102 lines
2.5 KiB
Markdown
102 lines
2.5 KiB
Markdown
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# Pytorch Chamfer Distance.
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Include a **CUDA** version, and a **PYTHON** version with pytorch standard operations.
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NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt thresholds accordingly.
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- [x] F - Score
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### CUDA VERSION
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- [x] JIT compilation
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- [x] Supports multi-gpu
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- [x] 2D point clouds.
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- [x] 3D point clouds.
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- [x] 5D point clouds.
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- [x] Contiguous() safe.
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### Python Version
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- [x] Supports any dimension
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### Usage
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```python
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import torch, chamfer3D.dist_chamfer_3D, fscore
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chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist()
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points1 = torch.rand(32, 1000, 3).cuda()
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points2 = torch.rand(32, 2000, 3, requires_grad=True).cuda()
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dist1, dist2, idx1, idx2 = chamLoss(points1, points2)
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f_score, precision, recall = fscore.fscore(dist1, dist2)
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```
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### Add it to your project as a submodule
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```shell
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git submodule add https://github.com/ThibaultGROUEIX/ChamferDistancePytorch
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```
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### Benchmark: [forward + backward] pass
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- [x] CUDA 10.1, NVIDIA 435, Pytorch 1.4
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- [x] p1 : 32 x 2000 x dim
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- [x] p2 : 32 x 1000 x dim
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| *Timing (sec * 1000)* | 2D | 3D | 5D |
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| ---------- | -------- | ------- | ------- |
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| **Cuda Compiled** | **1.2** | 1.4 |1.8 |
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| **Cuda JIT** | 1.3 | **1.4** |**1.5** |
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| **Python** | 37 | 37 | 37 |
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| *Memory (MB)* | 2D | 3D | 5D |
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| ---------- | -------- | ------- | ------- |
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| **Cuda Compiled** | 529 | 529 | 549 |
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| **Cuda JIT** | **520** | **529** |**549** |
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| **Python** | 2495 | 2495 | 2495 |
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### What is the chamfer distance ?
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[Stanford course](http://graphics.stanford.edu/courses/cs468-17-spring/LectureSlides/L14%20-%203d%20deep%20learning%20on%20point%20cloud%20representation%20(analysis).pdf) on 3D deep Learning
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### Aknowledgment
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Original backbone from [Fei Xia](https://github.com/fxia22/pointGAN/blob/master/nndistance/src/nnd_cuda.cu).
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JIT cool trick from [Christian Diller](https://github.com/chrdiller)
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### Troubleshoot
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- `Undefined symbol: Zxxxxxxxxxxxxxxxxx `:
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--> Fix: Make sure to `import torch` before you `import chamfer`.
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--> Use pytorch.version >= 1.1.0
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- [RuntimeError: Ninja is required to load C++ extension](https://github.com/zhanghang1989/PyTorch-Encoding/issues/167)
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```shell
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wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip
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sudo unzip ninja-linux.zip -d /usr/local/bin/
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sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force
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```
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#### TODO:
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* Discuss behaviour of torch.min() and tensor.min() which causes issues in some pytorch versions
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