From 190623b56c71a6d37c65fd2428582cff5c43b7fb Mon Sep 17 00:00:00 2001 From: Lingxiao Zhang <823951506@qq.com> Date: Mon, 11 Sep 2023 21:42:24 +0800 Subject: [PATCH] IGLICT add HSDF, NeRF-Editing, StylizedNeRF --- .../ChamferDistancePytorch/.gitignore | 2 + .../HSDF-Net/ChamferDistancePytorch/LICENSE | 21 + .../HSDF-Net/ChamferDistancePytorch/README.md | 103 ++ .../chamfer2D/chamfer2D.cu | 182 ++ .../chamfer2D/chamfer_cuda.cpp | 33 + .../chamfer2D/dist_chamfer_2D.py | 80 + .../ChamferDistancePytorch/chamfer2D/setup.py | 14 + .../chamfer3D/chamfer3D.cu | 196 +++ .../chamfer3D/chamfer_cuda.cpp | 33 + .../chamfer3D/dist_chamfer_3D.py | 81 + .../ChamferDistancePytorch/chamfer3D/setup.py | 14 + .../chamfer5D/chamfer5D.cu | 223 +++ .../chamfer5D/chamfer_cuda.cpp | 33 + .../chamfer5D/dist_chamfer_5D.py | 82 + .../ChamferDistancePytorch/chamfer5D/setup.py | 14 + .../chamfer6D/chamfer6D.cu | 237 +++ .../chamfer6D/chamfer_cuda.cpp | 33 + .../chamfer6D/dist_chamfer_6D.py | 82 + 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100644 contrib/StylizedNeRF/configs/fern.txt create mode 100644 contrib/StylizedNeRF/configs/flower.txt create mode 100644 contrib/StylizedNeRF/dataset.py create mode 100644 contrib/StylizedNeRF/load_llff.py create mode 100644 contrib/StylizedNeRF/models_jt.py create mode 100644 contrib/StylizedNeRF/rendering.py create mode 100644 contrib/StylizedNeRF/requirements.txt create mode 100644 contrib/StylizedNeRF/run_stylenerf.py create mode 100644 contrib/StylizedNeRF/teaser.png create mode 100644 contrib/StylizedNeRF/train_style_modules.py create mode 100644 contrib/StylizedNeRF/utils.py diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/.gitignore b/contrib/HSDF-Net/ChamferDistancePytorch/.gitignore new file mode 100644 index 00000000..ca5b6a7b --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/.gitignore @@ -0,0 +1,2 @@ +*__pycache__* +/tmp diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/LICENSE b/contrib/HSDF-Net/ChamferDistancePytorch/LICENSE new file mode 100644 index 00000000..794e2dfb --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 ThibaultGROUEIX + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/README.md b/contrib/HSDF-Net/ChamferDistancePytorch/README.md new file mode 100644 index 00000000..c3ad6619 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/README.md @@ -0,0 +1,103 @@ +`pip install torch ninja` + +# Pytorch Chamfer Distance. + +Include a **CUDA** version, and a **PYTHON** version with pytorch standard operations. +NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt thresholds accordingly. + +- [x] F - Score + + + +### CUDA VERSION + +- [x] JIT compilation +- [x] Supports multi-gpu +- [x] 2D point clouds. +- [x] 3D point clouds. +- [x] 5D point clouds. +- [x] Contiguous() safe. + + + +### Python Version + +- [x] Supports any dimension + + + +### Usage + +```python +import torch, chamfer3D.dist_chamfer_3D, fscore +chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist() +points1 = torch.rand(32, 1000, 3).cuda() +points2 = torch.rand(32, 2000, 3, requires_grad=True).cuda() +dist1, dist2, idx1, idx2 = chamLoss(points1, points2) +f_score, precision, recall = fscore.fscore(dist1, dist2) +``` + + + +### Add it to your project as a submodule + +```shell +git submodule add https://github.com/ThibaultGROUEIX/ChamferDistancePytorch +``` + + + +### Benchmark: [forward + backward] pass +- [x] CUDA 10.1, NVIDIA 435, Pytorch 1.4 +- [x] p1 : 32 x 2000 x dim +- [x] p2 : 32 x 1000 x dim + +| *Timing (sec * 1000)* | 2D | 3D | 5D | +| ---------- | -------- | ------- | ------- | +| **Cuda Compiled** | **1.2** | 1.4 |1.8 | +| **Cuda JIT** | 1.3 | **1.4** |**1.5** | +| **Python** | 37 | 37 | 37 | + + +| *Memory (MB)* | 2D | 3D | 5D | +| ---------- | -------- | ------- | ------- | +| **Cuda Compiled** | 529 | 529 | 549 | +| **Cuda JIT** | **520** | **529** |**549** | +| **Python** | 2495 | 2495 | 2495 | + + + +### What is the chamfer distance ? + +[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 + + + +### Aknowledgment + +Original backbone from [Fei Xia](https://github.com/fxia22/pointGAN/blob/master/nndistance/src/nnd_cuda.cu). + +JIT cool trick from [Christian Diller](https://github.com/chrdiller) + +### Troubleshoot + +- `Undefined symbol: Zxxxxxxxxxxxxxxxxx `: + +--> Fix: Make sure to `import torch` before you `import chamfer`. +--> Use pytorch.version >= 1.1.0 + +- [RuntimeError: Ninja is required to load C++ extension](https://github.com/zhanghang1989/PyTorch-Encoding/issues/167) + +```shell +wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip +sudo unzip ninja-linux.zip -d /usr/local/bin/ +sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force +``` + + + + + +#### TODO: + +* Discuss behaviour of torch.min() and tensor.min() which causes issues in some pytorch versions diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer2D.cu b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer2D.cu new file mode 100644 index 00000000..567dd1a0 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer2D.cu @@ -0,0 +1,182 @@ + +#include +#include + +#include +#include + +#include + + + +__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){ + const int batch=512; + __shared__ float buf[batch*2]; + for (int i=blockIdx.x;ibest){ + result[(i*n+j)]=best; + result_i[(i*n+j)]=best_i; + } + } + __syncthreads(); + } + } +} +// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){ +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){ + + const auto batch_size = xyz1.size(0); + const auto n = xyz1.size(1); //num_points point cloud A + const auto m = xyz2.size(1); //num_points point cloud B + + NmDistanceKernel<<>>(batch_size, n, xyz1.data(), m, xyz2.data(), dist1.data(), idx1.data()); + NmDistanceKernel<<>>(batch_size, m, xyz2.data(), n, xyz1.data(), dist2.data(), idx2.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + + +} +__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){ + for (int i=blockIdx.x;i>>(batch_size,n,xyz1.data(),m,xyz2.data(),graddist1.data(),idx1.data(),gradxyz1.data(),gradxyz2.data()); + NmDistanceGradKernel<<>>(batch_size,m,xyz2.data(),n,xyz1.data(),graddist2.data(),idx2.data(),gradxyz2.data(),gradxyz1.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd get grad: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + +} + diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer_cuda.cpp b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer_cuda.cpp new file mode 100644 index 00000000..67574e21 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/chamfer_cuda.cpp @@ -0,0 +1,33 @@ +#include +#include + +///TMP +//#include "common.h" +/// NOT TMP + + +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2); + + +int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2); + + + + +int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) { + return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2); +} + + +int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, + at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) { + + return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2); +} + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &chamfer_forward, "chamfer forward (CUDA)"); + m.def("backward", &chamfer_backward, "chamfer backward (CUDA)"); +} \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/dist_chamfer_2D.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/dist_chamfer_2D.py new file mode 100644 index 00000000..b013642c --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/dist_chamfer_2D.py @@ -0,0 +1,80 @@ +from torch import nn +from torch.autograd import Function +import torch +import importlib +import os +chamfer_found = importlib.find_loader("chamfer_2D") is not None +if not chamfer_found: + ## Cool trick from https://github.com/chrdiller + print("Jitting Chamfer 2D") + cur_path = os.path.dirname(os.path.abspath(__file__)) + build_path = cur_path.replace('chamfer2D', 'tmp') + os.makedirs(build_path, exist_ok=True) + + from torch.utils.cpp_extension import load + chamfer_2D = load(name="chamfer_2D", + sources=[ + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]), + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer2D.cu"]), + ], build_directory=build_path) + print("Loaded JIT 2D CUDA chamfer distance") + +else: + import chamfer_2D + print("Loaded compiled 2D CUDA chamfer distance") + +# Chamfer's distance module @thibaultgroueix +# GPU tensors only +class chamfer_2DFunction(Function): + @staticmethod + def forward(ctx, xyz1, xyz2): + batchsize, n, dim = xyz1.size() + assert dim==2, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + _, m, dim = xyz2.size() + assert dim==2, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + device = xyz1.device + + device = xyz1.device + + dist1 = torch.zeros(batchsize, n) + dist2 = torch.zeros(batchsize, m) + + idx1 = torch.zeros(batchsize, n).type(torch.IntTensor) + idx2 = torch.zeros(batchsize, m).type(torch.IntTensor) + + dist1 = dist1.to(device) + dist2 = dist2.to(device) + idx1 = idx1.to(device) + idx2 = idx2.to(device) + torch.cuda.set_device(device) + + chamfer_2D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2) + ctx.save_for_backward(xyz1, xyz2, idx1, idx2) + return dist1, dist2, idx1, idx2 + + @staticmethod + def backward(ctx, graddist1, graddist2, gradidx1, gradidx2): + xyz1, xyz2, idx1, idx2 = ctx.saved_tensors + graddist1 = graddist1.contiguous() + graddist2 = graddist2.contiguous() + device = graddist1.device + + gradxyz1 = torch.zeros(xyz1.size()) + gradxyz2 = torch.zeros(xyz2.size()) + + gradxyz1 = gradxyz1.to(device) + gradxyz2 = gradxyz2.to(device) + chamfer_2D.backward( + xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2 + ) + return gradxyz1, gradxyz2 + + +class chamfer_2DDist(nn.Module): + def __init__(self): + super(chamfer_2DDist, self).__init__() + + def forward(self, input1, input2): + input1 = input1.contiguous() + input2 = input2.contiguous() + return chamfer_2DFunction.apply(input1, input2) diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/setup.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/setup.py new file mode 100644 index 00000000..11d01237 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer2D/setup.py @@ -0,0 +1,14 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='chamfer_2D', + ext_modules=[ + CUDAExtension('chamfer_2D', [ + "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']), + "/".join(__file__.split('/')[:-1] + ['chamfer2D.cu']), + ]), + ], + cmdclass={ + 'build_ext': BuildExtension + }) \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer3D.cu b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer3D.cu new file mode 100644 index 00000000..d5b886df --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer3D.cu @@ -0,0 +1,196 @@ + +#include +#include + +#include +#include + +#include + + + +__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){ + const int batch=512; + __shared__ float buf[batch*3]; + for (int i=blockIdx.x;ibest){ + result[(i*n+j)]=best; + result_i[(i*n+j)]=best_i; + } + } + __syncthreads(); + } + } +} +// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){ +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){ + + const auto batch_size = xyz1.size(0); + const auto n = xyz1.size(1); //num_points point cloud A + const auto m = xyz2.size(1); //num_points point cloud B + + NmDistanceKernel<<>>(batch_size, n, xyz1.data(), m, xyz2.data(), dist1.data(), idx1.data()); + NmDistanceKernel<<>>(batch_size, m, xyz2.data(), n, xyz1.data(), dist2.data(), idx2.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + + +} +__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){ + for (int i=blockIdx.x;i>>(batch_size,n,xyz1.data(),m,xyz2.data(),graddist1.data(),idx1.data(),gradxyz1.data(),gradxyz2.data()); + NmDistanceGradKernel<<>>(batch_size,m,xyz2.data(),n,xyz1.data(),graddist2.data(),idx2.data(),gradxyz2.data(),gradxyz1.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd get grad: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + +} + diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer_cuda.cpp b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer_cuda.cpp new file mode 100644 index 00000000..67574e21 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/chamfer_cuda.cpp @@ -0,0 +1,33 @@ +#include +#include + +///TMP +//#include "common.h" +/// NOT TMP + + +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2); + + +int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2); + + + + +int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) { + return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2); +} + + +int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, + at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) { + + return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2); +} + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &chamfer_forward, "chamfer forward (CUDA)"); + m.def("backward", &chamfer_backward, "chamfer backward (CUDA)"); +} \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/dist_chamfer_3D.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/dist_chamfer_3D.py new file mode 100644 index 00000000..de26d2c2 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/dist_chamfer_3D.py @@ -0,0 +1,81 @@ +from torch import nn +from torch.autograd import Function +import torch +import importlib +import os +chamfer_found = importlib.find_loader("chamfer_3D") is not None +if not chamfer_found: + ## Cool trick from https://github.com/chrdiller + print("Jitting Chamfer 3D") + cur_path = os.path.dirname(os.path.abspath(__file__)) + build_path = cur_path.replace('chamfer3D', 'tmp') + os.makedirs(build_path, exist_ok=True) + + from torch.utils.cpp_extension import load + chamfer_3D = load(name="chamfer_3D", + sources=[ + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]), + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer3D.cu"]), + ], build_directory=build_path) + print("Loaded JIT 3D CUDA chamfer distance") + +else: + import chamfer_3D + print("Loaded compiled 3D CUDA chamfer distance") + + +# Chamfer's distance module @thibaultgroueix +# GPU tensors only +class chamfer_3DFunction(Function): + @staticmethod + def forward(ctx, xyz1, xyz2): + batchsize, n, dim = xyz1.size() + assert dim==3, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + _, m, dim = xyz2.size() + assert dim==3, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + device = xyz1.device + + device = xyz1.device + + dist1 = torch.zeros(batchsize, n) + dist2 = torch.zeros(batchsize, m) + + idx1 = torch.zeros(batchsize, n).type(torch.IntTensor) + idx2 = torch.zeros(batchsize, m).type(torch.IntTensor) + + dist1 = dist1.to(device) + dist2 = dist2.to(device) + idx1 = idx1.to(device) + idx2 = idx2.to(device) + torch.cuda.set_device(device) + + chamfer_3D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2) + ctx.save_for_backward(xyz1, xyz2, idx1, idx2) + return dist1, dist2, idx1, idx2 + + @staticmethod + def backward(ctx, graddist1, graddist2, gradidx1, gradidx2): + xyz1, xyz2, idx1, idx2 = ctx.saved_tensors + graddist1 = graddist1.contiguous() + graddist2 = graddist2.contiguous() + device = graddist1.device + + gradxyz1 = torch.zeros(xyz1.size()) + gradxyz2 = torch.zeros(xyz2.size()) + + gradxyz1 = gradxyz1.to(device) + gradxyz2 = gradxyz2.to(device) + chamfer_3D.backward( + xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2 + ) + return gradxyz1, gradxyz2 + + +class chamfer_3DDist(nn.Module): + def __init__(self): + super(chamfer_3DDist, self).__init__() + + def forward(self, input1, input2): + input1 = input1.contiguous() + input2 = input2.contiguous() + return chamfer_3DFunction.apply(input1, input2) diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/setup.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/setup.py new file mode 100644 index 00000000..9a23aada --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer3D/setup.py @@ -0,0 +1,14 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='chamfer_3D', + ext_modules=[ + CUDAExtension('chamfer_3D', [ + "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']), + "/".join(__file__.split('/')[:-1] + ['chamfer3D.cu']), + ]), + ], + cmdclass={ + 'build_ext': BuildExtension + }) \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer5D.cu b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer5D.cu new file mode 100644 index 00000000..650e8890 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer5D.cu @@ -0,0 +1,223 @@ + +#include +#include + +#include +#include + +#include + + + +__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){ + const int batch=2048; + __shared__ float buf[batch*5]; + for (int i=blockIdx.x;ibest){ + result[(i*n+j)]=best; + result_i[(i*n+j)]=best_i; + } + } + __syncthreads(); + } + } +} +// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){ +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){ + + const auto batch_size = xyz1.size(0); + const auto n = xyz1.size(1); //num_points point cloud A + const auto m = xyz2.size(1); //num_points point cloud B + + NmDistanceKernel<<>>(batch_size, n, xyz1.data(), m, xyz2.data(), dist1.data(), idx1.data()); + NmDistanceKernel<<>>(batch_size, m, xyz2.data(), n, xyz1.data(), dist2.data(), idx2.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + + +} +__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){ + for (int i=blockIdx.x;i>>(batch_size,n,xyz1.data(),m,xyz2.data(),graddist1.data(),idx1.data(),gradxyz1.data(),gradxyz2.data()); + NmDistanceGradKernel<<>>(batch_size,m,xyz2.data(),n,xyz1.data(),graddist2.data(),idx2.data(),gradxyz2.data(),gradxyz1.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd get grad: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + +} diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer_cuda.cpp b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer_cuda.cpp new file mode 100644 index 00000000..67574e21 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/chamfer_cuda.cpp @@ -0,0 +1,33 @@ +#include +#include + +///TMP +//#include "common.h" +/// NOT TMP + + +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2); + + +int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2); + + + + +int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) { + return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2); +} + + +int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, + at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) { + + return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2); +} + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &chamfer_forward, "chamfer forward (CUDA)"); + m.def("backward", &chamfer_backward, "chamfer backward (CUDA)"); +} \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/dist_chamfer_5D.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/dist_chamfer_5D.py new file mode 100644 index 00000000..9cf749d7 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/dist_chamfer_5D.py @@ -0,0 +1,82 @@ +from torch import nn +from torch.autograd import Function +import torch +import importlib +import os + +chamfer_found = importlib.find_loader("chamfer_5D") is not None +if not chamfer_found: + ## Cool trick from https://github.com/chrdiller + print("Jitting Chamfer 5D") + cur_path = os.path.dirname(os.path.abspath(__file__)) + build_path = cur_path.replace('chamfer5D', 'tmp') + os.makedirs(build_path, exist_ok=True) + + from torch.utils.cpp_extension import load + chamfer_5D = load(name="chamfer_5D", + sources=[ + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]), + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer5D.cu"]), + ], build_directory=build_path) + print("Loaded JIT 5D CUDA chamfer distance") + +else: + import chamfer_5D + print("Loaded compiled 5D CUDA chamfer distance") + + +# Chamfer's distance module @thibaultgroueix +# GPU tensors only +class chamfer_5DFunction(Function): + @staticmethod + def forward(ctx, xyz1, xyz2): + batchsize, n, dim = xyz1.size() + assert dim==5, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + _, m, dim = xyz2.size() + assert dim==5, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + device = xyz1.device + + device = xyz1.device + + dist1 = torch.zeros(batchsize, n) + dist2 = torch.zeros(batchsize, m) + + idx1 = torch.zeros(batchsize, n).type(torch.IntTensor) + idx2 = torch.zeros(batchsize, m).type(torch.IntTensor) + + dist1 = dist1.to(device) + dist2 = dist2.to(device) + idx1 = idx1.to(device) + idx2 = idx2.to(device) + torch.cuda.set_device(device) + + chamfer_5D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2) + ctx.save_for_backward(xyz1, xyz2, idx1, idx2) + return dist1, dist2, idx1, idx2 + + @staticmethod + def backward(ctx, graddist1, graddist2, gradidx1, gradidx2): + xyz1, xyz2, idx1, idx2 = ctx.saved_tensors + graddist1 = graddist1.contiguous() + graddist2 = graddist2.contiguous() + device = graddist1.device + + gradxyz1 = torch.zeros(xyz1.size()) + gradxyz2 = torch.zeros(xyz2.size()) + + gradxyz1 = gradxyz1.to(device) + gradxyz2 = gradxyz2.to(device) + chamfer_5D.backward( + xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2 + ) + return gradxyz1, gradxyz2 + + +class chamfer_5DDist(nn.Module): + def __init__(self): + super(chamfer_5DDist, self).__init__() + + def forward(self, input1, input2): + input1 = input1.contiguous() + input2 = input2.contiguous() + return chamfer_5DFunction.apply(input1, input2) diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/setup.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/setup.py new file mode 100644 index 00000000..24292354 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer5D/setup.py @@ -0,0 +1,14 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='chamfer_5D', + ext_modules=[ + CUDAExtension('chamfer_5D', [ + "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']), + "/".join(__file__.split('/')[:-1] + ['chamfer5D.cu']), + ]), + ], + cmdclass={ + 'build_ext': BuildExtension + }) \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer6D.cu b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer6D.cu new file mode 100644 index 00000000..bb515303 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer6D.cu @@ -0,0 +1,237 @@ + +#include +#include + +#include +#include + +#include + + + +__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){ + const int batch=2048; + __shared__ float buf[batch*6]; + for (int i=blockIdx.x;ibest){ + result[(i*n+j)]=best; + result_i[(i*n+j)]=best_i; + } + } + __syncthreads(); + } + } +} +// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){ +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){ + + const auto batch_size = xyz1.size(0); + const auto n = xyz1.size(1); //num_points point cloud A + const auto m = xyz2.size(1); //num_points point cloud B + + NmDistanceKernel<<>>(batch_size, n, xyz1.data(), m, xyz2.data(), dist1.data(), idx1.data()); + NmDistanceKernel<<>>(batch_size, m, xyz2.data(), n, xyz1.data(), dist2.data(), idx2.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + + +} +__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){ + for (int i=blockIdx.x;i>>(batch_size,n,xyz1.data(),m,xyz2.data(),graddist1.data(),idx1.data(),gradxyz1.data(),gradxyz2.data()); + NmDistanceGradKernel<<>>(batch_size,m,xyz2.data(),n,xyz1.data(),graddist2.data(),idx2.data(),gradxyz2.data(),gradxyz1.data()); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in nnd get grad: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + +} diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer_cuda.cpp b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer_cuda.cpp new file mode 100644 index 00000000..67574e21 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/chamfer_cuda.cpp @@ -0,0 +1,33 @@ +#include +#include + +///TMP +//#include "common.h" +/// NOT TMP + + +int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2); + + +int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2); + + + + +int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) { + return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2); +} + + +int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, + at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) { + + return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2); +} + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &chamfer_forward, "chamfer forward (CUDA)"); + m.def("backward", &chamfer_backward, "chamfer backward (CUDA)"); +} \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/dist_chamfer_6D.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/dist_chamfer_6D.py new file mode 100644 index 00000000..0f073f83 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/dist_chamfer_6D.py @@ -0,0 +1,82 @@ +from torch import nn +from torch.autograd import Function +import torch +import importlib +import os + +chamfer_found = importlib.find_loader("chamfer_6D") is not None +if not chamfer_found: + ## Cool trick from https://github.com/chrdiller + print("Jitting Chamfer 6D") + cur_path = os.path.dirname(os.path.abspath(__file__)) + build_path = cur_path.replace('chamfer6D', 'tmp') + os.makedirs(build_path, exist_ok=True) + + from torch.utils.cpp_extension import load + chamfer_6D = load(name="chamfer_6D", + sources=[ + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]), + "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer6D.cu"]), + ], build_directory=build_path) + print("Loaded JIT 6D CUDA chamfer distance") + +else: + import chamfer_6D + print("Loaded compiled 6D CUDA chamfer distance") + + +# Chamfer's distance module @thibaultgroueix +# GPU tensors only +class chamfer_6DFunction(Function): + @staticmethod + def forward(ctx, xyz1, xyz2): + batchsize, n, dim = xyz1.size() + assert dim==6, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + _, m, dim = xyz2.size() + assert dim==6, "Wrong last dimension for the chamfer distance 's input! Check with .size()" + device = xyz1.device + + device = xyz1.device + + dist1 = torch.zeros(batchsize, n) + dist2 = torch.zeros(batchsize, m) + + idx1 = torch.zeros(batchsize, n).type(torch.IntTensor) + idx2 = torch.zeros(batchsize, m).type(torch.IntTensor) + + dist1 = dist1.to(device) + dist2 = dist2.to(device) + idx1 = idx1.to(device) + idx2 = idx2.to(device) + torch.cuda.set_device(device) + + chamfer_6D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2) + ctx.save_for_backward(xyz1, xyz2, idx1, idx2) + return dist1, dist2, idx1, idx2 + + @staticmethod + def backward(ctx, graddist1, graddist2, gradidx1, gradidx2): + xyz1, xyz2, idx1, idx2 = ctx.saved_tensors + graddist1 = graddist1.contiguous() + graddist2 = graddist2.contiguous() + device = graddist1.device + + gradxyz1 = torch.zeros(xyz1.size()) + gradxyz2 = torch.zeros(xyz2.size()) + + gradxyz1 = gradxyz1.to(device) + gradxyz2 = gradxyz2.to(device) + chamfer_6D.backward( + xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2 + ) + return gradxyz1, gradxyz2 + + +class chamfer_6DDist(nn.Module): + def __init__(self): + super(chamfer_6DDist, self).__init__() + + def forward(self, input1, input2): + input1 = input1.contiguous() + input2 = input2.contiguous() + return chamfer_6DFunction.apply(input1, input2) diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/setup.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/setup.py new file mode 100644 index 00000000..4b9044c3 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer6D/setup.py @@ -0,0 +1,14 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='chamfer_6D', + ext_modules=[ + CUDAExtension('chamfer_6D', [ + "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']), + "/".join(__file__.split('/')[:-1] + ['chamfer6D.cu']), + ]), + ], + cmdclass={ + 'build_ext': BuildExtension + }) \ No newline at end of file diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/chamfer_python.py b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer_python.py new file mode 100644 index 00000000..9c3bc1d9 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/chamfer_python.py @@ -0,0 +1,44 @@ +import torch + + +def pairwise_dist(x, y): + xx, yy, zz = torch.mm(x, x.t()), torch.mm(y, y.t()), torch.mm(x, y.t()) + rx = xx.diag().unsqueeze(0).expand_as(xx) + ry = yy.diag().unsqueeze(0).expand_as(yy) + P = rx.t() + ry - 2 * zz + return P + + +def NN_loss(x, y, dim=0): + dist = pairwise_dist(x, y) + values, indices = dist.min(dim=dim) + return values.mean() + + +def batched_pairwise_dist(a, b): + x, y = a.double(), b.double() + bs, num_points_x, points_dim = x.size() + bs, num_points_y, points_dim = y.size() + + xx = torch.pow(x, 2).sum(2) + yy = torch.pow(y, 2).sum(2) + zz = torch.bmm(x, y.transpose(2, 1)) + rx = xx.unsqueeze(1).expand(bs, num_points_y, num_points_x) # Diagonal elements xx + ry = yy.unsqueeze(1).expand(bs, num_points_x, num_points_y) # Diagonal elements yy + P = rx.transpose(2, 1) + ry - 2 * zz + return P + +def distChamfer(a, b): + """ + :param a: Pointclouds Batch x nul_points x dim + :param b: Pointclouds Batch x nul_points x dim + :return: + -closest point on b of points from a + -closest point on a of points from b + -idx of closest point on b of points from a + -idx of closest point on a of points from b + Works for pointcloud of any dimension + """ + P = batched_pairwise_dist(a, b) + return torch.min(P, 2)[0].float(), torch.min(P, 1)[0].float(), torch.min(P, 2)[1].int(), torch.min(P, 1)[1].int() + diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/fscore.py b/contrib/HSDF-Net/ChamferDistancePytorch/fscore.py new file mode 100644 index 00000000..265378b1 --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/fscore.py @@ -0,0 +1,17 @@ +import torch + +def fscore(dist1, dist2, threshold=0.001): + """ + Calculates the F-score between two point clouds with the corresponding threshold value. + :param dist1: Batch, N-Points + :param dist2: Batch, N-Points + :param th: float + :return: fscore, precision, recall + """ + # NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt the threshold accordingly. + precision_1 = torch.mean((dist1 < threshold).float(), dim=1) + precision_2 = torch.mean((dist2 < threshold).float(), dim=1) + fscore = 2 * precision_1 * precision_2 / (precision_1 + precision_2) + fscore[torch.isnan(fscore)] = 0 + return fscore, precision_1, precision_2 + diff --git a/contrib/HSDF-Net/ChamferDistancePytorch/unit_test.py b/contrib/HSDF-Net/ChamferDistancePytorch/unit_test.py new file mode 100644 index 00000000..13af6a3a --- /dev/null +++ b/contrib/HSDF-Net/ChamferDistancePytorch/unit_test.py @@ -0,0 +1,69 @@ +import torch, time +import chamfer2D.dist_chamfer_2D +import chamfer3D.dist_chamfer_3D +import chamfer5D.dist_chamfer_5D +import chamfer_python + +cham2D = chamfer2D.dist_chamfer_2D.chamfer_2DDist() +cham3D = chamfer3D.dist_chamfer_3D.chamfer_3DDist() +cham5D = chamfer5D.dist_chamfer_5D.chamfer_5DDist() + +from torch.autograd import Variable +from fscore import fscore + +def test_chamfer(distChamfer, dim): + points1 = torch.rand(4, 100, dim).cuda() + points2 = torch.rand(4, 200, dim, requires_grad=True).cuda() + dist1, dist2, idx1, idx2= distChamfer(points1, points2) + + loss = torch.sum(dist1) + loss.backward() + + mydist1, mydist2, myidx1, myidx2 = chamfer_python.distChamfer(points1, points2) + d1 = (dist1 - mydist1) ** 2 + d2 = (dist2 - mydist2) ** 2 + assert ( + torch.mean(d1) + torch.mean(d2) < 0.00000001 + ), "chamfer cuda and chamfer normal are not giving the same results" + + xd1 = idx1 - myidx1 + xd2 = idx2 - myidx2 + assert ( + torch.norm(xd1.float()) + torch.norm(xd2.float()) == 0 + ), "chamfer cuda and chamfer normal are not giving the same results" + print(f"fscore :", fscore(dist1, dist2)) + print("Unit test passed") + + +def timings(distChamfer, dim): + p1 = torch.rand(32, 2000, dim).cuda() + p2 = torch.rand(32, 1000, dim).cuda() + print("Timings : Start CUDA version") + start = time.time() + num_it = 100 + for i in range(num_it): + points1 = Variable(p1, requires_grad=True) + points2 = Variable(p2) + mydist1, mydist2, idx1, idx2 = distChamfer(points1, points2) + loss = torch.sum(mydist1) + loss.backward() + print(f"Ellapsed time forward backward is {(time.time() - start)/num_it} seconds.") + + + print("Timings : Start Pythonic version") + start = time.time() + for i in range(num_it): + points1 = Variable(p1, requires_grad=True) + points2 = Variable(p2) + mydist1, mydist2, idx1, idx2 = chamfer_python.distChamfer(points1, points2) + loss = torch.sum(mydist1) + loss.backward() + print(f"Ellapsed time forward backward is {(time.time() - start)/num_it} seconds.") + + + +dims = [2,3,5] +for i,cham in enumerate([cham2D, cham3D, cham5D]): + print(f"testing Chamfer {dims[i]}D") + test_chamfer(cham, dims[i]) + timings(cham, dims[i]) diff --git a/contrib/HSDF-Net/LICENSE b/contrib/HSDF-Net/LICENSE new file mode 100644 index 00000000..552fd220 --- /dev/null +++ b/contrib/HSDF-Net/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 IGLICT + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/contrib/HSDF-Net/NDF_combine.py b/contrib/HSDF-Net/NDF_combine.py new file mode 100644 index 00000000..df2f9207 --- /dev/null +++ b/contrib/HSDF-Net/NDF_combine.py @@ -0,0 +1,192 @@ +from torch.distributions.utils import probs_to_logits +from trimesh.util import sigfig_round +import models.local_model as model +from models.data import dataloader_garments, voxelized_data_shapenet + +from models import generation +import torch +from torch.nn import functional as F +import mesh_to_sdf +import numpy as np + +chunk_num = 50 +cls_threshold=0.2 +cls_logits_threshold = np.log(cls_threshold) - np.log(1. - cls_threshold) +usesign_threshold = 0.005 + +def rot_YZ(points): + points_rot = points.copy() + points_rot[:, 1], points_rot[:, 2] = points[:, 2], points[:, 1] + return points_rot + +def to_grid(points): + grid_points = points.copy() + grid_points[:, 0], grid_points[:, 2] = points[:, 2], points[:, 0] + + return 2 * grid_points + +def from_grid(grid_points): + points = grid_points.copy() + points[:, 0], points[:, 2] = grid_points[:, 2], grid_points[:, 0] + + return 0.5 * points + +# 'test', 'val', 'train' +def loadNDF(index, pointcloud_samples, exp_name, data_dir, split_file, sample_distribution, sample_sigmas, res, mode = 'test'): + + global encoding + global net + global device + + net = model.NDF() + + device = torch.device("cuda") + + ''' + if 'garments' in exp_name.lower() : + + dataset = dataloader_garments.VoxelizedDataset(mode = mode, data_path = data_dir, split_file = split_file, + res = res, density =0, pointcloud_samples = pointcloud_samples, + sample_distribution=sample_distribution, + sample_sigmas=sample_sigmas, + ) + + + + checkpoint = 'checkpoint_127h:6m:33s_457593.9149734974' + + generator = generation.Generator(net,exp_name, checkpoint = checkpoint, device = device) + + if 'cars' in exp_name.lower() : + + dataset = voxelized_data_shapenet.VoxelizedDataset( mode = mode, res = res, pointcloud_samples = pointcloud_samples, + data_path = data_dir, split_file = split_file, + sample_distribution = sample_distribution, sample_sigmas = sample_sigmas, + batch_size = 1, num_sample_points = 1024, num_workers = 1 + ) + + + + checkpoint = 'checkpoint_108h:5m:50s_389150.3971107006' + + generator = generation.Generator(net, exp_name, checkpoint=checkpoint, device=device) + ''' + + dataset = voxelized_data_shapenet.VoxelizedDataset( mode = mode, res = res, pointcloud_samples = pointcloud_samples, + data_path = data_dir, split_file = split_file, + sample_distribution = sample_distribution, sample_sigmas = sample_sigmas, + batch_size = 1, num_sample_points = 1024, num_workers = 1 + ) + + + + generator = generation.Generator(net, exp_name, device=device) + + example = dataset[index] + + print('Object: ',example['path']) + inputs = torch.from_numpy(example['inputs']).unsqueeze(0).to(device) # lead inputs and samples including one batch channel + + for param in net.parameters(): + param.requires_grad = False + + encoding = net.encoder(inputs) + + return example['path'] + + + +def predictRotNDF(points): + + points = rot_YZ(points) + points = to_grid(points) + points = torch.from_numpy(points).unsqueeze(0).float().to(device) + + points_chunk = torch.chunk(points, chunk_num, dim=1) + + ndf = np.zeros((0)) + + for point in points_chunk: + point = point.detach() + point.requires_grad = True + + dist = net.decoder(point,*encoding)[0] + dist = torch.clamp(dist, max=0.1) + #logits = p_r.logits + + ''' + mask = distcls_logits_threshold).float()*2-1 + #sign_selected = ((grid_logit_grad * grid_dis_grad).sum(-1)>0).float()*2-1 + + dist[mask] = dist_s*sign_selected + + #sign = torch.ones_like(sign_all) + + #sign[dist + +Recent advances in neural implicit representation have set a new state of the art in 3D modeling and reconstruction by breaking the previous barrier in resolution and topology. However, as such approaches rely on the signed distance function (SDF) which divides the space into inside and outside of the object, they are limited to representing closed shapes. To lift the limitation, methods based on unsigned distance function (UDF) are proposed such that a much broader class of shapes containing open surfaces can be effectively represented and learned via deep neural networks. +However, as UDF is signless, directly applying the iso-surface extracting technique, e.g. the Marching Cubes algorithm, would convert all open surfaces into the closed mesh. To generate open structures, these approaches have to convert the resulting UDF field into discrete points and then apply the Ball-Pivoting algorithm (BPA) to obtain the meshing result. Nonetheless, the BPA technique is prone to introduce self-intersections and disconnected surface patches with inconsistent normals. In addition, BPA is highly sensitive to the input parameters and often requires per-shape parameter tuning in order to generate a complete meshing result. This hinders UDF-based approaches from being practically used in real-world applications as mesh remains the prominent standard for modeling and rendering in both industry and academia. +To address the above issue, we present a novel learnable implicit representation, named Hybrid Sign and Distance Function (HSDF), that can faithfully represent complex geometry containing both closed and open surfaces, while being compatible with off-the-shelf iso-surface extraction methods, e.g. the Marching Cubes algorithm, for easy and high-quality field-to-mesh conversion. The key idea of HSDF is to integrate the advantages of both SDF and UDF while avoiding their adversarial impacts. We empirically find that the learning of UDF is quite robust and can generalize well to novel data. Therefore, to inherit the benefit of UDF and overcome its limitation, we propose to learn an additional sign field in addition to UDF via a sign predictor. Unlike traditional SDF, HSDF is able to locate the surface of interest before performing level surface extraction. We achieve this by generating surface points via the gradient field of the unsigned distance function following NDF. Hence, we are able to create local SDFs by multiplying the UDF with the sign field and cast complex shapes containing both closed and open surfaces by incorporating an adaptive meshing algorithm that only instantiates the regions containing surface points into a polygon mesh. + +## Installation + +The code is tested with Ubuntu 18.04, Python 3.8.12 Jittor 1.3.5.21, CUDA 11.1 and cuDNN 8005. + +Set Up Environment + + install anaconda and Run 'conda env create -f environment.yml' to build a environment and 'conda activate HSDF_env' to activate it + + +For the jittor installation, please refer to [this link](https://cg.cs.tsinghua.edu.cn/jittor/download). + + +## Experiment Preparation + +First, create a configuration file in folder `configs/`, use `configs/example*.txt` as reference and see `configs/config_loader.py` for detailed explanation of all configuration options. + +Next, download the MGN data from official website https://datasets.d2.mpi-inf.mpg.de/MultiGarmentNetwork/Multi-Garmentdataset.zip + +Next, prepare the data for HSDF using +``` +python dataprocessing/preprocess.py --config configs/example.txt +``` + +and generate a random test/training/validation split of the data using +``` +python dataprocessing/create_split.py --config configs/example.txt +``` + +but replacing `configs/example.txt` in the commands with the desired configuration. + +> Note: The preprocessing with `dataprocessing/preprocess.py` can be time intensive. In case multiple compute machines are +> available, the script can be run in parralell on those machines by splitting the overall files to preprocess into +> chuncks. For this, use: \ +> `python dataprocessing/preprocess.py --num_chunks X --current_chunk Y` \ +> where X is the desired number of chunks (e.g. the number of availiable machines) and Y is the chunk to be processed +> with execution of this command. Y needs to be an integer between 0 to X-1, including O and X-1. In case you have SLURM +> available you can adapt `slurm_scripts/run_preprocessing.sh` and use it via +> ``` +> sbatch slurm_scripts/run_preprocessing.sh +> ``` + +## Training + + Train model 'python train.py --config ./configs/example.txt + +## Generate model + + Download pretrained models from [this link](https://mailsucaseducn-my.sharepoint.com/:u:/g/personal/yangjie161_mails_ucas_edu_cn/Ee6jnWgGLrpOru32pOSOHQoBRTHBfSnnB5KoPkrgtuBW3Q?e=u6LqZd). + Run 'python generate.py --config ./configs/example.txt' + +If you prefer Pytorch version, please switch to [this](https://github.com/IGLICT/HSDF-Net/tree/pytorch). + +## Citation + +If you find our work useful in your research, please consider citing: + + @inproceedings{Wang22HSDF, + author = {Wang, Li and Yang, Jie and Chen, Wei-Kai and Meng, Xiao-Xu and Yang, Bo and Li, Jin-Tao and Gao, Lin}, + title = {HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies }, + booktitle={Neural Information Processing Systems (NeurIPS)}, + year = {2022}, + } + + @article{hu2020jittor, + title={Jittor: a novel deep learning framework with meta-operators and unified graph execution}, + author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang}, + journal={Science China Information Sciences}, + volume={63}, + number={222103}, + pages={1--21}, + year={2020} + } diff --git a/contrib/HSDF-Net/configs/config_loader.py b/contrib/HSDF-Net/configs/config_loader.py new file mode 100644 index 00000000..c0ad9bad --- /dev/null +++ b/contrib/HSDF-Net/configs/config_loader.py @@ -0,0 +1,156 @@ +from ast import parse +import configargparse +import numpy as np +import os + +def str2bool(inp): + return inp.lower() in 'true' + +def config_parser(): + parser = configargparse.ArgumentParser() + + # Experiment Setup + parser.add_argument('--config', is_config_file=True, default='configs/shapenet_cars.txt', + help='config file path') + parser.add_argument("--exp_name", type=str, default=None, + help='Experiment name, used as folder name for the experiment. If left blank, a \ + name will be auto generated based on the configuration settings.') + parser.add_argument("--data_dir", type=str, + help='input data directory') + parser.add_argument("--input_data_glob", type=str, + help='glob expression to find raw input files') + parser.add_argument("--split_file", type=str, + help='Path to read and write the data split file. Needs to end with ".npz"') + + # Training Data Parameters + parser.add_argument("--sample_std_dev", action='append', type=float, + help='Standard deviations of gaussian samples. \ + Used for displacing surface points to sample the distance field.') + parser.add_argument("--sample_ratio", action='append', type=float, + help='Ratio of standard deviations for samples used for training. \ + Needs to have the same len as sample_std with floats between 0-1 \ + and summing to 1.') + parser.add_argument("--bb_min", default=-0.5, type=float, + help='Training and testing shapes are normalized to be in a common bounding box.\ + This value defines the min value in x,y and z for the bounding box.') + parser.add_argument("--bb_max", default=0.5, type=float, + help='Training and testing shapes are normalized to be in a common bounding box.\ + This value defines the max value in x,y and z for the bounding box.') + parser.add_argument("--input_res", type=int, default=256, + help='Training and testing shapes are normalized to be in a common bounding box.\ + This value defines the max value in x,y and z for the bounding box.') + parser.add_argument("--num_points", type=int, default=10000, + help='Number of points sampled from each ground truth shape.') + + # apex + parser.add_argument("--local_rank", type=int, default=0) + + # Preprocessing - Multiprocessing + parser.add_argument("--num_chunks", type=int, default=1, + help='The preprocessing can be distributed over num_chunks multiple machines.\ + For this the raw files are split into num_chunks chunks. \ + Default is preprocessing on a single machine.') + parser.add_argument("--current_chunk", type=int, default=0, + help='Tells the script which chunk it should process. \ + Value between 0 till num_chunks-1.') + parser.add_argument("--num_cpus", type=int, default=-1, + help='Number of cpu cores to use for running the script. \ + Default is -1, that is, using all available cpus.') + + # Creating a data test/train/validation split + parser.add_argument('--class_folders', type=str, default=None, + help='If set to None, the split is created by creating a random sample from all input files. ' + 'If not None, the split is created per class of objects. Objects of the same class need to ' + 'be in a common parent folder for this. Variable class_folder is interpreted as glob ' + 'pattern, suffix of data_dir - i.e. data_dir + class_folder, e.g. class_folder="/*/".') + + parser_nval = parser.add_mutually_exclusive_group() + parser_nval.add_argument('--n_val', type=int, + help='Size of validation set.') + parser_nval.add_argument('--r_val', type=float, default=0.1, + help='Relative size of validation set.') + + parser_ntest = parser.add_mutually_exclusive_group() + parser_ntest.add_argument('--n_test', type=int, + help='Size of test set.') + parser_ntest.add_argument('--r_test', type=float, default=0.2, + help='Relative size of test set.') + + # Generation + parser.add_argument("--num_sample_points_generation", type=int, default=50000, + help='Number of point samples per object provided to the NDF network during generation.\ + Influences generation speed (larger batches result in faster generation) but also GPU \ + memory usage (higher values need more memory). Tip: choose largest possible value on GPU.') + parser.add_argument("--threshold", type=float, default=0.2, + help='threshold follow conv-onet') + + # Training + parser.add_argument("--num_sample_points_training", type=int, default=50000, + help='Number of point samples per object provided to the NDF network during training.\ + Influences training speed (larger batches result in shorter epochs) but also GPU \ + memory usage (higher values need more memory). Needs to be balanced with batch_size.') + parser.add_argument("--batch_size", type=int, default=4, + help='Number of objects provided to the NDF network in one batch during training.\ + Influences training speed (larger batches result in shorter epochs) but also GPU \ + memory usage (higher values need more memory). Needs to be balanced with \ + num_sample_points_training') + parser.add_argument("--num_epochs", type=int, default=1500, + help='Stopping citron for duration of training. Model converges much earlier: model convergence\ + can be checked via tensorboard and is logged within the experiment folder.') + parser.add_argument("--lr", type=float, default=1e-6, + help='Learning rate used during training.') + parser.add_argument("--optimizer", type=str, default='Adam', + help='Optimizer used during training.') + + ## Rendering arguments + parser.add_argument("--pc_samples", type=int, help='input pointcloud size') + parser.add_argument("--index", type=int, help='index to be rendered') + + ### + parser.add_argument("--size", type=int, help="the size of image", default=512) + parser.add_argument("--max_depth", type=float, help="the max depth of projected rays", default=2) + parser.add_argument("--alpha", type=float, help="the value by which the stepping distance should be multiplied", + default=0.6) + parser.add_argument("--step_back", type=float, default=0.005, help="the value by which we step back after stopping criteria met") + parser.add_argument("--epsilon", type=float, default=0.0026, help="epsilon ball - stopping criteria") + parser.add_argument("--screen_bound", type=float, default=0.4) + parser.add_argument("--screen_depth", type=float, default=-1) + + parser.add_argument('--cam_position', nargs='+', type=float, help='3D position of camera', default=[0, 0, -1]) + parser.add_argument('--light_position', nargs='+', type=float, help='3D position of light source', + default=[-1, -1, -1]) + parser.add_argument("--cam_orientation", nargs='+', type=float, + help="Camera Orientation in xyz euler angles (degrees)", default=[180.0, 0.0, -180.0]) + + parser.add_argument("--folder", type=str, default='./save', + help="location where images are to be saved") + parser.add_argument("--shade", type=str2bool, default=True, help="whether to save shade image") + parser.add_argument("--depth", type=str2bool, default=True, help="whether to save depth image") + parser.add_argument("--normal", type=str2bool, default=True, help="whether to save normal image") + + parser.add_argument("--debug_mode", type=str2bool, default=True, + help="to visualize everything in debug mode or not") + + + return parser + + +def get_config(): + parser = config_parser() + cfg = parser.parse_args() + + cfg.sample_ratio = np.array(cfg.sample_ratio) + cfg.sample_std_dev = np.array(cfg.sample_std_dev) + + assert np.sum(cfg.sample_ratio) == 1 + assert np.any(cfg.sample_ratio < 0) == False + assert len(cfg.sample_ratio) == len(cfg.sample_std_dev) + + if cfg.exp_name is None: + cfg.exp_name = 'data-{}dist-{}sigmas-{}res-{}'.format( + os.path.basename(cfg.data_dir), + ''.join(str(e) + '_' for e in cfg.sample_ratio), + ''.join(str(e) + '_' for e in cfg.sample_std_dev), + cfg.input_res) + + return cfg diff --git a/contrib/HSDF-Net/configs/example1.txt b/contrib/HSDF-Net/configs/example1.txt new file mode 100644 index 00000000..e19d40cb --- /dev/null +++ b/contrib/HSDF-Net/configs/example1.txt @@ -0,0 +1,10 @@ +exp_name = example1 +data_dir = datasets/clothing/data/0/ +split_file = datasets/clothing/data/split_clothing_overfit.npz +input_data_glob = /*/model.obj +sample_std_dev = [0.08, 0.02, 0.003] +sample_ratio = [0.01, 0.49, 0.5] +lr = 1e-6 +num_epochs = 200000000 + +num_points = 1000 \ No newline at end of file diff --git a/contrib/HSDF-Net/configs/example2.txt b/contrib/HSDF-Net/configs/example2.txt new file mode 100644 index 00000000..72928331 --- /dev/null +++ b/contrib/HSDF-Net/configs/example2.txt @@ -0,0 +1,18 @@ +exp_name = example2 +data_dir = datasets/mixamo_data/data/0/ +split_file = datasets/mixamo_data/data/split_mixamo_overfit.npz +input_data_glob = /*/model.obj +sample_std_dev = [0.08, 0.02, 0.003] +sample_ratio = [0.01, 0.49, 0.5] +lr = 1e-6 +num_epochs = 200000000 + +index = 0 +pc_samples = 10000 + +cam_position = [0, 2, 0] +cam_orientation = [-90.0, 180.0, 0.0] +debug_mode = True +screen_depth = -1 +max_depth = 3 +step_back = 0.0 \ No newline at end of file diff --git a/contrib/HSDF-Net/dataprocessing/boundary_sampling.py b/contrib/HSDF-Net/dataprocessing/boundary_sampling.py new file mode 100644 index 00000000..6b073a3b --- /dev/null +++ b/contrib/HSDF-Net/dataprocessing/boundary_sampling.py @@ -0,0 +1,64 @@ +import trimesh +import igl +import numpy as np +import glob +import multiprocessing as mp +from multiprocessing import Pool +import os +import traceback +from functools import partial +import random +import gc +import configs.config_loader as cfg_loader +from mesh_to_sdf import mesh_to_sdf + +# number of distance field samples generated per object +sample_num = 1000000 + +def boundary_sampling(path, sigma): + try: + + out_path = os.path.dirname(path) + file_name = os.path.splitext(os.path.basename(path))[0] + input_file = os.path.join(out_path,file_name + '_scaled.off') + out_file = out_path + '/boundary_{}_samples.npz'.format( sigma) + + if os.path.exists(out_file): + print('Exists: {}'.format(out_file)) + return + + print('processing {}'.format(input_file)) + + mesh = trimesh.load(input_file) + points = mesh.sample(sample_num) + + if sigma == 0: + boundary_points = points + else: + boundary_points = points + sigma * np.random.randn(sample_num, 3) + + grid_coords = boundary_points.copy() + grid_coords[:, 0], grid_coords[:, 2] = boundary_points[:, 2], boundary_points[:, 0] + + grid_coords = 2 * grid_coords + + #print('before mesh_to_sdf') + + if sigma == 0: + df = np.zeros(boundary_points.shape[0]) + else: + #df = np.abs(igl.signed_distance(boundary_points, mesh.vertices, mesh.faces)[0]) + df = igl.signed_distance(boundary_points, mesh.vertices, mesh.faces, True)[0] + # df = mesh_to_sdf(mesh, boundary_points, sign_method='normal', surface_point_method='sample') + + #print('after mesh_to_sdf') + + np.savez(out_file, points=boundary_points, df = df, grid_coords= grid_coords) + print('Finished: {}'.format(path)) + + except: + print('Error with {}: {}'.format(path, traceback.format_exc())) + + + del mesh, df, boundary_points, grid_coords, points + gc.collect() diff --git a/contrib/HSDF-Net/dataprocessing/convert_to_scaled_off.py b/contrib/HSDF-Net/dataprocessing/convert_to_scaled_off.py new file mode 100644 index 00000000..0735f370 --- /dev/null +++ b/contrib/HSDF-Net/dataprocessing/convert_to_scaled_off.py @@ -0,0 +1,71 @@ +import os +import glob +import multiprocessing as mp +from multiprocessing import Pool +import trimesh +import random +import sys +import traceback +import logging +import configs.config_loader as cfg_loader + + +logger = logging.getLogger() +logger.setLevel(logging.ERROR) + +class HiddenPrints: + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + + +def as_mesh(scene_or_mesh): + """ + Convert a possible scene to a mesh. + + If conversion occurs, the returned mesh has only vertex and face data. + Suggested by https://github.com/mikedh/trimesh/issues/507 + """ + if isinstance(scene_or_mesh, trimesh.Scene): + if len(scene_or_mesh.geometry) == 0: + mesh = None # empty scene + else: + # we lose texture information here + mesh = trimesh.util.concatenate( + tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces) + for g in scene_or_mesh.geometry.values())) + else: + assert(isinstance(scene_or_mesh, trimesh.Trimesh)) + mesh = scene_or_mesh + return mesh + + +def to_off(path): + + file_path = os.path.dirname(path) + file_name = os.path.splitext(os.path.basename(path))[0] + output_file = os.path.join(file_path,file_name + '_scaled.off') + + if os.path.exists(output_file): + print('Exists: {}'.format(output_file)) + return + + try: + with HiddenPrints(): + input = trimesh.load(path) + mesh = as_mesh(input) + total_size = (mesh.bounds[1] - mesh.bounds[0]).max() + centers = (mesh.bounds[1] + mesh.bounds[0]) / 2 + + mesh.apply_translation(-centers) + mesh.apply_scale(1 / total_size) + mesh.export(output_file) + + print('Finished: {}'.format(path)) + except: + print('Error with {}: {}'.format(path, traceback.format_exc())) + diff --git a/contrib/HSDF-Net/dataprocessing/create_split.py b/contrib/HSDF-Net/dataprocessing/create_split.py new file mode 100644 index 00000000..54d8347d --- /dev/null +++ b/contrib/HSDF-Net/dataprocessing/create_split.py @@ -0,0 +1,64 @@ +import os +import random +from glob import glob +import numpy as np +import configs.config_loader as cfg_loader +from collections import defaultdict + +cfg = cfg_loader.get_config() + +train_all = [] +test_all = [] +val_all = [] + +# parse all input files +print('Finding raw files for preprocessing.') +paths = glob( cfg.data_dir + cfg.input_data_glob) +paths = [os.path.dirname(p) for p in paths] + +# sort according to class folders (optional) +if cfg.class_folders is None: + res = {'single_class': paths} +else: + class_paths = glob( cfg.data_dir + cfg.class_folders) + res = defaultdict(list) + for path in paths: + for class_path in class_paths: + if path.startswith(class_path): + res[class_path].append(path) + + +for class_path in res.keys(): + + all_samples = res[class_path] + + random.shuffle(all_samples) + + # Number of examples + n_total = len(all_samples) + + if cfg.n_val is not None: + n_val = cfg.n_val + else: + n_val = int(cfg.r_val * n_total) + + if cfg.n_test is not None: + n_test = cfg.n_test + else: + n_test = int(cfg.r_test * n_total) + + if n_total < n_val + n_test: + print('Error: too few training samples.') + exit() + + n_train = n_total - n_val - n_test + + assert(n_train >= 0) + + # Select elements + train_all.extend( all_samples[:n_train]) + val_all.extend( all_samples[n_train:n_train+n_val]) + test_all.extend( all_samples[n_train+n_val:]) + + +np.savez(cfg.data_dir + f'/../split_{cfg.exp_name}.npz', train = train_all, test = test_all, val = val_all) \ No newline at end of file diff --git a/contrib/HSDF-Net/dataprocessing/preprocess.py b/contrib/HSDF-Net/dataprocessing/preprocess.py new file mode 100644 index 00000000..045312ee --- /dev/null +++ b/contrib/HSDF-Net/dataprocessing/preprocess.py @@ -0,0 +1,48 @@ +from dataprocessing.convert_to_scaled_off import to_off +from dataprocessing.boundary_sampling import boundary_sampling +import dataprocessing.voxelized_pointcloud_sampling as voxelized_pointcloud_sampling +from glob import glob +import configs.config_loader as cfg_loader +import multiprocessing as mp +from multiprocessing import Pool +import numpy as np +from functools import partial + +cfg = cfg_loader.get_config() + + +print('Finding raw files for preprocessing.') +paths = glob( cfg.data_dir + cfg.input_data_glob) +paths = sorted(paths) + +chunks = np.array_split(paths,cfg.num_chunks) +paths = chunks[cfg.current_chunk] + + +if cfg.num_cpus == -1: + num_cpus = mp.cpu_count() + print('cpu count: {}'.format(num_cpus)) +else: + num_cpus = cfg.num_cpus + +def multiprocess(func): + p = Pool(num_cpus) + p.map(func, paths) + p.close() + p.join() + +print('Start scaling.') +multiprocess(to_off) + +print('Start distance field sampling.') +for sigma in cfg.sample_std_dev: + print(f'Start distance field sampling with sigma: {sigma}.') + #multiprocess(partial(boundary_sampling, sigma = sigma)) + # this process is multi-processed for each path: IGL parallelizes the distance field computation of multiple points. + for path in paths: + boundary_sampling(path, sigma) + +print('Start voxelized pointcloud sampling.') +voxelized_pointcloud_sampling.init(cfg) +multiprocess(voxelized_pointcloud_sampling.voxelized_pointcloud_sampling) + diff --git a/contrib/HSDF-Net/dataprocessing/voxelized_pointcloud_sampling.py b/contrib/HSDF-Net/dataprocessing/voxelized_pointcloud_sampling.py new file mode 100644 index 00000000..3ae598bd --- /dev/null +++ b/contrib/HSDF-Net/dataprocessing/voxelized_pointcloud_sampling.py @@ -0,0 +1,54 @@ +from scipy.spatial import cKDTree as KDTree +import numpy as np +import trimesh +import os +import traceback + +kdtree, grid_points, cfg = None, None, None +def voxelized_pointcloud_sampling(path): + try: + + out_path = os.path.dirname(path) + file_name = os.path.splitext(os.path.basename(path))[0] + input_file = os.path.join(out_path,file_name + '_scaled.off') + out_file = out_path + '/voxelized_point_cloud_{}res_{}points.npz'.format(cfg.input_res, cfg.num_points) + + + if os.path.exists(out_file): + print(f'Exists: {out_file}') + return + + + + mesh = trimesh.load(input_file) + point_cloud = mesh.sample(cfg.num_points) + + occupancies = np.zeros(len(grid_points), dtype=np.int8) + + _, idx = kdtree.query(point_cloud) + occupancies[idx] = 1 + + compressed_occupancies = np.packbits(occupancies) + + np.savez(out_file, point_cloud=point_cloud, compressed_occupancies = compressed_occupancies, bb_min = cfg.bb_min, bb_max = cfg.bb_max, res = cfg.input_res) + print('Finished: {}'.format(path)) + + except Exception as err: + print('Error with {}: {}'.format(path, traceback.format_exc())) + +def init(cfg_param): + global kdtree, grid_points, cfg + cfg = cfg_param + grid_points = create_grid_points_from_bounds(cfg.bb_min, cfg.bb_max, cfg.input_res) + kdtree = KDTree(grid_points) + +def create_grid_points_from_bounds(minimun, maximum, res): + x = np.linspace(minimun, maximum, res) + X, Y, Z = np.meshgrid(x, x, x, indexing='ij') + X = X.reshape((np.prod(X.shape),)) + Y = Y.reshape((np.prod(Y.shape),)) + Z = Z.reshape((np.prod(Z.shape),)) + + points_list = np.column_stack((X, Y, Z)) + del X, Y, Z, x + return points_list \ No newline at end of file diff --git a/contrib/HSDF-Net/environment.yml b/contrib/HSDF-Net/environment.yml new file mode 100644 index 00000000..1aecc1e6 --- /dev/null +++ b/contrib/HSDF-Net/environment.yml @@ -0,0 +1,171 @@ +name: HSDF_env +channels: + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=4.5=1_gnu + - ca-certificates=2021.10.8=ha878542_0 + - certifi=2021.10.8=py38h578d9bd_1 + - igl=2.2.1=py38h52fb889_1 + - ld_impl_linux-64=2.35.1=h7274673_9 + - libblas=3.9.0=11_linux64_openblas + - libcblas=3.9.0=11_linux64_openblas + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.3.0=h5101ec6_17 + - libgfortran-ng=11.2.0=h69a702a_14 + - libgfortran5=11.2.0=h5c6108e_14 + - libgomp=9.3.0=h5101ec6_17 + - liblapack=3.9.0=11_linux64_openblas + - libopenblas=0.3.17=pthreads_h8fe5266_1 + - libstdcxx-ng=9.3.0=hd4cf53a_17 + - ncurses=6.3=h7f8727e_2 + - openssl=1.1.1n=h7f8727e_0 + - python=3.8.12=h12debd9_0 + - python_abi=3.8=2_cp38 + - readline=8.1.2=h7f8727e_1 + - sqlite=3.37.2=hc218d9a_0 + - tk=8.6.11=h1ccaba5_0 + - wheel=0.37.1=pyhd3eb1b0_0 + - xz=5.2.5=h7b6447c_0 + - zlib=1.2.11=h7f8727e_4 + - pip: + - absl-py==1.0.0 + - addict==2.4.0 + - anyio==3.6.1 + - argon2-cffi==21.3.0 + - argon2-cffi-bindings==21.2.0 + - asttokens==2.0.5 + - astunparse==1.6.3 + - attrs==22.1.0 + - babel==2.10.3 + - backcall==0.2.0 + - beautifulsoup4==4.11.1 + - bleach==5.0.1 + - cachetools==5.0.0 + - cffi==1.15.1 + - charset-normalizer==2.0.12 + - colorama==0.4.4 + - configargparse==1.5.3 + - cycler==0.11.0 + - cython==0.29.30 + - debugpy==1.6.2 + - decorator==5.1.1 + - defusedxml==0.7.1 + - deprecation==2.1.0 + - entrypoints==0.4 + - executing==0.8.3 + - fastjsonschema==2.16.1 + - fonttools==4.34.4 + - freetype-py==2.2.0 + - google-auth==2.6.0 + - google-auth-oauthlib==0.4.6 + - grpcio==1.44.0 + - h5py==3.7.0 + - icecream==2.1.0 + - idna==3.3 + - imageio==2.16.1 + - importlib-metadata==4.11.2 + - importlib-resources==5.9.0 + - ipykernel==6.15.1 + - ipython==8.4.0 + - ipython-genutils==0.2.0 + - ipywidgets==7.7.1 + - jedi==0.18.1 + - jinja2==3.1.2 + - jittor==1.3.5.21 + - joblib==1.1.0 + - json5==0.9.9 + - jsonschema==4.9.0 + - kiwisolver==1.4.4 + - markdown==3.3.6 + - markupsafe==2.1.1 + - matplotlib==3.5.2 + - matplotlib-inline==0.1.3 + - mesh-to-sdf==0.0.14 + - mistune==0.8.4 + - mpi4py==3.1.3 + - nbclassic==0.4.3 + - nbclient==0.6.6 + - nbconvert==6.5.0 + - nbformat==5.4.0 + - nest-asyncio==1.5.5 + - networkx==2.7 + - notebook==6.4.12 + - notebook-shim==0.1.0 + - numexpr==2.8.1 + - numpy==1.22.2 + - oauthlib==3.2.0 + - open3d==0.15.2 + - opencv-python==4.5.2.52 + - packaging==21.3 + - pandas==1.4.3 + - pandocfilters==1.5.0 + - parso==0.8.3 + - pexpect==4.8.0 + - pickleshare==0.7.5 + - pillow==9.0.1 + - pip==22.0.3 + - pkgutil-resolve-name==1.3.10 + - prometheus-client==0.14.1 + - prompt-toolkit==3.0.30 + - protobuf==3.19.4 + - psutil==5.9.1 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - pyasn1==0.4.8 + - pyasn1-modules==0.2.8 + - pycparser==2.21 + - pyglet==1.5.23 + - pygments==2.11.2 + - pyhocon==0.3.57 + - pymcubes==0.1.2 + - pymeshlab==2022.2.post2 + - pyopengl==3.1.0 + - pyparsing==3.0.7 + - pyquaternion==0.9.9 + - pyrender==0.1.45 + - pyrsistent==0.18.1 + - python-dateutil==2.8.2 + - pytz==2022.1 + - pywavelets==1.2.0 + - pyyaml==6.0 + - pyzmq==23.2.0 + - requests==2.27.1 + - requests-oauthlib==1.3.1 + - rsa==4.8 + - rtree==1.0.1 + - scikit-image==0.19.2 + - scikit-learn==1.0.2 + - scipy==1.7.0 + - send2trash==1.8.0 + - setuptools==63.3.0 + - six==1.16.0 + - sklearn==0.0 + - sniffio==1.2.0 + - soupsieve==2.3.2.post1 + - stack-data==0.3.0 + - tables==3.7.0 + - tensorboard==2.8.0 + - tensorboard-data-server==0.6.1 + - tensorboard-plugin-wit==1.8.1 + - tensorboardx==2.5.1 + - terminado==0.15.0 + - threadpoolctl==3.1.0 + - tifffile==2022.2.9 + - tinycss2==1.1.1 + - tomlkit==0.11.1 + - torchaudio==0.8.0 + - torchvision==0.9.0+cu111 + - tornado==6.2 + - tqdm==4.50.2 + - traitlets==5.3.0 + - trimesh==3.9.8 + - typing-extensions==4.1.1 + - urllib3==1.26.8 + - wcwidth==0.2.5 + - webencodings==0.5.1 + - websocket-client==1.3.3 + - werkzeug==2.0.3 + - widgetsnbextension==3.6.1 + - zipp==3.7.0 diff --git a/contrib/HSDF-Net/generate.py b/contrib/HSDF-Net/generate.py new file mode 100644 index 00000000..2a0d92a2 --- /dev/null +++ b/contrib/HSDF-Net/generate.py @@ -0,0 +1,105 @@ +import models.local_model as model +import models.data.voxelized_data_shapenet as voxelized_data +from models.generation import Generator +import jittor +import configs.config_loader as cfg_loader +import os +import trimesh +import numpy as np +from tqdm import tqdm +from utils import voxel2obj + +jittor.flags.use_cuda=1 + +cfg = cfg_loader.get_config() + +#device = torch.device("cuda") +net = model.HSDF() + +dataset = voxelized_data.VoxelizedDataset('test', + res=cfg.input_res, + pointcloud_samples=cfg.num_points, + data_path=cfg.data_dir, + split_file=cfg.split_file, + batch_size=1, + num_sample_points=cfg.num_sample_points_generation, + num_workers=8, + sample_distribution=cfg.sample_ratio, + sample_sigmas=cfg.sample_std_dev) + +gen = Generator(net, cfg.exp_name, cls_threshold=cfg.threshold) + +out_path = 'experiments/{}/evaluation_0.02_128/'.format(cfg.exp_name) + + +def gen_iterator(out_path, dataset, gen_p): + global gen + gen = gen_p + + if not os.path.exists(out_path): + os.makedirs(out_path) + print(out_path) + + # can be run on multiple machines: dataset is shuffled and already generated objects are skipped. + #loader = dataset.get_loader(shuffle=True) + + for i, data in tqdm(enumerate(dataset)): + + path = os.path.normpath(data['path'][0]) + + ''' + # selected testing cases for car + if 'bceb15ddfb9fe56aa13d6c605d0084d3' not in path \ + and '2b4664cf53176418faeea7738551d104' not in path \ + and 'a7c0f3bcc2347710a312b3f0b49ff828' not in path \ + and '2e8c4fd40a1be2fa5f38ed4497f2c53c' not in path \ + and '32924c86ee4a2c69aa4eefa8f42b566e' not in path \ + and '7e412497b8ab74963f2c3a55558a78f' not in path \ + and '7d33cb52d556c1fe618e9d35559b7aa' not in path \ + and '5ce5d2b8c3a7b846d13b7e043606607d' not in path \ + and '1f43243b81df21277925d1ea63246010' not in path \ + and '1ffe99aba88ca413ca71c17c1eef7213' not in path: + continue + ''' + + export_path = out_path + '/generation/{}/{}/'.format(path.split(os.sep)[-2], path.split(os.sep)[-1]) + + if os.path.exists(export_path): + print('Path exists - skip! {}'.format(export_path)) + continue + else: + os.makedirs(export_path) + + for num_steps in [7]: + + #debug + verts, faces, verts_nomask, faces_nomask, duration, voxel, verts_udf, faces_udf, voxel_gradnorm = gen.generate_mesh(data, voxel_resolution=128, chunk_num=16) + #verts, faces, verts_nomask, faces_nomask, duration, voxel, verts_udf, faces_udf, voxel_gradnorm = gen.generate_mesh(data, voxel_resolution=512, chunk_num=4096) + #np.savez(export_path + 'dense_point_cloud_{}'.format(num_steps), point_cloud=verts, duration=duration) + #np.savez(export_path + 'dense_point_cloud_{}_nomask'.format(num_steps), point_cloud=verts_nomask, duration=duration) + print('num_steps', num_steps, 'duration', duration) + #trimesh.Trimesh(vertices=verts, faces=faces).export( + # export_path + 'dense_point_cloud_{}.off'.format(num_steps)) + trimesh.Trimesh(vertices=verts_nomask, faces=faces_nomask).export( + export_path + 'dense_point_cloud_{}_nomask.off'.format(num_steps)) + trimesh.Trimesh(vertices=verts_udf, faces=faces_udf).export( + export_path + 'dense_point_cloud_{}_udf.off'.format(num_steps)) + + #trimesh.Trimesh(vertices=pos_pts, faces=[]).export( + # export_path + 'dense_point_cloud_{}_pos.off'.format(num_steps)) + #trimesh.Trimesh(vertices=neg_pts, faces=[]).export( + # export_path + 'dense_point_cloud_{}_neg.off'.format(num_steps)) + #trimesh.Trimesh(vertices=zero_pts, faces=[]).export( + # export_path + 'dense_point_cloud_{}_zero.off'.format(num_steps)) + + voxel2obj(export_path + 'voxel_gradnorm.obj', voxel_gradnorm) + + + pc, duration = gen.generate_point_cloud(data, num_steps) + print('pc duration', duration) + trimesh.Trimesh(vertices=pc, faces=[]).export( + export_path + 'dense_point_cloud_{}_pc.off'.format(num_steps)) + + + +gen_iterator(out_path, dataset, gen) diff --git a/contrib/HSDF-Net/models/__init__.py b/contrib/HSDF-Net/models/__init__.py new file mode 100644 index 00000000..8d1c8b69 --- /dev/null +++ b/contrib/HSDF-Net/models/__init__.py @@ -0,0 +1 @@ + diff --git a/contrib/HSDF-Net/models/generation.py b/contrib/HSDF-Net/models/generation.py new file mode 100644 index 00000000..7f5b2131 --- /dev/null +++ b/contrib/HSDF-Net/models/generation.py @@ -0,0 +1,543 @@ +import jittor +import os +import numpy as np +from glob import glob +import jittor +import jittor.nn as nn +import time +from skimage.measure import marching_cubes +from sklearn.preprocessing import normalize + +class Generator(object): + def __init__(self, model, exp_name, threshold = 0.05, checkpoint = None, cls_threshold=0.2): + #self.model = model.to(device) + self.model = model + self.model.eval() + #self.device = device + self.checkpoint_path = os.path.dirname(__file__) + '/../experiments/{}/checkpoints/'.format( exp_name) + self.load_checkpoint(checkpoint) + self.threshold = threshold + + # compute binary cls threshold of Bernoulli logits + self.cls_logits_threshold = np.log(cls_threshold) - np.log(1. - cls_threshold) + + print("cls_logits_threshold: {}".format(self.cls_logits_threshold)) + + + + def generate_mesh(self, data, voxel_resolution=128, EPS=0, chunk_num=128, num_steps=5): + + start = time.time() + inputs = data['inputs'] + + # add noises + sigma = 0.02 + inputs += sigma * jittor.randn(inputs.shape) + + for param in self.model.parameters(): + param.requires_grad = False + + bound = 1. + points = np.meshgrid( + np.linspace(-bound, bound, voxel_resolution), + np.linspace(-bound, bound, voxel_resolution), + np.linspace(-bound, bound, voxel_resolution) + ) + points = np.stack(points) + points = np.swapaxes(points, 1, 2) + points = points.reshape(3, -1).transpose().reshape( + [voxel_resolution]*3 + [3]).astype(np.float32) + #print(points.shape) + + #points = torch.from_numpy( + # get_raster_points( + # voxel_resolution=voxel_resolution)).to(self.device) + #points = points.reshape([voxel_resolution]*3 + [3]) + points_gpu = jittor.array(points) + + avgpool3d = nn.AvgPool3d(kernel_size=2, stride=1) + centers = avgpool3d(jittor.array(np.expand_dims(points_gpu.permute(3,0,1,2), axis=0))).permute(0,2,3,4,1) + centers = centers.reshape([1,-1,3]) + points_gpu = points_gpu.reshape([1,-1,3]) + centers = centers.detach() + points_gpu = points_gpu.detach() + + # split into chunks + center_chunks = jittor.chunk(centers, chunks=chunk_num, dim=1) + points_gpu_chunks = jittor.chunk(points_gpu, chunks=chunk_num, dim=1) + + # encode + encoding = self.model.encoder(inputs) + + gradient_cpu = np.zeros((1,0,3)) + centers_sdf_cpu = np.zeros((1,0)) + centers_cpu = np.zeros((1,0,3)) + sign_cpu = np.zeros((1,0)) + + points_udf_cpu = np.zeros((1,0)) + + print('-------begin computing mask--------') + + for i in range(chunk_num): + point = points_gpu_chunks[i] + center = center_chunks[i] + + center = center.detach() + center.requires_grad = True + + with jittor.no_grad(): + udf, p_r_init = self.model.decoder(point, *encoding) + sign = (p_r_init>0.).float()*2-1 + + center_debug = self.model.decoder(center, *encoding)[0] + center_debug.sync() + #centers_df_pred = jittor.clamp(center_debug, min_v=None, max_v=self.threshold) + #np.savetxt("center_log.txt", center_debug.numpy()) + centers_df_pred = center_debug.minimum(self.threshold) + # decode + #centers_df_pred = jittor.clamp( + # self.model.decoder(center, *encoding)[0], + # max_v=self.threshold) + + #centers_df_pred.sum().backward() + center_grad = jittor.grad(centers_df_pred.sum(), center, retain_graph=False) + + #gradient_cpu = np.concatenate([gradient_cpu, center.grad.detach().numpy()], axis=1) + gradient_cpu = np.concatenate([gradient_cpu, center_grad.detach().numpy()], axis=1) + centers_cpu = np.concatenate([centers_cpu, center.detach().numpy()], axis=1) + centers_sdf_cpu = np.concatenate([centers_sdf_cpu, centers_df_pred.detach().numpy()], axis=1) + sign_cpu = np.concatenate([sign_cpu, sign.detach().numpy()], axis=1) + points_udf_cpu = np.concatenate([points_udf_cpu, udf.detach().numpy()], axis=1) + + + gradient = gradient_cpu.reshape([voxel_resolution-1]*3 + [3]) + centers_df_pred = centers_sdf_cpu.reshape([voxel_resolution-1]*3) + centers = centers_cpu.reshape([voxel_resolution-1]*3 + [3]) + sign_cpu = sign_cpu.reshape([voxel_resolution]*3) + points_udf_cpu = points_udf_cpu.reshape([voxel_resolution]*3) + + gradient_norm = np.linalg.norm(gradient, axis=-1) + #print('gradient norm max: {}'.format(np.max(gradient_norm))) + #print('gradient norm min: {}'.format(np.min(gradient_norm))) + #print('gradient > 1 cell: {}'.format(gradient_norm[gradient_norm>1.].shape)) + voxel_gradnorm = (gradient_norm>1.).astype(np.int) + + # generate mask + mask = np.zeros([voxel_resolution]*3) + voxel = np.zeros([voxel_resolution-1]*3) + + #max_sum = -1 + + for i in range(voxel_resolution-1): + for j in range(voxel_resolution-1): + for k in range(voxel_resolution-1): + + if np.abs(centers_df_pred[i,j,k])>=self.threshold: + continue + + # TODO: gradient normalize + closest_surfpt = centers[i,j,k] - normalize(gradient[i,j,k].reshape((-1,1)), axis=0).ravel()*centers_df_pred[i,j,k] + + # check if closest surfpt is inside this cube + if (points[i,j,k,0]-EPS < closest_surfpt[0] < points[i+1,j,k,0]+EPS and + points[i,j,k,1]-EPS < closest_surfpt[1] < points[i,j+1,k,1]+EPS and + points[i,j,k,2]-EPS < closest_surfpt[2] < points[i,j,k+1,2]+EPS): + + ''' + grid_sign_sum = 0 + + for ii in range(2): + for jj in range(2): + for kk in range(2): + grid_sign_sum += sign_by_value[i+ii,j+jj,k+kk] + ''' + + # get voxel to visualize + voxel[i,j,k] = 1 + #mask[i+1,j+1,k+1] = 1 + + # 8 corners of a cube + for ii in range(2): + for jj in range(2): + for kk in range(2): + + mask[i+ii,j+jj,k+kk] = 1 + ''' + # generate mask + mask_gradnorm = np.zeros([voxel_resolution]*3) + #voxel = np.zeros([voxel_resolution-1]*3) + + #max_sum = -1 + + for i in range(voxel_resolution-1): + for j in range(voxel_resolution-1): + for k in range(voxel_resolution-1): + + if voxel_gradnorm[i,j,k] == 0: + + # 8 corners of a cube + for ii in range(2): + for jj in range(2): + for kk in range(2): + + mask_gradnorm[i+ii,j+jj,k+kk] = 1 + ''' + + + #mask_cpu = np.array_split(mask.reshape([1,-1,1]), chunk_num, axis=1) + + print('-------begin computing sign and distance--------') + + #print('points: {}'.format(points)) + + #point_chunks = torch.chunk(points_gpu, chunks=chunk_num, dim=1) + + points_sdf_cpu = sign_cpu.copy() * 10 + points_sdf_cpu_filtered = np.zeros((1,0)) + #points_cpu = np.zeros((1,0,3)) + + sign_by_value_cpu = sign_cpu.copy() * 10 + sign_by_value_cpu_filtered = np.zeros((1,0)) + + points_filtered = jittor.array(points[mask==1]).reshape(1,-1,3) + + #print('points_filtered shape: {}'.format(points_filtered.shape)) + + point_chunks = jittor.chunk(points_filtered, chunks=chunk_num, dim=1) + + for i in range(len(point_chunks)): + + #mask_chunk = mask_cpu[i] + #if mask_chunk.sum() == 0: + # continue + + point = point_chunks[i] + point = point.detach() + point.requires_grad = True + + #print('point shape: {}'.format(point.shape)) + + # compute sign by mere value + #with torch.no_grad(): + udf, sign_by_value = self.model.decoder(point, *encoding) + + + ''' + for j in range(num_steps): + # generate grid gradient of UDF and OCC + #if j>0: + # dis_pred_laststep = dis_pred + + dis_pred = self.model.decoder(point, *encoding)[0] + + #print('sum: {}'.format((dis_pred0: + # dis_mask = dis_pred>=dis_pred_laststep + # dis_pred[dis_mask] = torch.zeros_like(dis_pred)[dis_mask] + + # debug + #dis_pred = torch.zeros_like(dis_pred) + + point = point - F.normalize(grid_dis_grad, dim=-1) * dis_pred.unsqueeze(-1) + point = point.detach() + point.requires_grad = True + + print('near ratio: {}'.format((dis_pred0).float()*2-1 + + #grid_point_shift = grid_point_init + grid_logits_grad * dis_pred_init.unsqueeze(-1) + #with torch.no_grad(): + # grid_dis_shift = self.model.decoder(grid_point_shift, *encoding)[0] + #sign = (grid_dis_shift>dis_pred_init).float()*2-1 + udf.sync() + sign.sync() + points_df_pred = udf * sign + + # DEBUG + #points_df_pred = (cls_pred.argmax(dim=1)*2-1).float() + #points_df_pred = p_r_init.logits + + #points_cpu = np.concatenate([points_cpu, point.detach().numpy()], axis=1) + points_sdf_cpu_filtered = np.concatenate([points_sdf_cpu_filtered, points_df_pred.detach().numpy()], axis=1) + #points_udf_cpu_filtered = np.concatenate([points_udf_cpu_filtered, dis_pred_init.detach().numpy()], axis=1) + + sign_by_value_cpu_filtered = np.concatenate([sign_by_value_cpu_filtered, sign_by_value.detach().numpy()], axis=1) + + ''' + # plot vector field + point_cpu = point[0].detach().numpy() + grid_logits_grad_cpu = grid_logits_grad[0].detach().numpy() + grid_dis_grad_cpu = grid_dis_grad[0].detach().numpy() + + fig = plt.figure() + ax = fig.gca(projection='3d') + + ax.quiver(point_cpu[:,0],point_cpu[:,1],point_cpu[:,2],grid_logits_grad_cpu[:,0],grid_logits_grad_cpu[:,1],grid_logits_grad_cpu[:,2],length=0.05) + + plt.savefig('chunk_{}_logits.png'.format(i)) + + plt.close() + ''' + + # CPU compute mask (could be parallelized) + #points_df_pred = points_sdf_cpu.reshape([voxel_resolution]*3) + #points = points_cpu.reshape([voxel_resolution]*3 + [3]) + + #points_udf_pred = points_udf_cpu.reshape([voxel_resolution]*3) + + #sign_by_value = sign_by_value_cpu.reshape([voxel_resolution]*3)#.astype(np.bool) + + points_sdf_cpu[mask==1] = points_sdf_cpu_filtered.ravel() + #points_udf_cpu[mask==1] = points_udf_cpu_filtered.ravel() + sign_by_value_cpu[mask==1] = sign_by_value_cpu_filtered.ravel() + + + #pos_pts = points[np.logical_and(points_df_pred>0, points_df_pred<0.1)] + #neg_pts = points[np.logical_and(points_df_pred<0, points_df_pred>-0.1)] + #zero_pts = points[points_df_pred==0] + + print('-------begin marching cube-------') + verts = [] + faces = [] + verts_nomask = [] + faces_nomask = [] + duration = time.time() - start + + #verts, faces, norms, vals = marching_cubes( + # points_sdf_cpu, + # #np.abs(points_sdf_cpu) * ((sign_by_value_cpu>self.cls_logits_threshold)*2-1), + # 0, + # mask=mask.astype(bool)) + + + + #verts_nomask, faces_nomask, _, _ = marching_cubes( + # points_df_pred, + # 0) + + verts_nomask, faces_nomask, _, _ = marching_cubes( + sign_by_value_cpu, + 0., + mask=mask.astype(bool) + ) + + duration = time.time() - start + + + verts_udf, faces_udf, _, _ = marching_cubes( + points_udf_cpu, + 6e-3 + ) + + ''' + verts_udf, faces_udf, _, _ = marching_cubes( + points_udf_cpu, + 0., + mask=mask_gradnorm.astype(bool) + ) + ''' + + #print(verts_nomask.shape) # verts_nomask shape (verts, 3) + centers = np.array((1,1,1)) + spacing = 2./(voxel_resolution-1) + scale = (points.reshape([-1, 3]).max(0) - points.reshape([-1, 3]).min(0))[0] + pts_center = points.reshape([-1, 3]).mean(0) + + + verts_nomask_normalized = (verts_nomask*spacing - centers)*0.5 + + verts_nomask = verts_nomask_normalized*scale + pts_center + + + return verts, faces, verts_nomask, faces_nomask, duration, voxel, verts_udf, faces_udf, voxel_gradnorm#, pos_pts, neg_pts, zero_pts + + + + def generate_point_cloud(self, data, num_steps = 5, num_points = 900000, filter_val = 0.009): + + jittor.gc() + start = time.time() + inputs = data['inputs'] + + + for param in self.model.parameters(): + param.requires_grad = False + + sample_num = 100000 + samples_cpu = np.zeros((0, 3)) + samples = jittor.rand(1, sample_num, 3).float() * 3 - 1.5 + samples.requires_grad = True + + encoding = self.model.encoder(inputs) + + i = 0 + while len(samples_cpu) < num_points: + print('iteration', i) + + for j in range(num_steps): + print('refinement', j) + df_pred = jittor.clamp(self.model.decoder(samples, *encoding)[0], max_v=self.threshold) + + sample_grad = jittor.grad(df_pred.sum(), samples, retain_graph=False) + sample_grad.sync() + #df_pred.sum().backward() + + gradient = sample_grad.detach() + samples = samples.detach() + df_pred = df_pred.detach() + inputs = inputs.detach() + samples = samples - jittor.normalize(gradient, dim=2) * df_pred.reshape(-1, 1) # better use Tensor.copy method? + samples = samples.detach() + samples.requires_grad = True + + + print('finished refinement') + + if not i == 0: + samples_cpu = np.vstack((samples_cpu, samples[df_pred < filter_val].detach().numpy())) + + samples = samples.detach() + df_pred = df_pred.detach() + df_pred.sync() + samples.sync() + samples_tmp = samples[df_pred < 0.03] + samples_tmp.sync() + samples = samples_tmp.unsqueeze(0) + indices = jittor.randint(samples.shape[1], shape=(1, sample_num)) + samples = samples[[[0, ] * sample_num], indices] + samples += (self.threshold / 3) * jittor.randn(samples.shape) # 3 sigma rule + samples = samples.detach() + samples.requires_grad = True + + i += 1 + print(samples_cpu.shape) + + duration = time.time() - start + + return samples_cpu, duration + + + + def load_checkpoint(self, checkpoint): + checkpoints = glob(self.checkpoint_path + '/*') + #print(checkpoints) + if checkpoint is None: + if len(checkpoints) == 0: + print('No checkpoints found at {}'.format(self.checkpoint_path)) + return 0, 0 + + checkpoints = [os.path.splitext(os.path.basename(path))[0].split('_')[-1] for path in checkpoints] + checkpoints = np.array(checkpoints, dtype=float) + checkpoints = np.sort(checkpoints) + + for name in glob(self.checkpoint_path + '/*'): + if str(checkpoints[-1]) in name: + path = self.checkpoint_path + os.path.basename(name) + + #path = self.checkpoint_path + 'checkpoint_{}h:{}m:{}s_{}.tar'.format( + # *[*convertSecs(checkpoints[-1]), checkpoints[-1]]) + else: + path = self.checkpoint_path + '{}.tar'.format(checkpoint) + print('Loaded checkpoint from: {}'.format(path)) + ''' + #torch to jittor + + import torch + check_torch = torch.load(path) + if 'module' in list(check_torch['model_state_dict'].keys())[0]: + self.model.load_state_dict({k[7:]:v for k,v in check_torch['model_state_dict'].items()}) + else: + self.model.load_state_dict(check_torch['model_state_dict']) + epoch = check_torch['epoch'] + training_time = check_torch['training_time'] + del torch + ''' + checkpoint = jittor.load(path) + if 'module' in list(checkpoint['model_state_dict'].keys())[0]: + self.model.load_state_dict({k[7:]:v for k,v in checkpoint['model_state_dict'].items()}) + else: + self.model.load_state_dict(checkpoint['model_state_dict']) + epoch = checkpoint['epoch'] + training_time = checkpoint['training_time'] + + return epoch, training_time + + +def convertMillis(millis): + seconds = int((millis / 1000) % 60) + minutes = int((millis / (1000 * 60)) % 60) + hours = int((millis / (1000 * 60 * 60))) + return hours, minutes, seconds + +def convertSecs(sec): + seconds = int(sec % 60) + minutes = int((sec / 60) % 60) + hours = int((sec / (60 * 60))) + return hours, minutes, seconds diff --git a/contrib/HSDF-Net/models/local_model.py b/contrib/HSDF-Net/models/local_model.py new file mode 100644 index 00000000..baf48712 --- /dev/null +++ b/contrib/HSDF-Net/models/local_model.py @@ -0,0 +1,153 @@ +import jittor +import jittor.nn as nn + +class HSDF(nn.Module): + + def __init__(self, hidden_dim=256): + super(HSDF, self).__init__() + self.conv_in = nn.Conv3d(1, 16, 3, padding=1) # out: 256 ->m.p. 128 + self.conv_0 = nn.Conv3d(16, 32, 3, padding=1) # out: 128 + self.conv_0_1 = nn.Conv3d(32, 32, 3, padding=1) # out: 128 ->m.p. 64 + self.conv_1 = nn.Conv3d(32, 64, 3, padding=1) # out: 64 + self.conv_1_1 = nn.Conv3d(64, 64, 3, padding=1) # out: 64 -> mp 32 + self.conv_2 = nn.Conv3d(64, 128, 3, padding=1) # out: 32 + self.conv_2_1 = nn.Conv3d(128, 128, 3, padding=1) # out: 32 -> mp 16 + self.conv_3 = nn.Conv3d(128, 128, 3, padding=1) # out: 16 + self.conv_3_1 = nn.Conv3d(128, 128, 3, padding=1) # out: 16 -> mp 8 + self.conv_4 = nn.Conv3d(128, 128, 3, padding=1) # out: 8 + self.conv_4_1 = nn.Conv3d(128, 128, 3, padding=1) # out: 8 + + feature_size = (1 + 16 + 32 + 64 + 128 + 128 + 128) * 7 + 3 + self.fc_0 = nn.Conv1d(feature_size, hidden_dim * 2, 1) + self.fc_1 = nn.Conv1d(hidden_dim *2, hidden_dim, 1) + self.fc_2 = nn.Conv1d(hidden_dim , hidden_dim, 1) + self.fc_out = nn.Conv1d(hidden_dim, 1, 1) + + # classification head + self.fc_0_cls = nn.Conv1d(feature_size, hidden_dim * 2, 1) + self.fc_1_cls = nn.Conv1d(hidden_dim *2, hidden_dim, 1) + self.fc_2_cls = nn.Conv1d(hidden_dim , hidden_dim, 1) + self.fc_out_cls = nn.Conv1d(hidden_dim, 1, 1) + + self.actvn = nn.ReLU() + + self.maxpool = nn.MaxPool3d(2) + + self.conv_in_bn = nn.BatchNorm3d(16) + self.conv0_1_bn = nn.BatchNorm3d(32) + self.conv1_1_bn = nn.BatchNorm3d(64) + self.conv2_1_bn = nn.BatchNorm3d(128) + self.conv3_1_bn = nn.BatchNorm3d(128) + self.conv4_1_bn = nn.BatchNorm3d(128) + + # add remez_net + #self.m = 7 + #self.n = 7 + #self.max_mn = self.m if self.m >= self.n else self.n + #self.rat = rational_net(self.max_mn, self.max_mn, feature_size=feature_size) + + + displacment = 0.0722 + displacments = [] + displacments.append([0, 0, 0]) + for x in range(3): + for y in [-1, 1]: + input = [0, 0, 0] + input[x] = y * displacment + displacments.append(input) + + self.displacments = jittor.array(displacments) + + def encoder(self,x): + x = x.unsqueeze(1) + f_0 = x + + net = self.actvn(self.conv_in(x)) + net = self.conv_in_bn(net) + f_1 = net + net = self.maxpool(net) # out 128 + + net = self.actvn(self.conv_0(net)) + net = self.actvn(self.conv_0_1(net)) + net = self.conv0_1_bn(net) + f_2 = net + net = self.maxpool(net) # out 64 + + net = self.actvn(self.conv_1(net)) + net = self.actvn(self.conv_1_1(net)) + net = self.conv1_1_bn(net) + f_3 = net + net = self.maxpool(net) + + net = self.actvn(self.conv_2(net)) + net = self.actvn(self.conv_2_1(net)) + net = self.conv2_1_bn(net) + f_4 = net + net = self.maxpool(net) + + net = self.actvn(self.conv_3(net)) + net = self.actvn(self.conv_3_1(net)) + net = self.conv3_1_bn(net) + f_5 = net + net = self.maxpool(net) + + net = self.actvn(self.conv_4(net)) + net = self.actvn(self.conv_4_1(net)) + net = self.conv4_1_bn(net) + f_6 = net + + return f_0, f_1, f_2, f_3, f_4, f_5, f_6 + + def decoder(self, p, f_0, f_1, f_2, f_3, f_4, f_5, f_6): + + p_features = p.transpose(1, -1) + p = p.unsqueeze(1).unsqueeze(1) + p = jittor.concat([p + d for d in self.displacments], dim=2) + + + # feature extraction + feature_0 = nn.grid_sample(f_0, p, padding_mode='border', align_corners=True) + feature_1 = nn.grid_sample(f_1, p, padding_mode='border', align_corners=True) + feature_2 = nn.grid_sample(f_2, p, padding_mode='border', align_corners=True) + feature_3 = nn.grid_sample(f_3, p, padding_mode='border', align_corners=True) + feature_4 = nn.grid_sample(f_4, p, padding_mode='border', align_corners=True) + feature_5 = nn.grid_sample(f_5, p, padding_mode='border', align_corners=True) + feature_6 = nn.grid_sample(f_6, p, padding_mode='border', align_corners=True) + + + # here every channel corresponds to one feature. + + features = jittor.concat((feature_0, feature_1, feature_2, feature_3, feature_4, feature_5, feature_6), + dim=1) # (B, features, 1,7,sample_num) + shape = features.shape + features = jittor.reshape(features, + (shape[0], shape[1] * shape[3], shape[4])) # (B, featues_per_sample, samples_num) + features = jittor.concat((features, p_features), dim=1) # (B, featue_size, samples_num) + + p_r = None + + net = self.actvn(self.fc_0(features)) + net = self.actvn(self.fc_1(net)) + net = self.actvn(self.fc_2(net)) + net = self.actvn(self.fc_out(net)) + #net = self.fc_out(net) + out = net.squeeze(1) + + # classification head + net_cls = self.actvn(self.fc_0_cls(features)) + net_cls = self.actvn(self.fc_1_cls(net_cls)) + net_cls = self.actvn(self.fc_2_cls(net_cls)) + + # classification task with no actvn + out_cls = self.fc_out_cls(net_cls).squeeze(1) # (B, 1, samples_num) -> (B, samples_num) + + # return occupancy probabilities for the sampled points + #print(out_cls) + #p_r = dist.Bernoulli(logits=out_cls) + + + return out, out_cls + + def execute(self, p, x): + out, p_r = self.decoder(p, *self.encoder(x)) + return out, p_r \ No newline at end of file diff --git a/contrib/HSDF-Net/models/training.py b/contrib/HSDF-Net/models/training.py new file mode 100644 index 00000000..7fdeaeb8 --- /dev/null +++ b/contrib/HSDF-Net/models/training.py @@ -0,0 +1,307 @@ +from __future__ import division +import jittor +import jittor.optim as optim +import jittor.nn as nn + +from trainers.losses.filtering_losses import loss_lap, loss_lap_dsdf +from trainers.losses.eikonal_loss import loss_eikonal, loss_eikonal_dsdf + +from glob import glob +import numpy as np +import time +import os + +from tensorboardX import SummaryWriter + +class Trainer(object): + + def __init__( + self, + model, + #device, + train_dataset, + val_dataset, + exp_name, + optimizer='Adam', + lr = 1e-4, + threshold = 0.1, + cls_threshold=0.2): + + self.model = model + + if optimizer == 'Adam': + self.optimizer = optim.Adam(self.model.parameters(), lr= lr) + if optimizer == 'RMSprop': + self.optimizer = optim.RMSprop(self.model.parameters(), momentum=0.9) + + + self.train_dataset = train_dataset + self.val_dataset = val_dataset + self.exp_path = os.path.dirname(__file__) + '/../experiments/{}/'.format( exp_name) + self.checkpoint_path = self.exp_path + 'checkpoints/'.format( exp_name) + if jittor.rank == 0: + if not os.path.exists(self.checkpoint_path): + print(self.checkpoint_path) + os.makedirs(self.checkpoint_path) + self.writer = SummaryWriter(self.exp_path + 'summary'.format(exp_name)) + self.val_min = None + self.max_dist = threshold + + # compute binary cls threshold of Bernoulli logits + self.cls_logits_threshold = np.log(cls_threshold) - np.log(1. - cls_threshold) + + + def train_step(self,batch): + self.model.train() + self.optimizer.zero_grad() + loss, acc = self.compute_loss(batch) + #loss.backward() + #self.optimizer.step(loss) + print("loss {}".format(loss)) + self.optimizer.backward(loss) + self.optimizer.step() + + # map-reduce loss and acc + reduced_loss, reduced_acc = loss, acc + if jittor.in_mpi: + reduced_loss = loss.mpi_all_reduce()# / jittor.world_size + reduced_acc = acc.mpi_all_reduce()# / jittor.world_size + + return reduced_loss.item(), reduced_acc.item() + + def compute_loss(self,batch): + #device = self.device + + p = batch.get('grid_coords')#.to(device) + + # clamp p to -1,1 + #p = torch.clamp(p, max=1., min=-1.) + + # for computing curvature + #p.requires_grad = True + + df_gt = batch.get('df')#.to(device) #(Batch,num_points) + inputs = batch.get('inputs')#.to(device) + + #print('input max: {}'.format(torch.max(inputs))) + #print('input min: {}'.format(torch.min(inputs))) + + #print('p max: {}'.format(torch.max(p))) + #print('p min: {}'.format(torch.min(p))) + + + df_pred, p_r = self.model(p,inputs) #(Batch,num_points) + + #print('df_pred max: {}'.format(torch.max(df_pred))) + #print('df_pred min: {}'.format(torch.min(df_pred))) + + # have to split abs val traning and sign traning, cauz they're conflicting at open regions + # regression loss + loss_r = nn.L1Loss()( + jittor.clamp(df_pred, max_v=self.max_dist, min_v=0.), + jittor.clamp(jittor.abs(df_gt), max_v=self.max_dist))# out = (B,num_points) by componentwise comparing vecots of size num_samples: + + #loss_r = torch.nn.L1Loss(reduction='none')( + # df_pred, + # torch.abs(df_gt))# out = (B,num_points) by componentwise comparing vecots of size num_samples: + + loss_c = nn.L1Loss()( + jittor.clamp(p_r, max_v=self.max_dist, min_v=-self.max_dist), + jittor.clamp(df_gt, max_v=self.max_dist, min_v=-self.max_dist))# out = (B,num_points) by componentwise comparing vecots of size num_samples: + + + # classification loss + #sign_gt = torch.sign(df_gt) + + #cls_gt = (sign_gt+1)/2 + + #loss_c = torch.nn.CrossEntropyLoss()( + # cls_pred.permute(0,2,1).reshape(-1,2), + # cls_gt.view(-1)) + + # soft label + #cls_gt = cls_gt - sign_gt * 0.5 * torch.exp(-1/torch.abs(df_gt)) + + #loss_c = F.binary_cross_entropy_with_logits( + # p_r.logits, cls_gt, reduction='none'#, weight = 1/torch.exp(df_pred.detach()) + #) + + # introduce curvature loss for sharpening + ''' + loss_lap_scaling = 1. * loss_lap_dsdf( + df_pred, + df_pred, + x = p, + npoints = 5000, + beta = 0., + masking_thr = 50 + ) + + loss_unit_grad_norm = loss_eikonal_dsdf( + y = df_pred, + x = p, + weights = 50. + ) + ''' + + #print('loss_lap_scaling: {}'.format(loss_lap_scaling.sum(-1).mean())) + #print('loss_unit_grad_norm: {}'.format(loss_unit_grad_norm.sum(-1).mean())) + + + # compute accuracy for multi-class classification + + acc = ((p_r>0.).long()==(df_gt>0.).long()).sum().double() / (df_gt.shape[0] * df_gt.shape[1]) + #acc = torch.zeros((1)) + + #print('loss_r: {}'.format(loss_r.sum(-1).mean())) + #print('loss_c: {}'.format(loss_c.sum(-1).mean())) + + loss = loss_r.sum(-1).mean() + loss_c.sum(-1).mean()# + loss_unit_grad_norm.sum(-1).mean() + loss_lap_scaling.sum(-1).mean() # loss_i summed over all #num_samples samples -> out = (B,1) and mean over batch -> out = (1) + + return loss, acc + + def train_model(self, epochs): + loss = 0 + train_data_loader = self.train_dataset + start, training_time = self.load_checkpoint() + iteration_start_time = time.time() + + for epoch in range(start, epochs): + sum_loss = 0 + sum_acc = 0 + if jittor.rank == 0: + print('Start epoch {}'.format(epoch)) + + # shuffle ditributed sampler + #train_data_loader.sampler.set_epoch(epoch) + + print("loader length: {}".format(len(train_data_loader))) + + for idx, batch in enumerate(train_data_loader): + #print('idx: {}'.format(idx)) + + if jittor.rank == 0: + #save model + iteration_duration = time.time() - iteration_start_time + if iteration_duration > 60 * 60: # eve model every X min and at start + #print('{} eval'.format(self.local_rank)) + + training_time += iteration_duration + iteration_start_time = time.time() + + self.save_checkpoint(epoch, training_time) + val_loss, val_acc = self.compute_val_loss() + + if self.val_min is None: + self.val_min = val_loss + + if val_loss < self.val_min: + self.val_min = val_loss + for path in glob(self.exp_path + 'val_min=*'): + os.remove(path) + np.save(self.exp_path + 'val_min={}'.format(epoch), [epoch, val_loss]) + + self.writer.add_scalar('val loss batch avg', val_loss, epoch) + self.writer.add_scalar('val acc batch avg', val_acc, epoch) + + + #optimize model + loss, acc = self.train_step(batch) + if jittor.rank == 0: + print("Current loss: {} acc: {}".format(loss / self.train_dataset.num_sample_points, acc)) + sum_loss += loss + sum_acc += acc + + + + if jittor.rank == 0: + self.writer.add_scalar('training loss last batch', loss, epoch) + self.writer.add_scalar('training loss batch avg', sum_loss / len(train_data_loader), epoch) + self.writer.add_scalar('training acc batch avg', sum_acc / len(train_data_loader), epoch) + + + + + def save_checkpoint(self, epoch, training_time): + path = self.checkpoint_path + 'checkpoint_{}h_{}m_{}s_{}.tar'.format(*[*convertSecs(training_time),training_time]) + if not os.path.exists(path) and jittor.rank == 0: + jittor.save({ #'state': torch.cuda.get_rng_state_all(), + 'training_time': training_time ,'epoch':epoch, + 'model_state_dict': self.model.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict()}, path) + + + + def load_checkpoint(self): + checkpoints = glob(self.checkpoint_path+'/*') + if len(checkpoints) == 0: + print('No checkpoints found at {}'.format(self.checkpoint_path)) + return 0,0 + + checkpoints = [os.path.splitext(os.path.basename(path))[0].split('_')[-1] for path in checkpoints] + checkpoints = np.array(checkpoints, dtype=float) + checkpoints = np.sort(checkpoints) + + for name in glob(self.checkpoint_path + '/*'): + if str(checkpoints[-1]) in name: + path = self.checkpoint_path + os.path.basename(name) + #path = self.checkpoint_path + 'checkpoint_{}h:{}m:{}s_{}.tar'.format(*[*convertSecs(checkpoints[-1]),checkpoints[-1]]) + + print('Loaded checkpoint from: {}'.format(path)) + checkpoint = jittor.load(path) + + # load partially + #print(checkpoint['optimizer_state_dict']['param_groups']) + #exit() + #print(self.optimizer.state_dict()['param_groups']) + #exit() + + if 'module' in list(checkpoint['model_state_dict'].keys())[0]: + self.model.load_state_dict(checkpoint['model_state_dict']) + else: + print('load from without apex') + self.model.load_state_dict({'module.'+k:v for k,v in checkpoint['model_state_dict'].items()}) + + epoch = 0 + training_time = 0 + + try: + self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + #amp.load_state_dict(checkpoint['amp_state_dict']) + epoch = checkpoint['epoch'] + training_time = checkpoint['training_time'] + except: + print('find pretrained weights, epoch reduce to 0') + # torch.cuda.set_rng_state_all(checkpoint['state']) # batch order is restored. unfortunately doesn't work like that. + return epoch, training_time + + def compute_val_loss(self): + self.model.eval() + + sum_val_loss = 0 + sum_val_acc = 0 + num_batches = 15 + for _ in range(num_batches): + try: + val_batch = self.val_data_iterator.next() + except: + self.val_data_iterator = self.val_dataset.__iter__() + val_batch = self.val_data_iterator.__next__() + + #val_loss, val_acc = self.compute_loss( val_batch) + sum_val_loss += self.compute_loss( val_batch)[0].data.item() + sum_val_acc += self.compute_loss( val_batch)[1].data.item() + + return sum_val_loss / num_batches, sum_val_acc / num_batches + +def convertMillis(millis): + seconds = int((millis / 1000) % 60) + minutes = int((millis / (1000 * 60)) % 60) + hours = int((millis / (1000 * 60 * 60))) + return hours, minutes, seconds + +def convertSecs(sec): + seconds = int(sec % 60) + minutes = int((sec / 60) % 60) + hours = int((sec / (60 * 60))) + return hours, minutes, seconds diff --git a/contrib/HSDF-Net/ndf_postprocess.mlx b/contrib/HSDF-Net/ndf_postprocess.mlx new file mode 100644 index 00000000..a76c4e8a --- /dev/null +++ b/contrib/HSDF-Net/ndf_postprocess.mlx @@ -0,0 +1,10 @@ + + + + + + + + + + diff --git a/contrib/HSDF-Net/renderer.py b/contrib/HSDF-Net/renderer.py new file mode 100644 index 00000000..932f89b9 --- /dev/null +++ b/contrib/HSDF-Net/renderer.py @@ -0,0 +1,386 @@ +import math +import numpy as np +import trimesh +import cv2 +import os + +import configs.config_loader as cfg_loader + +import NDF_combine as NDF + +def ray_trace_mesh(trimesh_mesh, xyz_world, rays, batch_size=20004): + intersector = trimesh.ray.ray_triangle.RayMeshIntersector(trimesh_mesh) + all_loc_intersect = [] + all_ray_index = [] + all_face_index = [] + + for x in range(0, rays.shape[0], batch_size): + loc_intersect, ray_index, face_index = \ + intersector.intersects_location(xyz_world[x:x+batch_size], rays[x:x+batch_size]) + + if len(ray_index) != 0: + ray_index = batch_size * int(x / batch_size) + ray_index + all_loc_intersect.append(loc_intersect) + all_ray_index.append(ray_index) + all_face_index.append(face_index) + + if rays.shape[0] % batch_size: + x = batch_size * int(rays.shape[0] / batch_size) + loc_intersect, ray_index, face_index = \ + intersector.intersects_location(xyz_world[x:], rays[x:]) + if len(ray_index) != 0: + ray_index = x + ray_index + all_loc_intersect.append(loc_intersect) + all_ray_index.append(ray_index) + all_face_index.append(face_index) + + dists = np.ones(rays.shape[0]) * 100 + normals = np.ones(rays.shape) * 0.1 + + try: + all_loc_intersect = np.concatenate(all_loc_intersect, 0) + all_face_index = np.concatenate(all_face_index, 0) + all_ray_index = np.concatenate(all_ray_index, 0) + + for ct, xx in enumerate(all_ray_index): + dist = np.sqrt(np.sum((xyz_world[xx] - all_loc_intersect[ct])**2, 0)) + + if dist < dists[xx]: + dists[xx] = dist + yy = all_face_index[ct] + normals_ = trimesh_mesh.face_normals[yy] + #reverse = np.sum(normals_ * rays[xx]) > 0 + normals__ = -normals_ + #if reverse: + # normals__ = -normals_ + normals[xx] = normals__ + except: + dists[dists==100] = -1 + return normals, dists, xyz_world + + + return normals, dists, all_loc_intersect + + +def str2bool(inp): + return inp.lower() in 'true' + +class Renderer(): + def __init__(self): + self.get_args() + self.create_plane_points_from_bounds() + self.define_screen_points() + self.define_unit_rays() + + def get_args(self): + """ + :return: + """ + self.args = cfg_loader.get_config() + + # print(self.args.cam_position) + # print(self.args.cam_orientation) + os.makedirs(self.args.folder, exist_ok=True) + + def create_plane_points_from_bounds(self): + """ + Creates a plane of points which acts as the screen for rendering + """ + # create an xy plane + x = np.linspace(-self.args.screen_bound, self.args.screen_bound, self.args.size) + y = np.linspace(-self.args.screen_bound, self.args.screen_bound, self.args.size) + X, Y = np.meshgrid(x, y, indexing='ij') + X = X.reshape((np.prod(X.shape),)) + Y = Y.reshape((np.prod(Y.shape),)) + + # append the third dimension coordinate to the xy plane + points_list = np.column_stack((X, Y)) + points_list = np.insert(points_list, 2, self.args.screen_depth, axis=1) + self.points_list = points_list + + def to_rotation_matrix(self): + """ + Creates rotation matrix from the input euler angles + """ + euler_angles = np.array(self.args.cam_orientation) + R_x = np.array([[1, 0, 0], + [0, math.cos(math.radians(euler_angles[0])), -math.sin(math.radians(euler_angles[0]))], + [0, math.sin(math.radians(euler_angles[0])), math.cos(math.radians(euler_angles[0]))] + ]) + + R_y = np.array([[math.cos(math.radians(euler_angles[1])), 0, math.sin(math.radians(euler_angles[1]))], + [0, 1, 0], + [-math.sin(math.radians(euler_angles[1])), 0, math.cos(math.radians(euler_angles[1]))] + ]) + + R_z = np.array([[math.cos(math.radians(euler_angles[2])), -math.sin(math.radians(euler_angles[2])), 0], + [math.sin(math.radians(euler_angles[2])), math.cos(math.radians(euler_angles[2])), 0], + [0, 0, 1] + ]) + + R = np.dot(R_z, np.dot(R_y, R_x)) + + self.rot_matrix = R + + def to_transf_matrix(self): + """ + Creates a transformation matrix from rotation matrix and translation vector + """ + self.to_rotation_matrix() + + temp_trans = np.array([0, 0, 0]) + temp_trans = np.reshape(temp_trans, (1, 3)) + rot = np.concatenate((self.rot_matrix, temp_trans), axis=0) + rot = np.concatenate((rot, np.reshape(np.array([0, 0, 0, 1]), (4, 1))), axis=1) + + inp_trans = np.reshape(self.args.cam_position, (3,)) + inp_trans = np.concatenate((inp_trans, [1]), axis=0) + + rot[:, 3] = inp_trans + + self.trans_mat = rot + + def append_one(self, arr): + """ + :param arr: + :return: + """ + append = np.ones(arr.shape[0]) + append = np.reshape(append, (append.shape[0], 1)) + new_arr = np.concatenate((arr, append), axis=1) + return new_arr + + def define_screen_points(self): + """ + Transforms the screen points and camera position using the camera translation and orientation information provided by the user + """ + self.create_plane_points_from_bounds() + self.to_transf_matrix() + + cam_loc = np.array([0, 0, 0]) + screen_and_cam = np.vstack((cam_loc, self.points_list)) + screen_and_cam_hom = self.append_one(screen_and_cam) + + # 4 X SIZE^2 + screen_and_cam_hom_T = np.transpose(screen_and_cam_hom, (1, 0)) + screen_and_cam_hom_T_transformed = np.matmul(self.trans_mat, screen_and_cam_hom_T) + + # SIZE^2 X 4 + screen_and_cam_hom_transformed = np.transpose(screen_and_cam_hom_T_transformed, (1, 0)) + + # SIZE^2 X 3 + self.screen_and_cam_transformed = screen_and_cam_hom_transformed[:, :3] + + if self.args.debug_mode: + trimesh.Trimesh(vertices=self.screen_and_cam_transformed, faces=[]).export('setup_camera_rot.off') + + def define_unit_rays(self): + """ + Defines rays from camera to the screen along which + """ + # Separate screen points and camera point + points = self.screen_and_cam_transformed[1:, :] + self.cam_trans = np.reshape(self.screen_and_cam_transformed[0, :], (1, 3)) + + # Define ray paths from camera + ray_vector = (points - self.cam_trans) + + # Normalize ray vectors + norm_ray = np.linalg.norm(ray_vector, ord=2, axis=1) + norm_ray = np.reshape(norm_ray, (self.args.size * self.args.size, 1)) + + self.unit_rays = ray_vector / norm_ray + + def get_lgth_rays(self): + """ + :return: + """ + src_batch = np.repeat([self.args.light_position], self.args.size * self.args.size, axis=0) + rays = src_batch - self.final_points + norm_ray = np.linalg.norm(rays, ord=2, axis=1) + norm_ray = np.reshape(norm_ray, (self.args.size * self.args.size, 1)) + + self.ray_to_src = rays / norm_ray + + def run(self): + """ + Runs the ray marching algorithm + """ + print(self.args) + path = NDF.loadNDF( + mode = 'test', index = self.args.index, + pointcloud_samples = self.args.pc_samples, + exp_name = self.args.exp_name, data_dir = self.args.data_dir, + split_file = self.args.split_file, sample_distribution = self.args.sample_ratio, + sample_sigmas = self.args.sample_std_dev, res = self.args.input_res + ) + + depth = np.zeros((self.args.size * self.args.size, 1)) + depth_gt = np.zeros((self.args.size * self.args.size, 1)) + + cam_batch = np.repeat(self.cam_trans, self.args.size * self.args.size, axis=0) + points = cam_batch.copy() + points_gt = cam_batch.copy() + + iter = 1 + + ray = self.unit_rays.copy() + ray_gt = self.unit_rays.copy() + + indices_cont_all = list(range(self.args.size * self.args.size)) + indices_cont_all_gt = list(range(self.args.size * self.args.size)) + + trimesh_mesh = trimesh.load(os.path.join(path, 'model_scaled.off')) + # compute gt surface points + + #dists_gt = NDF.predictGtNDF(points, trimesh_mesh) + #points_gt = points + ray * np.expand_dims(dists_gt, axis=-1) + + ''' + while len(indices_cont_all) > 0: + + print('Iter:', iter) + dists_points_gt = NDF.predictGtNDF(points_gt, trimesh_mesh) + dists_points_gt = np.reshape(dists_points_gt, (self.args.size * self.args.size, 1)) + + indices_stop_gt = np.where(dists_points_gt < self.args.epsilon)[0] + indices_stop2_gt = np.where(depth_gt > self.args.max_depth)[0] + indices_stop_all_gt = list(set(indices_stop_gt).union(set(indices_stop2_gt))) + # print(len(indices_stop_all)) + + ray_gt[indices_stop_all_gt] = 0 + setA = set(range(self.args.size * self.args.size)) + setB = set(indices_stop_all_gt) + indices_cont_all_gt = list(setA.difference(setB)) + + + # print(len(indices_cont_all)) + depth[indices_cont_all_gt] = depth[indices_cont_all_gt] + self.args.alpha * dists_points_gt[indices_cont_all_gt] + points_gt = points_gt + (ray_gt * (self.args.alpha * dists_points_gt)) + iter = iter + 1 + + iter = 1 + ''' + + #print(points.shape) + + + while len(indices_cont_all) > 0: + + print('Iter:', iter) + dists_points = NDF.predictRotNDF(points) + dists_points = np.reshape(dists_points, (self.args.size * self.args.size, 1)) + + indices_stop = np.where(dists_points < self.args.epsilon)[0] + indices_stop2 = np.where(depth > self.args.max_depth)[0] + indices_stop_all = list(set(indices_stop).union(set(indices_stop2))) + # print(len(indices_stop_all)) + + ray[indices_stop_all] = 0 + setA = set(range(self.args.size * self.args.size)) + setB = set(indices_stop_all) + indices_cont_all = list(setA.difference(setB)) + + + # print(len(indices_cont_all)) + depth[indices_cont_all] = depth[indices_cont_all] + self.args.alpha * dists_points[indices_cont_all] + points = points + (ray * (self.args.alpha * dists_points)) + iter = iter + 1 + + #print(np.max(dists_points)) + #print(points.shape) + #print(np.max(depth)) + #print(np.min(depth)) + #print(len(depth>self.args.max_depth)) + + points = points - (self.unit_rays * self.args.step_back) + + self.final_points = points.copy() + + ## NORMALS + self.depth_np = depth.copy() + self.depth_np[self.depth_np > self.args.max_depth] = self.args.max_depth + + dists, gradients = NDF.predictRotGradientNDF(points) + + #dist_gt, gradients_gt = NDF.predictGtGradientNDF(points_gt, trimesh_mesh) + vertices = trimesh_mesh.vertices.copy() + trimesh_mesh.vertices[:,1], trimesh_mesh.vertices[:,2] = vertices[:,2], vertices[:,1] + gradients_gt, depth_gt, all_loc_intersect = ray_trace_mesh(trimesh_mesh, points_gt, ray_gt) + + self.depth_gt = depth_gt.copy() + self.depth_gt[self.depth_gt > self.args.max_depth] = self.args.max_depth + + self.final_gradients = gradients.copy() + self.normals = np.reshape(gradients, (self.args.size * self.args.size, 3)) + + self.final_gradients_gt = gradients_gt.copy() + self.normals_gt = np.reshape(gradients_gt, (self.args.size * self.args.size, 3)) + + + def save(self, image, name, size, normalize): + """ + :param image: Input image as np array + :param name: Name of file to be stored + :param size: Size of the image + :param normalize: whether to normalize all values to 0-1 + Saves individual images + """ + if normalize: + image = (image + 1)/2 + image = np.reshape(image, (self.args.size, self.args.size, size)) + + image = cv2.transpose(image) + image = cv2.flip(image, 0) + image = image[90:610, :] + + cv2.imwrite(os.path.join(self.args.folder, name), np.uint8(255 * image)) + + def save_images(self): + """ + Saves Images after completion of the rendering algorithm + """ + shade = np.sum(np.multiply(-self.unit_rays, self.normals), axis=1) + shade = np.reshape(shade, (shade.shape[0], 1)) + + shade[self.depth_np == self.args.max_depth] = 1 + self.save(shade, 'shade.jpg', 1, True) + + # SHADE WITH LIGhT SOURCE + if self.args.shade: + self.get_lgth_rays() + shd_lgth = np.sum(np.multiply(self.ray_to_src, self.normals), axis=1) + shd_lgth = np.reshape(shd_lgth, (shd_lgth.shape[0], 1)) + shd_lgth[self.depth_np == self.args.max_depth ] = 1 + self.save(shd_lgth, 'shade_src.jpg', 1, True) + + if self.args.normal: + RGB_normals = self.final_gradients.copy() + inds = (self.depth_np == self.args.max_depth) + for j in range(3): + new_arr = np.reshape(RGB_normals[:, j], (self.args.size * self.args.size, 1)) + new_arr[inds] = 1 + + black_pixels_mask = np.all(RGB_normals == [0, 0, 0], axis=-1) + RGB_normals[black_pixels_mask] = np.array([1, 1, 1]) + self.save(RGB_normals, 'normals.jpg', 3, True) + + # save gt mesh normal map + RGB_normals_gt = self.final_gradients_gt.copy() + inds = (self.depth_gt == self.args.max_depth) + for j in range(3): + new_arr = np.reshape(RGB_normals_gt[:, j], (self.args.size * self.args.size, 1)) + new_arr[inds] = 1 + + black_pixels_mask_gt = np.all(RGB_normals_gt == [0, 0, 0], axis=-1) + RGB_normals_gt[black_pixels_mask_gt] = np.array([1, 1, 1]) + self.save(RGB_normals_gt, 'normals_gt.jpg', 3, True) + + if self.args.depth: + depth_normalized = np.copy(self.depth_np / self.args.max_depth) + self.save(depth_normalized, 'depth_final.jpg', 1, False) + +if __name__ == "__main__": + renderer = Renderer() + renderer.run() + renderer.save_images() \ No newline at end of file diff --git a/contrib/HSDF-Net/slurm_scripts/out/.gitkeep b/contrib/HSDF-Net/slurm_scripts/out/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/contrib/HSDF-Net/slurm_scripts/run_preprocessing.sh b/contrib/HSDF-Net/slurm_scripts/run_preprocessing.sh new file mode 100644 index 00000000..fc99ffc5 --- /dev/null +++ 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a/contrib/HSDF-Net/train.py b/contrib/HSDF-Net/train.py new file mode 100644 index 00000000..06b09364 --- /dev/null +++ b/contrib/HSDF-Net/train.py @@ -0,0 +1,57 @@ +import models.local_model as model +import models.data.voxelized_data_shapenet as voxelized_data +from models import training +import jittor +import configs.config_loader as cfg_loader +import os + +jittor.flags.use_cuda=1 + +cfg = cfg_loader.get_config() + +# # configure apex +# torch.cuda.set_device(cfg.local_rank) +# torch.distributed.init_process_group( +# 'nccl', +# init_method='env://' +# ) + +net = model.HSDF() + +print("local rank: {}".format(cfg.local_rank)) + +train_dataset = voxelized_data.VoxelizedDataset('train', + res=cfg.input_res, + pointcloud_samples=cfg.num_points, + data_path=cfg.data_dir, + split_file=cfg.split_file, + batch_size=cfg.batch_size, + num_sample_points=cfg.num_sample_points_training, + num_workers=2, + sample_distribution=cfg.sample_ratio, + sample_sigmas=cfg.sample_std_dev) +val_dataset = voxelized_data.VoxelizedDataset('val', + res=cfg.input_res, + pointcloud_samples=cfg.num_points, + data_path=cfg.data_dir, + split_file=cfg.split_file, + batch_size=cfg.batch_size, + num_sample_points=cfg.num_sample_points_training, + num_workers=2, + sample_distribution=cfg.sample_ratio, + sample_sigmas=cfg.sample_std_dev) + +# # debug for NaN +# torch.autograd.set_detect_anomaly(True) + +trainer = training.Trainer(net, + #torch.device('cuda:{}'.format(cfg.local_rank)), + train_dataset, + val_dataset, + cfg.exp_name, + optimizer=cfg.optimizer, + lr=cfg.lr, + # local_rank=cfg.local_rank, + cls_threshold=cfg.threshold) + +trainer.train_model(cfg.num_epochs) diff --git a/contrib/HSDF-Net/trainers/base_trainer.py b/contrib/HSDF-Net/trainers/base_trainer.py new file mode 100644 index 00000000..e58ce607 --- /dev/null +++ b/contrib/HSDF-Net/trainers/base_trainer.py @@ -0,0 +1,37 @@ + +class BaseTrainer(): + + def __init__(self, cfg, args): + pass + + def update(self, data, *args, **kwargs): + raise NotImplementedError("Trainer [update] not implemented.") + + def epoch_end(self, epoch, writer=None, **kwargs): + # Signal now that the epoch ends.... + pass + + def multi_gpu_wrapper(self, wrapper): + raise NotImplementedError("Trainer [multi_gpu_wrapper] not implemented.") + + def log_train(self, train_info, train_data, + writer=None, step=None, epoch=None, visualize=False, + **kwargs): + raise NotImplementedError("Trainer [log_train] not implemented.") + + def validate(self, test_loader, epoch, *args, **kwargs): + raise NotImplementedError("Trainer [validate] not implemented.") + + def log_val(self, val_info, writer=None, step=None, epoch=None, **kwargs): + if writer is not None: + for k, v in val_info.items(): + if step is not None: + writer.add_scalar(k, v, step) + else: + writer.add_scalar(k, v, epoch) + + def save(self, epoch=None, step=None, appendix=None, **kwargs): + raise NotImplementedError("Trainer [save] not implemented.") + + def resume(self, path, strict=True, **kwargs): + raise NotImplementedError("Trainer [resume] not implemented.") diff --git a/contrib/HSDF-Net/trainers/implicit_deform.py b/contrib/HSDF-Net/trainers/implicit_deform.py new file mode 100644 index 00000000..c9fec817 --- /dev/null +++ b/contrib/HSDF-Net/trainers/implicit_deform.py @@ -0,0 +1,317 @@ +import os +#import torch +import jittor +import importlib +import os.path as osp +from argparse import Namespace +#import torch.nn.functional as F +import jittor.nn as F +from trainers.base_trainer import BaseTrainer +from trainers.utils.utils import set_random_seed +from trainers.utils.igp_utils import sample_points +from trainers.losses.eikonal_loss import loss_eikonal +from models.igp_wrapper import distillation, deformation +from trainers.losses.implicit_thin_shell_losses import \ + stretch_loss, bending_loss + + +def deform_step( + net, opt, original, handles_ts, targets_ts, dim=3, + # Clip gradient + grad_clip=None, + # Sample points + sample_cfg=None, x=None, weights=1, + # Loss handle + loss_h_weight=1., use_l1_loss=False, loss_h_thr=None, + # Loss G + loss_g_weight=1e-2, n_g_pts=5000, + # Loss bending + loss_hess_weight=0., n_hess_pts=5000, hess_use_surf_points=True, + hess_invert_sample=True, hess_detach_weight=True, hess_use_rejection=False, + # Loss stretch + loss_stretch_weight=0., n_s_pts=5000, stretch_use_surf_points=True, + stretch_invert_sample=True, stretch_loss_type='area_length', + stretch_use_weight=False, stretch_detach_weight=True, + stretch_use_rejection=False, +): + opt.zero_grad() + + # Compute handle losses + # x + handles_ts = handles_ts.clone().detach().float().cuda() + # y + targets_ts = targets_ts.clone().detach().float().cuda() + constr = ( + net(targets_ts, return_delta=True)[0] + targets_ts - handles_ts + ).view(-1, dim).norm(dim=-1, keepdim=False) + if loss_h_thr is not None: + loss_h_thr = float(loss_h_thr) + constr = F.relu(constr - loss_h_thr) + if use_l1_loss: + loss_h = F.l1_loss( + constr, jittor.zeros_like(constr)) * loss_h_weight + else: + loss_h = F.mse_loss( + constr, jittor.zeros_like(constr)) * loss_h_weight + + if sample_cfg is not None and x is None: + x, weights = sample_points( + npoints=getattr(sample_cfg, "num_points", 5000), + dim=dim, inp_nf=original, out_nf=net, deform=net.deform, + sample_surf_points=getattr(sample_cfg, "use_surf_points", True), + invert_sampling=getattr(sample_cfg, "invert_sample", True), + detach_weight=getattr(sample_cfg, "detach_weight", True), + use_rejection=getattr(sample_cfg, "use_rejection", False) + ) + + if loss_g_weight > 0.: + loss_g = loss_eikonal(net, npoints=n_g_pts, dim=dim, x=x) * loss_g_weight + else: + loss_g = jittor.zeros(1).cuda().float() + + if loss_hess_weight > 0.: + loss_hess = bending_loss( + inp_nf=original, out_nf=net, deform=net.deform, + dim=dim, npoints=n_hess_pts, + use_surf_points=hess_use_surf_points, + invert_sampling=hess_invert_sample, + x=x, weights=weights, + detach_weight=hess_detach_weight, + use_rejection=hess_use_rejection, + ) + loss_hess *= loss_hess_weight + else: + loss_hess = jittor.zeros(1).cuda().float() + + if loss_stretch_weight > 0.: + loss_stretch = stretch_loss( + inp_nf=original, out_nf=net, deform=net.deform, + npoints=n_s_pts, dim=dim, + use_surf_points=stretch_use_surf_points, + invert_sampling=stretch_invert_sample, + loss_type=stretch_loss_type, + x=x, weights=weights, + detach_weight=stretch_detach_weight, + use_rejection=stretch_use_rejection, + ) + loss_stretch *= loss_stretch_weight + else: + loss_stretch = jittor.zeros(1).cuda().float() + + loss = loss_h + loss_g + loss_hess + loss_stretch + opt.backward(loss) + if grad_clip is not None: + opt.clip_grad_norm(net.deform.parameters(), grad_clip) + + opt.step() + + return { + 'loss': loss.detach().cpu().item(), + 'loss_h': loss_h.detach().cpu().item(), + # Repairing + 'loss_g': loss_g.detach().cpu().item(), + # Shell energy + 'loss_hess': loss_hess.detach().cpu().item(), + 'loss_stretch': loss_stretch.detach().cpu().item() + } + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args, original_decoder=None): + super().__init__(cfg, args) + self.cfg = cfg + self.args = args + set_random_seed(getattr(self.cfg.trainer, "seed", 666)) + + # The networks + # TODO: add recursive loading of trainers. + if original_decoder is None: + sn_lib = importlib.import_module(cfg.models.decoder.type) + self.original_net = sn_lib.Net(cfg, cfg.models.decoder) + self.original_net.cuda() + self.original_net.load_state_dict( + jittor.load(cfg.models.decoder.path)['net']) + print("Original Decoder:") + print(self.original_net) + else: + self.original_net = original_decoder + + # Get the wrapper for the operation + self.wrapper_type = getattr( + cfg.trainer, "wrapper_type", "distillation") + if self.wrapper_type in ['distillation']: + self.net, self.opt, self.sch = distillation( + cfg, self.original_net, + reload=getattr(self.cfg.trainer, "reload_decoder", True)) + elif self.wrapper_type in ['deformation']: + self.net, self.opt, self.sch = deformation( + cfg, self.original_net) + else: + raise ValueError("wrapper_type:", self.wrapper_type) + + # Prepare save directory + os.makedirs(osp.join(cfg.save_dir, "images"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "checkpoints"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "val"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "vis"), exist_ok=True) + + # Set-up counter + self.num_update_step = 0 + self.boundary_points = None + + # Set up basic parameters + self.dim = getattr(cfg.trainer, "dim", 3) + self.grad_clip = getattr(cfg.trainer, "grad_clip", None) + self.loss_h_weight = getattr(cfg.trainer, "loss_h_weight", 100) + self.loss_h_thr = getattr(cfg.trainer, "loss_h_thr", 1e-3) + + if hasattr(cfg.trainer, "loss_g"): + self.loss_g_cfg = cfg.trainer.loss_g + else: + self.loss_g_cfg = Namespace(**{}) + + if hasattr(cfg.trainer, "loss_bend"): + self.loss_bend_cfg = cfg.trainer.loss_bend + else: + self.loss_bend_cfg = Namespace(**{}) + + if hasattr(cfg.trainer, "loss_stretch"): + self.loss_stretch_cfg = cfg.trainer.loss_stretch + else: + self.loss_stretch_cfg = Namespace() + + if hasattr(cfg.trainer, "sample_cfg"): + self.sample_cfg = cfg.trainer.sample_cfg + else: + self.sample_cfg = None + + self.show_network_hist = getattr( + cfg.trainer, "show_network_hist", False) + + def update(self, data, *args, **kwargs): + self.num_update_step += 1 + handles_ts = data['handles'].cuda().float() + targets_ts = data['targets'].cuda().float() + if 'x' in data and 'weights' in data: + x_ts = data['x'].cuda().float() + w_ts = data['weights'].cuda().float() + else: + x_ts = None + w_ts = 1. + + loss_g_weight = float(getattr(self.loss_g_cfg, "weight", 1e-3)) + loss_hess_weight = float(getattr(self.loss_bend_cfg, "weight", 0.)) + loss_stretch_weight = float( + getattr(self.loss_stretch_cfg, "weight", 0)) + step_res = deform_step( + self.net, self.opt, self.original_net, + handles_ts, targets_ts, dim=self.dim, + x=x_ts, weights=w_ts, + sample_cfg=self.sample_cfg, + # Loss handle + loss_h_weight=self.loss_h_weight, + loss_h_thr=self.loss_h_thr, + # Loss G + loss_g_weight=loss_g_weight, + n_g_pts=getattr(self.loss_g_cfg, "num_points", 5000), + + # Loss Hessian + loss_hess_weight=loss_hess_weight, + n_hess_pts=getattr(self.loss_bend_cfg, "num_points", 5000), + hess_use_surf_points=getattr( + self.loss_bend_cfg, "use_surf_points", True), + hess_invert_sample=getattr( + self.loss_bend_cfg, "invert_sample", True), + hess_detach_weight=getattr( + self.loss_bend_cfg, "detach_weight", True), + hess_use_rejection=getattr( + self.loss_bend_cfg, "use_rejection", True), + + # Loss stretch + loss_stretch_weight=loss_stretch_weight, + n_s_pts=getattr(self.loss_stretch_cfg, "num_points", 5000), + stretch_use_surf_points=getattr( + self.loss_stretch_cfg, "use_surf_points", True), + stretch_invert_sample=getattr( + self.loss_stretch_cfg, "invert_sample", True), + stretch_loss_type=getattr( + self.loss_stretch_cfg, "loss_type", "l2"), + stretch_use_weight=getattr( + self.loss_stretch_cfg, "use_weight", True), + stretch_detach_weight=getattr( + self.loss_stretch_cfg, "detach_weight", True), + stretch_use_rejection=getattr( + self.loss_stretch_cfg, "use_rejection", True), + + # Gradient clipping + grad_clip=self.grad_clip, + ) + step_res = { + ('scalar/loss/%s' % k): v for k, v in step_res.items() + } + step_res['loss'] = step_res['scalar/loss/loss'] + step_res.update({ + "scalar/weight/loss_h_weight": self.loss_h_weight, + 'scalar/weight/loss_hess_weight': loss_hess_weight, + 'scalar/weight/loss_stretch_weight': loss_stretch_weight, + }) + return step_res + + def log_train(self, train_info, train_data, writer=None, + step=None, epoch=None, visualize=False, **kwargs): + if writer is None: + return + writer_step = step if step is not None else epoch + + # Log training information to tensorboard + train_info = {k: (v.cpu() if not isinstance(v, float) else v) + for k, v in train_info.items()} + for k, v in train_info.items(): + ktype = k.split("/")[0] + kstr = "/".join(k.split("/")[1:]) + if ktype == 'scalar': + writer.add_scalar(kstr, v, writer_step) + + if self.show_network_hist: + for name, p in self.net.named_parameters(): + writer.add_histogram("dec/%s" % name, p, writer_step) + for name, p in self.original_net.named_parameters(): + writer.add_histogram("orig_dec/%s" % name, p, writer_step) + + def validate(self, test_loader, epoch, *args, **kwargs): + # TODO: compute mesh and compute the manifold harmonics to + # see if the high frequencies signals are dimed/suppressed + return {} + + def save(self, epoch=None, step=None, appendix=None, **kwargs): + d = { + 'dec': self.original_net.state_dict(), + 'net_opt_dec': self.opt.state_dict(), + 'next_dec': self.net.state_dict(), + 'epoch': epoch, + 'step': step + } + if appendix is not None: + d.update(appendix) + save_name = "epoch_%s_iters_%s.pt" % (epoch, step) + jittor.save(d, osp.join(self.cfg.save_dir, "checkpoints", save_name)) + jittor.save(d, osp.join(self.cfg.save_dir, "latest.pt")) + + def resume(self, path, strict=True, **kwargs): + ckpt = jittor.load(path) + self.original_net.load_state_dict(ckpt['dec'], strict=strict) + self.net.load_state_dict(ckpt['next_dec'], strict=strict) + self.opt.load_state_dict(ckpt['net_opt_dec']) + start_epoch = ckpt['epoch'] + return start_epoch + + def multi_gpu_wrapper(self, wrapper): + self.net = wrapper(self.net) + + def epoch_end(self, epoch, writer=None, **kwargs): + if self.sch is not None: + self.sch.step(epoch=epoch) + if writer is not None: + writer.add_scalar( + 'lr/opt_dec_lr_sch', self.sch.get_lr()[0], epoch) diff --git a/contrib/HSDF-Net/trainers/implicit_deform_2D.py b/contrib/HSDF-Net/trainers/implicit_deform_2D.py new file mode 100644 index 00000000..a04ac07d --- /dev/null +++ b/contrib/HSDF-Net/trainers/implicit_deform_2D.py @@ -0,0 +1,184 @@ +#import torch +import jittor +import numpy as np +from trainers.utils.vis_utils import imf2img, make_2d_grid +from trainers.utils.igp_losses import get_surf_pcl +from trainers.implicit_deform import Trainer as BaseTrainer +from argparse import Namespace +import matplotlib.pyplot as plt + + +try: + from evaluation.evaluation_metrics import EMD_CD + eval_reconstruciton = True +except: # noqa + # Skip evaluation + eval_reconstruciton = False + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args, original_decoder=None): + super().__init__(cfg, args, original_decoder=original_decoder) + self.dim = 2 + self.vis_cfg = getattr(self.cfg.trainer, "vis", Namespace()) + + def visualize( + self, train_data, train_info, + writer=None, step=None, epoch=None, **kwargs): + figsize = getattr(self.vis_cfg, "figsize", 5) + res = getattr(self.vis_cfg, "res", 100) + handle_np = ( + (train_data['handles'] + 1.) * float(res) * 0.5 + ).detach().cpu().numpy().reshape(-1, 2) + new_handle_np = ( + (train_data['targets'] + 1) * float(res) * 0.5 + ).detach().cpu().numpy().reshape(-1, 2) + orig_img = imf2img( + lambda x: self.original_net(x, None), res=res + ).reshape(res, res) + img = imf2img( + lambda x: self.net(x, None), res=res).reshape(res, res) + + qres = getattr(self.vis_cfg, "qres", 15) + orig_loc = make_2d_grid(qres).view(qres * qres, 2).detach().numpy() + dfm= self.net( + jittor.Var(orig_loc).cuda().view(1, -1, 2).float(), + None, return_delta=True + )[0].detach().cpu().numpy().reshape(qres * qres, 2) + + fig_s = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + axs = fig_s.add_subplot(111) + axs.contour(orig_img, levels=[0], linestyles='dotted') + axs.contour(img, levels=[0]) + axs.scatter(handle_np[:, 0], handle_np[:, 1], c='b', marker='*') + axs.scatter(new_handle_np[:, 0], new_handle_np[:, 1], c='r', marker='o') + axs.set_title("Shape") + writer.add_figure( + "shape", fig_s, global_step=(step if step is not None else epoch)) + + fig_f = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + axf = fig_f.add_subplot(111) + axf.contour(orig_img, levels=[0], linestyles='dotted') + axf.contour(img) + axf.scatter(handle_np[:, 0], handle_np[:, 1], c='b', marker='*') + axf.scatter(new_handle_np[:, 0], new_handle_np[:, 1], c='r', marker='o') + axf.set_title("Field") + writer.add_figure( + "field", fig_f, global_step=(step if step is not None else epoch)) + + fig_d = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + axd = fig_d.add_subplot(111) + axd.set_title("Deform Direction") + dfm_norm = np.linalg.norm(dfm, axis=-1).reshape(-1, 1) + dfm_dirc = (dfm / dfm_norm).reshape(-1, 2) + loc_in_res = 0.5 * (orig_loc + 1.) * res + axd.contour(orig_img, levels=[0], linestyles='dotted', colors='r') + axd.contour(img, levels=[0], colors='r') + axd.quiver( + loc_in_res[:, 0], loc_in_res[:, 1], dfm_dirc[:, 0], dfm_dirc[:, 1]) + axd.scatter(handle_np[:, 0], handle_np[:, 1], c='b', marker='*') + axd.scatter(new_handle_np[:, 0], new_handle_np[:, 1], c='r', marker='o') + writer.add_figure( + "deform_direction", fig_d, + global_step=(step if step is not None else epoch)) + + fig_l = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + axl = fig_l.add_subplot(111) + dfm = imf2img( + lambda x: self.net(x, None, return_delta=True)[0], res=res) + dfm_norm_img = np.linalg.norm( + dfm.reshape(res, res, 2), axis=-1).reshape(res, res) + axl.contourf(dfm_norm_img) + axl.set_title("Deform Length") + axl.contour(orig_img, levels=[0], linestyles='dotted', colors='r') + axl.contour(img, levels=[0], colors='r') + axl.scatter(handle_np[:, 0], handle_np[:, 1], c='b', marker='*') + axl.scatter(new_handle_np[:, 0], new_handle_np[:, 1], c='r', marker='o') + writer.add_figure( + "deform_length", fig_l, + global_step=(step if step is not None else epoch)) + + # Sample points just to make sure this is also exampled + # 1. Sample directly with net + # 2. Sample directly with original + # 3. Inverse the originally sampled + n_pts_smp = getattr(self.vis_cfg, "n_pts_smp", 1000) + print("Sampling forward!") + x_forward = get_surf_pcl( + lambda x: self.net(x, None), npoints=n_pts_smp, dim=2 + ).view(-1, 2).detach().cpu().numpy() + fig_xfwd = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + ax_xfwd = fig_xfwd.add_subplot(111) + ax_xfwd.set_title("Npoints:%d" % int(x_forward.shape[0])) + if x_forward.shape[0] > 0: + ax_xfwd.scatter(x_forward[:, 0], x_forward[:, 1], s=5, marker='o') + ax_xfwd.set_xlim(-1, 1) + ax_xfwd.set_ylim(-1, 1) + writer.add_figure( + "x_forward", fig_xfwd, + global_step=(step if step is not None else epoch)) + + print("Sampling!") + x_orig = get_surf_pcl( + lambda x: self.original_net(x, None), + npoints=n_pts_smp, dim=2).view(-1, 2) + if hasattr(self.net, "deform") and x_orig.size(0) > 0 and \ + hasattr(self.net.deform, "invert"): + with jittor.no_grad(): + x_invert = self.net.deform.invert( + x_orig.view(1, -1, 2), iters=30) + x_invert = x_invert.detach().cpu().numpy().reshape(-1, 2) + + fig_xinv = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + ax_xinv = fig_xinv.add_subplot(111) + ax_xinv.set_title("Npoints:%d" % int(x_invert.shape[0])) + ax_xinv.scatter(x_invert[:, 0], x_invert[:, 1], s=5, marker='o') + ax_xinv.set_xlim(-1, 1) + ax_xinv.set_ylim(-1, 1) + writer.add_figure( + "x_invert", fig_xinv, + global_step=(step if step is not None else epoch)) + + x_orig = x_orig.detach().cpu().numpy().reshape(-1, 2) + fig_xorg = plt.figure(figsize=(figsize, figsize)) + plt.tight_layout() + ax_xorig = fig_xorg.add_subplot(111) + ax_xorig.set_title("Npoints:%d" % int(x_orig.shape[0])) + if x_orig.shape[0] > 0: + ax_xorig.scatter(x_orig[:, 0], x_orig[:, 1], s=5, marker='o') + ax_xorig.set_xlim(-1, 1) + ax_xorig.set_ylim(-1, 1) + writer.add_figure( + "x_orig", fig_xorg, + global_step=(step if step is not None else epoch)) + + + def validate(self, test_loader, epoch, *args, **kwargs): + # TODO: compute mesh and compute the manifold harmonics to + # see if the high frequencies signals are dimed/suppressed + val_res = getattr(self.cfg.trainer, "val_res", 128) + _, orig_stats = imf2img( + lambda x: self.original_net(x, None), res=val_res, + return_stats=True, verbose=True + ) + _, net_stats = imf2img( + lambda x: self.net(x, None), res=val_res, + return_stats=True, verbose=True + ) + + return { + 'val/org_area': orig_stats['area'], + 'val/new_area': net_stats['area'], + 'val/area_change_ratio': net_stats['area'] / (orig_stats['area'] + 1e-5), + 'val/org_length': orig_stats['len'], + 'val/new_length': net_stats['len'], + 'val/length_change_ratio': net_stats['len'] / (orig_stats['len'] + 1e-5), + } + diff --git a/contrib/HSDF-Net/trainers/implicit_deform_3D.py b/contrib/HSDF-Net/trainers/implicit_deform_3D.py new file mode 100644 index 00000000..824f3506 --- /dev/null +++ b/contrib/HSDF-Net/trainers/implicit_deform_3D.py @@ -0,0 +1,151 @@ +import os +#import torch +import jittor +import trimesh +import numpy as np +from argparse import Namespace +from trainers.utils.vis_utils import imf2mesh +from evaluation.evaluation_metrics import CD, EMD +from trainers.implicit_deform import Trainer as BaseTrainer +from trainers.utils.igp_utils import compute_invert_weight + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args, original_decoder=None): + super().__init__(cfg, args, original_decoder=original_decoder) + self.dim = 3 + self.vis_cfg = getattr(self.cfg.trainer, "vis", Namespace()) + + # same resolution as the one from + self.res = int(getattr(self.cfg.trainer, "mc_res", 256)) + self.thr = float(getattr(self.cfg.trainer, "mc_thr", 0.)) + self.original_mesh, self.original_mesh_stats = imf2mesh( + lambda x: self.original_net(x), + res=self.res, threshold=self.thr, + normalize=True, norm_type='res', return_stats=True + ) + + if hasattr(self.cfg.trainer, "mesh_presample"): + self.presample_cfg = self.cfg.trainer.mesh_presample + self.presmp_npoints = getattr( + self.presample_cfg, "num_points", 10000) + else: + self.presmp_npoints = None + + def update(self, data, *args, **kwargs): + if self.presmp_npoints is not None: + uniform_pcl_orig = self.original_mesh.sample(self.presmp_npoints) + with jittor.no_grad(): + x_invert_uniform = self.net.deform.invert( + jittor.Var(uniform_pcl_orig).float().cuda().view(-1, 3), + iters=30 + ).view(1, -1, 3).cuda().float() + + weights = compute_invert_weight( + x_invert_uniform, + inp_nf=self.original_net, + out_nf=self.net, + deform=self.net.deform, + surface=True, + ).cuda().float().view(1, -1) + + if getattr(self.presample_cfg, "detach_weight", False): + weights = weights.detach() + + data.update({ + 'x': x_invert_uniform, + 'weights': weights + }) + return super().update(data, *args, **kwargs) + + def visualize( + self, train_data, train_info, + writer=None, step=None, epoch=None, **kwargs): + res = int(getattr(self.cfg.trainer, "vis_mc_res", 64)) + thr = float(getattr(self.cfg.trainer, "vis_mc_thr", 0.)) + with jittor.no_grad(): + print("Visualize: %s %s" % (step, epoch)) + mesh = imf2mesh( + lambda x: self.net(x, None), res=res, threshold=thr, + normalize=True, norm_type='res' + ) + if mesh is not None: + save_name = "mesh_%diters.obj" \ + % (step if step is not None else epoch) + path = os.path.join(self.cfg.save_dir, "vis", save_name) + mesh.export(path) + + def validate(self, test_loader, epoch, *args, **kwargs): + print("Validating : %d" % epoch) + + cd_gtr = 0 + emd_gtr = 0 + cd_out = 0 + emd_out = 0 + cd_ratio = 0. + emd_ratio = 0 + area_ratio = 0. + vol_ratio = 0. + + with jittor.no_grad(): + new_mesh, new_mesh_stats = imf2mesh( + lambda x: self.net(x), + res=self.res, threshold=self.thr, + normalize=True, norm_type='res', return_stats=True + ) + if new_mesh is not None: + save_name = "mesh_%diters.obj" % epoch + path = os.path.join(self.cfg.save_dir, "val", save_name) + new_mesh.export(path) + + area_ratio = new_mesh_stats['area'] / (self.original_mesh_stats['area'] + 1e-5) + vol_ratio = new_mesh_stats['vol'] / (self.original_mesh_stats['vol'] + 1e-5) + + for test_data in test_loader: + break + if 'gtr_verts' in test_data and 'gtr_faces' in test_data: + npoints = getattr(self.cfg.trainer, "val_npoints", 2048) + gtr_verts = test_data['gtr_verts'].detach().view(-1, 3).cpu().numpy() + gtr_faces = test_data['gtr_faces'].detach().view(-1, 3).cpu().numpy() + gtr_mesh = trimesh.Trimesh(vertices=gtr_verts, faces=gtr_faces) + + gtr_pcl0 = gtr_mesh.sample(npoints)[np.newaxis, ...] + gtr_pcl1 = gtr_mesh.sample(npoints)[np.newaxis, ...] + out_pcl = new_mesh.sample(npoints)[np.newaxis, ...] + print(gtr_pcl0.shape, gtr_pcl1.shape, out_pcl.shape) + + cd_gtr, dists_gtr = CD( + jittor.Var(gtr_pcl0), jittor.Var(gtr_pcl1)) + cd_out, dists_out = CD( + jittor.Var(gtr_pcl0), jittor.Var(out_pcl)) + cd_ratio = cd_out / (cd_gtr + 1e-8) + + emd_gtr, _ = EMD( + jittor.Var(gtr_pcl0), jittor.Var(gtr_pcl1), + dist=dists_gtr + ) + emd_out, _ = EMD( + jittor.Var(gtr_pcl0), jittor.Var(out_pcl), + dist=dists_out + ) + emd_ratio = emd_out / (emd_gtr + 1e-8) + + res = { + 'val/org_mesh_area': self.original_mesh_stats['area'], + 'val/org_mesh_vol': self.original_mesh_stats['vol'], + 'val/new_mesh_area': new_mesh_stats['area'], + 'val/new_mesh_vol': new_mesh_stats['vol'], + 'val/area_change_ratio': area_ratio, + 'val/vol_change_ratio': vol_ratio, + 'val/cd_gtr': cd_gtr, + 'val/emd_gtr': emd_gtr, + 'val/cd_out': cd_out, + 'val/emd_out': emd_out, + 'val/cd_ratio': cd_ratio, + 'val/emd_ratio': emd_ratio + } + print(res) + return res + + diff --git a/contrib/HSDF-Net/trainers/losses/eikonal_loss.py b/contrib/HSDF-Net/trainers/losses/eikonal_loss.py new file mode 100644 index 00000000..1b69d6f7 --- /dev/null +++ b/contrib/HSDF-Net/trainers/losses/eikonal_loss.py @@ -0,0 +1,73 @@ +#import torch +#import torch.nn.functional as F +import jittor +import jittor.nn as F +from trainers.utils.diff_ops import gradient +from trainers.utils.igp_utils import sample_points + + +def loss_eikonal( + net, gtr=None, deform=None, + npoints=1000, use_surf_points=False, invert_sampling=True, + x=None, dim=3, reduction='mean', weights=None +): + if x is None: + x, weights = sample_points( + npoints, dim=dim, sample_surf_points=use_surf_points, + inp_nf=gtr, out_nf=net, deform=deform, + invert_sampling=invert_sampling, + ) + bs, npoints = x.size(0), x.size(1) + else: + assert weights is not None + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + x = x.view(bs, npoints, dim) + + x.requires_grad = True + y = net(x.view(1, -1, dim)) + grad_norm = gradient(y, x).view(-1, dim).norm(dim=-1) + loss_all = jittor.nn.mse_loss( + grad_norm, jittor.ones_like(grad_norm), reduction='none') + loss_all = loss_all * weights + + if reduction == 'none': + loss = loss_all + elif reduction == 'mean': + loss = loss_all.mean() + elif reduction == 'sum': + loss = loss_all.sum() + else: + raise NotImplementedError + return loss + +def loss_eikonal_dsdf( + y, x, deform=None, + npoints=1000, use_surf_points=False, invert_sampling=True, + dim=3, reduction='mean', weights=None +): + + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + #x = x.view(bs, npoints, dim) + + #x.requires_grad = True + #y = net(x.view(1, -1, dim)) + grad_norm = gradient(y, x).view(-1, dim).norm(dim=-1) + loss_all = jittor.nn.mse_loss( + grad_norm, jittor.ones_like(grad_norm), reduction='none') + loss_all = loss_all * weights + + if reduction == 'none': + loss = loss_all + elif reduction == 'mean': + loss = loss_all.mean() + elif reduction == 'sum': + loss = loss_all.sum() + else: + raise NotImplementedError + return loss diff --git a/contrib/HSDF-Net/trainers/losses/filtering_losses.py b/contrib/HSDF-Net/trainers/losses/filtering_losses.py new file mode 100644 index 00000000..69174d59 --- /dev/null +++ b/contrib/HSDF-Net/trainers/losses/filtering_losses.py @@ -0,0 +1,178 @@ +#import torch +#import torch.nn.functional as F +from trainers.utils.diff_ops import laplace +from trainers.utils.igp_utils import get_surf_pcl, sample_points +import jittor +import jittor.nn as F + +def loss_boundary(gtr, net, npoints=1000, dim=3, x=None, use_surf_points=False): + """ + This function tries to enforce that the field [gtr] and [net] are similar. + Basically computing |gtr(x) - net(x)| for some [x]. + [x] will be sampled from surface of [gtr] if [use_surf_points] is True + Otherwise, [x] is sampled from [-1, 1]^3 + + :param gtr: + :param net: + :param npoints: + :param dim: + :param x: + :param use_surf_points: + :return: + """ + if x is None: + x, _ = sample_points( + npoints, dim=dim, sample_surf_points=use_surf_points, + invert_sampling=False, out_nf=gtr, deform=None + ) + x = x.detach().cuda().float() + bs = 1 + x = x.view(bs, npoints, dim) + else: + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + x = x.view(bs, npoints, dim) + + if use_surf_points: + net_y = net(x) + loss_all = F.mse_loss(net_y, jittor.zeros_like(net_y), reduction='none') + else: + net_y = net(x) + gtr_y = gtr(x) + loss_all = F.mse_loss(net_y, gtr_y, reduction='none') + loss_all = loss_all.view(bs, npoints) + loss = loss_all.mean() + return loss, x + + +def loss_lap( + gtr, net, deform=None, + x=None, npoints=1000, dim=3, + beta=1., masking_thr=10, return_mask=False, use_weights=False, weights=1 +): + """ + Matching the Laplacian between [gtr] and [net] on sampled points. + + :param gtr: + :param net: + :param deform: + :param x: + :param npoints: + :param dim: + :param use_surf_points: + :param invert_sampling: + :param beta: + :param masking_thr: + :param return_mask: + :param use_weights: + :param weights: + :return: + """ + if x is None: + x, weights = sample_points( + npoints, dim=dim, sample_surf_points=False, + out_nf=gtr, inp_nf=None, deform=None, invert_sampling=False, + ) + bs, npoints = x.size(0), x.size(1) + else: + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + x = x.view(bs, npoints, dim) + + if deform is None: + gtr_x = x + else: + gtr_x = deform(x, None) + gtr_x = gtr_x.view(bs, npoints, dim).contiguous() + if gtr_x.is_leaf: + gtr_x.requires_grad = True + else: + gtr_x.retain_grad() + gtr_y = gtr(gtr_x) + lap_gtr = laplace(gtr_y, gtr_x, normalize=True).view(bs, npoints) + + if x.is_leaf: + x.requires_grad = True + else: + x.retain_grad() + net_y = net(x) + lap_net = laplace(net_y, x, normalize=True).view(*lap_gtr.shape) + + diff = lap_gtr * beta - lap_net + if masking_thr is not None: + mask = ((jittor.abs(lap_gtr) < masking_thr) & + (jittor.abs(lap_net) < masking_thr)) + else: + mask = jittor.ones_like(lap_gtr) > 0 + loss = F.mse_loss(diff, jittor.zeros_like(diff), reduction='none') + if use_weights: + loss = loss * weights + loss = loss[mask].mean() + if return_mask: + return loss, mask + else: + return loss + +# modify lap loss for DSDF +def loss_lap_dsdf( + gtr_y, net_y, + x=None, npoints=1000, dim=3, + beta=1., masking_thr=10, return_mask=False, use_weights=False, weights=1 +): + """ + Matching the Laplacian between [gtr] and [net] on sampled points. + + :param gtr: + :param net: + :param deform: + :param x: + :param npoints: + :param dim: + :param use_surf_points: + :param invert_sampling: + :param beta: + :param masking_thr: + :param return_mask: + :param use_weights: + :param weights: + :return: + """ + + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + #x = x.view(bs, npoints, dim) + + #print('x shape: {}'.format(x.shape)) + + ''' + if x.is_leaf: + x.requires_grad = True + else: + x.retain_grad() + ''' + + #print('x grad: {}'.format(x.requires_grad)) + + lap_gtr = laplace(gtr_y, x, normalize=True).view(bs, npoints) + lap_net = laplace(net_y, x, normalize=True).view(*lap_gtr.shape) + + diff = lap_gtr * beta - lap_net + if masking_thr is not None: + mask = ((jittor.abs(lap_gtr) < masking_thr) & + (jittor.abs(lap_net) < masking_thr)) + else: + mask = jittor.ones_like(lap_gtr) > 0 + loss = F.mse_loss(diff, jittor.zeros_like(diff), reduction='none') + if use_weights: + loss = loss * weights + loss = loss[mask].mean() + if return_mask: + return loss, mask + else: + return loss diff --git a/contrib/HSDF-Net/trainers/losses/implicit_thin_shell_losses.py b/contrib/HSDF-Net/trainers/losses/implicit_thin_shell_losses.py new file mode 100644 index 00000000..26875bc5 --- /dev/null +++ b/contrib/HSDF-Net/trainers/losses/implicit_thin_shell_losses.py @@ -0,0 +1,134 @@ +#import torch +#import torch.nn.functional as F +import jittor +import jittor.nn as F +from trainers.utils.diff_ops import hessian, jacobian +from trainers.utils.igp_utils import sample_points, tangential_projection_matrix + + +def bending_loss( + inp_nf, out_nf, + # Presampled points + x=None, weights=None, + # Sampling + npoints=1000, dim=3, use_surf_points=False, deform=None, + invert_sampling=False, detach_weight=True, use_rejection=False, + # Loss related + loss_type='l2', reduction='mean', +): + if x is None: + x, weights = sample_points( + npoints, dim=dim, sample_surf_points=use_surf_points, + inp_nf=inp_nf, out_nf=out_nf, deform=deform, + invert_sampling=invert_sampling, + detach_weight=detach_weight, use_rejection=use_rejection, + ) + bs, npoints = x.size(0), x.size(1) + else: + assert weights is not None + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + x = x.view(bs, npoints, dim) + + # Compute Hessian from the output space + if x.is_leaf: + x.requires_grad = True + else: + x.retain_grad() + y_out = out_nf(x) + h_out, h_out_status = hessian(y_out, x) + h_out = h_out.view(bs * npoints, dim, dim) + + # Compute the projection matrix from the output space + _, P = tangential_projection_matrix(y_out, x) + P = P.view(bs * npoints, dim, dim) + + # Compute points from the input space + x_inp = deform(x).view(bs, npoints, dim) + J, J_status = jacobian(x_inp, x) + J = J.view(bs * npoints, dim, dim) + + # Compute Hessian from the input space + x_inp.retain_grad() + y_inp = inp_nf(x_inp) + h_inp, h_inp_status = hessian(y_inp, x_inp) + h_inp = h_inp.view(bs * npoints, dim, dim) + + # Compute the projected hessians and their differences after adjustment + h_inp_J = jittor.bmm(J.transpose(1, 2).contiguous(), jittor.bmm(h_inp, J)) + diff = jittor.bmm( + P.transpose(1, 2).contiguous(), jittor.bmm(h_out - h_inp_J, P)) + + # Compute the Forbinius norm (weighted) + F_norm = diff.view(bs * npoints, -1).norm(dim=-1, keepdim=False) + F_norm = F_norm.view(bs, npoints) + F_norm = F_norm * weights + + if loss_type == 'l2': + loss = F.mse_loss( + F_norm, jittor.zeros_like(F_norm), reduction=reduction) + elif loss_type == 'l1': + loss = F.l1_loss( + F_norm, jittor.zeros_like(F_norm), reduction=reduction) + else: + raise ValueError + return loss + + +def stretch_loss( + inp_nf, out_nf, deform, + x=None, npoints=1000, dim=3, use_surf_points=False, invert_sampling=False, + loss_type='l2', reduction='mean', weights=1, + detach_weight=True, use_rejection=False, +): + if x is None: + x, weights = sample_points( + npoints, dim=dim, sample_surf_points=use_surf_points, + inp_nf=inp_nf, out_nf=out_nf, deform=deform, + invert_sampling=invert_sampling, + detach_weight=detach_weight, use_rejection=use_rejection, + ) + bs, npoints = x.size(0), x.size(1) + else: + assert weights is not None + if len(x.size()) == 2: + bs, npoints = 1, x.size(0) + else: + bs, npoints = x.size(0), x.size(1) + x = x.view(bs, npoints, dim) + + # Compute Projection on the output space + if x.is_leaf: + x.requires_grad = True + x.retain_grad() + y_out = out_nf(x) + _, P = tangential_projection_matrix(y_out, x) + P = P.view(bs * npoints, dim, dim) + + # Compute the deformation Jacobian + x_inp = deform(x).view(bs, npoints, dim) + J, J_status = jacobian(x_inp, x) + J = J.view(bs * npoints, dim, dim) + + # Compute the matrix of interests + I = jittor.eye(dim).view(1, dim, dim).to(J) + diff = I - jittor.bmm(J.transpose(1, 2), J) + diff = jittor.bmm(P.transpose(1, 2), jittor.bmm(diff, P)) + + # Compute the Forbinius norm (weighted) + F_norm = diff.view(bs * npoints, -1).norm(dim=-1, keepdim=False) + F_norm = F_norm.view(bs, npoints) + F_norm = F_norm * weights + + if loss_type == 'l2': + loss = F.mse_loss( + F_norm, jittor.zeros_like(F_norm), reduction=reduction) + elif loss_type == 'l1': + loss = F.l1_loss( + F_norm, jittor.zeros_like(F_norm), reduction=reduction) + else: + raise ValueError + return loss + diff --git a/contrib/HSDF-Net/trainers/nf_sdf_trainer_3D.py b/contrib/HSDF-Net/trainers/nf_sdf_trainer_3D.py new file mode 100644 index 00000000..b3eaea82 --- /dev/null +++ b/contrib/HSDF-Net/trainers/nf_sdf_trainer_3D.py @@ -0,0 +1,167 @@ +import os +#import torch +import jittor +import importlib +import os.path as osp +#import torch.nn.functional as F +from trainers.utils.diff_ops import gradient +from trainers.utils.vis_utils import imf2mesh +from trainers.base_trainer import BaseTrainer +from trainers.utils.utils import get_opt, set_random_seed + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args): + super().__init__(cfg, args) + self.cfg = cfg + self.args = args + set_random_seed(getattr(self.cfg.trainer, "seed", 666)) + + # The networks + lib = importlib.import_module(cfg.models.decoder.type) + self.net = lib.Net(cfg, cfg.models.decoder) + self.net.cuda() + print("Net:") + print(self.net) + + # The optimizer + self.opt, self.sch = get_opt( + self.net.parameters(), self.cfg.trainer.opt) + + # Prepare save directory + os.makedirs(osp.join(cfg.save_dir, "val"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "images"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "checkpoints"), exist_ok=True) + + def update(self, data, *args, **kwargs): + if 'no_update' in kwargs: + no_update = kwargs['no_update'] + else: + no_update = False + if not no_update: + self.net.train() + self.opt.zero_grad() + + xyz, dist = data['xyz'].cuda(), data['dist'].cuda() + bs = xyz.size(0) + out = self.net(xyz) + ndf_loss_weight = float(getattr( + self.cfg.trainer, "ndf_loss_weight", 1.)) + if ndf_loss_weight > 0: + loss_y_ndf = ((jittor.abs(out) - dist) ** 2).view(bs, -1).mean() + loss_y_ndf *= ndf_loss_weight + else: + loss_y_ndf = jittor.zeros(1).cuda().float() + + sdf_loss_weight = float(getattr( + self.cfg.trainer, "sdf_loss_weight", 0.)) + if 'sign' in data and sdf_loss_weight > 0: + sign = data['sign'].cuda().float() + loss_y_sdf = ((out - dist * sign) ** 2).view(bs, -1).mean() + loss_y_sdf *= sdf_loss_weight + else: + loss_y_sdf = 0. * jittor.zeros(1).to(loss_y_ndf) + + occ_loss_weight = float(getattr( + self.cfg.trainer, "occ_loss_weight", 0.)) + if 'sign' in data and occ_loss_weight > 0: + target = (data['sign'].cuda().float() >= 0).float() + loss_occ = F.binary_cross_entropy( + jittor.sigmoid(out), target + ) + loss_occ *= occ_loss_weight + else: + loss_occ = 0. * jittor.zeros(1).cuda().float() + + grad_norm_weight = float(getattr( + self.cfg.trainer, "grad_norm_weight", 0.)) + grad_norm_num_points = int(getattr( + self.cfg.trainer, "grad_norm_num_points", 0)) + if grad_norm_weight > 0. and grad_norm_num_points > 0: + xyz = jittor.rand( + bs, grad_norm_num_points, xyz.size(-1)).to(xyz) * 2 - 1 + xyz = xyz.cuda() + xyz.requires_grad = True + grad_norm = gradient(self.net(xyz), xyz).view( + bs, -1, xyz.size(-1)).norm(dim=-1) + loss_unit_grad_norm = F.mse_loss( + grad_norm, jittor.ones_like(grad_norm)) * grad_norm_weight + else: + loss_unit_grad_norm = 0. * jittor.zeros(1).to(loss_y_ndf) + loss = loss_unit_grad_norm + loss_y_ndf + loss_y_sdf + loss_occ + + if not no_update: + loss.backward() + self.opt.step() + + return { + 'loss': loss.detach().cpu().item(), + 'scalar/loss': loss.detach().cpu().item(), + 'scalar/loss_y_ndf': loss_y_ndf.detach().cpu().item(), + 'scalar/loss_y_sdf': loss_y_sdf.detach().cpu().item(), + 'scalar/loss_occ': loss_occ.detach().cpu().item(), + 'scalar/loss_grad_norm': loss_unit_grad_norm.detach().cpu().item(), + } + + def log_train(self, train_info, train_data, writer=None, + step=None, epoch=None, visualize=False, **kwargs): + if writer is None: + return + + # Log training information to tensorboard + writer_step = step if step is not None else epoch + assert writer_step is not None + for k, v in train_info.items(): + t, kn = k.split("/")[0], "/".join(k.split("/")[1:]) + if t not in ['scalar']: + continue + if t == 'scalar': + writer.add_scalar('train/' + kn, v, writer_step) + + if visualize: + with jittor.no_grad(): + print("Visualize: %s" % step) + res = int(getattr(self.cfg.trainer, "vis_mc_res", 256)) + thr = float(getattr(self.cfg.trainer, "vis_mc_thr", 0.)) + + mesh = imf2mesh( + lambda x: self.net(x), res=res, threshold=thr) + if mesh is not None: + save_name = "mesh_%diters.obj" \ + % (step if step is not None else epoch) + mesh.export(osp.join(self.cfg.save_dir, "val", save_name)) + mesh.export(osp.join(self.cfg.save_dir, "latest_mesh.obj")) + + def validate(self, test_loader, epoch, *args, **kwargs): + return {} + + def save(self, epoch=None, step=None, appendix=None, **kwargs): + d = { + 'opt': self.opt.state_dict(), + 'net': self.net.state_dict(), + 'epoch': epoch, + 'step': step + } + if appendix is not None: + d.update(appendix) + save_name = "epoch_%s_iters_%s.pt" % (epoch, step) + jittor.save(d, osp.join(self.cfg.save_dir, "checkpoints", save_name)) + jittor.save(d, osp.join(self.cfg.save_dir, "latest.pt")) + + def resume(self, path, strict=True, **kwargs): + ckpt = jittor.load(path) + self.net.load_state_dict(ckpt['net'], strict=strict) + self.opt.load_state_dict(ckpt['opt']) + start_epoch = ckpt['epoch'] + return start_epoch + + def multi_gpu_wrapper(self, wrapper): + self.net = wrapper(self.net) + + def epoch_end(self, epoch, writer=None, **kwargs): + if self.sch is not None: + self.sch.step(epoch=epoch) + if writer is not None: + writer.add_scalar( + 'train/opt_lr', self.sch.get_lr()[0], epoch) diff --git a/contrib/HSDF-Net/trainers/smooth_sharpen.py b/contrib/HSDF-Net/trainers/smooth_sharpen.py new file mode 100644 index 00000000..d3a658a9 --- /dev/null +++ b/contrib/HSDF-Net/trainers/smooth_sharpen.py @@ -0,0 +1,208 @@ +import os +#import torch +import jittor +import importlib +import os.path as osp +from trainers.base_trainer import BaseTrainer +from models.igp_wrapper import distillation, deformation, correction +from trainers.utils.utils import set_random_seed +from trainers.losses.eikonal_loss import loss_eikonal +from trainers.losses.filtering_losses import loss_boundary, loss_lap + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args, original_decoder=None): + super().__init__(cfg, args) + self.cfg = cfg + self.args = args + self.dim = 3 + + set_random_seed(getattr(self.cfg.trainer, "seed", 666)) + + # The networks + if original_decoder is None: + if not hasattr(cfg.models, "net"): + cfg.models.net = cfg.models.decoder + sn_lib = importlib.import_module(cfg.models.net.type) + self.original_decoder = sn_lib.Net(cfg, cfg.models.net) + self.original_decoder.cuda() + self.original_decoder.load_state_dict( + jittor.load(cfg.models.net.path)['net']) + print("Original Decoder:") + print(self.original_decoder) + else: + self.original_decoder = original_decoder + + # Get the wrapper for the operation + self.wrapper_type = getattr( + cfg.trainer, "wrapper_type", "distillation") + if self.wrapper_type in ['distillation']: + self.decoder, self.opt_dec, self.scheduler_dec = distillation( + cfg, self.original_decoder, + reload=getattr(self.cfg.trainer, "reload_decoder", True)) + elif self.wrapper_type in ['correction']: + self.decoder, self.opt_dec, self.scheduler_dec = correction( + cfg, self.original_decoder) + elif self.wrapper_type in ['deformation']: + self.decoder, self.opt_dec, self.scheduler_dec = deformation( + cfg, self.original_decoder) + else: + raise ValueError("wrapper_type:", self.wrapper_type) + + # Prepare save directory + os.makedirs(osp.join(cfg.save_dir, "images"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "checkpoints"), exist_ok=True) + os.makedirs(osp.join(cfg.save_dir, "val"), exist_ok=True) + + # Set-up counter + self.num_update_step = 0 + self.boundary_points = None + + # The [beta] that controlls how smooth/sharp the output shape should be + # If beta > 1, then the output shape will increase in curvature + # so it will be sharper + # If beta < 1, then the output shape will decrease in curvature + # so it will be smoother. + # beta should be > 0. + self.beta = getattr(self.cfg.trainer, "beta", 1.) + + # whether plot histogram for network weights + self.show_network_hist = getattr( + self.cfg.trainer, "show_network_hist", False) + + def update(self, _, *args, **kwargs): + self.num_update_step += 1 + if 'no_update' in kwargs: + no_update = kwargs['no_update'] + else: + no_update = False + if not no_update: + self.decoder.train() + self.opt_dec.zero_grad() + + boundary_loss_weight = float(getattr( + self.cfg.trainer, "boundary_weight", 1.)) + boundary_loss_num_points = int(getattr( + self.cfg.trainer, "boundary_num_points", 0)) + boundary_loss_points_update_step = int(getattr( + self.cfg.trainer, "boundary_loss_points_update_step", 1)) + boundary_loss_use_surf_points = int(getattr( + self.cfg.trainer, "boundary_loss_use_surf_points", True)) + if boundary_loss_weight > 0. and boundary_loss_num_points > 0: + if self.num_update_step % boundary_loss_points_update_step == 0: + self.boundary_points = None + loss_y_boundary, self.boundary_points = loss_boundary( + (lambda x: self.original_decoder(x)), + (lambda x: self.decoder(x)), + npoints=boundary_loss_num_points, + x=self.boundary_points, + dim=self.dim, + use_surf_points=boundary_loss_use_surf_points) + loss_y_boundary = loss_y_boundary * boundary_loss_weight + else: + loss_y_boundary = jittor.zeros(1).float().cuda() + + grad_norm_weight = float(getattr( + self.cfg.trainer, "grad_norm_weight", 1e-2)) + grad_norm_num_points = int(getattr( + self.cfg.trainer, "grad_norm_num_points", 5000)) + if grad_norm_weight > 0. and grad_norm_num_points > 0: + loss_unit_grad_norm = loss_eikonal( + lambda x: self.decoder(x), + npoints= grad_norm_num_points, + use_surf_points=False, invert_sampling=False + ) + loss_unit_grad_norm *= grad_norm_weight + else: + loss_unit_grad_norm = jittor.zeros(1).float().cuda() + + lap_loss_weight = float(getattr( + self.cfg.trainer, "lap_loss_weight", 1e-4)) + lap_loss_threshold = int(getattr( + self.cfg.trainer, "lap_loss_threshold", 50)) + lap_loss_num_points = int(getattr( + self.cfg.trainer, "lap_loss_num_points", 5000)) + if lap_loss_weight > 0. and lap_loss_num_points > 0: + loss_lap_scaling = loss_lap( + (lambda x: self.original_decoder(x)), + (lambda x: self.decoder(x)), + npoints=lap_loss_num_points, + beta=self.beta, + masking_thr=lap_loss_threshold, + ) + loss_lap_scaling = loss_lap_scaling * lap_loss_weight + else: + loss_lap_scaling = jittor.zeros(1).float().cuda() + + loss = loss_unit_grad_norm + loss_y_boundary + loss_lap_scaling + if not no_update: + loss.backward() + self.opt_dec.step() + + return { + 'loss': loss.detach().cpu().item(), + 'scalar/loss/loss': loss.detach().cpu().item(), + 'scalar/loss/loss_boundary': loss_y_boundary.detach().cpu().item(), + 'scalar/loss/loss_eikonal': loss_unit_grad_norm.detach().cpu().item(), + 'scalar/loss/loss_lap_scaling': loss_lap_scaling.detach().cpu().item(), + 'scalar/weight/loss_boundary': boundary_loss_weight, + 'scalar/weight/loss_eikonal': grad_norm_weight, + 'scalar/weight/loss_lap': lap_loss_weight, + } + + def log_train(self, train_info, train_data, writer=None, + step=None, epoch=None, visualize=False, **kwargs): + if writer is None: + return + writer_step = step if step is not None else epoch + + # Log training information to tensorboard + train_info = {k: (v.cpu() if not isinstance(v, float) else v) + for k, v in train_info.items()} + for k, v in train_info.items(): + ktype = k.split("/")[0] + kstr = "/".join(k.split("/")[1:]) + if ktype == 'scalar': + writer.add_scalar(kstr, v, writer_step) + + if self.show_network_hist: + for name, p in self.decoder.named_parameters(): + writer.add_histogram("hist/%s" % name, p, writer_step) + + if visualize: + # NOTE: trainer sub class should implement this function + self.visualize(train_info, train_data, writer=writer, step=step, + epoch=epoch, visualize=visualize, **kwargs) + + def validate(self, test_loader, epoch, *args, **kwargs): + return {} + + def save(self, epoch=None, step=None, appendix=None, **kwargs): + d = { + 'orig_dec': self.original_decoder.state_dict(), + 'opt_dec': self.opt_dec.state_dict(), + 'dec': self.decoder.state_dict(), + 'epoch': epoch, + 'step': step + } + if appendix is not None: + d.update(appendix) + save_name = "epoch_%s_iters_%s.pt" % (epoch, step) + path = osp.join(self.cfg.save_dir, "checkpoints", save_name) + jittor.save(d, path) + + def resume(self, path, strict=True, **kwargs): + ckpt = jittor.load(path) + self.original_decoder.load_state_dict(ckpt['orig_dec'], strict=strict) + self.decoder.load_state_dict(ckpt['dec'], strict=strict) + self.opt_dec.load_state_dict(ckpt['opt_dec']) + start_epoch = ckpt['epoch'] + return start_epoch + + def epoch_end(self, epoch, writer=None, **kwargs): + if self.scheduler_dec is not None: + self.scheduler_dec.step(epoch=epoch) + if writer is not None: + writer.add_scalar( + 'train/opt_lr', self.scheduler_dec.get_lr()[0], epoch) diff --git a/contrib/HSDF-Net/trainers/smooth_sharpen_3D.py b/contrib/HSDF-Net/trainers/smooth_sharpen_3D.py new file mode 100644 index 00000000..c6e3773c --- /dev/null +++ b/contrib/HSDF-Net/trainers/smooth_sharpen_3D.py @@ -0,0 +1,21 @@ +import os +from trainers.utils.vis_utils import imf2mesh +from trainers.smooth_sharpen import Trainer as BaseTrainer + + +class Trainer(BaseTrainer): + + def __init__(self, cfg, args, original_decoder=None): + super().__init__(cfg, args, original_decoder=original_decoder) + self.dim = 3 + + def visualize(self, train_info, train_data, writer=None, + step=None, epoch=None, visualize=False, **kwargs): + print("Visualize: %s" % step) + res = int(getattr(self.cfg.trainer, "vis_mc_res", 256)) + thr = float(getattr(self.cfg.trainer, "vis_mc_thr", 0.)) + mesh = imf2mesh(lambda x: self.decoder(x), res=res, threshold=thr) + if mesh is not None: + save_name = "mesh_%diters.obj" % self.num_update_step + mesh.export(os.path.join(self.cfg.save_dir, "val", save_name)) + mesh.export(os.path.join(self.cfg.save_dir, "latest_mesh.obj")) diff --git a/contrib/HSDF-Net/trainers/utils/diff_ops.py b/contrib/HSDF-Net/trainers/utils/diff_ops.py new file mode 100644 index 00000000..ae633779 --- /dev/null +++ b/contrib/HSDF-Net/trainers/utils/diff_ops.py @@ -0,0 +1,87 @@ +# Based on https://github.com/vsitzmann/siren/blob/master/diff_operators.py +#import torch +#from torch.autograd import grad +import jittor + + +def hessian(y, x): + """ + Hessian of y wrt x + y: shape (meta_batch_size, num_observations, channels) + x: shape (meta_batch_size, num_observations, dim) + return: + shape (meta_batch_size, num_observations, dim, channels) + """ + meta_batch_size, num_observations = y.shape[:2] + grad_y = jittor.ones_like(y[..., 0]).to(y.device) + h = jittor.zeros(meta_batch_size, num_observations, + y.shape[-1], x.shape[-1], x.shape[-1]).to(y.device) + for i in range(y.shape[-1]): + # calculate dydx over batches for each feature value of y + dydx = grad(y[..., i], x, grad_y, create_graph=True)[0] + + # calculate hessian on y for each x value + for j in range(x.shape[-1]): + h[..., i, j, :] = grad(dydx[..., j], x, grad_y, + create_graph=True)[0][..., :] + + status = 0 + if jittor.any(jittor.isnan(h)): + status = -1 + return h, status + + +def laplace(y, x, normalize=False, eps=0., return_grad=False): + grad = gradient(y, x) + if normalize: + grad = grad / (grad.norm(dim=-1, keepdim=True) + eps) + div = divergence(grad, x) + + if return_grad: + return div, grad + return div + + +def divergence(y, x): + div = 0. + for i in range(y.shape[-1]): + div += grad( + y[..., i], x, jittor.ones_like(y[..., i]), + create_graph=True)[0][..., i:i+1] + return div + + +def gradient(y, x, grad_outputs=None): + if grad_outputs is None: + grad_outputs = jittor.ones_like(y) + grad = jittor.grad( + y, [x], retain_graph=True)[0] + + #print('grad: {}'.format(grad)) + return grad + + +def jacobian(y, x): + """ + Jacobian of y wrt x + y: shape (meta_batch_size, num_observations, channels) + x: shape (meta_batch_size, num_observations, dim) + ret: shape (meta_batch_size, num_observations, channels, dim) + """ + meta_batch_size, num_observations = y.shape[:2] + # (meta_batch_size*num_points, 2, 2) + jac = jittor.zeros( + meta_batch_size, num_observations, + y.shape[-1], x.shape[-1]).to(y.device) + for i in range(y.shape[-1]): + # calculate dydx over batches for each feature value of y + y_flat = y[...,i].view(-1, 1) + jac[:, :, i, :] = grad( + y_flat, x, jittor.ones_like(y_flat), create_graph=True)[0] + + status = 0 + if jittor.any(jittor.isnan(jac)): + status = -1 + + return jac, status + diff --git a/contrib/HSDF-Net/trainers/utils/igp_utils.py b/contrib/HSDF-Net/trainers/utils/igp_utils.py new file mode 100644 index 00000000..80e21d20 --- /dev/null +++ b/contrib/HSDF-Net/trainers/utils/igp_utils.py @@ -0,0 +1,257 @@ +#import torch +import jittor +from trainers.utils.diff_ops import gradient, jacobian + + +def outter(v1, v2): + """ + Batched outter product of two vectors: [v1] [v2]^T + :param v1: (bs, dim) + :param v2: (bs, dim) + :return: (bs, dim, dim) + """ + bs = v1.size(0) + d = v1.size(1) + v1 = v1.view(bs, d, 1) + v2 = v2.view(bs, 1, d) + return jittor.bmm(v1, v2) + + +def _addr_(mat, vec1, vec2, alpha=1., beta=1.): + """ + Return + alpha * outter(vec1, vec2) + beta * [mat] + :param mat: (bs, npoints, dim, dim) + :param vec1: (bs, npoints, dim) + :param vec2: (bs, npoints, dim) + :param alpha: float + :param beta: float + :return: + """ + bs, npoints, dim =vec1.size(0), vec1.size(1), vec1.size(2) + assert len(mat.size()) == 4 + outter_n = outter(vec1.view(bs * npoints, dim), vec2.view(bs * npoints, dim)) + outter_n = outter_n.view(bs, npoints, dim, dim) + out = alpha * outter_n + beta * mat.view(bs, npoints, dim, dim) + return out + + +def get_surf_pcl(net, npoints=1000, dim=3, use_rejection=True, **kwargs): + if use_rejection: + return get_surf_pcl_rejection(net, npoints, dim, **kwargs) + else: + return get_surf_pcl_langevin_dynamic(net, npoints, dim, **kwargs) + + +def get_surf_pcl_rejection( + net, npoints, dim, batch_size=100000, thr=0.05, return_rej_x=False): + """ + Sampling points with rejection sampling. We first sample uniformly from + [-1, 1]^3, then reject all points with |distance| > [thr]. Once gathered + enough rejected points, we will take a gradient step toward the surface: + y = x - F(x)n(x) + :param net: Neural field + :param npoints: Number of points to sample + :param dim: Dimension of the points + :param batch_size: Batch size per iteration + :param thr: Rejection threshold + :param return_rej_x: Whether returned points right after rejection. + :return: + [x] Sampled points + [rej_x]? Obtained points after rejection. + """ + out = [] + cnt = 0 + with jittor.no_grad(): + while cnt < npoints: + x = jittor.rand(1, batch_size, dim).cuda().float() * 2 - 1 + y = jittor.abs(net(x)) + m = (y < thr).view(1, batch_size) + m_cnt = m.sum().detach().cpu().item() + if m_cnt < 1: + continue + x_eq = x[m].view(m_cnt, dim) + out.append(x_eq) + cnt += m_cnt + rej_x = x = jittor.cat(out, dim=0)[:npoints, :] + + if x.is_leaf: + x.requires_grad = True + else: + x.retain_grad() + y = net(x) + g = gradient(y, x).view(npoints, dim).detach() + g = g / g.norm(dim=-1, keepdim=True) + x = x - g * y + if return_rej_x: + return x, rej_x + return x + + +def get_surf_pcl_langevin_dynamic( + net, npoints, dim, steps=5, eps=1e-4, + noise_sigma=0.01, filtered=True, sigma_decay=1., + max_repeat=10, bound=(1 - 1e-4)): + out_cnt = 0 + out = None + already_repeated = 0 + while out_cnt < npoints and already_repeated < max_repeat: + already_repeated += 1 + x = jittor.rand(npoints, dim).cuda().float() * 2 - 1 + for i in range(steps): + sigma_i = noise_sigma * sigma_decay ** i + x = x.detach() + jittor.randn_like(x).to(x) * sigma_i + x.requires_grad = True + y = net(x) + if jittor.allclose(y, jittor.zeros_like(y)): + break + + g = gradient(y, x).view(npoints, dim).detach() + g = g / (g.norm(dim=-1, keepdim=True) + eps) + x = jittor.clamp(x - g * y, min_v=-bound, max_v=bound) + + if filtered: + with jittor.no_grad(): + y = net(x) + mask = (jittor.abs(y) < eps).view(-1, 1) + x = x.view(-1, dim).masked_select(mask).view(-1, dim) + out_cnt += x.shape[0] + if out is None: + out = x + else: + out = jittor.cat([x, out], dim=0) + else: + out = x + out_cnt = npoints + out = out[:npoints, :] + return out + + +def tangential_projection_matrix(y, x, norm=True, eps=1e-6): + """ + Compute the tangential projection matrix: + P = I - n(x)n(x)^T + where n(x) is the outward surface normal of x + :param x: (bs, npts, dim) input points + :param y: (bs, npts, 1) neural_field(x) + :param norm: Whether normalize the surface normal vector + :param eps: Numerical eps + :return: + [normals] (bs, npts, dim) The surface normal + [normals_proj] (bs, npts, dim, dim) The projector matrices + """ + bs, npoints, dim = x.size(0), x.size(1), x.size(2) + grad = gradient(y, x) + if norm: + normals = ( + grad / (grad.norm(dim=-1, keepdim=True) + eps) + ).view(bs, npoints, dim) + else: + normals = grad.view(bs, npoints, dim) + normals_proj = _addr_( + jittor.eye(dim).view(1, 1, dim, dim).expand(bs, npoints, -1, -1).to(y), + normals, normals, alpha=-1 + ) + return normals, normals_proj + + +def compute_invert_weight( + x, deform, inp_nf, out_nf, surface=False, normalize=True): + """ + Computing the weight in Section 5.3.3. + + :param x: (bs, npts, dim) Points from the output space + :param deform: Network that maps output space to input space + :param inp_nf: Neural fields of the input space + :param out_nf: Neural fields of the output space + :param surface: Whether the inverse is for surface integral. + :param normalize: + :return: + """ + bs, npoints, dim = x.size(0), x.size(1), x.size(2) + x = x.clone().detach() + x.requires_grad = True + y = deform(x).view(bs, npoints, dim) + J, status = jacobian(y, x) + assert status == 0 + + if surface: + # Find the change of area along the tangential plane + yn, yn_proj = tangential_projection_matrix(inp_nf(y), y) + xn, xn_proj = tangential_projection_matrix(out_nf(x), x) + + J = jittor.bmm( + J.view(-1, dim, dim), + xn_proj.view(-1, dim, dim) + ) + J = _addr_(J.view(bs, npoints, dim, dim), + yn.view(bs, npoints, dim), + xn.view(bs, npoints, dim)) + + weight = jittor.abs(jittor.linalg.det(J.view(bs * npoints, dim, dim))) + if int(dim) == 3: + weight = weight ** 2 + weight = 1. / weight.view(bs, npoints) + + if normalize: + weight = weight / weight.sum(dim=-1, keepdim=True) * npoints + + return weight + + +def sample_points( + npoints, dim=3, sample_surf_points=False, + inp_nf=None, out_nf=None, deform=None, + invert_sampling=False, + detach_weight=True, use_rejection=False): + """ + Sample points from the neural fields: inp_nf, out_nf, and deform. + + :param npoints: Number of points to sample. + :param dim: Dimension of the points. + :param sample_surf_points: + :param inp_nf: Input neural fields. F: (bs, npts, dim) -> (bs, npts, 1) + :param out_nf: Output neural fields. G: (bs, npts, dim) -> (bs, npts, 1) + :param deform: Neural fields that deofrm from output space to input space. + (bs, npts, dim) -> (bs, npts, dim) + :param invert_sampling: Whether sample from [inp_nf] then invert the points + through the [deform] to become samples of [out_nf] + :param detach_weight: Whether detach the weights. + :param use_rejection: Whether use rejection to sample. + :return: + [x] (1, npoints, dim) Sampled points on the surface of [out_nf](x) = 0. + [weights] the weights for inverting the surface intergral. + """ + if sample_surf_points: + if invert_sampling: + assert deform is not None + assert inp_nf is not None + y = get_surf_pcl( + inp_nf, npoints=npoints, dim=dim, use_rejection=use_rejection) + x = deform.invert(y, iters=30).detach().cuda().float() + + weight = compute_invert_weight( + x.view(1, -1, dim), + deform=deform, inp_nf=inp_nf, out_nf=out_nf, surface=True) + else: + assert out_nf is not None + x = get_surf_pcl( + out_nf, npoints=npoints, dim=dim, use_rejection=use_rejection + ).detach().cuda().float() + weight = jittor.ones(1, npoints).cuda().float() + else: + x = jittor.rand(1, npoints, dim).cuda().float() * 2 - 1 + weight = jittor.ones(1, npoints).cuda().float() + if invert_sampling: + assert deform is not None + y = x + x = deform.invert(y, iters=30).detach().cuda().float() + weight = compute_invert_weight( + x.view(1, -1, dim), + deform=deform, inp_nf=inp_nf, out_nf=out_nf, surface=False) + + x = x.view(1, npoints, dim) + weight = weight.view(1, npoints) + if detach_weight: + weight = weight.detach() + return x, weight diff --git a/contrib/HSDF-Net/trainers/utils/o3d_deformation.py b/contrib/HSDF-Net/trainers/utils/o3d_deformation.py new file mode 100644 index 00000000..6d31e2e6 --- /dev/null +++ b/contrib/HSDF-Net/trainers/utils/o3d_deformation.py @@ -0,0 +1,83 @@ +import trimesh +import numpy as np +import open3d as o3d +from trainers.utils.vis_utils import imf2mesh + + +def trimesh_to_o3dmesh(mesh): + o3d_mesh = o3d.geometry.TriangleMesh( + vertices=o3d.utility.Vector3dVector(np.array(mesh.vertices)), + triangles=o3d.utility.Vector3iVector(np.array(mesh.faces)) + ) + o3d_mesh.compute_vertex_normals() + return o3d_mesh + + +def o3dmesh_to_trimesh(mesh): + mesh = trimesh.Trimesh( + vertices=np.asarray(mesh.vertices).reshape(-1, 3).astype(np.float), + faces=np.asarray(mesh.triangles).reshape(-1, 3).astype(np.int) + ) + return mesh + + +def deform_mesh_o3d(imf, handles, targets, normalize=True, res=256, + imf_mesh=None, steps=50, smoothed_alpha=0.01, verbose=True): + """ + Use Open3D to do deformation + Args: + [imf] + [handles] (n, 3) Source points. + [targets] (n, 3) Target points. + [normalize] Whether normalize the mesh to unit sphere. Default (True). + [res] Resolution for MC. Default (256). + Returns: + """ + if imf_mesh is None: + mesh = imf2mesh(imf, res=res, threshold=0.00) + + if normalize: + verts = (mesh.vertices * 2 - res) / float(res) + mesh = trimesh.Trimesh(vertices=verts, faces=mesh.faces) + else: + mesh = imf_mesh + + vertices = np.asarray(mesh.vertices).reshape(-1, 3) + vert_ids = [] + vert_pos = [] + for i in range(handles.reshape(-1, 3).shape[0]): + dist = np.linalg.norm( + vertices - handles[i, :].reshape(1, 3), axis=-1 + ).flatten() + handle_idx = np.argmin(dist) + vert_ids.append(handle_idx) + vert_pos.append( + vertices[handle_idx].reshape(3) + targets[i].reshape(3) - + handles[i].reshape(3)) + + constraint_ids = o3d.utility.IntVector(vert_ids) + constraint_pos = o3d.utility.Vector3dVector(vert_pos) + o3d_vert0 = o3d.utility.Vector3dVector(mesh.vertices) + o3d_face0 = o3d.utility.Vector3iVector(mesh.faces) + o3d_mesh0 = o3d.geometry.TriangleMesh( + vertices=o3d_vert0, triangles=o3d_face0) + o3d_mesh0.compute_vertex_normals() + + with o3d.utility.VerbosityContextManager( + o3d.utility.VerbosityLevel.Debug) as cm: + if smoothed_alpha > 0: + print("Smoothing alphas:", smoothed_alpha, "Use Smoothed Energy") + mesh_deformed = o3d_mesh0.deform_as_rigid_as_possible( + constraint_ids, constraint_pos, max_iter=steps, + smoothed_alpha=smoothed_alpha, + energy=o3d.geometry.DeformAsRigidAsPossibleEnergy.Smoothed) + else: + print("Smoothing alphas:", smoothed_alpha, "Use Spokes Energy") + mesh_deformed = o3d_mesh0.deform_as_rigid_as_possible( + constraint_ids, constraint_pos, max_iter=steps, + smoothed_alpha=0, + energy=o3d.geometry.DeformAsRigidAsPossibleEnergy.Spokes) + + return o3dmesh_to_trimesh(mesh_deformed) + + diff --git a/contrib/HSDF-Net/trainers/utils/utils.py b/contrib/HSDF-Net/trainers/utils/utils.py new file mode 100644 index 00000000..2fdd9143 --- /dev/null +++ b/contrib/HSDF-Net/trainers/utils/utils.py @@ -0,0 +1,72 @@ +#import torch +import jittor +import random +import numpy as np +from jittor import optim + + +def get_opt(params, cfgopt, overwrite_lr=None): + if overwrite_lr is not None: + lr = float(overwrite_lr) + else: + lr = float(cfgopt.lr) + if cfgopt.type == 'adam': + optimizer = optim.Adam(params, lr=lr, + betas=(cfgopt.beta1, cfgopt.beta2), + weight_decay=float(cfgopt.weight_decay)) + elif cfgopt.type == 'sgd': + optimizer = jittor.optim.SGD(params, lr=lr, momentum=cfgopt.momentum) + else: + assert 0, "Optimizer type should be either 'adam' or 'sgd'" + + scheduler = None + scheduler_type = getattr(cfgopt, "scheduler", None) + if scheduler_type is not None: + if scheduler_type == 'exponential': + decay = float(getattr(cfgopt, "step_decay", 0.1)) + scheduler = optim.lr_scheduler.ExponentialLR(optimizer, decay) + elif scheduler_type == 'step': + step_size = int(getattr(cfgopt, "step_epoch", 500)) + decay = float(getattr(cfgopt, "step_decay", 0.1)) + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=decay) + elif scheduler_type == 'linear': + step_size = int(getattr(cfgopt, "step_epoch", 2000)) + final_ratio = float(getattr(cfgopt, "final_ratio", 0.01)) + start_ratio = float(getattr(cfgopt, "start_ratio", 0.5)) + duration_ratio = float(getattr(cfgopt, "duration_ratio", 0.45)) + + def lambda_rule(ep): + lr_l = 1.0 - min(1, max(0, ep - start_ratio * step_size) / float(duration_ratio * step_size)) * (1 - final_ratio) + return lr_l + + scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + + elif scheduler_type == 'cosine_anneal_nocycle': + final_lr_ratio = float(getattr(cfgopt, "final_lr_ratio", 0.01)) + eta_min = float(lr) * final_lr_ratio + eta_max = float(lr) + + total_epoch = int(getattr(cfgopt, "step_epoch", 2000)) + start_ratio = float(getattr(cfgopt, "start_ratio", 0.2)) + T_max = total_epoch * (1 - start_ratio) + + def lambda_rule(ep): + curr_ep = max(0., ep - start_ratio * total_epoch) + lr_l = eta_min + 0.5 * (eta_max - eta_min) * (1 + np.cos(np.pi * curr_ep / T_max)) + lr_l = lr_l / eta_max + return lr_l + + scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + + else: + assert 0, "args.schedulers should be either 'exponential' or 'linear' or 'step'" + return optimizer, scheduler + + +def set_random_seed(seed): + """set random seed""" + random.seed(seed) + np.random.seed(seed) + jittor.manual_seed(seed) + jittor.cuda.manual_seed(seed) + jittor.cuda.manual_seed_all(seed) diff --git a/contrib/HSDF-Net/trainers/utils/vis_utils.py b/contrib/HSDF-Net/trainers/utils/vis_utils.py new file mode 100644 index 00000000..2fd1d4d3 --- /dev/null +++ b/contrib/HSDF-Net/trainers/utils/vis_utils.py @@ -0,0 +1,137 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tqdm +#import torch +import jittor +import trimesh +import skimage +import numpy as np +import skimage.measure + + +def imf2mesh(imf, res=256, threshold=0.0, batch_size = 10000, verbose=True, + use_double=False, normalize=False, norm_type='res', + return_stats=False, bound=1.): + xs, ys, zs = np.meshgrid(np.arange(res), np.arange(res), np.arange(res)) + grid = np.concatenate([ + ys[..., np.newaxis], + xs[..., np.newaxis], + zs[..., np.newaxis] + ], axis=-1).astype(np.float) + grid = (grid / float(res - 1) - 0.5) * 2 * bound + grid = grid.reshape(-1, 3) + # Grid will be [-1, 1] * bound + + dists_lst = [] + pbar = range(0, grid.shape[0], batch_size) + if verbose: + pbar = tqdm.tqdm(pbar) + for i in pbar: + sidx, eidx = i, i + batch_size + eidx = min(grid.shape[0], eidx) + with jittor.no_grad(): + xyz = jittor.Var( + grid[sidx:eidx, :]).float().cuda().view(1, -1, 3) + if use_double: + xyz = xyz.double() + distances = imf(xyz) + distances = distances.cpu().numpy() + dists_lst.append(distances.reshape(-1)) + + dists = np.concatenate( + [x.reshape(-1, 1) for x in dists_lst], axis=0).reshape(-1) + field = dists.reshape(res, res, res) + try: + vert, face, _, _ = skimage.measure.marching_cubes( + field, level=threshold) + print(vert.max(), vert.min()) + # Vertices will be [0, res - 1] + + if normalize: + if norm_type == 'norm': + center = vert.mean(axis=0).view(1, -1) + vert_c = vert - center + length = np.linalg.norm(vert_c, axis=-1).max() + vert = vert_c / length + elif norm_type == 'res': + vert = (vert / float(res - 1) - 0.5) * 2 * bound + else: + raise ValueError + new_mesh = trimesh.Trimesh(vertices=vert, faces=face) + except ValueError as e: + print(field.max(), field.min()) + print(e) + new_mesh = None + except RuntimeError as e: + print(field.max(), field.min()) + print(e) + new_mesh = None + + if return_stats: + if new_mesh is not None: + area = new_mesh.area + vol = (field < threshold).astype(np.float).mean() * (2 * bound) ** 3 + else: + area = 0 + vol = 0 + return new_mesh, { + 'vol': vol, + 'area': area + } + + return new_mesh + + +def make_2d_grid(r, add_noise=False): + xs, ys = jittor.meshgrid(jittor.arange(r), jittor.arange(r)) + xy = jittor.cat([ys.reshape(-1, 1), xs.reshape(-1, 1)], dim=-1).float() + if add_noise: + xy += jittor.rand_like(xy) + else: + xy += 0.5 + xy = (xy / float(r) - 0.5) * 2 + return xy + + +def imf2img(imf, res=256, add_noise=False, batch_size=10000, threshold=0., + verbose=False, grid=None, return_stats=False, bound=1): + if grid is None: + grid = make_2d_grid(res, add_noise=add_noise).view(-1, 2) + dists_lst = [] + pbar = range(0, grid.shape[0], batch_size) + if verbose: + pbar = tqdm.tqdm(pbar) + for i in pbar: + sidx, eidx = i, i + batch_size + eidx = min(grid.shape[0], eidx) + with jittor.no_grad(): + xyz = grid[sidx:eidx, :].cuda().view(1, -1, 2) + n = xyz.size(1) + distances = imf(xyz) + distances = distances.cpu().numpy() + dists_lst.append(distances.reshape(n, -1)) + dists = np.concatenate( + [x for x in dists_lst], axis=0) + img = dists.reshape(res, res, -1) + if return_stats: + area = (img < threshold).astype(np.float).mean() * 2 ** 2 + contours = skimage.measure.find_contours( + img.reshape(res, res), level=threshold) + total_length = 0 + for vert in contours: + n_v_c = vert.shape[0] + n_v_c_idx = np.array( + (np.arange(n_v_c).astype(np.int) + 1) % n_v_c).astype(np.int) + v_next = vert[n_v_c_idx, :] + v_next = v_next.reshape(n_v_c, 2) + diff = (vert - v_next) / float(res) + dist = np.linalg.norm(diff, axis=-1).sum() + total_length += dist + return img, { + 'area' : area, + 'len': total_length, + 'contours': contours + } + return img diff --git a/contrib/HSDF-Net/utils.py b/contrib/HSDF-Net/utils.py new file mode 100644 index 00000000..b6af758a --- /dev/null +++ b/contrib/HSDF-Net/utils.py @@ -0,0 +1,314 @@ +from posixpath import dirname +import numpy as np +import sys +import os +from subprocess import call +import shutil +#from torch.import typename +import multiprocessing as mp +from multiprocessing import Pool +from functools import partial +#import torch +import jittor + +#from torch.nn.parallel.data_parallel import DataParallel +import configs.config_loader as cfg_loader +from glob import glob + +#import torch.distributed as dist +import pymeshlab as ml +import trimesh +from scipy.spatial import cKDTree as KDTree +import time + +#from mesh_to_sdf import sample_sdf_near_surface, mesh_to_voxels, mesh_to_sdf +#from mesh_to_sdf.utils import get_raster_points +from numpy.core.einsumfunc import einsum_path +from numpy.lib.twodim_base import mask_indices + +import trimesh +import pyrender +import numpy as np +from trimesh import points +#import igl +from skimage.measure import marching_cubes +#import torch +import os +#import igl + +# this is mostly from https://github.com/chrischoy/3D-R2N2/blob/master/lib/voxel.py +# though I sped up the voxel2mesh function considerably, now only surface voxels are saved +# this is only really important for very large models + +MGN_TYPE = [ + 'Pants', + 'ShortPants', + 'LongCoat', + 'ShirtNoCoat', + 'TShirtNoCoat'] + + +def voxel2mesh(voxels, threshold=.3): + cube_verts = [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], + [1, 1, 1]] # 8 points + + cube_faces = [[0, 1, 2], [1, 3, 2], [2, 3, 6], [3, 7, 6], [0, 2, 6], [0, 6, 4], [0, 5, 1], + [0, 4, 5], [6, 7, 5], [6, 5, 4], [1, 7, 3], [1, 5, 7]] # 12 face + + cube_verts = np.array(cube_verts) + cube_faces = np.array(cube_faces) + 1 + + l, m, n = voxels.shape + + scale = 0.01 + cube_dist_scale = 1.1 + verts = [] + faces = [] + curr_vert = 0 + + positions = np.where(voxels > threshold) # recieves position of all voxels + offpositions = np.where(voxels < threshold) # recieves position of all voxels + voxels[positions] = 1 # sets all voxels values to 1 + voxels[offpositions] = 0 + for i,j,k in zip(*positions): + if np.sum(voxels[i-1:i+2,j-1:j+2,k-1:k+2])< 27 : #identifies if current voxels has an exposed face + verts.extend(scale * (cube_verts + cube_dist_scale * np.array([[i, j, k]]))) + faces.extend(cube_faces + curr_vert) + curr_vert += len(cube_verts) + return np.array(verts), np.array(faces) + + +def write_obj(filename, verts, faces): + """ write the verts and faces on file.""" + with open(filename, 'w') as f: + # write vertices + f.write('g\n# %d vertex\n' % len(verts)) + for vert in verts: + f.write('v %f %f %f\n' % tuple(vert)) + + # write faces + f.write('# %d faces\n' % len(faces)) + for face in faces: + f.write('f %d %d %d\n' % tuple(face)) + + +def voxel2obj(filename, pred, threshold=.3): + verts, faces = voxel2mesh(pred, threshold ) + write_obj(filename, verts, faces) + + +# arange shapenet_improved dataset into folders +def preprocess_shapenet(data_path, obj_name='model.obj'): + file_list = os.listdir(data_path) + for obj_path in file_list: + dir_name = os.path.join(data_path, os.path.splitext(obj_path)[0]) + os.mkdir(dir_name) + shutil.move(os.path.join(data_path, obj_path), dir_name) + os.rename(os.path.join(dir_name, obj_path), os.path.join(dir_name, obj_name)) + + print('{} moved!'.format(obj_path)) + +def preprocess_mgn(data_path, obj_name='model.obj'): + file_list = os.listdir(data_path) + for obj_dir in file_list: + for type_name in MGN_TYPE: + type_path = os.path.join(data_path, obj_dir, type_name + '.obj') + if os.path.exists(type_path): + dir_name = os.path.join(data_path, obj_dir + '_' + type_name) + os.mkdir(dir_name) + shutil.move(type_path, dir_name) + os.rename(os.path.join(dir_name, type_name + '.obj'), os.path.join(dir_name, obj_name)) + + print('{} moved!'.format(dir_name)) + + shutil.rmtree(os.path.join(data_path, obj_dir)) + +def preprocess_mixamo(data_path, obj_name='model.obj'): + dir_list = os.listdir(data_path) + for dir_name in dir_list: + os.rename(os.path.join(data_path, dir_name, dir_name+'.obj'), os.path.join(data_path, dir_name, obj_name)) + +# fix npz files +def fix_npz(path): + dir_list = os.listdir(path) + bad_file_list = [] + for dir_name in dir_list: + file_list = os.listdir(os.path.join(path, dir_name)) + for file_name in file_list: + if '.npz' in file_name: + try: + np.load(os.path.join(path,dir_name,file_name), allow_pickle=True) + except: + print('bad file: {}'.format(file_name)) + bad_file_list.append(file_name) + + return bad_file_list + +def fix_npz_mp(file_path): + try: + np.load(file_path, allow_pickle=True) + except: + print('bad file: {}'.format(file_path)) + os.remove(file_path) +''' +def reduce_tensor(tensor): + rt = tensor.clone() + dist.all_reduce(rt, op=dist.ReduceOp.SUM) + rt /= dist.get_world_size() + return rt +''' +def optimizer_to(optim, device): + for param in optim.state.values(): + # Not sure there are any global tensors in the state dict + if isinstance(param, jittor.Var): + param.data = param.data.to(device) + if param._grad is not None: + param._grad.data = param._grad.data.to(device) + elif isinstance(param, dict): + for subparam in param.values(): + if isinstance(subparam, jittor.Var): + subparam.data = subparam.data.to(device) + if subparam._grad is not None: + subparam._grad.data = subparam._grad.data.to(device) + +def add_tail(name_list, tail='_old'): + ret_list = [] + for name in name_list: + ret_list.append(os.path.join(os.path.dirname(name)+tail, os.path.basename(name))) + + return ret_list + +def modify_npz(npz_path): + split = np.load(npz_path) + np.savez(npz_path, train=add_tail(split['train']), test=add_tail(split['test']), val=add_tail(split['val'])) + + +def pc2mesh(data_dir): + name_list = os.listdir(data_dir) + + for name in name_list: + + target_path = os.path.join(data_dir, name, 'dense_point_cloud_7_bpa.obj') + if os.path.exists(target_path): + print('{} exsits, skip!'.format(name)) + continue + + print('processing {}'.format(name)) + start = time.time() + path = os.path.join(data_dir, name, 'dense_point_cloud_7_pc.off') + + ms = ml.MeshSet() + ms.load_new_mesh(path) + ms.load_filter_script('ndf_postprocess.mlx') + ms.apply_filter_script() + + ms.save_current_mesh(os.path.join(data_dir, name, 'dense_point_cloud_7_bpa.obj')) + + duration = time.time() - start + + print('duration {}'.format(duration)) + +def preprocess_watertight(data_dir, src_dir): + name_list = os.listdir(data_dir) + + for name in name_list: + + target_path = os.path.join(src_dir, name, 'model_wt.obj') + if os.path.exists(target_path): + print('{} exsits, skip!'.format(name)) + continue + + print('processing {}'.format(name)) + start = time.time() + path = os.path.join(src_dir, name, 'model_scaled.off') + + ''' + ms = ml.MeshSet() + ms.load_new_mesh(path) + ms.load_filter_script('ndf_postprocess.mlx') + ms.apply_filter_script() + + ms.save_current_mesh(os.path.join(data_dir, name, 'dense_point_cloud_7_bpa.obj')) + ''' + + voxel_resolution = 256 + + mesh = trimesh.load(path) + + points = get_raster_points(voxel_resolution=voxel_resolution) + + sdf = igl.signed_distance(points, mesh.vertices, mesh.faces)[0] + + sdf = sdf.reshape([voxel_resolution]*3) + + verts, faces, norms, vals = marching_cubes(sdf, 0) + + trimesh.Trimesh(vertices=verts, faces=faces).export(target_path) + + duration = time.time() - start + + print('duration {}'.format(duration)) + +def create_grid_points_from_bounds(minimun, maximum, res): + x = np.linspace(minimun, maximum, res) + X, Y, Z = np.meshgrid(x, x, x, indexing='ij') + X = X.reshape((np.prod(X.shape),)) + Y = Y.reshape((np.prod(Y.shape),)) + Z = Z.reshape((np.prod(Z.shape),)) + + points_list = np.column_stack((X, Y, Z)) + del X, Y, Z, x + return points_list + +if __name__=='__main__': + #modify_npz('datasets/shapenet_improved/data/split_shapenet_cars_chen_old.npz') + #preprocess_shapenet('datasets/shapenet_improved/data/03001627') + #preprocess_mgn('datasets/MGN/data/0') + #preprocess_mixamo('datasets/mixamo_data/data/0') + + preprocess_watertight('experiments/shapenet_lamp_chen_apex_148_3000/evaluation/generation/03636649', 'datasets/shapenet_improved/data/03636649') + #pc2mesh('experiments/shapenet_chairs_chen_apex_148_3000/evaluation/generation/03001627') + + ''' + grid_points = create_grid_points_from_bounds(-0.5, 0.5, 64) + kdtree = KDTree(grid_points) + + mesh = trimesh.load('datasets/shapenet_improved/data/03001627/113016635d554d5171fb733891076ecf/model_scaled.off') + point_cloud = mesh.sample(3000) + + occupancies = np.zeros(len(grid_points), dtype=np.int8) + + _, idx = kdtree.query(point_cloud) + occupancies[idx] = 1 + + #npz_file = np.load('datasets/shapenet_improved/data/03001627/113016635d554d5171fb733891076ecf/voxelized_point_cloud_256res_3000points.npz') + #occupancies = np.unpackbits(npz_file['compressed_occupancies']) + voxel2obj('chair.obj', np.reshape(occupancies, (64,)*3)) + ''' + + ''' + cfg = cfg_loader.get_config() + + paths = glob( cfg.data_dir + '/*/*.npz') + print(len(paths)) + + paths = sorted(paths) + + chunks = np.array_split(paths,cfg.num_chunks) + paths = chunks[cfg.current_chunk] + + + if cfg.num_cpus == -1: + num_cpus = mp.cpu_count() + print('cpu count: {}'.format(num_cpus)) + else: + num_cpus = cfg.num_cpus + + def multiprocess(func): + p = Pool(num_cpus) + p.map(func, paths) + p.close() + p.join() + + multiprocess(fix_npz_mp) + ''' diff --git a/contrib/NeRF-Editing/LICENSE b/contrib/NeRF-Editing/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/contrib/NeRF-Editing/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. diff --git a/contrib/NeRF-Editing/OpenVolumeMesh/CMakeLists.txt b/contrib/NeRF-Editing/OpenVolumeMesh/CMakeLists.txt new file mode 100644 index 00000000..02a36a3b --- /dev/null +++ b/contrib/NeRF-Editing/OpenVolumeMesh/CMakeLists.txt @@ -0,0 +1,35 @@ +cmake_minimum_required(VERSION 3.12 FATAL_ERROR) +cmake_policy(VERSION 3.12.0) +project(OpenVolumeMeshExamples) + +if (NOT TARGET OpenVolumeMesh::OpenVolumeMesh) + find_package(OpenVolumeMesh REQUIRED) +endif() + +# Add target for first example +add_executable(simple_mesh simple_mesh/simple_mesh.cpp) + +target_link_libraries(simple_mesh OpenVolumeMesh::OpenVolumeMesh) + +set(OVM_CXX_STANDARD 11 CACHE STRING "C++ standard version to use") +set_property(CACHE OVM_CXX_STANDARD PROPERTY STRINGS 11 14 17 20) + +set_target_properties (simple_mesh PROPERTIES + CXX_STANDARD ${OVM_CXX_STANDARD} + CXX_STANDARD_REQUIRED YES + CXX_EXTENSIONS NO +) + +#[[= +if(WIN32) + # copy exe file to "Build" directory + # Visual studio will create this file in a subdirectory so we can't use + # RUNTIME_OUTPUT_DIRECTORY directly here + add_custom_command (TARGET simple_mesh POST_BUILD + COMMAND ${CMAKE_COMMAND} -E + copy_if_different + ${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_CFG_INTDIR}/simple_mesh.exe + ${CMAKE_BINARY_DIR}/Examples/simple_mesh.exe) +else() +endif() +=]] diff --git a/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/simple_mesh.cc b/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/simple_mesh.cc new file mode 100644 index 00000000..46e02db2 --- /dev/null +++ b/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/simple_mesh.cc @@ -0,0 +1,149 @@ +// C++ includes +#include +#include +#include +#include +#include + +// Include vector classes +#include + +// Include polyhedral mesh kernel +#include + +#include + +// Make some typedefs to facilitate your life +typedef OpenVolumeMesh::Geometry::Vec3f Vec3f; +typedef OpenVolumeMesh::GeometryKernel PolyhedralMeshV3f; + +// verts [NV,3]; simplicies [NC,4] +void readVerts(std::string file_path, std::vector>& verts, std::vector>& cells) { + std::ifstream myfile (file_path); + if (myfile.is_open()) { + int Num; + myfile >> Num; // read verts + for (int idx = 0; idx < Num; idx++) { + float v1, v2, v3; myfile >> v1; myfile>> v2; myfile >> v3; + std::vector cur = {v1, v2, v3}; + verts.push_back(cur); + } + myfile >> Num; // read cells + for (int idx = 0; idx < Num; idx++) { + int id1, id2, id3, id4; + myfile >> id1; myfile >> id2; myfile >> id3; myfile >> id4; + std::vector cur = {id1, id2, id3, id4}; + cells.push_back(cur); + } + myfile.close(); + } + + else std::cout << "Unable to open file \n"; +} + +int getHalfFaceID(std::vector& vertsPerCell) { + // determine the face[v1/v2/v3] ID wrt. v4 + Vec3f v01 = vertsPerCell[1] - vertsPerCell[0]; + Vec3f v02 = vertsPerCell[2] - vertsPerCell[0]; + Vec3f v03 = vertsPerCell[3] - vertsPerCell[0]; + + // for (Vec3f v : vertsPerCell) std::cout << v << ", "; + // std::cout << std::endl; + + if (v01.cross(v02).dot(v03) > 0) return 0; + else return 1; +} + +int main(int _argc, char** _argv) { + + + std::string input_file = _argv[1]; + std::string save_name = _argv[2]; + std::vector> verts; + std::vector> cells; + readVerts(input_file, verts, cells); + + // Create mesh object + PolyhedralMeshV3f myMesh; + + // add verts + std::vector vert_handles; + for (std::vector cur : verts) { + OpenVolumeMesh::VertexHandle vh = myMesh.add_vertex(Vec3f(cur[0], cur[1], cur[2])); + vert_handles.push_back(vh); + } + + // add faces + // to avoid duplicate faces, use hash to check + std::unordered_map fh_hash; + std::vector face_handles; + for (std::vector cur_cell : cells) { + std::vector cur_vh; + OpenVolumeMesh::FaceHandle fh; + std::vector halffaces; + std::vector vertsPerCell; + std::string fh_str; + + cur_vh.push_back(vert_handles[cur_cell[0]]); // 0 1 2 + cur_vh.push_back(vert_handles[cur_cell[1]]); + cur_vh.push_back(vert_handles[cur_cell[2]]); + fh_str = std::to_string(cur_cell[0]) + ", " + std::to_string(cur_cell[1]) + ", " + std::to_string(cur_cell[2]); + if (fh_hash.count(fh_str)) fh = fh_hash[fh_str]; + else {fh = myMesh.add_face(cur_vh); fh_hash[fh_str] = fh;} + for (OpenVolumeMesh::FaceVertexIter fv_it = myMesh.fv_iter(fh); fv_it.valid(); fv_it++) { + vertsPerCell.push_back(myMesh.vertex(*fv_it)); + } + vertsPerCell.push_back(myMesh.vertex(vert_handles[cur_cell[3]])); + halffaces.push_back(myMesh.halfface_handle(fh, getHalfFaceID(vertsPerCell))); + + cur_vh.clear(); vertsPerCell.clear(); + cur_vh.push_back(vert_handles[cur_cell[0]]); // 0 1 3 + cur_vh.push_back(vert_handles[cur_cell[1]]); + cur_vh.push_back(vert_handles[cur_cell[3]]); + fh_str = std::to_string(cur_cell[0]) + ", " + std::to_string(cur_cell[1]) + ", " + std::to_string(cur_cell[3]); + if (fh_hash.count(fh_str)) fh = fh_hash[fh_str]; + else {fh = myMesh.add_face(cur_vh); fh_hash[fh_str] = fh;} + for (OpenVolumeMesh::FaceVertexIter fv_it = myMesh.fv_iter(fh); fv_it.valid(); fv_it++) { + vertsPerCell.push_back(myMesh.vertex(*fv_it)); + } + vertsPerCell.push_back(myMesh.vertex(vert_handles[cur_cell[2]])); + halffaces.push_back(myMesh.halfface_handle(fh, getHalfFaceID(vertsPerCell))); + + cur_vh.clear(); vertsPerCell.clear(); + cur_vh.push_back(vert_handles[cur_cell[0]]); // 0 2 3 + cur_vh.push_back(vert_handles[cur_cell[2]]); + cur_vh.push_back(vert_handles[cur_cell[3]]); + fh_str = std::to_string(cur_cell[0]) + ", " + std::to_string(cur_cell[2]) + ", " + std::to_string(cur_cell[3]); + if (fh_hash.count(fh_str)) fh = fh_hash[fh_str]; + else {fh = myMesh.add_face(cur_vh); fh_hash[fh_str] = fh;} + for (OpenVolumeMesh::FaceVertexIter fv_it = myMesh.fv_iter(fh); fv_it.valid(); fv_it++) { + vertsPerCell.push_back(myMesh.vertex(*fv_it)); + } + vertsPerCell.push_back(myMesh.vertex(vert_handles[cur_cell[1]])); + halffaces.push_back(myMesh.halfface_handle(fh, getHalfFaceID(vertsPerCell))); + + cur_vh.clear(); vertsPerCell.clear(); + cur_vh.push_back(vert_handles[cur_cell[1]]); // 1 2 3 + cur_vh.push_back(vert_handles[cur_cell[2]]); + cur_vh.push_back(vert_handles[cur_cell[3]]); + fh_str = std::to_string(cur_cell[1]) + ", " + std::to_string(cur_cell[2]) + ", " + std::to_string(cur_cell[3]); + if (fh_hash.count(fh_str)) fh = fh_hash[fh_str]; + else {fh = myMesh.add_face(cur_vh); fh_hash[fh_str] = fh;} + for (OpenVolumeMesh::FaceVertexIter fv_it = myMesh.fv_iter(fh); fv_it.valid(); fv_it++) { + vertsPerCell.push_back(myMesh.vertex(*fv_it)); + } + vertsPerCell.push_back(myMesh.vertex(vert_handles[cur_cell[0]])); + halffaces.push_back(myMesh.halfface_handle(fh, getHalfFaceID(vertsPerCell))); + + myMesh.add_cell(halffaces); + } + + // Create file manager object + OpenVolumeMesh::IO::FileManager fileManager; + // Store mesh to file "myMesh.ovm" in the current directory + // std::string save_name = "myMesh_debug.ovm"; + fileManager.writeFile(save_name.c_str(), myMesh); + std::cout << "save mesh to " << save_name << std::endl; + + return 0; +} diff --git a/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/test.cpp b/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/test.cpp new file mode 100644 index 00000000..d9ea57c9 --- /dev/null +++ b/contrib/NeRF-Editing/OpenVolumeMesh/simple_mesh/test.cpp @@ -0,0 +1,90 @@ +// C++ includes +#include +#include + +// Include vector classes +#include + +// Include polyhedral mesh kernel +#include + +// Include the file manager header +#include + +#include // sqrt + +// Make some typedefs to facilitate your life +typedef OpenVolumeMesh::Geometry::Vec3f Vec3f; +typedef OpenVolumeMesh::GeometryKernel PolyhedralMeshV3f; + +float calDihedralAngle(std::vector dihedral) { + // v1, v2 constructs the common edge. v3 and v4 expand the dihedral angle + OpenVolumeMesh::Geometry::Vec3f v1 = dihedral[0]; + OpenVolumeMesh::Geometry::Vec3f v2 = dihedral[1]; + OpenVolumeMesh::Geometry::Vec3f v3 = dihedral[2]; + OpenVolumeMesh::Geometry::Vec3f v4 = dihedral[3]; + + // assume the order: v3 -> v1 -> v2 -> v4 + OpenVolumeMesh::Geometry::Vec3f d1 = v1 - v3; + OpenVolumeMesh::Geometry::Vec3f d2 = v2 - v1; + OpenVolumeMesh::Geometry::Vec3f d3 = v4 - v2; + + OpenVolumeMesh::Geometry::Vec3f n1 = d1.cross(d2); + OpenVolumeMesh::Geometry::Vec3f n2 = d2.cross(d3); + + float cos_angle = n1.dot(n2) / n1.norm() / n2.norm(); + + return cos_angle / sqrt(1 - cos_angle*cos_angle); +} + +int main(int argc, char const *argv[]) +{ + std::string ovm_path = "/mnt/2/sunyangtian/NeRF_Ali/volumeARAP_batch/test_file2/mesh_cage_.ovm"; + OpenVolumeMesh::GeometricPolyhedralMeshV3f myMesh; + OpenVolumeMesh::IO::FileManager fileManager; + fileManager.readFile(ovm_path, myMesh); + + for (int i = 0; i < myMesh.n_vertices(); i++) { + OpenVolumeMesh::VertexHandle vh(i); + for (OpenVolumeMesh::VertexVertexIter vvit=myMesh.vv_iter(vh); vvit.valid(); vvit++) { + OpenVolumeMesh::Geometry::Vec3f pos = myMesh.vertex(*vvit); + std::cout << "vertex index: " << (*vvit).idx() << std::endl; + } + std::cout << "---------------------------------" << std::endl; + for (OpenVolumeMesh::VertexOHalfEdgeIter voheit=myMesh.voh_iter(vh); voheit.valid(); voheit++) { + OpenVolumeMesh::VertexHandle vh2 = myMesh.to_vertex_handle(*voheit); + std::cout << "****** the edge between ****** " << vh.idx() << " and " << vh2.idx() << std::endl; + OpenVolumeMesh::EdgeHandle eh = myMesh.edge_handle(*voheit); + for (OpenVolumeMesh::EdgeCellIter ecit=myMesh.ec_iter(eh); ecit.valid(); ecit++) { + std::cout << "iterate cells ..." << std::endl; + std::vector dihedralAngle; + // vector size is 4. The first and second is the common edge + dihedralAngle.push_back(myMesh.vertex(vh)); + dihedralAngle.push_back(myMesh.vertex(vh2)); + std::cout << "specific verts are: " << vh.idx() << ", " << vh2.idx() << std::endl; + for (OpenVolumeMesh::CellVertexIter cvit=myMesh.cv_iter(*ecit); cvit.valid(); cvit++) { + if (*cvit != vh && *cvit != vh2) { + dihedralAngle.push_back(myMesh.vertex(*cvit)); + std::cout << "verts add " << (*cvit).idx() << std::endl; + } + } + std::cout << "the cot of dihedralAngle is: " << calDihedralAngle(dihedralAngle) << std::endl; + std::cout << "---------------------------------" << std::endl; + } + + } + if (i > 1) + break; + } + + std::cout << "TEST dihetral angle" << std::endl; + OpenVolumeMesh::Geometry::Vec3f v1(0,0,0); + OpenVolumeMesh::Geometry::Vec3f v2(1,0,0); + OpenVolumeMesh::Geometry::Vec3f v3(0,0,1); + OpenVolumeMesh::Geometry::Vec3f v4(0,1,-1); + + std::vector dihedral = {v1,v2,v3,v4}; + std::cout << "the cot of dihedralAngle is: " << calDihedralAngle(dihedral) << std::endl; + + return 0; +} diff --git a/contrib/NeRF-Editing/README.md b/contrib/NeRF-Editing/README.md new file mode 100644 index 00000000..dd56c4d7 --- /dev/null +++ b/contrib/NeRF-Editing/README.md @@ -0,0 +1,161 @@ +# NeRF-Editing: Geometry Editing of Neural Radiance Fields + +![Teaser image](./img/teaser.gif) + +## Abstract + +Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users. + +## Environment +* Install [jittor](https://github.com/Jittor/jittor) +
+ Other dependencies (click to expand) + + - opencv_python==4.5.2.52 + - imageio==2.17.0 + - trimesh==3.9.8 + - numpy==1.19.2 + - pyhocon==0.3.57 + - icecream==2.1.0 + - tqdm==4.50.2 + - scipy==1.7.0 + - PyMCubes==0.1.2 + - natsort==8.1.0 + - tensorboardX-2.5 + +
+ +We also use the pyrender to get the depth map. +``` +pip install pyrender +``` + +* Download [OpenVolumeMesh](https://www.graphics.rwth-aachen.de/software/openvolumemesh/download/) to the `OpenVolumeMesh` folder +* Download [Eigen](https://eigen.tuxfamily.org/index.php?title=Main_Page) to the `volumeARAP_batch/Eigen` folder + +## Data preparation + +Suppose the image data is in the `$data_dir/images` folder, we first estimate the camera poses with [colmap](https://github.com/colmap/colmap). Then we process the camera poses with the command +``` +python process_colmap.py $data_dir $data_dir +``` +Finally the data folder looks like +``` +$data_dir +├── colmap_output.txt (colmap output) +├── database.db (colmap output) +├── images ($data_dir/images) +├── intrinsics.txt +├── pose +├── rgb +└── sparse (colmap output) +``` + +We now provide a provide a ready-to-use dataset `hbychair` collected by ourselves in [google drive](https://drive.google.com/drive/folders/1OdHHNxMk9t9cDTZHlMHLSb11tf9LYHtG?usp=sharing), along with the pre-trained model and deformation results. You can put the data into the `data` folder. + +Or you can use [nerf-synthetic dataset](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) directly, see `./confs/wmask_lego.conf` as an example. + +## Technological process + +##### Training +we adopt the training strategy of [NeuS](https://github.com/Totoro97/NeuS). + +``` +python exp_runner.py --mode train --conf ./confs/womask_hbychair.conf --case hbychair_neus +``` + +##### Extract mesh + ``` +python exp_runner.py --mode validate_mesh --conf ./confs/womask_hbychair.conf --case hbychair_neus --is_continue # use latest checkpoint + ``` + *We have provided a simplified mesh `mesh_nofloor_simp.obj`* + +##### Render image before editing +``` +python exp_runner.py --mode circle --conf ./confs/womask_hbychair_render.conf --case hbychair_neus --is_continue --obj_path ./logs/hbychair_wo_mask/mesh_nofloor_simp.obj +``` + +*Note: `obj_path` is optional, which provides better rendering results.* + +##### Construct cage mesh + ``` +python exp_runner.py --mode validate_mesh --conf ./confs/womask_hbychair.conf --case hbychair_neus --is_continue --do_dilation + ``` + *We have provided a cage mesh `mesh_cage_nofloor.obj`* + +##### Construct tetrahedral mesh using [TetWild](https://github.com/Yixin-Hu/TetWild). +``` +./TetWild ../../src/logs/hbychair_wo_mask/mesh_cage_nofloor.obj +``` +*Note that we modify the tetrahedra storage format of Tetwild output. Therefore, please compile the `tetwild` in this repository following the instructions [here](https://github.com/Yixin-Hu/TetWild).* + +##### Change the output to `ovm` format. +``` +./simple_mesh ../../src/logs/hbychair_wo_mask/mesh_cage_nofloor_.txt ../../src/logs/hbychair_wo_mask/mesh_cage_nofloor_.ovm +``` +*`simple_mesh` can be obtained using the `CMakeLists.txt` in the `OpenVolumeMesh` folder.* + +##### Editing + Deform the extracted mesh *with any mesh editing tool*, and put the (sequence) mesh in `$deformed_dir` folder. + + *We have provided a deformed mesh `deformed_mesh.obj` and a folder named as `mesh_seq`* + +##### Propagate editing +Generate the controlpoint.txt to guide the deformation. +``` +python barycentric_control_pts_jittor.py +``` +Note that specify the `mesh_path` (extracted mesh), `tet_path` (tetrahedra mesh) and `deformed_dir`(deformed mesh sequence) first. + +And the format of controlpoint.txt is listed below. + +``` +10 (Number of sequence) +N (Num of control points) +x1 y1 z1 +x2 y2 z2 +... +N (Num of control points) +x1 y1 z1 +x2 y2 z2 +... +. +. +. +N (Num of barycentric coordinate) +id1 id2 id3 id4 (vert index of this tet) +u1 v1 w1 z1 +id1' id2' id3' id4' +u2 v2 w2 z2 +... +``` +Compile the `volumeARAP_batch` project to obtain `volumeARAP`, and deform the tetrehedra mesh. +``` +./volumeARAP ../../src/logs/hbychair_wo_mask/mesh_cage_nofloor_.ovm ../../src/logs/hbychair_wo_mask/mesh_seq/2_barycentric_control.txt ../../src/logs/hbychair_wo_mask/mesh_seq_ovm 0 +``` +##### Rendering after editing +``` +python exp_runner.py --mode circle --conf ./confs/womask_hbychair_render.conf --case hbychair_neus --is_continue --use_deform --reconstructed_mesh_file ./logs/hbychair_wo_mask/mesh_cage_nofloor_.txt --deformed_mesh_file ./logs/hbychair_wo_mask/mesh_seq_ovm/arap_result_0000_.ovm --obj_path ./logs/hbychair_wo_mask/deformed_mesh.obj +``` + +* fix camera (generate sequential editing results in a fixed camera) +``` +python exp_runner.py --mode circle --conf ./confs/womask_hbychair_render.conf --case hbychair_neus --is_continue --use_deform --reconstructed_mesh_file ./logs/hbychair_wo_mask/mesh_cage_nofloor_.txt --deformed_mesh_file ./logs/hbychair_wo_mask/mesh_seq_ovm/arap_result_0000_.ovm --obj_path ./logs/hbychair_wo_mask/deformed_mesh.obj --fix_camera +``` + +## Acknowledgement +This code borrows heavily from [NeuS](https://github.com/Totoro97/NeuS). + +## Citation + +If you found this code useful please cite our work as: + +``` +@inproceedings{yuan2022nerf, + title={NeRF-editing: geometry editing of neural radiance fields}, + author={Yuan, Yu-Jie and Sun, Yang-Tian and Lai, Yu-Kun and Ma, Yuewen and Jia, Rongfei and Gao, Lin}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={18353--18364}, + year={2022} +} +``` diff --git a/contrib/NeRF-Editing/TetWild/CMakeLists.txt b/contrib/NeRF-Editing/TetWild/CMakeLists.txt new file mode 100644 index 00000000..c0b90cb5 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/CMakeLists.txt @@ -0,0 +1,177 @@ +cmake_minimum_required(VERSION 3.3) +list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake) + +################################################################################ + + +################################################################################ +# Options +################################################################################ +# tetwild +option(TETWILD_WITH_HUNTER "Use Hunter to download and configure Boost" OFF) +option(TETWILD_WITH_ISPC "Use ISPC" OFF) +# libigl library +option(LIBIGL_USE_STATIC_LIBRARY "Use libigl as static library" OFF) +option(LIBIGL_WITH_ANTTWEAKBAR "Use AntTweakBar" OFF) +option(LIBIGL_WITH_CGAL "Use CGAL" ON) +option(LIBIGL_WITH_COMISO "Use CoMiso" OFF) +option(LIBIGL_WITH_CORK "Use Cork" OFF) +option(LIBIGL_WITH_LIM "Use LIM" OFF) +option(LIBIGL_WITH_MATLAB "Use Matlab" OFF) +option(LIBIGL_WITH_MOSEK "Use MOSEK" OFF) +option(LIBIGL_WITH_PNG "Use PNG" OFF) +option(LIBIGL_WITH_PYTHON "Use Python" OFF) +option(LIBIGL_WITH_TETGEN "Use Tetgen" OFF) +option(LIBIGL_WITH_TRIANGLE "Use Triangle" OFF) +option(LIBIGL_WITH_XML "Use XML" OFF) +# gui +option(LIBIGL_WITH_EMBREE "Use Embree" OFF) +option(LIBIGL_WITH_NANOGUI "Use Nanogui menu" OFF) +option(LIBIGL_WITH_OPENGL "Use OpenGL" OFF) +option(LIBIGL_WITH_OPENGL_GLFW "Use GLFW" OFF) +option(LIBIGL_WITH_VIEWER "Use OpenGL viewer" OFF) +#geogram +option(GEOGRAM_WITH_TRIANGLE "Use Triangle" OFF) + +if(TETWILD_WITH_HUNTER) + # Needs to be set before the main project... argh =/ + include(HunterGate) + HunterGate( + URL "https://github.com/ruslo/hunter/archive/v0.23.25.tar.gz" + SHA1 "cb75cce9a3a8d552e70e7118f3203eb4ac05c201" + ) +endif() + +################################################################################ +# Project name +################################################################################ + +project(TetWild) + +################################################################################ +# Settings +################################################################################ +if(NOT CMAKE_BUILD_TYPE) + message(STATUS "No build type selected, default to Release") + set(CMAKE_BUILD_TYPE "Release") +endif() + +set(TETWILD_EXTERNAL "${CMAKE_CURRENT_SOURCE_DIR}/extern") + +# Color output +include(UseColors) + +# Use folder in Visual Studio +set_property(GLOBAL PROPERTY USE_FOLDERS ON) + +# Extra warnings +include(Warnings) + +# Export compile flags (used for autocompletion of the C++ code) +set(CMAKE_EXPORT_COMPILE_COMMANDS 1) + +# Generate position independent code +set(CMAKE_POSITION_INDEPENDENT_CODE ON) + +################################################################################ +# 3rd party libraries +################################################################################ +include(TetWildDependencies) + +################################################################################ +# TetWild +################################################################################ +# Build static library for executable +add_library(libTetWild + include/tetwild/Args.h + include/tetwild/Exception.h + include/tetwild/Logger.h + include/tetwild/tetwild.h + src/tetwild/BSPSubdivision.cpp + src/tetwild/BSPSubdivision.h + src/tetwild/CGALTypes.h + src/tetwild/Common.cpp + src/tetwild/Common.h + src/tetwild/DelaunayTetrahedralization.cpp + src/tetwild/DelaunayTetrahedralization.h + src/tetwild/DistanceQuery.cpp + src/tetwild/DistanceQuery.h + src/tetwild/EdgeCollapser.cpp + src/tetwild/EdgeCollapser.h + src/tetwild/EdgeRemover.cpp + src/tetwild/EdgeRemover.h + src/tetwild/EdgeSplitter.cpp + src/tetwild/EdgeSplitter.h + src/tetwild/ForwardDecls.h + src/tetwild/InoutFiltering.cpp + src/tetwild/InoutFiltering.h + src/tetwild/LocalOperations.cpp + src/tetwild/LocalOperations.h + src/tetwild/Logger.cpp + src/tetwild/MeshConformer.cpp + src/tetwild/MeshConformer.h + src/tetwild/MeshRefinement.cpp + src/tetwild/MeshRefinement.h + src/tetwild/Preprocess.cpp + src/tetwild/Preprocess.h + src/tetwild/SimpleTetrahedralization.cpp + src/tetwild/SimpleTetrahedralization.h + src/tetwild/State.cpp + src/tetwild/State.h + src/tetwild/TetmeshElements.cpp + src/tetwild/TetmeshElements.h + src/tetwild/tetwild.cpp + src/tetwild/VertexSmoother.cpp + src/tetwild/VertexSmoother.h + src/tetwild/geogram/mesh_AABB.cpp + src/tetwild/geogram/mesh_AABB.h +) +target_include_directories(libTetWild + PRIVATE + src + PUBLIC + include +) +target_link_libraries(libTetWild + PUBLIC + geogram + igl::core + pymesh::pymesh + spdlog::spdlog + PRIVATE + igl::cgal + warnings::all +) +set_target_properties(libTetWild PROPERTIES OUTPUT_NAME "tetwild") + +# ispc +if(TETWILD_WITH_ISPC) + message(STATUS "Compiling energy with ISPC") + add_subdirectory(src/ispc) + ispc_add_energy(libTetWild) +endif() + +# Building executable +add_executable(TetWild src/main.cpp) +target_link_libraries(TetWild + libTetWild + CLI11::CLI11 + igl::cgal + warnings::all +) +target_include_directories(TetWild PRIVATE src) +igl_copy_cgal_dll(TetWild) + +# Install +install(TARGETS TetWild RUNTIME DESTINATION bin) + +################################################################################ +# Folders for Visual Studio/XCode IDEs +################################################################################ + +# geogram +set_target_properties(geogram PROPERTIES FOLDER extern/geogram) +set_target_properties(geogram_third_party PROPERTIES FOLDER extern/geogram) +set_target_properties(uninstall PROPERTIES FOLDER extern/geogram) +# pymesh +set_target_properties(pymesh_tiny PROPERTIES FOLDER extern/pymesh) diff --git a/contrib/NeRF-Editing/TetWild/Dockerfile b/contrib/NeRF-Editing/TetWild/Dockerfile new file mode 100644 index 00000000..11892a4b --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/Dockerfile @@ -0,0 +1,23 @@ +# Use an official Python runtime as a parent image +FROM ubuntu +ENV DEBIAN_FRONTEND=noninteractive + +# Install any needed packages specified in requirements.txt +RUN apt-get update && apt-get install -y git cmake g++ libcgal-dev + +# Set the working directory to /app +WORKDIR /app + +# Download and compile TetWild +RUN git clone https://github.com/Yixin-Hu/TetWild --recursive +WORKDIR /app/TetWild/build +RUN cmake .. && make + +WORKDIR /data + +ENTRYPOINT ["/app/TetWild/build/TetWild"] + +## Create TetWild image with: +# docker build -t tetwild . +## Run TetWild with: +# docker run --rm -v "$(pwd)":/data tetwild [TetWild arguments] diff --git a/contrib/NeRF-Editing/TetWild/LICENSE.GPL b/contrib/NeRF-Editing/TetWild/LICENSE.GPL new file mode 100644 index 00000000..20d40b6b --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/LICENSE.GPL @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/contrib/NeRF-Editing/TetWild/LICENSE.MPL2 b/contrib/NeRF-Editing/TetWild/LICENSE.MPL2 new file mode 100644 index 00000000..f4bbcd20 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/LICENSE.MPL2 @@ -0,0 +1,373 @@ +Mozilla Public License Version 2.0 +================================== + +1. Definitions +-------------- + +1.1. "Contributor" + means each individual or legal entity that creates, contributes to + the creation of, or owns Covered Software. + +1.2. "Contributor Version" + means the combination of the Contributions of others (if any) used + by a Contributor and that particular Contributor's Contribution. + +1.3. "Contribution" + means Covered Software of a particular Contributor. + +1.4. "Covered Software" + means Source Code Form to which the initial Contributor has attached + the notice in Exhibit A, the Executable Form of such Source Code + Form, and Modifications of such Source Code Form, in each case + including portions thereof. + +1.5. "Incompatible With Secondary Licenses" + means + + (a) that the initial Contributor has attached the notice described + in Exhibit B to the Covered Software; or + + (b) that the Covered Software was made available under the terms of + version 1.1 or earlier of the License, but not also under the + terms of a Secondary License. + +1.6. "Executable Form" + means any form of the work other than Source Code Form. + +1.7. "Larger Work" + means a work that combines Covered Software with other material, in + a separate file or files, that is not Covered Software. + +1.8. "License" + means this document. + +1.9. "Licensable" + means having the right to grant, to the maximum extent possible, + whether at the time of the initial grant or subsequently, any and + all of the rights conveyed by this License. + +1.10. "Modifications" + means any of the following: + + (a) any file in Source Code Form that results from an addition to, + deletion from, or modification of the contents of Covered + Software; or + + (b) any new file in Source Code Form that contains any Covered + Software. + +1.11. "Patent Claims" of a Contributor + means any patent claim(s), including without limitation, method, + process, and apparatus claims, in any patent Licensable by such + Contributor that would be infringed, but for the grant of the + License, by the making, using, selling, offering for sale, having + made, import, or transfer of either its Contributions or its + Contributor Version. + +1.12. "Secondary License" + means either the GNU General Public License, Version 2.0, the GNU + Lesser General Public License, Version 2.1, the GNU Affero General + Public License, Version 3.0, or any later versions of those + licenses. + +1.13. "Source Code Form" + means the form of the work preferred for making modifications. + +1.14. "You" (or "Your") + means an individual or a legal entity exercising rights under this + License. For legal entities, "You" includes any entity that + controls, is controlled by, or is under common control with You. For + purposes of this definition, "control" means (a) the power, direct + or indirect, to cause the direction or management of such entity, + whether by contract or otherwise, or (b) ownership of more than + fifty percent (50%) of the outstanding shares or beneficial + ownership of such entity. + +2. License Grants and Conditions +-------------------------------- + +2.1. Grants + +Each Contributor hereby grants You a world-wide, royalty-free, +non-exclusive license: + +(a) under intellectual property rights (other than patent or trademark) + Licensable by such Contributor to use, reproduce, make available, + modify, display, perform, distribute, and otherwise exploit its + Contributions, either on an unmodified basis, with Modifications, or + as part of a Larger Work; and + +(b) under Patent Claims of such Contributor to make, use, sell, offer + for sale, have made, import, and otherwise transfer either its + Contributions or its Contributor Version. + +2.2. Effective Date + +The licenses granted in Section 2.1 with respect to any Contribution +become effective for each Contribution on the date the Contributor first +distributes such Contribution. + +2.3. Limitations on Grant Scope + +The licenses granted in this Section 2 are the only rights granted under +this License. No additional rights or licenses will be implied from the +distribution or licensing of Covered Software under this License. +Notwithstanding Section 2.1(b) above, no patent license is granted by a +Contributor: + +(a) for any code that a Contributor has removed from Covered Software; + or + +(b) for infringements caused by: (i) Your and any other third party's + modifications of Covered Software, or (ii) the combination of its + Contributions with other software (except as part of its Contributor + Version); or + +(c) under Patent Claims infringed by Covered Software in the absence of + its Contributions. + +This License does not grant any rights in the trademarks, service marks, +or logos of any Contributor (except as may be necessary to comply with +the notice requirements in Section 3.4). + +2.4. Subsequent Licenses + +No Contributor makes additional grants as a result of Your choice to +distribute the Covered Software under a subsequent version of this +License (see Section 10.2) or under the terms of a Secondary License (if +permitted under the terms of Section 3.3). + +2.5. Representation + +Each Contributor represents that the Contributor believes its +Contributions are its original creation(s) or it has sufficient rights +to grant the rights to its Contributions conveyed by this License. + +2.6. Fair Use + +This License is not intended to limit any rights You have under +applicable copyright doctrines of fair use, fair dealing, or other +equivalents. + +2.7. Conditions + +Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted +in Section 2.1. + +3. Responsibilities +------------------- + +3.1. Distribution of Source Form + +All distribution of Covered Software in Source Code Form, including any +Modifications that You create or to which You contribute, must be under +the terms of this License. You must inform recipients that the Source +Code Form of the Covered Software is governed by the terms of this +License, and how they can obtain a copy of this License. You may not +attempt to alter or restrict the recipients' rights in the Source Code +Form. + +3.2. Distribution of Executable Form + +If You distribute Covered Software in Executable Form then: + +(a) such Covered Software must also be made available in Source Code + Form, as described in Section 3.1, and You must inform recipients of + the Executable Form how they can obtain a copy of such Source Code + Form by reasonable means in a timely manner, at a charge no more + than the cost of distribution to the recipient; and + +(b) You may distribute such Executable Form under the terms of this + License, or sublicense it under different terms, provided that the + license for the Executable Form does not attempt to limit or alter + the recipients' rights in the Source Code Form under this License. + +3.3. Distribution of a Larger Work + +You may create and distribute a Larger Work under terms of Your choice, +provided that You also comply with the requirements of this License for +the Covered Software. If the Larger Work is a combination of Covered +Software with a work governed by one or more Secondary Licenses, and the +Covered Software is not Incompatible With Secondary Licenses, this +License permits You to additionally distribute such Covered Software +under the terms of such Secondary License(s), so that the recipient of +the Larger Work may, at their option, further distribute the Covered +Software under the terms of either this License or such Secondary +License(s). + +3.4. Notices + +You may not remove or alter the substance of any license notices +(including copyright notices, patent notices, disclaimers of warranty, +or limitations of liability) contained within the Source Code Form of +the Covered Software, except that You may alter any license notices to +the extent required to remedy known factual inaccuracies. + +3.5. Application of Additional Terms + +You may choose to offer, and to charge a fee for, warranty, support, +indemnity or liability obligations to one or more recipients of Covered +Software. However, You may do so only on Your own behalf, and not on +behalf of any Contributor. You must make it absolutely clear that any +such warranty, support, indemnity, or liability obligation is offered by +You alone, and You hereby agree to indemnify every Contributor for any +liability incurred by such Contributor as a result of warranty, support, +indemnity or liability terms You offer. You may include additional +disclaimers of warranty and limitations of liability specific to any +jurisdiction. + +4. Inability to Comply Due to Statute or Regulation +--------------------------------------------------- + +If it is impossible for You to comply with any of the terms of this +License with respect to some or all of the Covered Software due to +statute, judicial order, or regulation then You must: (a) comply with +the terms of this License to the maximum extent possible; and (b) +describe the limitations and the code they affect. Such description must +be placed in a text file included with all distributions of the Covered +Software under this License. Except to the extent prohibited by statute +or regulation, such description must be sufficiently detailed for a +recipient of ordinary skill to be able to understand it. + +5. Termination +-------------- + +5.1. The rights granted under this License will terminate automatically +if You fail to comply with any of its terms. However, if You become +compliant, then the rights granted under this License from a particular +Contributor are reinstated (a) provisionally, unless and until such +Contributor explicitly and finally terminates Your grants, and (b) on an +ongoing basis, if such Contributor fails to notify You of the +non-compliance by some reasonable means prior to 60 days after You have +come back into compliance. Moreover, Your grants from a particular +Contributor are reinstated on an ongoing basis if such Contributor +notifies You of the non-compliance by some reasonable means, this is the +first time You have received notice of non-compliance with this License +from such Contributor, and You become compliant prior to 30 days after +Your receipt of the notice. + +5.2. If You initiate litigation against any entity by asserting a patent +infringement claim (excluding declaratory judgment actions, +counter-claims, and cross-claims) alleging that a Contributor Version +directly or indirectly infringes any patent, then the rights granted to +You by any and all Contributors for the Covered Software under Section +2.1 of this License shall terminate. + +5.3. In the event of termination under Sections 5.1 or 5.2 above, all +end user license agreements (excluding distributors and resellers) which +have been validly granted by You or Your distributors under this License +prior to termination shall survive termination. + +************************************************************************ +* * +* 6. Disclaimer of Warranty * +* ------------------------- * +* * +* Covered Software is provided under this License on an "as is" * +* basis, without warranty of any kind, either expressed, implied, or * +* statutory, including, without limitation, warranties that the * +* Covered Software is free of defects, merchantable, fit for a * +* particular purpose or non-infringing. The entire risk as to the * +* quality and performance of the Covered Software is with You. * +* Should any Covered Software prove defective in any respect, You * +* (not any Contributor) assume the cost of any necessary servicing, * +* repair, or correction. This disclaimer of warranty constitutes an * +* essential part of this License. No use of any Covered Software is * +* authorized under this License except under this disclaimer. * +* * +************************************************************************ + +************************************************************************ +* * +* 7. Limitation of Liability * +* -------------------------- * +* * +* Under no circumstances and under no legal theory, whether tort * +* (including negligence), contract, or otherwise, shall any * +* Contributor, or anyone who distributes Covered Software as * +* permitted above, be liable to You for any direct, indirect, * +* special, incidental, or consequential damages of any character * +* including, without limitation, damages for lost profits, loss of * +* goodwill, work stoppage, computer failure or malfunction, or any * +* and all other commercial damages or losses, even if such party * +* shall have been informed of the possibility of such damages. This * +* limitation of liability shall not apply to liability for death or * +* personal injury resulting from such party's negligence to the * +* extent applicable law prohibits such limitation. Some * +* jurisdictions do not allow the exclusion or limitation of * +* incidental or consequential damages, so this exclusion and * +* limitation may not apply to You. * +* * +************************************************************************ + +8. Litigation +------------- + +Any litigation relating to this License may be brought only in the +courts of a jurisdiction where the defendant maintains its principal +place of business and such litigation shall be governed by laws of that +jurisdiction, without reference to its conflict-of-law provisions. +Nothing in this Section shall prevent a party's ability to bring +cross-claims or counter-claims. + +9. Miscellaneous +---------------- + +This License represents the complete agreement concerning the subject +matter hereof. If any provision of this License is held to be +unenforceable, such provision shall be reformed only to the extent +necessary to make it enforceable. Any law or regulation which provides +that the language of a contract shall be construed against the drafter +shall not be used to construe this License against a Contributor. + +10. Versions of the License +--------------------------- + +10.1. New Versions + +Mozilla Foundation is the license steward. Except as provided in Section +10.3, no one other than the license steward has the right to modify or +publish new versions of this License. Each version will be given a +distinguishing version number. + +10.2. Effect of New Versions + +You may distribute the Covered Software under the terms of the version +of the License under which You originally received the Covered Software, +or under the terms of any subsequent version published by the license +steward. + +10.3. Modified Versions + +If you create software not governed by this License, and you want to +create a new license for such software, you may create and use a +modified version of this License if you rename the license and remove +any references to the name of the license steward (except to note that +such modified license differs from this License). + +10.4. Distributing Source Code Form that is Incompatible With Secondary +Licenses + +If You choose to distribute Source Code Form that is Incompatible With +Secondary Licenses under the terms of this version of the License, the +notice described in Exhibit B of this License must be attached. + +Exhibit A - Source Code Form License Notice +------------------------------------------- + + This Source Code Form is subject to the terms of the Mozilla Public + License, v. 2.0. If a copy of the MPL was not distributed with this + file, You can obtain one at http://mozilla.org/MPL/2.0/. + +If it is not possible or desirable to put the notice in a particular +file, then You may include the notice in a location (such as a LICENSE +file in a relevant directory) where a recipient would be likely to look +for such a notice. + +You may add additional accurate notices of copyright ownership. + +Exhibit B - "Incompatible With Secondary Licenses" Notice +--------------------------------------------------------- + + This Source Code Form is "Incompatible With Secondary Licenses", as + defined by the Mozilla Public License, v. 2.0. \ No newline at end of file diff --git a/contrib/NeRF-Editing/TetWild/README.md b/contrib/NeRF-Editing/TetWild/README.md new file mode 100644 index 00000000..78e808d6 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/README.md @@ -0,0 +1,238 @@ +# TetWild - Tetrahedral Meshing in the Wild +![](docs/teaser.png) +Yixin Hu, Qingnan Zhou, Xifeng Gao, Alec Jacobson, Denis Zorin, Daniele Panozzo. +ACM Transactions on Graphics (SIGGRAPH 2018). + +``` +@article{Hu:2018:TMW:3197517.3201353, + author = {Hu, Yixin and Zhou, Qingnan and Gao, Xifeng and Jacobson, Alec and Zorin, Denis and Panozzo, Daniele}, + title = {Tetrahedral Meshing in the Wild}, + journal = {ACM Trans. Graph.}, + issue_date = {August 2018}, + volume = {37}, + number = {4}, + month = jul, + year = {2018}, + issn = {0730-0301}, + pages = {60:1--60:14}, + articleno = {60}, + numpages = {14}, + url = {http://doi.acm.org/10.1145/3197517.3201353}, + doi = {10.1145/3197517.3201353}, + acmid = {3201353}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {mesh generation, robust geometry processing, tetrahedral meshing}, +} +``` + +## News + +🎉🎉🎉 We have 2D version "TetWild" - **TriWild** public! TriWild is able to deal with both linear and curved constraints. Now we are able to mesh curves robustly! Check it out 👉 **[TriWild](https://github.com/wildmeshing/TriWild)**. + +## Important Tips + +💡💡💡 **If you are interested in the algorithm details, please refer to our [paper](https://cs.nyu.edu/~yixinhu/tetwild.pdf) first. We provide plenty of examples and statistics in the paper. You can also refer to my SIGRRAPH talk [slides](https://slides.games-cn.org/pdf/GAMES201858%E8%83%A1%E8%AF%91%E5%BF%83.pdf).** + +💡💡💡 **Check our [license](https://github.com/Yixin-Hu/TetWild#license) first.** + +💡💡💡 **Our algorithm should be and is robust both in theory and in practice. If you do find TetWild crash (on your laptop), please test it (on cluster) with more resource given. The most complex model I tested requires >100GB memory.** + +💡💡💡 **The orientation of input faces is as important as the position of their vertices. Our algorithm is faithful to the input face position and orientation. The winding number algorithm we are using requires reasonably consistent orientation of the input triangle mesh. If the input has a large region of faces all inverted (i.e. with flipped normal), the output tetmesh could have some parts missing or includes some elements "outside" the surface around this region, which is expected because the inverted input faces defines the "inside" as outside. We provide three 2D examples as below for easier understanding. The left in each sub-figure is input boundary with normals and the right is output triangle meshes.** + +![](docs/Slide1.jpg) + + + +## Dataset +Here is pre-generated tetmeshes and the extracted surface meshes for research-purpose usage. **Please kindly cite our paper when using our pre-generated data.** + +- Input: [Thingi10k](https://ten-thousand-models.appspot.com/) + +- Output: +[10k tetmeshes](https://drive.google.com/file/d/17AZwaQaj_nxdCIUpiGFCQ7_khNQxfG4Y/view?usp=sharing), +[10k surface meshes](https://drive.google.com/open?id=1E_C1uVoG1ZGF3pfDpHFKIS8Qqd2VXLZQ) + +- Figures in the paper: [Input/output & scripts](https://drive.google.com/file/d/1P4wFOGOEebNp4pT-s4sFhl9GTSQcG0n_/view?usp=sharing) + +## Installation + +#### via Docker + +Install [Docker](https://docs.docker.com/) and run Docker. Pull TetWild Docker image and run the binary: + +```bash +docker pull yixinhu/tetwild +docker run --rm -v "$(pwd)":/data yixinhu/tetwild [TetWild arguments] +``` + +Important note: If you find TetWild termination unreasonably, it's largely due to the momery limit of Docker. Please check the momery limit using `docker stats`. + +#### via CMake + +Our code was originally developed on MacOS and has been tested on Linux and Windows. We provide the commands for installing TetWild in MacOS: + +- Clone the repository into your local machine: + +```bash +git clone https://github.com/Yixin-Hu/TetWild +``` + +- Compile the code using cmake (default in Release mode): + +```bash +cd TetWild +mkdir build +cd build +cmake .. +make +``` + +You may need to install `gmp` and `mpfr` or `CGAL` before compiling the code. You can install them via [homebrew](https://brew.sh/). + +``` +brew install gmp +brew install mpfr +``` +or + +``` +brew install cgal +``` + +- Check the installation: + +```bash +./TetWild --help +``` +This command should show a list of TetWild parameters. + +💡 If you do not have Boost installed (Boost-thread is needed for the logger spdlog), you can enable the cmake option `-DTETWILD_WITH_HUNTER=ON`. This will let CMake use [Hunter](https://github.com/ruslo/hunter) to download and configure Boost automatically. + +💡 If you find `Could not find Matlab` or `Could not find Mosek` in the output of cmake, it does not matter since they are not used. + +💡 We provide users an option to use [ISPC](https://ispc.github.io/index.html) for computing energy parallelly. It reduces the timimg for computing energy to 50% of the original, but it could result in more optimization iterations and more overall running time. According to our experiment on 1000 models, it reduces the overall running time by 4% in average. If you want to use ISPC, please [install it first](https://ispc.github.io/ispc.html#installing-ispc) and then turn on the flag `GTET_ISPC` in `CMakeLists.txt`. + +## Usage + +### Input/output Format + +The inputs of our software are triangle surface meshes in `.off/.obj/.stl/.ply` format. + +We support `.mesh/.msh` format output. The default output format is `.msh` with minimum dihedral angle recorded as element scalar field, which can be visualized by software [Gmsh](http://gmsh.info/). You can use `PyMesh::MshLoader` and `PyMesh::MshSaver` in `pymesh/` for read and write `.msh` meshes. + +💡 TetWild also outputs the surface of tetmesh in `.obj` format if the `is_quiet` flag is not turned on. + +### Features +Our software is quite easy to use. Basically, users only need to provide a surface triangle mesh as input and our mesher would output a tetrahedral mesh by using default settings. If you want to customize your own tetmeshes, we also provide some options. + +- Envelope of size *epsilon* + +Using smaller envelope preserves features better but also takes longer time. The default value of *epsilon* is *b/1000*, where *b* is the length of the diagonal of the bounding box. + +- Ideal edge length + +Using smaller ideal edge length gives a denser mesh but also takes longer time. The default ideal edge length is *b/20* + +- Filtering energy + +Our mesher stops optimizing the mesh when maximum energy is smaller than filtering energy. Thus, larger filtering energy means less optimization and sooner stopping. If you do not care about quality, then give a larger filtering energy would let you get the result earlier. The energy we used here is conformal AMIPS whose range is from 3 to +inf. The default filtering energy is 10. + +💡 We suggest not to set filtering energy smaller than 8 for complex input. + +- Maximum number of optimization passes + +Our mesher stops optimizing the mesh when the maximum number of passes is reached. The default number is 80. + +- Targeted number of vertices + +We allow users to input the targeted number of vertices and the mesher would try its best to match that number with 5% error. When the targeted number of vertices is unrealistically small, then the output tetmesh may not have number of vertices matched. + +💡 If you want a tetmesh with low resolution, please use larger envelop and larger ideal edge length. + +- Sizing field + +Users can provide a background tetmesh in .msh format with vertex scalar field `values` stored. The scalar field `values` is used for controlling edge length. The scalars inside an element of the background mesh are linearly interpolated. + +💡 [Here](https://drive.google.com/open?id=1-5AyoQ-CdZnX8IAqZoqgW1tiNBTNvFjJ) is an example including input surface mesh, background mesh and output tetmeshes with/without sizing control. + +- Smoothing open regions + +Our method can fill gaps and holes but the tetmesh faces on those parts could be bumpy. We provide users an option to do Lapacian smoothing on those faces to get a smoother surface. + +- With out winding number + +Please use `--save-mid-result 2`. + +### Command Line Switches +Our software supports usage via command line or via a C++ function wrapper. Here is an overview of all command line switches: + +``` +RobustTetMeshing +Usage: ./TetWild [OPTIONS] input [output] + +Positionals: + input TEXT REQUIRED Input surface mesh INPUT in .off/.obj/.stl/.ply format. (string, required) + output TEXT Output tetmesh OUTPUT in .msh format. (string, optional, default: input_file+postfix+'.msh') + +Options: + -h,--help Print this help message and exit + --input TEXT REQUIRED Input surface mesh INPUT in .off/.obj/.stl/.ply format. (string, required) + --output TEXT Output tetmesh OUTPUT in .msh or .mesh format. (string, optional, default: input_file+postfix+'.msh') + --postfix TEXT Postfix P for output files. (string, optional, default: '_') + -l,--ideal-edge-length FLOAT + ideal_edge_length = diag_of_bbox * L. (double, optional, default: 0.05) + -e,--epsilon FLOAT epsilon = diag_of_bbox * EPS. (double, optional, default: 1e-3) + --stage INT Run pipeline in stage STAGE. (integer, optional, default: 1) + --filter-energy FLOAT Stop mesh improvement when the maximum energy is smaller than ENERGY. (double, optional, default: 10) + --max-pass INT Do PASS mesh improvement passes in maximum. (integer, optional, default: 80) + --is-laplacian Do Laplacian smoothing for the surface of output on the holes of input (optional) + --targeted-num-v INT Output tetmesh that contains TV vertices. (integer, optional, tolerance: 5%) + --bg-mesh TEXT Background tetmesh BGMESH in .msh format for applying sizing field. (string, optional) + --save-mid-result 0: save result before optimization, 1: save mid-results during optimization, 2: save result without winding number. + -q,--is-quiet Mute console output. (optional) + --log TEXT Log info to given file. + --level INT Log level (0 = most verbose, 6 = off). +``` + + + +### Function Wrapper + +💡 We use [libigl](https://github.com/libigl/libigl) to read the input triangle mesh. If you encounter any issue loading your mesh with libigl, please open a ticket there. Alternatively, you could load the mesh yourself and use our function wrapper to pass the raw data directly to TetWild. + +We provide a wrapper for TetWild in `tetwild.h`, allowing users do the tetrahedaliztion without read/write data from/to files. One can use it in the following way: + +1. Include the header file `#include `. +2. Set parameters through a struct variable `tetwild::Args args`. The following table provides the correspondence between parameters and command line switches. + + | Switch | Parameter | + |:--------------------|:----------------------------| + | --input | N/A | + | --postfix | `args.postfix` | + | --output | N/A | + | --ideal-edge-length | `args.initial_edge_len_rel` | + | --epsilon | `args.eps_rel` | + | --stage | `args.stage` | + | --filter-energy | `args.filter_energy_thres` | + | --max-pass | `args.max_num_passes` | + | --is-quiet | `args.is_quiet` | + | --targeted-num-v | `args.target_num_vertices` | + | --bg-mesh | `args.background_mesh` | + | --is-laplacian | `args.smooth_open_boundary` | + +3. Call function `tetwild::tetrahedralization(v_in, f_in, v_out, t_out, a_out, args)`. The input/output arguments are described in the function docstring, and use libigl-style matrices for representing a mesh. + +## License +TetWild is MPL2 licensed. But it contains CGAL code under GPL license. + +TetWild is free for both commercial and non-commercial usage. However, you have to cite our work in your paper or put a reference of TetWild in your software. Whenever you fix bugs or make some improvement of TetWild, you should contribute back. + +## Acknowledgements + +We used several useful libraries in our implement, testing, and rendering listed as follows. We would like to especially thank their authors for their great work and publishing the code. + +- [PyMesh](https://github.com/qnzhou/PyMesh) +- [PyRenderer](https://github.com/qnzhou/PyRenderer) +- [CLI11](https://github.com/CLIUtils/CLI11) diff --git a/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.CMakeLists.cmake.in b/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.CMakeLists.cmake.in new file mode 100644 index 00000000..89be4fdd --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.CMakeLists.cmake.in @@ -0,0 +1,17 @@ +# Distributed under the OSI-approved MIT License. See accompanying +# file LICENSE or https://github.com/Crascit/DownloadProject for details. + +cmake_minimum_required(VERSION 2.8.2) + +project(${DL_ARGS_PROJ}-download NONE) + +include(ExternalProject) +ExternalProject_Add(${DL_ARGS_PROJ}-download + ${DL_ARGS_UNPARSED_ARGUMENTS} + SOURCE_DIR "${DL_ARGS_SOURCE_DIR}" + BINARY_DIR "${DL_ARGS_BINARY_DIR}" + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "" + TEST_COMMAND "" +) diff --git a/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.cmake b/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.cmake new file mode 100644 index 00000000..e300f426 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/DownloadProject.cmake @@ -0,0 +1,182 @@ +# Distributed under the OSI-approved MIT License. See accompanying +# file LICENSE or https://github.com/Crascit/DownloadProject for details. +# +# MODULE: DownloadProject +# +# PROVIDES: +# download_project( PROJ projectName +# [PREFIX prefixDir] +# [DOWNLOAD_DIR downloadDir] +# [SOURCE_DIR srcDir] +# [BINARY_DIR binDir] +# [QUIET] +# ... +# ) +# +# Provides the ability to download and unpack a tarball, zip file, git repository, +# etc. at configure time (i.e. when the cmake command is run). How the downloaded +# and unpacked contents are used is up to the caller, but the motivating case is +# to download source code which can then be included directly in the build with +# add_subdirectory() after the call to download_project(). Source and build +# directories are set up with this in mind. +# +# The PROJ argument is required. The projectName value will be used to construct +# the following variables upon exit (obviously replace projectName with its actual +# value): +# +# projectName_SOURCE_DIR +# projectName_BINARY_DIR +# +# The SOURCE_DIR and BINARY_DIR arguments are optional and would not typically +# need to be provided. They can be specified if you want the downloaded source +# and build directories to be located in a specific place. The contents of +# projectName_SOURCE_DIR and projectName_BINARY_DIR will be populated with the +# locations used whether you provide SOURCE_DIR/BINARY_DIR or not. +# +# The DOWNLOAD_DIR argument does not normally need to be set. It controls the +# location of the temporary CMake build used to perform the download. +# +# The PREFIX argument can be provided to change the base location of the default +# values of DOWNLOAD_DIR, SOURCE_DIR and BINARY_DIR. If all of those three arguments +# are provided, then PREFIX will have no effect. The default value for PREFIX is +# CMAKE_BINARY_DIR. +# +# The QUIET option can be given if you do not want to show the output associated +# with downloading the specified project. +# +# In addition to the above, any other options are passed through unmodified to +# ExternalProject_Add() to perform the actual download, patch and update steps. +# The following ExternalProject_Add() options are explicitly prohibited (they +# are reserved for use by the download_project() command): +# +# CONFIGURE_COMMAND +# BUILD_COMMAND +# INSTALL_COMMAND +# TEST_COMMAND +# +# Only those ExternalProject_Add() arguments which relate to downloading, patching +# and updating of the project sources are intended to be used. Also note that at +# least one set of download-related arguments are required. +# +# If using CMake 3.2 or later, the UPDATE_DISCONNECTED option can be used to +# prevent a check at the remote end for changes every time CMake is run +# after the first successful download. See the documentation of the ExternalProject +# module for more information. It is likely you will want to use this option if it +# is available to you. Note, however, that the ExternalProject implementation contains +# bugs which result in incorrect handling of the UPDATE_DISCONNECTED option when +# using the URL download method or when specifying a SOURCE_DIR with no download +# method. Fixes for these have been created, the last of which is scheduled for +# inclusion in CMake 3.8.0. Details can be found here: +# +# https://gitlab.kitware.com/cmake/cmake/commit/bdca68388bd57f8302d3c1d83d691034b7ffa70c +# https://gitlab.kitware.com/cmake/cmake/issues/16428 +# +# If you experience build errors related to the update step, consider avoiding +# the use of UPDATE_DISCONNECTED. +# +# EXAMPLE USAGE: +# +# include(DownloadProject) +# download_project(PROJ googletest +# GIT_REPOSITORY https://github.com/google/googletest.git +# GIT_TAG master +# UPDATE_DISCONNECTED 1 +# QUIET +# ) +# +# add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR}) +# +#======================================================================================== + + +set(_DownloadProjectDir "${CMAKE_CURRENT_LIST_DIR}") + +include(CMakeParseArguments) + +function(download_project) + + set(options QUIET) + set(oneValueArgs + PROJ + PREFIX + DOWNLOAD_DIR + SOURCE_DIR + BINARY_DIR + # Prevent the following from being passed through + CONFIGURE_COMMAND + BUILD_COMMAND + INSTALL_COMMAND + TEST_COMMAND + ) + set(multiValueArgs "") + + cmake_parse_arguments(DL_ARGS "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + + # Hide output if requested + if (DL_ARGS_QUIET) + set(OUTPUT_QUIET "OUTPUT_QUIET") + else() + unset(OUTPUT_QUIET) + message(STATUS "Downloading/updating ${DL_ARGS_PROJ}") + endif() + + # Set up where we will put our temporary CMakeLists.txt file and also + # the base point below which the default source and binary dirs will be. + # The prefix must always be an absolute path. + if (NOT DL_ARGS_PREFIX) + set(DL_ARGS_PREFIX "${CMAKE_BINARY_DIR}") + else() + get_filename_component(DL_ARGS_PREFIX "${DL_ARGS_PREFIX}" ABSOLUTE + BASE_DIR "${CMAKE_CURRENT_BINARY_DIR}") + endif() + if (NOT DL_ARGS_DOWNLOAD_DIR) + set(DL_ARGS_DOWNLOAD_DIR "${DL_ARGS_PREFIX}/${DL_ARGS_PROJ}-download") + endif() + + # Ensure the caller can know where to find the source and build directories + if (NOT DL_ARGS_SOURCE_DIR) + set(DL_ARGS_SOURCE_DIR "${DL_ARGS_PREFIX}/${DL_ARGS_PROJ}-src") + endif() + if (NOT DL_ARGS_BINARY_DIR) + set(DL_ARGS_BINARY_DIR "${DL_ARGS_PREFIX}/${DL_ARGS_PROJ}-build") + endif() + set(${DL_ARGS_PROJ}_SOURCE_DIR "${DL_ARGS_SOURCE_DIR}" PARENT_SCOPE) + set(${DL_ARGS_PROJ}_BINARY_DIR "${DL_ARGS_BINARY_DIR}" PARENT_SCOPE) + + # The way that CLion manages multiple configurations, it causes a copy of + # the CMakeCache.txt to be copied across due to it not expecting there to + # be a project within a project. This causes the hard-coded paths in the + # cache to be copied and builds to fail. To mitigate this, we simply + # remove the cache if it exists before we configure the new project. It + # is safe to do so because it will be re-generated. Since this is only + # executed at the configure step, it should not cause additional builds or + # downloads. + file(REMOVE "${DL_ARGS_DOWNLOAD_DIR}/CMakeCache.txt") + + # Create and build a separate CMake project to carry out the download. + # If we've already previously done these steps, they will not cause + # anything to be updated, so extra rebuilds of the project won't occur. + # Make sure to pass through CMAKE_MAKE_PROGRAM in case the main project + # has this set to something not findable on the PATH. + configure_file("${_DownloadProjectDir}/DownloadProject.CMakeLists.cmake.in" + "${DL_ARGS_DOWNLOAD_DIR}/CMakeLists.txt") + execute_process(COMMAND ${CMAKE_COMMAND} -G "${CMAKE_GENERATOR}" + -D "CMAKE_MAKE_PROGRAM:FILE=${CMAKE_MAKE_PROGRAM}" + . + RESULT_VARIABLE result + ${OUTPUT_QUIET} + WORKING_DIRECTORY "${DL_ARGS_DOWNLOAD_DIR}" + ) + if(result) + message(FATAL_ERROR "CMake step for ${DL_ARGS_PROJ} failed: ${result}") + endif() + execute_process(COMMAND ${CMAKE_COMMAND} --build . + RESULT_VARIABLE result + ${OUTPUT_QUIET} + WORKING_DIRECTORY "${DL_ARGS_DOWNLOAD_DIR}" + ) + if(result) + message(FATAL_ERROR "Build step for ${DL_ARGS_PROJ} failed: ${result}") + endif() + +endfunction() diff --git a/contrib/NeRF-Editing/TetWild/cmake/FindLIBIGL.cmake b/contrib/NeRF-Editing/TetWild/cmake/FindLIBIGL.cmake new file mode 100644 index 00000000..b0619fd4 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/FindLIBIGL.cmake @@ -0,0 +1,33 @@ +# - Try to find the LIBIGL library +# Once done this will define +# +# LIBIGL_FOUND - system has LIBIGL +# LIBIGL_INCLUDE_DIR - **the** LIBIGL include directory +if(LIBIGL_FOUND OR TARGET igl::core) + return() +endif() + +find_path(LIBIGL_INCLUDE_DIR igl/readOBJ.h + HINTS + # ENV LIBIGL + # ENV LIBIGLROOT + # ENV LIBIGL_ROOT + # ENV LIBIGL_DIR + PATHS + ${TETWILD_EXTERNAL}/libigl + # /usr + # /usr/local + # /usr/local/igl/libigl + PATH_SUFFIXES include + NO_CMAKE_SYSTEM_PATH +) + +include(FindPackageHandleStandardArgs) +find_package_handle_standard_args(LIBIGL + "\nlibigl not found --- You can download it using:\n\tgit clone --recursive https://github.com/libigl/libigl.git ${CMAKE_SOURCE_DIR}/../libigl" + LIBIGL_INCLUDE_DIR) +mark_as_advanced(LIBIGL_INCLUDE_DIR) + +#list(APPEND CMAKE_MODULE_PATH "${LIBIGL_INCLUDE_DIR}/../shared/cmake") +list(APPEND CMAKE_MODULE_PATH "${LIBIGL_INCLUDE_DIR}/../cmake") +include(libigl) diff --git a/contrib/NeRF-Editing/TetWild/cmake/HunterGate.cmake b/contrib/NeRF-Editing/TetWild/cmake/HunterGate.cmake new file mode 100644 index 00000000..887557a5 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/HunterGate.cmake @@ -0,0 +1,540 @@ +# Copyright (c) 2013-2018, Ruslan Baratov +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# This is a gate file to Hunter package manager. +# Include this file using `include` command and add package you need, example: +# +# cmake_minimum_required(VERSION 3.2) +# +# include("cmake/HunterGate.cmake") +# HunterGate( +# URL "https://github.com/path/to/hunter/archive.tar.gz" +# SHA1 "798501e983f14b28b10cda16afa4de69eee1da1d" +# ) +# +# project(MyProject) +# +# hunter_add_package(Foo) +# hunter_add_package(Boo COMPONENTS Bar Baz) +# +# Projects: +# * https://github.com/hunter-packages/gate/ +# * https://github.com/ruslo/hunter + +option(HUNTER_ENABLED "Enable Hunter package manager support" ON) + +if(HUNTER_ENABLED) + if(CMAKE_VERSION VERSION_LESS "3.2") + message( + FATAL_ERROR + "At least CMake version 3.2 required for Hunter dependency management." + " Update CMake or set HUNTER_ENABLED to OFF." + ) + endif() +endif() + +include(CMakeParseArguments) # cmake_parse_arguments + +option(HUNTER_STATUS_PRINT "Print working status" ON) +option(HUNTER_STATUS_DEBUG "Print a lot info" OFF) +option(HUNTER_TLS_VERIFY "Enable/disable TLS certificate checking on downloads" ON) + +set(HUNTER_WIKI "https://github.com/ruslo/hunter/wiki") + +function(hunter_gate_status_print) + if(HUNTER_STATUS_PRINT OR HUNTER_STATUS_DEBUG) + foreach(print_message ${ARGV}) + message(STATUS "[hunter] ${print_message}") + endforeach() + endif() +endfunction() + +function(hunter_gate_status_debug) + if(HUNTER_STATUS_DEBUG) + foreach(print_message ${ARGV}) + string(TIMESTAMP timestamp) + message(STATUS "[hunter *** DEBUG *** ${timestamp}] ${print_message}") + endforeach() + endif() +endfunction() + +function(hunter_gate_wiki wiki_page) + message("------------------------------ WIKI -------------------------------") + message(" ${HUNTER_WIKI}/${wiki_page}") + message("-------------------------------------------------------------------") + message("") + message(FATAL_ERROR "") +endfunction() + +function(hunter_gate_internal_error) + message("") + foreach(print_message ${ARGV}) + message("[hunter ** INTERNAL **] ${print_message}") + endforeach() + message("[hunter ** INTERNAL **] [Directory:${CMAKE_CURRENT_LIST_DIR}]") + message("") + hunter_gate_wiki("error.internal") +endfunction() + +function(hunter_gate_fatal_error) + cmake_parse_arguments(hunter "" "WIKI" "" "${ARGV}") + string(COMPARE EQUAL "${hunter_WIKI}" "" have_no_wiki) + if(have_no_wiki) + hunter_gate_internal_error("Expected wiki") + endif() + message("") + foreach(x ${hunter_UNPARSED_ARGUMENTS}) + message("[hunter ** FATAL ERROR **] ${x}") + endforeach() + message("[hunter ** FATAL ERROR **] [Directory:${CMAKE_CURRENT_LIST_DIR}]") + message("") + hunter_gate_wiki("${hunter_WIKI}") +endfunction() + +function(hunter_gate_user_error) + hunter_gate_fatal_error(${ARGV} WIKI "error.incorrect.input.data") +endfunction() + +function(hunter_gate_self root version sha1 result) + string(COMPARE EQUAL "${root}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("root is empty") + endif() + + string(COMPARE EQUAL "${version}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("version is empty") + endif() + + string(COMPARE EQUAL "${sha1}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("sha1 is empty") + endif() + + string(SUBSTRING "${sha1}" 0 7 archive_id) + + if(EXISTS "${root}/cmake/Hunter") + set(hunter_self "${root}") + else() + set( + hunter_self + "${root}/_Base/Download/Hunter/${version}/${archive_id}/Unpacked" + ) + endif() + + set("${result}" "${hunter_self}" PARENT_SCOPE) +endfunction() + +# Set HUNTER_GATE_ROOT cmake variable to suitable value. +function(hunter_gate_detect_root) + # Check CMake variable + string(COMPARE NOTEQUAL "${HUNTER_ROOT}" "" not_empty) + if(not_empty) + set(HUNTER_GATE_ROOT "${HUNTER_ROOT}" PARENT_SCOPE) + hunter_gate_status_debug("HUNTER_ROOT detected by cmake variable") + return() + endif() + + # Check environment variable + string(COMPARE NOTEQUAL "$ENV{HUNTER_ROOT}" "" not_empty) + if(not_empty) + set(HUNTER_GATE_ROOT "$ENV{HUNTER_ROOT}" PARENT_SCOPE) + hunter_gate_status_debug("HUNTER_ROOT detected by environment variable") + return() + endif() + + # Check HOME environment variable + string(COMPARE NOTEQUAL "$ENV{HOME}" "" result) + if(result) + set(HUNTER_GATE_ROOT "$ENV{HOME}/.hunter" PARENT_SCOPE) + hunter_gate_status_debug("HUNTER_ROOT set using HOME environment variable") + return() + endif() + + # Check SYSTEMDRIVE and USERPROFILE environment variable (windows only) + if(WIN32) + string(COMPARE NOTEQUAL "$ENV{SYSTEMDRIVE}" "" result) + if(result) + set(HUNTER_GATE_ROOT "$ENV{SYSTEMDRIVE}/.hunter" PARENT_SCOPE) + hunter_gate_status_debug( + "HUNTER_ROOT set using SYSTEMDRIVE environment variable" + ) + return() + endif() + + string(COMPARE NOTEQUAL "$ENV{USERPROFILE}" "" result) + if(result) + set(HUNTER_GATE_ROOT "$ENV{USERPROFILE}/.hunter" PARENT_SCOPE) + hunter_gate_status_debug( + "HUNTER_ROOT set using USERPROFILE environment variable" + ) + return() + endif() + endif() + + hunter_gate_fatal_error( + "Can't detect HUNTER_ROOT" + WIKI "error.detect.hunter.root" + ) +endfunction() + +function(hunter_gate_download dir) + string( + COMPARE + NOTEQUAL + "$ENV{HUNTER_DISABLE_AUTOINSTALL}" + "" + disable_autoinstall + ) + if(disable_autoinstall AND NOT HUNTER_RUN_INSTALL) + hunter_gate_fatal_error( + "Hunter not found in '${dir}'" + "Set HUNTER_RUN_INSTALL=ON to auto-install it from '${HUNTER_GATE_URL}'" + "Settings:" + " HUNTER_ROOT: ${HUNTER_GATE_ROOT}" + " HUNTER_SHA1: ${HUNTER_GATE_SHA1}" + WIKI "error.run.install" + ) + endif() + string(COMPARE EQUAL "${dir}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("Empty 'dir' argument") + endif() + + string(COMPARE EQUAL "${HUNTER_GATE_SHA1}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("HUNTER_GATE_SHA1 empty") + endif() + + string(COMPARE EQUAL "${HUNTER_GATE_URL}" "" is_bad) + if(is_bad) + hunter_gate_internal_error("HUNTER_GATE_URL empty") + endif() + + set(done_location "${dir}/DONE") + set(sha1_location "${dir}/SHA1") + + set(build_dir "${dir}/Build") + set(cmakelists "${dir}/CMakeLists.txt") + + hunter_gate_status_debug("Locking directory: ${dir}") + file(LOCK "${dir}" DIRECTORY GUARD FUNCTION) + hunter_gate_status_debug("Lock done") + + if(EXISTS "${done_location}") + # while waiting for lock other instance can do all the job + hunter_gate_status_debug("File '${done_location}' found, skip install") + return() + endif() + + file(REMOVE_RECURSE "${build_dir}") + file(REMOVE_RECURSE "${cmakelists}") + + file(MAKE_DIRECTORY "${build_dir}") # check directory permissions + + # Disabling languages speeds up a little bit, reduces noise in the output + # and avoids path too long windows error + file( + WRITE + "${cmakelists}" + "cmake_minimum_required(VERSION 3.2)\n" + "project(HunterDownload LANGUAGES NONE)\n" + "include(ExternalProject)\n" + "ExternalProject_Add(\n" + " Hunter\n" + " URL\n" + " \"${HUNTER_GATE_URL}\"\n" + " URL_HASH\n" + " SHA1=${HUNTER_GATE_SHA1}\n" + " DOWNLOAD_DIR\n" + " \"${dir}\"\n" + " TLS_VERIFY\n" + " ${HUNTER_TLS_VERIFY}\n" + " SOURCE_DIR\n" + " \"${dir}/Unpacked\"\n" + " CONFIGURE_COMMAND\n" + " \"\"\n" + " BUILD_COMMAND\n" + " \"\"\n" + " INSTALL_COMMAND\n" + " \"\"\n" + ")\n" + ) + + if(HUNTER_STATUS_DEBUG) + set(logging_params "") + else() + set(logging_params OUTPUT_QUIET) + endif() + + hunter_gate_status_debug("Run generate") + + # Need to add toolchain file too. + # Otherwise on Visual Studio + MDD this will fail with error: + # "Could not find an appropriate version of the Windows 10 SDK installed on this machine" + if(EXISTS "${CMAKE_TOOLCHAIN_FILE}") + get_filename_component(absolute_CMAKE_TOOLCHAIN_FILE "${CMAKE_TOOLCHAIN_FILE}" ABSOLUTE) + set(toolchain_arg "-DCMAKE_TOOLCHAIN_FILE=${absolute_CMAKE_TOOLCHAIN_FILE}") + else() + # 'toolchain_arg' can't be empty + set(toolchain_arg "-DCMAKE_TOOLCHAIN_FILE=") + endif() + + string(COMPARE EQUAL "${CMAKE_MAKE_PROGRAM}" "" no_make) + if(no_make) + set(make_arg "") + else() + # Test case: remove Ninja from PATH but set it via CMAKE_MAKE_PROGRAM + set(make_arg "-DCMAKE_MAKE_PROGRAM=${CMAKE_MAKE_PROGRAM}") + endif() + + execute_process( + COMMAND + "${CMAKE_COMMAND}" + "-H${dir}" + "-B${build_dir}" + "-G${CMAKE_GENERATOR}" + "${toolchain_arg}" + ${make_arg} + WORKING_DIRECTORY "${dir}" + RESULT_VARIABLE download_result + ${logging_params} + ) + + if(NOT download_result EQUAL 0) + hunter_gate_internal_error( + "Configure project failed." + "To reproduce the error run: ${CMAKE_COMMAND} -H${dir} -B${build_dir} -G${CMAKE_GENERATOR} ${toolchain_arg} ${make_arg}" + "In directory ${dir}" + ) + endif() + + hunter_gate_status_print( + "Initializing Hunter workspace (${HUNTER_GATE_SHA1})" + " ${HUNTER_GATE_URL}" + " -> ${dir}" + ) + execute_process( + COMMAND "${CMAKE_COMMAND}" --build "${build_dir}" + WORKING_DIRECTORY "${dir}" + RESULT_VARIABLE download_result + ${logging_params} + ) + + if(NOT download_result EQUAL 0) + hunter_gate_internal_error("Build project failed") + endif() + + file(REMOVE_RECURSE "${build_dir}") + file(REMOVE_RECURSE "${cmakelists}") + + file(WRITE "${sha1_location}" "${HUNTER_GATE_SHA1}") + file(WRITE "${done_location}" "DONE") + + hunter_gate_status_debug("Finished") +endfunction() + +# Must be a macro so master file 'cmake/Hunter' can +# apply all variables easily just by 'include' command +# (otherwise PARENT_SCOPE magic needed) +macro(HunterGate) + if(HUNTER_GATE_DONE) + # variable HUNTER_GATE_DONE set explicitly for external project + # (see `hunter_download`) + set_property(GLOBAL PROPERTY HUNTER_GATE_DONE YES) + endif() + + # First HunterGate command will init Hunter, others will be ignored + get_property(_hunter_gate_done GLOBAL PROPERTY HUNTER_GATE_DONE SET) + + if(NOT HUNTER_ENABLED) + # Empty function to avoid error "unknown function" + function(hunter_add_package) + endfunction() + + set( + _hunter_gate_disabled_mode_dir + "${CMAKE_CURRENT_LIST_DIR}/cmake/Hunter/disabled-mode" + ) + if(EXISTS "${_hunter_gate_disabled_mode_dir}") + hunter_gate_status_debug( + "Adding \"disabled-mode\" modules: ${_hunter_gate_disabled_mode_dir}" + ) + list(APPEND CMAKE_PREFIX_PATH "${_hunter_gate_disabled_mode_dir}") + endif() + elseif(_hunter_gate_done) + hunter_gate_status_debug("Secondary HunterGate (use old settings)") + hunter_gate_self( + "${HUNTER_CACHED_ROOT}" + "${HUNTER_VERSION}" + "${HUNTER_SHA1}" + _hunter_self + ) + include("${_hunter_self}/cmake/Hunter") + else() + set(HUNTER_GATE_LOCATION "${CMAKE_CURRENT_SOURCE_DIR}") + + string(COMPARE NOTEQUAL "${PROJECT_NAME}" "" _have_project_name) + if(_have_project_name) + hunter_gate_fatal_error( + "Please set HunterGate *before* 'project' command. " + "Detected project: ${PROJECT_NAME}" + WIKI "error.huntergate.before.project" + ) + endif() + + cmake_parse_arguments( + HUNTER_GATE "LOCAL" "URL;SHA1;GLOBAL;FILEPATH" "" ${ARGV} + ) + + string(COMPARE EQUAL "${HUNTER_GATE_SHA1}" "" _empty_sha1) + string(COMPARE EQUAL "${HUNTER_GATE_URL}" "" _empty_url) + string( + COMPARE + NOTEQUAL + "${HUNTER_GATE_UNPARSED_ARGUMENTS}" + "" + _have_unparsed + ) + string(COMPARE NOTEQUAL "${HUNTER_GATE_GLOBAL}" "" _have_global) + string(COMPARE NOTEQUAL "${HUNTER_GATE_FILEPATH}" "" _have_filepath) + + if(_have_unparsed) + hunter_gate_user_error( + "HunterGate unparsed arguments: ${HUNTER_GATE_UNPARSED_ARGUMENTS}" + ) + endif() + if(_empty_sha1) + hunter_gate_user_error("SHA1 suboption of HunterGate is mandatory") + endif() + if(_empty_url) + hunter_gate_user_error("URL suboption of HunterGate is mandatory") + endif() + if(_have_global) + if(HUNTER_GATE_LOCAL) + hunter_gate_user_error("Unexpected LOCAL (already has GLOBAL)") + endif() + if(_have_filepath) + hunter_gate_user_error("Unexpected FILEPATH (already has GLOBAL)") + endif() + endif() + if(HUNTER_GATE_LOCAL) + if(_have_global) + hunter_gate_user_error("Unexpected GLOBAL (already has LOCAL)") + endif() + if(_have_filepath) + hunter_gate_user_error("Unexpected FILEPATH (already has LOCAL)") + endif() + endif() + if(_have_filepath) + if(_have_global) + hunter_gate_user_error("Unexpected GLOBAL (already has FILEPATH)") + endif() + if(HUNTER_GATE_LOCAL) + hunter_gate_user_error("Unexpected LOCAL (already has FILEPATH)") + endif() + endif() + + hunter_gate_detect_root() # set HUNTER_GATE_ROOT + + # Beautify path, fix probable problems with windows path slashes + get_filename_component( + HUNTER_GATE_ROOT "${HUNTER_GATE_ROOT}" ABSOLUTE + ) + hunter_gate_status_debug("HUNTER_ROOT: ${HUNTER_GATE_ROOT}") + if(NOT HUNTER_ALLOW_SPACES_IN_PATH) + string(FIND "${HUNTER_GATE_ROOT}" " " _contain_spaces) + if(NOT _contain_spaces EQUAL -1) + hunter_gate_fatal_error( + "HUNTER_ROOT (${HUNTER_GATE_ROOT}) contains spaces." + "Set HUNTER_ALLOW_SPACES_IN_PATH=ON to skip this error" + "(Use at your own risk!)" + WIKI "error.spaces.in.hunter.root" + ) + endif() + endif() + + string( + REGEX + MATCH + "[0-9]+\\.[0-9]+\\.[0-9]+[-_a-z0-9]*" + HUNTER_GATE_VERSION + "${HUNTER_GATE_URL}" + ) + string(COMPARE EQUAL "${HUNTER_GATE_VERSION}" "" _is_empty) + if(_is_empty) + set(HUNTER_GATE_VERSION "unknown") + endif() + + hunter_gate_self( + "${HUNTER_GATE_ROOT}" + "${HUNTER_GATE_VERSION}" + "${HUNTER_GATE_SHA1}" + _hunter_self + ) + + set(_master_location "${_hunter_self}/cmake/Hunter") + if(EXISTS "${HUNTER_GATE_ROOT}/cmake/Hunter") + # Hunter downloaded manually (e.g. by 'git clone') + set(_unused "xxxxxxxxxx") + set(HUNTER_GATE_SHA1 "${_unused}") + set(HUNTER_GATE_VERSION "${_unused}") + else() + get_filename_component(_archive_id_location "${_hunter_self}/.." ABSOLUTE) + set(_done_location "${_archive_id_location}/DONE") + set(_sha1_location "${_archive_id_location}/SHA1") + + # Check Hunter already downloaded by HunterGate + if(NOT EXISTS "${_done_location}") + hunter_gate_download("${_archive_id_location}") + endif() + + if(NOT EXISTS "${_done_location}") + hunter_gate_internal_error("hunter_gate_download failed") + endif() + + if(NOT EXISTS "${_sha1_location}") + hunter_gate_internal_error("${_sha1_location} not found") + endif() + file(READ "${_sha1_location}" _sha1_value) + string(COMPARE EQUAL "${_sha1_value}" "${HUNTER_GATE_SHA1}" _is_equal) + if(NOT _is_equal) + hunter_gate_internal_error( + "Short SHA1 collision:" + " ${_sha1_value} (from ${_sha1_location})" + " ${HUNTER_GATE_SHA1} (HunterGate)" + ) + endif() + if(NOT EXISTS "${_master_location}") + hunter_gate_user_error( + "Master file not found:" + " ${_master_location}" + "try to update Hunter/HunterGate" + ) + endif() + endif() + include("${_master_location}") + set_property(GLOBAL PROPERTY HUNTER_GATE_DONE YES) + endif() +endmacro() diff --git a/contrib/NeRF-Editing/TetWild/cmake/TetWildDependencies.cmake b/contrib/NeRF-Editing/TetWild/cmake/TetWildDependencies.cmake new file mode 100644 index 00000000..795be6ad --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/TetWildDependencies.cmake @@ -0,0 +1,52 @@ +################################################################################ +# CMake download helpers +################################################################################ + +# download external dependencies +include(TetWildDownloadExternal) + +################################################################################ +# Required dependencies +################################################################################ + +# geogram +if(NOT TARGET geogram) + tetwild_download_geogram() + include(geogram) +endif() + +# Boost +if(TETWILD_WITH_HUNTER) + hunter_add_package(Boost COMPONENTS thread system) +endif() + +# fmt +if(NOT TARGET fmt::fmt) + tetwild_download_fmt() + add_subdirectory(${TETWILD_EXTERNAL}/fmt) +endif() + +# spdlog +if(NOT TARGET spdlog::spdlog) + tetwild_download_spdlog() + add_library(spdlog INTERFACE) + add_library(spdlog::spdlog ALIAS spdlog) + target_include_directories(spdlog INTERFACE ${TETWILD_EXTERNAL}/spdlog/include) + target_compile_definitions(spdlog INTERFACE -DSPDLOG_FMT_EXTERNAL) + target_link_libraries(spdlog INTERFACE fmt::fmt) +endif() + +# libigl +if(NOT TARGET igl::core) + tetwild_download_libigl() + find_package(LIBIGL REQUIRED) +endif() + +# pymesh loaders +add_subdirectory(${TETWILD_EXTERNAL}/pymesh) + +# CL11 +if(NOT TARGET CLI11::CLI11) + tetwild_download_cli11() + add_subdirectory(${TETWILD_EXTERNAL}/cli11) +endif() diff --git a/contrib/NeRF-Editing/TetWild/cmake/TetWildDownloadExternal.cmake b/contrib/NeRF-Editing/TetWild/cmake/TetWildDownloadExternal.cmake new file mode 100644 index 00000000..538e5e65 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/TetWildDownloadExternal.cmake @@ -0,0 +1,54 @@ +################################################################################ +include(DownloadProject) + +# Shortcut function +function(tetwild_download_project name) + download_project( + PROJ ${name} + SOURCE_DIR ${TETWILD_EXTERNAL}/${name} + DOWNLOAD_DIR ${TETWILD_EXTERNAL}/.cache/${name} + ${ARGN} + ) +endfunction() + +################################################################################ + +## libigl +function(tetwild_download_libigl) + tetwild_download_project(libigl + GIT_REPOSITORY https://github.com/libigl/libigl.git + GIT_TAG v2.2.0 + ) +endfunction() + +## geogram +function(tetwild_download_geogram) + tetwild_download_project(geogram + GIT_REPOSITORY https://github.com/alicevision/geogram.git + GIT_TAG v1.6.7 + ) +endfunction() + +## fmt +function(tetwild_download_fmt) + tetwild_download_project(fmt + GIT_REPOSITORY https://github.com/fmtlib/fmt.git + GIT_TAG 5.2.0 + ) +endfunction() + +## spdlog +function(tetwild_download_spdlog) + tetwild_download_project(spdlog + GIT_REPOSITORY https://github.com/gabime/spdlog.git + GIT_TAG v1.1.0 + ) +endfunction() + +## CLI11 +function(tetwild_download_cli11) + tetwild_download_project(cli11 + URL https://github.com/CLIUtils/CLI11/archive/v1.6.1.tar.gz + URL_MD5 48ef97262adb0b47a2f0a7edbda6e2aa + ) +endfunction() diff --git a/contrib/NeRF-Editing/TetWild/cmake/UseColors.cmake b/contrib/NeRF-Editing/TetWild/cmake/UseColors.cmake new file mode 100644 index 00000000..78017566 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/UseColors.cmake @@ -0,0 +1,47 @@ +################################################################################ +# When using Clang, there is nothing to do: colors are enabled by default +# When using GCC >= 4.9, colored diagnostics can be enabled natively +# When using an older version, one can use gccfilter (a perl script) +# +# I do not recommend using gccfilter as of now (May 2014), because it seems to +# be bugged. But if you still want to try, here is how to install it on Ubuntu: +# +# +# 1) Download the perl script and add it to you $PATH +# mkdir -p ~/.local/bin +# wget -P ~/.local/bin http://www.mixtion.org/gccfilter/gccfilter +# chmod +x ~/local/bin/gccfilter +# echo 'PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc +# +# 2) Install the dependencies +# * Term::ANSIColor +# sudo cpan +# cpan> install Term::ANSIColor +# * The module "Getopt::Long" is included in "perl-base" +# * For Getopt::ArgvFile and Regexp::Common ... +# sudo apt-get install libgetopt-argvfile-perl libregexp-common-perl +# +################################################################################ + +if(CMAKE_COMPILER_IS_GNUCXX) + # If GCC >= 4.9, just activate the right option + # We enable colorized diagnostics always instead of using "auto" so that + # they're still colored when using Ninja. + if(NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9) + message(STATUS "GCC >= 4.9 detected, enabling colored diagnostics") + add_definitions(-fdiagnostics-color=always) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fdiagnostics-color=always") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fdiagnostics-color=always") + return() + endif() + # If GCC < 4.9, maybe we can use gccfilter + find_program(GCC_FILTER gccfilter) + if(GCC_FILTER) + option(COLOR_GCC "Use GCCFilter to color compiler output messages" OFF) + set(COLOR_GCC_OPTIONS "-c -r -w" CACHE STRING "Arguments that are passed to gccfilter when output coloring is switchend on. Defaults to -c -r -w.") + if(COLOR_GCC) + set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${GCC_FILTER} ${COLOR_GCC_OPTIONS}") + message(STATUS "Using gccfilter for colored diagnostics") + endif() + endif() +endif() diff --git a/contrib/NeRF-Editing/TetWild/cmake/Warnings.cmake b/contrib/NeRF-Editing/TetWild/cmake/Warnings.cmake new file mode 100644 index 00000000..067fc404 --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/Warnings.cmake @@ -0,0 +1,160 @@ +################################################################################ +cmake_minimum_required(VERSION 3.1) +################################################################################ +# See comments and discussions here: +# http://stackoverflow.com/questions/5088460/flags-to-enable-thorough-and-verbose-g-warnings +################################################################################ + +if(TARGET warnings::all) + return() +endif() + +set(MY_FLAGS + -Wall + -Wextra + -pedantic + + # -Wconversion + #-Wunsafe-loop-optimizations # broken with C++11 loops + -Wunused + + -Wno-long-long + -Wpointer-arith + -Wformat=2 + -Wuninitialized + -Wcast-qual + -Wmissing-noreturn + -Wmissing-format-attribute + -Wredundant-decls + + -Werror=implicit + -Werror=nonnull + -Werror=init-self + -Werror=main + -Werror=missing-braces + -Werror=sequence-point + -Werror=return-type + -Werror=trigraphs + -Werror=array-bounds + -Werror=write-strings + -Werror=address + -Werror=int-to-pointer-cast + -Werror=pointer-to-int-cast + + -Wunused-variable + -Wunused-but-set-variable + -Wunused-parameter + + #-Weffc++ + -Wno-old-style-cast + #-Wno-sign-conversion + #-Wsign-conversion + + -Wshadow + + -Wstrict-null-sentinel + -Woverloaded-virtual + -Wsign-promo + -Wstack-protector + -Wstrict-aliasing + -Wstrict-aliasing=2 + -Wswitch-default + -Wswitch-enum + -Wswitch-unreachable + + -Wcast-align + -Wdisabled-optimization + #-Winline # produces warning on default implicit destructor + -Winvalid-pch + #-Wmissing-include-dirs + -Wpacked + -Wno-padded + -Wstrict-overflow + -Wstrict-overflow=2 + + -Wctor-dtor-privacy + -Wlogical-op + -Wnoexcept + -Woverloaded-virtual + # -Wundef + + -Wnon-virtual-dtor + -Wdelete-non-virtual-dtor + -Werror=non-virtual-dtor + -Werror=delete-non-virtual-dtor + + -Wno-sign-compare + + ########### + # GCC 6.1 # + ########### + + -Wnull-dereference + -fdelete-null-pointer-checks + -Wduplicated-cond + -Wmisleading-indentation + + #-Weverything + + ########################### + # Enabled by -Weverything # + ########################### + + #-Wdocumentation + #-Wdocumentation-unknown-command + #-Wfloat-equal + #-Wcovered-switch-default + + #-Wglobal-constructors + #-Wexit-time-destructors + #-Wmissing-variable-declarations + #-Wextra-semi + #-Wweak-vtables + #-Wno-source-uses-openmp + #-Wdeprecated + #-Wnewline-eof + #-Wmissing-prototypes + + #-Wno-c++98-compat + #-Wno-c++98-compat-pedantic + + ########################### + # Need to check if those are still valid today + ########################### + + #-Wimplicit-atomic-properties + #-Wmissing-declarations + #-Wmissing-prototypes + #-Wstrict-selector-match + #-Wundeclared-selector + #-Wunreachable-code + + # Not a warning, but enable link-time-optimization + # TODO: Check out modern CMake version of setting this flag + # https://cmake.org/cmake/help/latest/module/CheckIPOSupported.html + #-flto + + # Gives meaningful stack traces + -fno-omit-frame-pointer + -fno-optimize-sibling-calls +) + +# Flags above don't make sense for MSVC +if(MSVC) + set(MY_FLAGS) +endif() + +include(CheckCXXCompilerFlag) + +add_library(warnings_all INTERFACE) +add_library(warnings::all ALIAS warnings_all) + +foreach(FLAG IN ITEMS ${MY_FLAGS}) + string(REPLACE "=" "-" FLAG_VAR "${FLAG}") + if(NOT DEFINED IS_SUPPORTED_${FLAG_VAR}) + check_cxx_compiler_flag("${FLAG}" IS_SUPPORTED_${FLAG_VAR}) + endif() + if(IS_SUPPORTED_${FLAG_VAR}) + target_compile_options(warnings_all INTERFACE ${FLAG}) + endif() +endforeach() diff --git a/contrib/NeRF-Editing/TetWild/cmake/geogram.cmake b/contrib/NeRF-Editing/TetWild/cmake/geogram.cmake new file mode 100644 index 00000000..ee18db5d --- /dev/null +++ b/contrib/NeRF-Editing/TetWild/cmake/geogram.cmake @@ -0,0 +1,84 @@ +################################################################################ +# Find Geogram and build it as part of the current build +################################################################################ + +if(TARGET geogram) + return() +endif() + +################################################################################ + +if(TETWILD_EXTERNAL) + set(GEOGRAM_SEARCH_PATHS ${TETWILD_EXTERNAL}) +else() + set(GEOGRAM_SEARCH_PATHS + ${GEOGRAM_INSTALL_PREFIX} + "$ENV{GEOGRAM_INSTALL_PREFIX}" + "/usr/local/" + "$ENV{PROGRAMFILES}/Geogram" + "$ENV{PROGRAMW6432}/Geogram" + "$ENV{HOME}/.local/") +endif() + +find_path(GEOGRAM_SOURCE_INCLUDE_DIR + geogram/basic/common.h + PATHS ${GEOGRAM_SEARCH_PATHS} + PATH_SUFFIXES geogram/src/lib + ) + +set(GEOGRAM_ROOT ${GEOGRAM_SOURCE_INCLUDE_DIR}/../..) + +################################################################################ + +if(${CMAKE_SYSTEM_NAME} MATCHES "Windows") + set(VORPALINE_ARCH_64 TRUE CACHE BOOL "" FORCE) + set(VORPALINE_PLATFORM Win-vs-generic CACHE STRING "" FORCE) +elseif(${CMAKE_SYSTEM_NAME} MATCHES "Linux") + set(VORPALINE_PLATFORM Linux64-gcc-dynamic CACHE STRING "" FORCE) +elseif(${CMAKE_SYSTEM_NAME} MATCHES "Darwin") + set(VORPALINE_PLATFORM Darwin-clang-dynamic CACHE STRING "" FORCE) +endif() + +option(GEOGRAM_WITH_GRAPHICS "Viewers and geogram_gfx library" OFF) +option(GEOGRAM_WITH_LEGACY_NUMERICS "Legacy numerical libraries" OFF) +option(GEOGRAM_WITH_HLBFGS "Non-linear solver (Yang Liu's HLBFGS)" OFF) +option(GEOGRAM_WITH_TETGEN "Tetrahedral mesher (Hang Si's TetGen)" OFF) +option(GEOGRAM_WITH_TRIANGLE "Triangle mesher (Jonathan Shewchuk's triangle)" ON) +option(GEOGRAM_WITH_EXPLORAGRAM "Experimental code (hexahedral meshing vpipeline and optimal transport)" OFF) +option(GEOGRAM_WITH_LUA "Built-in LUA interpreter" OFF) +option(GEOGRAM_LIB_ONLY "Libraries only (no example programs/no viewer)" ON) +option(GEOGRAM_WITH_FPG "Predicate generator (Sylvain Pion's FPG)" OFF) +option(GEOGRAM_USE_SYSTEM_GLFW3 "Use the version of GLFW3 installed in the system if found" OFF) + +################################################################################ + +add_subdirectory(${GEOGRAM_ROOT} geogram) +target_include_directories(geogram SYSTEM PUBLIC ${GEOGRAM_SOURCE_INCLUDE_DIR}) + +################################################################################ + +if(${CMAKE_SYSTEM_NAME} MATCHES "Windows") + # remove warning for multiply defined symbols (caused by multiple + # instanciations of STL templates) + target_compile_options(geogram INTERFACE /wd4251) + + # remove all unused stuff from windows.h + target_compile_definitions(geogram INTERFACE -DWIN32_LEAN_AND_MEAN) + target_compile_definitions(geogram INTERFACE -DVC_EXTRALEAN) + + # do not define a min() and a max() macro, breaks + # std::min() and std::max() !! + target_compile_definitions(geogram INTERFACE -DNOMINMAX) + + # we want M_PI etc... + target_compile_definitions(geogram INTERFACE -D_USE_MATH_DEFINES) + + if(NOT VORPALINE_BUILD_DYNAMIC) + # If we use static library, we link with the static C++ runtime. + foreach(config ${CMAKE_CONFIGURATION_TYPES}) + string(TOUPPER ${config} config) + string(REPLACE /MD /MT CMAKE_C_FLAGS_${config} "${CMAKE_C_FLAGS_${config}}") + string(REPLACE /MD /MT CMAKE_CXX_FLAGS_${config} "${CMAKE_CXX_FLAGS_${config}}") + endforeach() + endif() +endif() diff --git a/contrib/NeRF-Editing/TetWild/docs/Slide1.jpg b/contrib/NeRF-Editing/TetWild/docs/Slide1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9ecb9fecf704988fa933d0d625fcc37c6835bf5d GIT binary patch literal 567466 zcmeFa2|U!@_c;C@yKLDaWJD3!cfuqg31#2K82dV7Po`Z_WXWDC$xf0bVT$Zq2&L>> 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