeatures < 8 + || numInFeatures%MULTIPLE_IN_FEATURES==0), + errors::InvalidArgument("BasisProjOp expects a valid number of input features, " + "between 1 and "+std::to_string(MULTIPLE_IN_FEATURES)+" or multiple of " + +std::to_string(MULTIPLE_IN_FEATURES))); + OP_REQUIRES(pContext, inBasisIns.shape().dim_size(0) == numNeighbors, + errors::InvalidArgument("BasisProjOp expects the same number of basis as the number of neighbors.")); + OP_REQUIRES(pContext, inNeighbors.dims() == 2 && inNeighbors.shape().dim_size(1) == 2, + errors::InvalidArgument("BasisProjOp expects a neighbor tensor with 2 dimensions " + "and 2 indices per neighbor.")); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Create temporal tensor. + //float* tmpBuffer = gpuDevice->getFloatTmpGPUBuffer(numSamples*numInFeatures*numBasis); + float* outputGPUPtr = nullptr; + TensorShape outShape = TensorShape{numSamples, numInFeatures, numBasis}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape, &outputGPUPtr)); + + //Compute the projected points. + BASIS_SWITCH_CALL(numBasis, + mccnn::basis_proj_gpu, + gpuDevice, + numSamples, numInFeatures, inPtFeaturesGPUPtr, + inBasisGPUPtr, inNeighborsGPUPtr, + inSampleNeighIGPUPtr, outputGPUPtr) + } + + }; + + /** + * Operation to compute a monte carlo convolution. + */ + class BasisProjGradsOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit BasisProjGradsOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inBasisIns = pContext->input(0); + const Tensor& inPtFeatures = pContext->input(1); + const Tensor& inNeighbors = pContext->input(2); + const Tensor& inSampleNeighIndices = pContext->input(3); + const Tensor& inGradients = pContext->input(4); + + //Get variables from tensors. + unsigned int numPts = inPtFeatures.shape().dim_size(0); + unsigned int numSamples = inSampleNeighIndices.shape().dim_size(0); + unsigned int numNeighbors = inNeighbors.shape().dim_size(0); + unsigned int numBasis = inBasisIns.shape().dim_size(1); + unsigned int numInFeatures = inPtFeatures.shape().dim_size(1); + + //Get the pointers to GPU data from the tensors. + const float* inBasisGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inBasisIns); + const float* inPtFeaturesGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPtFeatures); + const int* inNeighborsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNeighbors); + const int* inSampleNeighIGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inSampleNeighIndices); + const float* inGradientsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inGradients); + + //Check for the correctness of the input. + bool correct = false; + for(int i = MIN_BASIS; i <= MAX_BASIS; i*=2) + correct = correct || (numBasis == i); + OP_REQUIRES(pContext, correct, + errors::InvalidArgument("BasisProjOpGrads expects a valid number of basis functions.")); + OP_REQUIRES(pContext, numInFeatures > 0 && (numInFeatures < 8 + || numInFeatures%MULTIPLE_IN_FEATURES==0), + errors::InvalidArgument("BasisProjOpGrads expects a valid number of input features, " + "between 1 and "+std::to_string(MULTIPLE_IN_FEATURES)+" or multiple of " + +std::to_string(MULTIPLE_IN_FEATURES))); + OP_REQUIRES(pContext, inBasisIns.shape().dim_size(0) == numNeighbors, + errors::InvalidArgument("BasisProjOpGrads expects the same number of basis as the number of neighbors.")); + OP_REQUIRES(pContext, inNeighbors.dims() == 2 && inNeighbors.shape().dim_size(1) == 2, + errors::InvalidArgument("BasisProjOpGrads expects a neighbor tensor with 2 dimensions " + "and 2 indices per neighbor.")); + OP_REQUIRES(pContext, inGradients.dims() == 3 && inGradients.shape().dim_size(0) == numSamples && + inGradients.shape().dim_size(1) == numInFeatures && inGradients.shape().dim_size(2) == numBasis, + errors::InvalidArgument("BasisProjOpGrads expects a neighbor tensor with 2 dimensions " + "and 2 indices per neighbor.")); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Create temporal tensor. + float* output1GPUPtr = nullptr; + float* output2GPUPtr = nullptr; + TensorShape out1Shape = TensorShape{numNeighbors, numBasis}; + TensorShape out2Shape = TensorShape{numPts, numInFeatures}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, out1Shape, &output1GPUPtr)); + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (1, pContext, out2Shape, &output2GPUPtr)); + + //Compute the projected points. + BASIS_SWITCH_CALL(numBasis, + mccnn::basis_proj_grads_gpu, + gpuDevice, + numPts, numSamples, numNeighbors, + numInFeatures, inPtFeaturesGPUPtr, + inBasisGPUPtr, inNeighborsGPUPtr, + inSampleNeighIGPUPtr, inGradientsGPUPtr, + output2GPUPtr, output1GPUPtr) + } + }; +} + +REGISTER_KERNEL_BUILDER(Name("BasisProj").Device(DEVICE_GPU), mccnn::BasisProjOp); +REGISTER_KERNEL_BUILDER(Name("BasisProjGrads").Device(DEVICE_GPU), mccnn::BasisProjGradsOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/ops/basis_proj_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/ops/basis_proj_ops.py new file mode 100644 index 000000000..4ad0a57d6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/basis_proj/python/ops/basis_proj_ops.py @@ -0,0 +1,21 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use basis_proj op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +basis_proj_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_basis_proj_ops.so')) +basis_proj = basis_proj_ops.basis_proj +basis_proj_grads = basis_proj_ops.basis_proj_grads diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/BUILD new file mode 100644 index 000000000..c8b2068de --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/BUILD @@ -0,0 +1,102 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "build_grid_ds_ops_gpu", + srcs = ["cc/kernels/build_grid_ds.h", "cc/kernels/build_grid_ds_kernels.cu.cc"], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +cc_binary( + name = 'python/ops/_build_grid_ds_ops.so', + srcs = [ + "cc/ops/build_grid_ds_ops.cpp", + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + ] + if_cuda_is_configured([":build_grid_ds_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "build_grid_ds_ops_py", + srcs = ([ + "python/ops/build_grid_ds_ops.py", + ]), + data = [ + ":python/ops/_build_grid_ds_ops.so" + ], + srcs_version = "PY2AND3", +) + +# py_test( +# name = "build_grid_ds_ops_py_test", +# srcs = [ +# "python/ops/build_grid_ds_ops_test.py" +# ], +# main = "python/ops/build_grid_ds_ops_test.py", +# deps = [ +# ":build_grid_ds_ops_py", +# ], +# srcs_version = "PY2AND3", +# ) + +py_library( + name = "build_grid_ds_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":build_grid_ds_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds.h new file mode 100755 index 000000000..e08a248a5 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds.h @@ -0,0 +1,52 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to build the data structure to +/// access the sparse regular grid. +///////////////////////////////////////////////////////////////////////////// + +#ifndef BUILD_GRID_DS_CUH_ +#define BUILD_GRID_DS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to compute the data structure to access a sparse regular grid. + * @param pDevice Device. + * @param pDSSize Size of the data structure. + * @param pNumPts Number of points. + * @param pInGPUPtrKeys Input pointer to the vector of keys + * on the GPU. + * @param pInGPUPtrNumCells Input pointer to the vector of number + * of cells on the GPU. + * @param pInGPUpOutGPUPtrKeys Output pointer to the data structure + * on the GPU. + * @paramT D Number of dimensions. + */ + template + void build_grid_ds_gpu( + std::unique_ptr& pDevice, + const unsigned int pDSSize, + const unsigned int pNumPts, + const mccnn::int64_m* pInGPUPtrKeys, + const int* pInGPUPtrNumCells, + int* pOutGPUPtrDS); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds_kernels.cu.cc new file mode 100755 index 000000000..c0b37e530 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds_kernels.cu.cc @@ -0,0 +1,127 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to build the data structure to +/// access the sparse regular grid. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds.h" + +///////////////////////// GPU + +/** + * GPU kernel to compute the grid data structure. + * @param pNumPts Number of points. + * @param pKeys Array of keys. + * @param pNumCells Number of cells. + * @param pOutDS Output array with the data structure. + * @paramT D Number of dimensions. + */ + template + __global__ void build_grid_gpu_kernel( + const unsigned int pNumPts, + const mccnn::int64_m* __restrict__ pKeys, + const mccnn::ipoint* __restrict__ pNumCells, + int2* __restrict__ pOutDS) +{ + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(int curPtIndex = initPtIndex; curPtIndex < pNumPts; curPtIndex += totalThreads) + { + //Get the key and compute the index into the ds. + mccnn::int64_m curKey = pKeys[curPtIndex]; + int dsIndex = mccnn::compute_ds_index_from_key_gpu_funct(curKey, pNumCells[0]); + + //Check if it is the first point in the ds cell. + int prevPtIndex = curPtIndex-1; + if(prevPtIndex >= 0){ + if(dsIndex != + mccnn::compute_ds_index_from_key_gpu_funct(pKeys[prevPtIndex], pNumCells[0])){ + pOutDS[dsIndex].x = curPtIndex; + } + } + + //Check if it is the last point in the ds cell. + int nextPtIndex = curPtIndex+1; + if(nextPtIndex == pNumPts){ + pOutDS[dsIndex].y = pNumPts; + }else if(dsIndex != + mccnn::compute_ds_index_from_key_gpu_funct(pKeys[nextPtIndex], pNumCells[0])){ + pOutDS[dsIndex].y = nextPtIndex; + } + } +} + +///////////////////////// CPU + +template +void mccnn::build_grid_ds_gpu( + std::unique_ptr& pDevice, + const unsigned int pDSSize, + const unsigned int pNumPts, + const mccnn::int64_m* pInGPUPtrKeys, + const int* pInGPUPtrNumCells, + int* pOutGPUPtrDS) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)build_grid_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Initialize to zero the output array. + pDevice->memset(pOutGPUPtrDS, 0, sizeof(int)*pDSSize); + pDevice->check_error(__FILE__, __LINE__); + + //Execute the cuda kernel. + build_grid_gpu_kernel<<>>( + pNumPts, + pInGPUPtrKeys, + (const mccnn::ipoint*)pInGPUPtrNumCells, + (int2*)pOutGPUPtrDS); + pDevice->check_error(__FILE__, __LINE__); +} + +///////////////////////// CPU Template declaration + +#define BUILD_GRID_DS_TEMP_DECL(Dims) \ + template void mccnn::build_grid_ds_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pDSSize, \ + const unsigned int pNumPts, \ + const mccnn::int64_m* pInGPUPtrKeys, \ + const int* pInGPUPtrNumCells, \ + int* pOutGPUPtrDS); + +DECLARE_TEMPLATE_DIMS(BUILD_GRID_DS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/ops/build_grid_ds_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/ops/build_grid_ds_ops.cpp new file mode 100755 index 000000000..36b81f31e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/cc/ops/build_grid_ds_ops.cpp @@ -0,0 +1,115 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief C++ operations definition to build the data structure to access +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" +#include "tfg_custom_ops/build_grid_ds/cc/kernels/build_grid_ds.h" + +/** + * Declaration of the tensorflow operation. + */ +REGISTER_OP("BuildGridDs") + .Input("keys: int64") + .Input("num_cells_gpu: int32") + .Input("num_cells_cpu: int32") + .Output("acc_ds: int32") + .Attr("batch_size: int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + int batchSize; + TF_RETURN_IF_ERROR(pIC->GetAttr("batch_size", &batchSize)); + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({batchSize, -1, -1, 2}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to build the data structure to access a regular grid. + */ + class BuildGridDsOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit BuildGridDsOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + OP_REQUIRES_OK(pContext, pContext->GetAttr("batch_size", &batchSize_)); + OP_REQUIRES(pContext, batchSize_ > 0, + errors::InvalidArgument("BuildGridDsOp requires a positive batch size")); + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inKeys = pContext->input(0); + const Tensor& inNumCellsGPU = pContext->input(1); + const Tensor& inNumCellsCPU = pContext->input(2); + + //Get variables from tensors. + unsigned int numPts = inKeys.shape().dim_size(0); + unsigned int numDimensions = inNumCellsGPU.shape().dim_size(0); + + //Get the pointers to GPU/CPU data from the tensors. + const mccnn::int64_m* keysGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inKeys); + const int* numCellsGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inNumCellsGPU); + auto numCellsCPU = inNumCellsCPU.flat(); + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("BuildGridDsOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inNumCellsCPU.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("BuildGridDsOp expects number of cells with the" + " same dimensions in both parameters")); + + //Create the output tensor. + int* outputGPUPtr = nullptr; + TensorShape outShape = TensorShape{batchSize_, numCellsCPU(0), numCellsCPU(1), 2}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape, &outputGPUPtr)); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Compute the data structure. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::build_grid_ds_gpu, + gpuDevice, batchSize_*numCellsCPU(0)*numCellsCPU(1)*2, + numPts, keysGPUPtr, numCellsGPUPtr, outputGPUPtr); + } + + private: + + /**Batch size.*/ + int batchSize_; + }; +} + +REGISTER_KERNEL_BUILDER(Name("BuildGridDs").Device(DEVICE_GPU).HostMemory("num_cells_cpu"), mccnn::BuildGridDsOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/ops/build_grid_ds_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/ops/build_grid_ds_ops.py new file mode 100644 index 000000000..e5d21e90e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/build_grid_ds/python/ops/build_grid_ds_ops.py @@ -0,0 +1,20 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use build_grid_ds op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +build_grid_ds_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_build_grid_ds_ops.so')) +build_grid_ds = build_grid_ds_ops.build_grid_ds diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/BUILD new file mode 100644 index 000000000..07f8d1836 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/BUILD @@ -0,0 +1,103 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "compute_keys_ops_gpu", + srcs = ["cc/kernels/compute_keys.h", "cc/kernels/compute_keys_kernels.cu.cc"], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), + visibility = ["//tfg_custom_ops/find_neighbors:__pkg__"], +) + +cc_binary( + name = 'python/ops/_compute_keys_ops.so', + srcs = [ + "cc/ops/compute_keys_ops.cpp", + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + ] + if_cuda_is_configured([":compute_keys_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "compute_keys_ops_py", + srcs = ([ + "python/ops/compute_keys_ops.py", + ]), + data = [ + ":python/ops/_compute_keys_ops.so" + ], + srcs_version = "PY2AND3", +) + +py_test( + name = "compute_keys_ops_py_test", + srcs = [ + "python/ops/compute_keys_ops_test.py" + ], + main = "python/ops/compute_keys_ops_test.py", + deps = [ + ":compute_keys_ops_py", + ], + srcs_version = "PY2AND3", +) + +py_library( + name = "compute_keys_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":compute_keys_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys.h new file mode 100755 index 000000000..bd94f089b --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys.h @@ -0,0 +1,60 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to compute the keys indices +/// of a point cloud into a regular grid. +///////////////////////////////////////////////////////////////////////////// + +#ifndef COMPUTE_KEYS_CUH_ +#define COMPUTE_KEYS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to compute the keys on the gpu. + * @param pDevice Device. + * @param pNumPts Number of points. + * @param pInGPUPtrPts Input pointer to the vector of points + * on the GPU. + * @param pInGPUPtrBatchIds Input pointer to the vector of batch + * ids on the GPU. + * @param pInGPUPtrSAABBMin Input pointer to the vector of minimum + * points of the bounding boxes on the GPU scaled by the inverse + * cell size. + * @param pInGPUPtrNumCells Input pointer to the vector of number + * of cells on the GPU. + * @param pInGPUPtrInvCellSizes Input pointer to the vector with the + * inverse sizes of each cell. + * @param pInGPUpOutGPUPtrKeys Output pointer to the vector of keys + * on the GPU. + * @paramT D Number of dimensions. + */ + template + void compute_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const float* pInGPUPtrPts, + const int* pInGPUPtrBatchIds, + const float* pInGPUPtrSAABBMin, + const int* pInGPUPtrNumCells, + const float* pInGPUPtrInvCellSizes, + mccnn::int64_m* pOutGPUPtrKeys); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys_kernels.cu.cc new file mode 100755 index 000000000..4b9bdda7c --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/kernels/compute_keys_kernels.cu.cc @@ -0,0 +1,130 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to compute the keys indices +/// of a point cloud into a regular grid. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "compute_keys.h" + +///////////////////////// GPU + +/** + * GPU kernel to compute the keys of each point. + * @param pNumPts Number of points. + * @param pPts Array of points. + * @param pBatchIds Array of batch ids. + * @param pSAABBMin Array of scaled minimum point of bounding + * boxes. + * @param pNumCells Number of cells. + * @param pInvCellSize Inverse cell size. + * @param pOutKeys Output array with the point keys. + */ +template +__global__ void compute_keys_gpu_kernel( + const unsigned int pNumPts, + const mccnn::fpoint* __restrict__ pPts, + const int* __restrict__ pBatchIds, + const mccnn::fpoint* __restrict__ pSAABBMin, + const mccnn::ipoint* __restrict__ pNumCells, + const mccnn::fpoint* __restrict__ pInvCellSize, + mccnn::int64_m* __restrict__ pOutKeys) +{ + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(int curPtIndex = initPtIndex; curPtIndex < pNumPts; curPtIndex += totalThreads) + { + //Get the values for the point. + int curBatchId = pBatchIds[curPtIndex]; + mccnn::fpoint curPt = pPts[curPtIndex]; + mccnn::fpoint curSAABBMin = pSAABBMin[curBatchId]; + + //Compute the current cell indices. + mccnn::ipoint cell = mccnn::compute_cell_gpu_funct( + curPt, curSAABBMin, pNumCells[0], pInvCellSize[0]); + + //Compute the key index of the cell. + mccnn::int64_m keyIndex = mccnn::compute_key_gpu_funct( + cell, pNumCells[0], curBatchId); + + //Save the key index. + pOutKeys[curPtIndex] = keyIndex; + } +} + +///////////////////////// CPU + +template +void mccnn::compute_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const float* pInGPUPtrPts, + const int* pInGPUPtrBatchIds, + const float* pInGPUPtrSAABBMin, + const int* pInGPUPtrNumCells, + const float* pInGPUPtrInvCellSizes, + mccnn::int64_m* pOutGPUPtrKeys) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)compute_keys_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + compute_keys_gpu_kernel<<>>( + pNumPts, + (const mccnn::fpoint*)pInGPUPtrPts, + pInGPUPtrBatchIds, + (const mccnn::fpoint*)pInGPUPtrSAABBMin, + (const mccnn::ipoint*)pInGPUPtrNumCells, + (const mccnn::fpoint*)pInGPUPtrInvCellSizes, + pOutGPUPtrKeys); + pDevice->check_error(__FILE__, __LINE__); +} + +///////////////////////// CPU Template declaration + +#define COMPUTE_KEYS_TEMP_DECL(Dims) \ + template void mccnn::compute_keys_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const float* pInGPUPtrPts, \ + const int* pInGPUPtrBatchIds, \ + const float* pInGPUPtrSAABBMin, \ + const int* pInGPUPtrNumCells, \ + const float* pInGPUPtrInvCellSizes, \ + mccnn::int64_m* pOutGPUPtrKeys); + +DECLARE_TEMPLATE_DIMS(COMPUTE_KEYS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/ops/compute_keys_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/ops/compute_keys_ops.cpp new file mode 100755 index 000000000..abdd94885 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/cc/ops/compute_keys_ops.cpp @@ -0,0 +1,121 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief C++ operations definition to compute the keys indices of a point +/// cloud into a regular grid. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" + +#include "tfg_custom_ops/compute_keys/cc/kernels/compute_keys.h" + +/** + * Declaration of the tensorflow operation. + */ +REGISTER_OP("ComputeKeys") + .Input("points: float32") + .Input("batch_ids: int32") + .Input("scaled_aabb_min: float32") + .Input("num_cells: int32") + .Input("inv_cell_sizes: float32") + .Output("keys: int64") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({pIC->Dim(pIC->input(0), 0)}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to compute the keys of each point in a regular grid. + */ + class ComputeKeysOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit ComputeKeysOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inPts = pContext->input(0); + const Tensor& inBatchIds = pContext->input(1); + const Tensor& inSAABBMin = pContext->input(2); + const Tensor& inNumCells = pContext->input(3); + const Tensor& inInvCellSizes = pContext->input(4); + + //Get variables from tensors. + unsigned int numPts = inPts.shape().dim_size(0); + unsigned int numDimensions = inPts.shape().dim_size(1); + unsigned int batchSize = inSAABBMin.shape().dim_size(0); + + //Get the pointers to GPU data from the tensors. + const float* ptsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPts); + const int* batchIdsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inBatchIds); + const float* aabbMinGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inSAABBMin); + const int* numCellsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNumCells); + const float* invCellSizesGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inInvCellSizes); + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("ComputeKeysOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inBatchIds.shape().dim_size(0) == numPts, + errors::InvalidArgument("ComputeKeysOp expects the same number of batch" + " ids as number of points.")); + OP_REQUIRES(pContext, inSAABBMin.shape().dim_size(1) == numDimensions, + errors::InvalidArgument("ComputeKeysOp expects aabb with the same " + "number of dimensions as the points.")); + OP_REQUIRES(pContext, inNumCells.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputeKeysOp expects number of cells with the same " + "number of dimensions as the points.")); + OP_REQUIRES(pContext, inInvCellSizes.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputeKeysOp expects cell sizes with the same " + "number of dimensions as the points.")); + + //Create the output tensor. + mccnn::int64_m* outputGPUPtr = nullptr; + TensorShape outShape = TensorShape{numPts}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape, &outputGPUPtr)); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Compute the keys. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::compute_keys_gpu, + gpuDevice, numPts, ptsGPUPtr, batchIdsGPUPtr, + aabbMinGPUPtr, numCellsGPUPtr, invCellSizesGPUPtr, + outputGPUPtr); + } + }; +} + +REGISTER_KERNEL_BUILDER(Name("ComputeKeys").Device(DEVICE_GPU), mccnn::ComputeKeysOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops.py new file mode 100644 index 000000000..f4f208d0e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops.py @@ -0,0 +1,20 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use compute_keys op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +compute_keys_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_compute_keys_ops.so')) +compute_keys = compute_keys_ops.compute_keys diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops_test.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops_test.py new file mode 100644 index 000000000..e6e847719 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_keys/python/ops/compute_keys_ops_test.py @@ -0,0 +1,95 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +"""Class to test key computation of regular grid""" + +import os +import sys +import numpy as np +import tensorflow as tf +from absl.testing import parameterized +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.framework import test_util +try: + from tfg_custom_ops.compute_keys.python.ops.compute_keys_ops import\ + compute_keys +except ImportError: + import compute_keys + + +def _create_random_point_cloud_segmented(batch_size, + num_points, + dimension=3, + sizes=None, + scale=1, + clean_aabb=False, + equal_sized_batches=False): + points = np.random.uniform(0, scale, [num_points, dimension]) + if sizes is None: + if not equal_sized_batches: + batch_ids = np.random.randint(0, batch_size, num_points) + batch_ids[:batch_size] = np.arange(0, batch_size) + else: + batch_ids = np.repeat(np.arange(0, batch_size), num_points // batch_size) + else: + sizes = np.array(sizes, dtype=int) + batch_ids = np.repeat(np.arange(0, batch_size), sizes) + if clean_aabb: + # adds points such that the aabb is [0,0,0] [1,1,1]*scale + # to prevent rounding errors + points = np.concatenate( + (points, scale * np.ones([batch_size, dimension]), + np.zeros([batch_size, dimension]))) + batch_ids = np.concatenate( + (batch_ids, np.arange(0, batch_size), np.arange(0, batch_size))) + return points, batch_ids + + +class GridTest(test.TestCase): + + @test_util.run_gpu_only + def test_compute_keys_with_sort(self): + num_points = 10000 + batch_size = 32 + radius = 0.1 + dimension = 3 + radius_array = np.repeat(radius, dimension) + points, batch_ids = _create_random_point_cloud_segmented( + batch_size, num_points * batch_size, dimension=dimension, + sizes=np.ones(batch_size, dtype=int) * num_points, clean_aabb=False) + aabb_min_per_batch = np.empty([batch_size, dimension]) + aabb_max_per_batch = np.empty([batch_size, dimension]) + for i in range(batch_size): + aabb_min_per_batch[i] = np.amin(points[batch_ids == i], axis=0) + aabb_max_per_batch[i] = np.amax(points[batch_ids == i], axis=0) + aabb_sizes = aabb_max_per_batch - aabb_min_per_batch + total_num_cells = np.max(np.ceil(aabb_sizes / radius), axis=0) + custom_keys = compute_keys( + points, batch_ids, aabb_min_per_batch / radius, + total_num_cells, 1 / radius_array) + + aabb_min_per_point = aabb_min_per_batch[batch_ids, :] + cell_ind = np.floor((points - aabb_min_per_point) / radius).astype(int) + cell_ind = np.minimum(np.maximum(cell_ind, [0] * dimension), + total_num_cells) + ref_keys = batch_ids * total_num_cells[0] * \ + total_num_cells[1] * total_num_cells[2] + \ + cell_ind[:, 0] * total_num_cells[1] * total_num_cells[2] + \ + cell_ind[:, 1] * total_num_cells[2] + cell_ind[:, 2] + + # check unsorted keys + self.assertAllEqual(custom_keys, ref_keys) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/BUILD new file mode 100644 index 000000000..f40319d13 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/BUILD @@ -0,0 +1,102 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "compute_pdf_ops_gpu", + srcs = ["cc/kernels/compute_pdf.h", "cc/kernels/compute_pdf_kernels.cu.cc"], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +cc_binary( + name = 'python/ops/_compute_pdf_ops.so', + srcs = [ + "cc/ops/compute_pdf_ops.cpp", + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + ] + if_cuda_is_configured([":compute_pdf_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "compute_pdf_ops_py", + srcs = ([ + "python/ops/compute_pdf_ops.py", + ]), + data = [ + ":python/ops/_compute_pdf_ops.so" + ], + srcs_version = "PY2AND3", +) + +# py_test( +# name = "compute_pdf_ops_py_test", +# srcs = [ +# "python/ops/compute_pdf_ops_test.py" +# ], +# main = "python/ops/compute_pdf_ops_test.py", +# deps = [ +# ":compute_pdf_ops_py", +# ], +# srcs_version = "PY2AND3", +# ) + +py_library( + name = "compute_pdf_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":compute_pdf_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf.h new file mode 100755 index 000000000..ff361083d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf.h @@ -0,0 +1,101 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to compute the pdf of each +/// neighboring point. +///////////////////////////////////////////////////////////////////////////// + +#ifndef COMPUTE_PDF_CUH_ +#define COMPUTE_PDF_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to compute the pdfs on the gpu. + * @param pDevice Device. + * @param pMode Mode used to compute the sigma of the KDE: + * - 0: Constant. + * - 1: Based on the number of points in the receptive field. 1/sqrt(N) + * @param pNumSamples Number of samples. + * @param pNumNeighbors Number of neighbors. + * @param pInGPUPtrInvRadii Inverse radii used to select the neighbors. + * @param pInGPUPtrInvBandwidth Inverse bandwidth value used in mode 0 for + * the KDE. + * @param pInGPUPtrPts Input pointer to the vector of point + * coordinates on the GPU. + * @param pInGPUPtrNeighbors Input pointer to the vector of neighbors + * on the GPU. + * @param pInGPUPtrSampleNeighI Input pointer to the vector of number of + * neighbors for each sample on the GPU. + * @param pOutGPUPtrPDFs Output pointer to the vector of pdfs + * on the GPU. + * @paramt D Number of dimensions. + */ + template + void compute_pdf_gpu( + std::unique_ptr& pDevice, + const unsigned int pMode, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const float* pInGPUPtrInvRadii, + const float* pInGPUPtrInvBandwidth, + const float* pInGPUPtrPts, + const int* pInGPUPtrNeighbors, + const int* pInGPUPtrSampleNeighI, + float* pOutGPUPtrPDFs); + + /** + * Method to compute the gradients of the points wrt the pdfs values. + * @param pDevice Device. + * @param pMode Mode used to compute the sigma of the KDE: + * - 0: Constant. + * - 1: Based on the number of points in the receptive field. 1/sqrt(N) + * @param pNumPts Number of points. + * @param pNumSamples Number of samples. + * @param pNumNeighbors Number of neighbors. + * @param pInGPUPtrInvRadii Inverse radii used to select the neighbors. + * @param pInGPUPtrInvBandwidth Inverse bandwidth value used in mode 0 for + * the KDE. + * @param pInGPUPtrPts Input pointer to the vector of point + * coordinates on the GPU. + * @param pInGPUPtrNeighbors Input pointer to the vector of neighbors + * on the GPU. + * @param pInGPUPtrSampleNeighI Input pointer to the vector of number of + * neighbors for each sample on the GPU. + * @param pInGPUPtrPDFGrad Input gradient for each pdf value. + * @param pOutGPUPtrPtGrads Output pointer to the vector of pdfs + * on the GPU. + * @paramt D Number of dimensions. + */ + template + void compute_pdf_grads_gpu( + std::unique_ptr& pDevice, + const unsigned int pMode, + const unsigned int pNumPts, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const float* pInGPUPtrInvRadii, + const float* pInGPUPtrInvBandwidth, + const float* pInGPUPtrPts, + const int* pInGPUPtrNeighbors, + const int* pInGPUPtrSampleNeighI, + const float* pInGPUPtrPDFGrad, + float* pOutGPUPtrPtGrads); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf_kernels.cu.cc new file mode 100755 index 000000000..3e0554742 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf_kernels.cu.cc @@ -0,0 +1,368 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to compute the pdf of each +/// neighboring point. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf.h" + +///////////////////////// GPU + +/** + * Template method to get the bandwidth. + */ +template +struct bandwidth_struct{ + __forceinline__ __device__ mccnn::fpoint operator()( + const mccnn::fpoint& pConstBandwidth, + const int pNumNeighbors){return mccnn::fpoint(0.0f);} +}; + +//Mode 0: Constant bandwidth. +template +struct bandwidth_struct{ + __forceinline__ __device__ mccnn::fpoint operator()( + const mccnn::fpoint& pConstBandwidth, + const int pNumNeighbors){ + return pConstBandwidth; + } +}; + +//Mode 1: Based on the number of points. +template +struct bandwidth_struct{ + __forceinline__ __device__ mccnn::fpoint operator()( + const mccnn::fpoint& pConstBandwidth, + const int pNumNeighbors){ + return mccnn::fpoint(sqrt((float)pNumNeighbors)); + } +}; + +/** + * GPU kernel to compute the pdfs on the gpu. + * @param pNumSamples Number of samples. + * @param pNumNeighbors Number of neighbors. + * @param pInvRadii Inverse radii used to select the + * neighbors. + * @param pInvBandwidth Constant inverse banwidth. + * @param pPts Array of points. + * @param pNeighbors Array of neighbors. + * @param pNeighIndexXSample Indices of neighbors x sample. + * @param pOutPDF Output array with the pdfs. + * @paramt D Number of dimensions. + * @paramt M Mode used to compute the bandwidth. + */ +template +__global__ void compute_pdf_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const mccnn::fpoint* __restrict__ pInvRadii, + const mccnn::fpoint* __restrict__ pInvBandwidth, + const mccnn::fpoint* __restrict__ pPts, + const int2* __restrict__ pNeighbors, + const int* __restrict__ pNeighIndexXSample, + float* __restrict__ pOutPDF) +{ + //Get the global thread index. + int iniPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curIter = iniPtIndex; + curIter < pNumSamples; + curIter += totalThreads) + { + //Get the current point coordinates. + mccnn::fpoint curPt = pPts[curIter]; + + //Get the range of points for this receptive field. + int2 rangePts; + rangePts.x = (curIter > 0)?pNeighIndexXSample[curIter-1]:0; + rangePts.y = pNeighIndexXSample[curIter]; + int numPts = rangePts.y-rangePts.x; + + //Get the proper badnwidth. + bandwidth_struct getBwStruct; + mccnn::fpoint curBandwidth = getBwStruct(pInvBandwidth[0], pNumSamples); + + //Iterate over the points in the receptive field and compute the PDF. + float accumPdf = 0.0f; + float accumError = 0.0f; //Kahan summation algorithm for numerical stability. + for(int i = 0; i < numPts; ++i) + { + //Get the neighbor coordinate. + int2 neighIndex = pNeighbors[rangePts.x+i]; + + //Comopute the contribution to the KDE. + mccnn::fpoint auxVec = (pPts[neighIndex.x] - curPt)*curBandwidth*pInvRadii[0]; + auxVec = curBandwidth*mccnn::expf(auxVec*auxVec*(-0.5f))*(0.39894228f); + float localPDF = 1.0f; +#pragma unroll + for(int d = 0; d < D; ++d) + localPDF *= auxVec[d]; + + //Accumulate the contribution (Kahan summation algorithm for numerical stabitility). + float auxVar1 = localPDF - accumError; + float auxVar2 = accumPdf + auxVar1; + accumError = (auxVar2 - accumPdf) - auxVar1; + accumPdf = auxVar2; + } + + //Save the PDF. + pOutPDF[curIter] = accumPdf; + } +} + +/** + * GPU kernel to compute gradients of the point wrt the pdf values + * on the gpu. + * @param pNumSamples Number of samples. + * @param pNumNeighbors Number of neighbors. + * @param pInvRadii Inverse radii used to select the + * neighbors. + * @param pInvBandwidth Constant inverse banwidth. + * @param pPts Array of points. + * @param pNeighbors Array of neighbors. + * @param pNeighIndexXSample Indices of neighbors x sample. + * @param pPDFGrads Input pdf gradients. + * @param pOutPtGrads Output array with the point gradients. + * @paramt D Number of dimensions. + * @paramt M Mode used to compute the bandwidth. + */ + template + __global__ void compute_pdf_grads_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const mccnn::fpoint* __restrict__ pInvRadii, + const mccnn::fpoint* __restrict__ pInvBandwidth, + const mccnn::fpoint* __restrict__ pPts, + const int2* __restrict__ pNeighbors, + const int* __restrict__ pNeighIndexXSample, + const float* __restrict__ pPDFGrads, + mccnn::fpoint* __restrict__ pOutPtGrads) + { + //Get the global thread index. + int iniPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curIter = iniPtIndex; + curIter < pNumSamples; + curIter += totalThreads) + { + //Get the current point coordinates. + mccnn::fpoint curPt = pPts[curIter]; + + //Get the range of points for this receptive field. + int2 rangePts; + rangePts.x = (curIter > 0)?pNeighIndexXSample[curIter-1]:0; + rangePts.y = pNeighIndexXSample[curIter]; + int numPts = rangePts.y-rangePts.x; + + //Get the proper badnwidth. + bandwidth_struct getBwStruct; + mccnn::fpoint curBandwidth = getBwStruct(pInvBandwidth[0], pNumSamples); + + //Get the current pdf gradient. + float curPDFGrad = pPDFGrads[curIter]; + + //Iterate over the points in the receptive field and compute the PDF. + mccnn::fpoint accumGradient(0.0f); + mccnn::fpoint accumError(0.0f); //Kahan summation algorithm for numerical stability. + for(int i = 0; i < numPts; ++i) + { + //Get the neighbor coordinate. + int2 neighIndex = pNeighbors[rangePts.x+i]; + + //Comopute the contribution to the gradient. + mccnn::fpoint diffVec = (pPts[neighIndex.x] - curPt)*curBandwidth*pInvRadii[0]; + mccnn::fpoint auxVec = curBandwidth*mccnn::expf(diffVec*diffVec*(-0.5f))*(0.39894228f); + float localPDF = curPDFGrad; +#pragma unroll + for(int d = 0; d < D; ++d) + localPDF *= auxVec[d]; + diffVec = diffVec*curBandwidth*pInvRadii[0]*localPDF; + + //Accumulate the contribution (Kahan summation algorithm for numerical stabitility). + mccnn::fpoint auxVar1 = diffVec - accumError; + mccnn::fpoint auxVar2 = accumGradient + auxVar1; + accumError = (auxVar2 - accumGradient) - auxVar1; + accumGradient = auxVar2; + +#pragma unroll + for(int d = 0; d < D; ++d) + atomicAdd(&pOutPtGrads[neighIndex.x][d], -diffVec[d]); + } +#pragma unroll + for(int d = 0; d < D; ++d) + atomicAdd(&pOutPtGrads[curIter][d], accumGradient[d]); + } + } + +///////////////////////// CPU + +template +void mccnn::compute_pdf_gpu( + std::unique_ptr& pDevice, + const unsigned int pMode, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const float* pInGPUPtrInvRadii, + const float* pInGPUPtrInvBandwidth, + const float* pInGPUPtrPts, + const int* pInGPUPtrNeighbors, + const int* pInGPUPtrSampleNeighI, + float* pOutGPUPtrPDFs) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)compute_pdf_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumSamples/blockSize; + execBlocks += (pNumSamples%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the appropriate cuda kernel based on the selected mode. + if(pMode == 0){ + compute_pdf_gpu_kernel<<>>( + pNumSamples, pNumNeighbors, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + (const mccnn::fpoint*)pInGPUPtrInvBandwidth, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrNeighbors, + pInGPUPtrSampleNeighI, + pOutGPUPtrPDFs); + }else if(pMode == 1){ + compute_pdf_gpu_kernel<<>>( + pNumSamples, pNumNeighbors, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + (const mccnn::fpoint*)pInGPUPtrInvBandwidth, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrNeighbors, + pInGPUPtrSampleNeighI, + pOutGPUPtrPDFs); + } + pDevice->check_error(__FILE__, __LINE__); +} + +template +void mccnn::compute_pdf_grads_gpu( + std::unique_ptr& pDevice, + const unsigned int pMode, + const unsigned int pNumPts, + const unsigned int pNumSamples, + const unsigned int pNumNeighbors, + const float* pInGPUPtrInvRadii, + const float* pInGPUPtrInvBandwidth, + const float* pInGPUPtrPts, + const int* pInGPUPtrNeighbors, + const int* pInGPUPtrSampleNeighI, + const float* pInGPUPtrPDFGrad, + float* pOutGPUPtrPtGrads) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize to zero the output array. + pDevice->memset(pOutGPUPtrPtGrads, 0, sizeof(float)*pNumPts*D); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)compute_pdf_grads_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumSamples/blockSize; + execBlocks += (pNumSamples%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the appropriate cuda kernel based on the selected mode. + if(pMode == 0){ + compute_pdf_grads_gpu_kernel<<>>( + pNumSamples, pNumNeighbors, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + (const mccnn::fpoint*)pInGPUPtrInvBandwidth, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrNeighbors, + pInGPUPtrSampleNeighI, + pInGPUPtrPDFGrad, + (mccnn::fpoint*)pOutGPUPtrPtGrads); + }else if(pMode == 1){ + compute_pdf_grads_gpu_kernel<<>>( + pNumSamples, pNumNeighbors, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + (const mccnn::fpoint*)pInGPUPtrInvBandwidth, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrNeighbors, + pInGPUPtrSampleNeighI, + pInGPUPtrPDFGrad, + (mccnn::fpoint*)pOutGPUPtrPtGrads); + } + pDevice->check_error(__FILE__, __LINE__); +} + +///////////////////////// CPU Template declaration + +#define COMPUTE_PDF_TEMP_DECL(Dims) \ + template void mccnn::compute_pdf_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pMode, \ + const unsigned int pNumSamples, \ + const unsigned int pNumNeighbors, \ + const float* pInGPUPtrInvRadii, \ + const float* pInGPUPtrInvBandwidth, \ + const float* pInGPUPtrPts, \ + const int* pInGPUPtrNeighbors, \ + const int* pInGPUPtrSampleNeighI, \ + float* pOutGPUPtrPDFs); + +#define COMPUTE_PDF_GRADS_TEMP_DECL(Dims) \ + template void mccnn::compute_pdf_grads_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pMode, \ + const unsigned int pNumPts, \ + const unsigned int pNumSamples, \ + const unsigned int pNumNeighbors, \ + const float* pInGPUPtrInvRadii, \ + const float* pInGPUPtrInvBandwidth, \ + const float* pInGPUPtrPts, \ + const int* pInGPUPtrNeighbors, \ + const int* pInGPUPtrSampleNeighI, \ + const float* pInGPUPtrPDFGrad, \ + float* pOutGPUPtrPtGrads); + +DECLARE_TEMPLATE_DIMS(COMPUTE_PDF_TEMP_DECL) +DECLARE_TEMPLATE_DIMS(COMPUTE_PDF_GRADS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/ops/compute_pdf_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/ops/compute_pdf_ops.cpp new file mode 100755 index 000000000..196516316 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/cc/ops/compute_pdf_ops.cpp @@ -0,0 +1,259 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief C++ operations definition to compute the pdf of each neighbor. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" + +#include "tfg_custom_ops/compute_pdf/cc/kernels/compute_pdf.h" + +/** + * Declaration of the tensorflow operations. + */ +REGISTER_OP("ComputePdf") + .Input("points: float32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Input("inv_badnwidth: float32") + .Input("inv_radii: float32") + .Output("pdfs: float32") + .Attr("mode: int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({pIC->Dim(pIC->input(2), 0)}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +REGISTER_OP("ComputePdfWithPtGrads") + .Input("points: float32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Input("inv_badnwidth: float32") + .Input("inv_radii: float32") + .Output("pdfs: float32") + .Attr("mode: int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({pIC->Dim(pIC->input(2), 0)}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +REGISTER_OP("ComputePdfPtGrads") + .Input("points: float32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Input("inv_badnwidth: float32") + .Input("inv_radii: float32") + .Input("pdf_grads: float32") + .Output("pt_grads: float32") + .Attr("mode: int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims = + pIC->MakeShape({ + pIC->Dim(pIC->input(0), 0), + pIC->Dim(pIC->input(0), 1)}); + pIC->set_output(0, outputDims); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to compute the pdf of each neighbor. + */ + class ComputePDFOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit ComputePDFOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + OP_REQUIRES_OK(pContext, pContext->GetAttr("mode", &mode_)); + OP_REQUIRES(pContext, mode_ >= 0 && mode_ < 2, + errors::InvalidArgument("ComputePDFOp requires a mode between 0 and 1")); + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inPts = pContext->input(0); + const Tensor& inNeighbors = pContext->input(1); + const Tensor& inSampleNeighIndices = pContext->input(2); + const Tensor& inInvBandwidth = pContext->input(3); + const Tensor& inInvRadii = pContext->input(4); + + //Get variables from tensors. + unsigned int numPts = inPts.shape().dim_size(0); + unsigned int numSamples = inSampleNeighIndices.shape().dim_size(0); + unsigned int numNeighbors = inNeighbors.shape().dim_size(0); + unsigned int numDimensions = inPts.shape().dim_size(1); + + //Get the pointers to GPU data from the tensors. + const float* ptsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPts); + const int* neighborsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNeighbors); + const int* sampleNeighIGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inSampleNeighIndices); + const float* invBandwidthGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inInvBandwidth); + const float* inInvRadiiGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inInvRadii); + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numSamples == numPts, + errors::InvalidArgument("ComputePDFOp expects the same points as samples.")); + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("ComputePDFOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inInvBandwidth.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputePDFOp expects a number of dimensions in" + " inInvBandwidth equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inInvRadii.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputePDFOp expects a number of dimensions in" + " inInvRadii equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inNeighbors.dims() == 2 && + inNeighbors.shape().dim_size(1) == 2, + errors::InvalidArgument("ComputePDFOp expects a neighbor tensor with 2 dimensions " + "and 2 indices per neighbor.")); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Create the output tensor. + float* outputGPUPtr = nullptr; + TensorShape outShape = TensorShape{numSamples}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape, &outputGPUPtr)); + + //Compute the PDF + DIMENSION_SWITCH_CALL(numDimensions, mccnn::compute_pdf_gpu, + gpuDevice, mode_, numSamples, numNeighbors, inInvRadiiGPUPtr, + invBandwidthGPUPtr, ptsGPUPtr, neighborsGPUPtr, sampleNeighIGPUPtr, + outputGPUPtr); + } + + private: + + /**Mode used to compute the sigma value: + * - 0: Constant. + * - 1: Based on the number of points in the receptive field. 1/sqrt(N)*/ + int mode_; + }; + + /** + * Operation to compute the gradients of each point wrt the pdf values. + */ + class ComputePDFPtGradsOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit ComputePDFPtGradsOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + OP_REQUIRES_OK(pContext, pContext->GetAttr("mode", &mode_)); + OP_REQUIRES(pContext, mode_ >= 0 && mode_ < 2, + errors::InvalidArgument("ComputePDFPtGradsOp requires a mode" + " between 0 and 1")); + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inPts = pContext->input(0); + const Tensor& inNeighbors = pContext->input(1); + const Tensor& inSampleNeighIndices = pContext->input(2); + const Tensor& inInvBandwidth = pContext->input(3); + const Tensor& inInvRadii = pContext->input(4); + const Tensor& inPDFGrads = pContext->input(5); + + //Get variables from tensors. + unsigned int numSamples = inSampleNeighIndices.shape().dim_size(0); + unsigned int numPts = inPts.shape().dim_size(0); + unsigned int numNeighbors = inNeighbors.shape().dim_size(0); + unsigned int numDimensions = inPts.shape().dim_size(1); + + //Get the pointers to GPU data from the tensors. + const float* ptsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPts); + const int* neighborsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNeighbors); + const int* sampleNeighIGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inSampleNeighIndices); + const float* invBandwidthGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inInvBandwidth); + const float* inInvRadiiGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inInvRadii); + const float* inPDFGradsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPDFGrads); + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numSamples == numPts, + errors::InvalidArgument("ComputePDFPtGradsOp expects the same points as samples.")); + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("ComputePDFPtGradsOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inInvBandwidth.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputePDFPtGradsOp expects a number of dimensions in" + " inInvBandwidth equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inInvRadii.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("ComputePDFPtGradsOp expects a number of dimensions in" + " inInvRadii equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inNeighbors.dims() == 2 && + inNeighbors.shape().dim_size(1) == 2, + errors::InvalidArgument("ComputePDFPtGradsOp expects a neighbor tensor with 2 " + "dimensions and 2 indices per neighbor.")); + OP_REQUIRES(pContext, inPDFGrads.shape().dim_size(0) == numSamples, + errors::InvalidArgument("ComputePDFPtGradsOp expects a number of pdf " + " gradients equal to the number of neighbors.")); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Create the output tensor. + float* outputGPUPtr = nullptr; + TensorShape outShape = TensorShape{numPts, numDimensions}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape, &outputGPUPtr)); + + //Compute the PDF + DIMENSION_SWITCH_CALL(numDimensions, mccnn::compute_pdf_grads_gpu, + gpuDevice, mode_, numPts, numSamples, numNeighbors, inInvRadiiGPUPtr, + invBandwidthGPUPtr, ptsGPUPtr, neighborsGPUPtr, sampleNeighIGPUPtr, + inPDFGradsGPUPtr, outputGPUPtr); + } + + private: + + /**Mode used to compute the sigma value: + * - 0: Constant. + * - 1: Based on the number of points in the receptive field. 1/sqrt(N)*/ + int mode_; + }; +} + +REGISTER_KERNEL_BUILDER(Name("ComputePdf").Device(DEVICE_GPU), mccnn::ComputePDFOp); +REGISTER_KERNEL_BUILDER(Name("ComputePdfWithPtGrads").Device(DEVICE_GPU), mccnn::ComputePDFOp); +REGISTER_KERNEL_BUILDER(Name("ComputePdfPtGrads").Device(DEVICE_GPU), mccnn::ComputePDFPtGradsOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/ops/compute_pdf_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/ops/compute_pdf_ops.py new file mode 100644 index 000000000..5a94ed4d4 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/compute_pdf/python/ops/compute_pdf_ops.py @@ -0,0 +1,21 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use compute_pdf op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +compute_pdf_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_compute_pdf_ops.so')) +compute_pdf_with_pt_grads = compute_pdf_ops.compute_pdf_with_pt_grads +compute_pdf_pt_grads = compute_pdf_ops.compute_pdf_pt_grads diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/BUILD new file mode 100644 index 000000000..b9ef7257d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/BUILD @@ -0,0 +1,117 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "find_neighbors_ops_gpu", + srcs = [ + "cc/kernels/count_neighbors.h", + "cc/kernels/count_neighbors_kernels.cu.cc", + "cc/kernels/elem_wise_min.h", + "cc/kernels/elem_wise_min_kernels.cu.cc", + "cc/kernels/find_ranges_grid_ds.h", + "cc/kernels/find_ranges_grid_ds_kernels.cu.cc", + "cc/kernels/scan_alg.h", + "cc/kernels/scan_alg_kernels.cu.cc", + "cc/kernels/store_neighbors.h", + "cc/kernels/store_neighbors_kernels.cu.cc", + "cc/kernels/store_pooled_pts.h", + "cc/kernels/store_pooled_pts_kernels.cu.cc", + ], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), + visibility = ["//tfg_custom_ops/find_neighbors:__pkg__"], +) + +cc_binary( + name = 'python/ops/_find_neighbors_ops.so', + srcs = [ + "cc/ops/find_neighbors_ops.cpp", + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/compute_keys:compute_keys_ops_gpu", + ] + if_cuda_is_configured([":find_neighbors_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "find_neighbors_ops_py", + srcs = ([ + "python/ops/find_neighbors_ops.py", + ]), + data = [ + ":python/ops/_find_neighbors_ops.so" + ], + srcs_version = "PY2AND3", +) + +# py_test( +# name = "find_neighbors_ops_py_test", +# srcs = [ +# "python/ops/find_neighbors_ops_test.py" +# ], +# main = "python/ops/find_neighbors_ops_test.py", +# deps = [ +# ":find_neighbors_ops_py", +# ], +# srcs_version = "PY2AND3", +# ) + +py_library( + name = "find_neighbors_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":find_neighbors_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors.h new file mode 100755 index 000000000..85e8424c0 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors.h @@ -0,0 +1,60 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to count the neighbors for each +/// point. +///////////////////////////////////////////////////////////////////////////// + +#ifndef COUNT_NEIGHBORS_CUH_ +#define COUNT_NEIGHBORS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to count the number of neighbors. + * @param pDevice GPU device. + * @param pNumSamples Number of samples. + * @param pNumRanges Number of ranges per point. + * @param pInGPUPtrSamples Input pointer to the vector of samples + * coordinates on the GPU. + * @param pInGPUPtrPts Input pointer to the vector of point + * coordinates on the GPU. + * @param pInGPUPtrRanges Input pointer to the vector of sample + * ranges on the GPU. + * @param pInvRadii Inverse of the radius used on the + * search of neighbors in each dimension. + * @param pOutGPUPtrNumNeighs Output pointer to the vector with the + * number of neighbors for each sample on the GPU. The memory + * should be initialized to 0 outside this function. + * @tparam D Number of dimensions. + */ + template + void count_neighbors( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const float* pInGPUPtrSamples, + const float* pInGPUPtrPts, + const int* pInGPUPtrRanges, + const float* pInGPUPtrInvRadii, + int* pOutGPUPtrNumNeighs); + +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors_kernels.cu.cc new file mode 100755 index 000000000..aa8b10f71 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors_kernels.cu.cc @@ -0,0 +1,163 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to count the neighbors for +/// each point. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors.h" + +#define NUM_THREADS_X_RANGE 16 + +///////////////////////// GPU + +/** + * GPU kernel to count the number of neighbors for each sample. + * @param pNumSamples Number of samples. + * @param pNumRanges Number of ranges per sample. + * @param pSamples 3D coordinates of each sample. + * @param pPts 3D coordinates of each point. + * @param pRanges Search ranges for each sample. + * @param pInvRadii Inverse of the radius used on the + * search of neighbors in each dimension. + * @param pOutNumNeighs Number of neighbors for each sample. + * @tparam D Number of dimensions + */ + template + __global__ void count_neighbors_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const mccnn::fpoint* __restrict__ pSamples, + const mccnn::fpoint* __restrict__ pPts, + const int2* __restrict__ pRanges, + const mccnn::fpoint* __restrict__ pInvRadii, + int* __restrict__ pOutNumNeighs) +{ + //Declare shared memory. + extern __shared__ int localCounter[]; + + //Get the global thread index. + int initSampleIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(long long curIter = initSampleIndex; + curIter < pNumSamples*pNumRanges*NUM_THREADS_X_RANGE; curIter += totalThreads) + { + //Get the point id. + int sampleIndex = curIter/(pNumRanges*NUM_THREADS_X_RANGE); + + //Initialize the shared memory + localCounter[threadIdx.x] = sampleIndex; + localCounter[blockDim.x + threadIdx.x] = 0; + + __syncthreads(); + + //Get the offset and index of the local counter. + int localIndex = curIter%NUM_THREADS_X_RANGE; + int offsetCounter = sampleIndex-localCounter[0]; + + //Get the current sample coordinates and the search range. + mccnn::fpoint curSampleCoords = pSamples[sampleIndex]; + int2 curRange = pRanges[curIter/NUM_THREADS_X_RANGE]; + + //Iterate over the points. + for(int curPtIter = curRange.x+localIndex; + curPtIter < curRange.y; curPtIter+=NUM_THREADS_X_RANGE) + { + //Check if the point is closer than the selected radius. + mccnn::fpoint curPtCoords = pPts[curPtIter]; + if(length((curSampleCoords - curPtCoords)*pInvRadii[0]) < 1.0f){ + //Increment the shared counters. + atomicAdd(&localCounter[blockDim.x+offsetCounter], 1); + } + } + + __syncthreads(); + + //Update the global counters. + if(threadIdx.x == 0){ + atomicAdd(&pOutNumNeighs[sampleIndex], localCounter[blockDim.x]); + }else if(sampleIndex != localCounter[threadIdx.x-1]){ + atomicAdd(&pOutNumNeighs[sampleIndex], localCounter[blockDim.x+offsetCounter]); + } + + __syncthreads(); + } +} + +///////////////////////// CPU + +template +void mccnn::count_neighbors( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const float* pInGPUPtrSamples, + const float* pInGPUPtrPts, + const int* pInGPUPtrRanges, + const float* pInGPUPtrInvRadii, + int* pOutGPUPtrNumNeighs) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, + (const void*)count_neighbors_gpu_kernel, + blockSize*2*sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = (pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)/blockSize; + execBlocks += ((pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + count_neighbors_gpu_kernel<<>>( + pNumSamples, + pNumRanges, + (const mccnn::fpoint*)pInGPUPtrSamples, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrRanges, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + pOutGPUPtrNumNeighs); + pDevice->check_error(__FILE__, __LINE__); +} + +///////////////////////// CPU Template declaration + +#define COUNT_NEIGHS_TEMP_DECL(Dims) \ + template void mccnn::count_neighbors( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumSamples, \ + const unsigned int pNumRanges, \ + const float* pInGPUPtrSamples, \ + const float* pInGPUPtrPts, \ + const int* pInGPUPtrRanges, \ + const float* pInvRadii, \ + int* pOutGPUPtrNumNeighs); + +DECLARE_TEMPLATE_DIMS(COUNT_NEIGHS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min.h new file mode 100755 index 000000000..9a0c2bc6a --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min.h @@ -0,0 +1,47 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to perform a min operation +/// element wise. +///////////////////////////////////////////////////////////////////////////// + +#ifndef ELEM_WISE_MIN_CUH_ +#define ELEM_WISE_MIN_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to perform element wise minimum operation with a given + * minimum value. + * @param pDevice Device. + * @param pNumElements Number of elements in the array. + * @param pMinValue Minimum value. + * @param pValuesGPUPtr Input/Output pointer to the vector of + * values in GPU memory + * @rparamt T Type of the elements. + */ + template + void elem_wise_min_value( + std::unique_ptr& pDevice, + const unsigned int pNumElements, + const T pMinValue, + T* pValuesGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min_kernels.cu.cc new file mode 100755 index 000000000..759294f24 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min_kernels.cu.cc @@ -0,0 +1,100 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to perform a min operation +/// element wise. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min.h" + +///////////////////////// GPU + +/** + * GPU kernel to perform element wise minimum operation with a given + * maximum value. + * @param pNumElements Number of elements in the array. + * @param pMinValue Minimum value. + * @param pValues Input/Output pointer to the vector of + * values in GPU memory + * @rparamt T Type of the elements. + */ + template + __global__ void elem_wise_min_value_gpu( + const unsigned int pNumElements, + const T pMinValue, + T* __restrict__ pValues) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curIndex = initPtIndex; + curIndex < pNumElements; + curIndex += totalThreads) + { + //Perform the max operation. + pValues[curIndex] = MCCNN_MIN(pValues[curIndex], pMinValue); + } +} + +///////////////////////// CPU + +template +void mccnn::elem_wise_min_value( + std::unique_ptr& pDevice, + const unsigned int pNumElements, + const T pMinValue, + T* pValuesGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)elem_wise_min_value_gpu, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumElements/blockSize; + execBlocks += (pNumElements%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + elem_wise_min_value_gpu<<>>( + pNumElements, pMinValue, pValuesGPUPtr); + pDevice->check_error(__FILE__, __LINE__); +} + +//TEMPLATE INSTANTIATION +template void mccnn::elem_wise_min_value( + std::unique_ptr& pDevice, + const unsigned int pNumElements, + const float pMinValue, + float* pValuesGPUPtr); +template void mccnn::elem_wise_min_value( + std::unique_ptr& pDevice, + const unsigned int pNumElements, + const int pMinValue, + int* pValuesGPUPtr); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds.h new file mode 100755 index 000000000..e1a23becb --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds.h @@ -0,0 +1,81 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to find the ranges in the list +/// of points for a grid cell and its 26 neighbors. +///////////////////////////////////////////////////////////////////////////// + +#ifndef BUILD_GRID_DS_CUH_ +#define BUILD_GRID_DS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include +#include + +namespace mccnn{ + + /** + * Method to find the ranges in the list of points for a grid cell + * and its 26 neighbors. + * @param pDevice Device. + * @param pNumSamples Number of samples. + * @param pNumPts Number of points. + * @param pLastDOffsets Number of displacement in the last + * dimension in the positive and negative axis. + * @param pNumOffsets Number of offsets applied to the + * keys. + * @param pInGPUPtrOffsets List of offsets to apply. + * @param pInGPUPtrSampleKeys Input pointer to the vector of keys + * of each sample on the GPU. + * @param pInGPUPtrPtsKeys Input pointer to the vector of keys + * of each point on the GPU. + * @param pInGPUPtrGridDS Input grid acceleration data + * structure. + * @param pInGPUPtrNumCells Input pointer to the vector of number + * of cells on the GPU. + * @param pOutGPUPtrRanges Output pointer to the array containing + * the search ranges for each sample. + * @paramT D Number of dimensions. + */ + template + void find_ranges_grid_ds_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumPts, + const unsigned int pLastDOffsets, + const unsigned int pNumOffsets, + const int* pInGPUPtrOffsets, + const mccnn::int64_m* pInGPUPtrSampleKeys, + const mccnn::int64_m* pInGPUPtrPtsKeys, + const int* pInGPUPtrGridDS, + const int* pInGPUPtrNumCells, + int* pOutGPUPtrRanges); + + /** + * Method to compute the total number of offsets + * to apply for each range search. + * @param pNumDimensions Number of dimensions. + * @param pAxisOffset Offset apply to each axis. + * @param pOutVector Output parameter with the + * displacements applied to each axis. + */ + unsigned int computeTotalNumOffsets( + const unsigned int pNumDimensions, + const unsigned int pAxisOffset, + std::vector& pOutVector); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds_kernels.cu.cc new file mode 100755 index 000000000..aa4dfb396 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds_kernels.cu.cc @@ -0,0 +1,353 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to compute the keys indices +/// of a point cloud into a regular grid. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds.h" + +#include + +///////////////////////// GPU + +/** + * GPU kernel to find the ranges in the list of points for a grid cell + * and its neighbors. + * @param pNumSamples Number of samples. + * @param pNumPts Number of points. + * @param pLastDOffsets Number of displacement in the last + * dimension in the positive and negative axis. + * @param pNumOffsets Number of offsets applied to the + * keys. + * @param pOffsets List of offsets to apply. + * @param pSampleKeys Array of keys for each sample. + * @param pPtKeys Array of keys for each point. + * @param pGridDs Grid data structure. + * @param pNumCells Number of cells. + * @param pOutDS Output array with the ranges. + * @paramT D Number of dimensions. + */ +template +__global__ void find_ranges_grid_ds_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumPts, + const unsigned int pLastDOffsets, + const unsigned int pNumOffsets, + const mccnn::ipoint* __restrict__ pOffsets, + const mccnn::int64_m* __restrict__ pSampleKeys, + const mccnn::int64_m* __restrict__ pPtKeys, + const int2* __restrict__ pGridDs, + const mccnn::ipoint* __restrict__ pNumCells, + int2* __restrict__ pOutRanges) +{ + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + //Calculate the total number of cells. + mccnn::int64_m totalCells = mccnn::compute_total_num_cells_gpu_funct(pNumCells[0]); + + for(int curIter = initPtIndex; curIter < pNumSamples*pNumOffsets; curIter += totalThreads) + { + //Calculate the point and offset index. + int curPtIndex = curIter/pNumOffsets; + int curOffset = curIter%pNumOffsets; + + //Get the current offset. + mccnn::ipoint cellOffset = pOffsets[curOffset]; + + //Get the key of the current point. + mccnn::int64_m curKey = pSampleKeys[curPtIndex]; + + //Get the new cell with the offset. + mccnn::ipoint cellIndex = + mccnn::compute_cell_from_key_gpu_funct(curKey, pNumCells[0]); +#pragma unroll + for(int i=0; i < D; ++i) + cellIndex[i+1] += cellOffset[i]; + + //Check if we are out of the bounding box. + bool inside = true; +#pragma unroll + for(int i=0; i < D-1; ++i) + inside = inside && cellIndex[i+1] >= 0 && cellIndex[i+1] < pNumCells[0][i]; + if(inside) + { + //Get the range of pts to check in the data structure. + int curDsIndex = mccnn::compute_ds_index_from_cell_gpu_funct(cellIndex, pNumCells[0]); + int2 dsRange = pGridDs[curDsIndex]; + int rangeSize = dsRange.y-dsRange.x-1; + + //Tube has at least 1 element. + if(rangeSize >= 0){ + + //Compute max key of the range. + mccnn::ipoint auxCell(&cellIndex[1]); + mccnn::int64_m auxKey = mccnn::compute_key_gpu_funct(auxCell, pNumCells[0], cellIndex[0]); + mccnn::int64_m maxKey = auxKey+pLastDOffsets; + mccnn::int64_m minKey = auxKey-pLastDOffsets; + maxKey = (auxKey/totalCells == maxKey/totalCells)?maxKey:((auxKey/totalCells)*totalCells) + totalCells - 1; + minKey = (auxKey/totalCells == minKey/totalCells)?minKey:((auxKey/totalCells)*totalCells); + + //Declare iterators and auxiliar variables. + int2 curMinRange = make_int2(0, rangeSize); + int2 curMaxRange = make_int2(0, rangeSize); + + //Search for the range. + bool stopMinRange = rangeSize <= 1; + bool stopMaxRange = stopMinRange; + while(!stopMinRange || !stopMaxRange){ + + //Compute the pivots. + int minPivot = (curMinRange.y + curMinRange.x)/2; + int maxPivot = (curMaxRange.y + curMaxRange.x)/2; + + //Check the minimum range. + if(!stopMinRange){ + mccnn::int64_m curMinKey = pPtKeys[minPivot+dsRange.x]; + if(curMinKey > maxKey) curMinRange.x = minPivot; + else curMinRange.y = minPivot; + } + + //Check the maximum range. + if(!stopMaxRange){ + mccnn::int64_m curMaxKey = pPtKeys[maxPivot+dsRange.x]; + if(curMaxKey >= minKey) curMaxRange.x = maxPivot; + else curMaxRange.y = maxPivot; + } + + //Check the stopping condition. + stopMinRange = (curMinRange.y - curMinRange.x) <= 1; + stopMaxRange = (curMaxRange.y - curMaxRange.x) <= 1; + } + + int2 resultingRange = make_int2(0, 0); + + //Get the values of the keys. + mccnn::int64_m lastMinKey1 = pPtKeys[curMinRange.x+dsRange.x]; + mccnn::int64_m lastMinKey2 = pPtKeys[curMinRange.y+dsRange.x]; + + //Test for the init of the range. + if(lastMinKey1 >= minKey && lastMinKey1 <= maxKey){ + resultingRange.x = curMinRange.x+dsRange.x; + }else if(lastMinKey2 >= minKey && lastMinKey2 <= maxKey){ + resultingRange.x = curMinRange.y+dsRange.x; + } + + mccnn::int64_m lastMaxKey1 = pPtKeys[curMaxRange.x+dsRange.x]; + mccnn::int64_m lastMaxKey2 = pPtKeys[curMaxRange.y+dsRange.x]; + + //Test for the end of the range. + if(lastMaxKey2 >= minKey && lastMaxKey2 <= maxKey){ + resultingRange.y = curMaxRange.y+dsRange.x+1; + }else if(lastMaxKey1 >= minKey && lastMaxKey1 <= maxKey){ + resultingRange.y = curMaxRange.x+dsRange.x+1; + } + + //Store in memory the resulting range. + pOutRanges[curIter] = resultingRange; + } + } + } +} + +/** + * GPU kernel for the 2D case. + * @param pNumSamples Number of samples. + * @param pNumPts Number of points. + * @param pNumOffsets Number of offsets applied to the + * keys. + * @param pOffsets List of offsets to apply. + * @param pSampleKeys Array of keys for each sample. + * @param pGridDs Grid data structure. + * @param pNumCells Number of cells. + * @param pOutDS Output array with the ranges. + * @paramT D Number of dimensions. + */ +template +__global__ void find_ranges_grid_ds_2d_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumPts, + const unsigned int pNumOffsets, + const mccnn::ipoint* __restrict__ pOffsets, + const mccnn::int64_m* __restrict__ pSampleKeys, + const int2* __restrict__ pGridDs, + const mccnn::ipoint* __restrict__ pNumCells, + int2* __restrict__ pOutRanges) +{ + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + //Calculate the total number of cells. + mccnn::int64_m totalCells = mccnn::compute_total_num_cells_gpu_funct(pNumCells[0]); + + for(int curIter = initPtIndex; curIter < pNumSamples*pNumOffsets; curIter += totalThreads) + { + //Calculate the point and offset index. + int curPtIndex = curIter/pNumOffsets; + int curOffset = curIter%pNumOffsets; + + //Get the current offset. + mccnn::ipoint cellOffset = pOffsets[curOffset]; + + //Get the key of the current point. + mccnn::int64_m curKey = pSampleKeys[curPtIndex]; + + //Get the new cell with the offset. + mccnn::ipoint cellIndex = + mccnn::compute_cell_from_key_gpu_funct(curKey, pNumCells[0]); +#pragma unroll + for(int i=0; i < D; ++i) + cellIndex[i+1] += cellOffset[i]; + + //Check if we are out of the bounding box. + bool inside = true; +#pragma unroll + for(int i=0; i < D; ++i) + inside = inside && cellIndex[i+1] >= 0 && cellIndex[i+1] < pNumCells[0][i]; + if(inside) + { + //Get the range of pts to check in the data structure. + int curDsIndex = mccnn::compute_ds_index_from_cell_gpu_funct(cellIndex, pNumCells[0]); + int2 dsRange = pGridDs[curDsIndex]; + + //Store in memory the resulting range. + pOutRanges[curIter] = dsRange; + } + } +} + +///////////////////////// CPU + +template +void mccnn::find_ranges_grid_ds_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumSamples, + const unsigned int pNumPts, + const unsigned int pLastDOffsets, + const unsigned int pNumOffsets, + const int* pInGPUPtrOffsets, + const mccnn::int64_m* pInGPUPtrSampleKeys, + const mccnn::int64_m* pInGPUPtrPtsKeys, + const int* pInGPUPtrGridDS, + const int* pInGPUPtrNumCells, + int* pOutGPUPtrRanges) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize to zero the output array. + pDevice->memset(pOutGPUPtrRanges, 0, sizeof(int)*pNumSamples*pNumOffsets*2); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*2; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)find_ranges_grid_ds_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = (pNumSamples*pNumOffsets)/blockSize; + execBlocks += ((pNumSamples*pNumOffsets)%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + if(D > 2){ + find_ranges_grid_ds_gpu_kernel<<>>( + pNumSamples, + pNumPts, + pLastDOffsets, + pNumOffsets, + (const mccnn::ipoint*)pInGPUPtrOffsets, + pInGPUPtrSampleKeys, + pInGPUPtrPtsKeys, + (const int2*)pInGPUPtrGridDS, + (const mccnn::ipoint*)pInGPUPtrNumCells, + (int2*)pOutGPUPtrRanges); + }else{ + find_ranges_grid_ds_2d_gpu_kernel<<>>( + pNumSamples, + pNumPts, + pNumOffsets, + (const mccnn::ipoint*)pInGPUPtrOffsets, + pInGPUPtrSampleKeys, + (const int2*)pInGPUPtrGridDS, + (const mccnn::ipoint*)pInGPUPtrNumCells, + (int2*)pOutGPUPtrRanges); + } + pDevice->check_error(__FILE__, __LINE__); +} + +unsigned int mccnn::computeTotalNumOffsets( + const unsigned int pNumDimensions, + const unsigned int pAxisOffset, + std::vector& pOutVector) +{ + //Calculate the total number of offsets. + unsigned int cellsXAxis = pAxisOffset*2 + 1; + unsigned int numOffsets = cellsXAxis; + for(int i = 0 ; i < std::max((int)pNumDimensions-2, 1); ++i) + numOffsets *= cellsXAxis; + + //Calculate each offset. + pOutVector.clear(); + std::vector curOffset(pNumDimensions, 0); + for(int i = 0; i < numOffsets; ++i) + { + int auxInt = i; + + for(int j = std::max((int)pNumDimensions-2, 1); j >= 0 ; --j) + { + int auxInt2 = auxInt%cellsXAxis; + auxInt2 = auxInt2-pAxisOffset; + curOffset[j] = auxInt2; + auxInt = auxInt/cellsXAxis; + } + if(pNumDimensions != 2) curOffset[pNumDimensions-1] = 0; + + for(int j = 0; j < pNumDimensions; ++j) + pOutVector.push_back(curOffset[j]); + } + + return numOffsets; +} + +///////////////////////// CPU Template declaration + +#define FIND_RANGES_GRID_DS_TEMP_DECL(Dims) \ + template void mccnn::find_ranges_grid_ds_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumSamples, \ + const unsigned int pNumPts, \ + const unsigned int pLastDOffsets, \ + const unsigned int pNumOffsets, \ + const int* pInGPUPtrOffsets, \ + const mccnn::int64_m* pInGPUPtrSampleKeys, \ + const mccnn::int64_m* pInGPUPtrPtsKeys, \ + const int* pInGPUPtrGridDS, \ + const int* pInGPUPtrNumCells, \ + int* pOutGPUPtrRanges); + +DECLARE_TEMPLATE_DIMS(FIND_RANGES_GRID_DS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg.h new file mode 100755 index 000000000..fe0c5b98c --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg.h @@ -0,0 +1,44 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to execute the parallel scan +/// algorithm in an int array. +///////////////////////////////////////////////////////////////////////////// + +#ifndef SCAN_ALG_CUH_ +#define SCAN_ALG_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to execute the parallel scan algorithm in an int array. + * @param pDevice GPU device. + * @param pNumElems Number of elements in the array. + * The number of elements should be multiple of T*2. + * @param pInGPUPtrElems Input pointer to the array on + * the GPU. + * @return The total accumulation of elements at the end of the array. + */ + unsigned int scan_alg( + std::unique_ptr& pDevice, + const unsigned int pNumElems, + int* pInGPUPtrElems); + +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg_kernels.cu.cc new file mode 100755 index 000000000..a4ab33aa6 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/scan_alg_kernels.cu.cc @@ -0,0 +1,266 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to execute the parallel scan +/// algorithm in an int array. +///////////////////////////////////////////////////////////////////////////// + +#include + +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/scan_alg.h" + +#define NUM_THREADS 256 + +///////////////////////// GPU + +/** + * GPU kernel to execute the parallel scan algorithm in an int array. + * Code based on the paper: Parallel Prefix Sum (Scan) with CUDA + * https://developer.nvidia.com/gpugems/GPUGems3/gpugems3_ch39.html + * @param pNumElems Number of elements in the array. + * @param pNumProcBlocks Number of blocks to process. + * @param pElems Input pointer to the array. + * @param pOutAuxBlockCounter Output pointer to the array containing + * the counter of each block. If null only the individual blocks + * are executed (for arrays of length smaller than T). + */ + __global__ void scan_alg_gpu_kernel( + const unsigned int pNumElems, + const unsigned int pNumProcBlocks, + int* __restrict__ pElems, + int* __restrict__ pOutAuxBlockCounter) +{ + //Declare shared memory. + __shared__ int temp[NUM_THREADS*2]; + + //Get the local and global thread index. + int localThread = threadIdx.x; + int initElemIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(int curElemIndex = initElemIndex; + curElemIndex < pNumProcBlocks*NUM_THREADS; + curElemIndex+=totalThreads) + { + //Initialize the offset. + int offset = 1; + + //Load the global memory into shared memory. + if((curElemIndex*2) < pNumElems){ + temp[2*localThread] = pElems[2*curElemIndex]; + }else{ + temp[2*localThread] = 0; + } + if((curElemIndex*2+1) < pNumElems){ + temp[2*localThread+1] = pElems[2*curElemIndex+1]; + }else{ + temp[2*localThread+1] = 0; + } + + //Build sum in place up the tree + for (int d = NUM_THREADS; d > 0; d >>= 1) + { + __syncthreads(); + + if (localThread < d) + { + int ai = offset*(2*localThread+1)-1; + int bi = offset*(2*localThread+2)-1; + temp[bi] += temp[ai]; + } + + offset *= 2; + } + + //Clear the last element + if (localThread == 0){ + temp[(NUM_THREADS*2) - 1] = 0; + } + + //Traverse down tree & build scan + for (int d = 1; d < (NUM_THREADS*2); d *= 2) + { + offset >>= 1; + + __syncthreads(); + + if (localThread < d) + { + int ai = offset*(2*localThread+1)-1; + int bi = offset*(2*localThread+2)-1; + int t = temp[ai]; + temp[ai] = temp[bi]; + temp[bi] += t; + } + } + + __syncthreads(); + + //Update the block counters. + if(localThread == 0){ + int blockId = curElemIndex/NUM_THREADS; + int elemIndex = min((2*NUM_THREADS-1), pNumElems-(curElemIndex*2)-1); + pOutAuxBlockCounter[blockId] = temp[elemIndex] + pElems[2*curElemIndex+elemIndex]; + } + + __syncthreads(); + + //Write the results to device memory + if((curElemIndex*2) < pNumElems) + pElems[2*curElemIndex] = temp[2*localThread]; + if((curElemIndex*2+1) < pNumElems) + pElems[2*curElemIndex+1] = temp[2*localThread+1]; + + __syncthreads(); + } +} + +/** + * GPU kernel to execute the parallel scan algorithm in an int array. + * NOTE: The number of threads per block have to be a divisor of + * pCBlockSize in order to work. + * @param pNumElems Number of elements in the array. + * @param pCBlockSize Block size used for the counters. + * @param pBlockCounter Counter values for each block. + * @param pElems Input pointer to the array. + */ + __global__ void propagate_down_counters_gpu_kernel( + const unsigned int pNumElems, + const unsigned int pCBlockSize, + const int* __restrict__ pBlockCounter, + int* __restrict__ pElems) +{ + //Declare the shared memory. + __shared__ int blockOffset; + + //Get the local and global thread index. + int localThread = threadIdx.x; + int initElemIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(int curElemIndex = initElemIndex+pCBlockSize; + curElemIndex < pNumElems; + curElemIndex+=totalThreads) + { + //The the offset of the block. + if(localThread == 0){ + blockOffset = pBlockCounter[curElemIndex/pCBlockSize]; + } + + __syncthreads(); + + //Update the counters. + pElems[curElemIndex] += blockOffset; + } +} + +///////////////////////// CPU + +unsigned int mccnn::scan_alg( + std::unique_ptr& pDevice, + const unsigned int pNumElems, + int* pInGPUPtrElems) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = NUM_THREADS; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, + (const void*)scan_alg_gpu_kernel, + blockSize*2*sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Compute the hierarchical scan. + unsigned int numIterations = 0; + int curNumElems = pNumElems; + int* curGPUPtr = pInGPUPtrElems; + std::vector > counters; + counters.push_back(std::make_pair(curGPUPtr, curNumElems)); + while(curNumElems > 1 || numIterations == 0) + { + //Calculate the total number of blocks to execute. + unsigned int execBlocks = curNumElems/(blockSize*2); + execBlocks += (curNumElems%(blockSize*2) != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Create the auxiliar counter array. + int* auxiliarCoutner = pDevice->getIntTmpGPUBuffer(execBlocks); + pDevice->memset(auxiliarCoutner, 0, sizeof(int)*execBlocks); + + //Store the auxiliar counter in the vector. + counters.push_back(std::make_pair(auxiliarCoutner, execBlocks)); + + //Execute the cuda kernel. + scan_alg_gpu_kernel<<>>( + curNumElems, + execBlocks, + curGPUPtr, + auxiliarCoutner); + + pDevice->check_error(__FILE__, __LINE__); + + //Set the variables for the next pass. + curNumElems = execBlocks; + curGPUPtr = auxiliarCoutner; + + //Increment the iteration counter. + numIterations++; + } + + //Propagate down the counters. + unsigned int propBlockSize = gpuProps.warpSize_*2; + for(int i = counters.size()-1; i > 0; i--) + { + curNumElems = counters[i-1].second-(blockSize*2); + if(curNumElems > 0){ + //Calculate the total number of blocks to execute. + unsigned int execBlocks = curNumElems/propBlockSize; + execBlocks += (curNumElems%propBlockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Propagate the offsets. + propagate_down_counters_gpu_kernel<<>>( + counters[i-1].second, + blockSize*2, + counters[i].first, + counters[i-1].first); + } + } + + //Get the total accumulated value. + int* accumScan = pDevice->getIntTmpGPUBuffer(1, true); + pDevice->memcpy_device_to_host( + (void*)accumScan, + (void*)counters[counters.size()-1].first, + sizeof(int)); + + //Wait for the result. + cudaEvent_t resEvent; + cudaEventCreate(&resEvent); + cudaEventRecord(resEvent, cudaStream); + cudaEventSynchronize(resEvent); + + return accumScan[0]; +} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors.h new file mode 100755 index 000000000..68a941d83 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors.h @@ -0,0 +1,68 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to store the neighbors for each +/// point. +///////////////////////////////////////////////////////////////////////////// + +#ifndef STORE_NEIGHBORS_CUH_ +#define STORE_NEIGHBORS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to store the number of neighbors. + * @param pDevice GPU device. + * @param pMaxNeighbors Maximum number of neighbors. If zero or less, + * there is not limit. + * @param pNumSamples Number of samples. + * @param pNumRanges Number of ranges per point. + * @param pInGPUPtrSamples Input pointer to the vector of samples + * coordinates on the GPU. + * @param pInGPUPtrPts Input pointer to the vector of point + * coordinates on the GPU. + * @param pInGPUPtrRanges Input pointer to the vector of sample + * ranges on the GPU. + * @param pInGPUPtrInvRadii Inverse of the radius used on the + * search of neighbors in each dimension. + * @param pOutGPUPtrNumNeighsU Input/Output pointer to the vector + * with the number of neighbors for each sample without the limit of + * pMaxNeighbors. + * @param pOutGPUPtrNumNeighs Input/Output pointer to the vector + * with the number of neighbors for each sample on the GPU. + * @param pOutGPUPtrNeighs Output pointer to the vector with the + * number of neighbors for each sample on the GPU. + * @tparam D Number of dimensions. + */ + template + void store_neighbors( + std::unique_ptr& pDevice, + const int pMaxNeighbors, + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const float* pInGPUPtrSamples, + const float* pInGPUPtrPts, + const int* pInGPUPtrRanges, + const float* pInGPUPtrInvRadii, + int* pOutGPUPtrNumNeighsU, + int* pOutGPUPtrNumNeighs, + int* pOutGPUPtrNeighs); + +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors_kernels.cu.cc new file mode 100755 index 000000000..011ea22d0 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors_kernels.cu.cc @@ -0,0 +1,288 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to store the neighbors for +/// each point. +///////////////////////////////////////////////////////////////////////////// + +#include +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/rnd_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors.h" + +#define NUM_THREADS_X_RANGE 16 + +///////////////////////// GPU + +/** + * GPU kernel to store the number of neighbors for each sample. + * @param pSeed Seed used to initialize the random state. + * @param pMaxNeighbors Maximum neighbors allowed per sample. + * @param pNumSamples Number of samples. + * @param pNumRanges Number of ranges per sample. + * @param pSamples 3D coordinates of each sample. + * @param pPts 3D coordinates of each point. + * @param pRanges Search ranges for each sample. + * @param pInvRadii Inverse of the radius used on the + * search of neighbors in each dimension. + * @param pOutNumNeighsU Number of neighbors for each sample + * without the limit imposed by pMaxNeighbors. + * @param pAuxCounter Auxiliar counter. + * @param pOutNumNeighs Number of neighbors for each sample. + * @param pOutNeighs Final beighbors. + * @tparam D Number of dimensions. + */ + template + __global__ void store_neighbors_limited_gpu_kernel( + const int pSeed, + const int pMaxNeighbors, + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const mccnn::fpoint* __restrict__ pSamples, + const mccnn::fpoint* __restrict__ pPts, + const int2* __restrict__ pRanges, + const mccnn::fpoint* __restrict__ pInvRadii, + const int* __restrict__ pOutNumNeighsU, + int* __restrict__ pAuxCounter, + int* __restrict__ pOutNumNeighs, + int2* __restrict__ pOutNeighs) +{ + //Get the global thread index. + int initSampleIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + //Initialize the random seed generator. + int curSeed = mccnn::wang_hash(pSeed+initSampleIndex); + + for(long long curIter = initSampleIndex; + curIter < pNumSamples*pNumRanges*NUM_THREADS_X_RANGE; + curIter += totalThreads) + { + //Get the point id. + int sampleIndex = curIter/(pNumRanges*NUM_THREADS_X_RANGE); + + //Get the offset and index of the local counter. + int localIndex = curIter%NUM_THREADS_X_RANGE; + + //Get the current sample coordinates and the search range. + mccnn::fpoint curSampleCoords = pSamples[sampleIndex]; + int2 curRange = pRanges[curIter/NUM_THREADS_X_RANGE]; + + //Iterate over the points. + for(int curPtIter = curRange.x+localIndex; + curPtIter < curRange.y; curPtIter+=NUM_THREADS_X_RANGE) + { + //Check if the point is closer than the selected radius. + mccnn::fpoint curPtCoors = pPts[curPtIter]; + if(length((curSampleCoords - curPtCoors)*pInvRadii[0]) < 1.0f){ + //Increment the shared counters. + int neighIndex = atomicAdd(&pAuxCounter[sampleIndex], 1); + if(neighIndex < pMaxNeighbors){ + neighIndex = atomicAdd(&pOutNumNeighs[sampleIndex], 1); + pOutNeighs[neighIndex] = make_int2(curPtIter, sampleIndex); + }else{ + float ratio = (float)pMaxNeighbors/(float)pOutNumNeighsU[sampleIndex]; + curSeed = mccnn::rand_xorshift(curSeed); + float uniformVal = mccnn::seed_to_float(curSeed); + if(uniformVal < ratio){ + curSeed = mccnn::rand_xorshift(curSeed); + uniformVal = mccnn::seed_to_float(curSeed); + neighIndex = (int)floor(uniformVal*((float)pMaxNeighbors))+1; + pOutNeighs[pOutNumNeighs[sampleIndex] - neighIndex] = make_int2(curPtIter, sampleIndex); + } + } + } + } + } +} + +/** + * GPU kernel to store the number of neighbors for each sample. + * @param pNumSamples Number of samples. + * @param pNumRanges Number of ranges per sample. + * @param pSamples 3D coordinates of each sample. + * @param pPts 3D coordinates of each point. + * @param pRanges Search ranges for each sample. + * @param pInvRadii Inverse of the radius used on the + * search of neighbors in each dimension. + * @param pOutNumNeighs Number of neighbors for each sample. + * @param pOutNeighs Final beighbors. + * @tparam D Number of dimensions. + */ + template + __global__ void store_neighbors_gpu_kernel( + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const mccnn::fpoint* __restrict__ pSamples, + const mccnn::fpoint* __restrict__ pPts, + const int2* __restrict__ pRanges, + const mccnn::fpoint* __restrict__ pInvRadii, + int* __restrict__ pOutNumNeighs, + int2* __restrict__ pOutNeighs) +{ + //Get the global thread index. + int initSampleIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(long long curIter = initSampleIndex; + curIter < pNumSamples*pNumRanges*NUM_THREADS_X_RANGE; + curIter += totalThreads) + { + //Get the point id. + int sampleIndex = curIter/(pNumRanges*NUM_THREADS_X_RANGE); + + //Get the offset and index of the local counter. + int localIndex = curIter%NUM_THREADS_X_RANGE; + + //Get the current sample coordinates and the search range. + mccnn::fpoint curSampleCoords = pSamples[sampleIndex]; + int2 curRange = pRanges[curIter/NUM_THREADS_X_RANGE]; + + //Iterate over the points. + for(int curPtIter = curRange.x+localIndex; + curPtIter < curRange.y; curPtIter+=NUM_THREADS_X_RANGE) + { + //Check if the point is closer than the selected radius. + mccnn::fpoint curPtCoors = pPts[curPtIter]; + if(length((curSampleCoords - curPtCoors)*pInvRadii[0]) < 1.0f){ + //Increment the shared counters. + int neighIndex = atomicAdd(&pOutNumNeighs[sampleIndex], 1); + pOutNeighs[neighIndex] = make_int2(curPtIter, sampleIndex); + } + } + } +} + +///////////////////////// CPU + +template +void mccnn::store_neighbors( + std::unique_ptr& pDevice, + const int pMaxNeighbors, + const unsigned int pNumSamples, + const unsigned int pNumRanges, + const float* pInGPUPtrSamples, + const float* pInGPUPtrPts, + const int* pInGPUPtrRanges, + const float* pInGPUPtrInvRadii, + int* pOutGPUPtrNumNeighsU, + int* pOutGPUPtrNumNeighs, + int* pOutGPUPtrNeighs) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + unsigned int numBlocks; + unsigned int blockSize; + if(pMaxNeighbors > 0){ + //Create a temporal tensor. + int* tmpVect = pDevice->getIntTmpGPUBuffer(pNumSamples); + pDevice->memset(tmpVect, 0, sizeof(int)*pNumSamples); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + blockSize = gpuProps.warpSize_*2; + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, (const void*)store_neighbors_limited_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = (pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)/blockSize; + execBlocks += ((pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + store_neighbors_limited_gpu_kernel<<>>( + time(NULL), + pMaxNeighbors, + pNumSamples, + pNumRanges, + (const mccnn::fpoint*)pInGPUPtrSamples, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrRanges, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + pOutGPUPtrNumNeighsU, + tmpVect, + pOutGPUPtrNumNeighs, + (int2*)pOutGPUPtrNeighs); + pDevice->check_error(__FILE__, __LINE__); + }else{ + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + blockSize = gpuProps.warpSize_*2; + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize, (const void*)store_neighbors_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = (pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)/blockSize; + execBlocks += ((pNumSamples*pNumRanges*NUM_THREADS_X_RANGE)%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + store_neighbors_gpu_kernel<<>>( + pNumSamples, + pNumRanges, + (const mccnn::fpoint*)pInGPUPtrSamples, + (const mccnn::fpoint*)pInGPUPtrPts, + (const int2*)pInGPUPtrRanges, + (const mccnn::fpoint*)pInGPUPtrInvRadii, + pOutGPUPtrNumNeighs, + (int2*)pOutGPUPtrNeighs); + pDevice->check_error(__FILE__, __LINE__); + } + +#ifdef DEBUG_INFO + cudaEventRecord(stop, cudaStream); + cudaEventSynchronize(stop); + float milliseconds = 0; + cudaEventElapsedTime(&milliseconds, start, stop); + + float gpuOccupancy = (float)(numBlocks*blockSize)/(float)gpuProps.maxThreadsXMP_; + + fprintf(stderr, "### STORE NEIGHBORS ###\n"); + fprintf(stderr, "Num samples: %d\n", pNumSamples); + fprintf(stderr, "Occupancy: %f\n", gpuOccupancy); + fprintf(stderr, "Execution time: %f\n", milliseconds); + fprintf(stderr, "\n"); +#endif +} + +///////////////////////// CPU Template declaration + +#define STORE_NEIGHS_TEMP_DECL(Dims) \ + template void mccnn::store_neighbors( \ + std::unique_ptr& pDevice, \ + const int pMaxNeighbors, \ + const unsigned int pNumSamples, \ + const unsigned int pNumRanges, \ + const float* pInGPUPtrSamples, \ + const float* pInGPUPtrPts, \ + const int* pInGPUPtrRanges, \ + const float* pInGPUPtrInvRadii, \ + int* pOutGPUPtrNumNeighsU, \ + int* pOutGPUPtrNumNeighs, \ + int* pOutGPUPtrNeighs); + +DECLARE_TEMPLATE_DIMS(STORE_NEIGHS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts.h new file mode 100755 index 000000000..a646968ed --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts.h @@ -0,0 +1,61 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to store in memory the pooled +/// points. +///////////////////////////////////////////////////////////////////////////// + +#ifndef STORED_POOLED_PTS_CUH_ +#define STORED_POOLED_PTS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to pool a set of points from a point cloud. + * @param pDevice Device. + * @param pNumPts Number of input points. + * @param pNumPooledPts Number of pooled points. + * @param pPtsGPUPtr Input pointer to the gpu array with + * the point coordinates. + * @param pBatchIdsGPUPtr Input pointer to the gpu array with + * the batch ids. + * @param pSelectedGPUPtr Input pointer to the gpu array with + * the selected points. + * @param pOutPtsGPUPtr Output pointer to the gpu array with + * the selected point coordinates. + * @param pOutBatchIdsGPUPtr Output pointer to the gpu array with + * the selected batch ids. + * @param pOutIndicesGPUPtr Output pointer to the gpu array with + * the selected indices. + * @paramt D Number of dimensions. + */ + template + void store_pooled_pts_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumPooledPts, + const float* pPtsGPUPtr, + const int* pBatchIdsGPUPtr, + const int* pSelectedGPUPtr, + float* pOutPtsGPUPtr, + int* pOutBatchIdsGPUPtr, + int* pOutIndicesGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts_kernels.cu.cc new file mode 100755 index 000000000..4d629ccbd --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts_kernels.cu.cc @@ -0,0 +1,135 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to store in memory the pooled +/// points. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/store_pooled_pts.h" + +///////////////////////// GPU + +/** + * GPU kernel to count the unique keys in a list. + * @param pNumPts Number of input points. + * @param pPts Input pointer to the array with the points. + * @param pBatchIds Input pointer to the array with the batch ids. + * @param pSelected Input pointer to the array with the selected pts. + * @param pNumPooledPoints Input pointer to the counter of already saved pts. + * @param pOutPts Output pointer to the array with the output pts. + * @param pBatchIds Output pointer to the array with the output batchids. + * @param pOutIndices Output pointer to the array with the output indices. + * @paramt D Number of dimensions. + */ + template + __global__ void store_pooled_pts_gpu_kernel( + const unsigned int pNumPts, + const mccnn::fpoint* __restrict__ pPts, + const int* __restrict__ pBatchIds, + const int* __restrict__ pSelected, + int* __restrict__ pNumPooledPoints, + mccnn::fpoint* __restrict__ pOutPts, + int* __restrict__ pOutBatchIds, + int* __restrict__ pOutIndices) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumPts; + curPtIndex += totalThreads) + { + //Check if the point was selected. + if(pSelected[curPtIndex] == 1){ + //Get the index where to save the current point. + int saveIndex = atomicAdd(pNumPooledPoints, 1); + pOutPts[saveIndex] = pPts[curPtIndex]; + pOutBatchIds[saveIndex] = pBatchIds[curPtIndex]; + pOutIndices[saveIndex] = curPtIndex; + } + } +} + +///////////////////////// CPU +template +void mccnn::store_pooled_pts_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumPooledPts, + const float* pPtsGPUPtr, + const int* pBatchIdsGPUPtr, + const int* pSelectedGPUPtr, + float* pOutPtsGPUPtr, + int* pOutBatchIdsGPUPtr, + int* pOutIndicesGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize memory. + int* tmpCounter = pDevice->getIntTmpGPUBuffer(1); + pDevice->memset(tmpCounter, 0, sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + + //Execute the cuda kernel. + unsigned int numBlocks = 0; + + //Calculate the total number of blocks to execute in parallel. + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)store_pooled_pts_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Call kernel. + store_pooled_pts_gpu_kernel + <<>>( + pNumPts, (const mccnn::fpoint*)pPtsGPUPtr, + pBatchIdsGPUPtr, pSelectedGPUPtr, tmpCounter, + (mccnn::fpoint*)pOutPtsGPUPtr, pOutBatchIdsGPUPtr, + pOutIndicesGPUPtr); + pDevice->check_error(__FILE__, __LINE__); +} + +#define STORE_POOLED_PTS_TEMP_DECL(Dims) \ + template void mccnn::store_pooled_pts_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const unsigned int pNumPooledPts, \ + const float* pPtsGPUPtr, \ + const int* pBatchIdsGPUPtr, \ + const int* pSelectedGPUPtr, \ + float* pOutPtsGPUPtr, \ + int* pOutBatchIdsGPUPtr, \ + int* pOutIndicesGPUPtr); + +DECLARE_TEMPLATE_DIMS(STORE_POOLED_PTS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/ops/find_neighbors_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/ops/find_neighbors_ops.cpp new file mode 100755 index 000000000..4371275d4 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/cc/ops/find_neighbors_ops.cpp @@ -0,0 +1,259 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief C++ operations definition to build the data structure to access +/// the sparse regular grid. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" + +#include "tfg_custom_ops/compute_keys/cc/kernels/compute_keys.h" + +#include "tfg_custom_ops/find_neighbors/cc/kernels/find_ranges_grid_ds.h" +#include "tfg_custom_ops/find_neighbors/cc/kernels/count_neighbors.h" +#include "tfg_custom_ops/find_neighbors/cc/kernels/elem_wise_min.h" +#include "tfg_custom_ops/find_neighbors/cc/kernels/scan_alg.h" +#include "tfg_custom_ops/find_neighbors/cc/kernels/store_neighbors.h" + +//This value determines how many cuda kernel call are made to search in +//the neighboring cells: +// - 3 Dimensions 9 offsets per block -> 1 cuda kernel executions +// - 3 Dimensions 3 offsets per block -> 3 cuda kernel executions +// - 6 Dimensions 9 offsets per block -> 27 cuda kernel executions +// - 6 Dimensions 3 offsets per block -> 81 cuda kernel executions +#define NUM_OFFSETS_X_COMPUTE_BLOCK 9 + +/** + * Declaration of the tensorflow operation. + */ +REGISTER_OP("FindNeighbors") + .Input("samples: float32") + .Input("samples_batch_ids: int32") + .Input("points: float32") + .Input("point_keys: int64") + .Input("grid_acc_ds: int32") + .Input("num_cells: int32") + .Input("scaled_aabb_min: float32") + .Input("inv_cell_sizes: float32") + .Input("inv_radii: float32") + .Output("samples_neigh_indices: int32") + .Output("neigh_indices: int32") + .Attr("max_neighs:int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims1 = pIC->MakeShape({pIC->Dim(pIC->input(0), 0)}); + shape_inference::ShapeHandle outputDims2 = pIC->MakeShape({-1, 2}); + pIC->set_output(0, outputDims1); + pIC->set_output(1, outputDims2); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to find the neighboring points to a set of samples. + */ + class FindNeighborsOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit FindNeighborsOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + OP_REQUIRES_OK(pContext, pContext->GetAttr("max_neighs", &maxNeighs_)); + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inSamples = pContext->input(0); + const Tensor& inBatchIds = pContext->input(1); + const Tensor& inPts = pContext->input(2); + const Tensor& inPtKeys = pContext->input(3); + const Tensor& inGridDs = pContext->input(4); + const Tensor& inNumCells = pContext->input(5); + const Tensor& inSAABBMin = pContext->input(6); + const Tensor& inInvCellSizes = pContext->input(7); + const Tensor& inInvRadii = pContext->input(8); + + //Get variables from tensors. + unsigned int numSamples = inSamples.shape().dim_size(0); + unsigned int numPts = inPts.shape().dim_size(0); + unsigned int numDimensions = inSamples.shape().dim_size(1); + + //Get the pointers to GPU/CPU data from the tensors. + const float* samplesGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inSamples); + const int* batchIdsGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inBatchIds); + const float* ptsGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inPts); + const mccnn::int64_m* ptKeysGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inPtKeys); + const int* gridDsGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inGridDs); + const int* numCellsGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inNumCells); + const float* sAABBMinGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inSAABBMin); + const float* invCellSizeGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inInvCellSizes); + const float* invRadiiGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inInvRadii); + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("FindNeighborsOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inNumCells.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("FindNeighborsOp expects a number of dimensions in" + " inNumCells equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inPts.shape().dim_size(1) == numDimensions, + errors::InvalidArgument("FindNeighborsOp expects a number of dimensions in" + " inPts equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inSAABBMin.shape().dim_size(1) == numDimensions, + errors::InvalidArgument("FindNeighborsOp expects a number of dimensions in" + " inSAABBMin equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inInvCellSizes.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("FindNeighborsOp expects a number of dimensions in" + " inInvCellSizes equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inInvRadii.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("FindNeighborsOp expects a number of dimensions in" + " inInvRadii equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inBatchIds.shape().dim_size(0) == numSamples, + errors::InvalidArgument("FindNeighborsOp expects the same number of batch" + " ids as samples.")); + OP_REQUIRES(pContext, inPtKeys.shape().dim_size(0) == numPts, + errors::InvalidArgument("FindNeighborsOp expects the same number of keys" + " as points.")); + OP_REQUIRES(pContext, inGridDs.dims() == 4 && + inGridDs.shape().dim_size(3) == 2, + errors::InvalidArgument("FindNeighborsOp expects a grid acceleration data" + " structure with the right format (B, X, Y, 2).")); + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Create the first output. + int* outputGPUPtr1 = nullptr; + TensorShape outShape1 = TensorShape{numSamples}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape1, &outputGPUPtr1)); + gpuDevice->memset(outputGPUPtr1, 0, sizeof(int)*numSamples); + gpuDevice->check_error(__FILE__, __LINE__); + + //Create the temporal tensors. + mccnn::int64_m* tmpGPUPtr1 = gpuDevice->getInt64TmpGPUBuffer(numSamples); + int* tmpGPUPtr2 = gpuDevice->getIntTmpGPUBuffer( + numSamples*NUM_OFFSETS_X_COMPUTE_BLOCK*2); + int* tmpGPUPtr3 = gpuDevice->getIntTmpGPUBuffer( + NUM_OFFSETS_X_COMPUTE_BLOCK*numDimensions); + + //Compute the keys of the samples. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::compute_keys_gpu, + gpuDevice, numSamples, samplesGPUPtr, batchIdsGPUPtr, + sAABBMinGPUPtr, numCellsGPUPtr, invCellSizeGPUPtr, tmpGPUPtr1); + + //Compute the number of offsets to used in the search. + std::vector combOffsets; + unsigned int numOffsets = mccnn::computeTotalNumOffsets( + numDimensions, 1, combOffsets); + unsigned int numOfComputations = numOffsets/NUM_OFFSETS_X_COMPUTE_BLOCK; + + //Created the pinned cpu memory to store the offsets. + int bufferSize = (int)combOffsets.size(); + int* combOffsetsPinnedMem = gpuDevice->getIntTmpGPUBuffer(bufferSize, true); + memcpy((void*)combOffsetsPinnedMem, (void*)&combOffsets[0], sizeof(int)*bufferSize); + + for(int i = 0; i < numOfComputations; ++i) + { + //Copy to device the offsets. + gpuDevice->memcpy_host_to_device(tmpGPUPtr3, + &combOffsetsPinnedMem[numDimensions*NUM_OFFSETS_X_COMPUTE_BLOCK*i], + sizeof(int)*numDimensions*NUM_OFFSETS_X_COMPUTE_BLOCK); + gpuDevice->check_error(__FILE__, __LINE__); + + //Find ranges of points to check in the data structure. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::find_ranges_grid_ds_gpu, + gpuDevice, numSamples, numPts, 1, NUM_OFFSETS_X_COMPUTE_BLOCK, + tmpGPUPtr3, tmpGPUPtr1, ptKeysGPUPtr, + gridDsGPUPtr, numCellsGPUPtr, tmpGPUPtr2); + + //Count neighbors. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::count_neighbors, + gpuDevice, numSamples, NUM_OFFSETS_X_COMPUTE_BLOCK, samplesGPUPtr, + ptsGPUPtr, tmpGPUPtr2, invRadiiGPUPtr, outputGPUPtr1); + } + + //Clamp the number of neighbors if required. + int* tmpGPUPtr4 = nullptr; + if(maxNeighs_ > 0){ + tmpGPUPtr4 = gpuDevice->getIntTmpGPUBuffer(numSamples); + gpuDevice->memcpy_device_to_device(tmpGPUPtr4, outputGPUPtr1, sizeof(int)*numSamples); + mccnn::elem_wise_min_value(gpuDevice, numSamples, maxNeighs_, outputGPUPtr1); + } + + //Scan algorithm of the number of neighbors per sample. + unsigned int numNeighbors = mccnn::scan_alg( + gpuDevice, numSamples, outputGPUPtr1); + + //Create the second output tensor. + int* outputGPUPtr2 = nullptr; + TensorShape outShape2 = TensorShape{numNeighbors, 2}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (1, pContext, outShape2, &outputGPUPtr2)); + gpuDevice->memset(outputGPUPtr2, 0, sizeof(int)*numNeighbors*2); + gpuDevice->check_error(__FILE__, __LINE__); + + for(int i = 0; i < numOfComputations; ++i) + { + if(numOfComputations > 1){ + //Copy to device the offsets. + gpuDevice->memcpy_host_to_device(tmpGPUPtr3, + &combOffsetsPinnedMem[numDimensions*NUM_OFFSETS_X_COMPUTE_BLOCK*i], + sizeof(int)*numDimensions*NUM_OFFSETS_X_COMPUTE_BLOCK); + gpuDevice->check_error(__FILE__, __LINE__); + + //Find ranges of points to check in the data structure. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::find_ranges_grid_ds_gpu, + gpuDevice, numSamples, numPts, 1, NUM_OFFSETS_X_COMPUTE_BLOCK, + tmpGPUPtr3, tmpGPUPtr1, ptKeysGPUPtr, + gridDsGPUPtr, numCellsGPUPtr, tmpGPUPtr2); + } + + //Store neighbors. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::store_neighbors, + gpuDevice, maxNeighs_, numSamples, NUM_OFFSETS_X_COMPUTE_BLOCK, + samplesGPUPtr, ptsGPUPtr, tmpGPUPtr2, invRadiiGPUPtr, + tmpGPUPtr4, outputGPUPtr1, outputGPUPtr2); + } + } + + private: + + /**Maximum number of neighbors to store per sample.*/ + int maxNeighs_; + }; +} + +REGISTER_KERNEL_BUILDER(Name("FindNeighbors").Device(DEVICE_GPU), mccnn::FindNeighborsOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/ops/find_neighbors_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/ops/find_neighbors_ops.py new file mode 100644 index 000000000..90f8fe0a3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/find_neighbors/python/ops/find_neighbors_ops.py @@ -0,0 +1,20 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use find_neighbors op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +find_neighbors_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_find_neighbors_ops.so')) +find_neighbors = find_neighbors_ops.find_neighbors diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/BUILD new file mode 100644 index 000000000..72d0e650d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/BUILD @@ -0,0 +1,113 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "sampling_ops_gpu", + srcs = [ + "cc/kernels/count_sampling_pd.h", + "cc/kernels/count_sampling_pd_kernels.cu.cc", + "cc/kernels/count_unique_keys.h", + "cc/kernels/count_unique_keys_kernels.cu.cc", + "cc/kernels/sampling_avg.h", + "cc/kernels/sampling_avg_kernels.cu.cc", + "cc/kernels/store_sampled_pts.h", + "cc/kernels/store_sampled_pts_kernels.cu.cc", + "cc/kernels/store_unique_keys.h", + "cc/kernels/store_unique_keys_kernels.cu.cc", + ], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + "//tfg_custom_ops/shared:pointcloud_common_kernels", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + alwayslink = 1, + linkopts = [], + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +cc_binary( + name = 'python/ops/_sampling_ops.so', + srcs = [ + "cc/ops/sampling_ops.cpp", + ], + linkshared = 1, + features = select({ + ":windows": ["windows_export_all_symbols"], + "//conditions:default": [], + }), + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + ] + if_cuda_is_configured([":sampling_ops_gpu"]), + copts = select({ + ":windows": ["/D__CLANG_SUPPORT_DYN_ANNOTATION__", "/DEIGEN_MPL2_ONLY", "/DEIGEN_MAX_ALIGN_BYTES=64", "/DEIGEN_HAS_TYPE_TRAITS=0", "/DTF_USE_SNAPPY", "/showIncludes", "/MD", "/O2", "/DNDEBUG", "/w", "-DWIN32_LEAN_AND_MEAN", "-DNOGDI", "/d2ReducedOptimizeHugeFunctions", "/arch:AVX", "/std:c++14", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"], + "//conditions:default": ["-pthread", "-std=c++11", "-D_GLIBCXX_USE_CXX11_ABI=0"], + }) + if_cuda_is_configured(["-DTENSORFLOW_USE_NVCC=1", "-DGOOGLE_CUDA=1", "-x cuda", "-nvcc_options=relaxed-constexpr", "-nvcc_options=ftz=true"]), +) + +py_library( + name = "sampling_ops_py", + srcs = ([ + "python/ops/sampling_ops.py", + ]), + data = [ + ":python/ops/_sampling_ops.so" + ], + srcs_version = "PY2AND3", +) + +# py_test( +# name = "sampling_ops_py_test", +# srcs = [ +# "python/ops/sampling_ops_test.py" +# ], +# main = "python/ops/sampling_ops_test.py", +# deps = [ +# ":sampling_ops_py", +# ], +# srcs_version = "PY2AND3", +# ) + +py_library( + name = "sampling_py", + srcs = ([ + "__init__.py", + "python/__init__.py", + "python/ops/__init__.py", + ]), + deps = [ + ":sampling_ops_py" + ], + srcs_version = "PY2AND3", +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd.h new file mode 100755 index 000000000..a2352ccfe --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd.h @@ -0,0 +1,66 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to sample a set of points from +/// a point cloud. +///////////////////////////////////////////////////////////////////////////// + +#ifndef COUNT_SAMPLING_PD_CUH_ +#define COUNT_SAMPLING_PD_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to sample a set of points from a point cloud. + * @param pDevice Device. + * @param pNumPts Number of input points. + * @param pNumUniqueKeys Number of unique keys. + * @param pUniqueKeyIndexs Index of the start of each + * unique key. + * @param pInKeysGPUPtr Input pointer to the vector of keys + * sorted from bigger to smaller residing on GPU memory. + * @param pPtsGPUPtr Input point coordinates. + * @param pNeighbors Input list of neighbors. + * @param pNeighStartIndex Input list of indicies of each list + * of neighbors. + * @param pNumCellsGPUPtr Number of cells. + * @param pOutNumSampledPts Output integer with the number + * of sampled points. + * @param pOutPtsGPUPtr Output array with the new point + * coordinates. + * @param pBatchIdsGPUPtr Output array with the batch ids + * of the new points. + * @paramt D Number of dimensions. + */ + template + void count_sampling_pd_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumUniqueKeys, + const int* pUniqueKeyIndexs, + const mccnn::int64_m* pInKeysGPUPtr, + const float* pPtsGPUPtr, + const int* pNeighbors, + const int* pNeighStartIndex, + const int* pNumCellsGPUPtr, + int& pOutNumSampledPts, + int* pSelectedGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd_kernels.cu.cc new file mode 100755 index 000000000..6efa0c68a --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_sampling_pd_kernels.cu.cc @@ -0,0 +1,214 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to sample a set of points +/// from a point cloud. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/sampling/cc/kernels/count_sampling_pd.h" + +///////////////////////// GPU + +/** + * GPU kernel to count the unique keys in a list. + * @param pNumPts Number of points. + * @param pNumSampledPts Number of sampled points. + * @param pKeys Pointer to the array of keys. + * @param pPts Input points. + * @param pNumCells Number of cells. + * @param pUniqueKeyIndices Input index of the first point of + * each cell. + * @param pOutPts Output sampled points. + * @param pOutBatchIds Output batch ids. + */ + template + __global__ void count_sampled_pd_gpu_kernel( + const unsigned int pNumPts, + const unsigned int pNumUniqueKeys, + const mccnn::ipoint pCurrentCellBlock, + const mccnn::int64_m* __restrict__ pKeys, + const mccnn::fpoint* __restrict__ pPts, + const mccnn::ipoint* __restrict__ pNumCells, + const int2* __restrict__ pNeighbors, + const int* __restrict__ pNeighPtIndices, + const int* __restrict__ pUniqueKeyIndices, + int* __restrict__ pNumSampledPoints, + int* __restrict__ pUsed) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumUniqueKeys; + curPtIndex += totalThreads) + { + //Find the start index. + int curPtCellIter = pUniqueKeyIndices[curPtIndex]; + + //Get the key and batch id. + mccnn::int64_m curKey = pKeys[curPtCellIter]; + mccnn::ipoint cellIndex = mccnn::compute_cell_from_key_gpu_funct( + curKey, pNumCells[0]); + + //Check that it is a valid cell. + bool validCell = true; +#pragma unroll + for(int dimIter = 0; dimIter < D; ++dimIter) + validCell = validCell && ((cellIndex[dimIter+1]%2)==pCurrentCellBlock[dimIter]); + + if(validCell){ + + //Iterate until there are no more points in the cell. + while(curPtCellIter < pNumPts && pKeys[curPtCellIter] == curKey) + { + //Check the range of neighbors. + int startNeighIndex = (curPtCellIter > 0)? + pNeighPtIndices[curPtCellIter-1]: 0; + int endNeighIndex = pNeighPtIndices[curPtCellIter]; + + //Check if some of the neighbors have been previously selected. + bool valid = true; + for(int neighIter = startNeighIndex; + neighIter < endNeighIndex; + ++neighIter) + { + int neighIndex = pNeighbors[neighIter].x; + valid = valid && (pUsed[neighIndex] == 0); + } + + //If this is a valid point, we select it. + if(valid){ + atomicAdd(pNumSampledPoints, 1); + pUsed[curPtCellIter] = 1; + } + + //Increment counter. + curPtCellIter++; + } + } + } +} + +///////////////////////// CPU + +template +void mccnn::count_sampling_pd_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumUniqueKeys, + const int* pUniqueKeyIndexs, + const mccnn::int64_m* pInKeysGPUPtr, + const float* pPtsGPUPtr, + const int* pNeighbors, + const int* pNeighStartIndex, + const int* pNumCellsGPUPtr, + int& pOutNumSampledPts, + int* pSelectedGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize memory. + int* tmpCounter = pDevice->getIntTmpGPUBuffer(1); + pDevice->memset(tmpCounter, 0, sizeof(int)); + pDevice->memset(pSelectedGPUPtr, 0, sizeof(int)*pNumPts); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumUniqueKeys/blockSize; + execBlocks += (pNumUniqueKeys%blockSize != 0)?1:0; + + //Execute the cuda kernel. + unsigned int numBlocks = 0; + + //Calculate the total number of blocks to execute in parallel. + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)count_sampled_pd_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Compute the number of calls we need to do. + unsigned int numCalls = 1; + for(int i = 0; i < D; ++i) + numCalls *= 2; + + //Iterate over the different calls required. + for(int callIter = 0; callIter < numCalls; ++callIter) + { + //Compute valid cells mod. + int auxInt = callIter; + mccnn::ipoint validCode; + for(int i = 0; i < D; ++i){ + validCode[i] = auxInt%2; + auxInt = auxInt/2; + } + + //Call kernel. + count_sampled_pd_gpu_kernel + <<>>( + pNumPts, pNumUniqueKeys, validCode, + pInKeysGPUPtr, (const mccnn::fpoint*)pPtsGPUPtr, + (const mccnn::ipoint*)pNumCellsGPUPtr, + (const int2*)pNeighbors, pNeighStartIndex, + pUniqueKeyIndexs, tmpCounter, pSelectedGPUPtr); + } + pDevice->check_error(__FILE__, __LINE__); + + //Copy the number of points into cpu memory. + int* auxNumSampledPts = pDevice->getIntTmpGPUBuffer(1, true); + pDevice->memcpy_device_to_host( + (void*)auxNumSampledPts, + (void*)tmpCounter, + sizeof(int)); + + //Wait for the result. + cudaEvent_t resEvent; + cudaEventCreate(&resEvent); + cudaEventRecord(resEvent, cudaStream); + cudaEventSynchronize(resEvent); + + //Collect the result. + pOutNumSampledPts = auxNumSampledPts[0]; +} + +#define COUNT_SAMPLING_PD_TEMP_DECL(Dims) \ + template void mccnn::count_sampling_pd_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const unsigned int pNumUniqueKeys, \ + const int* pUniqueKeyIndexs, \ + const mccnn::int64_m* pInKeysGPUPtr, \ + const float* pPtsGPUPtr, \ + const int* pNeighbors, \ + const int* pNeighStartIndex, \ + const int* pNumCellsGPUPtr, \ + int& pOutNumSampledPts, \ + int* pSelectedGPUPtr); + +DECLARE_TEMPLATE_DIMS(COUNT_SAMPLING_PD_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys.h new file mode 100755 index 000000000..77e559490 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys.h @@ -0,0 +1,43 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to count the unique keys +/// in a sorted array of keys. +///////////////////////////////////////////////////////////////////////////// + +#ifndef COUNT_UNIQUE_KEYS_CUH_ +#define COUNT_UNIQUE_KEYS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to count the number of unique keys on the gpu. + * @param pDevice Device. + * @param pNumPts Number of points. + * @param pInKeysGPUPtr Input pointer to the vector of keys + * sorted from bigger to smaller residing on GPU memory. + * @returns Number of unique keys in the array. + */ + unsigned int count_unique_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const mccnn::int64_m* pInKeysGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys_kernels.cu.cc new file mode 100755 index 000000000..5d9ebd6c8 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/count_unique_keys_kernels.cu.cc @@ -0,0 +1,120 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to count the unique keys +/// in a sorted array of keys. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/sampling/cc/kernels/count_unique_keys.h" + +///////////////////////// GPU + +/** + * GPU kernel to count the unique keys in a list. + * @param pNumPts Number of points. + * @param pKeys Pointer to the array of keys. + * @param pGlobalCounter Pointer to the global counter. + */ + __global__ void count_unique_keys_gpu_kernel( + const unsigned int pNumPts, + const mccnn::int64_m* __restrict__ pKeys, + int* __restrict__ pGlobalCounter) +{ + __shared__ int localCounter; + + //Initialize the local counter. + if(threadIdx.x == 0){ + localCounter = 0; + } + + __syncthreads(); + + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumPts-1; + curPtIndex += totalThreads) + { + //Check if it is the last element of the cell. + if(pKeys[curPtIndex] != pKeys[curPtIndex+1]){ + atomicAdd(&localCounter, 1); + } + } + + __syncthreads(); + + //Add the local counter into the glocal counter. + if(threadIdx.x == 0){ + atomicAdd(pGlobalCounter, localCounter); + } +} + +///////////////////////// CPU + +unsigned int mccnn::count_unique_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const mccnn::int64_m* pInKeysGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the gpu counter. + int* tmpCounter = pDevice->getIntTmpGPUBuffer(1); + pDevice->memset(tmpCounter, 0, sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)count_unique_keys_gpu_kernel, sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + count_unique_keys_gpu_kernel<<>>( + pNumPts, pInKeysGPUPtr, tmpCounter); + pDevice->check_error(__FILE__, __LINE__); + + //Get the total number of keys. + int* numUniqueKeys = pDevice->getIntTmpGPUBuffer(1, true);; + pDevice->memcpy_device_to_host( + (void*)numUniqueKeys, + (void*)tmpCounter, + sizeof(int)); + + //Wait for the result. + cudaEvent_t resEvent; + cudaEventCreate(&resEvent); + cudaEventRecord(resEvent, cudaStream); + cudaEventSynchronize(resEvent); + + return numUniqueKeys[0]+1; +} diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg.h new file mode 100755 index 000000000..a3e95d6de --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg.h @@ -0,0 +1,58 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to sample a set of points from +/// a point cloud. +///////////////////////////////////////////////////////////////////////////// + +#ifndef SAMPLING_AVG_CUH_ +#define SAMPLING_AVG_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to sample a set of points from a point cloud. + * @param pDevice Device. + * @param pNumPts Number of input points. + * @param pNumSampledPts Number of sampled points. + * @param pInKeysGPUPtr Input pointer to the vector of keys + * sorted from bigger to smaller residing on GPU memory. + * @param pPts GPUPtr Input point coordinates. + * @param pNumCellsGPUPtr Number of cells. + * @param pUniqueKeyIndexs Unique key indices. + * @param pOutPtsGPUPtr Output array with the new point + * coordinates. + * @param pBatchIdsGPUPtr Output array with the batch ids + * of the new points. + * @paramt D Number of dimensions. + */ + template + void sampling_avg_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSampledPts, + const mccnn::int64_m* pInKeysGPUPtr, + const float* pPtsGPUPtr, + const int* pNumCellsGPUPtr, + const int* pUniqueKeyIndexs, + float* pOutPtsGPUPtr, + int* pBatchIdsGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg_kernels.cu.cc new file mode 100755 index 000000000..71b2a21d3 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/sampling_avg_kernels.cu.cc @@ -0,0 +1,168 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to sample a set of points +/// from a point cloud. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/sampling/cc/kernels/sampling_avg.h" + +///////////////////////// GPU + + +/** + * GPU kernel to count the unique keys in a list. + * @param pNumPts Number of points. + * @param pNumSampledPts Number of sampled points. + * @param pKeys Pointer to the array of keys. + * @param pPts Input points. + * @param pNumCells Number of cells. + * @param pUniqueKeyIndices Input index of the first point of + * each cell. + * @param pOutPts Output sampled points. + * @param pOutBatchIds Output batch ids. + */ + template + __global__ void sampling_avg_gpu_kernel( + const unsigned int pNumPts, + const unsigned int pNumSampledPts, + const mccnn::int64_m* __restrict__ pKeys, + const mccnn::fpoint* __restrict__ pPts, + const mccnn::ipoint* __restrict__ pNumCells, + const int* __restrict__ pUniqueKeyIndices, + mccnn::fpoint* __restrict__ pOutPts, + int* __restrict__ pOutBatchIds) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumSampledPts; + curPtIndex += totalThreads) + { + //Find the start index. + int curPtCellIter = pUniqueKeyIndices[curPtIndex]; + + //Get the key and batch id. + mccnn::int64_m curKey = pKeys[curPtCellIter]; + mccnn::int64_m totalNumCells = mccnn::compute_total_num_cells_gpu_funct(pNumCells[0]); + int batchId = curKey/totalNumCells; + + //Declare the initial point coordinate and the counter of points. + mccnn::fpoint curOutPt(0.0f); + int curNumPts = 0; + + //Iterate until there are no more points in the cell. + while(curPtCellIter < pNumPts && pKeys[curPtCellIter] == curKey) + { + curOutPt += pPts[curPtCellIter]; + curNumPts++; + curPtCellIter++; + } + + //Finish the selection process. + curOutPt = curOutPt/((float)curNumPts); + + //Store the result. + pOutPts[curPtIndex] = curOutPt; + pOutBatchIds[curPtIndex] = batchId; + } +} + +///////////////////////// CPU + +template +void mccnn::sampling_avg_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSampledPts, + const mccnn::int64_m* pInKeysGPUPtr, + const float* pPtsGPUPtr, + const int* pNumCellsGPUPtr, + const int* pUniqueKeyIndexs, + float* pOutPtsGPUPtr, + int* pBatchIdsGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumSampledPts/blockSize; + execBlocks += (pNumSampledPts%blockSize != 0)?1:0; + + //Execute the cuda kernel. + unsigned int numBlocks = 0; + + //Calculate the total number of blocks to execute in parallel. + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)sampling_avg_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the kernel. + sampling_avg_gpu_kernel<<>>( + pNumPts, pNumSampledPts, pInKeysGPUPtr, + (const mccnn::fpoint*)pPtsGPUPtr, + (const mccnn::ipoint*)pNumCellsGPUPtr, + pUniqueKeyIndexs, + (mccnn::fpoint*)pOutPtsGPUPtr, + pBatchIdsGPUPtr); + + pDevice->check_error(__FILE__, __LINE__); + +#ifdef DEBUG_INFO + cudaEventRecord(stop, cudaStream); + cudaEventSynchronize(stop); + float milliseconds = 0; + cudaEventElapsedTime(&milliseconds, start, stop); + + float gpuOccupancy = (float)(numBlocks*blockSize)/(float)gpuProps.maxThreadsXMP_; + + fprintf(stderr, "### SAMPLING AVG ###\n"); + fprintf(stderr, "Num points: %d\n", pNumPts); + fprintf(stderr, "Num sampled pts: %d\n", pNumSampledPts); + fprintf(stderr, "Occupancy: %f\n", gpuOccupancy); + fprintf(stderr, "Execution time: %f\n", milliseconds); + fprintf(stderr, "\n"); +#endif +} + +#define SAMPLING_AVG_TEMP_DECL(Dims) \ + template void mccnn::sampling_avg_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const unsigned int pNumSampledPts, \ + const mccnn::int64_m* pInKeysGPUPtr, \ + const float* pPtsGPUPtr, \ + const int* pNumCellsGPUPtr, \ + const int* pUniqueKeyIndexs, \ + float* pOutPtsGPUPtr, \ + int* pBatchIdsGPUPtr); + +DECLARE_TEMPLATE_DIMS(SAMPLING_AVG_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts.h new file mode 100755 index 000000000..d44f77dd8 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts.h @@ -0,0 +1,61 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaraion of the CUDA operations to store in memory the sampled +/// points. +///////////////////////////////////////////////////////////////////////////// + +#ifndef STORED_SAMPLED_PTS_CUH_ +#define STORED_SAMPLED_PTS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to sample a set of points from a point cloud. + * @param pDevice Device. + * @param pNumPts Number of input points. + * @param pNumSampledPts Number of sampled points. + * @param pPtsGPUPtr Input pointer to the gpu array with + * the point coordinates. + * @param pBatchIdsGPUPtr Input pointer to the gpu array with + * the batch ids. + * @param pSelectedGPUPtr Input pointer to the gpu array with + * the selected points. + * @param pOutPtsGPUPtr Output pointer to the gpu array with + * the selected point coordinates. + * @param pOutBatchIdsGPUPtr Output pointer to the gpu array with + * the selected batch ids. + * @param pOutIndicesGPUPtr Output pointer to the gpu array with + * the selected indices. + * @paramt D Number of dimensions. + */ + template + void store_sampled_pts_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSampledPts, + const float* pPtsGPUPtr, + const int* pBatchIdsGPUPtr, + const int* pSelectedGPUPtr, + float* pOutPtsGPUPtr, + int* pOutBatchIdsGPUPtr, + int* pOutIndicesGPUPtr); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts_kernels.cu.cc new file mode 100755 index 000000000..7d15c588d --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_sampled_pts_kernels.cu.cc @@ -0,0 +1,135 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to store in memory the sampled +/// points. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" +#include "tfg_custom_ops/shared/cc/kernels/grid_utils.h" + +#include "tfg_custom_ops/sampling/cc/kernels/store_sampled_pts.h" + +///////////////////////// GPU + +/** + * GPU kernel to count the unique keys in a list. + * @param pNumPts Number of input points. + * @param pPts Input pointer to the array with the points. + * @param pBatchIds Input pointer to the array with the batch ids. + * @param pSelected Input pointer to the array with the selected pts. + * @param pNumSampledPoints Input pointer to the counter of already saved pts. + * @param pOutPts Output pointer to the array with the output pts. + * @param pBatchIds Output pointer to the array with the output batchids. + * @param pOutIndices Output pointer to the array with the output indices. + * @paramt D Number of dimensions. + */ + template + __global__ void store_sampled_pts_gpu_kernel( + const unsigned int pNumPts, + const mccnn::fpoint* __restrict__ pPts, + const int* __restrict__ pBatchIds, + const int* __restrict__ pSelected, + int* __restrict__ pNumSampledPoints, + mccnn::fpoint* __restrict__ pOutPts, + int* __restrict__ pOutBatchIds, + int* __restrict__ pOutIndices) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumPts; + curPtIndex += totalThreads) + { + //Check if the point was selected. + if(pSelected[curPtIndex] == 1){ + //Get the index where to save the current point. + int saveIndex = atomicAdd(pNumSampledPoints, 1); + pOutPts[saveIndex] = pPts[curPtIndex]; + pOutBatchIds[saveIndex] = pBatchIds[curPtIndex]; + pOutIndices[saveIndex] = curPtIndex; + } + } +} + +///////////////////////// CPU +template +void mccnn::store_sampled_pts_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const unsigned int pNumSampledPts, + const float* pPtsGPUPtr, + const int* pBatchIdsGPUPtr, + const int* pSelectedGPUPtr, + float* pOutPtsGPUPtr, + int* pOutBatchIdsGPUPtr, + int* pOutIndicesGPUPtr) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Initialize memory. + int* tmpCounter = pDevice->getIntTmpGPUBuffer(1); + pDevice->memset(tmpCounter, 0, sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + + //Execute the cuda kernel. + unsigned int numBlocks = 0; + + //Calculate the total number of blocks to execute in parallel. + numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)store_sampled_pts_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Call kernel. + store_sampled_pts_gpu_kernel + <<>>( + pNumPts, (const mccnn::fpoint*)pPtsGPUPtr, + pBatchIdsGPUPtr, pSelectedGPUPtr, tmpCounter, + (mccnn::fpoint*)pOutPtsGPUPtr, pOutBatchIdsGPUPtr, + pOutIndicesGPUPtr); + pDevice->check_error(__FILE__, __LINE__); +} + +#define STORE_SAMPLED_PTS_TEMP_DECL(Dims) \ + template void mccnn::store_sampled_pts_gpu( \ + std::unique_ptr& pDevice, \ + const unsigned int pNumPts, \ + const unsigned int pNumSampledPts, \ + const float* pPtsGPUPtr, \ + const int* pBatchIdsGPUPtr, \ + const int* pSelectedGPUPtr, \ + float* pOutPtsGPUPtr, \ + int* pOutBatchIdsGPUPtr, \ + int* pOutIndicesGPUPtr); + +DECLARE_TEMPLATE_DIMS(STORE_SAMPLED_PTS_TEMP_DECL) \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys.h new file mode 100755 index 000000000..4fecca939 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys.h @@ -0,0 +1,44 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +/// \brief Declaraion of the CUDA operations to store the first point index +/// of each unique key. +///////////////////////////////////////////////////////////////////////////// + +#ifndef STORE_UNIQUE_KEYS_CUH_ +#define STORE_UNIQUE_KEYS_CUH_ + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/gpu_device.hpp" +#include + +namespace mccnn{ + + /** + * Method to store the first point index for each unique keys on the gpu. + * @param pDevice Device. + * @param pNumPts Number of points. + * @param pInKeysGPUPtr Input pointer to the vector of keys + * sorted from bigger to smaller residing on GPU memory. + * @param pFIndexKeys Output pointer to the array with + * the first point index for each unique key. + */ + void store_unique_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const mccnn::int64_m* pInKeysGPUPtr, + int* pFIndexKeys); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys_kernels.cu.cc b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys_kernels.cu.cc new file mode 100755 index 000000000..1cdc5f661 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/kernels/store_unique_keys_kernels.cu.cc @@ -0,0 +1,94 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to store the first point index +/// of each unique key. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/math_helper.h" +#include "tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h" + +#include "tfg_custom_ops/sampling/cc/kernels/store_unique_keys.h" + +///////////////////////// GPU + +/** + * GPU kernel to store in the index of the first point of each cell. + * @param pNumPts Number of points. + * @param pKeys Pointer to the array of keys. + * @param pGlobalCounter Pointer to the global counter. + * @param pOutFIndices Output pointer with the starting + * index for each unique key. + */ + __global__ void store_unique_keys_gpu_kernel( + const unsigned int pNumPts, + const mccnn::int64_m* __restrict__ pKeys, + int* __restrict__ pGlobalCounter, + int* __restrict__ pOutFIndices) +{ + //Get the global thread index. + int initPtIndex = mccnn::compute_global_index_gpu_funct(); + int totalThreads = mccnn::compute_total_threads_gpu_funct(); + + for(unsigned int curPtIndex = initPtIndex; + curPtIndex < pNumPts; + curPtIndex += totalThreads) + { + //Check if it is the first element of the cell. + if(curPtIndex == 0 || pKeys[curPtIndex] != pKeys[curPtIndex-1]){ + int curIndex = atomicAdd(pGlobalCounter, 1); + pOutFIndices[curIndex] = curPtIndex; + } + } +} + +///////////////////////// CPU + +void mccnn::store_unique_keys_gpu( + std::unique_ptr& pDevice, + const unsigned int pNumPts, + const mccnn::int64_m* pInKeysGPUPtr, + int* pFIndexKeys) +{ + //Get the cuda stream. + auto cudaStream = pDevice->getCUDAStream(); + + //Get the gpu counter. + int* tmpCounter = pDevice->getIntTmpGPUBuffer(sizeof(int)); + pDevice->memset(tmpCounter, 0, sizeof(int)); + pDevice->check_error(__FILE__, __LINE__); + + //Get the device properties. + const GpuDeviceProperties& gpuProps = pDevice->get_device_properties(); + + //Calculate the ideal number of blocks for the selected block size. + unsigned int numMP = gpuProps.numMPs_; + unsigned int blockSize = gpuProps.warpSize_*4; + unsigned int numBlocks = pDevice->get_max_active_block_x_sm( + blockSize,(const void*)store_unique_keys_gpu_kernel, 0); + pDevice->check_error(__FILE__, __LINE__); + + //Calculate the total number of blocks to execute. + unsigned int execBlocks = pNumPts/blockSize; + execBlocks += (pNumPts%blockSize != 0)?1:0; + unsigned int totalNumBlocks = numMP*numBlocks; + totalNumBlocks = (totalNumBlocks > execBlocks)?execBlocks:totalNumBlocks; + + //Execute the cuda kernel. + store_unique_keys_gpu_kernel<<>>( + pNumPts, pInKeysGPUPtr, tmpCounter, pFIndexKeys); + pDevice->check_error(__FILE__, __LINE__); +} diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/ops/sampling_ops.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/ops/sampling_ops.cpp new file mode 100755 index 000000000..5a3e5b588 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/cc/ops/sampling_ops.cpp @@ -0,0 +1,204 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief C++ operations definition to perform a sample operation in a point +/// cloud. +///////////////////////////////////////////////////////////////////////////// + +#include "tfg_custom_ops/shared/cc/kernels/defines.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_utils.hpp" +#include "tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp" + +#include "tfg_custom_ops/sampling/cc/kernels/count_unique_keys.h" +#include "tfg_custom_ops/sampling/cc/kernels/store_unique_keys.h" +#include "tfg_custom_ops/sampling/cc/kernels/sampling_avg.h" +#include "tfg_custom_ops/sampling/cc/kernels/count_sampling_pd.h" +#include "tfg_custom_ops/sampling/cc/kernels/store_sampled_pts.h" + +/** + * Declaration of the tensorflow operation. + */ +REGISTER_OP("Sampling") + .Input("points: float32") + .Input("batch_ids: int32") + .Input("point_keys: int64") + .Input("num_cells: int32") + .Input("neighbors: int32") + .Input("sample_neigh_indices: int32") + .Output("sample_pts: float32") + .Output("sample_batch_ids: int32") + .Output("sample_indices: int32") + .Attr("mode: int") + .SetShapeFn([](shape_inference::InferenceContext* pIC) { + shape_inference::ShapeHandle outputDims1 = + pIC->MakeShape({-1, pIC->Dim(pIC->input(0), 1)}); + shape_inference::ShapeHandle outputDims2 = + pIC->MakeShape({-1}); + pIC->set_output(0, outputDims1); + pIC->set_output(1, outputDims2); + pIC->set_output(2, outputDims2); + return Status::OK(); + }); + +namespace mccnn{ + + /** + * Operation to perform a sampling operation on a regular grid. + */ + class SamplingOp: public OpKernel{ + + public: + + /** + * Constructor. + * @param pContext Constructor context of the operation. + */ + explicit SamplingOp( + OpKernelConstruction* pContext) + :OpKernel(pContext){ + + OP_REQUIRES_OK(pContext, pContext->GetAttr("mode", &mode_)); + OP_REQUIRES(pContext, mode_ >= 0 && mode_ < 2, + errors::InvalidArgument("SamplingOp requires a valid sampling mode.")); + } + + /** + * Method to compute the operation. + * @param pContext Context of the operation. + */ + void Compute(OpKernelContext * pContext) override{ + + //Get the input tensors. + const Tensor& inPts = pContext->input(0); + const Tensor& inBatchIds = pContext->input(1); + const Tensor& inPtKeys = pContext->input(2); + const Tensor& inNumCells = pContext->input(3); + const Tensor& inNeighbors = pContext->input(4); + const Tensor& inStartNeighIds = pContext->input(5); + + //Get variables from tensors. + unsigned int numPts = inPts.shape().dim_size(0); + unsigned int numDimensions = inPts.shape().dim_size(1); + unsigned int numNeighbors = inNeighbors.shape().dim_size(0); + + //Get the pointers to GPU data from the tensors. + const float* ptsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inPts); + const int* batchIdsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inBatchIds); + const mccnn::int64_m* ptKeysGPUPtr = mccnn::tensorflow_utils:: + get_const_tensor_pointer(inPtKeys); + const int* numCellsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNumCells); + const int* inNeighborsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inNeighbors); + const int* inStartNeighIdsGPUPtr = mccnn::tensorflow_utils::get_const_tensor_pointer(inStartNeighIds); + + + //Check for the correctness of the input. + OP_REQUIRES(pContext, numDimensions >= MIN_DIMENSIONS + && numDimensions <= MAX_DIMENSIONS, + errors::InvalidArgument("SamplingOp expects a valid number of dimension")); + OP_REQUIRES(pContext, inBatchIds.shape().dim_size(0) == numPts, + errors::InvalidArgument("SamplingOp expects the same number of keys" + " as number of points.")); + OP_REQUIRES(pContext, inPtKeys.shape().dim_size(0) == numPts, + errors::InvalidArgument("SamplingOp expects the same number of keys" + " as number of points.")); + OP_REQUIRES(pContext, inNumCells.shape().dim_size(0) == numDimensions, + errors::InvalidArgument("SamplingOp expects a number of dimensions in" + " inNumCells equal to the number of dimensions in the input points")); + OP_REQUIRES(pContext, inNeighbors.dims() == 2 && + inNeighbors.shape().dim_size(1) == 2, + errors::InvalidArgument("SamplingOp expects a neighbor tensor with 2 dimensions " + "and 2 indices per neighbor.")); + OP_REQUIRES(pContext, inStartNeighIds.shape().dim_size(0) == numPts, + errors::InvalidArgument("SamplingOp expects the same number of points " + "in inStartNeighIds as in the points tensor.")); + + + //Get the gpu device. + std::unique_ptr gpuDevice = make_unique(pContext); + + //Count the number of unique keys. + unsigned int numKeys = mccnn::count_unique_keys_gpu(gpuDevice, numPts, ptKeysGPUPtr); + + //Declare the temporal buffers. + int* tmpGPUPtr1 = gpuDevice->getIntTmpGPUBuffer(numKeys); + + //Store the first indices of each key. + mccnn::store_unique_keys_gpu(gpuDevice, numPts, ptKeysGPUPtr, tmpGPUPtr1); + + //Poisson disk sampling. + if(mode_ == 0){ + + //Declare temporal buffers. + int* tmpGPUPtr2 = gpuDevice->getIntTmpGPUBuffer(numPts); + + //Count the number of sampleed points. + int numSampledPts; + DIMENSION_SWITCH_CALL(numDimensions, mccnn::count_sampling_pd_gpu, + gpuDevice, numPts, numKeys, tmpGPUPtr1, ptKeysGPUPtr, + ptsGPUPtr, inNeighborsGPUPtr, inStartNeighIdsGPUPtr, + numCellsGPUPtr, numSampledPts, tmpGPUPtr2); + + //Create the output tensors. + float* outPtsGPUPtr = nullptr; + int* outBatchIdsGPUPtr = nullptr; + int* outIndicesGPUPtr = nullptr; + TensorShape outShape1 = TensorShape{numSampledPts, numDimensions}; + TensorShape outShape2 = TensorShape{numSampledPts}; + TensorShape outShape3 = TensorShape{numSampledPts}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape1, &outPtsGPUPtr)); + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (1, pContext, outShape2, &outBatchIdsGPUPtr)); + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (2, pContext, outShape3, &outIndicesGPUPtr)); + + //Store the sampled points. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::store_sampled_pts_gpu, + gpuDevice, numPts, numSampledPts, ptsGPUPtr, + batchIdsGPUPtr, tmpGPUPtr2, outPtsGPUPtr, + outBatchIdsGPUPtr, outIndicesGPUPtr); + + //Cell average. + }else{ + //Create the output tensors. + float* outPtsGPUPtr = nullptr; + int* outBatchIdsGPUPtr = nullptr; + int* outIndicesGPUPtr = nullptr; + TensorShape outShape1 = TensorShape{numKeys, numDimensions}; + TensorShape outShape2 = TensorShape{numKeys}; + TensorShape outShape3 = TensorShape{1}; + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (0, pContext, outShape1, &outPtsGPUPtr)); + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (1, pContext, outShape2, &outBatchIdsGPUPtr)); + OP_REQUIRES_OK(pContext, mccnn::tensorflow_utils::allocate_output_tensor + (2, pContext, outShape3, &outIndicesGPUPtr)); + + + //Select the new point. + DIMENSION_SWITCH_CALL(numDimensions, mccnn::sampling_avg_gpu, + gpuDevice, numPts, numKeys, ptKeysGPUPtr, ptsGPUPtr, + numCellsGPUPtr, tmpGPUPtr1, outPtsGPUPtr, outBatchIdsGPUPtr); + } + } + + private: + + /**Mode used to sample points.*/ + int mode_; + }; +} + +REGISTER_KERNEL_BUILDER(Name("Sampling").Device(DEVICE_GPU), mccnn::SamplingOp); \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/ops/__init__.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/ops/sampling_ops.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/ops/sampling_ops.py new file mode 100644 index 000000000..6b68aaef7 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/sampling/python/ops/sampling_ops.py @@ -0,0 +1,21 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific +""" use sampling op in python """ + +from tensorflow.python.framework import load_library +from tensorflow.python.platform import resource_loader + +sampling_ops = load_library.load_op_library( + resource_loader.get_path_to_datafile('_sampling_ops.so')) +# sampling = sampling_ops.sampling +sampling = sampling_ops.sampling diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/BUILD new file mode 100644 index 000000000..9e12bd425 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/BUILD @@ -0,0 +1,51 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured", "if_cuda") + +config_setting( + name = "windows", + constraint_values = ["@bazel_tools//platforms:windows"], +) + +cc_library( + name = "cuda", + data = [ + "@local_config_cuda//cuda:cudart", + ], + linkopts = select({ + "@local_config_cuda//cuda:darwin": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib", + ], + ":windows": [], + "//conditions:default": [ + "-Wl,-rpath,../local_config_cuda/cuda/lib64", + "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64", + ], + }), + deps = [ + "@local_config_cuda//cuda:cudart", + ], +) + +cc_library( + name = "pointcloud_common_kernels", + srcs = [ + "cc/kernels/cuda_kernel_utils.h", + "cc/kernels/defines.hpp", + "cc/kernels/gpu_device.hpp", + "cc/kernels/tf_utils.hpp", + "cc/kernels/tf_gpu_device.hpp", + "cc/kernels/tf_gpu_device.cpp", + "cc/kernels/math_helper.h", + "cc/kernels/grid_utils.h", + "cc/kernels/rnd_utils.h", + "cc/kernels/nn_utils.h", + ], + deps = [ + "@local_config_tf//:libtensorflow_framework", + "@local_config_tf//:tf_header_lib", + ] + if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]), + # visibility = ["//tfg_custom_ops/compute_keys:__pkg__"], +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h new file mode 100755 index 000000000..0526b3014 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/cuda_kernel_utils.h @@ -0,0 +1,67 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Utilities for the cuda implementations of the tensor operations. +///////////////////////////////////////////////////////////////////////////// + +#ifndef CUDA_KERNEL_UTILS_H_ +#define CUDA_KERNEL_UTILS_H_ + +#include +#include "cuda_runtime.h" +#include "cuda_fp16.h" + +namespace mccnn{ + + ///////////////////////// DEVICE FUNCTIONS + + /** + * Function to compute the global index of the current thread. + * @return Current thread index. + */ + __device__ __forceinline__ unsigned long long int compute_global_index_gpu_funct() + { + return threadIdx.x + blockDim.x*blockIdx.x; + } + + /** + * Function to compute the total number of threads in execution.. + * @return Total number of threads. + */ + __device__ __forceinline__ unsigned long long int compute_total_threads_gpu_funct() + { + return gridDim.x*blockDim.x; + } + + /** + * Function to do an atomic max operation on floats. + * @param pAddress Address in which we want to perform the atomic operation. + * @param pVal Value we want to input. + * @return Stored value. + */ + __device__ static float atomicMax(float* pAddress, const float pVal) + { + int* address_as_i = (int*) pAddress; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fmaxf(pVal, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + } +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/defines.hpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/defines.hpp new file mode 100755 index 000000000..2eafb5352 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/defines.hpp @@ -0,0 +1,68 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Definitions. +///////////////////////////////////////////////////////////////////////////// + +#ifndef DEFINES_H_ +#define DEFINES_H_ + +#include + +//Definition of the minimum and maximum number of dimensions. +#define MIN_DIMENSIONS 2 +#define MAX_DIMENSIONS 6 + +//Macros to declare and call a template function with a variable +//number of dimensions. +#define DECLARE_TEMPLATE_DIMS(Func) \ + Func(2) \ + Func(3) \ + Func(4) \ + Func(5) \ + Func(6) + +#define DIMENSION_CASE_SWITCH(Dim, Func, ...) \ + case Dim: \ + Func(__VA_ARGS__); \ + break; + +#define DIMENSION_SWITCH_CALL(Var, Func, ...) \ + switch(Var){ \ + DIMENSION_CASE_SWITCH(2, Func, __VA_ARGS__) \ + DIMENSION_CASE_SWITCH(3, Func, __VA_ARGS__) \ + DIMENSION_CASE_SWITCH(4, Func, __VA_ARGS__) \ + DIMENSION_CASE_SWITCH(5, Func, __VA_ARGS__) \ + DIMENSION_CASE_SWITCH(6, Func, __VA_ARGS__) \ + } + + +//Definition of the min and max operation for cuda code. +#define MCCNN_MAX(a, b) (a < b) ? b : a; +#define MCCNN_MIN(a, b) (a > b) ? b : a; + +namespace mccnn{ + //Definition of the int 64 bit. + typedef long long int64_m; +} + +//Definition of make unique for C++11 (Only available in C++14) +template +std::unique_ptr make_unique(Args&&... args) +{ + return std::unique_ptr(new T(std::forward(args)...)); +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/gpu_device.hpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/gpu_device.hpp new file mode 100755 index 000000000..0edca9c4c --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/gpu_device.hpp @@ -0,0 +1,184 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Interface of the gpu device. +///////////////////////////////////////////////////////////////////////////// + +#ifndef IGPU_DEVICE_H_ +#define IGPU_DEVICE_H_ + +#include "cuda_runtime.h" +#include "defines.hpp" + +namespace mccnn{ + + /** + * Struct containing the properties of the current GPU device. + */ + struct GpuDeviceProperties{ + /**Warp size.*/ + int warpSize_; + /**Number of multiprocessors.*/ + int numMPs_; + /**Maximum threads per block.*/ + int maxThreadsXBlock_; + /**Maximum threads per multiprocessor.*/ + int maxThreadsXMP_; + /**Maximum number of registers per block.*/ + int maxRegistersXBlock_; + /**Maximum number of registers per multiprocessor.*/ + int maxRegistersXMP_; + /**Shared memory per block.*/ + size_t sharedMemXBlock_; + /**Shared memory per multiprocessor.*/ + size_t sharedMemXMP_; + /**Major version of the compute capability.*/ + int majorVersion_; + /**Minor version of the compute capability.*/ + int minorVersion_; + }; + + /** + * GPU Device object. + */ + class IGPUDevice + { + public: + + /** + * Constructor. + */ + explicit IGPUDevice(){} + + /** + * Destructor. + */ + virtual ~IGPUDevice(void){} + + /** + * Method to get the device properties. + * @return Device properties. + */ + inline const GpuDeviceProperties& get_device_properties(){ + return deviceProps_; + } + + /** + * Method to set a block of gpu memory to a certain value. + * @param pDest Pointer to the gpu memory we want to change. + * @param pVal Value we want to initialize the memory to. + * @param pSize Size of the buffer. + */ + virtual void memset(void* pDest, int pVal, size_t pSize) = 0; + + /** + * Method to copy memory from gpu to gpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_device_to_device(void* pDest, void* pSrc, size_t pSize) = 0; + + /** + * Method to copy memory from gpu to cpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_device_to_host(void* pDest, void* pSrc, size_t pSize) = 0; + + /** + * Method to copy memory from cpu to gpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_host_to_device(void* pDest, void* pSrc, size_t pSize) = 0; + + /** + * Method to copy memory from cpu to symbol. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_host_to_symbol(void* pDest, void* pSrc, size_t pSize) = 0; + + /** + * Method to get the maximum number of active blocks per multiprocessor. + * @param pBlockSize Number of Threads per block. + * @param pFunct Function for which compute the number of blocks. + * @param pSharedMemXBlock Share memory per block. + * @return Number of active blocks per multiprocessor. + */ + virtual int get_max_active_block_x_sm( + const unsigned int pBlockSize, + const void* pFunct, + const size_t pSharedMemXBlock) = 0; + + /** + * Method to evaluate if was an error in the last operation. + * @param pFile File from which the cuda operation was called. + * @param pLine Line of the file from which the cuda operation + * was called. + */ + virtual void check_error( + const char* pFile, + int pLine) = 0; + + /** + * Method to get a temporal gpu memory buffer of floats. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual float* getFloatTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false) = 0; + + /** + * Method to get a temporal gpu memory buffer of ints. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual int* getIntTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false) = 0; + + /** + * Method to get a temporal gpu memory buffer of int64_m. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual mccnn::int64_m* getInt64TmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false) = 0; + + /** + * Method to get the cuda stream used. + * @return Cuda stream. + */ + virtual const cudaStream_t& getCUDAStream() = 0; + + protected: + + /**Device properties.*/ + GpuDeviceProperties deviceProps_; + + }; +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/grid_utils.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/grid_utils.h new file mode 100755 index 000000000..e30b4a2db --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/grid_utils.h @@ -0,0 +1,163 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Utilities for the cuda implementations of the tensor operations. +///////////////////////////////////////////////////////////////////////////// + +#ifndef GRID_UTILS_H_ +#define GRID_UTILS_H_ + +#include + +#include "defines.hpp" +#include "math_helper.h" + +namespace mccnn{ + + ///////////////////////// DEVICE FUNCTIONS + + /** + * Function to compute the total number of cells. + * @param pPosition Position of the point. + * @param pSMinPoint Minimum point of the bounding box scaled by the + * inverse of the cell size. + * @param pNumCells Number of cells. + * @param pInvCellSize Cell size. + * @return Cell indices. + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ mccnn::int64_m + compute_total_num_cells_gpu_funct( + const mccnn::ipoint pNumCells) + { + mccnn::int64_m result = 1; +#pragma unroll + for(int i = 0; i < D; ++i) + result *= pNumCells[i]; + return result; + } + + /** + * Function to compute the cell for a given point. + * @param pPosition Position of the point. + * @param pSMinPoint Minimum point of the bounding box scaled by the + * inverse of the cell size. + * @param pNumCells Number of cells. + * @param pInvCellSize Cell size. + * @return Cell indices. + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ mccnn::ipoint + compute_cell_gpu_funct( + const mccnn::fpoint pPosition, + const mccnn::fpoint pSMinPoint, + const mccnn::ipoint pNumCells, + const mccnn::fpoint pInvCellSize) + { + mccnn::fpoint relPoint = pPosition*pInvCellSize - pSMinPoint; + mccnn::ipoint curCell = (mccnn::ipoint)floorf(relPoint); + return minp(maxp(curCell, mccnn::ipoint(0)), pNumCells-1); + } + + + /** + * Function to compute the key for a given cell. + * @param pCell Cell index. + * @param pNumCells Number of cells. + * @param pBatchId Current batch id. + * @return Key of the cell. + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ mccnn::int64_m compute_key_gpu_funct( + const mccnn::ipoint pCell, + const mccnn::ipoint pNumCells, + const int pBatchId) + { + mccnn::int64_m key = 0; + mccnn::int64_m accumKey = 1; +#pragma unroll + for(int i = D-1; i >=0 ; --i) + { + key += pCell[i]*accumKey; + accumKey *= pNumCells[i]; + } + return key + accumKey*pBatchId; + } + + /** + * Function to compute the cell from a given key. + * @param pKey Input key. + * @param pNumCells Number of cells. + * @return Cell indices and batch id (b, d1, d2, ..., dn). + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ mccnn::ipoint compute_cell_from_key_gpu_funct( + const mccnn::int64_m pKey, + const mccnn::ipoint pNumCells) + { + mccnn::int64_m auxInt = pKey; + mccnn::ipoint result; +#pragma unroll + for(int i = D-1; i >=0; --i){ + result[i+1] = auxInt%pNumCells[i]; + auxInt = auxInt/pNumCells[i]; + } + result[0] = auxInt; + + return result; + } + + /** + * Function to compute data structure index from a given key. + * @param pKey Input key. + * @param pNumCells Number of cells. + * @return Index to the data structure. + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ int compute_ds_index_from_key_gpu_funct( + const mccnn::int64_m pKey, + const mccnn::ipoint pNumCells) + { + mccnn::int64_m divVal = 1; +#pragma unroll + for(int i = 0; i < D-2; ++i) + divVal *= pNumCells[i+2]; + return (int)(pKey/divVal); + } + + /** + * Function to compute data structure index from a given cell. + * @param pExtCell Input cell in which the batch id is in the + * first position. + * @param pNumCells Number of cells. + * @return Index to the data structure. + * @paramT D Number of dimensions. + */ + template + __device__ __forceinline__ int compute_ds_index_from_cell_gpu_funct( + const mccnn::ipoint pExtCell, + const mccnn::ipoint pNumCells) + { + return pExtCell[0]*pNumCells[0]*pNumCells[1]+ + pExtCell[1]*pNumCells[1] + pExtCell[2]; + } +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/math_helper.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/math_helper.h new file mode 100755 index 000000000..69f60e5d1 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/math_helper.h @@ -0,0 +1,389 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Definition of math helper functions. +///////////////////////////////////////////////////////////////////////////// + +#ifndef MATH_HELPER_H_ +#define MATH_HELPER_H_ + +#include +#include +#include "defines.hpp" +#include "cuda_runtime.h" +#include "cuda_fp16.h" + +namespace mccnn +{ + //Definition of a point with D dimensions. + template + struct point{ + + /**Position of the point.*/ + T pos_[D]; + + ///////////////// CONSTRUCTORS + + __host__ __device__ point(){ +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = 0; + } + + __host__ __device__ point(T* pPtr){ +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = pPtr[i]; + } + + __host__ __device__ point(const T* pPtr){ +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = pPtr[i]; + } + + __host__ __device__ point(T pVal){ +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = pVal; + } + + __host__ __device__ point(const point& pPt){ +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = pPt[i]; + } + + ///////////////// OPERATORS + + ///////// ACCESS + + __host__ __device__ __forceinline__ T& + operator[](int pIndex) + { + return pos_[pIndex]; + } + + __host__ __device__ __forceinline__ const T& + operator[](int pIndex) const + { + return pos_[pIndex]; + } + + ///////// EQUAL + + __host__ __device__ __forceinline__ point& + operator=(const point& pPt1) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] = pPt1[i]; + return *this; + } + + ///////// CONVERSION + + template + __host__ __device__ __forceinline__ operator + point() const + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = static_cast(pos_[i]); + return result; + } + + ///////// SUMATION + + __host__ __device__ __forceinline__ point& + operator+=(const point& pPt1) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] += pPt1[i]; + return *this; + } + + __host__ __device__ __forceinline__ point& + operator+=(const T pVal) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] += pVal; + return *this; + } + + ///////// SUBSTRACTION + + __host__ __device__ __forceinline__ point& + operator-=(const point& pPt1) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] -= pPt1[i]; + return *this; + } + + __host__ __device__ __forceinline__ point& + operator-=(const T pVal) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] -= pVal; + return *this; + } + + ///////// MULTIPLICATION + + __host__ __device__ __forceinline__ point& + operator*=(const point& pPt1) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] *= pPt1[i]; + return *this; + } + + __host__ __device__ __forceinline__ point& + operator*=(const T pVal) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] *= pVal; + return *this; + } + + ///////// DIVISION + + __host__ __device__ __forceinline__ point& + operator/=(const point& pPt1) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] /= pPt1[i]; + return *this; + } + + __host__ __device__ __forceinline__ point& + operator/=(const T pVal) + { +#pragma unroll + for(int i=0; i < D; ++i) + pos_[i] /= pVal; + return *this; + } + }; + + template + using fpoint = point; + + template + using hpoint = point; + + template + using ipoint = point; + + ///////////////// OPERATORS + + ///////// SUMATION + + template + __host__ __device__ __forceinline__ point operator+( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] + pPt2[i]; + return result; + } + + template + __host__ __device__ __forceinline__ point operator+( + const point& pPt1, const T pVal) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] + pVal; + return result; + } + + ///////// SUBTRACTION + + template + __host__ __device__ __forceinline__ point operator-( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] - pPt2[i]; + return result; + } + + template + __host__ __device__ __forceinline__ point operator-( + const point& pPt1, const T pVal) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] - pVal; + return result; + } + + ///////// MULTIPLICATION + + template + __host__ __device__ __forceinline__ point operator*( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] * pPt2[i]; + return result; + } + + template + __host__ __device__ __forceinline__ point operator*( + const point& pPt1, const T pVal) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] * pVal; + return result; + } + + ///////// DIVISION + + template + __host__ __device__ __forceinline__ point operator/( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] / pPt2[i]; + return result; + } + + template + __host__ __device__ __forceinline__ point operator/( + const point& pPt1, const T pVal) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = pPt1[i] / pVal; + return result; + } + + + ///////////////// MATH + + template + __host__ __device__ __forceinline__ point cross( + const point& pPt1, const point& pPt2) + { + point result; + result[0] = pPt1[1]*pPt2[2] - pPt1[2]*pPt2[1]; + result[1] = pPt1[2]*pPt2[0] - pPt1[0]*pPt2[2]; + result[2] = pPt1[0]*pPt2[1] - pPt1[1]*pPt2[0]; + return result; + } + + template + __host__ __device__ __forceinline__ T dot( + const point& pPt1, const point& pPt2) + { + T result = (T)0; +#pragma unroll + for(int i=0; i < D; ++i) + result += pPt1[i] * pPt2[i]; + return result; + } + + template + __host__ __device__ __forceinline__ float length( + const point& pPt) + { + return sqrt(dot(pPt, pPt)); + } + + template + __host__ __device__ __forceinline__ void normalize(point& pPt) + { + float curInvLength = 1.0f/length(pPt); + pPt *= curInvLength; + } + + template + __host__ __device__ __forceinline__ point normalize(point pPt) + { + float curInvLength = 1.0f/length(pPt); + pPt *= curInvLength; + return pPt; + } + + template + __host__ __device__ __forceinline__ point floorf( + const point& pPt) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = floor(pPt[i]); + return result; + } + + template + __host__ __device__ __forceinline__ point maxp( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = MCCNN_MAX(pPt1[i], pPt2[i]); + return result; + } + + template + __host__ __device__ __forceinline__ point minp( + const point& pPt1, const point& pPt2) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = MCCNN_MIN(pPt1[i], pPt2[i]); + return result; + } + + template + __host__ __device__ __forceinline__ point expf( + const point& pPt) + { + point result; +#pragma unroll + for(int i=0; i < D; ++i) + result[i] = exp(pPt[i]); + return result; + } +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/nn_utils.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/nn_utils.h new file mode 100755 index 000000000..b1a1bc8d7 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/nn_utils.h @@ -0,0 +1,99 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Neural network utils. +///////////////////////////////////////////////////////////////////////////// + +#ifndef NN_UTILS_H_ +#define NN_UTILS_H_ + +//Leaky relu weight. +#define LEAKY_RELU_WEIGHT 0.2f + +//ELU weight +#define ELU_WEIGHT 1.0f + +namespace mccnn{ + + /** + * Template to define activation functions. + */ + template + struct activation_function_struct{ + __forceinline__ __device__ float forward( + float pValue){return 0.0f;} + + __forceinline__ __device__ float backward( + float pValue){return 0.0f;} + }; + + // A = 0 : RELU + template<> + struct activation_function_struct<0>{ + __forceinline__ __device__ float forward( + float pValue){ + return max(pValue, 0.0f); + } + + __forceinline__ __device__ float backward( + float pValue){ + return (pValue > 0.0f)? 1.0f: 0.0f; + } + }; + + // A = 1 : Leaky RELU + template<> + struct activation_function_struct<1>{ + __forceinline__ __device__ float forward( + float pValue){ + return (pValue >= 0.0f)?pValue:pValue*LEAKY_RELU_WEIGHT; + } + + __forceinline__ __device__ float backward( + float pValue){ + return (pValue > 0.0f)?1.0f:LEAKY_RELU_WEIGHT; + } + }; + + // A = 2 : ELU + template<> + struct activation_function_struct<2>{ + __forceinline__ __device__ float forward( + const float pValue){ + return (pValue <= 0.0f)?ELU_WEIGHT*(exp(pValue) - 1.0f):pValue; + } + + __forceinline__ __device__ float backward( + const float pValue){ + return (pValue <= 0.0f)?ELU_WEIGHT + pValue:1.0f; + } + }; + + // A = 2 : Exp + template<> + struct activation_function_struct<3>{ + __forceinline__ __device__ float forward( + const float pValue){ + return exp(pValue); + } + + __forceinline__ __device__ float backward( + const float pValue){ + return pValue; + } + }; +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/rnd_utils.h b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/rnd_utils.h new file mode 100755 index 000000000..97c8152fe --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/rnd_utils.h @@ -0,0 +1,52 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the CUDA operations to create pseudo-random +/// numbers +///////////////////////////////////////////////////////////////////////////// + +#ifndef RND_UTILS_CUH_ +#define RND_UTILS_CUH_ + +namespace mccnn{ + + /** + * Pseudo random number generator: http://www.reedbeta.com/blog/quick-and-easy-gpu-random-numbers-in-d3d11/ + */ + __device__ __forceinline__ unsigned int wang_hash(unsigned int pSeed) + { + pSeed = (pSeed ^ 61) ^ (pSeed >> 16); + pSeed *= 9; + pSeed = pSeed ^ (pSeed >> 4); + pSeed *= 0x27d4eb2d; + pSeed = pSeed ^ (pSeed >> 15); + return pSeed; + } + + __device__ __forceinline__ unsigned int rand_xorshift(unsigned int pSeed) + { + pSeed ^= (pSeed << 13); + pSeed ^= (pSeed >> 17); + pSeed ^= (pSeed << 5); + return pSeed; + } + + __device__ __forceinline__ unsigned int seed_to_float(unsigned int pSeed) + { + return float(pSeed) * (1.0 / 4294967296.0); + } +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.cpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.cpp new file mode 100755 index 000000000..7b8d93455 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.cpp @@ -0,0 +1,148 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Implementation of the tensorflow gpu device. +///////////////////////////////////////////////////////////////////////////// + +#include "cuda_runtime.h" + +#include "tf_gpu_device.hpp" +#include "tf_utils.hpp" + +#include +#include + +namespace mccnn{ + + TFGPUDevice::TFGPUDevice(tensorflow::OpKernelContext* pContext): + IGPUDevice(), context_(pContext) + { + //Get GPU device. + cudaDeviceProp prop; + int device; + cudaGetDevice(&device); + cudaGetDeviceProperties(&prop, device); + + deviceProps_.warpSize_ = prop.warpSize; + deviceProps_.numMPs_ = prop.multiProcessorCount; + deviceProps_.maxThreadsXBlock_ = prop.maxThreadsPerBlock; + deviceProps_.maxThreadsXMP_ = prop.maxThreadsPerMultiProcessor; + deviceProps_.maxRegistersXBlock_ = prop.regsPerBlock; + deviceProps_.maxRegistersXMP_ = prop.regsPerMultiprocessor; + deviceProps_.sharedMemXBlock_ = prop.sharedMemPerBlock; + deviceProps_.sharedMemXMP_ = prop.sharedMemPerMultiprocessor; + deviceProps_.majorVersion_ = prop.major; + deviceProps_.minorVersion_ = prop.minor; + } + + TFGPUDevice::~TFGPUDevice() + {} + + void TFGPUDevice::memset(void* pDest, int pVal, size_t pSize) + { + cudaMemsetAsync(pDest, pVal, pSize, context_->eigen_device().stream()); + } + + void TFGPUDevice::memcpy_device_to_device(void* pDest, void* pSrc, size_t pSize) + { + cudaMemcpyAsync(pDest, pSrc, pSize, cudaMemcpyDeviceToDevice, context_->eigen_device().stream()); + } + + void TFGPUDevice::memcpy_device_to_host(void* pDest, void* pSrc, size_t pSize) + { + cudaMemcpyAsync(pDest, pSrc, pSize, cudaMemcpyDeviceToHost, context_->eigen_device().stream()); + } + + void TFGPUDevice::memcpy_host_to_device(void* pDest, void* pSrc, size_t pSize) + { + cudaMemcpyAsync(pDest, pSrc, pSize, cudaMemcpyHostToDevice, context_->eigen_device().stream()); + } + + void TFGPUDevice::memcpy_host_to_symbol(void* pDest, void* pSrc, size_t pSize) + { + cudaMemcpyToSymbolAsync(pDest, pSrc, pSize, 0, cudaMemcpyHostToDevice, context_->eigen_device().stream()); + } + + int TFGPUDevice::get_max_active_block_x_sm( + const unsigned int pBlockSize, + const void* pFunct, + const size_t pSharedMemXBlock) + { + int outputNumBlocks; + cudaOccupancyMaxActiveBlocksPerMultiprocessor ( + &outputNumBlocks, pFunct, pBlockSize, pSharedMemXBlock); + return outputNumBlocks; + } + + void TFGPUDevice::check_error( + const char* pFile, + int pLine) + { + cudaError_t errorCode = cudaPeekAtLastError(); + if (errorCode != cudaSuccess) + { + fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(errorCode), pFile, pLine); + exit(errorCode); + //TODO - Proper error handling, exceptions. + } + } + + float* TFGPUDevice::getFloatTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged) + { + return this->getTmpGPUBuffer(pSize, pCPUManaged); + } + + int* TFGPUDevice::getIntTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged) + { + return this->getTmpGPUBuffer(pSize, pCPUManaged); + } + + int64_m* TFGPUDevice::getInt64TmpGPUBuffer(const unsigned int pSize, bool pCPUManaged) + { + return this->getTmpGPUBuffer(pSize, pCPUManaged); + } + + const cudaStream_t& TFGPUDevice::getCUDAStream() + { + return context_->eigen_device().stream(); + } + + template + T* TFGPUDevice::getTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged) + { + tensorflow::AllocatorAttributes allocatorAtt; + if(pCPUManaged){ + allocatorAtt.set_on_host(true); + allocatorAtt.set_gpu_compatible(true); + } + + std::unique_ptr pTmpTensor = make_unique(); + TensorShape tmpShape = TensorShape{pSize}; + if(!TF_PREDICT_TRUE(context_->allocate_temp( + DataTypeToEnum::value, tmpShape, pTmpTensor.get(), allocatorAtt).ok())){ + fprintf(stderr,"Error allocating temporal tensor of %ld bytes.\n", sizeof(T)*pSize); + exit(-1); + //TODO - Proper error handling, exceptions. + } + auto tmpTensorFlat = pTmpTensor->flat(); + tmpTensors_.push_back(std::move(pTmpTensor)); + return &(tmpTensorFlat(0)); + } + + template int* TFGPUDevice::getTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged); + template mccnn::int64_m* TFGPUDevice::getTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged); + template float* TFGPUDevice::getTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged); + +} \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp new file mode 100755 index 000000000..530c5213a --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_gpu_device.hpp @@ -0,0 +1,172 @@ +///////////////////////////////////////////////////////////////////////////// +// Copyright 2020 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Declaration of the tensorflow gpu device. +///////////////////////////////////////////////////////////////////////////// + +#ifndef TF_GPU_DEVICE_H_ +#define TF_GPU_DEVICE_H_ + +#include +#include + +#include "gpu_device.hpp" + +namespace tensorflow{ + class OpKernelContext; + class Tensor; +} + +namespace mccnn{ + + /** + * Tensorflow GPU Device object. + */ + class TFGPUDevice: public IGPUDevice + { + public: + + /** + * Constructor. + * @param pContext Operation context. + */ + explicit TFGPUDevice( + tensorflow::OpKernelContext* pContext); + + /** + * Destructor. + */ + virtual ~TFGPUDevice(void); + + /** + * Method to set a block of gpu memory to a certain value. + * @param pDest Pointer to the gpu memory we want to change. + * @param pVal Value we want to initialize the memory to. + * @param pSize Size of the buffer. + */ + virtual void memset(void* pDest, int pVal, size_t pSize); + + /** + * Method to copy memory from gpu to gpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_device_to_device(void* pDest, void* pSrc, size_t pSize); + + /** + * Method to copy memory from gpu to cpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_device_to_host(void* pDest, void* pSrc, size_t pSize); + + /** + * Method to copy memory from cpu to gpu. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_host_to_device(void* pDest, void* pSrc, size_t pSize); + + /** + * Method to copy memory from cpu to symbol. + * @param pDest Destiantion pointer. + * @param pSrc Source pointer. + * @param pSize Number of bytes to copy. + */ + virtual void memcpy_host_to_symbol(void* pDest, void* pSrc, size_t pSize); + + /** + * Method to get the maximum number of active blocks per multiprocessor. + * @param pBlockSize Number of Threads per block. + * @param pFunct Function for which compute the number of blocks. + * @param pSharedMemXBlock Share memory per block. + * @return Number of active blocks per multiprocessor. + */ + virtual int get_max_active_block_x_sm( + const unsigned int pBlockSize, + const void* pFunct, + const size_t pSharedMemXBlock); + + /** + * Method to evaluate if was an error in the last operation. + * @param pFile File from which the cuda operation was called. + * @param pLine Line of the file from which the cuda operation + * was called. + */ + virtual void check_error( + const char* pFile, + int pLine); + + /** + * Method to get a temporal gpu memory buffer of floats. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual float* getFloatTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false); + + /** + * Method to get a temporal gpu memory buffer of ints. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual int* getIntTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false); + + /** + * Method to get a temporal gpu memory buffer of int64_m. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + virtual mccnn::int64_m* getInt64TmpGPUBuffer(const unsigned int pSize, bool pCPUManaged = false); + + /** + * Method to get the cuda stream used. + * @return Cuda stream. + */ + virtual const cudaStream_t& getCUDAStream(); + + private: + + /** + * Private method to get a temporal gpu memory buffer. + * @param pSize Number of elements in the buffer. + * @param pCPUManaged Booleant that indicates if the buffer will + * be allocated on CPU for host device data transfers. + * @paramt Type of the elements in the buffer. + * @return Pointer to the buffer in memory. + */ + template + T* getTmpGPUBuffer(const unsigned int pSize, bool pCPUManaged); + + /**Operation context.*/ + tensorflow::OpKernelContext* context_; + /**Vector of temporal tensors allocated.*/ + std::vector> tmpTensors_; + + }; +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_utils.hpp b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_utils.hpp new file mode 100755 index 000000000..013e9df98 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/tfg_custom_ops/shared/cc/kernels/tf_utils.hpp @@ -0,0 +1,85 @@ +///////////////////////////////////////////////////////////////////////////// +/// Copyright 2020 Google LLC +/// +/// Licensed under the Apache License, Version 2.0 (the "License"); +/// you may not use this file except in compliance with the License. +/// You may obtain a copy of the License at +/// +/// https://www.apache.org/licenses/LICENSE-2.0 +/// +/// Unless required by applicable law or agreed to in writing, software +/// distributed under the License is distributed on an "AS IS" BASIS, +/// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +/// See the License for the specific language governing permissions and +/// limitations under the License. +///////////////////////////////////////////////////////////////////////////// +/// \brief Utilities for the tensorflow interface. +///////////////////////////////////////////////////////////////////////////// +#ifndef TF_UTILS_H_ +#define TF_UTILS_H_ + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_types.h" + +using namespace tensorflow; + +namespace mccnn{ + + namespace tensorflow_utils + { + /** + * Template to get the pointer of a tensor. + * @param pTensor Tensor from which the pointer will be obtained. + * @return Pointer of the GPU array containing the tensor. + */ + template + inline T* get_tensor_pointer(const Tensor& pTensor) + { + auto tensorFlat = pTensor.flat(); + return &(tensorFlat(0)); + } + + /** + * Template to get the constant pointer of a tensor. + * @param pTensor Tensor from which the pointer will be obtained. + * @return Pointer of the GPU array containing the tensor. + */ + template + inline const T* get_const_tensor_pointer(const Tensor& pTensor) + { + auto tensorFlat = pTensor.flat(); + return &(tensorFlat(0)); + } + + /** + * Template to allocate an output for a tensorflow operation. + * @param pIndex Index of the output. + * @param pContext Context of the operation. + * @param pShape Shape of the output tensor. + * @param pOutPtr Output parameter with the GPU array pointer. + * @return Status of the allocation operation. + */ + template + inline Status allocate_output_tensor( + const unsigned int pIndex, + OpKernelContext* pContext, + TensorShape& pShape, + T** pOutPtr) + { + Tensor* outTensor = nullptr; + Status retStatus = pContext->allocate_output( + pIndex, pShape, &outTensor); + auto outTensorFlat = outTensor->flat(); + *pOutPtr = &(outTensorFlat(0)); + return retStatus; + } + } +} + +#endif \ No newline at end of file diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/BUILD new file mode 100755 index 000000000..18b97f663 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/BUILD @@ -0,0 +1,167 @@ +# This file is expanded from a template by cuda_configure.bzl +# Update cuda_configure.bzl#verify_build_defines when adding new variables. + +load(":cc_toolchain_config.bzl", "cc_toolchain_config") + +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +toolchain( + name = "toolchain-linux-x86_64", + exec_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + target_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + toolchain = ":cc-compiler-local", + toolchain_type = "@bazel_tools//tools/cpp:toolchain_type", +) + +cc_toolchain_suite( + name = "toolchain", + toolchains = { + "local|compiler": ":cc-compiler-local", + "darwin|compiler": ":cc-compiler-darwin", + "x64_windows|msvc-cl": ":cc-compiler-windows", + "x64_windows": ":cc-compiler-windows", + "arm": ":cc-compiler-local", + "aarch64": ":cc-compiler-local", + "k8": ":cc-compiler-local", + "piii": ":cc-compiler-local", + "ppc": ":cc-compiler-local", + "darwin": ":cc-compiler-darwin", + }, +) + +cc_toolchain( + name = "cc-compiler-local", + all_files = ":crosstool_wrapper_driver_is_not_gcc", + compiler_files = ":empty", + dwp_files = ":empty", + linker_files = ":crosstool_wrapper_driver_is_not_gcc", + objcopy_files = ":empty", + strip_files = ":empty", + # To support linker flags that need to go to the start of command line + # we need the toolchain to support parameter files. Parameter files are + # last on the command line and contain all shared libraries to link, so all + # regular options will be left of them. + supports_param_files = 1, + toolchain_config = ":cc-compiler-local-config", + toolchain_identifier = "local_linux", +) + +cc_toolchain_config( + name = "cc-compiler-local-config", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.0/targets/x86_64-linux/include", + "/usr/local/cuda-10.0/include", + "/usr/local/cuda-10.0/extras/CUPTI/include", + "/usr/include", + ], + cpu = "local", + extra_no_canonical_prefixes_flags = ["-fno-canonical-system-headers"], + host_compiler_path = "clang/bin/crosstool_wrapper_driver_is_not_gcc", + host_compiler_prefix = "/usr/bin", + host_compiler_warnings = [], + host_unfiltered_compile_flags = [], + linker_bin_path = "/usr/bin", +) + +cc_toolchain( + name = "cc-compiler-darwin", + all_files = ":crosstool_wrapper_driver_is_not_gcc", + compiler_files = ":empty", + dwp_files = ":empty", + linker_files = ":crosstool_wrapper_driver_is_not_gcc", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 0, + toolchain_config = ":cc-compiler-local-darwin", + toolchain_identifier = "local_darwin", +) + +cc_toolchain_config( + name = "cc-compiler-local-darwin", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.0/targets/x86_64-linux/include", + "/usr/local/cuda-10.0/include", + "/usr/local/cuda-10.0/extras/CUPTI/include", + "/usr/include", + ], + cpu = "darwin", + extra_no_canonical_prefixes_flags = ["-fno-canonical-system-headers"], + host_compiler_path = "clang/bin/crosstool_wrapper_driver_is_not_gcc", + host_compiler_prefix = "/usr/bin", + host_compiler_warnings = [], + host_unfiltered_compile_flags = [], + linker_bin_path = "/usr/bin", +) + +cc_toolchain( + name = "cc-compiler-windows", + all_files = ":windows_msvc_wrapper_files", + compiler_files = ":empty", + dwp_files = ":empty", + linker_files = ":windows_msvc_wrapper_files", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 1, + toolchain_config = ":cc-compiler-windows-config", + toolchain_identifier = "local_windows", +) + +cc_toolchain_config( + name = "cc-compiler-windows-config", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.0/targets/x86_64-linux/include", + "/usr/local/cuda-10.0/include", + "/usr/local/cuda-10.0/extras/CUPTI/include", + "/usr/include", + ], + cpu = "x64_windows", + msvc_cl_path = "msvc_not_used", + msvc_env_include = "msvc_not_used", + msvc_env_lib = "msvc_not_used", + msvc_env_path = "msvc_not_used", + msvc_env_tmp = "msvc_not_used", + msvc_lib_path = "msvc_not_used", + msvc_link_path = "msvc_not_used", + msvc_ml_path = "msvc_not_used", +) + +filegroup( + name = "empty", + srcs = [], +) + +filegroup( + name = "crosstool_wrapper_driver_is_not_gcc", + srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"], +) + +filegroup( + name = "windows_msvc_wrapper_files", + srcs = glob(["windows/msvc_*"]), +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/cc_toolchain_config.bzl b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/cc_toolchain_config.bzl new file mode 100755 index 000000000..282ba08cd --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/cc_toolchain_config.bzl @@ -0,0 +1,1486 @@ +"""cc_toolchain_config rule for configuring CUDA toolchains on Linux, Mac, and Windows.""" + +load( + "@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", + "action_config", + "env_entry", + "env_set", + "feature", + "feature_set", + "flag_group", + "flag_set", + "tool", + "tool_path", + "variable_with_value", +) +load( + "@bazel_tools//tools/build_defs/cc:action_names.bzl", + "ASSEMBLE_ACTION_NAME", + "CC_FLAGS_MAKE_VARIABLE_ACTION_NAME", + "CLIF_MATCH_ACTION_NAME", + "CPP_COMPILE_ACTION_NAME", + "CPP_HEADER_PARSING_ACTION_NAME", + "CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_EXECUTABLE_ACTION_NAME", + "CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_STATIC_LIBRARY_ACTION_NAME", + "CPP_MODULE_CODEGEN_ACTION_NAME", + "CPP_MODULE_COMPILE_ACTION_NAME", + "C_COMPILE_ACTION_NAME", + "LINKSTAMP_COMPILE_ACTION_NAME", + "LTO_BACKEND_ACTION_NAME", + "LTO_INDEXING_ACTION_NAME", + "OBJCPP_COMPILE_ACTION_NAME", + "OBJCPP_EXECUTABLE_ACTION_NAME", + "OBJC_ARCHIVE_ACTION_NAME", + "OBJC_COMPILE_ACTION_NAME", + "OBJC_EXECUTABLE_ACTION_NAME", + "OBJC_FULLY_LINK_ACTION_NAME", + "PREPROCESS_ASSEMBLE_ACTION_NAME", + "STRIP_ACTION_NAME", +) + +ACTION_NAMES = struct( + c_compile = C_COMPILE_ACTION_NAME, + cpp_compile = CPP_COMPILE_ACTION_NAME, + linkstamp_compile = LINKSTAMP_COMPILE_ACTION_NAME, + cc_flags_make_variable = CC_FLAGS_MAKE_VARIABLE_ACTION_NAME, + cpp_module_codegen = CPP_MODULE_CODEGEN_ACTION_NAME, + cpp_header_parsing = CPP_HEADER_PARSING_ACTION_NAME, + cpp_module_compile = CPP_MODULE_COMPILE_ACTION_NAME, + assemble = ASSEMBLE_ACTION_NAME, + preprocess_assemble = PREPROCESS_ASSEMBLE_ACTION_NAME, + lto_indexing = LTO_INDEXING_ACTION_NAME, + lto_backend = LTO_BACKEND_ACTION_NAME, + cpp_link_executable = CPP_LINK_EXECUTABLE_ACTION_NAME, + cpp_link_dynamic_library = CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_nodeps_dynamic_library = CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_static_library = CPP_LINK_STATIC_LIBRARY_ACTION_NAME, + strip = STRIP_ACTION_NAME, + objc_archive = OBJC_ARCHIVE_ACTION_NAME, + objc_compile = OBJC_COMPILE_ACTION_NAME, + objc_executable = OBJC_EXECUTABLE_ACTION_NAME, + objc_fully_link = OBJC_FULLY_LINK_ACTION_NAME, + objcpp_compile = OBJCPP_COMPILE_ACTION_NAME, + objcpp_executable = OBJCPP_EXECUTABLE_ACTION_NAME, + clif_match = CLIF_MATCH_ACTION_NAME, + objcopy_embed_data = "objcopy_embed_data", + ld_embed_data = "ld_embed_data", +) + +def _impl(ctx): + if (ctx.attr.cpu == "darwin"): + toolchain_identifier = "local_darwin" + elif (ctx.attr.cpu == "local"): + toolchain_identifier = "local_linux" + elif (ctx.attr.cpu == "x64_windows"): + toolchain_identifier = "local_windows" + else: + fail("Unreachable") + + host_system_name = "local" + + target_system_name = "local" + + if (ctx.attr.cpu == "darwin"): + target_cpu = "darwin" + elif (ctx.attr.cpu == "local"): + target_cpu = "local" + elif (ctx.attr.cpu == "x64_windows"): + target_cpu = "x64_windows" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "local"): + target_libc = "local" + elif (ctx.attr.cpu == "darwin"): + target_libc = "macosx" + elif (ctx.attr.cpu == "x64_windows"): + target_libc = "msvcrt" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + compiler = "compiler" + elif (ctx.attr.cpu == "x64_windows"): + compiler = "msvc-cl" + else: + fail("Unreachable") + + abi_version = "local" + + abi_libc_version = "local" + + cc_target_os = None + + builtin_sysroot = None + + all_link_actions = [ + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ] + + cpp_link_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_nodeps_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_nodeps_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_static_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_static_library, + implies = [ + "nologo", + "archiver_flags", + "input_param_flags", + "linker_param_file", + "msvc_env", + ], + tools = [tool(path = ctx.attr.msvc_lib_path)], + ) + + assemble_action = action_config( + action_name = ACTION_NAMES.assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + preprocess_assemble_action = action_config( + action_name = ACTION_NAMES.preprocess_assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + c_compile_action = action_config( + action_name = ACTION_NAMES.c_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_compile_action = action_config( + action_name = ACTION_NAMES.cpp_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_link_executable_action = action_config( + action_name = ACTION_NAMES.cpp_link_executable, + implies = [ + "nologo", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + action_configs = [] + elif (ctx.attr.cpu == "x64_windows"): + action_configs = [ + assemble_action, + preprocess_assemble_action, + c_compile_action, + cpp_compile_action, + cpp_link_executable_action, + cpp_link_dynamic_library_action, + cpp_link_nodeps_dynamic_library_action, + cpp_link_static_library_action, + ] + else: + fail("Unreachable") + + no_windows_export_all_symbols_feature = feature(name = "no_windows_export_all_symbols") + + pic_feature = feature( + name = "pic", + enabled = True, + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group(flags = ["-fPIC"], expand_if_available = "pic"), + flag_group( + flags = ["-fPIE"], + expand_if_not_available = "pic", + ), + ], + ), + ], + ) + + preprocessor_defines_feature = feature( + name = "preprocessor_defines", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/D%{preprocessor_defines}"], + iterate_over = "preprocessor_defines", + ), + ], + ), + ], + ) + + generate_pdb_file_feature = feature( + name = "generate_pdb_file", + requires = [ + feature_set(features = ["dbg"]), + feature_set(features = ["fastbuild"]), + ], + ) + + linkstamps_feature = feature( + name = "linkstamps", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{linkstamp_paths}"], + iterate_over = "linkstamp_paths", + expand_if_available = "linkstamp_paths", + ), + ], + ), + ], + ) + + unfiltered_compile_flags_feature = feature( + name = "unfiltered_compile_flags", + flag_sets = ([ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ctx.attr.host_unfiltered_compile_flags, + ), + ], + ), + ] if ctx.attr.host_unfiltered_compile_flags else []), + ) + + determinism_feature = feature( + name = "determinism", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-Wno-builtin-macro-redefined", + "-D__DATE__=\"redacted\"", + "-D__TIMESTAMP__=\"redacted\"", + "-D__TIME__=\"redacted\"", + ], + ), + ], + ), + ], + ) + + nologo_feature = feature( + name = "nologo", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + flag_groups = [flag_group(flags = ["/nologo"])], + ), + ], + ) + + supports_pic_feature = feature(name = "supports_pic", enabled = True) + + output_execpath_flags_feature = feature( + name = "output_execpath_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + default_link_flags_feature = feature( + name = "default_link_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/MACHINE:X64"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-Wl,-z,relro,-z,now"])], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie", "-Wl,-z,relro,-z,now"])], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie"])], + ), + ], + ) + else: + hardening_feature = None + + supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) + + targets_windows_feature = feature( + name = "targets_windows", + enabled = True, + implies = ["copy_dynamic_libraries_to_binary"], + ) + + msvc_env_feature = feature( + name = "msvc_env", + env_sets = [ + env_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + env_entries = [ + env_entry(key = "PATH", value = ctx.attr.msvc_env_path), + env_entry( + key = "INCLUDE", + value = ctx.attr.msvc_env_include, + ), + env_entry(key = "LIB", value = ctx.attr.msvc_env_lib), + env_entry(key = "TMP", value = ctx.attr.msvc_env_tmp), + env_entry(key = "TEMP", value = ctx.attr.msvc_env_tmp), + ], + ), + ], + ) + + linker_subsystem_flag_feature = feature( + name = "linker_subsystem_flag", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/SUBSYSTEM:CONSOLE"])], + ), + ], + ) + + dynamic_link_msvcrt_no_debug_feature = feature( + name = "dynamic_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MD"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + warnings_feature = feature( + name = "warnings", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-Wall"] + ctx.attr.host_compiler_warnings, + ), + ], + ), + ], + ) + + dynamic_link_msvcrt_debug_feature = feature( + name = "dynamic_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MDd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + compiler_output_flags_feature = feature( + name = "compiler_output_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.assemble], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}", "/Zi"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fa%{output_file}"], + expand_if_available = "output_assembly_file", + ), + ], + expand_if_available = "output_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/P", "/Fi%{output_file}"], + expand_if_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + ), + ], + ), + ], + ) + + default_compile_flags_feature = feature( + name = "default_compile_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.linkstamp_compile, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.lto_backend, + ACTION_NAMES.clif_match, + ], + flag_groups = [ + flag_group( + flags = [ + "/DCOMPILER_MSVC", + "/DNOMINMAX", + "/D_WIN32_WINNT=0x0600", + "/D_CRT_SECURE_NO_DEPRECATE", + "/D_CRT_SECURE_NO_WARNINGS", + "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS", + "/bigobj", + "/Zm500", + "/J", + "/Gy", + "/GF", + "/EHsc", + "/wd4351", + "/wd4291", + "/wd4250", + "/wd4996", + ], + ), + ], + ), + ], + ) + + static_link_msvcrt_debug_feature = feature( + name = "static_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MTd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + static_link_msvcrt_feature = feature(name = "static_link_msvcrt") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-g"])], + ), + ], + implies = ["common"], + ) + elif (ctx.attr.cpu == "x64_windows"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEBUG:FULL", "/INCREMENTAL:NO"])], + ), + ], + implies = ["generate_pdb_file"], + ) + else: + dbg_feature = None + + undefined_dynamic_feature = feature( + name = "undefined-dynamic", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-undefined", "dynamic_lookup"])], + ), + ], + ) + + parse_showincludes_feature = feature( + name = "parse_showincludes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_header_parsing, + ], + flag_groups = [flag_group(flags = ["/showIncludes"])], + ), + ], + ) + + linker_param_file_feature = feature( + name = "linker_param_file", + flag_sets = [ + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["@%{linker_param_file}"], + expand_if_available = "linker_param_file", + ), + ], + ), + ], + ) + + static_link_msvcrt_no_debug_feature = feature( + name = "static_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MT"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + supports_interface_shared_libraries_feature = feature( + name = "supports_interface_shared_libraries", + enabled = True, + ) + + disable_assertions_feature = feature( + name = "disable-assertions", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-DNDEBUG"])], + ), + ], + ) + + if (ctx.attr.cpu == "x64_windows"): + fastbuild_feature = feature( + name = "fastbuild", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group(flags = ["/DEBUG:FASTLINK", "/INCREMENTAL:NO"]), + ], + ), + ], + implies = ["generate_pdb_file"], + ) + elif (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + fastbuild_feature = feature(name = "fastbuild", implies = ["common"]) + else: + fastbuild_feature = None + + user_compile_flags_feature = feature( + name = "user_compile_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["%{user_compile_flags}"], + iterate_over = "user_compile_flags", + expand_if_available = "user_compile_flags", + ), + ], + ), + ], + ) + + compiler_input_flags_feature = feature( + name = "compiler_input_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["/c", "%{source_file}"], + expand_if_available = "source_file", + ), + ], + ), + ], + ) + + no_legacy_features_feature = feature(name = "no_legacy_features") + + archiver_flags_feature = feature( + name = "archiver_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + redirector_feature = feature( + name = "redirector", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ], + flag_groups = [ + flag_group( + flags = [ + "-B", + "external/local_config_cuda/crosstool/windows/msvc_wrapper_for_nvcc.py", + ], + ), + ], + ), + ], + ) + + linker_bin_path_feature = feature( + name = "linker-bin-path", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-B" + ctx.attr.linker_bin_path])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,--gc-sections"])], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "darwin"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "x64_windows"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/O2", "/DNDEBUG"])], + ), + ], + ) + else: + opt_feature = None + + include_paths_feature = feature( + name = "include_paths", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/I%{quote_include_paths}"], + iterate_over = "quote_include_paths", + ), + flag_group( + flags = ["/I%{include_paths}"], + iterate_over = "include_paths", + ), + flag_group( + flags = ["/I%{system_include_paths}"], + iterate_over = "system_include_paths", + ), + ], + ), + ], + ) + + shared_flag_feature = feature( + name = "shared_flag", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["/DLL"])], + ), + ], + ) + + windows_export_all_symbols_feature = feature(name = "windows_export_all_symbols") + + frame_pointer_feature = feature( + name = "frame-pointer", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-fno-omit-frame-pointer"])], + ), + ], + ) + + build_id_feature = feature( + name = "build-id", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["-Wl,--build-id=md5", "-Wl,--hash-style=gnu"], + ), + ], + ), + ], + ) + + sysroot_feature = feature( + name = "sysroot", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["--sysroot=%{sysroot}"], + iterate_over = "sysroot", + expand_if_available = "sysroot", + ), + ], + ), + ], + ) + + def_file_feature = feature( + name = "def_file", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/DEF:%{def_file_path}", "/ignore:4070"], + expand_if_available = "def_file_path", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "darwin"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lc++"])], + ), + ], + ) + elif (ctx.attr.cpu == "local"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lstdc++"])], + ), + ], + ) + else: + stdlib_feature = None + + no_stripping_feature = feature(name = "no_stripping") + + alwayslink_feature = feature( + name = "alwayslink", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,-no-as-needed"])], + ), + ], + ) + + input_param_flags_feature = feature( + name = "input_param_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["/IMPLIB:%{interface_library_output_path}"], + expand_if_available = "interface_library_output_path", + ), + ], + ), + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link", + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link.object_files", + flag_groups = [flag_group(flags = ["%{libraries_to_link.object_files}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file_group", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "interface_library", + ), + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["%{libraries_to_link.name}"], + expand_if_false = "libraries_to_link.is_whole_archive", + ), + flag_group( + flags = ["/WHOLEARCHIVE:%{libraries_to_link.name}"], + expand_if_true = "libraries_to_link.is_whole_archive", + ), + ], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "static_library", + ), + ), + ], + expand_if_available = "libraries_to_link", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = [ + "-no-canonical-prefixes", + ] + ctx.attr.extra_no_canonical_prefixes_flags, + ), + ], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-no-canonical-prefixes"])], + ), + ], + ) + else: + no_canonical_prefixes_feature = None + + has_configured_linker_path_feature = feature(name = "has_configured_linker_path") + + copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") + + user_link_flags_feature = feature( + name = "user_link_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{user_link_flags}"], + iterate_over = "user_link_flags", + expand_if_available = "user_link_flags", + ), + ], + ), + ], + ) + + cpp11_feature = feature( + name = "c++11", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-std=c++11"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "alwayslink", + "hardening", + "warnings", + "frame-pointer", + "build-id", + "no-canonical-prefixes", + "linker-bin-path", + ], + ) + elif (ctx.attr.cpu == "darwin"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "hardening", + "warnings", + "frame-pointer", + "no-canonical-prefixes", + "linker-bin-path", + "undefined-dynamic", + ], + ) + else: + common_feature = None + + if (ctx.attr.cpu == "local"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + alwayslink_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + build_id_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + elif (ctx.attr.cpu == "darwin"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + undefined_dynamic_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + elif (ctx.attr.cpu == "x64_windows"): + features = [ + no_legacy_features_feature, + redirector_feature, + nologo_feature, + has_configured_linker_path_feature, + no_stripping_feature, + targets_windows_feature, + copy_dynamic_libraries_to_binary_feature, + default_compile_flags_feature, + msvc_env_feature, + include_paths_feature, + preprocessor_defines_feature, + parse_showincludes_feature, + generate_pdb_file_feature, + shared_flag_feature, + linkstamps_feature, + output_execpath_flags_feature, + archiver_flags_feature, + input_param_flags_feature, + linker_subsystem_flag_feature, + user_link_flags_feature, + default_link_flags_feature, + linker_param_file_feature, + static_link_msvcrt_feature, + static_link_msvcrt_no_debug_feature, + dynamic_link_msvcrt_no_debug_feature, + static_link_msvcrt_debug_feature, + dynamic_link_msvcrt_debug_feature, + dbg_feature, + fastbuild_feature, + opt_feature, + user_compile_flags_feature, + sysroot_feature, + unfiltered_compile_flags_feature, + compiler_output_flags_feature, + compiler_input_flags_feature, + def_file_feature, + windows_export_all_symbols_feature, + no_windows_export_all_symbols_feature, + supports_dynamic_linker_feature, + supports_interface_shared_libraries_feature, + ] + else: + fail("Unreachable") + + cxx_builtin_include_directories = ctx.attr.builtin_include_directories + + if (ctx.attr.cpu == "x64_windows"): + tool_paths = [ + tool_path(name = "ar", path = ctx.attr.msvc_lib_path), + tool_path(name = "ml", path = ctx.attr.msvc_ml_path), + tool_path(name = "cpp", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcc", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcov", path = "wrapper/bin/msvc_nop.bat"), + tool_path(name = "ld", path = ctx.attr.msvc_link_path), + tool_path(name = "nm", path = "wrapper/bin/msvc_nop.bat"), + tool_path( + name = "objcopy", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "objdump", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "strip", + path = "wrapper/bin/msvc_nop.bat", + ), + ] + elif (ctx.attr.cpu == "local"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/ar"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + elif (ctx.attr.cpu == "darwin"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/libtool"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + else: + fail("Unreachable") + + out = ctx.actions.declare_file(ctx.label.name) + ctx.actions.write(out, "Fake executable") + return [ + cc_common.create_cc_toolchain_config_info( + ctx = ctx, + features = features, + action_configs = action_configs, + artifact_name_patterns = [], + cxx_builtin_include_directories = cxx_builtin_include_directories, + toolchain_identifier = toolchain_identifier, + host_system_name = host_system_name, + target_system_name = target_system_name, + target_cpu = target_cpu, + target_libc = target_libc, + compiler = compiler, + abi_version = abi_version, + abi_libc_version = abi_libc_version, + tool_paths = tool_paths, + make_variables = [], + builtin_sysroot = builtin_sysroot, + cc_target_os = cc_target_os, + ), + DefaultInfo( + executable = out, + ), + ] + +cc_toolchain_config = rule( + implementation = _impl, + attrs = { + "cpu": attr.string(mandatory = True, values = ["darwin", "local", "x64_windows"]), + "builtin_include_directories": attr.string_list(), + "extra_no_canonical_prefixes_flags": attr.string_list(), + "host_compiler_path": attr.string(), + "host_compiler_prefix": attr.string(), + "host_compiler_warnings": attr.string_list(), + "host_unfiltered_compile_flags": attr.string_list(), + "linker_bin_path": attr.string(), + "msvc_cl_path": attr.string(default = "msvc_not_used"), + "msvc_env_include": attr.string(default = "msvc_not_used"), + "msvc_env_lib": attr.string(default = "msvc_not_used"), + "msvc_env_path": attr.string(default = "msvc_not_used"), + "msvc_env_tmp": attr.string(default = "msvc_not_used"), + "msvc_lib_path": attr.string(default = "msvc_not_used"), + "msvc_link_path": attr.string(default = "msvc_not_used"), + "msvc_ml_path": attr.string(default = "msvc_not_used"), + }, + provides = [CcToolchainConfigInfo], + executable = True, +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/clang/bin/crosstool_wrapper_driver_is_not_gcc b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/clang/bin/crosstool_wrapper_driver_is_not_gcc new file mode 100755 index 000000000..9800b7689 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/clang/bin/crosstool_wrapper_driver_is_not_gcc @@ -0,0 +1,267 @@ +#!/usr/bin/env python +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Crosstool wrapper for compiling CUDA programs. + +SYNOPSIS: + crosstool_wrapper_is_not_gcc [options passed in by cc_library() + or cc_binary() rule] + +DESCRIPTION: + This script is expected to be called by the cc_library() or cc_binary() bazel + rules. When the option "-x cuda" is present in the list of arguments passed + to this script, it invokes the nvcc CUDA compiler. Most arguments are passed + as is as a string to --compiler-options of nvcc. When "-x cuda" is not + present, this wrapper invokes hybrid_driver_is_not_gcc with the input + arguments as is. + +NOTES: + Changes to the contents of this file must be propagated from + //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc to + //third_party/gpus/crosstool/v*/*/clang/bin/crosstool_wrapper_is_not_gcc +""" + +from __future__ import print_function + +__author__ = 'keveman@google.com (Manjunath Kudlur)' + +from argparse import ArgumentParser +import os +import subprocess +import re +import sys +import pipes + +# Template values set by cuda_autoconf. +CPU_COMPILER = ('/dt7/usr/bin/gcc') +GCC_HOST_COMPILER_PATH = ('/dt7/usr/bin/gcc') + +NVCC_PATH = '/usr/local/cuda-10.0/bin/nvcc' +PREFIX_DIR = os.path.dirname(GCC_HOST_COMPILER_PATH) +NVCC_VERSION = '10.0' + +def Log(s): + print('gpus/crosstool: {0}'.format(s)) + + +def GetOptionValue(argv, option): + """Extract the list of values for option from the argv list. + + Args: + argv: A list of strings, possibly the argv passed to main(). + option: The option whose value to extract, without the leading '-'. + + Returns: + A list of values, either directly following the option, + (eg., -opt val1 val2) or values collected from multiple occurrences of + the option (eg., -opt val1 -opt val2). + """ + + parser = ArgumentParser() + parser.add_argument('-' + option, nargs='*', action='append') + args, _ = parser.parse_known_args(argv) + if not args or not vars(args)[option]: + return [] + else: + return sum(vars(args)[option], []) + + +def GetHostCompilerOptions(argv): + """Collect the -isystem, -iquote, and --sysroot option values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be used as the --compiler-options to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-isystem', nargs='*', action='append') + parser.add_argument('-iquote', nargs='*', action='append') + parser.add_argument('--sysroot', nargs=1) + parser.add_argument('-g', nargs='*', action='append') + parser.add_argument('-fno-canonical-system-headers', action='store_true') + parser.add_argument('-no-canonical-prefixes', action='store_true') + + args, _ = parser.parse_known_args(argv) + + opts = '' + + if args.isystem: + opts += ' -isystem ' + ' -isystem '.join(sum(args.isystem, [])) + if args.iquote: + opts += ' -iquote ' + ' -iquote '.join(sum(args.iquote, [])) + if args.g: + opts += ' -g' + ' -g'.join(sum(args.g, [])) + if args.fno_canonical_system_headers: + opts += ' -fno-canonical-system-headers' + if args.no_canonical_prefixes: + opts += ' -no-canonical-prefixes' + if args.sysroot: + opts += ' --sysroot ' + args.sysroot[0] + + return opts + +def _update_options(nvcc_options): + if NVCC_VERSION in ("7.0",): + return nvcc_options + + update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" } + return [ update_options[opt] if opt in update_options else opt + for opt in nvcc_options ] + +def GetNvccOptions(argv): + """Collect the -nvcc_options values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be passed directly to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-nvcc_options', nargs='*', action='append') + + args, _ = parser.parse_known_args(argv) + + if args.nvcc_options: + options = _update_options(sum(args.nvcc_options, [])) + return ' '.join(['--'+a for a in options]) + return '' + + +def InvokeNvcc(argv, log=False): + """Call nvcc with arguments assembled from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + log: True if logging is requested. + + Returns: + The return value of calling os.system('nvcc ' + args) + """ + + host_compiler_options = GetHostCompilerOptions(argv) + nvcc_compiler_options = GetNvccOptions(argv) + opt_option = GetOptionValue(argv, 'O') + m_options = GetOptionValue(argv, 'm') + m_options = ''.join([' -m' + m for m in m_options if m in ['32', '64']]) + include_options = GetOptionValue(argv, 'I') + out_file = GetOptionValue(argv, 'o') + depfiles = GetOptionValue(argv, 'MF') + defines = GetOptionValue(argv, 'D') + defines = ''.join([' -D' + define for define in defines]) + undefines = GetOptionValue(argv, 'U') + undefines = ''.join([' -U' + define for define in undefines]) + std_options = GetOptionValue(argv, 'std') + # currently only c++11 is supported by Cuda 7.0 std argument + nvcc_allowed_std_options = ["c++11"] + std_options = ''.join([' -std=' + define + for define in std_options if define in nvcc_allowed_std_options]) + + # The list of source files get passed after the -c option. I don't know of + # any other reliable way to just get the list of source files to be compiled. + src_files = GetOptionValue(argv, 'c') + + # Pass -w through from host to nvcc, but don't do anything fancier with + # warnings-related flags, since they're not necessarily the same across + # compilers. + warning_options = ' -w' if '-w' in argv else '' + + if len(src_files) == 0: + return 1 + if len(out_file) != 1: + return 1 + + opt = (' -O2' if (len(opt_option) > 0 and int(opt_option[0]) > 0) + else ' -g -G') + + includes = (' -I ' + ' -I '.join(include_options) + if len(include_options) > 0 + else '') + + # Unfortunately, there are other options that have -c prefix too. + # So allowing only those look like C/C++ files. + src_files = [f for f in src_files if + re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)] + srcs = ' '.join(src_files) + out = ' -o ' + out_file[0] + + supported_cuda_compute_capabilities = [ "3.0", "6.0" ] + nvccopts = '-D_FORCE_INLINES ' + for capability in supported_cuda_compute_capabilities: + capability = capability.replace('.', '') + nvccopts += r'-gencode=arch=compute_%s,\"code=sm_%s,compute_%s\" ' % ( + capability, capability, capability) + nvccopts += ' ' + nvcc_compiler_options + nvccopts += undefines + nvccopts += defines + nvccopts += std_options + nvccopts += m_options + nvccopts += warning_options + + if depfiles: + # Generate the dependency file + depfile = depfiles[0] + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + '"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' ' + srcs + ' -M -o ' + depfile) + if log: Log(cmd) + exit_status = os.system(cmd) + if exit_status != 0: + return exit_status + + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + ' -fPIC"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' -c ' + srcs + out) + + # TODO(zhengxq): for some reason, 'gcc' needs this help to find 'as'. + # Need to investigate and fix. + cmd = 'PATH=' + PREFIX_DIR + ':$PATH ' + cmd + if log: Log(cmd) + return os.system(cmd) + + +def main(): + parser = ArgumentParser() + parser.add_argument('-x', nargs=1) + parser.add_argument('--cuda_log', action='store_true') + args, leftover = parser.parse_known_args(sys.argv[1:]) + + if args.x and args.x[0] == 'cuda': + if args.cuda_log: Log('-x cuda') + leftover = [pipes.quote(s) for s in leftover] + if args.cuda_log: Log('using nvcc') + return InvokeNvcc(leftover, log=args.cuda_log) + + # Strip our flags before passing through to the CPU compiler for files which + # are not -x cuda. We can't just pass 'leftover' because it also strips -x. + # We not only want to pass -x to the CPU compiler, but also keep it in its + # relative location in the argv list (the compiler is actually sensitive to + # this). + cpu_compiler_flags = [flag for flag in sys.argv[1:] + if not flag.startswith(('--cuda_log'))] + + return subprocess.call([CPU_COMPILER] + cpu_compiler_flags) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/windows/msvc_wrapper_for_nvcc.py b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/windows/msvc_wrapper_for_nvcc.py new file mode 100755 index 000000000..79b98e587 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.0/windows/msvc_wrapper_for_nvcc.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Crosstool wrapper for compiling CUDA programs with nvcc on Windows. + +DESCRIPTION: + This script is the Windows version of //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc +""" + +from __future__ import print_function + +from argparse import ArgumentParser +import os +import subprocess +import re +import sys +import pipes + +# Template values set by cuda_autoconf. +CPU_COMPILER = ('/dt7/usr/bin/gcc') +GCC_HOST_COMPILER_PATH = ('/dt7/usr/bin/gcc') + +NVCC_PATH = '/usr/local/cuda-10.0/bin/nvcc' +NVCC_VERSION = '10.0' +NVCC_TEMP_DIR = "C:\\Windows\\Temp\\nvcc_inter_files_tmp_dir" +supported_cuda_compute_capabilities = [ "3.0", "6.0" ] + +def Log(s): + print('gpus/crosstool: {0}'.format(s)) + + +def GetOptionValue(argv, option): + """Extract the list of values for option from options. + + Args: + option: The option whose value to extract, without the leading '/'. + + Returns: + 1. A list of values, either directly following the option, + (eg., /opt val1 val2) or values collected from multiple occurrences of + the option (eg., /opt val1 /opt val2). + 2. The leftover options. + """ + + parser = ArgumentParser(prefix_chars='/') + parser.add_argument('/' + option, nargs='*', action='append') + args, leftover = parser.parse_known_args(argv) + if args and vars(args)[option]: + return (sum(vars(args)[option], []), leftover) + return ([], leftover) + +def _update_options(nvcc_options): + if NVCC_VERSION in ("7.0",): + return nvcc_options + + update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" } + return [ update_options[opt] if opt in update_options else opt + for opt in nvcc_options ] + +def GetNvccOptions(argv): + """Collect the -nvcc_options values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + 1. The string that can be passed directly to nvcc. + 2. The leftover options. + """ + + parser = ArgumentParser() + parser.add_argument('-nvcc_options', nargs='*', action='append') + + args, leftover = parser.parse_known_args(argv) + + if args.nvcc_options: + options = _update_options(sum(args.nvcc_options, [])) + return (['--' + a for a in options], leftover) + return ([], leftover) + + +def InvokeNvcc(argv, log=False): + """Call nvcc with arguments assembled from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + log: True if logging is requested. + + Returns: + The return value of calling os.system('nvcc ' + args) + """ + + src_files = [f for f in argv if + re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)] + if len(src_files) == 0: + raise Error('No source files found for cuda compilation.') + + out_file = [ f for f in argv if f.startswith('/Fo') ] + if len(out_file) != 1: + raise Error('Please sepecify exactly one output file for cuda compilation.') + out = ['-o', out_file[0][len('/Fo'):]] + + nvcc_compiler_options, argv = GetNvccOptions(argv) + + opt_option, argv = GetOptionValue(argv, 'O') + opt = ['-g', '-G'] + if (len(opt_option) > 0 and opt_option[0] != 'd'): + opt = ['-O2'] + + include_options, argv = GetOptionValue(argv, 'I') + includes = ["-I " + include for include in include_options] + + defines, argv = GetOptionValue(argv, 'D') + defines = ['-D' + define for define in defines] + + undefines, argv = GetOptionValue(argv, 'U') + undefines = ['-U' + define for define in undefines] + + # The rest of the unrecongized options should be passed to host compiler + host_compiler_options = [option for option in argv if option not in (src_files + out_file)] + + m_options = ["-m64"] + + nvccopts = ['-D_FORCE_INLINES'] + for capability in supported_cuda_compute_capabilities: + capability = capability.replace('.', '') + nvccopts += [r'-gencode=arch=compute_%s,"code=sm_%s,compute_%s"' % ( + capability, capability, capability)] + nvccopts += nvcc_compiler_options + nvccopts += undefines + nvccopts += defines + nvccopts += m_options + nvccopts += ['--compiler-options="' + " ".join(host_compiler_options) + '"'] + nvccopts += ['-x', 'cu'] + opt + includes + out + ['-c'] + src_files + # If we don't specify --keep-dir, nvcc will generate intermediate files under TEMP + # Put them under NVCC_TEMP_DIR instead, then Bazel can ignore files under NVCC_TEMP_DIR during dependency check + # http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#options-for-guiding-compiler-driver + # Different actions are sharing NVCC_TEMP_DIR, so we cannot remove it if the directory already exists. + if os.path.isfile(NVCC_TEMP_DIR): + os.remove(NVCC_TEMP_DIR) + if not os.path.exists(NVCC_TEMP_DIR): + os.makedirs(NVCC_TEMP_DIR) + nvccopts += ['--keep', '--keep-dir', NVCC_TEMP_DIR] + cmd = [NVCC_PATH] + nvccopts + if log: + Log(cmd) + proc = subprocess.Popen(cmd, + stdout=sys.stdout, + stderr=sys.stderr, + env=os.environ.copy(), + shell=True) + proc.wait() + return proc.returncode + +def main(): + parser = ArgumentParser() + parser.add_argument('-x', nargs=1) + parser.add_argument('--cuda_log', action='store_true') + args, leftover = parser.parse_known_args(sys.argv[1:]) + + if args.x and args.x[0] == 'cuda': + if args.cuda_log: Log('-x cuda') + leftover = [pipes.quote(s) for s in leftover] + if args.cuda_log: Log('using nvcc') + return InvokeNvcc(leftover, log=args.cuda_log) + + # Strip our flags before passing through to the CPU compiler for files which + # are not -x cuda. We can't just pass 'leftover' because it also strips -x. + # We not only want to pass -x to the CPU compiler, but also keep it in its + # relative location in the argv list (the compiler is actually sensitive to + # this). + cpu_compiler_flags = [flag for flag in sys.argv[1:] + if not flag.startswith(('--cuda_log')) + and not flag.startswith(('-nvcc_options'))] + + return subprocess.call([CPU_COMPILER] + cpu_compiler_flags) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/BUILD b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/BUILD new file mode 100755 index 000000000..1537a032b --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/BUILD @@ -0,0 +1,175 @@ +# This file is expanded from a template by cuda_configure.bzl +# Update cuda_configure.bzl#verify_build_defines when adding new variables. + +load(":cc_toolchain_config.bzl", "cc_toolchain_config") + +licenses(["restricted"]) + +package(default_visibility = ["//visibility:public"]) + +toolchain( + name = "toolchain-linux-x86_64", + exec_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + target_compatible_with = [ + "@bazel_tools//platforms:linux", + "@bazel_tools//platforms:x86_64", + ], + toolchain = ":cc-compiler-local", + toolchain_type = "@bazel_tools//tools/cpp:toolchain_type", +) + +cc_toolchain_suite( + name = "toolchain", + toolchains = { + "local|compiler": ":cc-compiler-local", + "darwin|compiler": ":cc-compiler-darwin", + "x64_windows|msvc-cl": ":cc-compiler-windows", + "x64_windows": ":cc-compiler-windows", + "arm": ":cc-compiler-local", + "aarch64": ":cc-compiler-local", + "k8": ":cc-compiler-local", + "piii": ":cc-compiler-local", + "ppc": ":cc-compiler-local", + "darwin": ":cc-compiler-darwin", + }, +) + +cc_toolchain( + name = "cc-compiler-local", + all_files = ":crosstool_wrapper_driver_is_not_gcc", + ar_files = ":crosstool_wrapper_driver_is_not_gcc", + as_files = ":crosstool_wrapper_driver_is_not_gcc", + compiler_files = ":crosstool_wrapper_driver_is_not_gcc", + dwp_files = ":empty", + linker_files = ":crosstool_wrapper_driver_is_not_gcc", + objcopy_files = ":empty", + strip_files = ":empty", + # To support linker flags that need to go to the start of command line + # we need the toolchain to support parameter files. Parameter files are + # last on the command line and contain all shared libraries to link, so all + # regular options will be left of them. + supports_param_files = 1, + toolchain_config = ":cc-compiler-local-config", + toolchain_identifier = "local_linux", +) + +cc_toolchain_config( + name = "cc-compiler-local-config", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.1/targets/x86_64-linux/include", + "/usr/local/cuda-10.1/include", + "/usr/local/cuda-10.1/extras/CUPTI/include", + "/usr/include", + ], + builtin_sysroot = "", + cpu = "local", + cuda_path = "", + extra_no_canonical_prefixes_flags = ["-fno-canonical-system-headers"], + host_compiler_path = "clang/bin/crosstool_wrapper_driver_is_not_gcc", + host_compiler_prefix = "/usr/bin", + host_compiler_warnings = [], + host_unfiltered_compile_flags = [], + linker_bin_path = "/usr/bin", +) + +cc_toolchain( + name = "cc-compiler-darwin", + all_files = ":crosstool_wrapper_driver_is_not_gcc", + ar_files = ":crosstool_wrapper_driver_is_not_gcc", + as_files = ":crosstool_wrapper_driver_is_not_gcc", + compiler_files = ":crosstool_wrapper_driver_is_not_gcc", + dwp_files = ":empty", + linker_files = ":crosstool_wrapper_driver_is_not_gcc", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 0, + toolchain_config = ":cc-compiler-local-darwin", + toolchain_identifier = "local_darwin", +) + +cc_toolchain_config( + name = "cc-compiler-local-darwin", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.1/targets/x86_64-linux/include", + "/usr/local/cuda-10.1/include", + "/usr/local/cuda-10.1/extras/CUPTI/include", + "/usr/include", + ], + cpu = "darwin", + extra_no_canonical_prefixes_flags = ["-fno-canonical-system-headers"], + host_compiler_path = "clang/bin/crosstool_wrapper_driver_is_not_gcc", + host_compiler_prefix = "/usr/bin", + host_compiler_warnings = [], + host_unfiltered_compile_flags = [], + linker_bin_path = "/usr/bin", +) + +cc_toolchain( + name = "cc-compiler-windows", + all_files = ":windows_msvc_wrapper_files", + ar_files = ":windows_msvc_wrapper_files", + as_files = ":windows_msvc_wrapper_files", + compiler_files = ":windows_msvc_wrapper_files", + dwp_files = ":empty", + linker_files = ":windows_msvc_wrapper_files", + objcopy_files = ":empty", + strip_files = ":empty", + supports_param_files = 1, + toolchain_config = ":cc-compiler-windows-config", + toolchain_identifier = "local_windows", +) + +cc_toolchain_config( + name = "cc-compiler-windows-config", + builtin_include_directories = [ + "/dt7/usr/include/c++/7", + "/dt7/usr/include/c++/7/x86_64-pc-linux-gnu", + "/dt7/usr/include/c++/7/backward", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include", + "/dt7/usr/lib/gcc/x86_64-pc-linux-gnu/7/include-fixed", + "/dt7/usr/include", + "/usr/local/cuda-10.1/targets/x86_64-linux/include", + "/usr/local/cuda-10.1/include", + "/usr/local/cuda-10.1/extras/CUPTI/include", + "/usr/include", + ], + cpu = "x64_windows", + msvc_cl_path = "msvc_not_used", + msvc_env_include = "msvc_not_used", + msvc_env_lib = "msvc_not_used", + msvc_env_path = "msvc_not_used", + msvc_env_tmp = "msvc_not_used", + msvc_lib_path = "msvc_not_used", + msvc_link_path = "msvc_not_used", + msvc_ml_path = "msvc_not_used", +) + +filegroup( + name = "empty", + srcs = [], +) + +filegroup( + name = "crosstool_wrapper_driver_is_not_gcc", + srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"], +) + +filegroup( + name = "windows_msvc_wrapper_files", + srcs = glob(["windows/msvc_*"]), +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/cc_toolchain_config.bzl b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/cc_toolchain_config.bzl new file mode 100755 index 000000000..3d4d41aa2 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/cc_toolchain_config.bzl @@ -0,0 +1,1516 @@ +"""cc_toolchain_config rule for configuring CUDA toolchains on Linux, Mac, and Windows.""" + +load( + "@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", + "action_config", + "env_entry", + "env_set", + "feature", + "feature_set", + "flag_group", + "flag_set", + "tool", + "tool_path", + "variable_with_value", +) +load( + "@bazel_tools//tools/build_defs/cc:action_names.bzl", + "ASSEMBLE_ACTION_NAME", + "CC_FLAGS_MAKE_VARIABLE_ACTION_NAME", + "CLIF_MATCH_ACTION_NAME", + "CPP_COMPILE_ACTION_NAME", + "CPP_HEADER_PARSING_ACTION_NAME", + "CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_EXECUTABLE_ACTION_NAME", + "CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME", + "CPP_LINK_STATIC_LIBRARY_ACTION_NAME", + "CPP_MODULE_CODEGEN_ACTION_NAME", + "CPP_MODULE_COMPILE_ACTION_NAME", + "C_COMPILE_ACTION_NAME", + "LINKSTAMP_COMPILE_ACTION_NAME", + "LTO_BACKEND_ACTION_NAME", + "LTO_INDEXING_ACTION_NAME", + "OBJCPP_COMPILE_ACTION_NAME", + "OBJCPP_EXECUTABLE_ACTION_NAME", + "OBJC_ARCHIVE_ACTION_NAME", + "OBJC_COMPILE_ACTION_NAME", + "OBJC_EXECUTABLE_ACTION_NAME", + "OBJC_FULLY_LINK_ACTION_NAME", + "PREPROCESS_ASSEMBLE_ACTION_NAME", + "STRIP_ACTION_NAME", +) + +ACTION_NAMES = struct( + c_compile = C_COMPILE_ACTION_NAME, + cpp_compile = CPP_COMPILE_ACTION_NAME, + linkstamp_compile = LINKSTAMP_COMPILE_ACTION_NAME, + cc_flags_make_variable = CC_FLAGS_MAKE_VARIABLE_ACTION_NAME, + cpp_module_codegen = CPP_MODULE_CODEGEN_ACTION_NAME, + cpp_header_parsing = CPP_HEADER_PARSING_ACTION_NAME, + cpp_module_compile = CPP_MODULE_COMPILE_ACTION_NAME, + assemble = ASSEMBLE_ACTION_NAME, + preprocess_assemble = PREPROCESS_ASSEMBLE_ACTION_NAME, + lto_indexing = LTO_INDEXING_ACTION_NAME, + lto_backend = LTO_BACKEND_ACTION_NAME, + cpp_link_executable = CPP_LINK_EXECUTABLE_ACTION_NAME, + cpp_link_dynamic_library = CPP_LINK_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_nodeps_dynamic_library = CPP_LINK_NODEPS_DYNAMIC_LIBRARY_ACTION_NAME, + cpp_link_static_library = CPP_LINK_STATIC_LIBRARY_ACTION_NAME, + strip = STRIP_ACTION_NAME, + objc_archive = OBJC_ARCHIVE_ACTION_NAME, + objc_compile = OBJC_COMPILE_ACTION_NAME, + objc_executable = OBJC_EXECUTABLE_ACTION_NAME, + objc_fully_link = OBJC_FULLY_LINK_ACTION_NAME, + objcpp_compile = OBJCPP_COMPILE_ACTION_NAME, + objcpp_executable = OBJCPP_EXECUTABLE_ACTION_NAME, + clif_match = CLIF_MATCH_ACTION_NAME, + objcopy_embed_data = "objcopy_embed_data", + ld_embed_data = "ld_embed_data", +) + +def _impl(ctx): + if (ctx.attr.cpu == "darwin"): + toolchain_identifier = "local_darwin" + elif (ctx.attr.cpu == "local"): + toolchain_identifier = "local_linux" + elif (ctx.attr.cpu == "x64_windows"): + toolchain_identifier = "local_windows" + else: + fail("Unreachable") + + host_system_name = "local" + + target_system_name = "local" + + if (ctx.attr.cpu == "darwin"): + target_cpu = "darwin" + elif (ctx.attr.cpu == "local"): + target_cpu = "local" + elif (ctx.attr.cpu == "x64_windows"): + target_cpu = "x64_windows" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "local"): + target_libc = "local" + elif (ctx.attr.cpu == "darwin"): + target_libc = "macosx" + elif (ctx.attr.cpu == "x64_windows"): + target_libc = "msvcrt" + else: + fail("Unreachable") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + compiler = "compiler" + elif (ctx.attr.cpu == "x64_windows"): + compiler = "msvc-cl" + else: + fail("Unreachable") + + abi_version = "local" + + abi_libc_version = "local" + + cc_target_os = None + + builtin_sysroot = ctx.attr.builtin_sysroot + + all_link_actions = [ + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ] + + cpp_link_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_nodeps_dynamic_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_nodeps_dynamic_library, + implies = [ + "nologo", + "shared_flag", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + "has_configured_linker_path", + "def_file", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + cpp_link_static_library_action = action_config( + action_name = ACTION_NAMES.cpp_link_static_library, + implies = [ + "nologo", + "archiver_flags", + "input_param_flags", + "linker_param_file", + "msvc_env", + ], + tools = [tool(path = ctx.attr.msvc_lib_path)], + ) + + assemble_action = action_config( + action_name = ACTION_NAMES.assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + preprocess_assemble_action = action_config( + action_name = ACTION_NAMES.preprocess_assemble, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "sysroot", + ], + tools = [tool(path = ctx.attr.msvc_ml_path)], + ) + + c_compile_action = action_config( + action_name = ACTION_NAMES.c_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_compile_action = action_config( + action_name = ACTION_NAMES.cpp_compile, + implies = [ + "compiler_input_flags", + "compiler_output_flags", + "nologo", + "msvc_env", + "parse_showincludes", + "user_compile_flags", + "sysroot", + "unfiltered_compile_flags", + ], + tools = [tool(path = ctx.attr.msvc_cl_path)], + ) + + cpp_link_executable_action = action_config( + action_name = ACTION_NAMES.cpp_link_executable, + implies = [ + "nologo", + "linkstamps", + "output_execpath_flags", + "input_param_flags", + "user_link_flags", + "linker_subsystem_flag", + "linker_param_file", + "msvc_env", + "no_stripping", + ], + tools = [tool(path = ctx.attr.msvc_link_path)], + ) + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + action_configs = [] + elif (ctx.attr.cpu == "x64_windows"): + action_configs = [ + assemble_action, + preprocess_assemble_action, + c_compile_action, + cpp_compile_action, + cpp_link_executable_action, + cpp_link_dynamic_library_action, + cpp_link_nodeps_dynamic_library_action, + cpp_link_static_library_action, + ] + else: + fail("Unreachable") + + no_windows_export_all_symbols_feature = feature(name = "no_windows_export_all_symbols") + + pic_feature = feature( + name = "pic", + enabled = True, + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group(flags = ["-fPIC"], expand_if_available = "pic"), + flag_group( + flags = ["-fPIE"], + expand_if_not_available = "pic", + ), + ], + ), + ], + ) + + preprocessor_defines_feature = feature( + name = "preprocessor_defines", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/D%{preprocessor_defines}"], + iterate_over = "preprocessor_defines", + ), + ], + ), + ], + ) + + generate_pdb_file_feature = feature( + name = "generate_pdb_file", + requires = [ + feature_set(features = ["dbg"]), + feature_set(features = ["fastbuild"]), + ], + ) + + linkstamps_feature = feature( + name = "linkstamps", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{linkstamp_paths}"], + iterate_over = "linkstamp_paths", + expand_if_available = "linkstamp_paths", + ), + ], + ), + ], + ) + + unfiltered_compile_flags_feature = feature( + name = "unfiltered_compile_flags", + flag_sets = ([ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ctx.attr.host_unfiltered_compile_flags, + ), + ], + ), + ] if ctx.attr.host_unfiltered_compile_flags else []), + ) + + determinism_feature = feature( + name = "determinism", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-Wno-builtin-macro-redefined", + "-D__DATE__=\"redacted\"", + "-D__TIMESTAMP__=\"redacted\"", + "-D__TIME__=\"redacted\"", + ], + ), + ], + ), + ], + ) + + nologo_feature = feature( + name = "nologo", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + flag_groups = [flag_group(flags = ["/nologo"])], + ), + ], + ) + + supports_pic_feature = feature(name = "supports_pic", enabled = True) + + output_execpath_flags_feature = feature( + name = "output_execpath_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + default_link_flags_feature = feature( + name = "default_link_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/MACHINE:X64"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-Wl,-z,relro,-z,now"])], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie", "-Wl,-z,relro,-z,now"])], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + hardening_feature = feature( + name = "hardening", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = [ + "-U_FORTIFY_SOURCE", + "-D_FORTIFY_SOURCE=1", + "-fstack-protector", + ], + ), + ], + ), + flag_set( + actions = [ACTION_NAMES.cpp_link_executable], + flag_groups = [flag_group(flags = ["-pie"])], + ), + ], + ) + else: + hardening_feature = None + + supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) + + targets_windows_feature = feature( + name = "targets_windows", + enabled = True, + implies = ["copy_dynamic_libraries_to_binary"], + ) + + msvc_env_feature = feature( + name = "msvc_env", + env_sets = [ + env_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_static_library, + ], + env_entries = [ + env_entry(key = "PATH", value = ctx.attr.msvc_env_path), + env_entry( + key = "INCLUDE", + value = ctx.attr.msvc_env_include, + ), + env_entry(key = "LIB", value = ctx.attr.msvc_env_lib), + env_entry(key = "TMP", value = ctx.attr.msvc_env_tmp), + env_entry(key = "TEMP", value = ctx.attr.msvc_env_tmp), + ], + ), + ], + ) + + linker_subsystem_flag_feature = feature( + name = "linker_subsystem_flag", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/SUBSYSTEM:CONSOLE"])], + ), + ], + ) + + dynamic_link_msvcrt_no_debug_feature = feature( + name = "dynamic_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MD"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + warnings_feature = feature( + name = "warnings", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-Wall"] + ctx.attr.host_compiler_warnings, + ), + ], + ), + ], + ) + + dynamic_link_msvcrt_debug_feature = feature( + name = "dynamic_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MDd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + compiler_output_flags_feature = feature( + name = "compiler_output_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.assemble], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}", "/Zi"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fo%{output_file}"], + expand_if_not_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + expand_if_not_available = "output_assembly_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/Fa%{output_file}"], + expand_if_available = "output_assembly_file", + ), + ], + expand_if_available = "output_file", + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["/P", "/Fi%{output_file}"], + expand_if_available = "output_preprocess_file", + ), + ], + expand_if_available = "output_file", + ), + ], + ), + ], + ) + + default_compile_flags_feature = feature( + name = "default_compile_flags", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.linkstamp_compile, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.lto_backend, + ACTION_NAMES.clif_match, + ], + flag_groups = [ + flag_group( + flags = [ + "/DCOMPILER_MSVC", + "/DNOMINMAX", + "/D_WIN32_WINNT=0x0600", + "/D_CRT_SECURE_NO_DEPRECATE", + "/D_CRT_SECURE_NO_WARNINGS", + "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS", + "/bigobj", + "/Zm500", + "/J", + "/Gy", + "/GF", + "/EHsc", + "/wd4351", + "/wd4291", + "/wd4250", + "/wd4996", + ], + ), + ], + ), + ], + ) + + static_link_msvcrt_debug_feature = feature( + name = "static_link_msvcrt_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MTd"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmtd.lib"])], + ), + ], + requires = [feature_set(features = ["dbg"])], + ) + + static_link_msvcrt_feature = feature(name = "static_link_msvcrt") + + if (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-g"])], + ), + ], + implies = ["common"], + ) + elif (ctx.attr.cpu == "x64_windows"): + dbg_feature = feature( + name = "dbg", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEBUG:FULL", "/INCREMENTAL:NO"])], + ), + ], + implies = ["generate_pdb_file"], + ) + else: + dbg_feature = None + + undefined_dynamic_feature = feature( + name = "undefined-dynamic", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-undefined", "dynamic_lookup"])], + ), + ], + ) + + parse_showincludes_feature = feature( + name = "parse_showincludes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_header_parsing, + ], + flag_groups = [flag_group(flags = ["/showIncludes"])], + ), + ], + ) + + linker_param_file_feature = feature( + name = "linker_param_file", + flag_sets = [ + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["@%{linker_param_file}"], + expand_if_available = "linker_param_file", + ), + ], + ), + ], + ) + + static_link_msvcrt_no_debug_feature = feature( + name = "static_link_msvcrt_no_debug", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/MT"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmt.lib"])], + ), + ], + requires = [ + feature_set(features = ["fastbuild"]), + feature_set(features = ["opt"]), + ], + ) + + supports_interface_shared_libraries_feature = feature( + name = "supports_interface_shared_libraries", + enabled = True, + ) + + disable_assertions_feature = feature( + name = "disable-assertions", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-DNDEBUG"])], + ), + ], + ) + + if (ctx.attr.cpu == "x64_windows"): + fastbuild_feature = feature( + name = "fastbuild", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/Od", "/Z7", "/DDEBUG"])], + ), + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group(flags = ["/DEBUG:FASTLINK", "/INCREMENTAL:NO"]), + ], + ), + ], + implies = ["generate_pdb_file"], + ) + elif (ctx.attr.cpu == "darwin" or + ctx.attr.cpu == "local"): + fastbuild_feature = feature(name = "fastbuild", implies = ["common"]) + else: + fastbuild_feature = None + + user_compile_flags_feature = feature( + name = "user_compile_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["%{user_compile_flags}"], + iterate_over = "user_compile_flags", + expand_if_available = "user_compile_flags", + ), + ], + ), + ], + ) + + compiler_input_flags_feature = feature( + name = "compiler_input_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ], + flag_groups = [ + flag_group( + flags = ["/c", "%{source_file}"], + expand_if_available = "source_file", + ), + ], + ), + ], + ) + + no_legacy_features_feature = feature(name = "no_legacy_features") + + archiver_flags_feature = feature( + name = "archiver_flags", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + flags = ["/OUT:%{output_execpath}"], + expand_if_available = "output_execpath", + ), + ], + ), + ], + ) + + redirector_feature = feature( + name = "redirector", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ], + flag_groups = [ + flag_group( + flags = [ + "-B", + "external/local_config_cuda/crosstool/windows/msvc_wrapper_for_nvcc.py", + ], + ), + ], + ), + ], + ) + + linker_bin_path_feature = feature( + name = "linker-bin-path", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-B" + ctx.attr.linker_bin_path])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,--gc-sections"])], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "darwin"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [ + flag_group( + flags = ["-g0", "-O2", "-ffunction-sections", "-fdata-sections"], + ), + ], + ), + ], + implies = ["common", "disable-assertions"], + ) + elif (ctx.attr.cpu == "x64_windows"): + opt_feature = feature( + name = "opt", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["/O2", "/DNDEBUG"])], + ), + ], + ) + else: + opt_feature = None + + include_paths_feature = feature( + name = "include_paths", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ], + flag_groups = [ + flag_group( + flags = ["/I%{quote_include_paths}"], + iterate_over = "quote_include_paths", + ), + flag_group( + flags = ["/I%{include_paths}"], + iterate_over = "include_paths", + ), + flag_group( + flags = ["/I%{system_include_paths}"], + iterate_over = "system_include_paths", + ), + ], + ), + ], + ) + + shared_flag_feature = feature( + name = "shared_flag", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["/DLL"])], + ), + ], + ) + + windows_export_all_symbols_feature = feature(name = "windows_export_all_symbols") + + frame_pointer_feature = feature( + name = "frame-pointer", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-fno-omit-frame-pointer"])], + ), + ], + ) + + build_id_feature = feature( + name = "build-id", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["-Wl,--build-id=md5", "-Wl,--hash-style=gnu"], + ), + ], + ), + ], + ) + + sysroot_feature = feature( + name = "sysroot", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["--sysroot=%{sysroot}"], + iterate_over = "sysroot", + expand_if_available = "sysroot", + ), + ], + ), + ], + ) + + cuda_path_feature = feature( + name = "cuda_path", + enabled = True, + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.assemble, + ACTION_NAMES.preprocess_assemble, + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_header_parsing, + ACTION_NAMES.cpp_module_compile, + ACTION_NAMES.cpp_module_codegen, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["--cuda-path=" + ctx.attr.cuda_path], + ), + ], + ), + ], + ) + + def_file_feature = feature( + name = "def_file", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["/DEF:%{def_file_path}", "/ignore:4070"], + expand_if_available = "def_file_path", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "darwin"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lc++"])], + ), + ], + ) + elif (ctx.attr.cpu == "local"): + stdlib_feature = feature( + name = "stdlib", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [flag_group(flags = ["-lstdc++"])], + ), + ], + ) + else: + stdlib_feature = None + + no_stripping_feature = feature(name = "no_stripping") + + alwayslink_feature = feature( + name = "alwayslink", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ACTION_NAMES.cpp_link_executable, + ], + flag_groups = [flag_group(flags = ["-Wl,-no-as-needed"])], + ), + ], + ) + + input_param_flags_feature = feature( + name = "input_param_flags", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = ["/IMPLIB:%{interface_library_output_path}"], + expand_if_available = "interface_library_output_path", + ), + ], + ), + flag_set( + actions = all_link_actions + + [ACTION_NAMES.cpp_link_static_library], + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link", + flag_groups = [ + flag_group( + iterate_over = "libraries_to_link.object_files", + flag_groups = [flag_group(flags = ["%{libraries_to_link.object_files}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file_group", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "object_file", + ), + ), + flag_group( + flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "interface_library", + ), + ), + flag_group( + flag_groups = [ + flag_group( + flags = ["%{libraries_to_link.name}"], + expand_if_false = "libraries_to_link.is_whole_archive", + ), + flag_group( + flags = ["/WHOLEARCHIVE:%{libraries_to_link.name}"], + expand_if_true = "libraries_to_link.is_whole_archive", + ), + ], + expand_if_equal = variable_with_value( + name = "libraries_to_link.type", + value = "static_library", + ), + ), + ], + expand_if_available = "libraries_to_link", + ), + ], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [ + flag_group( + flags = [ + "-no-canonical-prefixes", + ] + ctx.attr.extra_no_canonical_prefixes_flags, + ), + ], + ), + ], + ) + elif (ctx.attr.cpu == "darwin"): + no_canonical_prefixes_feature = feature( + name = "no-canonical-prefixes", + flag_sets = [ + flag_set( + actions = [ + ACTION_NAMES.c_compile, + ACTION_NAMES.cpp_compile, + ACTION_NAMES.cpp_link_executable, + ACTION_NAMES.cpp_link_dynamic_library, + ACTION_NAMES.cpp_link_nodeps_dynamic_library, + ], + flag_groups = [flag_group(flags = ["-no-canonical-prefixes"])], + ), + ], + ) + else: + no_canonical_prefixes_feature = None + + has_configured_linker_path_feature = feature(name = "has_configured_linker_path") + + copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") + + user_link_flags_feature = feature( + name = "user_link_flags", + flag_sets = [ + flag_set( + actions = all_link_actions, + flag_groups = [ + flag_group( + flags = ["%{user_link_flags}"], + iterate_over = "user_link_flags", + expand_if_available = "user_link_flags", + ), + ], + ), + ], + ) + + cpp11_feature = feature( + name = "c++11", + flag_sets = [ + flag_set( + actions = [ACTION_NAMES.cpp_compile], + flag_groups = [flag_group(flags = ["-std=c++11"])], + ), + ], + ) + + if (ctx.attr.cpu == "local"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "alwayslink", + "hardening", + "warnings", + "frame-pointer", + "build-id", + "no-canonical-prefixes", + "linker-bin-path", + ], + ) + elif (ctx.attr.cpu == "darwin"): + common_feature = feature( + name = "common", + implies = [ + "stdlib", + "c++11", + "determinism", + "hardening", + "warnings", + "frame-pointer", + "no-canonical-prefixes", + "linker-bin-path", + "undefined-dynamic", + ], + ) + else: + common_feature = None + + if (ctx.attr.cpu == "local"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + alwayslink_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + build_id_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + if ctx.attr.cuda_path: + features += [cuda_path_feature] + elif (ctx.attr.cpu == "darwin"): + features = [ + cpp11_feature, + stdlib_feature, + determinism_feature, + pic_feature, + hardening_feature, + warnings_feature, + frame_pointer_feature, + no_canonical_prefixes_feature, + disable_assertions_feature, + linker_bin_path_feature, + undefined_dynamic_feature, + common_feature, + opt_feature, + fastbuild_feature, + dbg_feature, + supports_dynamic_linker_feature, + supports_pic_feature, + ] + elif (ctx.attr.cpu == "x64_windows"): + features = [ + no_legacy_features_feature, + redirector_feature, + nologo_feature, + has_configured_linker_path_feature, + no_stripping_feature, + targets_windows_feature, + copy_dynamic_libraries_to_binary_feature, + default_compile_flags_feature, + msvc_env_feature, + include_paths_feature, + preprocessor_defines_feature, + parse_showincludes_feature, + generate_pdb_file_feature, + shared_flag_feature, + linkstamps_feature, + output_execpath_flags_feature, + archiver_flags_feature, + input_param_flags_feature, + linker_subsystem_flag_feature, + user_link_flags_feature, + default_link_flags_feature, + linker_param_file_feature, + static_link_msvcrt_feature, + static_link_msvcrt_no_debug_feature, + dynamic_link_msvcrt_no_debug_feature, + static_link_msvcrt_debug_feature, + dynamic_link_msvcrt_debug_feature, + dbg_feature, + fastbuild_feature, + opt_feature, + user_compile_flags_feature, + sysroot_feature, + unfiltered_compile_flags_feature, + compiler_output_flags_feature, + compiler_input_flags_feature, + def_file_feature, + windows_export_all_symbols_feature, + no_windows_export_all_symbols_feature, + supports_dynamic_linker_feature, + supports_interface_shared_libraries_feature, + ] + else: + fail("Unreachable") + + cxx_builtin_include_directories = ctx.attr.builtin_include_directories + + if (ctx.attr.cpu == "x64_windows"): + tool_paths = [ + tool_path(name = "ar", path = ctx.attr.msvc_lib_path), + tool_path(name = "ml", path = ctx.attr.msvc_ml_path), + tool_path(name = "cpp", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcc", path = ctx.attr.msvc_cl_path), + tool_path(name = "gcov", path = "wrapper/bin/msvc_nop.bat"), + tool_path(name = "ld", path = ctx.attr.msvc_link_path), + tool_path(name = "nm", path = "wrapper/bin/msvc_nop.bat"), + tool_path( + name = "objcopy", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "objdump", + path = "wrapper/bin/msvc_nop.bat", + ), + tool_path( + name = "strip", + path = "wrapper/bin/msvc_nop.bat", + ), + ] + elif (ctx.attr.cpu == "local"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/ar"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + elif (ctx.attr.cpu == "darwin"): + tool_paths = [ + tool_path(name = "gcc", path = ctx.attr.host_compiler_path), + tool_path(name = "ar", path = ctx.attr.host_compiler_prefix + "/libtool"), + tool_path(name = "compat-ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "cpp", path = ctx.attr.host_compiler_prefix + "/cpp"), + tool_path(name = "dwp", path = ctx.attr.host_compiler_prefix + "/dwp"), + tool_path(name = "gcov", path = ctx.attr.host_compiler_prefix + "/gcov"), + tool_path(name = "ld", path = ctx.attr.host_compiler_prefix + "/ld"), + tool_path(name = "nm", path = ctx.attr.host_compiler_prefix + "/nm"), + tool_path(name = "objcopy", path = ctx.attr.host_compiler_prefix + "/objcopy"), + tool_path(name = "objdump", path = ctx.attr.host_compiler_prefix + "/objdump"), + tool_path(name = "strip", path = ctx.attr.host_compiler_prefix + "/strip"), + ] + else: + fail("Unreachable") + + out = ctx.actions.declare_file(ctx.label.name) + ctx.actions.write(out, "Fake executable") + return [ + cc_common.create_cc_toolchain_config_info( + ctx = ctx, + features = features, + action_configs = action_configs, + artifact_name_patterns = [], + cxx_builtin_include_directories = cxx_builtin_include_directories, + toolchain_identifier = toolchain_identifier, + host_system_name = host_system_name, + target_system_name = target_system_name, + target_cpu = target_cpu, + target_libc = target_libc, + compiler = compiler, + abi_version = abi_version, + abi_libc_version = abi_libc_version, + tool_paths = tool_paths, + make_variables = [], + builtin_sysroot = builtin_sysroot, + cc_target_os = cc_target_os, + ), + DefaultInfo( + executable = out, + ), + ] + +cc_toolchain_config = rule( + implementation = _impl, + attrs = { + "cpu": attr.string(mandatory = True, values = ["darwin", "local", "x64_windows"]), + "builtin_include_directories": attr.string_list(), + "extra_no_canonical_prefixes_flags": attr.string_list(), + "host_compiler_path": attr.string(), + "host_compiler_prefix": attr.string(), + "host_compiler_warnings": attr.string_list(), + "host_unfiltered_compile_flags": attr.string_list(), + "linker_bin_path": attr.string(), + "builtin_sysroot": attr.string(), + "cuda_path": attr.string(), + "msvc_cl_path": attr.string(default = "msvc_not_used"), + "msvc_env_include": attr.string(default = "msvc_not_used"), + "msvc_env_lib": attr.string(default = "msvc_not_used"), + "msvc_env_path": attr.string(default = "msvc_not_used"), + "msvc_env_tmp": attr.string(default = "msvc_not_used"), + "msvc_lib_path": attr.string(default = "msvc_not_used"), + "msvc_link_path": attr.string(default = "msvc_not_used"), + "msvc_ml_path": attr.string(default = "msvc_not_used"), + }, + provides = [CcToolchainConfigInfo], + executable = True, +) diff --git a/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/clang/bin/crosstool_wrapper_driver_is_not_gcc b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/clang/bin/crosstool_wrapper_driver_is_not_gcc new file mode 100755 index 000000000..44e745f69 --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/custom_ops/third_party/toolchains/preconfig/ubuntu16.04/gcc7_manylinux2010-nvcc-cuda10.1/clang/bin/crosstool_wrapper_driver_is_not_gcc @@ -0,0 +1,272 @@ +#!/usr/bin/env python +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Crosstool wrapper for compiling CUDA programs. + +SYNOPSIS: + crosstool_wrapper_is_not_gcc [options passed in by cc_library() + or cc_binary() rule] + +DESCRIPTION: + This script is expected to be called by the cc_library() or cc_binary() bazel + rules. When the option "-x cuda" is present in the list of arguments passed + to this script, it invokes the nvcc CUDA compiler. Most arguments are passed + as is as a string to --compiler-options of nvcc. When "-x cuda" is not + present, this wrapper invokes hybrid_driver_is_not_gcc with the input + arguments as is. + +NOTES: + Changes to the contents of this file must be propagated from + //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc to + //third_party/gpus/crosstool/v*/*/clang/bin/crosstool_wrapper_is_not_gcc +""" + +from __future__ import print_function + +__author__ = 'keveman@google.com (Manjunath Kudlur)' + +from argparse import ArgumentParser +import os +import subprocess +import re +import sys +import pipes + +# Template values set by cuda_autoconf. +CPU_COMPILER = ('/dt7/usr/bin/gcc') +GCC_HOST_COMPILER_PATH = ('/dt7/usr/bin/gcc') + +NVCC_PATH = '/usr/local/cuda-10.1/bin/nvcc' +PREFIX_DIR = os.path.dirname(GCC_HOST_COMPILER_PATH) +NVCC_VERSION = '10.1' + +# Environment variable for supported TF CUDA Compute Capabilities +# eg. export TF_CUDA_COMPUTE_CAPABILITIES=3.5,3.7,5.2,6.0,6.1,7.0 +CUDA_COMPUTE_ENV_VAR = 'TF_CUDA_COMPUTE_CAPABILITIES' +DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,6.0' + +def Log(s): + print('gpus/crosstool: {0}'.format(s)) + + +def GetOptionValue(argv, option): + """Extract the list of values for option from the argv list. + + Args: + argv: A list of strings, possibly the argv passed to main(). + option: The option whose value to extract, without the leading '-'. + + Returns: + A list of values, either directly following the option, + (eg., -opt val1 val2) or values collected from multiple occurrences of + the option (eg., -opt val1 -opt val2). + """ + + parser = ArgumentParser() + parser.add_argument('-' + option, nargs='*', action='append') + args, _ = parser.parse_known_args(argv) + if not args or not vars(args)[option]: + return [] + else: + return sum(vars(args)[option], []) + + +def GetHostCompilerOptions(argv): + """Collect the -isystem, -iquote, and --sysroot option values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be used as the --compiler-options to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-isystem', nargs='*', action='append') + parser.add_argument('-iquote', nargs='*', action='append') + parser.add_argument('--sysroot', nargs=1) + parser.add_argument('-g', nargs='*', action='append') + parser.add_argument('-fno-canonical-system-headers', action='store_true') + parser.add_argument('-no-canonical-prefixes', action='store_true') + + args, _ = parser.parse_known_args(argv) + + opts = '' + + if args.isystem: + opts += ' -isystem ' + ' -isystem '.join(sum(args.isystem, [])) + if args.iquote: + opts += ' -iquote ' + ' -iquote '.join(sum(args.iquote, [])) + if args.g: + opts += ' -g' + ' -g'.join(sum(args.g, [])) + if args.fno_canonical_system_headers: + opts += ' -fno-canonical-system-headers' + if args.no_canonical_prefixes: + opts += ' -no-canonical-prefixes' + if args.sysroot: + opts += ' --sysroot ' + args.sysroot[0] + + return opts + +def _update_options(nvcc_options): + if NVCC_VERSION in ("7.0",): + return nvcc_options + + update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" } + return [ update_options[opt] if opt in update_options else opt + for opt in nvcc_options ] + +def GetNvccOptions(argv): + """Collect the -nvcc_options values from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + + Returns: + The string that can be passed directly to nvcc. + """ + + parser = ArgumentParser() + parser.add_argument('-nvcc_options', nargs='*', action='append') + + args, _ = parser.parse_known_args(argv) + + if args.nvcc_options: + options = _update_options(sum(args.nvcc_options, [])) + return ' '.join(['--'+a for a in options]) + return '' + + +def InvokeNvcc(argv, log=False): + """Call nvcc with arguments assembled from argv. + + Args: + argv: A list of strings, possibly the argv passed to main(). + log: True if logging is requested. + + Returns: + The return value of calling os.system('nvcc ' + args) + """ + + host_compiler_options = GetHostCompilerOptions(argv) + nvcc_compiler_options = GetNvccOptions(argv) + opt_option = GetOptionValue(argv, 'O') + m_options = GetOptionValue(argv, 'm') + m_options = ''.join([' -m' + m for m in m_options if m in ['32', '64']]) + include_options = GetOptionValue(argv, 'I') + out_file = GetOptionValue(argv, 'o') + depfiles = GetOptionValue(argv, 'MF') + defines = GetOptionValue(argv, 'D') + defines = ''.join([' -D' + define for define in defines]) + undefines = GetOptionValue(argv, 'U') + undefines = ''.join([' -U' + define for define in undefines]) + std_options = GetOptionValue(argv, 'std') + # currently only c++11 is supported by Cuda 7.0 std argument + nvcc_allowed_std_options = ["c++11"] + std_options = ''.join([' -std=' + define + for define in std_options if define in nvcc_allowed_std_options]) + + # The list of source files get passed after the -c option. I don't know of + # any other reliable way to just get the list of source files to be compiled. + src_files = GetOptionValue(argv, 'c') + + # Pass -w through from host to nvcc, but don't do anything fancier with + # warnings-related flags, since they're not necessarily the same across + # compilers. + warning_options = ' -w' if '-w' in argv else '' + + if len(src_files) == 0: + return 1 + if len(out_file) != 1: + return 1 + + opt = (' -O2' if (len(opt_option) > 0 and int(opt_option[0]) > 0) + else ' -g -G') + + includes = (' -I ' + ' -I '.join(include_options) + if len(include_options) > 0 + else '') + + # Unfortunately, there are other options that have -c prefix too. + # So allowing only those look like C/C++ files. + src_files = [f for f in src_files if + re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)] + srcs = ' '.join(src_files) + out = ' -o ' + out_file[0] + + supported_cuda_compute_capabilities = os.environ.get(CUDA_COMPUTE_ENV_VAR, DEFAULT_CUDA_COMPUTE_CAPABILITIES).split(',') + nvccopts = '-D_FORCE_INLINES ' + for capability in supported_cuda_compute_capabilities: + capability = capability.replace('.', '') + nvccopts += r'-gencode=arch=compute_%s,\"code=sm_%s,compute_%s\" ' % ( + capability, capability, capability) + nvccopts += ' ' + nvcc_compiler_options + nvccopts += undefines + nvccopts += defines + nvccopts += std_options + nvccopts += m_options + nvccopts += warning_options + + if depfiles: + # Generate the dependency file + depfile = depfiles[0] + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + '"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' ' + srcs + ' -M -o ' + depfile) + if log: Log(cmd) + exit_status = os.system(cmd) + if exit_status != 0: + return exit_status + + cmd = (NVCC_PATH + ' ' + nvccopts + + ' --compiler-options "' + host_compiler_options + ' -fPIC"' + + ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH + + ' -I .' + + ' -x cu ' + opt + includes + ' -c ' + srcs + out) + + # TODO(zhengxq): for some reason, 'gcc' needs this help to find 'as'. + # Need to investigate and fix. + cmd = 'PATH=' + PREFIX_DIR + ':$PATH ' + cmd + if log: Log(cmd) + return os.system(cmd) + + +def main(): + parser = ArgumentParser() + parser.add_argument('-x', nargs=1) + parser.add_argument('--cuda_log', action='store_true') + args, leftover = parser.parse_known_args(sys.argv[1:]) + + if args.x and args.x[0] == 'cuda': + if args.cuda_log: Log('-x cuda') + leftover = [pipes.quote(s) for s in leftover] + if args.cuda_log: Log('using nvcc') + return InvokeNvcc(leftover, log=args.cuda_log) + + # Strip our flags before passing through to the CPU compiler for files which + # are not -x cuda. We can't just pass 'leftover' because it also strips -x. + # We not only want to pass -x to the CPU compiler, but also keep it in its + # relative location in the argv list (the compiler is actually sensitive to + # this). + cpu_compiler_flags = [flag for flag in sys.argv[1:] + if not flag.startswith(('--cuda_log'))] + + return subprocess.call([CPU_COMPILER] + cpu_compiler_flags) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py new file mode 100644 index 000000000..694ee4d6e --- /dev/null +++ b/tensorflow_graphics/projects/point_convolutions/pylib/pc/custom_ops/custom_ops_wrapper.py @@ -0,0 +1,198 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Wrappers for point cloud CUDA functions """ + +import tensorflow as tf +import tfg_custom_ops + + +def compute_keys(point_cloud, num_cells, cell_size, name=None): + """ Method to compute the regular grid keys of a point cloud. + + For a point in cell `c` the key is computed as + \\(key = batch_id * prod_{d=0}^{D} num_cells_{d} + \\) + \\(sum_{d=0}^{D}( c_{d} prod_{d'=d+1}^{D} num_cells_{d'} ) \\). + Args: + point_cloud: A `PointCloud` instance. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + cell_size: An `int` `Tensor` of shape `[D]`, the cell sizes per + dimension. + + Returns: + An `int` `Tensor` of shape `[N]`, the keys per point. + + """ + aabb = point_cloud.get_AABB() + return tfg_custom_ops.compute_keys( + point_cloud._points, + point_cloud._batch_ids, + aabb._aabb_min / cell_size, + num_cells, + tf.math.reciprocal(cell_size)) +tf.no_gradient('ComputeKeys') + + +def build_grid_ds(sorted_keys, num_cells, batch_size, name=None): + """ Method to build a fast access data structure for point clouds. + + Creates a 2D regular grid in the first two dimension, saving the first and + last index belonging to that cell array. + Args: + sorted_keys: An `int` `Tensor` of shape `[N]`, the sorted keys. + num_cells: An `int` `Tensor` of shape `[D]`, the total number of cells + per dimension. + batch_size: An `int`. + + Returns: + An `int` `Tensor` of shape `[batch_size, num_cells[0], num_cells[1], 2]`. + + """ + return tfg_custom_ops.build_grid_ds( + sorted_keys, + num_cells, + num_cells, + batch_size) +tf.no_gradient('BuildGridDs') + + +def find_neighbors(grid, + point_cloud_centers, + radii, + max_neighbors=0, + name=None): + """ Method to find the neighbors of a center point cloud in another + point cloud. + + Args: + grid: A Grid instance, from which the neighbors are chosen. + point_cloud_centers: A `PointCloud` instance, containing the center points. + radii: An `float` `Tensor` of shape `[D]`, the radii to select neighbors + from. + max_neighbors: An `int`, if `0` all neighbors are selected. + + Returns: + center_neigh_ranges: An `int` `Tensor` of shape `[N]`, end of the ranges per + center point. You can get the neighbor ids of point `i` (i>0) with + `neighbors[center_neigh_ranges[i-1]:center_neigh_ranges[i]]`. + neighbors: An `int` `Tensor` of shape [M, 2], indices of the neighbor point + and the center for each neighbor. Follows the order of + `grid._sorted_points`. + + """ + return tfg_custom_ops.find_neighbors( + point_cloud_centers._points, + point_cloud_centers._batch_ids, + grid._sorted_points, + grid._sorted_keys, + grid.get_DS(), + grid._num_cells, + grid._aabb._aabb_min / grid._cell_sizes, + tf.math.reciprocal(grid._cell_sizes), + tf.math.reciprocal(radii), + max_neighbors) +tf.no_gradient('FindNeighbors') + + +def sampling(neighborhood, sample_mode, name=None): + """ Method to sample the points of a point cloud. + + Args: + neighborhood: A `Neighborhood` instance, which contains a point cloud with + its neighbors. + sample_mode: An `int`specifiying the sample mode, + `0` for average, `1` for poisson. + + Returns: + sampled_points: A `float` `Tensor` of shape `[S, D]`, the sampled points. + sampled_batch_ids: An `int` `Tensor` of shape `[S]`, the batch ids. + sampled_indices: An `int` `Tensor` of shape `[S]`, the indices to the + unsampled points. + Following the order of neighborhood._grid._sorted_points. + + """ + return tfg_custom_ops.sampling( + neighborhood._grid._sorted_points, + neighborhood._grid._sorted_batch_ids, + neighborhood._grid._sorted_keys, + neighborhood._grid._num_cells, + neighborhood._neighbors, + neighborhood._samples_neigh_ranges, + sample_mode) +tf.no_gradient('Sampling') + + +def compute_pdf(neighborhood, bandwidth, mode, name=None): + """ Method to compute the density distribution inside the neighborhoods of a + point cloud in euclidean space using kernel density estimation (KDE). + + Args: + neighborhood: A `Neighborhood` instance. + bandwidth: An `int` `Tensor` of shape `[D]`, the bandwidth of the KDE. + mode: A `KDEMode` value. + + Returns: + A `float` `Tensor` of shape `[N]`, the estimated density per point, + with respect to the sorted points of the grid in `neighborhood`. + + """ + return tfg_custom_ops.compute_pdf_with_pt_grads( + neighborhood._grid._sorted_points, + neighborhood._neighbors, + neighborhood._samples_neigh_ranges, + tf.math.reciprocal(bandwidth), + tf.math.reciprocal(neighborhood._radii), + mode) + + +@tf.RegisterGradient("ComputePdfWithPtGrads") +def _compute_pdf_grad(op, *grads): + inPtsGrad = tfg_custom_ops.compute_pdf_pt_grads( + op.inputs[0], + op.inputs[1], + op.inputs[2], + op.inputs[3], + op.inputs[4], + grads[0], + op.get_attr("mode")) + return [inPtsGrad, None, None, None, None] + + +def basis_proj(neigh_basis, features, neighborhood): + """ Method to aggregate the features*basis for different neighborhoods. + + Args: + neigh_basis: A `float` `Tensor` of shape `[M, H]`, the projection of + each neighbor to the different basis. + features: A `float` `Tensor` of shape `[N_in, C]`, the input features. + neighborhood: A `Neighborhood` instance. + + Returns: + A `float` `Tensor` of shape ``[N_out, C, H]`, the weighted latent features. + + """ + return tfg_custom_ops.basis_proj( + neigh_basis, + features, + neighborhood._original_neigh_ids, + neighborhood._samples_neigh_ranges) + + +@tf.RegisterGradient("BasisProj") +def _basis_proj_grad(op, *grads): + basis_grads, feature_grads = \ + tfg_custom_ops.basis_proj_grads( + op.inputs[0], op.inputs[1], op.inputs[2], + op.inputs[3], grads[0]) + return [basis_grads, feature_grads, None, None]