Summary:
1. changed IsInsideTriangle in geometry_utils to take in min_triangle_area parameter instead of hardcoded value
2. updated point_mesh_cpu.cpp and point_mesh_cuda.[h/cu] to adapt to changes in geometry_utils function signatures
3. updated point_mesh_distance.py and test_point_mesh_distance.py to modify _C. calls
Reviewed By: bottler
Differential Revision: D34459764
fbshipit-source-id: 0549e78713c6d68f03d85fb597a13dd88e09b686
Summary: Update all FB license strings to the new format.
Reviewed By: patricklabatut
Differential Revision: D33403538
fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
Summary:
CUDA implementation of farthest point sampling algorithm.
## Visual comparison
Compared to random sampling, farthest point sampling gives better coverage of the shape.
{F658631262}
## Reduction
Parallelized block reduction to find the max value at each iteration happens as follows:
1. First split the points into two equal sized parts (e.g. for a list with 8 values):
`[20, 27, 6, 8 | 11, 10, 2, 33]`
2. Use half of the thread (4 threads) to compare pairs of elements from each half (e.g elements [0, 4], [1, 5] etc) and store the result in the first half of the list:
`[20, 27, 6, 33 | 11, 10, 2, 33]`
Now we no longer care about the second part but again divide the first part into two
`[20, 27 | 6, 33| -, -, -, -]`
Now we can use 2 threads to compare the 4 elements
4. Finally we have gotten down to a single pair
`[20 | 33 | -, - | -, -, -, -]`
Use 1 thread to compare the remaining two elements
5. The max will now be at thread id = 0
`[33 | - | -, - | -, -, -, -]`
The reduction will give the farthest point for the selected batch index at this iteration.
Reviewed By: bottler, jcjohnson
Differential Revision: D30401803
fbshipit-source-id: 525bd5ae27c4b13b501812cfe62306bb003827d2
Summary:
Removes the now-unnecessary kernels from point mesh edge file
Migrates all point mesh functionality into one file.
Reviewed By: gkioxari
Differential Revision: D24550086
fbshipit-source-id: f924996cd38a7c2c1cf189d8a01611de4506cfa3
Summary: This diff creates the generic MeshBackwardKernel which can handle distance calculations between point, edge and faces in either direction. Replaces only point_mesh_face code for now.
Reviewed By: gkioxari
Differential Revision: D24549374
fbshipit-source-id: 2853c1da1c2a6b6de8d0e40007ba0735b8959044
Summary: This diff creates the generic MeshForwardKernel which can handle distance calculations between point, edge and faces in either direction. Replaces only point_mesh_face code for now.
Reviewed By: gkioxari
Differential Revision: D24543316
fbshipit-source-id: 302707d7cec2d77a899738adf40481035c240da8
Summary: Fix the new CPU implementation of point_mesh functionality to be compatible with older C++.
Reviewed By: nikhilaravi
Differential Revision: D22066785
fbshipit-source-id: a245849342019a93ff68e186a10985458b007436
Summary:
point_mesh functions were missing CPU implementations.
The indices returned are not always matching, possibly due to numerical instability.
Reviewed By: gkioxari
Differential Revision: D21594264
fbshipit-source-id: 3016930e2a9a0f3cd8b3ac4c94a92c9411c0989d
Summary:
Update the cuda kernels to:
- remove contiguous checks for the grad tensors and for cpu functions which use accessors
- for cuda implementations call `.contiguous()` on all tensors in the host function before invoking the kernel
Reviewed By: gkioxari
Differential Revision: D21598008
fbshipit-source-id: 9b97bda4582fd4269c8a00999874d4552a1aea2d
Summary:
Updates to:
- enable cuda kernel launches on any GPU (not just the default)
- cuda and contiguous checks for all kernels
- checks to ensure all tensors are on the same device
- error reporting in the cuda kernels
- cuda tests now run on a random device not just the default
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21215280
fbshipit-source-id: 1bedc9fe6c35e9e920bdc4d78ed12865b1005519
Summary:
Use aten instead of torch interface in all cuda code. This allows the cuda build to work with pytorch 1.5 with GCC 5 (e.g. the compiler of ubuntu 16.04LTS). This wasn't working. It has been failing with errors like the below, perhaps due to a bug in nvcc.
```
torch/include/torch/csrc/api/include/torch/nn/cloneable.h:68:61: error: invalid static_cast from type ‘const torch::OrderedDict<std::basic_string<char>, std::shared_ptr<torch::nn::Module> >’ to type ‘torch::OrderedDict<std::basic_string<char>, std::shared_ptr<torch::nn::Module> >
```
Reviewed By: nikhilaravi
Differential Revision: D21204029
fbshipit-source-id: ca6bdbcecf42493365e1c23a33fe35e1759fe8b6
