[ { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n.idea/" }, { "path": "LICENSE.txt", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2022 Haoxi Ran.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "README.md", "content": "# RepSurf - Surface Representation for Point Clouds
[CVPR 2022 Oral]\n\nBy *[Haoxi Ran\\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/3d-point-cloud-classification-on-scanobjectnn)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=surface-representation-for-point-clouds)
\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/3d-object-detection-on-sun-rgbd-val)](https://paperswithcode.com/sota/3d-object-detection-on-sun-rgbd-val?p=surface-representation-for-point-clouds)
\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=surface-representation-for-point-clouds)
\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/semantic-segmentation-on-s3dis)](https://paperswithcode.com/sota/semantic-segmentation-on-s3dis?p=surface-representation-for-point-clouds)
\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/3d-object-detection-on-scannetv2)](https://paperswithcode.com/sota/3d-object-detection-on-scannetv2?p=surface-representation-for-point-clouds)
\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/surface-representation-for-point-clouds/semantic-segmentation-on-s3dis-area5)](https://paperswithcode.com/sota/semantic-segmentation-on-s3dis-area5?p=surface-representation-for-point-clouds) \n\n### The pytorch official implementation of \"[Surface Representation for Point Clouds](http://arxiv.org/abs/2205.05740)\" \n### [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.pdf) | [arXiv](http://arxiv.org/abs/2205.05740)\n\n\n
\n \n
\n\n\n## News:\n- (**Sep 10** NEW :fire:) We have uploaded the implementation of RepSurf on S3DIS along with its training log and pretrained weights.\n- (**June 24** :fire:) We sucessfully finished our Oral presentation at CVPR 2022!\n- (**May 11**) We have uploaded the implementation of RepSurf on ScanObjectNN along with its training log and pretrained weights. \n\n## Tasks:\n\n### We conduct experiments of different tasks on different codebases:\n\n> Classification: **[3D Object Classification](./classification)**
\n> Segmentation: **[3D Semantic Segmentation](./segmentation)**\n\n\n## Visualization\n\nWe provide several visualization results in the folder **./visualization** for a closer look at the construction of\nRepSurf.\n\n\n## License\n\nRepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for\ncommercial use.\n" }, { "path": "classification/README.md", "content": "# RepSurf for Classification
\n\nBy *[Haoxi Ran\\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact)\n\n### [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.pdf) | [arXiv](http://arxiv.org/abs/2205.05740)\n\n\n## Preparation\n\n### Environment\n\nWe tested under the environment:\n\n* python 3.7\n* pytorch 1.6.0\n* cuda 10.1\n* gcc 7.2.0\n* h5py\n\nFor anaconda user, initialize the conda environment **repsurf-cls** by:\n\n```\nsh init.sh\n```\n\n## Experiments\n\n### ScanObjectNN (Data & Logs: [Google Drive](https://drive.google.com/drive/folders/1DGWT9W46MKVI0-lu18hJhB-R3BFVWuCs?usp=sharing))\n\n* Performance:\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ModelAccuracy#ParamsAugmentCodeLogCheckpoint
MVTN82.84.24MNonelinkN/Alink
PointMLP85.712.6MScale, Shiftlinklinklink
PointNet++ SSG77.91.475MRotate, JitterlinkN/AN/A
Umbrella RepSurf (PointNet++ SSG)84.871.483MNonelinkgoogle drivegoogle drive (6MB)
Umbrella RepSurf (PointNet++ SSG, 2x)86.056.806MNonelinkgoogle drivegoogle drive (27MB)
\n
\n\n* To download dataset:\n\n```\nwget https://download.cs.stanford.edu/orion/scanobjectnn/h5_files.zip\nunzip h5_files.zip\nln -s [PATH]/h5_files data/ScanObjectNN\n```\n\n**Note**: We conduct all experiments on the hardest variant of ScanObjectNN (**PB_T50_RS**).\n
\n\n* To train **Umbrella RepSurf** on ScanObjectNN:\n\n```\nsh scripts/scanobjectnn/repsurf_ssg_umb.sh\n```\n\n* To train **Umbrella RepSurf (2x setting)** on ScanObjectNN:\n\n```\nsh scripts/scanobjectnn/repsurf_ssg_umb_2x.sh\n```\n\n## Acknowledgment\n\nWe use part of the library [pointops](https://github.com/hszhao/PointWeb/tree/master/lib/pointops)\nfrom [PointWeb](https://github.com/hszhao/PointWeb).\n\n## License\n\nRepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for\ncommercial use.\n" }, { "path": "classification/dataset/ScanObjectNNDataLoader.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport h5py\nimport warnings\nfrom torch.utils.data import Dataset\n\nwarnings.filterwarnings('ignore')\n\n\nclass ScanObjectNNDataLoader(Dataset):\n def __init__(self, root, split='training', bg=True):\n self.root = root\n\n assert (split == 'training' or split == 'test')\n if bg:\n print('Use data with background points')\n dir_name = 'main_split'\n else:\n print('Use data without background points')\n dir_name = 'main_split_nobg'\n file_name = '_objectdataset_augmentedrot_scale75.h5'\n h5_name = '{}/{}/{}'.format(self.root, dir_name, split + file_name)\n with h5py.File(h5_name, mode=\"r\") as f:\n self.data = f['data'][:].astype('float32')\n self.label = f['label'][:].astype('int64')\n print('The size of %s data is %d' % (split, self.data.shape[0]))\n\n def __len__(self):\n return self.data.shape[0]\n\n def __getitem__(self, index):\n return self.data[index].T, self.label[index]\n" }, { "path": "classification/dataset/__init__.py", "content": "" }, { "path": "classification/init.sh", "content": "#!/bin/sh\n\nmkdir -p log/PointAnalysis/log/ScanObjectNN\nmkdir -p data/\n\nconda create -n repsurf-cls python=3.7 -y\nconda activate repsurf-cls\n\nconda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch -c conda-forge -y\nconda install -c anaconda h5py -y\n\ncd modules/pointops\npython3 setup.py install\ncd -\n" }, { "path": "classification/models/__init__.py", "content": "" }, { "path": "classification/models/repsurf/__init__.py", "content": "" }, { "path": "classification/models/repsurf/repsurf_ssg_umb.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom modules.repsurface_utils import SurfaceAbstractionCD, UmbrellaSurfaceConstructor\n\n\nclass Model(nn.Module):\n def __init__(self, args):\n super(Model, self).__init__()\n center_channel = 0 if not args.return_center else (6 if args.return_polar else 3)\n repsurf_channel = 10\n\n self.init_nsample = args.num_point\n self.return_dist = args.return_dist\n self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_channel,\n return_dist=args.return_dist, aggr_type=args.umb_pool,\n cuda=args.cuda_ops)\n self.sa1 = SurfaceAbstractionCD(npoint=512, radius=0.2, nsample=32, feat_channel=repsurf_channel,\n pos_channel=center_channel, mlp=[64, 64, 128], group_all=False,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n self.sa2 = SurfaceAbstractionCD(npoint=128, radius=0.4, nsample=64, feat_channel=128 + repsurf_channel,\n pos_channel=center_channel, mlp=[128, 128, 256], group_all=False,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n self.sa3 = SurfaceAbstractionCD(npoint=None, radius=None, nsample=None, feat_channel=256 + repsurf_channel,\n pos_channel=center_channel, mlp=[256, 512, 1024], group_all=True,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n # modelnet40\n self.classfier = nn.Sequential(\n nn.Linear(1024, 512),\n nn.BatchNorm1d(512),\n nn.ReLU(True),\n nn.Dropout(0.4),\n nn.Linear(512, 256),\n nn.BatchNorm1d(256),\n nn.ReLU(True),\n nn.Dropout(0.4),\n nn.Linear(256, args.num_class))\n\n def forward(self, points):\n # init\n center = points[:, :3, :]\n\n normal = self.surface_constructor(center)\n\n center, normal, feature = self.sa1(center, normal, None)\n center, normal, feature = self.sa2(center, normal, feature)\n center, normal, feature = self.sa3(center, normal, feature)\n\n feature = feature.view(-1, 1024)\n feature = self.classfier(feature)\n feature = F.log_softmax(feature, -1)\n\n return feature\n" }, { "path": "classification/models/repsurf/repsurf_ssg_umb_2x.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom modules.repsurface_utils import SurfaceAbstractionCD, UmbrellaSurfaceConstructor\n\n\nclass Model(nn.Module):\n def __init__(self, args):\n super(Model, self).__init__()\n center_channel = 0 if not args.return_center else (6 if args.return_polar else 3)\n repsurf_channel = 10\n\n self.init_nsample = args.num_point\n self.return_dist = args.return_dist\n self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_channel,\n return_dist=args.return_dist, aggr_type=args.umb_pool,\n cuda=args.cuda_ops)\n self.sa1 = SurfaceAbstractionCD(npoint=512, radius=0.1, nsample=24, feat_channel=repsurf_channel,\n pos_channel=center_channel, mlp=[128, 128, 256], group_all=False,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n self.sa2 = SurfaceAbstractionCD(npoint=128, radius=0.2, nsample=24, feat_channel=256 + repsurf_channel,\n pos_channel=center_channel, mlp=[256, 256, 512], group_all=False,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n self.sa3 = SurfaceAbstractionCD(npoint=32, radius=0.4, nsample=24, feat_channel=512 + repsurf_channel,\n pos_channel=center_channel, mlp=[512, 512, 1024], group_all=False,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n self.sa4 = SurfaceAbstractionCD(npoint=None, radius=None, nsample=None, feat_channel=1024 + repsurf_channel,\n pos_channel=center_channel, mlp=[1024, 1024, 2048], group_all=True,\n return_polar=args.return_polar, cuda=args.cuda_ops)\n # modelnet40\n self.classfier = nn.Sequential(\n nn.Linear(2048, 512),\n nn.BatchNorm1d(512),\n nn.ReLU(True),\n nn.Dropout(0.4),\n nn.Linear(512, 256),\n nn.BatchNorm1d(256),\n nn.ReLU(True),\n nn.Dropout(0.4),\n nn.Linear(256, args.num_class))\n\n def forward(self, points):\n # init\n center = points[:, :3, :]\n\n normal = self.surface_constructor(center)\n\n center, normal, feature = self.sa1(center, normal, None)\n center, normal, feature = self.sa2(center, normal, feature)\n center, normal, feature = self.sa3(center, normal, feature)\n center, normal, feature = self.sa4(center, normal, feature)\n\n feature = feature.view(-1, 2048)\n feature = self.classfier(feature)\n feature = F.log_softmax(feature, -1)\n\n return feature\n" }, { "path": "classification/modules/__init__.py", "content": "" }, { "path": "classification/modules/pointnet2_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch\n\ntry:\n from modules.pointops.functions.pointops import furthestsampling, gathering, ballquery, knnquery, \\\n grouping, interpolation, nearestneighbor\nexcept:\n raise Exception('Failed to load pointops')\n\n\ndef square_distance(src, dst):\n \"\"\"\n Calculate Squared distance between each two points.\n\n \"\"\"\n B, N, _ = src.shape\n _, M, _ = dst.shape\n dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))\n dist += torch.sum(src ** 2, -1).view(B, N, 1)\n dist += torch.sum(dst ** 2, -1).view(B, 1, M)\n return dist\n\n\ndef index_points(points, idx, cuda=False, is_group=False):\n if cuda:\n if is_group:\n points = grouping(points.transpose(1, 2).contiguous(), idx)\n return points.permute(0, 2, 3, 1).contiguous()\n else:\n points = gathering(points.transpose(1, 2).contiguous(), idx)\n return points.permute(0, 2, 1).contiguous()\n device = points.device\n B = points.shape[0]\n view_shape = list(idx.shape)\n view_shape[1:] = [1] * (len(view_shape) - 1)\n repeat_shape = list(idx.shape)\n repeat_shape[0] = 1\n batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)\n new_points = points[batch_indices, idx, :]\n return new_points\n\n\ndef farthest_point_sample(xyz, npoint, cuda=False):\n \"\"\"\n Input:\n xyz: pointcloud data, [B, N, 3]\n npoint: number of samples\n Return:\n centroids: sampled pointcloud index, [B, npoint]\n\n FLOPs:\n S * (3 + 3 + 2)\n \"\"\"\n if cuda:\n if not xyz.is_contiguous():\n xyz = xyz.contiguous()\n return furthestsampling(xyz, npoint)\n device = xyz.device\n B, N, C = xyz.shape\n centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)\n distance = torch.ones(B, N).to(device) * 1e10\n farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)\n batch_indices = torch.arange(B, dtype=torch.long).to(device)\n for i in range(npoint):\n centroids[:, i] = farthest\n centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)\n dist = torch.sum((xyz - centroid) ** 2, -1)\n mask = dist < distance\n distance[mask] = dist[mask]\n farthest = torch.max(distance, -1)[1]\n return centroids\n\n\ndef query_ball_point(radius, nsample, xyz, new_xyz, debug=False, cuda=False):\n if cuda:\n if not xyz.is_contiguous():\n xyz = xyz.contiguous()\n if not new_xyz.is_contiguous():\n new_xyz = new_xyz.contiguous()\n return ballquery(radius, nsample, xyz, new_xyz)\n device = xyz.device\n B, N, C = xyz.shape\n _, S, _ = new_xyz.shape\n group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])\n sqrdists = square_distance(new_xyz, xyz)\n group_idx[sqrdists > radius ** 2] = N\n group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]\n group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])\n mask = group_idx == N\n group_idx[mask] = group_first[mask]\n if debug:\n num_miss = torch.sum(mask)\n num_over = torch.sum(torch.clamp(torch.sum(sqrdists < radius ** 2, dim=2) - nsample, min=0))\n return num_miss, num_over\n return group_idx\n\n\ndef query_knn_point(k, xyz, new_xyz, cuda=False):\n if cuda:\n if not xyz.is_contiguous():\n xyz = xyz.contiguous()\n if not new_xyz.is_contiguous():\n new_xyz = new_xyz.contiguous()\n return knnquery(k, xyz, new_xyz)\n dist = square_distance(new_xyz, xyz)\n group_idx = dist.sort(descending=False, dim=-1)[1][:, :, :k]\n return group_idx\n\n\ndef sample(nsample, feature, cuda=False):\n feature = feature.permute(0, 2, 1)\n xyz = feature[:, :, :3]\n\n fps_idx = farthest_point_sample(xyz, nsample, cuda=cuda) # [B, npoint, C]\n torch.cuda.empty_cache()\n feature = index_points(feature, fps_idx, cuda=cuda, is_group=False)\n torch.cuda.empty_cache()\n feature = feature.permute(0, 2, 1)\n\n return feature\n" }, { "path": "classification/modules/pointops/__init__.py", "content": "" }, { "path": "classification/modules/pointops/functions/__init__.py", "content": "from .pointops import *\n" }, { "path": "classification/modules/pointops/functions/pointops.py", "content": "from typing import Tuple\nimport numpy as np\nimport torch\nfrom torch.autograd import Function\nimport torch.nn as nn\n\ntry:\n import pointops_cuda\nexcept ImportError:\n import warnings\n import os\n from torch.utils.cpp_extension import load\n\n warnings.warn(\"Unable to load pointops_cuda cpp extension.\")\n pointops_cuda_src = os.path.join(os.path.dirname(__file__), \"../src\")\n pointops_cuda = load('pointops_cuda', [\n pointops_cuda_src + '/pointops_api.cpp',\n pointops_cuda_src + '/ballquery/ballquery_cuda.cpp',\n pointops_cuda_src + '/ballquery/ballquery_cuda_kernel.cu',\n pointops_cuda_src + '/knnquery/knnquery_cuda.cpp',\n pointops_cuda_src + '/knnquery/knnquery_cuda_kernel.cu',\n pointops_cuda_src + '/knnquery_heap/knnquery_heap_cuda.cpp',\n pointops_cuda_src + '/knnquery_heap/knnquery_heap_cuda_kernel.cu',\n pointops_cuda_src + '/grouping/grouping_cuda.cpp',\n pointops_cuda_src + '/grouping/grouping_cuda_kernel.cu',\n pointops_cuda_src + '/grouping_int/grouping_int_cuda.cpp',\n pointops_cuda_src + '/grouping_int/grouping_int_cuda_kernel.cu',\n pointops_cuda_src + '/interpolation/interpolation_cuda.cpp',\n pointops_cuda_src + '/interpolation/interpolation_cuda_kernel.cu',\n pointops_cuda_src + '/sampling/sampling_cuda.cpp',\n pointops_cuda_src + '/sampling/sampling_cuda_kernel.cu'\n ], build_directory=pointops_cuda_src, verbose=False)\n\n\nclass FurthestSampling(Function):\n @staticmethod\n def forward(ctx, xyz, m):\n \"\"\"\n input: xyz: (b, n, 3) and n > m, m: int32\n output: idx: (b, m)\n \"\"\"\n assert xyz.is_contiguous()\n b, n, _ = xyz.size()\n idx = torch.cuda.IntTensor(b, m)\n temp = torch.cuda.FloatTensor(b, n).fill_(1e10)\n pointops_cuda.furthestsampling_cuda(b, n, m, xyz, temp, idx)\n return idx\n\n @staticmethod\n def backward(xyz, a=None):\n return None, None\n\n\nfurthestsampling = FurthestSampling.apply\n\n\nclass Gathering(Function):\n @staticmethod\n def forward(ctx, features, idx):\n \"\"\"\n input: features: (b, c, n), idx : (b, m) tensor\n output: (b, c, m)\n \"\"\"\n assert features.is_contiguous()\n assert idx.is_contiguous()\n b, c, n = features.size()\n m = idx.size(1)\n output = torch.cuda.FloatTensor(b, c, m)\n pointops_cuda.gathering_forward_cuda(b, c, n, m, features, idx, output)\n ctx.for_backwards = (idx, c, n)\n return output\n\n @staticmethod\n def backward(ctx, grad_out):\n idx, c, n = ctx.for_backwards\n b, m = idx.size()\n grad_features = torch.cuda.FloatTensor(b, c, n).zero_()\n grad_out_data = grad_out.data.contiguous()\n pointops_cuda.gathering_backward_cuda(b, c, n, m, grad_out_data, idx, grad_features.data)\n return grad_features, None\n\n\ngathering = Gathering.apply\n\n\nclass NearestNeighbor(Function):\n @staticmethod\n def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Find the three nearest neighbors of unknown in known\n input: unknown: (b, n, 3), known: (b, m, 3)\n output: dist2: (b, n, 3) l2 distance to the three nearest neighbors\n idx: (b, n, 3) index of 3 nearest neighbors\n \"\"\"\n assert unknown.is_contiguous()\n assert known.is_contiguous()\n b, n, _ = unknown.size()\n m = known.size(1)\n dist2 = torch.cuda.FloatTensor(b, n, 3)\n idx = torch.cuda.IntTensor(b, n, 3)\n pointops_cuda.nearestneighbor_cuda(b, n, m, unknown, known, dist2, idx)\n return torch.sqrt(dist2), idx\n\n @staticmethod\n def backward(ctx, a=None, b=None):\n return None, None\n\n\nnearestneighbor = NearestNeighbor.apply\n\n\nclass Interpolation(Function):\n @staticmethod\n def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Performs weight linear interpolation on 3 features\n input: features: (b, c, m) features descriptors to be interpolated from\n idx: (b, n, 3) three nearest neighbors of the target features in features\n weight: (b, n, 3) weights\n output: (b, c, n) tensor of the interpolated features\n \"\"\"\n features = features.contiguous()\n assert features.is_contiguous()\n assert idx.is_contiguous()\n assert weight.is_contiguous()\n b, c, m = features.size()\n n = idx.size(1)\n ctx.interpolation_for_backward = (idx, weight, m)\n output = torch.cuda.FloatTensor(b, c, n)\n pointops_cuda.interpolation_forward_cuda(b, c, m, n, features, idx, weight, output)\n return output\n\n @staticmethod\n def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"\n input: grad_out: (b, c, n)\n output: grad_features: (b, c, m), None, None\n \"\"\"\n idx, weight, m = ctx.interpolation_for_backward\n b, c, n = grad_out.size()\n grad_features = torch.cuda.FloatTensor(b, c, m).zero_()\n grad_out_data = grad_out.data.contiguous()\n pointops_cuda.interpolation_backward_cuda(b, c, n, m, grad_out_data, idx, weight, grad_features.data)\n return grad_features, None, None\n\n\ninterpolation = Interpolation.apply\n\n\nclass Grouping(Function):\n @staticmethod\n def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:\n \"\"\"\n input: features: (b, c, n), idx : (b, m, nsample) containing the indicies of features to group with\n output: (b, c, m, nsample)\n \"\"\"\n assert features.is_contiguous()\n assert idx.is_contiguous()\n b, c, n = features.size()\n _, m, nsample = idx.size()\n output = torch.cuda.FloatTensor(b, c, m, nsample)\n pointops_cuda.grouping_forward_cuda(b, c, n, m, nsample, features, idx, output)\n ctx.for_backwards = (idx, n)\n return output\n\n @staticmethod\n def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n input: grad_out: (b, c, m, nsample)\n output: (b, c, n), None\n \"\"\"\n idx, n = ctx.for_backwards\n b, c, m, nsample = grad_out.size()\n grad_features = torch.cuda.FloatTensor(b, c, n).zero_()\n grad_out_data = grad_out.data.contiguous()\n pointops_cuda.grouping_backward_cuda(b, c, n, m, nsample, grad_out_data, idx, grad_features.data)\n return grad_features, None\n\n\ngrouping = Grouping.apply\n\n\nclass GroupingInt(Function):\n @staticmethod\n def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:\n \"\"\"\n input: features: (b, c, n), idx : (b, m, nsample) containing the indicies of features to group with\n output: (b, c, m, nsample)\n \"\"\"\n assert features.is_contiguous()\n assert idx.is_contiguous()\n b, c, n = features.size()\n _, m, nsample = idx.size()\n output = torch.cuda.LongTensor(b, c, m, nsample)\n pointops_cuda.grouping_int_forward_cuda(b, c, n, m, nsample, features, idx, output)\n return output\n\n @staticmethod\n def backward(ctx, a=None):\n return None, None\n\n\ngrouping_int = GroupingInt.apply\n\n\nclass BallQuery(Function):\n @staticmethod\n def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:\n \"\"\"\n input: radius: float, radius of the balls\n nsample: int, maximum number of features in the balls\n xyz: torch.Tensor, (b, n, 3) xyz coordinates of the features\n new_xyz: torch.Tensor, (b, m, 3) centers of the ball query\n output: (b, m, nsample) tensor with the indicies of the features that form the query balls\n \"\"\"\n assert xyz.is_contiguous()\n assert new_xyz.is_contiguous()\n b, n, _ = xyz.size()\n m = new_xyz.size(1)\n idx = torch.cuda.IntTensor(b, m, nsample).zero_()\n pointops_cuda.ballquery_cuda(b, n, m, radius, nsample, new_xyz, xyz, idx)\n return idx\n\n @staticmethod\n def backward(ctx, a=None):\n return None, None, None, None\n\n\nballquery = BallQuery.apply\n\n\ndef pairwise_distances(x, y=None):\n '''\n Input: x is a Nxd matrix\n y is an optional Mxd matirx\n Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:]\n if y is not given then use 'y=x'.\n i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2\n '''\n x_norm = (x ** 2).sum(1).view(-1, 1)\n if y is not None:\n y_t = torch.transpose(y, 0, 1)\n y_norm = (y ** 2).sum(1).view(1, -1)\n else:\n y_t = torch.transpose(x, 0, 1)\n y_norm = x_norm.view(1, -1)\n dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)\n import numpy as np\n return torch.clamp(dist, 0.0, np.inf)\n\n\nclass KNNQueryNaive(Function):\n @staticmethod\n def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]:\n \"\"\"\n KNN Indexing\n input: nsample: int32, Number of neighbor\n xyz: (b, n, 3) coordinates of the features\n new_xyz: (b, m, 3) centriods\n output: idx: (b, m, nsample)\n \"\"\"\n if new_xyz is None:\n new_xyz = xyz\n b, m, _ = new_xyz.size()\n n = xyz.size(1)\n\n '''\n idx = torch.zeros(b, m, nsample).int().cuda()\n for i in range(b):\n dist = pairwise_distances(new_xyz[i, :, :], xyz[i, :, :])\n [_, idxs] = torch.sort(dist, dim=1)\n idx[i, :, :] = idxs[:, 0:nsample]\n '''\n\n # '''\n # new_xyz_repeat = new_xyz.repeat(1, 1, n).view(b, m * n, 3)\n # xyz_repeat = xyz.repeat(1, m, 1).view(b, m * n, 3)\n # dist = (new_xyz_repeat - xyz_repeat).pow(2).sum(dim=2).view(b, m, n)\n dist = (new_xyz.repeat(1, 1, n).view(b, m * n, 3) - xyz.repeat(1, m, 1).view(b, m * n, 3)).pow(2).sum(\n dim=2).view(b, m, n)\n [_, idxs] = torch.sort(dist, dim=2)\n idx = idxs[:, :, 0:nsample].int()\n # '''\n return idx\n\n @staticmethod\n def backward(ctx):\n return None, None, None\n\n\nknnquery_naive = KNNQueryNaive.apply\n\n\nclass KNNQuery(Function):\n @staticmethod\n def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]:\n \"\"\"\n KNN Indexing\n input: nsample: int32, Number of neighbor\n xyz: (b, n, 3) coordinates of the features\n new_xyz: (b, m, 3) centriods\n output: idx: (b, m, nsample)\n ( dist2: (b, m, nsample) )\n \"\"\"\n if new_xyz is None:\n new_xyz = xyz\n xyz = xyz.contiguous()\n new_xyz = new_xyz.contiguous()\n assert xyz.is_contiguous()\n assert new_xyz.is_contiguous()\n b, m, _ = new_xyz.size()\n n = xyz.size(1)\n idx = torch.cuda.IntTensor(b, m, nsample).zero_()\n dist2 = torch.cuda.FloatTensor(b, m, nsample).zero_()\n pointops_cuda.knnquery_cuda(b, n, m, nsample, xyz, new_xyz, idx, dist2)\n return idx\n\n @staticmethod\n def backward(ctx, a=None):\n return None, None, None\n\n\nknnquery = KNNQuery.apply\n\n\nclass KNNQuery_Heap(Function):\n @staticmethod\n def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]:\n \"\"\"\n KNN Indexing\n input: nsample: int32, Number of neighbor\n xyz: (b, n, 3) coordinates of the features\n new_xyz: (b, m, 3) centriods\n output: idx: (b, m, nsample)\n ( dist2: (b, m, nsample) )\n \"\"\"\n if new_xyz is None:\n new_xyz = xyz\n assert xyz.is_contiguous()\n assert new_xyz.is_contiguous()\n b, m, _ = new_xyz.size()\n n = xyz.size(1)\n idx = torch.cuda.IntTensor(b, m, nsample).zero_()\n dist2 = torch.cuda.FloatTensor(b, m, nsample).zero_()\n pointops_cuda.knnquery_heap_cuda(b, n, m, nsample, xyz, new_xyz, idx, dist2)\n ctx.mark_non_differentiable(idx)\n return idx\n\n @staticmethod\n def backward(ctx, a=None):\n return None, None, None\n\n\nknnquery_heap = KNNQuery_Heap.apply\n\n\nclass QueryAndGroup(nn.Module):\n \"\"\"\n Groups with a ball query of radius\n parameters:\n radius: float32, Radius of ball\n nsample: int32, Maximum number of features to gather in the ball\n \"\"\"\n\n def __init__(self, radius=None, nsample=32, use_xyz=True, return_idx=False):\n super(QueryAndGroup, self).__init__()\n self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz\n self.return_idx = return_idx\n\n def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor = None, features: torch.Tensor = None,\n idx: torch.Tensor = None) -> torch.Tensor:\n \"\"\"\n input: xyz: (b, n, 3) coordinates of the features\n new_xyz: (b, m, 3) centriods\n features: (b, c, n)\n idx: idx of neighbors\n # idxs: (b, n)\n output: new_features: (b, c+3, m, nsample)\n # grouped_idxs: (b, m, nsample)\n \"\"\"\n if new_xyz is None:\n new_xyz = xyz\n if idx is None:\n if self.radius is not None:\n idx = ballquery(self.radius, self.nsample, xyz, new_xyz)\n else:\n # idx = knnquery_naive(self.nsample, xyz, new_xyz) # (b, m, nsample)\n # idx = knnquery(self.nsample, xyz, new_xyz) # (b, m, nsample)\n idx = knnquery_heap(self.nsample, xyz, new_xyz) # (b, m, nsample)\n xyz_trans = xyz.transpose(1, 2).contiguous()\n grouped_xyz = grouping(xyz_trans, idx) # (b, 3, m, nsample)\n # grouped_idxs = grouping(idxs.unsqueeze(1).float(), idx).squeeze(1).int() # (b, m, nsample)\n grouped_xyz_diff = grouped_xyz - new_xyz.transpose(1, 2).unsqueeze(-1)\n if features is not None:\n grouped_features = grouping(features, idx)\n if self.use_xyz:\n new_features = torch.cat([grouped_xyz_diff, grouped_features], dim=1) # (b, 3+c, m, nsample)\n else:\n new_features = grouped_features\n else:\n assert self.use_xyz, \"Cannot have not features and not use xyz as a feature!\"\n new_features = grouped_xyz_diff\n\n if self.return_idx:\n return new_features, grouped_xyz, idx.long()\n # (b,c,m,k), (b,3,m,k), (b,m,k)\n else:\n return new_features, grouped_xyz\n" }, { "path": "classification/modules/pointops/setup.py", "content": "#python3 setup.py install\n\nfrom setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\nimport os\nfrom distutils.sysconfig import get_config_vars\n\n(opt,) = get_config_vars('OPT')\nos.environ['OPT'] = \" \".join(\n flag for flag in opt.split() if flag != '-Wstrict-prototypes'\n)\n\nsetup(\n name='pointops',\n ext_modules=[\n CUDAExtension('pointops_cuda', [\n 'src/pointops_api.cpp',\n\n 'src/ballquery/ballquery_cuda.cpp',\n 'src/ballquery/ballquery_cuda_kernel.cu',\n 'src/knnquery/knnquery_cuda.cpp',\n 'src/knnquery/knnquery_cuda_kernel.cu',\n 'src/knnquery_heap/knnquery_heap_cuda.cpp',\n 'src/knnquery_heap/knnquery_heap_cuda_kernel.cu',\n 'src/grouping/grouping_cuda.cpp',\n 'src/grouping/grouping_cuda_kernel.cu',\n 'src/grouping_int/grouping_int_cuda.cpp',\n 'src/grouping_int/grouping_int_cuda_kernel.cu',\n 'src/interpolation/interpolation_cuda.cpp',\n 'src/interpolation/interpolation_cuda_kernel.cu',\n 'src/sampling/sampling_cuda.cpp',\n 'src/sampling/sampling_cuda_kernel.cu',\n ],\n extra_compile_args={'cxx': ['-g'],\n 'nvcc': ['-O2']})\n ],\n cmdclass={'build_ext': BuildExtension})\n" }, { "path": "classification/modules/pointops/src/__init__.py", "content": "" }, { "path": "classification/modules/pointops/src/ballquery/ballquery_cuda.cpp", "content": "#include \n#include \n#include \n#include \n\n#include \"ballquery_cuda_kernel.h\"\n\nextern THCState *state;\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, \" must be a CUDAtensor \")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, \" must be contiguous \")\n#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)\n\nvoid ballquery_cuda(int b, int n, int m, float radius, int nsample, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor)\n{\n const float *new_xyz = new_xyz_tensor.data_ptr();\n const float *xyz = xyz_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n\n ballquery_cuda_launcher(b, n, m, radius, nsample, new_xyz, xyz, idx);\n}\n\n\nvoid ballquery_cuda_fast(int b, int n, int m, float radius, int nsample, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor)\n{\n CHECK_INPUT(new_xyz_tensor);\n CHECK_INPUT(xyz_tensor);\n\n const float *new_xyz = new_xyz_tensor.data_ptr();\n const float *xyz = xyz_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n ballquery_cuda_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx, stream);\n}\n" }, { "path": "classification/modules/pointops/src/ballquery/ballquery_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"ballquery_cuda_kernel.h\"\n\n// input: new_xyz(b, m, 3) xyz(b, n, 3)\n// output: idx(b, m, nsample)\n__global__ void ballquery_cuda_kernel(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx)\n{\n int batch_index = blockIdx.x;\n xyz += batch_index * n * 3;\n new_xyz += batch_index * m * 3;\n idx += m * nsample * batch_index;\n int index = threadIdx.x;\n int stride = blockDim.x;\n\n float radius2 = radius * radius;\n for (int j = index; j < m; j += stride)\n {\n float new_x = new_xyz[j * 3 + 0];\n float new_y = new_xyz[j * 3 + 1];\n float new_z = new_xyz[j * 3 + 2];\n for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k)\n {\n float x = xyz[k * 3 + 0];\n float y = xyz[k * 3 + 1];\n float z = xyz[k * 3 + 2];\n float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);\n if (d2 < radius2)\n {\n if (cnt == 0)\n {\n for (int l = 0; l < nsample; ++l)\n idx[j * nsample + l] = k;\n }\n idx[j * nsample + cnt] = k;\n ++cnt;\n }\n }\n }\n}\n\nvoid ballquery_cuda_launcher(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx)\n{\n ballquery_cuda_kernel<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx);\n}\n\n\n__global__ void ballquery_cuda_kernel_fast(int b, int n, int m, float radius, int nsample, const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) {\n int bs_idx = blockIdx.y;\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (bs_idx >= b || pt_idx >= m) return;\n\n new_xyz += bs_idx * m * 3 + pt_idx * 3;\n xyz += bs_idx * n * 3;\n idx += bs_idx * m * nsample + pt_idx * nsample;\n\n float radius2 = radius * radius;\n float new_x = new_xyz[0];\n float new_y = new_xyz[1];\n float new_z = new_xyz[2];\n\n int cnt = 0;\n for (int k = 0; k < n; ++k) {\n float x = xyz[k * 3 + 0];\n float y = xyz[k * 3 + 1];\n float z = xyz[k * 3 + 2];\n float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);\n if (d2 < radius2){\n if (cnt == 0){\n for (int l = 0; l < nsample; ++l) {\n idx[l] = k;\n }\n }\n idx[cnt] = k;\n ++cnt;\n if (cnt >= nsample){\n break;\n }\n }\n }\n}\n\n\nvoid ballquery_cuda_launcher_fast(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx, cudaStream_t stream) {\n // param new_xyz: (B, m, 3)\n // param xyz: (B, n, 3)\n // param idx: (B, m, nsample)\n\n cudaError_t err;\n\n dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n ballquery_cuda_kernel_fast<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}\n" }, { "path": "classification/modules/pointops/src/ballquery/ballquery_cuda_kernel.h", "content": "#ifndef _BALLQUERY_CUDA_KERNEL\n#define _BALLQUERY_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid ballquery_cuda(int b, int n, int m, float radius, int nsample, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor);\n\nvoid ballquery_cuda_fast(int b, int n, int m, float radius, int nsample, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid ballquery_cuda_launcher(int b, int n, int m, float radius, int nsample, const float *xyz, const float *new_xyz, int *idx);\n\nvoid ballquery_cuda_launcher_fast(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx, cudaStream_t stream);\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif\n" }, { "path": "classification/modules/pointops/src/cuda_utils.h", "content": "#ifndef _CUDA_UTILS_H\n#define _CUDA_UTILS_H\n\n#include \n\n#define TOTAL_THREADS 1024\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, \" must be a CUDAtensor \")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, \" must be contiguous \")\n#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)\n\n#define THREADS_PER_BLOCK 256\n#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))\n\ninline int opt_n_threads(int work_size) {\n const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0);\n return max(min(1 << pow_2, TOTAL_THREADS), 1);\n}\n\ninline dim3 opt_block_config(int x, int y) {\n const int x_threads = opt_n_threads(x);\n const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);\n dim3 block_config(x_threads, y_threads, 1);\n return block_config;\n}\n\n#endif" }, { "path": "classification/modules/pointops/src/grouping/grouping_cuda.cpp", "content": "#include \n#include \n#include \n#include \n\n#include \"grouping_cuda_kernel.h\"\n\nextern THCState *state;\n\nvoid grouping_forward_cuda(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor)\n{\n const float *points = points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *out = out_tensor.data_ptr();\n grouping_forward_cuda_launcher(b, c, n, m, nsample, points, idx, out);\n}\n\nvoid grouping_backward_cuda(int b, int c, int n, int m, int nsample, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor)\n{\n float *grad_points = grad_points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n const float *grad_out = grad_out_tensor.data_ptr();\n grouping_backward_cuda_launcher(b, c, n, m, nsample, grad_out, idx, grad_points);\n}\n\nvoid grouping_forward_cuda_fast(int b, int c, int n, int npoints, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) {\n\n const float *points = points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *out = out_tensor.data_ptr();\n grouping_forward_cuda_launcher_fast(b, c, n, npoints, nsample, points, idx, out);\n}" }, { "path": "classification/modules/pointops/src/grouping/grouping_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"grouping_cuda_kernel.h\"\n\n// input: points(b, c, n) idx(b, m, nsample)\n// output: out(b, c, m, nsample)\n__global__ void grouping_forward_cuda_kernel(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out)\n{\n int batch_index = blockIdx.x;\n points += batch_index * n * c;\n idx += batch_index * m * nsample;\n out += batch_index * m * nsample * c;\n const int index = threadIdx.y * blockDim.x + threadIdx.x;\n const int stride = blockDim.y * blockDim.x;\n for (int i = index; i < c * m; i += stride)\n {\n const int l = i / m;\n const int j = i % m;\n for (int k = 0; k < nsample; ++k)\n {\n int ii = idx[j * nsample + k];\n out[(l * m + j) * nsample + k] = points[l * n + ii];\n }\n }\n}\n\n// input: grad_out(b, c, m, nsample), idx(b, m, nsample)\n// output: grad_points(b, c, n)\n__global__ void grouping_backward_cuda_kernel(int b, int c, int n, int m, int nsample, const float *grad_out, const int *idx, float *grad_points)\n{\n int batch_index = blockIdx.x;\n grad_out += batch_index * m * nsample * c;\n idx += batch_index * m * nsample;\n grad_points += batch_index * n * c;\n const int index = threadIdx.y * blockDim.x + threadIdx.x;\n const int stride = blockDim.y * blockDim.x;\n for (int i = index; i < c * m; i += stride)\n {\n const int l = i / m;\n const int j = i % m;\n for (int k = 0; k < nsample; ++k)\n {\n int ii = idx[j * nsample + k];\n atomicAdd(grad_points + l * n + ii, grad_out[(l * m + j) * nsample + k]);\n }\n }\n}\n\nvoid grouping_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out)\n{\n grouping_forward_cuda_kernel<<>>(b, c, n, m, nsample, points, idx, out);\n}\n\nvoid grouping_backward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *grad_out, const int *idx, float *grad_points)\n{\n grouping_backward_cuda_kernel<<>>(b, c, n, m, nsample, grad_out, idx, grad_points);\n}\n\n// input: points(b, c, n) idx(b, npoints, nsample)\n// output: out(b, c, npoints, nsample)\n__global__ void grouping_forward_cuda_kernel_fast(int b, int c, int n, int npoints, int nsample, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {\n int bs_idx = blockIdx.z;\n int c_idx = blockIdx.y;\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n int pt_idx = index / nsample;\n if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;\n\n int sample_idx = index % nsample;\n\n idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;\n int in_idx = bs_idx * c * n + c_idx * n + idx[0];\n int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;\n\n out[out_idx] = points[in_idx];\n}\n\n// input: points(b, c, n) idx(b, npoints, nsample)\n// output: out(b, c, npoints, nsample)\nvoid grouping_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out) {\n\n cudaError_t err;\n\n dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n grouping_forward_cuda_kernel_fast<<>>(b, c, n, npoints, nsample, points, idx, out);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}\n\n\n" }, { "path": "classification/modules/pointops/src/grouping/grouping_cuda_kernel.h", "content": "#ifndef _GROUPING_CUDA_KERNEL\n#define _GROUPING_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid grouping_forward_cuda(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out);\nvoid grouping_backward_cuda(int b, int c, int n, int m, int nsample, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);\n\nvoid grouping_forward_cuda_fast(int b, int c, int n, int npoints, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid grouping_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out);\nvoid grouping_backward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *grad_out, const int *idx, float *grad_points);\n\nvoid grouping_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "classification/modules/pointops/src/grouping_int/grouping_int_cuda.cpp", "content": "#include \n#include \n#include \n#include \n\n#include \"grouping_int_cuda_kernel.h\"\n\nextern THCState *state;\n\nvoid grouping_int_forward_cuda(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor)\n{\n const long int *points = points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n long int *out = out_tensor.data_ptr();\n grouping_int_forward_cuda_launcher(b, c, n, m, nsample, points, idx, out);\n}\n\nvoid grouping_int_forward_cuda_fast(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor)\n{\n const long int *points = points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n long int *out = out_tensor.data_ptr();\n grouping_int_forward_cuda_launcher_fast(b, c, n, m, nsample, points, idx, out);\n}" }, { "path": "classification/modules/pointops/src/grouping_int/grouping_int_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"grouping_int_cuda_kernel.h\"\n\n// input: points(b, c, n) idx(b, m, nsample)\n// output: out(b, c, m, nsample)\n__global__ void grouping_int_forward_cuda_kernel(int b, int c, int n, int m, int nsample, const long int *points, const int *idx, long int *out)\n{\n int batch_index = blockIdx.x;\n points += batch_index * n * c;\n idx += batch_index * m * nsample;\n out += batch_index * m * nsample * c;\n const int index = threadIdx.y * blockDim.x + threadIdx.x;\n const int stride = blockDim.y * blockDim.x;\n for (int i = index; i < c * m; i += stride)\n {\n const int l = i / m;\n const int j = i % m;\n for (int k = 0; k < nsample; ++k)\n {\n int ii = idx[j * nsample + k];\n out[(l * m + j) * nsample + k] = points[l * n + ii];\n }\n }\n}\n\n\nvoid grouping_int_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const long int *points, const int *idx, long int *out)\n{\n grouping_int_forward_cuda_kernel<<>>(b, c, n, m, nsample, points, idx, out);\n}\n\n\n__global__ void grouping_int_forward_cuda_kernel_fast(int b, int c, int n, int npoints, int nsample, const long int *__restrict__ points, const int *__restrict__ idx, long int *__restrict__ out)\n{\n int bs_idx = blockIdx.z;\n int c_idx = blockIdx.y;\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n int pt_idx = index / nsample;\n if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;\n\n int sample_idx = index % nsample;\n\n idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;\n int in_idx = bs_idx * c * n + c_idx * n + idx[0];\n int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;\n\n out[out_idx] = points[in_idx];\n}\n\n\nvoid grouping_int_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const long int *points, const int *idx, long int *out)\n{\n cudaError_t err;\n\n dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n grouping_int_forward_cuda_kernel_fast<<>>(b, c, n, npoints, nsample, points, idx, out);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}" }, { "path": "classification/modules/pointops/src/grouping_int/grouping_int_cuda_kernel.h", "content": "#ifndef _GROUPING_INT_CUDA_KERNEL\n#define _GROUPING_INT_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid grouping_int_forward_cuda(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out);\n\nvoid grouping_int_forward_cuda_fast(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid grouping_int_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const long int *points, const int *idx, long int *out);\n\nvoid grouping_int_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const long int *points, const int *idx, long int *out);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "classification/modules/pointops/src/interpolation/interpolation_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"interpolation_cuda_kernel.h\"\n\nextern THCState *state;\n\nvoid nearestneighbor_cuda(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor)\n{\n const float *unknown = unknown_tensor.data_ptr();\n const float *known = known_tensor.data_ptr();\n float *dist2 = dist2_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n nearestneighbor_cuda_launcher(b, n, m, unknown, known, dist2, idx);\n}\n\nvoid interpolation_forward_cuda(int b, int c, int m, int n, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor)\n{\n const float *points = points_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n float *out = out_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n interpolation_forward_cuda_launcher(b, c, m, n, points, idx, weight, out);\n}\n\nvoid interpolation_backward_cuda(int b, int c, int n, int m, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor)\n{\n const float *grad_out = grad_out_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n float *grad_points = grad_points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n interpolation_backward_cuda_launcher(b, c, n, m, grad_out, idx, weight, grad_points);\n}\n\nvoid nearestneighbor_cuda_fast(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) {\n const float *unknown = unknown_tensor.data_ptr();\n const float *known = known_tensor.data_ptr();\n float *dist2 = dist2_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n nearestneighbor_cuda_launcher_fast(b, n, m, unknown, known, dist2, idx);\n}\n\nvoid interpolation_forward_cuda_fast(int b, int c, int m, int n, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor) {\n\n const float *points = points_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n float *out = out_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n interpolation_forward_cuda_launcher_fast(b, c, m, n, points, idx, weight, out);\n}" }, { "path": "classification/modules/pointops/src/interpolation/interpolation_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"interpolation_cuda_kernel.h\"\n\n// input: unknown(b, n, 3) known(b, m, 3)\n// output: dist2(b, n, 3), idx(b, n, 3)\n__global__ void nearestneighbor_cuda_kernel(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx)\n{\n int batch_index = blockIdx.x;\n unknown += batch_index * n * 3;\n known += batch_index * m * 3;\n dist2 += batch_index * n * 3;\n idx += batch_index * n * 3;\n\n int index = threadIdx.x;\n int stride = blockDim.x;\n for (int j = index; j < n; j += stride)\n {\n\t\tfloat ux = unknown[j * 3 + 0];\n\t\tfloat uy = unknown[j * 3 + 1];\n\t\tfloat uz = unknown[j * 3 + 2];\n\n\t\tdouble best1 = 1e40, best2 = 1e40, best3 = 1e40;\n\t\tint besti1 = 0, besti2 = 0, besti3 = 0;\n\t\tfor (int k = 0; k < m; ++k)\n\t\t{\n\t\t float x = known[k * 3 + 0];\n\t\t float y = known[k * 3 + 1];\n\t\t float z = known[k * 3 + 2];\n\t\t float d =\n\t\t\t(ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);\n\t\t if (d < best1)\n\t\t {\n\t\t\t\tbest3 = best2;\n\t\t\t\tbesti3 = besti2;\n\t\t\t\tbest2 = best1;\n\t\t\t\tbesti2 = besti1;\n\t\t\t\tbest1 = d;\n\t\t\t\tbesti1 = k;\n\t\t\t}\n\t\t\telse if (d < best2)\n\t\t\t{\n\t\t\t\tbest3 = best2;\n\t\t\t\tbesti3 = besti2;\n\t\t\t\tbest2 = d;\n\t\t\t\tbesti2 = k;\n\t\t\t}\n\t\t\telse if (d < best3)\n\t\t\t{\n\t\t\t\tbest3 = d;\n\t\t\t\tbesti3 = k;\n\t\t }\n\t\t}\n\t\tdist2[j * 3 + 0] = best1;\n\t\tdist2[j * 3 + 1] = best2;\n\t\tdist2[j * 3 + 2] = best3;\n\t\tidx[j * 3 + 0] = besti1;\n\t\tidx[j * 3 + 1] = besti2;\n\t\tidx[j * 3 + 2] = besti3;\n }\n}\n\n// input: points(b, c, m), idx(b, n, 3), weight(b, n, 3)\n// output: out(b, c, n)\n__global__ void interpolation_forward_cuda_kernel(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out)\n{\n int batch_index = blockIdx.x;\n points += batch_index * m * c;\n idx += batch_index * n * 3;\n weight += batch_index * n * 3;\n out += batch_index * n * c;\n\n const int index = threadIdx.y * blockDim.x + threadIdx.x;\n const int stride = blockDim.y * blockDim.x;\n for (int i = index; i < c * n; i += stride)\n {\n\t\tconst int l = i / n;\n\t\tconst int j = i % n;\n\t\tfloat w1 = weight[j * 3 + 0];\n\t\tfloat w2 = weight[j * 3 + 1];\n\t\tfloat w3 = weight[j * 3 + 2];\n\t\tint i1 = idx[j * 3 + 0];\n\t\tint i2 = idx[j * 3 + 1];\n\t\tint i3 = idx[j * 3 + 2];\n\t\tout[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + points[l * m + i3] * w3;\n }\n}\n\n// input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3)\n// output: grad_points(b, c, m)\n__global__ void interpolation_backward_cuda_kernel( int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points)\n{\n int batch_index = blockIdx.x;\n grad_out += batch_index * n * c;\n idx += batch_index * n * 3;\n weight += batch_index * n * 3;\n grad_points += batch_index * m * c;\n\n const int index = threadIdx.y * blockDim.x + threadIdx.x;\n const int stride = blockDim.y * blockDim.x;\n for (int i = index; i < c * n; i += stride)\n {\n\t\tconst int l = i / n;\n\t\tconst int j = i % n;\n\t\tfloat w1 = weight[j * 3 + 0];\n\t\tfloat w2 = weight[j * 3 + 1];\n\t\tfloat w3 = weight[j * 3 + 2];\n\t\tint i1 = idx[j * 3 + 0];\n\t\tint i2 = idx[j * 3 + 1];\n\t\tint i3 = idx[j * 3 + 2];\n\t\tatomicAdd(grad_points + l * m + i1, grad_out[i] * w1);\n\t\tatomicAdd(grad_points + l * m + i2, grad_out[i] * w2);\n\t\tatomicAdd(grad_points + l * m + i3, grad_out[i] * w3);\n }\n}\n\nvoid nearestneighbor_cuda_launcher(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx)\n{\n nearestneighbor_cuda_kernel<<>>(b, n, m, unknown, known, dist2, idx);\n}\n\nvoid interpolation_forward_cuda_launcher(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out)\n{\n interpolation_forward_cuda_kernel<<>>(b, c, m, n, points, idx, weight, out);\n}\n\nvoid interpolation_backward_cuda_launcher(int b, int n, int c, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points)\n{\n interpolation_backward_cuda_kernel<<>>(b, n, c, m, grad_out, idx, weight, grad_points);\n}\n\n\n// input: unknown(b, n, 3) known(b, m, 3)\n// output: dist2(b, n, 3), idx(b, n, 3)\n__global__ void nearestneighbor_cuda_kernel_fast(int b, int n, int m, const float *__restrict__ unknown, const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {\n\n int bs_idx = blockIdx.y;\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (bs_idx >= b || pt_idx >= n) return;\n\n unknown += bs_idx * n * 3 + pt_idx * 3;\n known += bs_idx * m * 3;\n dist2 += bs_idx * n * 3 + pt_idx * 3;\n idx += bs_idx * n * 3 + pt_idx * 3;\n\n float ux = unknown[0];\n float uy = unknown[1];\n float uz = unknown[2];\n\n double best1 = 1e40, best2 = 1e40, best3 = 1e40;\n int besti1 = 0, besti2 = 0, besti3 = 0;\n for (int k = 0; k < m; ++k) {\n float x = known[k * 3 + 0];\n float y = known[k * 3 + 1];\n float z = known[k * 3 + 2];\n float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);\n if (d < best1) {\n best3 = best2; besti3 = besti2;\n best2 = best1; besti2 = besti1;\n best1 = d; besti1 = k;\n }\n else if (d < best2) {\n best3 = best2; besti3 = besti2;\n best2 = d; besti2 = k;\n }\n else if (d < best3) {\n best3 = d; besti3 = k;\n }\n }\n dist2[0] = best1;\n dist2[1] = best2;\n dist2[2] = best3;\n\n idx[0] = besti1;\n idx[1] = besti2;\n idx[2] = besti3;\n}\n\n\n// input: points(b, c, m), idx(b, n, 3), weight(b, n, 3)\n// output: out(b, c, n)\n__global__ void interpolation_forward_cuda_kernel_fast(int b, int c, int m, int n, const float *__restrict__ points, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {\n\n int bs_idx = blockIdx.z;\n int c_idx = blockIdx.y;\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n\n if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;\n\n weight += bs_idx * n * 3 + pt_idx * 3;\n points += bs_idx * c * m + c_idx * m;\n idx += bs_idx * n * 3 + pt_idx * 3;\n out += bs_idx * c * n + c_idx * n;\n\n out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];\n}\n\n\nvoid nearestneighbor_cuda_launcher_fast(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx)\n{\n cudaError_t err;\n\n dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n nearestneighbor_cuda_kernel_fast<<>>(b, n, m, unknown, known, dist2, idx);\n\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}\n\nvoid interpolation_forward_cuda_launcher_fast(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out) {\n\n cudaError_t err;\n\n dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n interpolation_forward_cuda_kernel_fast<<>>(b, c, m, n, points, idx, weight, out);\n\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\",\n cudaGetErrorString(err));\n exit(-1);\n }\n}\n" }, { "path": "classification/modules/pointops/src/interpolation/interpolation_cuda_kernel.h", "content": "#ifndef _INTERPOLATION_CUDA_KERNEL\n#define _INTERPOLATION_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid nearestneighbor_cuda(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);\nvoid interpolation_forward_cuda(int b, int c, int m, int n, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);\nvoid interpolation_backward_cuda(int b, int c, int n, int m, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor);\n\nvoid nearestneighbor_cuda_fast(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);\nvoid interpolation_forward_cuda_fast(int b, int c, int m, int n, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid nearestneighbor_cuda_launcher(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx);\nvoid interpolation_forward_cuda_launcher(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out);\nvoid interpolation_backward_cuda_launcher(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points);\n\nvoid nearestneighbor_cuda_launcher_fast(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx);\nvoid interpolation_forward_cuda_launcher_fast(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "classification/modules/pointops/src/knnquery/__init__.py", "content": "" }, { "path": "classification/modules/pointops/src/knnquery/knnquery_cuda.cpp", "content": "#include \n#include \n#include \n#include \n\n#include \"knnquery_cuda_kernel.h\"\n\nextern THCState *state;\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, \" must be a CUDAtensor \")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, \" must be contiguous \")\n#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)\n\n\nvoid knnquery_cuda(int b, int n, int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor)\n{\n CHECK_INPUT(new_xyz_tensor);\n CHECK_INPUT(xyz_tensor);\n\n const float *new_xyz = new_xyz_tensor.data_ptr();\n const float *xyz = xyz_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n float *dist2 = dist2_tensor.data_ptr();\n\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n knnquery_cuda_launcher(b, n, m, nsample, xyz, new_xyz, idx, dist2, stream);\n}\n" }, { "path": "classification/modules/pointops/src/knnquery/knnquery_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"knnquery_cuda_kernel.h\"\n\n// input: xyz (b, n, 3) new_xyz (b, m, 3)\n// output: idx (b, m, nsample) dist2 (b, m, nsample)\n__global__ void knnquery_cuda_kernel(int b, int n, int m, int nsample, const float *__restrict__ xyz, const float *__restrict__ new_xyz, int *__restrict__ idx, float *__restrict__ dist2) {\n int bs_idx = blockIdx.y;\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (bs_idx >= b || pt_idx >= m) return;\n\n new_xyz += bs_idx * m * 3 + pt_idx * 3;\n xyz += bs_idx * n * 3;\n idx += bs_idx * m * nsample + pt_idx * nsample;\n\n float new_x = new_xyz[0];\n float new_y = new_xyz[1];\n float new_z = new_xyz[2];\n\n //double* best = new double[nsample];\n //int* besti = new int[nsample];\n double best[200];\n int besti[200];\n for(int i = 0; i < nsample; i++){\n best[i] = 1e40;\n besti[i] = 0;\n }\n for(int k = 0; k < n; k++){\n float x = xyz[k * 3 + 0];\n float y = xyz[k * 3 + 1];\n float z = xyz[k * 3 + 2];\n float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);\n for(int j = 0; j < nsample; j++){\n if(d2 < best[j]){\n for(int i = nsample - 1; i > j; i--){\n best[i] = best[i - 1];\n besti[i] = besti[i - 1];\n }\n best[j] = d2;\n besti[j] = k;\n break;\n }\n }\n }\n for(int i = 0; i < nsample; i++){\n idx[i] = besti[i];\n dist2[i] = best[i];\n }\n //delete []best;\n //delete []besti;\n}\n\n\nvoid knnquery_cuda_launcher(int b, int n, int m, int nsample, const float *xyz, const float *new_xyz, int *idx, float *dist2, cudaStream_t stream) {\n // param new_xyz: (B, m, 3)\n // param xyz: (B, n, 3)\n // param idx: (B, m, nsample)\n\n cudaError_t err;\n\n dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n \n // fprintf('%d, %d', blocks, threads);\n knnquery_cuda_kernel<<>>(b, n, m, nsample, xyz, new_xyz, idx, dist2);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n\n // err = cudaGetLastError();\n // if (cudaSuccess != err) {\n // fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n // exit(-1);\n // }\n}" }, { "path": "classification/modules/pointops/src/knnquery/knnquery_cuda_kernel.h", "content": "#ifndef _KNNQUERY_CUDA_KERNEL\n#define _KNNQUERY_CUDA_KERNEL\n\n#include \n#include \n#include \n\nvoid knnquery_cuda(int b, int n, int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid knnquery_cuda_launcher(int b, int n, int m, int nsample, const float *xyz, const float *new_xyz, int *idx, float *dist2, cudaStream_t stream);\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif" }, { "path": "classification/modules/pointops/src/knnquery_heap/__init__.py", "content": "" }, { "path": "classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda.cpp", "content": "#include \n#include \n#include \n#include \n\n#include \"knnquery_heap_cuda_kernel.h\"\n\nextern THCState *state;\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, \" must be a CUDAtensor \")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, \" must be contiguous \")\n#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)\n\n\nvoid knnquery_heap_cuda(int b, int n, int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor)\n{\n CHECK_INPUT(new_xyz_tensor);\n CHECK_INPUT(xyz_tensor);\n\n const float *new_xyz = new_xyz_tensor.data_ptr();\n const float *xyz = xyz_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n float *dist2 = dist2_tensor.data_ptr();\n\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n knnquery_heap_cuda_launcher(b, n, m, nsample, xyz, new_xyz, idx, dist2, stream);\n}\n" }, { "path": "classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"knnquery_heap_cuda_kernel.h\"\n\n\n__device__ void swap_float(float *x, float *y)\n{\n float tmp = *x;\n *x = *y;\n *y = tmp;\n}\n\n\n__device__ void swap_int(int *x, int *y)\n{\n int tmp = *x;\n *x = *y;\n *y = tmp;\n}\n\n\n__device__ void reheap(float *dist, int *idx, int k)\n{\n int root = 0;\n int child = root * 2 + 1;\n while (child < k)\n {\n if(child + 1 < k && dist[child+1] > dist[child])\n child++;\n if(dist[root] > dist[child])\n return;\n swap_float(&dist[root], &dist[child]);\n swap_int(&idx[root], &idx[child]);\n root = child;\n child = root * 2 + 1;\n }\n}\n\n\n__device__ void heap_sort(float *dist, int *idx, int k)\n{\n int i;\n for (i = k - 1; i > 0; i--)\n {\n swap_float(&dist[0], &dist[i]);\n swap_int(&idx[0], &idx[i]);\n reheap(dist, idx, i);\n }\n}\n\n\n// input: xyz (b, n, 3) new_xyz (b, m, 3)\n// output: idx (b, m, nsample) dist2 (b, m, nsample)\n__global__ void knnquery_heap_cuda_kernel(int b, int n, int m, int nsample, const float *__restrict__ xyz, const float *__restrict__ new_xyz, int *__restrict__ idx, float *__restrict__ dist2) {\n int bs_idx = blockIdx.y;\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (bs_idx >= b || pt_idx >= m) return;\n\n new_xyz += bs_idx * m * 3 + pt_idx * 3;\n xyz += bs_idx * n * 3;\n idx += bs_idx * m * nsample + pt_idx * nsample;\n dist2 += bs_idx * m * nsample + pt_idx * nsample;\n\n float new_x = new_xyz[0];\n float new_y = new_xyz[1];\n float new_z = new_xyz[2];\n\n float best_dist[100];\n int best_idx[100];\n for(int i = 0; i < nsample; i++){\n best_dist[i] = 1e10;\n best_idx[i] = 0;\n }\n for(int i = 0; i < n; i++){\n float x = xyz[i * 3 + 0];\n float y = xyz[i * 3 + 1];\n float z = xyz[i * 3 + 2];\n float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);\n if (d2 < best_dist[0]){\n best_dist[0] = d2;\n best_idx[0] = i;\n reheap(best_dist, best_idx, nsample);\n }\n }\n heap_sort(best_dist, best_idx, nsample);\n for(int i = 0; i < nsample; i++){\n idx[i] = best_idx[i];\n dist2[i] = best_dist[i];\n }\n}\n\n\nvoid knnquery_heap_cuda_launcher(int b, int n, int m, int nsample, const float *xyz, const float *new_xyz, int *idx, float *dist2, cudaStream_t stream) {\n // param new_xyz: (B, m, 3)\n // param xyz: (B, n, 3)\n // param idx: (B, m, nsample)\n\n cudaError_t err;\n\n dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n knnquery_heap_cuda_kernel<<>>(b, n, m, nsample, xyz, new_xyz, idx, dist2);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}" }, { "path": "classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda_kernel.h", "content": "#ifndef _KNNQUERY_HEAP_CUDA_KERNEL\n#define _KNNQUERY_HEAP_CUDA_KERNEL\n\n#include \n#include \n#include \n\nvoid knnquery_heap_cuda(int b, int n, int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid knnquery_heap_cuda_launcher(int b, int n, int m, int nsample, const float *xyz, const float *new_xyz, int *idx, float *dist2, cudaStream_t stream);\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif" }, { "path": "classification/modules/pointops/src/pointops_api.cpp", "content": "#include \n#include \n\n#include \"ballquery/ballquery_cuda_kernel.h\"\n#include \"grouping/grouping_cuda_kernel.h\"\n#include \"grouping_int/grouping_int_cuda_kernel.h\"\n#include \"sampling/sampling_cuda_kernel.h\"\n#include \"interpolation/interpolation_cuda_kernel.h\"\n#include \"knnquery/knnquery_cuda_kernel.h\"\n#include \"knnquery_heap/knnquery_heap_cuda_kernel.h\"\n\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"ballquery_cuda\", &ballquery_cuda_fast, \"ballquery_cuda_fast\"); // name in python, cpp function address, docs\n\n m.def(\"knnquery_cuda\", &knnquery_cuda, \"knnquery_cuda\");\n m.def(\"knnquery_heap_cuda\", &knnquery_heap_cuda, \"knnquery_heap_cuda\");\n\n m.def(\"grouping_forward_cuda\", &grouping_forward_cuda_fast, \"grouping_forward_cuda_fast\");\n m.def(\"grouping_backward_cuda\", &grouping_backward_cuda, \"grouping_backward_cuda\");\n\n m.def(\"grouping_int_forward_cuda\", &grouping_int_forward_cuda_fast, \"grouping_int_forward_cuda_fast\");\n\n m.def(\"gathering_forward_cuda\", &gathering_forward_cuda, \"gathering_forward_cuda\");\n m.def(\"gathering_backward_cuda\", &gathering_backward_cuda, \"gathering_backward_cuda\");\n m.def(\"furthestsampling_cuda\", &furthestsampling_cuda, \"furthestsampling_cuda\");\n\n m.def(\"nearestneighbor_cuda\", &nearestneighbor_cuda_fast, \"nearestneighbor_cuda_fast\");\n m.def(\"interpolation_forward_cuda\", &interpolation_forward_cuda_fast, \"interpolation_forward_cuda_fast\");\n m.def(\"interpolation_backward_cuda\", &interpolation_backward_cuda, \"interpolation_backward_cuda\");\n}\n" }, { "path": "classification/modules/pointops/src/sampling/sampling_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"sampling_cuda_kernel.h\"\n\nextern THCState *state;\n\nvoid gathering_forward_cuda(int b, int c, int n, int m, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor)\n{\n const float *points = points_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *out = out_tensor.data_ptr();\n gathering_forward_cuda_launcher(b, c, n, m, points, idx, out);\n}\n\nvoid gathering_backward_cuda(int b, int c, int n, int m, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor)\n{\n\n const float *grad_out = grad_out_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *grad_points = grad_points_tensor.data_ptr();\n gathering_backward_cuda_launcher(b, c, n, m, grad_out, idx, grad_points);\n}\n\nvoid furthestsampling_cuda(int b, int n, int m, at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor)\n{\n const float *points = points_tensor.data_ptr();\n float *temp = temp_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n furthestsampling_cuda_launcher(b, n, m, points, temp, idx);\n}\n" }, { "path": "classification/modules/pointops/src/sampling/sampling_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"sampling_cuda_kernel.h\"\n\n// input: points(b, c, n) idx(b, m)\n// output: out(b, c, m)\n__global__ void gathering_forward_cuda_kernel(int b, int c, int n, int m, const float *points, const int *idx, float *out)\n{\n for (int i = blockIdx.x; i < b; i += gridDim.x)\n {\n for (int l = blockIdx.y; l < c; l += gridDim.y)\n {\n for (int j = threadIdx.x; j < m; j += blockDim.x)\n {\n int a = idx[i * m + j];\n out[(i * c + l) * m + j] = points[(i * c + l) * n + a];\n }\n }\n }\n}\n\n// input: grad_out(b, c, m) idx(b, m)\n// output: grad_points(b, c, n)\n__global__ void gathering_backward_cuda_kernel(int b, int c, int n, int m, const float *grad_out, const int *idx, float *grad_points)\n{\n for (int i = blockIdx.x; i < b; i += gridDim.x)\n {\n for (int l = blockIdx.y; l < c; l += gridDim.y)\n {\n for (int j = threadIdx.x; j < m; j += blockDim.x)\n {\n int a = idx[i * m + j];\n atomicAdd(grad_points + (i * c + l) * n + a, grad_out[(i * c + l) * m + j]);\n }\n }\n }\n}\n\nvoid gathering_forward_cuda_launcher(int b, int c, int n, int m, const float *points, const int *idx, float *out)\n{\n gathering_forward_cuda_kernel<<>>(b, c, n, m, points, idx, out);\n}\n\nvoid gathering_backward_cuda_launcher(int b, int c, int n, int m, const float *grad_out, const int *idx, float *grad_points)\n{\n gathering_backward_cuda_kernel<<>>(b, c, n, m, grad_out, idx, grad_points);\n}\n\n__device__ void __update(float *dists, int *dists_i,\n\t\t\t int idx1, int idx2) {\n const float v1 = dists[idx1], v2 = dists[idx2];\n const int i1 = dists_i[idx1], i2 = dists_i[idx2];\n dists[idx1] = max(v1, v2);\n dists_i[idx1] = v2 > v1 ? i2 : i1;\n}\n\n// Input dataset: (b, n, 3), tmp: (b, n)\n// Ouput idxs (b, m)\ntemplate \n__global__ void furthestsampling_cuda_kernel(int b, int n, int m, const float *dataset, float *temp, int *idxs)\n{\n if (m <= 0)\n\t return;\n __shared__ float dists[block_size];\n __shared__ int dists_i[block_size];\n\n int batch_index = blockIdx.x;\n dataset += batch_index * n * 3;\n temp += batch_index * n;\n idxs += batch_index * m;\n int tid = threadIdx.x;\n const int stride = block_size;\n int old = 0;\n if (threadIdx.x == 0)\n\t idxs[0] = old;\n\n __syncthreads();\n for (int j = 1; j < m; j++)\n {\n int besti = 0;\n float best = -1;\n float x1 = dataset[old * 3 + 0];\n float y1 = dataset[old * 3 + 1];\n float z1 = dataset[old * 3 + 2];\n for (int k = tid; k < n; k += stride)\n {\n float x2, y2, z2;\n x2 = dataset[k * 3 + 0];\n y2 = dataset[k * 3 + 1];\n z2 = dataset[k * 3 + 2];\n //float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);\n //if (mag <= 1e-3)\n // continue;\n float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);\n float d2 = min(d, temp[k]);\n temp[k] = d2;\n besti = d2 > best ? k : besti;\n best = d2 > best ? d2 : best;\n }\n dists[tid] = best;\n dists_i[tid] = besti;\n __syncthreads();\n\n if (block_size >= 1024) {\n if (tid < 512) {\n __update(dists, dists_i, tid, tid + 512);\n }\n __syncthreads();\n }\n if (block_size >= 512) {\n if (tid < 256) {\n __update(dists, dists_i, tid, tid + 256);\n }\n __syncthreads();\n }\n if (block_size >= 256) {\n if (tid < 128) {\n __update(dists, dists_i, tid, tid + 128);\n }\n __syncthreads();\n }\n if (block_size >= 128) {\n if (tid < 64) {\n __update(dists, dists_i, tid, tid + 64);\n }\n __syncthreads();\n }\n if (block_size >= 64) {\n if (tid < 32) {\n __update(dists, dists_i, tid, tid + 32);\n }\n __syncthreads();\n }\n if (block_size >= 32) {\n if (tid < 16) {\n __update(dists, dists_i, tid, tid + 16);\n }\n __syncthreads();\n }\n if (block_size >= 16) {\n if (tid < 8) {\n __update(dists, dists_i, tid, tid + 8);\n }\n __syncthreads();\n }\n if (block_size >= 8) {\n if (tid < 4) {\n __update(dists, dists_i, tid, tid + 4);\n }\n __syncthreads();\n }\n if (block_size >= 4) {\n if (tid < 2) {\n __update(dists, dists_i, tid, tid + 2);\n }\n __syncthreads();\n }\n if (block_size >= 2) {\n if (tid < 1) {\n __update(dists, dists_i, tid, tid + 1);\n }\n __syncthreads();\n }\n\n old = dists_i[0];\n if (tid == 0)\n idxs[j] = old;\n }\n}\n\nvoid furthestsampling_cuda_launcher(int b, int n, int m, const float *dataset, float *temp, int *idxs)\n{ \n\tunsigned int n_threads = opt_n_threads(n);\n\tswitch (n_threads) {\n\t case 1024:\n\t furthestsampling_cuda_kernel<1024><<>>(b, n, m, dataset, temp, idxs);\n\t break;\n\t\tcase 512:\n\t\t\tfurthestsampling_cuda_kernel<512><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 256:\n\t\t\tfurthestsampling_cuda_kernel<256><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 128:\n\t\t\tfurthestsampling_cuda_kernel<128><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 64:\n\t\t\tfurthestsampling_cuda_kernel<64><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 32:\n\t\t\tfurthestsampling_cuda_kernel<32><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 16:\n\t\t\tfurthestsampling_cuda_kernel<16><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n \tcase 8:\n\t\t\tfurthestsampling_cuda_kernel<8><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n\t case 4:\n\t\t\tfurthestsampling_cuda_kernel<4><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n\t case 2:\n\t\t\tfurthestsampling_cuda_kernel<2><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n\t case 1:\n\t\t\tfurthestsampling_cuda_kernel<1><<>>(b, n, m, dataset, temp, idxs);\n\t\t\tbreak;\n\t default:\n\t\t\tfurthestsampling_cuda_kernel<512><<>>(b, n, m, dataset, temp, idxs);\n\t }\n}\n" }, { "path": "classification/modules/pointops/src/sampling/sampling_cuda_kernel.h", "content": "#ifndef _SAMPLING_CUDA_KERNEL\n#define _SAMPLING_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid gathering_forward_cuda(int b, int c, int n, int m, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);\nvoid gathering_backward_cuda(int b, int c, int n, int m, at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);\nvoid furthestsampling_cuda(int b, int n, int m, at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid gathering_forward_cuda_launcher(int b, int c, int n, int m, const float *points, const int *idx, float *out);\nvoid gathering_backward_cuda_launcher(int b, int c, int n, int m, const float *grad_out, const int *idx, float *grad_points);\nvoid furthestsampling_cuda_launcher(int b, int n, int m, const float *dataset, float *temp, int *idxs);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "classification/modules/polar_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch\nimport numpy as np\n\n\ndef xyz2sphere(xyz, normalize=True):\n \"\"\"\n Convert XYZ to Spherical Coordinate\n\n reference: https://en.wikipedia.org/wiki/Spherical_coordinate_system\n\n :param xyz: [B, N, 3] / [B, N, G, 3]\n :return: (rho, theta, phi) [B, N, 3] / [B, N, G, 3]\n \"\"\"\n rho = torch.sqrt(torch.sum(torch.pow(xyz, 2), dim=-1, keepdim=True))\n rho = torch.clamp(rho, min=0) # range: [0, inf]\n theta = torch.acos(xyz[..., 2, None] / rho) # range: [0, pi]\n phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi]\n # check nan\n idx = rho == 0\n theta[idx] = 0\n\n if normalize:\n theta = theta / np.pi # [0, 1]\n phi = phi / (2 * np.pi) + .5 # [0, 1]\n out = torch.cat([rho, theta, phi], dim=-1)\n return out\n\n\ndef xyz2cylind(xyz, normalize=True):\n \"\"\"\n Convert XYZ to Cylindrical Coordinate\n\n reference: https://en.wikipedia.org/wiki/Cylindrical_coordinate_system\n\n :param normalize: Normalize phi & z\n :param xyz: [B, N, 3] / [B, N, G, 3]\n :return: (rho, phi, z) [B, N, 3]\n \"\"\"\n rho = torch.sqrt(torch.sum(torch.pow(xyz[..., :2], 2), dim=-1, keepdim=True))\n rho = torch.clamp(rho, 0, 1) # range: [0, 1]\n phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi]\n z = xyz[..., 2, None]\n z = torch.clamp(z, -1, 1) # range: [-1, 1]\n\n if normalize:\n phi = phi / (2 * np.pi) + .5\n z = (z + 1.) / 2.\n out = torch.cat([rho, phi, z], dim=-1)\n return out\n" }, { "path": "classification/modules/ptaug_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch\n\n\n#################\n# MAIN\n#################\n\ndef get_aug_args(args):\n dataset = args.dataset\n if dataset == 'ScanObjectNN':\n aug_args = {'scale_factor': 0.5, 'shift_factor': 0.3}\n return aug_args\n else:\n raise Exception('No such dataset')\n\n\ndef transform_point_cloud(batch, args, aug_args, train=True, label=None):\n \"\"\"batch: B x 3/6 x N\"\"\"\n if args.aug_scale:\n batch[:, 0:3] = scale_point_cloud(batch[:, 0:3], aug_args['scale_factor'])\n if args.aug_shift:\n batch[:, 0:3] = shift_point_cloud(batch[:, 0:3], shift_range=aug_args['shift_factor'])\n if label is not None:\n return batch, label\n return batch\n\n\n#################\n# Shift\n#################\n\ndef shift_point_cloud(batch_data, shift_range=0.2):\n \"\"\" Randomly shift point cloud. Shift is per point cloud.\n Input:\n B x C x N array, original batch of point clouds\n Return:\n B x C x N array, shifted batch of point clouds\n \"\"\"\n shifts = (torch.rand(batch_data.shape[0], 3, 1, device=batch_data.device) * 2. - 1.) * shift_range\n batch_data += shifts\n return batch_data\n\n\n#################\n# Scale\n#################\n\ndef scale_point_cloud(batch_data, scale_range=0.2):\n \"\"\" Randomly scale the point cloud. Scale is per point cloud.\n Input:\n B x C x N array, original batch of point clouds\n Return:\n B x C x N array, scaled batch of point clouds\n \"\"\"\n scales = (torch.rand(batch_data.shape[0], 3, 1, device=batch_data.device) * 2. - 1.) * scale_range + 1.\n batch_data *= scales\n return batch_data\n" }, { "path": "classification/modules/recons_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch\nfrom torch import nn\nfrom modules.pointnet2_utils import query_knn_point, index_points\n\n\ndef _recons_factory(type):\n if type == 'knn':\n return knn_recons\n else:\n raise Exception('Not Implemented Reconstruction Type')\n\n\ndef knn_recons(k, center, context, cuda=False):\n idx = query_knn_point(k, context, center, cuda=cuda)\n torch.cuda.empty_cache()\n\n group_xyz = index_points(context, idx, cuda=cuda, is_group=True) # [B, N, K, C]\n torch.cuda.empty_cache()\n return group_xyz\n\n\ndef cal_normal(group_xyz, random_inv=False, is_group=False):\n \"\"\"\n Calculate Normal Vector (Unit Form + First Term Positive)\n\n :param group_xyz: [B, N, K=3, 3] / [B, N, G, K=3, 3]\n :param random_inv:\n :param return_intersect:\n :param return_const:\n :return: [B, N, 3]\n \"\"\"\n edge_vec1 = group_xyz[..., 1, :] - group_xyz[..., 0, :] # [B, N, 3]\n edge_vec2 = group_xyz[..., 2, :] - group_xyz[..., 0, :] # [B, N, 3]\n\n nor = torch.cross(edge_vec1, edge_vec2, dim=-1)\n unit_nor = nor / torch.norm(nor, dim=-1, keepdim=True) # [B, N, 3] / [B, N, G, 3]\n if not is_group:\n pos_mask = (unit_nor[..., 0] > 0).float() * 2. - 1. # keep x_n positive\n else:\n pos_mask = (unit_nor[..., 0:1, 0] > 0).float() * 2. - 1.\n unit_nor = unit_nor * pos_mask.unsqueeze(-1)\n\n # batch-wise random inverse normal vector (prob: 0.5)\n if random_inv:\n random_mask = torch.randint(0, 2, (group_xyz.size(0), 1, 1)).float() * 2. - 1.\n random_mask = random_mask.to(unit_nor.device)\n if not is_group:\n unit_nor = unit_nor * random_mask\n else:\n unit_nor = unit_nor * random_mask.unsqueeze(-1)\n\n return unit_nor\n\n\ndef pca(X, k, center=True):\n \"\"\"\n Principal Components Analysis impl. with SVD function\n\n :param X:\n :param k:\n :param center:\n :return:\n \"\"\"\n\n n = X.size()[0]\n ones = torch.ones(n).view([n, 1])\n h = ((1 / n) * torch.mm(ones, ones.t())) if center else torch.zeros(n * n).view([n, n])\n H = torch.eye(n) - h\n X_center = torch.mm(H.double(), X.double())\n u, s, v = torch.svd(X_center)\n components = v[:k].t()\n explained_variance = torch.mul(s[:k], s[:k]) / (n - 1)\n return {'X': X, 'k': k, 'components': components,\n 'explained_variance': explained_variance}\n\n\ndef cal_center(group_xyz):\n \"\"\"\n Calculate Global Coordinates of the Center of Triangle\n\n :param group_xyz: [B, N, K, 3] / [B, N, G, K, 3]; K >= 3\n :return: [B, N, 3] / [B, N, G, 3]\n \"\"\"\n center = torch.mean(group_xyz, dim=-2)\n return center\n\n\ndef cal_area(group_xyz):\n \"\"\"\n Calculate Area of Triangle\n\n :param group_xyz: [B, N, K, 3] / [B, N, G, K, 3]; K = 3\n :return: [B, N, 1] / [B, N, G, 1]\n \"\"\"\n pad_shape = group_xyz[..., 0, None].shape\n det_xy = torch.det(torch.cat([group_xyz[..., 0, None], group_xyz[..., 1, None], torch.ones(pad_shape)], dim=-1))\n det_yz = torch.det(torch.cat([group_xyz[..., 1, None], group_xyz[..., 2, None], torch.ones(pad_shape)], dim=-1))\n det_zx = torch.det(torch.cat([group_xyz[..., 2, None], group_xyz[..., 0, None], torch.ones(pad_shape)], dim=-1))\n area = torch.sqrt(det_xy ** 2 + det_yz ** 2 + det_zx ** 2).unsqueeze(-1)\n return area\n\n\ndef cal_const(normal, center, is_normalize=True):\n \"\"\"\n Calculate Constant Term (Standard Version, with x_normal to be 1)\n\n math::\n const = x_nor * x_0 + y_nor * y_0 + z_nor * z_0\n\n :param is_normalize:\n :param normal: [B, N, 3] / [B, N, G, 3]\n :param center: [B, N, 3] / [B, N, G, 3]\n :return: [B, N, 1] / [B, N, G, 1]\n \"\"\"\n const = torch.sum(normal * center, dim=-1, keepdim=True)\n factor = torch.sqrt(torch.Tensor([3])).to(normal.device)\n const = const / factor if is_normalize else const\n\n return const\n\n\ndef check_nan(normal, center, pos=None):\n \"\"\"\n Check & Remove NaN in normal tensor\n\n :param pos: [B, N, 1]\n :param center: [B, N, 3]\n :param normal: [B, N, 3]\n :return:\n \"\"\"\n B, N, _ = normal.shape\n mask = torch.sum(torch.isnan(normal), dim=-1) > 0\n mask_first = torch.argmax((~mask).int(), dim=-1)\n\n normal_first = normal[torch.arange(B), None, mask_first].repeat([1, N, 1])\n normal[mask] = normal_first[mask]\n center_first = center[torch.arange(B), None, mask_first].repeat([1, N, 1])\n center[mask] = center_first[mask]\n\n if pos is not None:\n pos_first = pos[torch.arange(B), None, mask_first].repeat([1, N, 1])\n pos[mask] = pos_first[mask]\n return normal, center, pos\n return normal, center\n\n\ndef check_nan_umb(normal, center, pos=None):\n \"\"\"\n Check & Remove NaN in normal tensor\n\n :param pos: [B, N, G, 1]\n :param center: [B, N, G, 3]\n :param normal: [B, N, G, 3]\n :return:\n \"\"\"\n B, N, G, _ = normal.shape\n mask = torch.sum(torch.isnan(normal), dim=-1) > 0\n mask_first = torch.argmax((~mask).int(), dim=-1)\n b_idx = torch.arange(B).unsqueeze(1).repeat([1, N])\n n_idx = torch.arange(N).unsqueeze(0).repeat([B, 1])\n\n normal_first = normal[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1])\n normal[mask] = normal_first[mask]\n center_first = center[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1])\n center[mask] = center_first[mask]\n\n if pos is not None:\n pos_first = pos[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1])\n pos[mask] = pos_first[mask]\n return normal, center, pos\n return normal, center\n\n\nclass SurfaceConstructor(nn.Module):\n \"\"\"\n Surface Constructor for Point Clouds\n\n Formulation of A Surface:\n A * (x - x_0) + B * (y - y_0) + C * (z - z_0) = 0,\n where A^2 + B^2 + C^2 = 1 & A > 0\n \"\"\"\n\n def __init__(self, r=None, k=3, recons_type='knn', return_dist=False, random_inv=True, cuda=False):\n super(SurfaceConstructor, self).__init__()\n self.K = k\n self.R = r\n self.recons = _recons_factory(recons_type)\n self.cuda = cuda\n\n self.return_dist = return_dist\n self.random_inv = random_inv\n\n def forward(self, center, context):\n \"\"\"\n Input:\n center: input points position as centroid points, [B, 3, N]\n context: input points position as context points, [B, 3, N']\n\n Output:\n normal: normals of constructed triangles, [B, 3, N]\n center: centroids of constructed triangles, [B, 3, N]\n pos: position info of constructed triangles, [B, 1, N]\n \"\"\"\n center = center.permute(0, 2, 1)\n context = context.permute(0, 2, 1)\n\n group_xyz = self.recons(self.K, center, context, cuda=self.cuda)\n normal = cal_normal(group_xyz, random_inv=self.random_inv)\n center = cal_center(group_xyz)\n\n if self.return_dist:\n pos = cal_const(normal, center)\n normal, center, pos = check_nan(normal, center, pos)\n normal = normal.permute(0, 2, 1)\n center = center.permute(0, 2, 1)\n pos = pos.permute(0, 2, 1)\n return normal, center, pos\n\n normal, center = check_nan(normal, center)\n normal = normal.permute(0, 2, 1)\n center = center.permute(0, 2, 1)\n\n return normal, center\n\n\nif __name__ == '__main__':\n xyz = torch.rand(1, 3, 1024) * 2. - 1.\n constructor = SurfaceConstructor(return_dist=True)\n\n normal, center, pos = constructor(xyz, xyz)\n print(normal.shape)\n print(center.shape)\n" }, { "path": "classification/modules/repsurface_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom modules.pointnet2_utils import farthest_point_sample, index_points, query_knn_point, query_ball_point\nfrom modules.polar_utils import xyz2sphere\nfrom modules.recons_utils import cal_const, cal_normal, cal_center, check_nan_umb\n\n\ndef sample_and_group(npoint, radius, nsample, center, normal, feature, return_normal=True, return_polar=False, cuda=False):\n \"\"\"\n Input:\n center: input points position data\n normal: input points normal data\n feature: input points feature\n Return:\n new_center: sampled points position data\n new_normal: sampled points normal data\n new_feature: sampled points feature\n \"\"\"\n # sample\n fps_idx = farthest_point_sample(center, npoint, cuda=cuda) # [B, npoint, A]\n torch.cuda.empty_cache()\n # sample center\n new_center = index_points(center, fps_idx, cuda=cuda, is_group=False)\n torch.cuda.empty_cache()\n # sample normal\n new_normal = index_points(normal, fps_idx, cuda=cuda, is_group=False)\n torch.cuda.empty_cache()\n\n # group\n idx = query_ball_point(radius, nsample, center, new_center, cuda=cuda)\n torch.cuda.empty_cache()\n # group normal\n group_normal = index_points(normal, idx, cuda=cuda, is_group=True) # [B, npoint, nsample, B]\n torch.cuda.empty_cache()\n # group center\n group_center = index_points(center, idx, cuda=cuda, is_group=True) # [B, npoint, nsample, A]\n torch.cuda.empty_cache()\n group_center_norm = group_center - new_center.unsqueeze(2)\n torch.cuda.empty_cache()\n\n # group polar\n if return_polar:\n group_polar = xyz2sphere(group_center_norm)\n group_center_norm = torch.cat([group_center_norm, group_polar], dim=-1)\n if feature is not None:\n group_feature = index_points(feature, idx, cuda=cuda, is_group=True)\n new_feature = torch.cat([group_center_norm, group_normal, group_feature], dim=-1) if return_normal \\\n else torch.cat([group_center_norm, group_feature], dim=-1)\n else:\n new_feature = torch.cat([group_center_norm, group_normal], dim=-1)\n\n return new_center, new_normal, new_feature\n\n\ndef sample_and_group_all(center, normal, feature, return_normal=True, return_polar=False):\n \"\"\"\n Input:\n center: input centroid position data\n normal: input normal data\n feature: input feature data\n Return:\n new_center: sampled points position data\n new_normal: sampled points position data\n new_feature: sampled points data\n \"\"\"\n device = center.device\n B, N, C = normal.shape\n\n new_center = torch.zeros(B, 1, 3).to(device)\n new_normal = new_center\n\n group_normal = normal.view(B, 1, N, C)\n group_center = center.view(B, 1, N, 3)\n if return_polar:\n group_polar = xyz2sphere(group_center)\n group_center = torch.cat([group_center, group_polar], dim=-1)\n\n new_feature = torch.cat([group_center, group_normal, feature.view(B, 1, N, -1)], dim=-1) if return_normal \\\n else torch.cat([group_center, feature.view(B, 1, N, -1)], dim=-1)\n\n return new_center, new_normal, new_feature\n\n\ndef resort_points(points, idx):\n \"\"\"\n Resort Set of points along G dim\n\n \"\"\"\n device = points.device\n B, N, G, _ = points.shape\n\n view_shape = [B, 1, 1]\n repeat_shape = [1, N, G]\n b_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)\n\n view_shape = [1, N, 1]\n repeat_shape = [B, 1, G]\n n_indices = torch.arange(N, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)\n\n new_points = points[b_indices, n_indices, idx, :]\n\n return new_points\n\n\ndef group_by_umbrella(xyz, new_xyz, k=9, cuda=False):\n \"\"\"\n Group a set of points into umbrella surfaces\n\n \"\"\"\n idx = query_knn_point(k, xyz, new_xyz, cuda=cuda)\n torch.cuda.empty_cache()\n group_xyz = index_points(xyz, idx, cuda=cuda, is_group=True)[:, :, 1:] # [B, N', K-1, 3]\n torch.cuda.empty_cache()\n\n group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2)\n group_phi = xyz2sphere(group_xyz_norm)[..., 2] # [B, N', K-1]\n sort_idx = group_phi.argsort(dim=-1) # [B, N', K-1]\n\n # [B, N', K-1, 1, 3]\n sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2)\n sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3)\n group_centriod = torch.zeros_like(sorted_group_xyz)\n umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2)\n\n return umbrella_group_xyz\n\n\nclass SurfaceAbstraction(nn.Module):\n \"\"\"\n Surface Abstraction Module\n\n \"\"\"\n\n def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all, return_polar=True, return_normal=True, cuda=False):\n super(SurfaceAbstraction, self).__init__()\n self.npoint = npoint\n self.radius = radius\n self.nsample = nsample\n self.return_normal = return_normal\n self.return_polar = return_polar\n self.cuda = cuda\n self.group_all = group_all\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n\n last_channel = in_channel\n for out_channel in mlp:\n self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))\n self.mlp_bns.append(nn.BatchNorm2d(out_channel))\n last_channel = out_channel\n\n def forward(self, center, normal, feature):\n normal = normal.permute(0, 2, 1)\n center = center.permute(0, 2, 1)\n if feature is not None:\n feature = feature.permute(0, 2, 1)\n\n if self.group_all:\n new_center, new_normal, new_feature = sample_and_group_all(center, normal, feature,\n return_polar=self.return_polar,\n return_normal=self.return_normal)\n else:\n new_center, new_normal, new_feature = sample_and_group(self.npoint, self.radius, self.nsample, center,\n normal, feature, return_polar=self.return_polar,\n return_normal=self.return_normal, cuda=self.cuda)\n\n new_feature = new_feature.permute(0, 3, 2, 1)\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_feature = F.relu(bn(conv(new_feature)))\n new_feature = torch.max(new_feature, 2)[0]\n\n new_center = new_center.permute(0, 2, 1)\n new_normal = new_normal.permute(0, 2, 1)\n\n return new_center, new_normal, new_feature\n\n\nclass SurfaceAbstractionCD(nn.Module):\n \"\"\"\n Surface Abstraction Module\n\n \"\"\"\n\n def __init__(self, npoint, radius, nsample, feat_channel, pos_channel, mlp, group_all,\n return_normal=True, return_polar=False, cuda=False):\n super(SurfaceAbstractionCD, self).__init__()\n self.npoint = npoint\n self.radius = radius\n self.nsample = nsample\n self.return_normal = return_normal\n self.return_polar = return_polar\n self.cuda = cuda\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n self.pos_channel = pos_channel\n self.group_all = group_all\n\n self.mlp_l0 = nn.Conv2d(self.pos_channel, mlp[0], 1)\n self.mlp_f0 = nn.Conv2d(feat_channel, mlp[0], 1)\n self.bn_l0 = nn.BatchNorm2d(mlp[0])\n self.bn_f0 = nn.BatchNorm2d(mlp[0])\n\n # mlp_l0+mlp_f0 can be considered as the first layer of mlp_convs\n last_channel = mlp[0]\n for out_channel in mlp[1:]:\n self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))\n self.mlp_bns.append(nn.BatchNorm2d(out_channel))\n last_channel = out_channel\n\n def forward(self, center, normal, feature):\n normal = normal.permute(0, 2, 1)\n center = center.permute(0, 2, 1)\n if feature is not None:\n feature = feature.permute(0, 2, 1)\n\n if self.group_all:\n new_center, new_normal, new_feature = sample_and_group_all(center, normal, feature,\n return_normal=self.return_normal,\n return_polar=self.return_polar)\n else:\n new_center, new_normal, new_feature = sample_and_group(self.npoint, self.radius, self.nsample, center,\n normal, feature, return_normal=self.return_normal,\n return_polar=self.return_polar, cuda=self.cuda)\n\n new_feature = new_feature.permute(0, 3, 2, 1)\n\n # init layer\n loc = self.bn_l0(self.mlp_l0(new_feature[:, :self.pos_channel]))\n feat = self.bn_f0(self.mlp_f0(new_feature[:, self.pos_channel:]))\n new_feature = loc + feat\n new_feature = F.relu(new_feature)\n\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_feature = F.relu(bn(conv(new_feature)))\n new_feature = torch.max(new_feature, 2)[0]\n\n new_center = new_center.permute(0, 2, 1)\n new_normal = new_normal.permute(0, 2, 1)\n\n return new_center, new_normal, new_feature\n\n\nclass UmbrellaSurfaceConstructor(nn.Module):\n \"\"\"\n Umbrella-based Surface Abstraction Module\n\n \"\"\"\n\n def __init__(self, k, in_channel, aggr_type='sum', return_dist=False, random_inv=True, cuda=False):\n super(UmbrellaSurfaceConstructor, self).__init__()\n self.k = k\n self.return_dist = return_dist\n self.random_inv = random_inv\n self.aggr_type = aggr_type\n self.cuda = cuda\n\n self.mlps = nn.Sequential(\n nn.Conv2d(in_channel, in_channel, 1, bias=False),\n nn.BatchNorm2d(in_channel),\n nn.ReLU(True),\n nn.Conv2d(in_channel, in_channel, 1, bias=True),\n nn.BatchNorm2d(in_channel),\n nn.ReLU(True),\n nn.Conv2d(in_channel, in_channel, 1, bias=True),\n )\n\n def forward(self, center):\n center = center.permute(0, 2, 1)\n # surface construction\n group_xyz = group_by_umbrella(center, center, k=self.k, cuda=self.cuda) # [B, N, K-1, 3 (points), 3 (coord.)]\n\n # normal\n group_normal = cal_normal(group_xyz, random_inv=self.random_inv, is_group=True)\n # coordinate\n group_center = cal_center(group_xyz)\n # polar\n group_polar = xyz2sphere(group_center)\n if self.return_dist:\n group_pos = cal_const(group_normal, group_center)\n group_normal, group_center, group_pos = check_nan_umb(group_normal, group_center, group_pos)\n new_feature = torch.cat([group_center, group_polar, group_normal, group_pos], dim=-1) # N+P+CP: 10\n else:\n group_normal, group_center = check_nan_umb(group_normal, group_center)\n new_feature = torch.cat([group_center, group_polar, group_normal], dim=-1)\n new_feature = new_feature.permute(0, 3, 2, 1) # [B, C, G, N]\n\n # mapping\n new_feature = self.mlps(new_feature)\n\n # aggregation\n if self.aggr_type == 'max':\n new_feature = torch.max(new_feature, 2)[0]\n elif self.aggr_type == 'avg':\n new_feature = torch.mean(new_feature, 2)\n else:\n new_feature = torch.sum(new_feature, 2)\n\n return new_feature\n" }, { "path": "classification/scripts/scanobjectnn/repsurf_ssg_umb.sh", "content": "#!/usr/bin/env bash\nset -v\n\npython3 tool/train_cls_scanobjectnn.py \\\n --cuda_ops \\\n --batch_size 64 \\\n --model repsurf.repsurf_ssg_umb \\\n --epoch 250 \\\n --log_dir repsurf_cls_ssg_umb \\\n --gpus 0 \\\n --n_workers 12 \\\n --return_center \\\n --return_dist \\\n --return_polar \\\n --group_size 8 \\\n --umb_pool sum \\\n --num_point 1024" }, { "path": "classification/scripts/scanobjectnn/repsurf_ssg_umb_2x.sh", "content": "#!/usr/bin/env bash\nset -v\n\npython3 tool/train_cls_scanobjectnn.py \\\n --cuda_ops \\\n --batch_size 64 \\\n --model repsurf.repsurf_ssg_umb_2x \\\n --epoch 250 \\\n --log_dir repsurf_cls_ssg_umb_2x \\\n --gpus 0 \\\n --n_workers 12 \\\n --return_center \\\n --return_dist \\\n --return_polar \\\n --group_size 8 \\\n --umb_pool sum \\\n --num_point 1024" }, { "path": "classification/tool/train_cls_scanobjectnn.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 05/10/2022\n\"\"\"\n\nfrom functools import partial\n\nimport argparse\nimport numpy as np\nimport os\nimport torch\nimport datetime\nimport logging\nfrom pathlib import Path\n\nfrom dataset.ScanObjectNNDataLoader import ScanObjectNNDataLoader\nfrom modules.ptaug_utils import transform_point_cloud, scale_point_cloud, get_aug_args\nfrom modules.pointnet2_utils import sample\nfrom util.utils import get_model, get_loss, set_seed, weight_init\n\n\ndef parse_args():\n \"\"\"PARAMETERS\"\"\"\n parser = argparse.ArgumentParser('RepSurf')\n # Basic\n parser.add_argument('--log_dir', type=str, default=None, help='experiment root')\n parser.add_argument('--data_dir', type=str, default='./data', help='data dir')\n parser.add_argument('--log_root', type=str, default='./log', help='log root dir')\n parser.add_argument('--model', default='repsurf.scanobjectnn.repsurf_ssg_umb',\n help='model file name [default: repsurf_ssg_umb]')\n parser.add_argument('--gpus', nargs='+', type=str, default=None)\n parser.add_argument('--seed', type=int, default=2800, help='Training Seed')\n parser.add_argument('--cuda_ops', action='store_true', default=False,\n help='Whether to use cuda version operations [default: False]')\n\n # Training\n parser.add_argument('--batch_size', type=int, default=64, help='batch size in training [default: 64]')\n parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training [Adam, SGD]')\n parser.add_argument('--scheduler', type=str, default='step', help='scheduler for training')\n parser.add_argument('--epoch', default=500, type=int, help='number of epoch in training [default: 500]')\n parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training [default: 0.001]')\n parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate [default: 1e-4]')\n parser.add_argument('--decay_step', default=20, type=int, help='number of epoch per decay [default: 20]')\n parser.add_argument('--n_workers', type=int, default=4, help='DataLoader Workers Number [default: 4]')\n parser.add_argument('--init', type=str, default=None, help='initializer for model [kaiming, xavier]')\n\n # Evaluation\n parser.add_argument('--min_val', type=int, default=100, help='Min val epoch [default: 100]')\n\n # Augmentation\n parser.add_argument('--aug_scale', action='store_true', default=False,\n help='Whether to augment by scaling [default: False]')\n parser.add_argument('--aug_shift', action='store_true', default=False,\n help='Whether to augment by shifting [default: False]')\n\n # Modeling\n parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]')\n parser.add_argument('--return_dist', action='store_true', default=False,\n help='Whether to use signed distance [default: False]')\n parser.add_argument('--return_center', action='store_true', default=False,\n help='Whether to return center in surface abstraction [default: False]')\n parser.add_argument('--return_polar', action='store_true', default=False,\n help='Whether to return polar coordinate in surface abstraction [default: False]')\n parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]')\n parser.add_argument('--umb_pool', type=str, default='sum', help='pooling for umbrella repsurf [sum, mean, max]')\n\n return parser.parse_args()\n\n\ndef test(model, loader, num_class=15, num_point=1024, num_votes=10, total_num=1):\n vote_correct = 0\n sing_correct = 0\n classifier = model.eval()\n\n for j, data in enumerate(loader):\n points, target = data\n points, target = points.cuda(), target.cuda()\n\n # preprocess\n points = sample(num_point, points)\n\n # vote\n vote_pool = torch.zeros(target.shape[0], num_class).cuda()\n for i in range(num_votes):\n new_points = points.clone()\n # scale\n if i > 0:\n new_points[:, :3] = scale_point_cloud(new_points[:, :3])\n # predict\n pred = classifier(new_points)\n # single\n if i == 0:\n sing_pred = pred\n # vote\n vote_pool += pred\n vote_pred = vote_pool / num_votes\n\n # single pred\n sing_pred_choice = sing_pred.data.max(1)[1]\n sing_correct += sing_pred_choice.eq(target.long().data).cpu().sum()\n # vote pred\n vote_pred_choice = vote_pred.data.max(1)[1]\n vote_correct += vote_pred_choice.eq(target.long().data).cpu().sum()\n\n sing_acc = sing_correct.item() / total_num\n vote_acc = vote_correct.item() / total_num\n\n return sing_acc, vote_acc\n\n\ndef main(args):\n def log_string(s):\n logger.info(s)\n print(s)\n\n '''HYPER PARAMETER'''\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = ','.join(args.gpus)\n set_seed(args.seed)\n\n '''CREATE DIR'''\n experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log'))\n experiment_dir.mkdir(exist_ok=True)\n experiment_dir = experiment_dir.joinpath('ScanObjectNN')\n experiment_dir.mkdir(exist_ok=True)\n if args.log_dir is None:\n timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))\n experiment_dir = experiment_dir.joinpath(timestr)\n else:\n experiment_dir = experiment_dir.joinpath(args.log_dir)\n experiment_dir.mkdir(exist_ok=True)\n checkpoints_dir = experiment_dir.joinpath('checkpoints/')\n checkpoints_dir.mkdir(exist_ok=True)\n log_dir = experiment_dir.joinpath('logs/')\n log_dir.mkdir(exist_ok=True)\n\n '''LOG'''\n args = parse_args()\n logger = logging.getLogger(\"Model\")\n logger.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model))\n file_handler.setLevel(logging.INFO)\n file_handler.setFormatter(formatter)\n logger.addHandler(file_handler)\n log_string('PARAMETER ...')\n log_string(args)\n\n '''DATA LOADING'''\n log_string('Load dataset ...')\n args.num_class = 15\n args.dataset = 'ScanObjectNN'\n args.normal = False\n aug_args = get_aug_args(args)\n DATA_PATH = os.path.join(args.data_dir, 'ScanObjectNN')\n TRAIN_DATASET = ScanObjectNNDataLoader(root=DATA_PATH, split='training')\n TEST_DATASET = ScanObjectNNDataLoader(root=DATA_PATH, split='test')\n trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True,\n num_workers=args.n_workers, drop_last=True)\n testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False,\n num_workers=args.n_workers)\n\n '''MODEL BUILDING'''\n classifier = torch.nn.DataParallel(get_model(args)).cuda()\n criterion = get_loss().cuda()\n\n try:\n checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth')\n start_epoch = checkpoint['epoch']\n classifier.load_state_dict(checkpoint['model_state_dict'])\n log_string('Use pretrain model')\n except:\n log_string('No existing model, starting training from scratch...')\n start_epoch = 0\n if args.init:\n init_func = partial(weight_init, init_type=args.init)\n classifier = classifier.apply(init_func)\n\n '''OPTIMIZER'''\n if args.optimizer == 'Adam':\n optimizer = torch.optim.Adam(\n classifier.parameters(),\n lr=args.learning_rate,\n betas=(0.9, 0.999),\n eps=1e-08,\n weight_decay=args.decay_rate)\n elif args.optimizer == 'SGD':\n optimizer = torch.optim.SGD(\n classifier.parameters(),\n lr=args.learning_rate,\n momentum=0.9)\n\n '''LR SCHEDULER'''\n if args.scheduler == 'step':\n scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.decay_step, gamma=0.7)\n else:\n raise Exception('No Such Scheduler')\n\n global_epoch = 0\n global_step = 0\n best_sing_acc = 0.0\n best_vote_acc = 0.0\n loader_len = len(trainDataLoader)\n\n '''TRANING'''\n logger.info('Start training...')\n for epoch in range(start_epoch, args.epoch):\n log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))\n train_loss = []\n train_correct = 0\n\n scheduler.step()\n for batch_id, data in enumerate(trainDataLoader):\n '''INPUT'''\n points, target = data\n points, target = points.cuda(), target.cuda()\n\n '''PREPROCESS'''\n points = sample(args.num_point, points)\n points = transform_point_cloud(points, args, aug_args)\n\n '''FORWARD'''\n optimizer.zero_grad()\n lr = optimizer.state_dict()['param_groups'][0]['lr']\n classifier = classifier.train()\n pred = classifier(points)\n loss = criterion(pred, target.long())\n pred_choice = pred.data.max(1)[1]\n correct = pred_choice.eq(target.long().data).cpu().sum()\n train_correct += correct\n train_loss.append(loss.item())\n\n '''BACKWARD'''\n loss.backward()\n optimizer.step()\n global_step += 1\n\n if batch_id % 80 == 0:\n print('Epoch: [{0}][{1}/{2}] lr {lr:.6f} loss {loss:.4f}'.\n format(epoch, batch_id, len(trainDataLoader), lr=lr, loss=loss.item()))\n\n train_instance_acc = train_correct.item() / (loader_len * args.batch_size)\n train_mean_loss = np.mean(train_loss)\n log_string('Train Instance Accuracy: %.2f, Loss: %f' % (train_instance_acc * 100, train_mean_loss))\n\n if epoch >= args.min_val:\n with torch.no_grad():\n sing_acc, vote_acc = test(classifier.eval(), testDataLoader, num_point=args.num_point,\n total_num=len(TEST_DATASET))\n\n if sing_acc >= best_sing_acc:\n best_sing_acc = sing_acc\n if vote_acc >= best_vote_acc:\n best_vote_acc = vote_acc\n best_epoch = epoch + 1\n\n log_string('Test Single Accuracy: %.2f' % (sing_acc * 100))\n log_string('Best Single Accuracy: %.2f' % (best_sing_acc * 100))\n log_string('Test Vote Accuracy: %.2f' % (vote_acc * 100))\n log_string('Best Vote Accuracy: %.2f' % (best_vote_acc * 100))\n\n if vote_acc >= best_vote_acc:\n logger.info('Save model...')\n savepath = str(checkpoints_dir) + '/best_model.pth'\n log_string('Saving at %s' % savepath)\n state = {\n 'epoch': best_epoch,\n 'vote_acc': vote_acc,\n 'model_state_dict': classifier.state_dict(),\n 'optimizer_state_dict': optimizer.state_dict(),\n }\n torch.save(state, savepath)\n global_epoch += 1\n\n logger.info('End of training...')\n\n\nif __name__ == '__main__':\n args = parse_args()\n main(args)\n" }, { "path": "classification/util/__init__.py", "content": "" }, { "path": "classification/util/utils.py", "content": "import importlib\nimport argparse\nimport random\n\nimport numpy as np\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\ndef set_seed(seed):\n \"\"\"\n Setting of Global Seed\n\n \"\"\"\n torch.backends.cudnn.enabled = True\n torch.backends.cudnn.deterministic = True # consistent results on the cpu and gpu\n torch.backends.cudnn.benchmark = True\n\n np.random.seed(seed)\n random.seed(seed)\n torch.manual_seed(seed) # cpu\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed) # gpu\n\n\ndef weight_init(m, init_type):\n if init_type == 'xavier':\n init_func = torch.nn.init.xavier_normal_\n elif init_type == 'kaiming':\n init_func = torch.nn.init.kaiming_normal_\n else:\n raise Exception('No such init type')\n\n if isinstance(m, (torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv1d)):\n init_func(m.weight)\n if m.bias is not None:\n torch.nn.init.constant_(m.bias, 0)\n elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm1d)):\n torch.nn.init.constant_(m.weight, 1) # constant\n # torch.nn.init.normal_(m.weight, 1.0, 0.02) # normal\n torch.nn.init.constant_(m.bias, 0)\n\n\nclass ClsLoss(nn.Module):\n def __init__(self):\n super(ClsLoss, self).__init__()\n\n def forward(self, pred, target):\n total_loss = F.nll_loss(pred, target)\n\n return total_loss\n\n\nclass SmoothClsLoss(nn.Module):\n def __init__(self, smoothing_ratio=0.1):\n super(SmoothClsLoss, self).__init__()\n self.smoothing_ratio = smoothing_ratio\n\n def forward(self, pred, target):\n eps = self.smoothing_ratio\n n_class = pred.size(1)\n\n one_hot = torch.zeros_like(pred).scatter(1, target.view(-1, 1), 1)\n one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)\n # log_prb = F.log_softmax(pred, dim=1)\n\n loss = -(one_hot * pred).sum(dim=1).mean()\n return loss\n\n\ndef get_model(args):\n module = importlib.import_module('models.%s' % args.model)\n return module.Model(args)\n\n\ndef get_loss():\n return SmoothClsLoss()\n\n\ndef get_test_args():\n return argparse.Namespace()\n" }, { "path": "segmentation/README.md", "content": "# RepSurf for Segmentation
\n\nBy *[Haoxi Ran\\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact)\n\n### [PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.pdf) | [arXiv](http://arxiv.org/abs/2205.05740)\n\n\n## Preparation\n\n### Environment\n\nWe tested under the environment:\n\n* python 3.7\n* pytorch 1.6.0 / 1.8.0\n* cuda 10.1 / 11.1\n* gcc 7.2.0\n* h5py\n* sharedarray\n* tensorboardx\n\nFor anaconda user, initialize the conda environment **repsurf-seg** by:\n\n```\nsh init.sh\n```\n\n## Experiments\n\n### S3DIS Area-5 (Data & Logs: [Google Drive](https://drive.google.com/drive/folders/1jIZuy4RPFJ4YHAE8ScVQgwtBwNGgfKnv?usp=sharing))\n\n* Performance using the same settings:\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ModelmIoUmAccOA#ParamsTraining TimeCodeTraining LogTest LogCheckpoint
Point Transformer
(our settings)		70.37 (official: 70.4)77.02 (official: 76.5)90.80 (official: 90.8)7.767M19.91hpointtransformer.pygoogle drivegoogle drivegoogle drive
(30 MB)
PointNet++ SSG (our settings)64.0571.5287.920.968M9.08hpointnet2_ssg.pygoogle drivegoogle drivegoogle drive
(4 MB)
PointNet++ SSG w/ Umbrella RepSurf (ours)68.8676.5490.220.976M9.18hrepsurf_umb_ssg.pygoogle drivegoogle drivegoogle drive
(4 MB)
\n
\n\n**Note**: \n1. The performance (mIoU/mAcc/OA) are from the final predictions on the whole scenes of S3DIS Area-5, while the results during training is on sub-sampled scenes for fast validation. \n2. The training time of all above implementations is estimated on four NVIDIA RTX 3090. The time in the logs contains both training and validating time.\n3. To speed up the training process, we apply Sectorized FPS (in the first stage) for all above methods. It can save 30～40% training time and does not affect the performance. \n4. To lessen the instability from grid sampling during inference, we apply median filtering to all the above implementations. Besides, it can slightly improve the results (~0.4 mIoU).\n\n* To (firstly install gdown by **pip install gdown** and) download dataset:\n\n```\ncd ./data/S3DIS\ngdown https://drive.google.com/u/1/uc?id=1UDM-bjrtqoIR9FWoIRyqLUJGyKEs22fP\ntar zxf s3dis.tar.gz && rm s3dis.tar.gz && cd -\n```\n\n* To train one model (**Umbrella RepSurf, Point Transformer, PointNet2**) for S3DIS Area-5:\n\n```\nsh scripts/s3dis/train_[MODEL].sh # MODEL: repsurf_umb, pointnet2, pointtransformer\n```\n\n* To test one model (**Our Umbrella RepSurf, Point Transformer, PointNet2**) for S3DIS Area-5 on whole scenes:\n\n```\nsh scripts/s3dis/test_[MODEL].sh # MODEL: repsurf_umb, pointnet2, pointtransformer\n```\n\n## Acknowledgment\n\nWe thank the [Point Transformer Implementation](https://github.com/POSTECH-CVLab/point-transformer) for the library pointops.\n\n## License\n\nRepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for\ncommercial use.\n" }, { "path": "segmentation/dataset/S3DISDataLoader.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport os\nimport numpy as np\nimport SharedArray as SA\nfrom torch.utils.data import Dataset\n\nfrom util.data_util import sa_create, data_prepare\n\nNUM_CLASS = 13\n\n\nclass S3DIS(Dataset):\n def __init__(self, args, split, coord_transform=None, rgb_transform=None,\n rgb_mean=None, rgb_std=None, shuffle_index=False):\n super().__init__()\n self.args, self.split, self.coord_transform, self.rgb_transform, self.rgb_mean, self.rgb_std, self.shuffle_index = \\\n args, split, coord_transform, rgb_transform, rgb_mean, rgb_std, shuffle_index\n self.stop_aug = False\n data_list = sorted(os.listdir(args.data_dir))\n data_list = [item[:-4] for item in data_list if 'Area_' in item]\n if split == 'train':\n self.data_list = [item for item in data_list if not 'Area_{}'.format(args.test_area) in item]\n else:\n self.data_list = [item for item in data_list if 'Area_{}'.format(args.test_area) in item]\n self.data_idx = np.arange(len(self.data_list))\n\n for item in self.data_list:\n if not os.path.exists(\"/dev/shm/s3dis_{}\".format(item)):\n data_path = os.path.join(args.data_dir, item + '.npy')\n data = np.load(data_path).astype(np.float32) # xyzrgbl, N*7\n sa_create(\"shm://s3dis_{}\".format(item), data)\n\n def __getitem__(self, idx):\n data_idx = self.data_idx[idx % len(self.data_idx)]\n data = SA.attach(\"shm://s3dis_{}\".format(self.data_list[data_idx])).copy()\n coord, feat, label = data[:, 0:3], data[:, 3:6], data[:, 6]\n coord, feat, label = \\\n data_prepare(coord, feat, label, self.args, self.split, self.coord_transform, self.rgb_transform,\n self.rgb_mean, self.rgb_std, self.shuffle_index, self.stop_aug)\n\n return coord, feat, label\n\n def __len__(self):\n return len(self.data_idx) * self.args.loop\n\n @staticmethod\n def print_weight(data_root, data_list):\n print('Computing label weight...')\n num_point_list = []\n label_freq = np.zeros(NUM_CLASS)\n label_total = np.zeros(NUM_CLASS)\n # load data\n for idx, item in enumerate(data_list):\n data_path = os.path.join(data_root, item + '.npy')\n data = np.load(data_path)\n labels = data[:, 6]\n freq = np.histogram(labels, range(NUM_CLASS + 1))[0]\n label_freq += freq\n label_total += (freq > 0).astype(np.float) * labels.size\n num_point_list.append(labels.size)\n\n # label weight\n label_freq = label_freq / label_total\n label_weight = np.median(label_freq) / label_freq\n print(label_weight)\n\n @staticmethod\n def print_mean_std(data_root, data_list):\n print('Computing color mean & std...')\n point_list = []\n for idx, item in enumerate(data_list):\n data_path = os.path.join(data_root, item + '.npy')\n data = np.load(data_path)\n point_list.append(data[:, 3:6])\n\n points = np.vstack(point_list) / 255.\n mean = np.mean(points, 0)\n std = np.std(points, 0)\n print(f'mean: {mean}, std:{std}')\n" }, { "path": "segmentation/dataset/__init__.py", "content": "" }, { "path": "segmentation/init.sh", "content": "#!/bin/sh\n\nmkdir -p log/PointAnalysis/log/S3DIS\nmkdir -p log/PointAnalysis/log/ScanNet\nmkdir -p data/S3DIS\nmkdir -p data/ScanNet\n\nconda create -n repsurf-seg python=3.7 -y\nconda activate repsurf-seg\n\n#conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch -c conda-forge -y\npip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\nconda install -c anaconda h5py pyyaml -y\nconda install -c conda-forge sharedarray tensorboardx -y\n\ncd modules/pointops\npython3 setup.py install\ncd -\n" }, { "path": "segmentation/models/__init__.py", "content": "" }, { "path": "segmentation/models/pointnet2/__init__.py", "content": "" }, { "path": "segmentation/models/pointnet2/pointnet2_ssg.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom modules.pointnet2_utils import PointNetSetAbstraction, PointNetFeaturePropagation\n\n\nclass Model(nn.Module):\n def __init__(self, args):\n super(Model, self).__init__()\n self.sa1 = PointNetSetAbstraction(4, 32, 6 + 3, [32, 32, 64], num_sector=4)\n self.sa2 = PointNetSetAbstraction(4, 32, 64 + 3, [64, 64, 128])\n self.sa3 = PointNetSetAbstraction(4, 32, 128 + 3, [128, 128, 256])\n self.sa4 = PointNetSetAbstraction(4, 32, 256 + 3, [256, 256, 512])\n\n self.fp4 = PointNetFeaturePropagation(768, [256, 256])\n self.fp3 = PointNetFeaturePropagation(384, [256, 256])\n self.fp2 = PointNetFeaturePropagation(320, [256, 128])\n self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])\n\n self.classifier = nn.Sequential(\n nn.Linear(128, 128),\n nn.BatchNorm1d(128),\n nn.ReLU(True),\n nn.Dropout(0.5),\n nn.Linear(128, args.num_class),\n )\n\n def forward(self, pos_feat_off0):\n pos_feat_off0[1] = torch.cat([pos_feat_off0[0], pos_feat_off0[1]], 1)\n\n pos_feat_off1 = self.sa1(pos_feat_off0)\n pos_feat_off2 = self.sa2(pos_feat_off1)\n pos_feat_off3 = self.sa3(pos_feat_off2)\n pos_feat_off4 = self.sa4(pos_feat_off3)\n\n pos_feat_off3[1] = self.fp4(pos_feat_off3, pos_feat_off4)\n pos_feat_off2[1] = self.fp3(pos_feat_off2, pos_feat_off3)\n pos_feat_off1[1] = self.fp2(pos_feat_off1, pos_feat_off2)\n pos_feat_off0[1] = self.fp1([pos_feat_off0[0], None, pos_feat_off0[2]], pos_feat_off1)\n\n feature = self.classifier(pos_feat_off0[1])\n\n return feature\n" }, { "path": "segmentation/models/pointtransformer/__init__.py", "content": "" }, { "path": "segmentation/models/pointtransformer/pointtransformer.py", "content": "import torch\nimport torch.nn as nn\nfrom modules.pointtransformer_utils import PointTransformerBlock, TransitionDown, TransitionUp\n\n\nclass Model(nn.Module):\n def __init__(self, args):\n super().__init__()\n block = PointTransformerBlock\n num_block = [2, 3, 4, 6, 3]\n self.in_c = args.in_channel\n self.in_planes, planes = self.in_c, [32, 64, 128, 256, 512]\n fpn_planes, fpnhead_planes, share_planes = 128, 64, 8\n stride, nsample = [1, 4, 4, 4, 4], [16, 16, 16, 16, 16]\n self.enc1 = self._make_enc(block, planes[0], num_block[0], share_planes, stride=stride[0],\n nsample=nsample[0]) # N/1\n self.enc2 = self._make_enc(block, planes[1], num_block[1], share_planes, stride=stride[1],\n nsample=nsample[1], num_sector=4) # N/4\n self.enc3 = self._make_enc(block, planes[2], num_block[2], share_planes, stride=stride[2],\n nsample=nsample[2]) # N/16\n self.enc4 = self._make_enc(block, planes[3], num_block[3], share_planes, stride=stride[3],\n nsample=nsample[3]) # N/64\n self.enc5 = self._make_enc(block, planes[4], num_block[4], share_planes, stride=stride[4],\n nsample=nsample[4]) # N/256\n self.dec5 = self._make_dec(block, planes[4], 2, share_planes, nsample=nsample[4], is_head=True) # transform p5\n self.dec4 = self._make_dec(block, planes[3], 2, share_planes, nsample=nsample[3]) # fusion p5 and p4\n self.dec3 = self._make_dec(block, planes[2], 2, share_planes, nsample=nsample[2]) # fusion p4 and p3\n self.dec2 = self._make_dec(block, planes[1], 2, share_planes, nsample=nsample[1]) # fusion p3 and p2\n self.dec1 = self._make_dec(block, planes[0], 2, share_planes, nsample=nsample[0]) # fusion p2 and p1\n self.cls = nn.Sequential(nn.Linear(planes[0], planes[0]), nn.BatchNorm1d(planes[0]), nn.ReLU(inplace=True),\n nn.Linear(planes[0], args.num_class))\n\n def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16, num_sector=1):\n layers = [TransitionDown(self.in_planes, planes * block.expansion, stride, nsample, num_sector)]\n self.in_planes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self.in_planes, self.in_planes, share_planes, nsample=nsample))\n return nn.Sequential(*layers)\n\n def _make_dec(self, block, planes, blocks, share_planes=8, nsample=16, is_head=False):\n layers = [TransitionUp(self.in_planes, None if is_head else planes * block.expansion)]\n self.in_planes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self.in_planes, self.in_planes, share_planes, nsample=nsample))\n return nn.Sequential(*layers)\n\n def forward(self, pxo, *args):\n p0, x0, o0 = pxo # (n, 3), (n, c), (b)\n x0 = p0 if self.in_c == 3 else torch.cat((p0, x0), 1)\n p1, x1, o1 = self.enc1([p0, x0, o0])\n p2, x2, o2 = self.enc2([p1, x1, o1])\n p3, x3, o3 = self.enc3([p2, x2, o2])\n p4, x4, o4 = self.enc4([p3, x3, o3])\n p5, x5, o5 = self.enc5([p4, x4, o4])\n x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1]\n x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1]\n x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1]\n x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1]\n x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1]\n x = self.cls(x1)\n return x\n" }, { "path": "segmentation/models/repsurf/__init__.py", "content": "" }, { "path": "segmentation/models/repsurf/repsurf_umb_ssg.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom modules.repsurface_utils import UmbrellaSurfaceConstructor, SurfaceAbstractionCD, SurfaceFeaturePropagationCD\n\n\nclass Model(nn.Module):\n def __init__(self, args):\n super(Model, self).__init__()\n center_channel = 6 if args.return_polar else 3\n repsurf_in_channel = 10\n repsurf_out_channel = 10\n\n self.sa1 = SurfaceAbstractionCD(4, 32, args.in_channel + repsurf_out_channel, center_channel, [32, 32, 64],\n True, args.return_polar, num_sector=4)\n self.sa2 = SurfaceAbstractionCD(4, 32, 64 + repsurf_out_channel, center_channel, [64, 64, 128],\n True, args.return_polar)\n self.sa3 = SurfaceAbstractionCD(4, 32, 128 + repsurf_out_channel, center_channel, [128, 128, 256],\n True, args.return_polar)\n self.sa4 = SurfaceAbstractionCD(4, 32, 256 + repsurf_out_channel, center_channel, [256, 256, 512],\n True, args.return_polar)\n\n self.fp4 = SurfaceFeaturePropagationCD(512, 256, [256, 256])\n self.fp3 = SurfaceFeaturePropagationCD(256, 128, [256, 256])\n self.fp2 = SurfaceFeaturePropagationCD(256, 64, [256, 128])\n self.fp1 = SurfaceFeaturePropagationCD(128, None, [128, 128, 128])\n\n self.classifier = nn.Sequential(\n nn.Linear(128, 128),\n nn.BatchNorm1d(128),\n nn.ReLU(True),\n nn.Dropout(0.5),\n nn.Linear(128, args.num_class),\n )\n\n self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_in_channel, repsurf_out_channel)\n\n def forward(self, pos_feat_off0):\n pos_nor_feat_off0 = [\n pos_feat_off0[0],\n self.surface_constructor(pos_feat_off0[0], pos_feat_off0[2]),\n torch.cat([pos_feat_off0[0], pos_feat_off0[1]], 1),\n pos_feat_off0[2]\n ]\n\n pos_nor_feat_off1 = self.sa1(pos_nor_feat_off0)\n pos_nor_feat_off2 = self.sa2(pos_nor_feat_off1)\n pos_nor_feat_off3 = self.sa3(pos_nor_feat_off2)\n pos_nor_feat_off4 = self.sa4(pos_nor_feat_off3)\n\n del pos_nor_feat_off0[1], pos_nor_feat_off1[1], pos_nor_feat_off2[1], pos_nor_feat_off3[1], pos_nor_feat_off4[1]\n pos_nor_feat_off3[1] = self.fp4(pos_nor_feat_off3, pos_nor_feat_off4)\n pos_nor_feat_off2[1] = self.fp3(pos_nor_feat_off2, pos_nor_feat_off3)\n pos_nor_feat_off1[1] = self.fp2(pos_nor_feat_off1, pos_nor_feat_off2)\n pos_nor_feat_off0[1] = self.fp1([pos_nor_feat_off0[0], None, pos_nor_feat_off0[2]], pos_nor_feat_off1)\n\n feature = self.classifier(pos_nor_feat_off0[1])\n\n return feature\n" }, { "path": "segmentation/modules/__init__.py", "content": "" }, { "path": "segmentation/modules/aug_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport numpy as np\n\n\ndef transform_point_cloud_coord(args):\n transform_list = []\n aug_args = args.aug_args\n if args.aug_scale:\n transform_list.append(\n RandomScale(aug_args['scale_factor'], aug_args['scale_ani'], aug_args['scale_prob']))\n if args.aug_rotate:\n if args.aug_rotate == 'pert':\n transform_list.append(\n RandomRotatePerturb(aug_args['pert_factor'], 3 * aug_args['pert_factor'], aug_args['pert_prob']))\n elif args.aug_rotate == 'pert_z':\n transform_list.append(\n RandomRotatePerturbAligned(aug_args['pert_factor'], 3 * aug_args['pert_factor'], aug_args['pert_prob']))\n elif args.aug_rotate == 'rot':\n transform_list.append(\n RandomRotate(prob=aug_args['rot_prob']))\n elif args.aug_rotate == 'rot_z':\n transform_list.append(\n RandomRotateAligned(prob=aug_args['rot_prob']))\n if args.aug_jitter:\n transform_list.append(\n RandomJitter(aug_args['jitter_factor'], 5 * aug_args['jitter_factor'], aug_args['jitter_prob'], args.lidar))\n if args.aug_flip:\n transform_list.append(RandomFlip())\n if args.aug_shift:\n transform_list.append(RandomShift(aug_args['shifts'], aug_args['shift_prob']))\n return Compose(transform_list) if len(transform_list) > 0 else None\n\n\ndef transform_point_cloud_rgb(args):\n transform_list = []\n aug_args = args.aug_args\n if args.color_contrast:\n transform_list.append(ChromaticAutoContrast())\n if args.color_shift:\n transform_list.append(ChromaticTranslation())\n if args.color_jitter:\n transform_list.append(ChromaticJitter())\n if args.hs_shift:\n transform_list.append(HueSaturationTranslation())\n if args.color_drop:\n transform_list.append(RandomDropColor())\n return Compose(transform_list) if len(transform_list) > 0 else None\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, coord, feat, label, mask=None):\n for t in self.transforms:\n coord, feat, label = t(coord, feat, label, mask)\n return coord, feat, label\n\n\nclass RandomRotate(object):\n def __init__(self, rot=(np.pi/24, np.pi/24, np.pi/4), prob=1.):\n self.rot = rot\n self.prob = prob\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.prob:\n angle_x = np.random.uniform(-self.rot[0], self.rot[0])\n angle_y = np.random.uniform(-self.rot[1], self.rot[1])\n angle_z = np.random.uniform(-self.rot[2], self.rot[2])\n cos_x, sin_x = np.cos(angle_x), np.sin(angle_x)\n cos_y, sin_y = np.cos(angle_y), np.sin(angle_y)\n cos_z, sin_z = np.cos(angle_z), np.sin(angle_z)\n R_x = np.array([[1, 0, 0], [0, cos_x, -sin_x], [0, sin_x, cos_x]])\n R_y = np.array([[cos_y, 0, sin_y], [0, 1, 0], [-sin_y, 0, cos_y]])\n R_z = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]])\n R = np.dot(R_z, np.dot(R_y, R_x))\n coord = np.dot(coord, np.transpose(R))\n return coord, feat, label\n\n\nclass RandomRotateAligned(object):\n def __init__(self, rot=np.pi, prob=1.):\n self.rot = rot\n self.prob = prob\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.prob:\n angle_z = np.random.uniform(-self.rot, self.rot)\n cos_z, sin_z = np.cos(angle_z), np.sin(angle_z)\n R = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]])\n coord = np.dot(coord, R)\n return coord, feat, label\n\n\nclass RandomRotatePerturb(object):\n def __init__(self, sigma=0.03, clip=0.09, prob=1.):\n self.sigma = sigma\n self.clip = clip\n self.prob = prob\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.prob:\n angle_x = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip)\n angle_y = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip)\n angle_z = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip)\n cos_x, sin_x = np.cos(angle_x), np.sin(angle_x)\n cos_y, sin_y = np.cos(angle_y), np.sin(angle_y)\n cos_z, sin_z = np.cos(angle_z), np.sin(angle_z)\n R_x = np.array([[1, 0, 0], [0, cos_x, -sin_x], [0, sin_x, cos_x]])\n R_y = np.array([[cos_y, 0, sin_y], [0, 1, 0], [-sin_y, 0, cos_y]])\n R_z = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]])\n R = np.dot(R_z, np.dot(R_y, R_x))\n coord = np.dot(coord, np.transpose(R))\n return coord, feat, label\n\n\nclass RandomRotatePerturbAligned(object):\n def __init__(self, sigma=0.03, clip=0.09, prob=1.):\n self.sigma = sigma\n self.clip = clip\n self.prob = prob\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.prob:\n angle_z = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip)\n cos_z, sin_z = np.cos(angle_z), np.sin(angle_z)\n R = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]])\n coord = np.dot(coord, R)\n return coord, feat, label\n\n\nclass RandomScale(object):\n def __init__(self, scale=0.1, anisotropic=False, prob=1.):\n self.scale = scale\n self.anisotropic = anisotropic\n self.prob = prob\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.prob:\n scale = np.random.uniform(1 - self.scale, 1 + self.scale, 3 if self.anisotropic else 1)\n coord *= scale\n return coord, feat, label\n\n\nclass RandomShift(object):\n def __init__(self, shift=(0.2, 0.2, 0), p=0.95):\n self.shift = shift\n self.p = p\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n shift_x = np.random.uniform(-self.shift[0], self.shift[0])\n shift_y = np.random.uniform(-self.shift[1], self.shift[1])\n shift_z = np.random.uniform(-self.shift[2], self.shift[2])\n coord += [shift_x, shift_y, shift_z]\n return coord, feat, label\n\n\nclass RandomFlip(object):\n def __init__(self, p=1.):\n self.p = p\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n if np.random.rand() < 0.5:\n coord[:, 0] = -coord[:, 0]\n if np.random.rand() < 0.5:\n coord[:, 1] = -coord[:, 1]\n return coord, feat, label\n\n\nclass RandomJitter(object):\n def __init__(self, sigma=0.01, clip=0.05, p=1., is_lidar=False):\n self.sigma = sigma\n self.clip = clip\n self.p = p\n self.is_lidar = is_lidar\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n assert (self.clip > 0)\n jitter = np.clip(self.sigma * np.random.randn(coord.shape[0], 3), -1 * self.clip, self.clip)\n if self.is_lidar:\n jitter[:, 2] *= 0.1 # re-scale z-axis jitter\n coord += jitter\n return coord, feat, label\n\n\nclass ChromaticAutoContrast(object):\n def __init__(self, p=0.2, blend_factor=None):\n self.p = p\n self.blend_factor = blend_factor\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n tmp_feat = feat[mask] if mask is not None else feat\n lo = np.min(tmp_feat, 0, keepdims=True)\n hi = np.max(tmp_feat, 0, keepdims=True)\n scale = 255 / (hi - lo)\n contrast_feat = (tmp_feat[:, :3] - lo) * scale\n blend_factor = np.random.rand() if self.blend_factor is None else self.blend_factor\n tmp_feat[:, :3] = (1 - blend_factor) * tmp_feat[:, :3] + blend_factor * contrast_feat\n if mask is not None:\n feat[mask] = tmp_feat\n else:\n feat = tmp_feat\n return coord, feat, label\n\n\nclass ChromaticTranslation(object):\n def __init__(self, p=0.95, ratio=0.05):\n self.p = p\n self.ratio = ratio\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n tr = (np.random.rand(1, feat.shape[1]) - 0.5) * 255 * 2 * self.ratio\n feat[:, :3] = np.clip(tr + feat[:, :3], 0, 255)\n if mask is not None:\n feat[:, :3][~mask] = 0.\n return coord, feat, label\n\n\nclass ChromaticJitter(object):\n def __init__(self, p=0.95, std=0.005):\n self.p = p\n self.std = std\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n noise = np.random.randn(*feat.shape)\n noise *= self.std * 255\n feat[:, :3] = np.clip(noise + feat[:, :3], 0, 255)\n if mask is not None:\n feat[:, :3][~mask] = 0.\n return coord, feat, label\n\n\nclass HueSaturationTranslation(object):\n @staticmethod\n def rgb_to_hsv(rgb):\n # Translated from source of colorsys.rgb_to_hsv\n # r,g,b should be a numpy arrays with values between 0 and 255\n # rgb_to_hsv returns an array of floats between 0.0 and 1.0.\n rgb = rgb.astype('float')\n hsv = np.zeros_like(rgb)\n # in case an RGBA array was passed, just copy the A channel\n hsv[..., 3:] = rgb[..., 3:]\n r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]\n maxc = np.max(rgb[..., :3], axis=-1)\n minc = np.min(rgb[..., :3], axis=-1)\n hsv[..., 2] = maxc\n mask = maxc != minc\n hsv[mask, 1] = (maxc - minc)[mask] / maxc[mask]\n rc = np.zeros_like(r)\n gc = np.zeros_like(g)\n bc = np.zeros_like(b)\n rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask]\n gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask]\n bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask]\n hsv[..., 0] = np.select([r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc)\n hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0\n return hsv\n\n @staticmethod\n def hsv_to_rgb(hsv):\n # Translated from source of colorsys.hsv_to_rgb\n # h,s should be a numpy arrays with values between 0.0 and 1.0\n # v should be a numpy array with values between 0.0 and 255.0\n # hsv_to_rgb returns an array of uints between 0 and 255.\n rgb = np.empty_like(hsv)\n rgb[..., 3:] = hsv[..., 3:]\n h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]\n i = (h * 6.0).astype('uint8')\n f = (h * 6.0) - i\n p = v * (1.0 - s)\n q = v * (1.0 - s * f)\n t = v * (1.0 - s * (1.0 - f))\n i = i % 6\n conditions = [s == 0.0, i == 1, i == 2, i == 3, i == 4, i == 5]\n rgb[..., 0] = np.select(conditions, [v, q, p, p, t, v], default=v)\n rgb[..., 1] = np.select(conditions, [v, v, v, q, p, p], default=t)\n rgb[..., 2] = np.select(conditions, [v, p, t, v, v, q], default=p)\n return rgb.astype('uint8')\n\n def __init__(self, hue_max=0.5, saturation_max=0.2, p=1.):\n self.hue_max = hue_max\n self.saturation_max = saturation_max\n self.p = p\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n # Assume feat[:, :3] is rgb\n tmp_feat = feat[mask] if mask is not None else feat\n hsv = HueSaturationTranslation.rgb_to_hsv(tmp_feat[:, :3])\n hue_val = (np.random.rand() - 0.5) * 2 * self.hue_max\n sat_ratio = 1 + (np.random.rand() - 0.5) * 2 * self.saturation_max\n hsv[..., 0] = np.remainder(hue_val + hsv[..., 0] + 1, 1)\n hsv[..., 1] = np.clip(sat_ratio * hsv[..., 1], 0, 1)\n tmp_feat[:, :3] = np.clip(HueSaturationTranslation.hsv_to_rgb(hsv), 0, 255)\n if mask is not None:\n feat[mask] = tmp_feat\n else:\n feat = tmp_feat\n return coord, feat, label\n\n\nclass RandomDropColor(object):\n def __init__(self, p=0.2):\n self.p = p\n\n def __call__(self, coord, feat, label, mask=None):\n if np.random.rand() < self.p:\n feat[:, :3] = 0\n return coord, feat, label\n" }, { "path": "segmentation/modules/pointnet2_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom modules.pointops.functions import pointops\n\n\ndef sample_and_group(stride, nsample, xyz, points, offset, return_idx=False, num_sector=1):\n # sample\n if stride > 1:\n new_offset, sample_idx = [offset[0].item() // stride], offset[0].item() // stride\n for i in range(1, offset.shape[0]):\n sample_idx += (offset[i].item() - offset[i - 1].item()) // stride\n new_offset.append(sample_idx)\n new_offset = torch.cuda.IntTensor(new_offset)\n if num_sector > 1:\n fps_idx = pointops.sectorized_fps(xyz, offset, new_offset, num_sector) # [M]\n else:\n fps_idx = pointops.furthestsampling(xyz, offset, new_offset) # [M]\n new_xyz = xyz[fps_idx.long(), :] # [M, 3]\n else:\n new_xyz = xyz\n new_offset = offset\n\n # group\n N, M = xyz.shape[0], new_xyz.shape[0]\n group_idx, _ = pointops.knnquery(nsample, xyz, new_xyz, offset, new_offset) # [M, nsample]\n group_xyz = xyz[group_idx.view(-1).long(), :].view(M, nsample, 3) # [M, nsample, 3]\n group_xyz_norm = group_xyz - new_xyz.unsqueeze(1)\n\n if points is not None and not return_idx:\n C = points.shape[1]\n group_points = points[group_idx.view(-1).long(), :].view(M, nsample, C)\n new_points = torch.cat([group_xyz_norm, group_points], dim=-1) # [M, nsample, 3/6+C]\n else:\n new_points = group_xyz_norm\n\n if return_idx:\n return new_xyz, new_points, new_offset, group_idx\n else:\n return new_xyz, new_points, new_offset\n\n\nclass PointNetSetAbstraction(nn.Module):\n \"\"\"\n PointNet2 SA Module\n\n \"\"\"\n\n def __init__(self, stride, nsample, in_channel, mlp, num_sector=1):\n super(PointNetSetAbstraction, self).__init__()\n self.stride = stride\n self.nsample = nsample\n self.num_sector = num_sector\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n\n last_channel = in_channel\n for out_channel in mlp:\n self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))\n self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n last_channel = out_channel\n\n def forward(self, pos_feat_off):\n xyz, points, offset = pos_feat_off # [N, 3], [N, C], [B]\n\n new_xyz, new_points, new_offset = sample_and_group(self.stride, self.nsample, xyz, points, offset,\n num_sector=self.num_sector)\n\n # new_xyz: sampled points position data, [M, 3]\n # new_points: sampled points data, [M, nsample, 3+C]\n new_points = new_points.transpose(1, 2).contiguous() # [M, 3+C, nsample]\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_points = F.relu(bn(conv(new_points)))\n new_points = torch.max(new_points, 2)[0]\n\n return [new_xyz, new_points, new_offset]\n\n\nclass PointNetFeaturePropagation(nn.Module):\n \"\"\"\n PointNet2 FP Module\n\n \"\"\"\n\n def __init__(self, in_channel, mlp):\n super(PointNetFeaturePropagation, self).__init__()\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n last_channel = in_channel\n for out_channel in mlp:\n self.mlp_convs.append(nn.Linear(last_channel, out_channel))\n self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n last_channel = out_channel\n\n def forward(self, pos_feat_off1, pos_feat_off2):\n xyz1, points1, offset1 = pos_feat_off1 # [N, 3], [N, C], [B]\n xyz2, points2, offset2 = pos_feat_off2 # [M, 3], [M, C], [B]\n\n idx, dist = pointops.knnquery(3, xyz2, xyz1, offset2, offset1) # [M, 3], [M, 3]\n dist_recip = 1.0 / (dist + 1e-8) # [M, 3]\n norm = torch.sum(dist_recip, dim=1, keepdim=True)\n weight = dist_recip / norm # [M, 3]\n\n interpolated_points = torch.cuda.FloatTensor(xyz1.shape[0], points2.shape[1]).zero_()\n for i in range(3):\n interpolated_points += points2[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)\n\n # skip connection\n if points1 is not None:\n new_points = torch.cat([points1, interpolated_points], dim=1) # [M, C1+C2]\n else:\n new_points = interpolated_points\n\n # mlp\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_points = F.relu(bn(conv(new_points)))\n\n return new_points\n" }, { "path": "segmentation/modules/pointops/__init__.py", "content": "" }, { "path": "segmentation/modules/pointops/functions/__init__.py", "content": "" }, { "path": "segmentation/modules/pointops/functions/pointops.py", "content": "from typing import Tuple\n\nimport torch\nfrom torch.autograd import Function\nimport torch.nn as nn\n\ntry:\n import pointops_cuda\nexcept ImportError:\n import warnings\n import os\n from torch.utils.cpp_extension import load\n\n warnings.warn(\"Unable to load pointops_cuda cpp extension.\")\n pointops_cuda_src = os.path.join(os.path.dirname(__file__), \"../src\")\n pointops_cuda = load('pointops_cuda', [\n pointops_cuda_src + '/pointops_api.cpp',\n pointops_cuda_src + '/knnquery/knnquery_cuda.cpp',\n pointops_cuda_src + '/knnquery/knnquery_cuda_kernel.cu',\n pointops_cuda_src + '/interpolation/interpolation_cuda.cpp',\n pointops_cuda_src + '/interpolation/interpolation_cuda_kernel.cu',\n pointops_cuda_src + '/sampling/sampling_cuda.cpp',\n pointops_cuda_src + '/sampling/sampling_cuda_kernel.cu',\n pointops_cuda_src + '/subtraction/subtraction_cuda.cpp',\n pointops_cuda_src + '/subtraction/subtraction_cuda_kernel.cu',\n pointops_cuda_src + '/aggregation/aggregation_cuda.cpp',\n pointops_cuda_src + '/aggregation/aggregation_cuda_kernel.cu',\n ], build_directory=pointops_cuda_src, verbose=False)\n\n\nclass FurthestSampling(Function):\n @staticmethod\n def forward(ctx, xyz, offset, new_offset):\n \"\"\"\n input: xyz: (n, 3), offset: (b), new_offset: (b)\n output: idx: (m)\n \"\"\"\n assert xyz.is_contiguous()\n n, b, n_max = xyz.shape[0], offset.shape[0], offset[0]\n for i in range(1, b):\n n_max = max(offset[i] - offset[i - 1], n_max)\n idx = torch.cuda.IntTensor(new_offset[b - 1].item()).zero_()\n tmp = torch.cuda.FloatTensor(n).fill_(1e10)\n pointops_cuda.furthestsampling_cuda(b, n_max, xyz, offset, new_offset, tmp, idx)\n del tmp\n return idx\n\n\nfurthestsampling = FurthestSampling.apply\n\n\nclass SectorizedFurthestSampling(Function):\n @staticmethod\n def forward(ctx, xyz, offset, new_offset, num_sectors, min_points=10000):\n \"\"\"\n input: xyz: (n, 3), offset: (b), new_offset: (b)\n output: idx: (m)\n \"\"\"\n assert xyz.is_contiguous()\n\n # cut into batches\n last_offset = 0\n sizes = []\n new_sizes = []\n indices = []\n for i in range(offset.shape[0]):\n size = offset[i] - last_offset\n if size < min_points:\n tmp_num_sectors = 1\n else:\n tmp_num_sectors = num_sectors\n batch_xyz = xyz[last_offset:last_offset + size]\n angle = torch.atan2(batch_xyz[:, 0], batch_xyz[:, 1]) # [0, 2*pi]\n sector_range = torch.linspace(angle.min(), angle.max() + 1e-4, tmp_num_sectors + 1)\n for s in range(tmp_num_sectors):\n indices.append(\n torch.where((angle >= sector_range[s]) & (angle < sector_range[s + 1]))[0] + last_offset\n )\n sizes.append(indices[-1].shape[0])\n if i > 0:\n new_size = (new_offset[i] - new_offset[i - 1]).item()\n else:\n new_size = new_offset[i].item()\n new_sizes_this_batch = [new_size // tmp_num_sectors for i in range(tmp_num_sectors)]\n new_sizes_this_batch[-1] += new_size % tmp_num_sectors\n new_sizes += new_sizes_this_batch\n last_offset = offset[i]\n\n sizes = torch.tensor(sizes, dtype=torch.long).to(offset)\n sector_offset = sizes.cumsum(dim=0)\n new_sizes = torch.tensor(new_sizes, dtype=torch.long).to(offset)\n new_sector_offset = new_sizes.cumsum(dim=0)\n indices = torch.cat(indices).long().to(offset.device)\n sector_xyz = xyz[indices].contiguous()\n\n # transform to sectors\n new_xyz = []\n n, b, n_max = sector_xyz.shape[0], sector_offset.shape[0], sector_offset[0]\n for i in range(1, b):\n n_max = max(sector_offset[i] - sector_offset[i - 1], n_max)\n idx = torch.cuda.IntTensor(new_sector_offset[b - 1].item()).zero_()\n tmp = torch.cuda.FloatTensor(n).fill_(1e10)\n pointops_cuda.furthestsampling_cuda(b, n_max, sector_xyz, sector_offset.int(), new_sector_offset.int(), tmp,\n idx)\n idx = indices[idx.long()]\n del tmp\n del sector_xyz\n return idx\n\n\nsectorized_fps = SectorizedFurthestSampling.apply\n\n\nclass KNNQuery(Function):\n @staticmethod\n def forward(ctx, nsample, xyz, new_xyz, offset, new_offset):\n \"\"\"\n input: xyz: (n, 3), new_xyz: (m, 3), offset: (b), new_offset: (b)\n output: idx: (m, nsample), dist2: (m, nsample)\n \"\"\"\n if new_xyz is None: new_xyz = xyz\n assert xyz.is_contiguous() and new_xyz.is_contiguous()\n m = new_xyz.shape[0]\n idx = torch.cuda.IntTensor(m, nsample).zero_()\n dist2 = torch.cuda.FloatTensor(m, nsample).zero_()\n pointops_cuda.knnquery_cuda(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2)\n return idx, torch.sqrt(dist2)\n\n\nknnquery = KNNQuery.apply\n\n\nclass Grouping(Function):\n @staticmethod\n def forward(ctx, input, idx):\n \"\"\"\n input: input: (n, c), idx : (m, nsample)\n output: (m, nsample, c)\n \"\"\"\n assert input.is_contiguous() and idx.is_contiguous()\n m, nsample, n, c = idx.shape[0], idx.shape[1], input.shape[0], input.shape[1]\n output = torch.cuda.FloatTensor(m, nsample, c)\n pointops_cuda.grouping_forward_cuda(m, nsample, c, input, idx, output)\n ctx.n = n\n ctx.save_for_backward(idx)\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n \"\"\"\n input: grad_out: (m, c, nsample)\n output: (n, c), None\n \"\"\"\n n = ctx.n\n idx, = ctx.saved_tensors\n m, nsample, c = grad_output.shape\n grad_input = torch.cuda.FloatTensor(n, c).zero_()\n pointops_cuda.grouping_backward_cuda(m, nsample, c, grad_output, idx, grad_input)\n return grad_input, None\n\n\ngrouping = Grouping.apply\n\n\ndef queryandgroup(nsample, xyz, new_xyz, feat, idx, offset, new_offset, use_xyz=True):\n \"\"\"\n input: xyz: (n, 3), new_xyz: (m, 3), feat: (n, c), idx: (m, nsample), offset: (b), new_offset: (b)\n output: new_feat: (m, c+3, nsample), grouped_idx: (m, nsample)\n \"\"\"\n assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous()\n if new_xyz is None:\n new_xyz = xyz\n if idx is None:\n idx, _ = knnquery(nsample, xyz, new_xyz, offset, new_offset) # (m, nsample)\n\n n, m, c = xyz.shape[0], new_xyz.shape[0], feat.shape[1]\n grouped_xyz = xyz[idx.view(-1).long(), :].view(m, nsample, 3) # (m, nsample, 3)\n # grouped_xyz = grouping(xyz, idx) # (m, nsample, 3)\n grouped_xyz -= new_xyz.unsqueeze(1) # (m, nsample, 3)\n grouped_feat = feat[idx.view(-1).long(), :].view(m, nsample, c) # (m, nsample, c)\n # grouped_feat = grouping(feat, idx) # (m, nsample, c)\n\n if use_xyz:\n return torch.cat((grouped_xyz, grouped_feat), -1) # (m, nsample, 3+c)\n else:\n return grouped_feat\n\n\nclass Subtraction(Function):\n @staticmethod\n def forward(ctx, input1, input2, idx):\n \"\"\"\n input: input1: (n, c), input2: (n, c), idx: (n, nsample)\n output: (n, nsample, c)\n \"\"\"\n assert input1.is_contiguous() and input2.is_contiguous()\n n, c = input1.shape;\n nsample = idx.shape[-1]\n output = torch.cuda.FloatTensor(n, nsample, c).zero_()\n pointops_cuda.subtraction_forward_cuda(n, nsample, c, input1, input2, idx, output)\n ctx.save_for_backward(idx)\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n \"\"\"\n input: grad_out: (n, nsample, c)\n output: grad_input1: (n, c), grad_input2: (n, c)\n \"\"\"\n idx, = ctx.saved_tensors\n n, nsample, c = grad_output.shape\n grad_input1 = torch.cuda.FloatTensor(n, c).zero_()\n grad_input2 = torch.cuda.FloatTensor(n, c).zero_()\n pointops_cuda.subtraction_backward_cuda(n, nsample, c, idx, grad_output, grad_input1, grad_input2)\n return grad_input1, grad_input2, None\n\n\nsubtraction = Subtraction.apply\n\n\nclass Aggregation(Function):\n @staticmethod\n def forward(ctx, input, position, weight, idx):\n \"\"\"\n input: input: (n, c), position: (n, nsample, c), weight : (n, nsample, c'), idx: (n, nsample)\n output: (n, c)\n \"\"\"\n assert input.is_contiguous() and position.is_contiguous() and weight.is_contiguous()\n n, nsample, c = position.shape;\n w_c = weight.shape[-1]\n output = torch.cuda.FloatTensor(n, c).zero_()\n pointops_cuda.aggregation_forward_cuda(n, nsample, c, w_c, input, position, weight, idx, output)\n ctx.save_for_backward(input, position, weight, idx)\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n \"\"\"\n input: grad_out: (n, c)\n output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight : (n, nsample, c')\n \"\"\"\n input, position, weight, idx = ctx.saved_tensors\n n, nsample, c = position.shape;\n w_c = weight.shape[-1]\n grad_input = torch.cuda.FloatTensor(n, c).zero_()\n grad_position = torch.cuda.FloatTensor(n, nsample, c).zero_()\n grad_weight = torch.cuda.FloatTensor(n, nsample, w_c).zero_()\n pointops_cuda.aggregation_backward_cuda(n, nsample, c, w_c, input, position, weight, idx, grad_output,\n grad_input, grad_position, grad_weight)\n return grad_input, grad_position, grad_weight, None\n\n\naggregation = Aggregation.apply\n\n\ndef interpolation(xyz, new_xyz, feat, offset, new_offset, k=3):\n \"\"\"\n input: xyz: (m, 3), new_xyz: (n, 3), feat: (m, c), offset: (b), new_offset: (b)\n output: (n, c)\n \"\"\"\n assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous()\n idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, 3), (n, 3)\n dist_recip = 1.0 / (dist + 1e-8) # (n, 3)\n norm = torch.sum(dist_recip, dim=1, keepdim=True)\n weight = dist_recip / norm # (n, 3)\n\n new_feat = torch.cuda.FloatTensor(new_xyz.shape[0], feat.shape[1]).zero_()\n for i in range(k):\n new_feat += feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)\n return new_feat\n\n\nclass Interpolation(Function):\n @staticmethod\n def forward(ctx, xyz, new_xyz, input, offset, new_offset, k=3):\n \"\"\"\n input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b)\n output: (n, c)\n \"\"\"\n assert xyz.is_contiguous() and new_xyz.is_contiguous() and input.is_contiguous()\n idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, k), (n, k)\n dist_recip = 1.0 / (dist + 1e-8) # (n, k)\n norm = torch.sum(dist_recip, dim=1, keepdim=True)\n weight = dist_recip / norm # (n, k)\n\n n, c, m = new_xyz.shape[0], input.shape[1], input.shape[0]\n output = torch.cuda.FloatTensor(n, c).zero_()\n pointops_cuda.interpolation_forward_cuda(n, c, k, input, idx, weight, output)\n ctx.m, ctx.k = m, k\n ctx.save_for_backward(idx, weight)\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n \"\"\"\n input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b)\n output: (n, c)\n \"\"\"\n m, k = ctx.m, ctx.k\n idx, weight = ctx.saved_tensors\n n, c = grad_output.shape\n grad_input = torch.cuda.FloatTensor(m, c).zero_()\n pointops_cuda.interpolation_backward_cuda(n, c, k, grad_output, idx, weight, grad_input)\n return None, None, grad_input, None, None, None\n\n\ninterpolation2 = Interpolation.apply\n" }, { "path": "segmentation/modules/pointops/setup.py", "content": "#python3 setup.py install\nfrom setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\nimport os\nfrom distutils.sysconfig import get_config_vars\n\n(opt,) = get_config_vars('OPT')\nos.environ['OPT'] = \" \".join(\n flag for flag in opt.split() if flag != '-Wstrict-prototypes'\n)\n\nsetup(\n name='pointops_cuda',\n author='Hengshuang Zhao',\n ext_modules=[\n CUDAExtension('pointops_cuda', [\n 'src/pointops_api.cpp',\n 'src/knnquery/knnquery_cuda.cpp',\n 'src/knnquery/knnquery_cuda_kernel.cu',\n 'src/sampling/sampling_cuda.cpp',\n 'src/sampling/sampling_cuda_kernel.cu',\n 'src/grouping/grouping_cuda.cpp',\n 'src/grouping/grouping_cuda_kernel.cu',\n 'src/interpolation/interpolation_cuda.cpp',\n 'src/interpolation/interpolation_cuda_kernel.cu',\n 'src/subtraction/subtraction_cuda.cpp',\n 'src/subtraction/subtraction_cuda_kernel.cu',\n 'src/aggregation/aggregation_cuda.cpp',\n 'src/aggregation/aggregation_cuda_kernel.cu',\n ],\n extra_compile_args={'cxx': ['-g'], 'nvcc': ['-O2']}\n )\n ],\n cmdclass={'build_ext': BuildExtension}\n)\n" }, { "path": "segmentation/modules/pointops/src/__init__.py", "content": "" }, { "path": "segmentation/modules/pointops/src/aggregation/aggregation_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"aggregation_cuda_kernel.h\"\n\n\nvoid aggregation_forward_cuda(int n, int nsample, int c, int w_c, at::Tensor input_tensor, at::Tensor position_tensor, at::Tensor weight_tensor, at::Tensor idx_tensor, at::Tensor output_tensor)\n{\n const float *input = input_tensor.data_ptr();\n const float *position = position_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *output = output_tensor.data_ptr();\n aggregation_forward_cuda_launcher(n, nsample, c, w_c, input, position, weight, idx, output);\n}\n\nvoid aggregation_backward_cuda(int n, int nsample, int c, int w_c, at::Tensor input_tensor, at::Tensor position_tensor, at::Tensor weight_tensor, at::Tensor idx_tensor, at::Tensor grad_output_tensor, at::Tensor grad_input_tensor, at::Tensor grad_position_tensor, at::Tensor grad_weight_tensor)\n{\n\tconst float *input = input_tensor.data_ptr();\n const float *position = position_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n const float *grad_output = grad_output_tensor.data_ptr();\n float *grad_input = grad_input_tensor.data_ptr();\n float *grad_position = grad_position_tensor.data_ptr();\n float *grad_weight = grad_weight_tensor.data_ptr();\n aggregation_backward_cuda_launcher(n, nsample, c, w_c, input, position, weight, idx, grad_output, grad_input, grad_position, grad_weight);\n}\n" }, { "path": "segmentation/modules/pointops/src/aggregation/aggregation_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"aggregation_cuda_kernel.h\"\n\n\n__global__ void aggregation_forward_cuda_kernel(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, float *output) {\n // input: input: (n, c), position: (n, nsample, c), weight: (n, nsample, w_c), idx: (n, nsample), output: (n, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * c) return;\n const int c_idx = index % c;\n const int n_idx = index / c;\n const int w_c_idx = c_idx % w_c;\n for (int nsample_idx = 0; nsample_idx < nsample; nsample_idx++)\n { \n int idx_idx = n_idx * nsample + nsample_idx;\n int input_idx = idx[idx_idx] * c + c_idx;\n int position_idx = n_idx * nsample * c + nsample_idx * c + c_idx;\n int weight_idx = n_idx * nsample * w_c + nsample_idx * w_c + w_c_idx;\n output[index] += (input[input_idx] + position[position_idx]) * weight[weight_idx];\n }\n}\n\n__global__ void aggregation_backward_cuda_kernel(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, const float *grad_output, float *grad_input, float *grad_position, float *grad_weight) {\n // input: grad_output: (n, c), output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight: (n, nsample, w_c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * c) return;\n const int c_idx = index % c;\n const int n_idx = index / c;\n const int w_c_idx = c_idx % w_c;\n for (int nsample_idx = 0; nsample_idx < nsample; nsample_idx++)\n { \n int idx_idx = n_idx * nsample + nsample_idx;\n int input_idx = idx[idx_idx] * c + c_idx;\n int position_idx = n_idx * nsample * c + nsample_idx * c + c_idx;\n int weight_idx = n_idx * nsample * w_c + nsample_idx * w_c + w_c_idx;\n atomicAdd(grad_input + input_idx, grad_output[index] * weight[weight_idx]);\n grad_position[position_idx] = grad_output[index] * weight[weight_idx];\n atomicAdd(grad_weight + weight_idx, grad_output[index] * (input[input_idx] + position[position_idx]));\n }\n}\n\nvoid aggregation_forward_cuda_launcher(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, float *output) {\n // input: input: (n, c), position: (n, nsample, c), weight: (n, nsample, w_c), idx: (n, nsample), output: (n, c)\n dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n aggregation_forward_cuda_kernel<<>>(n, nsample, c, w_c, input, position, weight, idx, output);\n}\n\nvoid aggregation_backward_cuda_launcher(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, const float *grad_output, float *grad_input, float *grad_position, float *grad_weight) { \n // input: grad_output: (n, c), output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight: (n, nsample, w_c)\n dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n aggregation_backward_cuda_kernel<<>>(n, nsample, c, w_c, input, position, weight, idx, grad_output, grad_input, grad_position, grad_weight);\n}\n" }, { "path": "segmentation/modules/pointops/src/aggregation/aggregation_cuda_kernel.h", "content": "#ifndef _AGGREGATION_CUDA_KERNEL\n#define _AGGREGATION_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid aggregation_forward_cuda(int n, int nsample, int c, int w_c, at::Tensor input_tensor, at::Tensor position_tensor, at::Tensor weight_tensor, at::Tensor idx_tensor, at::Tensor output_tensor);\nvoid aggregation_backward_cuda(int n, int nsample, int c, int w_c, at::Tensor input_tensor, at::Tensor position_tensor, at::Tensor weight_tensor, at::Tensor idx_tensor, at::Tensor grad_output_tensor, at::Tensor grad_input_tensor, at::Tensor grad_position_tensor, at::Tensor grad_weight_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid aggregation_forward_cuda_launcher(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, float *output);\nvoid aggregation_backward_cuda_launcher(int n, int nsample, int c, int w_c, const float *input, const float *position, const float *weight, const int *idx, const float *grad_output, float *grad_input, float *grad_position, float *grad_weight);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointops/src/cuda_utils.h", "content": "#ifndef _CUDA_UTILS_H\n#define _CUDA_UTILS_H\n\n#include \n#include \n\n#define TOTAL_THREADS 1024\n#define THREADS_PER_BLOCK 256\n#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))\n\ninline int opt_n_threads(int work_size) {\n const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0);\n return std::max(std::min(1 << pow_2, TOTAL_THREADS), 1);\n}\n\ninline dim3 opt_block_config(int x, int y) {\n const int x_threads = opt_n_threads(x);\n const int y_threads = std::max(std::min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);\n dim3 block_config(x_threads, y_threads, 1);\n return block_config;\n}\n\n#endif\n" }, { "path": "segmentation/modules/pointops/src/grouping/grouping_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"grouping_cuda_kernel.h\"\n\n\nvoid grouping_forward_cuda(int m, int nsample, int c, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor output_tensor)\n{\n const float *input = input_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *output = output_tensor.data_ptr();\n grouping_forward_cuda_launcher(m, nsample, c, input, idx, output);\n}\n\nvoid grouping_backward_cuda(int m, int nsample, int c, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor grad_input_tensor)\n{\n const float *grad_output = grad_output_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *grad_input = grad_input_tensor.data_ptr();\n grouping_backward_cuda_launcher(m, nsample, c, grad_output, idx, grad_input);\n}\n" }, { "path": "segmentation/modules/pointops/src/grouping/grouping_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"grouping_cuda_kernel.h\"\n\n\n__global__ void grouping_forward_cuda_kernel(int m, int nsample, int c, const float *__restrict__ input, const int *__restrict__ idx, float *__restrict__ output) {\n // input: input: (n, c), idx: (m, nsample), output: (m, nsample, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= m * nsample * c) return;\n const int c_idx = index % c;\n const int nsample_idx = (index / c) % nsample;\n const int m_idx = index / nsample / c;\n const int input_idx = idx[m_idx * nsample + nsample_idx] * c + c_idx;\n output[index] = input[input_idx];\n}\n\n__global__ void grouping_backward_cuda_kernel(int m, int nsample, int c, const float *__restrict__ grad_output, const int *__restrict__ idx, float *__restrict__ grad_input) {\n // input: grad_output: (m, nsample, c), idx: (m, nsample), output: grad_input: (n, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= m * nsample * c) return;\n const int c_idx = index % c;\n const int nsample_idx = (index / c) % nsample;\n const int m_idx = index / nsample / c;\n const int input_idx = idx[m_idx * nsample + nsample_idx] * c + c_idx;\n atomicAdd(grad_input + input_idx, grad_output[index]);\n}\n\nvoid grouping_forward_cuda_launcher(int m, int nsample, int c, const float *input, const int *idx, float *output) {\n // input: input: (n, c), idx: (m, nsample), output: (m, nsample, c)\n dim3 blocks(DIVUP(m * nsample * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n grouping_forward_cuda_kernel<<>>(m, nsample, c, input, idx, output);\n}\n\nvoid grouping_backward_cuda_launcher(int m, int nsample, int c, const float *grad_output, const int *idx, float *grad_input)\n{ \n // input: grad_output: (m, nsample, c), idx: (m, nsample), output: grad_input: (n, c)\n dim3 blocks(DIVUP(m * nsample * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n grouping_backward_cuda_kernel<<>>(m, nsample, c, grad_output, idx, grad_input);\n}\n" }, { "path": "segmentation/modules/pointops/src/grouping/grouping_cuda_kernel.h", "content": "#ifndef _GROUPING_CUDA_KERNEL\n#define _GROUPING_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid grouping_forward_cuda(int m, int nsample, int c, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor output_tensor);\nvoid grouping_backward_cuda(int m, int nsample, int c, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor grad_input_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid grouping_forward_cuda_launcher(int m, int nsample, int c, const float *input, const int *idx, float *output);\nvoid grouping_backward_cuda_launcher(int m, int nsample, int c, const float *grad_output, const int *idx, float *grad_input);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointops/src/interpolation/interpolation_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"interpolation_cuda_kernel.h\"\n\n\nvoid interpolation_forward_cuda(int n, int c, int k, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor output_tensor)\n{\n const float *input = input_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n float *output = output_tensor.data_ptr();\n interpolation_forward_cuda_launcher(n, c, k, input, idx, weight, output);\n}\n\nvoid interpolation_backward_cuda(int n, int c, int k, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_input_tensor)\n{\n const float *grad_output = grad_output_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n const float *weight = weight_tensor.data_ptr();\n float *grad_input = grad_input_tensor.data_ptr();\n interpolation_backward_cuda_launcher(n, c, k, grad_output, idx, weight, grad_input);\n}\n" }, { "path": "segmentation/modules/pointops/src/interpolation/interpolation_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"interpolation_cuda_kernel.h\"\n\n\n__global__ void interpolation_forward_cuda_kernel(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output)\n{\n // input: input: (m, c), idx: (n, k), weight: (n, k), output: output (n, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * c) return;\n int c_idx = index % c;\n int n_idx = index / c;\n for (int i = 0; i < k; i++)\n {\n int idx_idx = n_idx * k + i;\n int input_idx = idx[idx_idx] * c + c_idx;\n output[index] += input[input_idx] * weight[idx_idx];\n }\n}\n\n__global__ void interpolation_backward_cuda_kernel(int n, int c, int k, const float *grad_output, const int *idx, const float *weight, float *grad_input)\n{\n // input: grad_output: (n, c), idx: (n, k), weight: (n, k), output: grad_input (m, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * c) return;\n int c_idx = index % c;\n int n_idx = index / c;\n for (int i = 0; i < k; i++)\n {\n int idx_idx = n_idx * k + i;\n int input_idx = idx[idx_idx] * c + c_idx;\n atomicAdd(grad_input + input_idx, grad_output[index] * weight[idx_idx]);\n }\n}\n\nvoid interpolation_forward_cuda_launcher(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output) {\n // input: input: (m, c), idx: (n, k), weight: (n, k), output: output (n, c)\n dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n interpolation_forward_cuda_kernel<<>>(n, c, k, input, idx, weight, output);\n}\n\nvoid interpolation_backward_cuda_launcher(int n, int c, int k, const float *grad_output, const int *idx, const float *weight, float *grad_input) {\n // input: grad_output: (n, c), idx: (n, k), weight: (n, k), output: grad_input (m, c)\n dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n interpolation_backward_cuda_kernel<<>>(n, c, k, grad_output, idx, weight, grad_input);\n}\n" }, { "path": "segmentation/modules/pointops/src/interpolation/interpolation_cuda_kernel.h", "content": "#ifndef _INTERPOLATION_CUDA_KERNEL\n#define _INTERPOLATION_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid interpolation_forward_cuda(int n, int c, int k, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor output_tensor);\nvoid interpolation_backward_cuda(int n, int c, int k, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_input_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid interpolation_forward_cuda_launcher(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output);\nvoid interpolation_backward_cuda_launcher(int n, int c, int k, const float *grad_output, const int *idx, const float *weight, float *grad_input);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointops/src/knnquery/knnquery_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"knnquery_cuda_kernel.h\"\n\n\nvoid knnquery_cuda(int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor offset_tensor, at::Tensor new_offset_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor)\n{\n const float *xyz = xyz_tensor.data_ptr();\n const float *new_xyz = new_xyz_tensor.data_ptr();\n const int *offset = offset_tensor.data_ptr();\n const int *new_offset = new_offset_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n float *dist2 = dist2_tensor.data_ptr();\n knnquery_cuda_launcher(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2);\n}\n" }, { "path": "segmentation/modules/pointops/src/knnquery/knnquery_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"knnquery_cuda_kernel.h\"\n\n\n__device__ void swap_float(float *x, float *y)\n{\n float tmp = *x;\n *x = *y;\n *y = tmp;\n}\n\n\n__device__ void swap_int(int *x, int *y)\n{\n int tmp = *x;\n *x = *y;\n *y = tmp;\n}\n\n\n__device__ void reheap(float *dist, int *idx, int k)\n{\n int root = 0;\n int child = root * 2 + 1;\n while (child < k)\n {\n if(child + 1 < k && dist[child+1] > dist[child])\n child++;\n if(dist[root] > dist[child])\n return;\n swap_float(&dist[root], &dist[child]);\n swap_int(&idx[root], &idx[child]);\n root = child;\n child = root * 2 + 1;\n }\n}\n\n\n__device__ void heap_sort(float *dist, int *idx, int k)\n{\n int i;\n for (i = k - 1; i > 0; i--)\n {\n swap_float(&dist[0], &dist[i]);\n swap_int(&idx[0], &idx[i]);\n reheap(dist, idx, i);\n }\n}\n\n\n__device__ int get_bt_idx(int idx, const int *offset)\n{\n int i = 0;\n while (1)\n {\n if (idx < offset[i])\n break;\n else\n i++;\n }\n return i;\n}\n\n\n__global__ void knnquery_cuda_kernel(int m, int nsample, const float *__restrict__ xyz, const float *__restrict__ new_xyz, const int *__restrict__ offset, const int *__restrict__ new_offset, int *__restrict__ idx, float *__restrict__ dist2) {\n // input: xyz (n, 3) new_xyz (m, 3)\n // output: idx (m, nsample) dist2 (m, nsample)\n int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (pt_idx >= m) return;\n\n new_xyz += pt_idx * 3;\n idx += pt_idx * nsample;\n dist2 += pt_idx * nsample;\n int bt_idx = get_bt_idx(pt_idx, new_offset);\n int start;\n if (bt_idx == 0)\n start = 0;\n else\n start = offset[bt_idx - 1];\n int end = offset[bt_idx];\n\n float new_x = new_xyz[0];\n float new_y = new_xyz[1];\n float new_z = new_xyz[2];\n\n float best_dist[100];\n int best_idx[100];\n for(int i = 0; i < nsample; i++){\n best_dist[i] = 1e10;\n best_idx[i] = start;\n }\n for(int i = start; i < end; i++){\n float x = xyz[i * 3 + 0];\n float y = xyz[i * 3 + 1];\n float z = xyz[i * 3 + 2];\n float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);\n if (d2 < best_dist[0]){\n best_dist[0] = d2;\n best_idx[0] = i;\n reheap(best_dist, best_idx, nsample);\n }\n }\n heap_sort(best_dist, best_idx, nsample);\n for(int i = 0; i < nsample; i++){\n idx[i] = best_idx[i];\n dist2[i] = best_dist[i];\n }\n}\n\n\nvoid knnquery_cuda_launcher(int m, int nsample, const float *xyz, const float *new_xyz, const int *offset, const int *new_offset, int *idx, float *dist2) {\n // input: new_xyz: (m, 3), xyz: (n, 3), idx: (m, nsample)\n dim3 blocks(DIVUP(m, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n knnquery_cuda_kernel<<>>(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2);\n}\n" }, { "path": "segmentation/modules/pointops/src/knnquery/knnquery_cuda_kernel.h", "content": "#ifndef _KNNQUERY_CUDA_KERNEL\n#define _KNNQUERY_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid knnquery_cuda(int m, int nsample, at::Tensor xyz_tensor, at::Tensor new_xyz_tensor, at::Tensor offset_tensor, at::Tensor new_offset_tensor, at::Tensor idx_tensor, at::Tensor dist2_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid knnquery_cuda_launcher(int m, int nsample, const float *xyz, const float *new_xyz, const int *offset, const int *new_offset, int *idx, float *dist2);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointops/src/pointops_api.cpp", "content": "#include \n#include \n\n#include \"knnquery/knnquery_cuda_kernel.h\"\n#include \"sampling/sampling_cuda_kernel.h\"\n#include \"grouping/grouping_cuda_kernel.h\"\n#include \"interpolation/interpolation_cuda_kernel.h\"\n#include \"aggregation/aggregation_cuda_kernel.h\"\n#include \"subtraction/subtraction_cuda_kernel.h\"\n\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"knnquery_cuda\", &knnquery_cuda, \"knnquery_cuda\");\n m.def(\"furthestsampling_cuda\", &furthestsampling_cuda, \"furthestsampling_cuda\");\n m.def(\"grouping_forward_cuda\", &grouping_forward_cuda, \"grouping_forward_cuda\");\n m.def(\"grouping_backward_cuda\", &grouping_backward_cuda, \"grouping_backward_cuda\");\n m.def(\"interpolation_forward_cuda\", &interpolation_forward_cuda, \"interpolation_forward_cuda\");\n m.def(\"interpolation_backward_cuda\", &interpolation_backward_cuda, \"interpolation_backward_cuda\");\n m.def(\"subtraction_forward_cuda\", &subtraction_forward_cuda, \"subtraction_forward_cuda\");\n m.def(\"subtraction_backward_cuda\", &subtraction_backward_cuda, \"subtraction_backward_cuda\");\n m.def(\"aggregation_forward_cuda\", &aggregation_forward_cuda, \"aggregation_forward_cuda\");\n m.def(\"aggregation_backward_cuda\", &aggregation_backward_cuda, \"aggregation_backward_cuda\");\n}\n" }, { "path": "segmentation/modules/pointops/src/sampling/sampling_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"sampling_cuda_kernel.h\"\n\n\nvoid furthestsampling_cuda(int b, int n, at::Tensor xyz_tensor, at::Tensor offset_tensor, at::Tensor new_offset_tensor, at::Tensor tmp_tensor, at::Tensor idx_tensor)\n{\n const float *xyz = xyz_tensor.data_ptr();\n const int *offset = offset_tensor.data_ptr();\n const int *new_offset = new_offset_tensor.data_ptr();\n float *tmp = tmp_tensor.data_ptr();\n int *idx = idx_tensor.data_ptr();\n furthestsampling_cuda_launcher(b, n, xyz, offset, new_offset, tmp, idx);\n}\n" }, { "path": "segmentation/modules/pointops/src/sampling/sampling_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"sampling_cuda_kernel.h\"\n\n\n__device__ void __update(float *dists, int *dists_i, int idx1, int idx2) {\n const float v1 = dists[idx1], v2 = dists[idx2];\n const int i1 = dists_i[idx1], i2 = dists_i[idx2];\n dists[idx1] = max(v1, v2);\n dists_i[idx1] = v2 > v1 ? i2 : i1;\n}\n\n// input xyz: (n, 3), tmp: (b, n_max)\n// ouput idx (m)\ntemplate \n__global__ void furthestsampling_cuda_kernel(const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx)\n{\n __shared__ float dists[block_size];\n __shared__ int dists_i[block_size];\n\n int bid = blockIdx.x;\n int start_n, end_n, start_m, end_m, old;\n if (bid == 0) {\n start_n = 0;\n end_n = offset[0];\n start_m = 0;\n end_m = new_offset[0];\n old = 0;\n }\n else {\n start_n = offset[bid - 1];\n end_n = offset[bid];\n start_m = new_offset[bid - 1];\n end_m = new_offset[bid];\n old = offset[bid - 1];\n }\n\n const int stride = block_size;\n int tid = threadIdx.x;\n if (tid == 0) idx[start_m] = start_n;\n\n __syncthreads();\n for (int j = start_m + 1; j < end_m; j++)\n {\n int besti = start_n;\n float best = -1;\n float x1 = xyz[old * 3 + 0];\n float y1 = xyz[old * 3 + 1];\n float z1 = xyz[old * 3 + 2];\n for (int k = start_n + tid; k < end_n; k += stride)\n {\n float x2 = xyz[k * 3 + 0];\n float y2 = xyz[k * 3 + 1];\n float z2 = xyz[k * 3 + 2];\n float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);\n float d2 = min(d, tmp[k]);\n tmp[k] = d2;\n besti = d2 > best ? k : besti;\n best = d2 > best ? d2 : best;\n }\n dists[tid] = best;\n dists_i[tid] = besti;\n __syncthreads();\n\n if (block_size >= 1024) {\n if (tid < 512) {\n __update(dists, dists_i, tid, tid + 512);\n }\n __syncthreads();\n }\n if (block_size >= 512) {\n if (tid < 256) {\n __update(dists, dists_i, tid, tid + 256);\n }\n __syncthreads();\n }\n if (block_size >= 256) {\n if (tid < 128) {\n __update(dists, dists_i, tid, tid + 128);\n }\n __syncthreads();\n }\n if (block_size >= 128) {\n if (tid < 64) {\n __update(dists, dists_i, tid, tid + 64);\n }\n __syncthreads();\n }\n if (block_size >= 64) {\n if (tid < 32) {\n __update(dists, dists_i, tid, tid + 32);\n }\n __syncthreads();\n }\n if (block_size >= 32) {\n if (tid < 16) {\n __update(dists, dists_i, tid, tid + 16);\n }\n __syncthreads();\n }\n if (block_size >= 16) {\n if (tid < 8) {\n __update(dists, dists_i, tid, tid + 8);\n }\n __syncthreads();\n }\n if (block_size >= 8) {\n if (tid < 4) {\n __update(dists, dists_i, tid, tid + 4);\n }\n __syncthreads();\n }\n if (block_size >= 4) {\n if (tid < 2) {\n __update(dists, dists_i, tid, tid + 2);\n }\n __syncthreads();\n }\n if (block_size >= 2) {\n if (tid < 1) {\n __update(dists, dists_i, tid, tid + 1);\n }\n __syncthreads();\n }\n\n old = dists_i[0];\n if (tid == 0)\n idx[j] = old;\n }\n}\n\nvoid furthestsampling_cuda_launcher(int b, int n, const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx)\n{ \n\tunsigned int n_threads = opt_n_threads(n);\n\tswitch (n_threads) {\n case 1024:\n furthestsampling_cuda_kernel<1024><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 512:\n furthestsampling_cuda_kernel<512><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 256:\n furthestsampling_cuda_kernel<256><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 128:\n furthestsampling_cuda_kernel<128><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 64:\n furthestsampling_cuda_kernel<64><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 32:\n furthestsampling_cuda_kernel<32><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 16:\n furthestsampling_cuda_kernel<16><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 8:\n furthestsampling_cuda_kernel<8><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 4:\n furthestsampling_cuda_kernel<4><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 2:\n furthestsampling_cuda_kernel<2><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n case 1:\n furthestsampling_cuda_kernel<1><<>>(xyz, offset, new_offset, tmp, idx);\n break;\n default:\n furthestsampling_cuda_kernel<512><<>>(xyz, offset, new_offset, tmp, idx);\n }\n}\n" }, { "path": "segmentation/modules/pointops/src/sampling/sampling_cuda_kernel.h", "content": "#ifndef _SAMPLING_CUDA_KERNEL\n#define _SAMPLING_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid furthestsampling_cuda(int b, int n, at::Tensor xyz_tensor, at::Tensor offset_tensor, at::Tensor new_offset_tensor, at::Tensor tmp_tensor, at::Tensor idx_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid furthestsampling_cuda_launcher(int b, int n, const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointops/src/subtraction/subtraction_cuda.cpp", "content": "#include \n#include \n#include \n#include \n#include \"subtraction_cuda_kernel.h\"\n\n\nvoid subtraction_forward_cuda(int n, int nsample, int c, at::Tensor input1_tensor, at::Tensor input2_tensor, at::Tensor idx_tensor, at::Tensor output_tensor)\n{\n const float *input1 = input1_tensor.data_ptr();\n const float *input2 = input2_tensor.data_ptr();\n const int *idx = idx_tensor.data_ptr();\n float *output = output_tensor.data_ptr();\n subtraction_forward_cuda_launcher(n, nsample, c, input1, input2, idx, output);\n}\n\nvoid subtraction_backward_cuda(int n, int nsample, int c, at::Tensor idx_tensor, at::Tensor grad_output_tensor, at::Tensor grad_input1_tensor, at::Tensor grad_input2_tensor)\n{\n const int *idx = idx_tensor.data_ptr();\n const float *grad_output = grad_output_tensor.data_ptr();\n float *grad_input1 = grad_input1_tensor.data_ptr();\n float *grad_input2 = grad_input2_tensor.data_ptr();\n subtraction_backward_cuda_launcher(n, nsample, c, idx, grad_output, grad_input1, grad_input2);\n}\n" }, { "path": "segmentation/modules/pointops/src/subtraction/subtraction_cuda_kernel.cu", "content": "#include \"../cuda_utils.h\"\n#include \"subtraction_cuda_kernel.h\"\n\n\n__global__ void subtraction_forward_cuda_kernel(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output) {\n // input: input1: (n, c), input2: (n, c), idx: (n, nsample), output: (n, nsample, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * nsample * c) return;\n const int c_idx = index % c;\n const int nsample_idx = (index / c) % nsample;\n const int n_idx = index / nsample / c;\n const int idx_idx = n_idx * nsample + nsample_idx;\n const int input1_idx = n_idx * c + c_idx;\n const int input2_idx = idx[idx_idx] * c + c_idx;\n output[index] = input1[input1_idx] - input2[input2_idx];\n}\n\n__global__ void subtraction_backward_cuda_kernel(int n, int nsample, int c, const int *idx, const float *grad_output, float *grad_input1, float *grad_input2) {\n // input: grad_output: (n, nsample, c), output: grad_input1: (n, c), grad_input2: (n, c)\n int index = blockIdx.x * blockDim.x + threadIdx.x;\n if (index >= n * nsample * c) return;\n const int c_idx = index % c;\n const int nsample_idx = (index / c) % nsample;\n const int n_idx = index / nsample / c;\n const int idx_idx = n_idx * nsample + nsample_idx;\n const int input1_idx = n_idx * c + c_idx;\n const int input2_idx = idx[idx_idx] * c + c_idx;\n atomicAdd(grad_input1 + input1_idx, grad_output[index]);\n atomicAdd(grad_input2 + input2_idx, -grad_output[index]);\n}\n\nvoid subtraction_forward_cuda_launcher(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output) {\n // input: input1: (n, c), input2: (n, c), idx: (n, nsample), output: (n, nsample, c)\n dim3 blocks(DIVUP(n * nsample * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n subtraction_forward_cuda_kernel<<>>(n, nsample, c, input1, input2, idx, output);\n}\n\nvoid subtraction_backward_cuda_launcher(int n, int nsample, int c, const int *idx, const float *grad_output, float *grad_input1, float *grad_input2) { \n // input: grad_output: (n, nsample, c), output: grad_input1: (n, c), grad_input2: (n, c)\n dim3 blocks(DIVUP(n * nsample * c, THREADS_PER_BLOCK));\n dim3 threads(THREADS_PER_BLOCK);\n subtraction_backward_cuda_kernel<<>>(n, nsample, c, idx, grad_output, grad_input1, grad_input2);\n}\n" }, { "path": "segmentation/modules/pointops/src/subtraction/subtraction_cuda_kernel.h", "content": "#ifndef _SUBTRACTION_CUDA_KERNEL\n#define _SUBTRACTION_CUDA_KERNEL\n#include \n#include \n#include \n\nvoid subtraction_forward_cuda(int n, int nsample, int c, at::Tensor input1_tensor, at::Tensor input2_tensor, at::Tensor idx_tensor, at::Tensor output_tensor);\nvoid subtraction_backward_cuda(int n, int nsample, int c, at::Tensor idx_tensor, at::Tensor grad_output_tensor, at::Tensor grad_input1_tensor, at::Tensor grad_input2_tensor);\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nvoid subtraction_forward_cuda_launcher(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output);\nvoid subtraction_backward_cuda_launcher(int n, int nsample, int c, const int *idx, const float *grad_output, float *grad_input1, float *grad_input2);\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n" }, { "path": "segmentation/modules/pointtransformer_utils.py", "content": "import torch\nimport torch.nn as nn\n\nfrom modules.pointops.functions import pointops\n\n\nclass PointTransformerLayer(nn.Module):\n def __init__(self, in_planes, out_planes, share_planes=8, nsample=16):\n super().__init__()\n self.mid_planes = mid_planes = out_planes // 1\n self.out_planes = out_planes\n self.share_planes = share_planes\n self.nsample = nsample\n self.linear_q = nn.Linear(in_planes, mid_planes)\n self.linear_k = nn.Linear(in_planes, mid_planes)\n self.linear_v = nn.Linear(in_planes, out_planes)\n self.linear_p = nn.Sequential(nn.Linear(3, 3), nn.BatchNorm1d(3), nn.ReLU(inplace=True),\n nn.Linear(3, out_planes))\n self.linear_w = nn.Sequential(nn.BatchNorm1d(mid_planes), nn.ReLU(inplace=True),\n nn.Linear(mid_planes, mid_planes // share_planes),\n nn.BatchNorm1d(mid_planes // share_planes), nn.ReLU(inplace=True),\n nn.Linear(out_planes // share_planes, out_planes // share_planes))\n self.softmax = nn.Softmax(dim=1)\n\n def forward(self, pxo) -> torch.Tensor:\n p, x, o = pxo # (n, 3), (n, c), (b)\n x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x) # (n, c)\n x_k = pointops.queryandgroup(self.nsample, p, p, x_k, None, o, o, use_xyz=True) # (n, nsample, 3+c)\n x_v = pointops.queryandgroup(self.nsample, p, p, x_v, None, o, o, use_xyz=False) # (n, nsample, c)\n p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:]\n for i, layer in enumerate(self.linear_p):\n # (n, nsample, c)\n p_r = layer(p_r.transpose(1, 2).contiguous()).transpose(1, 2).contiguous() if i == 1 else layer(p_r)\n w = x_k - x_q.unsqueeze(1) + p_r.view(p_r.shape[0], p_r.shape[1], self.out_planes // self.mid_planes,\n self.mid_planes).sum(2) # (n, nsample, c)\n for i, layer in enumerate(self.linear_w):\n w = layer(w.transpose(1, 2).contiguous()).transpose(1, 2).contiguous() if i % 3 == 0 else layer(w)\n w = self.softmax(w) # (n, nsample, c)\n n, nsample, c = x_v.shape\n s = self.share_planes\n x = ((x_v + p_r).view(n, nsample, s, c // s) * w.unsqueeze(2)).sum(1).view(n, c)\n return x\n\n\nclass TransitionDown(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1, nsample=16, num_sector=1):\n super().__init__()\n self.stride, self.nsample, self.num_sector = stride, nsample, num_sector\n if stride != 1:\n self.linear = nn.Linear(3 + in_planes, out_planes, bias=False)\n self.pool = nn.MaxPool1d(nsample)\n else:\n self.linear = nn.Linear(in_planes, out_planes, bias=False)\n self.bn = nn.BatchNorm1d(out_planes)\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, pxo):\n p, x, o = pxo # (n, 3), (n, c), (b)\n if self.stride != 1:\n n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride\n for i in range(1, o.shape[0]):\n count += (o[i].item() - o[i - 1].item()) // self.stride\n n_o.append(count)\n n_o = torch.cuda.IntTensor(n_o)\n if self.num_sector > 1 and self.training:\n idx = pointops.sectorized_fps(p, o, n_o, self.num_sector) # [M]\n else:\n idx = pointops.furthestsampling(p, o, n_o) # [M]\n n_p = p[idx.long(), :] # (m, 3)\n x = pointops.queryandgroup(self.nsample, p, n_p, x, None, o, n_o, use_xyz=True) # (m, 3+c, nsample)\n x = self.relu(self.bn(self.linear(x).transpose(1, 2).contiguous())) # (m, c, nsample)\n x = self.pool(x).squeeze(-1) # (m, c)\n p, o = n_p, n_o\n else:\n x = self.relu(self.bn(self.linear(x))) # (n, c)\n return [p, x, o]\n\n\nclass TransitionUp(nn.Module):\n def __init__(self, in_planes, out_planes=None):\n super().__init__()\n if out_planes is None:\n self.linear1 = nn.Sequential(nn.Linear(2 * in_planes, in_planes), nn.BatchNorm1d(in_planes),\n nn.ReLU(inplace=True))\n self.linear2 = nn.Sequential(nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True))\n else:\n self.linear1 = nn.Sequential(nn.Linear(out_planes, out_planes), nn.BatchNorm1d(out_planes),\n nn.ReLU(inplace=True))\n self.linear2 = nn.Sequential(nn.Linear(in_planes, out_planes), nn.BatchNorm1d(out_planes),\n nn.ReLU(inplace=True))\n\n def forward(self, pxo1, pxo2=None):\n if pxo2 is None:\n _, x, o = pxo1 # (n, 3), (n, c), (b)\n x_tmp = []\n for i in range(o.shape[0]):\n if i == 0:\n s_i, e_i, cnt = 0, o[0], o[0]\n else:\n s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1]\n x_b = x[s_i:e_i, :]\n x_b = torch.cat((x_b, self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1)), 1)\n x_tmp.append(x_b)\n x = torch.cat(x_tmp, 0)\n x = self.linear1(x)\n else:\n p1, x1, o1 = pxo1\n p2, x2, o2 = pxo2\n x = self.linear1(x1) + pointops.interpolation(p2, p1, self.linear2(x2), o2, o1)\n return x\n\n\nclass PointTransformerBlock(nn.Module):\n expansion = 1\n\n def __init__(self, in_planes, planes, share_planes=8, nsample=16):\n super(PointTransformerBlock, self).__init__()\n self.linear1 = nn.Linear(in_planes, planes, bias=False)\n self.bn1 = nn.BatchNorm1d(planes)\n self.transformer2 = PointTransformerLayer(planes, planes, share_planes, nsample)\n self.bn2 = nn.BatchNorm1d(planes)\n self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False)\n self.bn3 = nn.BatchNorm1d(planes * self.expansion)\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, pxo):\n p, x, o = pxo # (n, 3), (n, c), (b)\n identity = x\n x = self.relu(self.bn1(self.linear1(x)))\n x = self.relu(self.bn2(self.transformer2([p, x, o])))\n x = self.bn3(self.linear3(x))\n x += identity\n x = self.relu(x)\n return [p, x, o]\n" }, { "path": "segmentation/modules/polar_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport numpy as np\n\n\ndef xyz2sphere(xyz, normalize=True):\n \"\"\"\n Convert XYZ to Spherical Coordinate\n\n reference: https://en.wikipedia.org/wiki/Spherical_coordinate_system\n\n :param xyz: [N, 3] / [N, G, 3]\n :return: (rho, theta, phi) [N, 3] / [N, G, 3]\n \"\"\"\n rho = torch.sqrt(torch.sum(torch.pow(xyz, 2), dim=-1, keepdim=True))\n rho = torch.clamp(rho, min=0) # range: [0, inf]\n theta = torch.acos(xyz[..., 2, None] / rho) # range: [0, pi]\n phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi]\n # check nan\n idx = rho == 0\n theta[idx] = 0\n\n if normalize:\n theta = theta / np.pi # [0, 1]\n phi = phi / (2 * np.pi) + .5 # [0, 1]\n out = torch.cat([rho, theta, phi], dim=-1)\n return out\n\n\ndef xyz2cylind(xyz, normalize=True):\n \"\"\"\n Convert XYZ to Cylindrical Coordinate\n\n reference: https://en.wikipedia.org/wiki/Cylindrical_coordinate_system\n\n :param normalize: Normalize phi & z\n :param xyz: [N, 3] / [N, G, 3]\n :return: (rho, phi, z) [N, 3] / [N, G, 3]\n \"\"\"\n rho = torch.sqrt(torch.sum(torch.pow(xyz[..., :2], 2), dim=-1, keepdim=True))\n rho = torch.clamp(rho, 0, 1) # range: [0, 1]\n phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi]\n z = xyz[..., 2, None]\n z = torch.clamp(z, -1, 1) # range: [-1, 1]\n\n if normalize:\n phi = phi / (2 * np.pi) + .5\n z = (z + 1.) / 2.\n out = torch.cat([rho, phi, z], dim=-1)\n return out\n" }, { "path": "segmentation/modules/recons_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport numpy as np\n\n\ndef cal_normal(group_xyz, offset, random_inv=False, is_group=False):\n \"\"\"\n Calculate Normal Vector (Unit Form + First Term Positive)\n\n :param group_xyz: [N, K=3, 3] / [N, G, K=3, 3]\n \"\"\"\n edge_vec1 = group_xyz[..., 1, :] - group_xyz[..., 0, :] # [N, 3]\n edge_vec2 = group_xyz[..., 2, :] - group_xyz[..., 0, :] # [N, 3]\n\n nor = torch.cross(edge_vec1, edge_vec2, dim=-1)\n unit_nor = nor / torch.norm(nor, dim=-1, keepdim=True) # [B, N, 3] / [B, N, G, 3]\n if not is_group:\n pos_mask = (unit_nor[..., 0] > 0).float() * 2. - 1. # keep x_n positive\n else:\n pos_mask = (unit_nor[..., 0:1, 0] > 0).float() * 2. - 1.\n unit_nor = unit_nor * pos_mask.unsqueeze(-1)\n\n # batch-wise random inverse normal vector (prob: 0.5)\n if random_inv:\n batch_prob = np.random.rand(offset.shape[0]) < 0.5\n random_mask = []\n sample_offset = [0] + list(offset.cpu().numpy())\n for idx in range(len(sample_offset) - 1):\n sample_mask = torch.ones((sample_offset[idx+1] - sample_offset[idx], 1), dtype=torch.float32)\n if not batch_prob[idx]:\n sample_mask *= -1\n random_mask.append(sample_mask)\n random_mask = torch.cat(random_mask, dim=0).to(unit_nor.device)\n # random_mask = torch.randint(0, 2, (group_xyz.size(0), 1)).float() * 2. - 1.\n # random_mask = random_mask.to(unit_nor.device)\n if not is_group:\n unit_nor = unit_nor * random_mask\n else:\n unit_nor = unit_nor * random_mask.unsqueeze(-1)\n\n return unit_nor\n\n\ndef cal_center(group_xyz):\n \"\"\"\n Calculate Global Coordinates of the Center of Triangle\n\n :param group_xyz: [N, K, 3] / [N, G, K, 3]; K >= 3\n :return: [N, 3] / [N, G, 3]\n \"\"\"\n center = torch.mean(group_xyz, dim=-2)\n return center\n\n\ndef cal_area(group_xyz):\n \"\"\"\n Calculate Area of Triangle\n\n :param group_xyz: [N, K, 3] / [N, G, K, 3]; K = 3\n :return: [N, 1] / [N, G, 1]\n \"\"\"\n pad_shape = group_xyz[..., 0, None].shape\n det_xy = torch.det(torch.cat([group_xyz[..., 0, None], group_xyz[..., 1, None], torch.ones(pad_shape)], dim=-1))\n det_yz = torch.det(torch.cat([group_xyz[..., 1, None], group_xyz[..., 2, None], torch.ones(pad_shape)], dim=-1))\n det_zx = torch.det(torch.cat([group_xyz[..., 2, None], group_xyz[..., 0, None], torch.ones(pad_shape)], dim=-1))\n area = torch.sqrt(det_xy ** 2 + det_yz ** 2 + det_zx ** 2).unsqueeze(-1)\n return area\n\n\ndef cal_const(normal, center, is_normalize=True):\n \"\"\"\n Calculate Constant Term (Standard Version, with x_normal to be 1)\n\n math::\n const = x_nor * x_0 + y_nor * y_0 + z_nor * z_0\n\n :param is_normalize:\n :param normal: [N, 3] / [N, G, 3]\n :param center: [N, 3] / [N, G, 3]\n :return: [N, 1] / [N, G, 1]\n \"\"\"\n const = torch.sum(normal * center, dim=-1, keepdim=True)\n factor = torch.sqrt(torch.Tensor([3])).to(normal.device)\n const = const / factor if is_normalize else const\n\n return const\n\n\ndef check_nan(normal, center, pos=None):\n \"\"\"\n Check & Remove NaN in normal tensor\n\n :param pos: [N, 1]\n :param center: [N, 3]\n :param normal: [N, 3]\n \"\"\"\n N, _ = normal.shape\n mask = torch.sum(torch.isnan(normal), dim=-1) > 0\n mask_first = torch.argmax((~mask).int(), dim=-1)\n\n normal_first = normal[None, mask_first].repeat([N, 1])\n normal[mask] = normal_first[mask]\n center_first = center[None, mask_first].repeat([N, 1])\n center[mask] = center_first[mask]\n\n if pos is not None:\n pos_first = pos[None, mask_first].repeat([N, 1])\n pos[mask] = pos_first[mask]\n return normal, center, pos\n return normal, center\n\n\ndef check_nan_umb(normal, center, pos=None):\n \"\"\"\n Check & Remove NaN in normal tensor\n\n :param pos: [N, G, 1]\n :param center: [N, G, 3]\n :param normal: [N, G, 3]\n \"\"\"\n N, G, _ = normal.shape\n mask = torch.sum(torch.isnan(normal), dim=-1) > 0\n mask_first = torch.argmax((~mask).int(), dim=-1)\n\n normal_first = normal[torch.arange(N), None, mask_first].repeat([1, G, 1])\n normal[mask] = normal_first[mask]\n center_first = center[torch.arange(N), None, mask_first].repeat([1, G, 1])\n center[mask] = center_first[mask]\n\n if pos is not None:\n pos_first = pos[torch.arange(N), None, mask_first].repeat([1, G, 1])\n pos[mask] = pos_first[mask]\n return normal, center, pos\n return normal, center\n" }, { "path": "segmentation/modules/repsurface_utils.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom modules.polar_utils import xyz2sphere\nfrom modules.recons_utils import cal_const, cal_normal, cal_center, check_nan_umb\nfrom modules.pointops.functions import pointops\n\n\ndef sample_and_group(stride, nsample, center, normal, feature, offset, return_polar=False, num_sector=1, training=True):\n # sample\n if stride > 1:\n new_offset, sample_idx = [offset[0].item() // stride], offset[0].item() // stride\n for i in range(1, offset.shape[0]):\n sample_idx += (offset[i].item() - offset[i - 1].item()) // stride\n new_offset.append(sample_idx)\n new_offset = torch.cuda.IntTensor(new_offset)\n if num_sector > 1 and training:\n fps_idx = pointops.sectorized_fps(center, offset, new_offset, num_sector) # [M]\n else:\n fps_idx = pointops.furthestsampling(center, offset, new_offset) # [M]\n new_center = center[fps_idx.long(), :] # [M, 3]\n new_normal = normal[fps_idx.long(), :] # [M, 3]\n else:\n new_center = center\n new_normal = normal\n new_offset = offset\n\n # group\n N, M, D = center.shape[0], new_center.shape[0], normal.shape[1]\n group_idx, _ = pointops.knnquery(nsample, center, new_center, offset, new_offset) # [M, nsample]\n group_center = center[group_idx.view(-1).long(), :].view(M, nsample, 3) # [M, nsample, 3]\n group_normal = normal[group_idx.view(-1).long(), :].view(M, nsample, D) # [M, nsample, 10]\n group_center_norm = group_center - new_center.unsqueeze(1)\n if return_polar:\n group_polar = xyz2sphere(group_center_norm)\n group_center_norm = torch.cat([group_center_norm, group_polar], dim=-1)\n\n if feature is not None:\n C = feature.shape[1]\n group_feature = feature[group_idx.view(-1).long(), :].view(M, nsample, C)\n new_feature = torch.cat([group_center_norm, group_normal, group_feature], dim=-1) # [npoint, nsample, C+D]\n else:\n new_feature = torch.cat([group_center_norm, group_normal], dim=-1)\n\n return new_center, new_normal, new_feature, new_offset\n\n\ndef resort_points(points, idx):\n \"\"\"\n Resort Set of points along G dim\n\n :param points: [N, G, 3]\n :param idx: [N, G]\n :return: [N, G, 3]\n \"\"\"\n device = points.device\n N, G, _ = points.shape\n\n n_indices = torch.arange(N, dtype=torch.long).to(device).view([N, 1]).repeat([1, G])\n new_points = points[n_indices, idx, :]\n\n return new_points\n\n\ndef _fixed_rotate(xyz):\n # y-axis:45deg -> z-axis:45deg\n rot_mat = torch.FloatTensor([[0.5, -0.5, 0.7071], [0.7071, 0.7071, 0.], [-0.5, 0.5, 0.7071]]).to(xyz.device)\n return xyz @ rot_mat\n\n\ndef group_by_umbrella_v2(xyz, new_xyz, offset, new_offset, k=9):\n \"\"\"\n Group a set of points into umbrella surfaces\n\n :param xyz: [N, 3]\n :param new_xyz: [N', 3]\n :param k: number of homogenous neighbors\n :return: [N', K-1, 3, 3]\n \"\"\"\n group_idx, _ = pointops.knnquery(k, xyz, new_xyz, offset, new_offset) # [M, K]\n group_xyz = xyz[group_idx.view(-1).long(), :].view(new_xyz.shape[0], k, 3) # [M, K, 3]\n group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2)\n group_phi = xyz2sphere(_fixed_rotate(group_xyz_norm))[..., 2] # [M, K-1]\n sort_idx = group_phi.argsort(dim=-1) # [M, K-1]\n\n # [M, K-1, 1, 3]\n sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2)\n sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3)\n group_centriod = torch.zeros_like(sorted_group_xyz)\n umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2)\n\n return umbrella_group_xyz\n\n\ndef group_by_umbrella(xyz, new_xyz, offset, new_offset, k=9):\n \"\"\"\n Group a set of points into umbrella surfaces\n\n :param xyz: [N, 3]\n :param new_xyz: [N', 3]\n :param k: number of homogenous neighbors\n :return: [N', K-1, 3, 3]\n \"\"\"\n group_idx, _ = pointops.knnquery(k, xyz, new_xyz, offset, new_offset) # [M, K]\n group_xyz = xyz[group_idx.view(-1).long(), :].view(new_xyz.shape[0], k, 3) # [M, K, 3]\n group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2)\n group_phi = xyz2sphere(group_xyz_norm)[..., 2] # [M, K-1]\n sort_idx = group_phi.argsort(dim=-1) # [M, K-1]\n\n # [M, K-1, 1, 3]\n sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2)\n sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3)\n group_centriod = torch.zeros_like(sorted_group_xyz)\n umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2)\n\n return umbrella_group_xyz\n\n\ndef sort_factory(s_type):\n if s_type is None:\n return group_by_umbrella\n elif s_type == 'fix':\n return group_by_umbrella_v2\n else:\n raise Exception('No such sorting method')\n\n\nclass SurfaceAbstraction(nn.Module):\n \"\"\"\n Surface Abstraction Module\n\n \"\"\"\n\n def __init__(self, stride, nsample, in_channel, mlp, return_polar=True, num_sector=1):\n super(SurfaceAbstraction, self).__init__()\n self.stride = stride\n self.nsample = nsample\n self.num_sector = num_sector\n self.return_polar = return_polar\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n\n last_channel = in_channel\n for out_channel in mlp:\n self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))\n self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n last_channel = out_channel\n\n def forward(self, pos_nor_feat_off):\n center, normal, feature, offset = pos_nor_feat_off # [N, 3], [N, 10], [N, C], [B]\n\n new_center, new_normal, new_feature, new_offset = sample_and_group(self.stride, self.nsample, center,\n normal, feature, offset,\n return_polar=self.return_polar,\n num_sector=self.num_sector,\n training=self.training)\n\n # new_center: sampled points position data, [M, 3]\n # new_normal: sampled normal feature data, [M, 3/10]\n # new_feature: sampled feature, [M, nsample, 3+3/10+C]\n new_feature = new_feature.transpose(1, 2).contiguous() # [M, 3+C, nsample]\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_feature = F.relu(bn(conv(new_feature)))\n new_feature = torch.max(new_feature, 2)[0]\n\n return [new_center, new_normal, new_feature, new_offset]\n\n\nclass SurfaceAbstractionCD(nn.Module):\n \"\"\"\n Surface Abstraction Module w/ Channel De-differentiation\n\n \"\"\"\n\n def __init__(self, stride, nsample, feat_channel, pos_channel, mlp, return_normal=True, return_polar=False,\n num_sector=1):\n super(SurfaceAbstractionCD, self).__init__()\n self.stride = stride\n self.nsample = nsample\n self.return_normal = return_normal\n self.return_polar = return_polar\n self.num_sector = num_sector\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n self.pos_channel = pos_channel\n\n self.mlp_l0 = nn.Conv1d(self.pos_channel, mlp[0], 1)\n self.mlp_f0 = nn.Conv1d(feat_channel, mlp[0], 1)\n self.bn_l0 = nn.BatchNorm1d(mlp[0])\n self.bn_f0 = nn.BatchNorm1d(mlp[0])\n\n last_channel = mlp[0]\n for out_channel in mlp[1:]:\n self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))\n self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n last_channel = out_channel\n\n def forward(self, pos_nor_feat_off):\n center, normal, feature, offset = pos_nor_feat_off # [N, 3], [N, 10], [N, C], [B]\n\n new_center, new_normal, new_feature, new_offset = sample_and_group(self.stride, self.nsample, center,\n normal, feature, offset,\n return_polar=self.return_polar,\n num_sector=self.num_sector,\n training=self.training)\n\n # new_center: sampled points position data, [M, 3]\n # new_normal: sampled normal feature data, [M, 3/10]\n # new_feature: sampled feature, [M, nsample, 3+3/10+C]\n new_feature = new_feature.transpose(1, 2).contiguous() # [M, 3+C, nsample]\n\n # init layer\n loc = self.bn_l0(self.mlp_l0(new_feature[:, :self.pos_channel]))\n feat = self.bn_f0(self.mlp_f0(new_feature[:, self.pos_channel:]))\n new_feature = loc + feat\n new_feature = F.relu(new_feature)\n\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_feature = F.relu(bn(conv(new_feature)))\n new_feature = torch.max(new_feature, 2)[0]\n\n return [new_center, new_normal, new_feature, new_offset]\n\n\nclass SurfaceFeaturePropagationCD(nn.Module):\n \"\"\"\n Surface FP Module w/ Channel De-differentiation\n\n \"\"\"\n\n def __init__(self, prev_channel, skip_channel, mlp):\n super(SurfaceFeaturePropagationCD, self).__init__()\n self.mlp_convs = nn.ModuleList()\n self.mlp_bns = nn.ModuleList()\n self.skip = skip_channel is not None\n\n self.mlp_f0 = nn.Linear(prev_channel, mlp[0])\n self.norm_f0 = nn.BatchNorm1d(mlp[0])\n if skip_channel is not None:\n self.mlp_s0 = nn.Linear(skip_channel, mlp[0])\n self.norm_s0 = nn.BatchNorm1d(mlp[0])\n\n last_channel = mlp[0]\n for out_channel in mlp[1:]:\n self.mlp_convs.append(nn.Linear(last_channel, out_channel))\n self.mlp_bns.append(nn.BatchNorm1d(out_channel))\n last_channel = out_channel\n\n def forward(self, pos_feat_off1, pos_feat_off2):\n xyz1, points1, offset1 = pos_feat_off1 # [N, 3], [N, C], [B]\n xyz2, points2, offset2 = pos_feat_off2 # [M, 3], [M, C], [B]\n\n # interpolation\n idx, dist = pointops.knnquery(3, xyz2, xyz1, offset2, offset1) # [M, 3], [M, 3]\n dist_recip = 1.0 / (dist + 1e-8) # [M, 3]\n norm = torch.sum(dist_recip, dim=1, keepdim=True)\n weight = dist_recip / norm # [M, 3]\n\n points2 = self.norm_f0(self.mlp_f0(points2))\n interpolated_points = torch.cuda.FloatTensor(xyz1.shape[0], points2.shape[1]).zero_()\n for i in range(3):\n interpolated_points += points2[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)\n\n # init layer\n if self.skip:\n skip = self.norm_s0(self.mlp_s0(points1))\n new_points = F.relu(interpolated_points + skip)\n else:\n new_points = F.relu(interpolated_points)\n\n # mlp\n for i, conv in enumerate(self.mlp_convs):\n bn = self.mlp_bns[i]\n new_points = F.relu(bn(conv(new_points)))\n\n return new_points\n\n\nclass UmbrellaSurfaceConstructor(nn.Module):\n \"\"\"\n Umbrella Surface Representation Constructor\n\n \"\"\"\n\n def __init__(self, k, in_channel, out_channel, random_inv=True, sort='fix'):\n super(UmbrellaSurfaceConstructor, self).__init__()\n self.k = k\n self.random_inv = random_inv\n\n self.mlps = nn.Sequential(\n nn.Conv1d(in_channel, out_channel, 1, bias=True),\n nn.BatchNorm1d(out_channel),\n nn.ReLU(True),\n nn.Conv1d(out_channel, out_channel, 1, bias=True),\n )\n self.sort_func = sort_factory(sort)\n\n def forward(self, center, offset):\n # umbrella surface reconstruction\n group_xyz = self.sort_func(center, center, offset, offset, k=self.k) # [N, K-1, 3 (points), 3 (coord.)]\n\n # normal\n group_normal = cal_normal(group_xyz, offset, random_inv=self.random_inv, is_group=True)\n # coordinate\n group_center = cal_center(group_xyz)\n # polar\n group_polar = xyz2sphere(group_center)\n # surface position\n group_pos = cal_const(group_normal, group_center)\n\n group_normal, group_center, group_pos = check_nan_umb(group_normal, group_center, group_pos)\n new_feature = torch.cat([group_polar, group_normal, group_pos, group_center], dim=-1) # P+N+SP+C: 10\n new_feature = new_feature.transpose(1, 2).contiguous() # [N, C, G]\n\n # mapping\n new_feature = self.mlps(new_feature)\n\n # aggregation\n new_feature = torch.sum(new_feature, 2)\n\n return new_feature\n" }, { "path": "segmentation/modules/voxelize_utils.py", "content": "import numpy as np\n\n\ndef fnv_hash_vec(arr):\n \"\"\"\n FNV64-1A\n\n \"\"\"\n assert arr.ndim == 2\n # Floor first for negative coordinates\n arr = arr.copy()\n arr = arr.astype(np.uint64, copy=False)\n hashed_arr = np.uint64(14695981039346656037) * np.ones(arr.shape[0], dtype=np.uint64)\n for j in range(arr.shape[1]):\n hashed_arr *= np.uint64(1099511628211)\n hashed_arr = np.bitwise_xor(hashed_arr, arr[:, j])\n return hashed_arr\n\n\ndef ravel_hash_vec(arr):\n \"\"\"\n Ravel the coordinates after subtracting the min coordinates.\n\n \"\"\"\n assert arr.ndim == 2\n arr = arr.copy()\n arr -= arr.min(0)\n arr = arr.astype(np.uint64, copy=False)\n arr_max = arr.max(0).astype(np.uint64) + 1\n\n keys = np.zeros(arr.shape[0], dtype=np.uint64)\n # Fortran style indexing\n for j in range(arr.shape[1] - 1):\n keys += arr[:, j]\n keys *= arr_max[j + 1]\n keys += arr[:, -1]\n return keys\n\n\ndef voxelize(coord, voxel_size=0.05, hash_type='fnv', mode=0):\n # voxelize coordinates\n discrete_coord = np.floor(coord / np.array(voxel_size))\n\n # shuffle coordinates\n if hash_type == 'ravel':\n key = ravel_hash_vec(discrete_coord)\n else:\n key = fnv_hash_vec(discrete_coord)\n\n idx_sort = np.argsort(key)\n key_sort = key[idx_sort]\n _, count = np.unique(key_sort, return_counts=True)\n if mode == 0: # train mode\n idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + np.random.randint(0, count.max(), count.size) % count\n idx_unique = idx_sort[idx_select]\n return idx_unique\n else: # val mode\n return idx_sort, count\n" }, { "path": "segmentation/scripts/s3dis/test_pointnet2.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='pointnet2_A5'\n\npython3 tool/test_s3dis.py --log_dir ${log_dir} \\\n --batch_size_test 12 \\\n --gpu_id 0 \\\n --model pointnet2.pointnet2_ssg \\\n --test_area 5 \\\n --filter" }, { "path": "segmentation/scripts/s3dis/test_pointtransformer.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='pointtransformer_A5'\n\npython3 tool/test_s3dis.py --log_dir ${log_dir} \\\n --batch_size_test 12 \\\n --gpu_id 0 \\\n --model pointtransformer.pointtransformer \\\n --test_area 5 \\\n --filter" }, { "path": "segmentation/scripts/s3dis/test_repsurf_umb.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='repsurf_umb_A5'\n\npython3 tool/test_s3dis.py --log_dir ${log_dir} \\\n --batch_size_test 12 \\\n --gpu_id 0 \\\n --model repsurf.repsurf_umb_ssg \\\n --test_area 5 \\\n --filter" }, { "path": "segmentation/scripts/s3dis/train_pointnet2.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='pointnet2_A5'\n\npython3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \\\n --batch_size 8 \\\n --batch_size_val 24 \\\n --workers 24 \\\n --gpus 0 1 2 3 \\\n --model pointnet2.pointnet2_ssg \\\n --optimizer AdamW \\\n --min_val 60 \\\n --epoch 100 \\\n --lr_decay_epochs 60 80 \\\n --test_area 5 \\\n --learning_rate 0.006 \\\n --lr_decay 0.1 \\\n --weight_decay 1e-2 \\\n --aug_scale \\\n --color_contrast \\\n --color_shift \\\n --color_jitter \\\n --hs_shift" }, { "path": "segmentation/scripts/s3dis/train_pointtransformer.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='pointtransformer_A5'\n\npython3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \\\n --batch_size 8 \\\n --batch_size_val 24 \\\n --workers 24 \\\n --gpus 0 1 2 3 \\\n --model pointtransformer.pointtransformer \\\n --optimizer AdamW \\\n --min_val 60 \\\n --epoch 100 \\\n --lr_decay_epochs 60 80 \\\n --test_area 5 \\\n --learning_rate 0.006 \\\n --lr_decay 0.1 \\\n --weight_decay 1e-2 \\\n --aug_scale \\\n --color_contrast \\\n --color_shift \\\n --color_jitter \\\n --hs_shift" }, { "path": "segmentation/scripts/s3dis/train_repsurf_umb.sh", "content": "#!/bin/bash\n\nexport PYTHONPATH=./\n\nlog_dir='repsurf_umb_A5'\n\npython3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \\\n --batch_size 8 \\\n --batch_size_val 24 \\\n --workers 24 \\\n --gpus 0 1 2 3 \\\n --model repsurf.repsurf_umb_ssg \\\n --optimizer AdamW \\\n --min_val 60 \\\n --epoch 100 \\\n --lr_decay_epochs 60 80 \\\n --test_area 5 \\\n --learning_rate 0.006 \\\n --lr_decay 0.1 \\\n --weight_decay 1e-2 \\\n --freeze_epoch 10 \\\n --color_contrast \\\n --color_shift \\\n --color_jitter \\\n --hs_shift\n" }, { "path": "segmentation/tool/test_s3dis.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport json\nimport os\nimport time\nimport random\nimport numpy as np\nimport argparse\nimport collections\nfrom pathlib import Path\n\nimport torch\nimport torch.nn.parallel\nimport torch.optim\nimport torch.utils.data\n\nfrom util.utils import AverageMeter, intersectionAndUnion, get_rgb_stat, pc_median_filter_gpu\nfrom util.utils import get_model, get_logger\nfrom modules.voxelize_utils import voxelize\n\n\nLABEL2COLOR = collections.OrderedDict([\n ('ceiling', [0, 255, 0]), ('floor', [0, 0, 255]), ('wall', [0, 255, 255]), ('beam', [255, 255, 0]),\n ('column', [255, 0, 255]), ('window', [100, 100, 255]), ('door', [200, 200, 100]), ('chair', [170, 120, 200]),\n ('table', [255, 0, 0]), ('bookcase', [200, 100, 100]), ('sofa', [10, 200, 100]), ('board', [200, 200, 200]),\n ('clutter', [50, 50, 50])])\nLABEL2CLASS = list(LABEL2COLOR.keys())\nPALETTE = np.array(list(LABEL2COLOR.values()), dtype=np.int64)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser('Model')\n\n # Basic\n parser.add_argument('--log_dir', type=str, default=None, help='experiment root')\n parser.add_argument('--data_dir', type=str, default='./data/S3DIS/trainval_fullarea', help='data dir')\n parser.add_argument('--log_root', type=str, default='./log', help='log root dir')\n parser.add_argument('--model_path', type=str, default=None, help='saved model weight')\n parser.add_argument('--model', default='pointnet_sem_seg', help='model name [default: pointnet_sem_seg]')\n parser.add_argument('--gpu_id', type=str, default='0')\n parser.add_argument('--seed', type=int, default=1000, help='Test Seed')\n\n # Test\n parser.add_argument('--batch_size_test', type=int, default=12, help='batch size in test [default: 24]')\n parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test [default: 5]')\n parser.add_argument('--filter', action='store_true', default=False, help='Apply median filter [default: False]')\n parser.add_argument('--data_norm', type=str, default='mean', help='initializer for model [mean, min, z_min]')\n parser.add_argument('--visual', action='store_true', default=False, help='Output visual results [default: False]')\n\n # Modeling\n parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]')\n parser.add_argument('--return_polar', action='store_true', default=False,\n help='Whether to return polar coordinate in surface abstraction [default: False]')\n\n return parser.parse_args()\n\n\ndef main():\n global args, logger\n\n args = parse_args()\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu_id\n random.seed(args.seed)\n np.random.seed(args.seed)\n\n args.dataset, args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \\\n 'S3DIS', 13, 80000, 0.04, 6, 255\n\n experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log', 'S3DIS'))\n experiment_dir = experiment_dir.joinpath(args.log_dir)\n checkpoints_dir = experiment_dir.joinpath('checkpoints/')\n args.ckpt_dir = str(checkpoints_dir)\n log_dir = experiment_dir.joinpath('logs/')\n args.log_dir = str(log_dir)\n result_dir = experiment_dir.joinpath('visual/')\n result_dir.mkdir(exist_ok=True)\n args.result_dir = str(result_dir)\n\n logger = get_logger(args.log_dir, 'test_%s' % args.model)\n logger.info(json.dumps(vars(args), indent=4, sort_keys=True))\n logger.info(\"=> creating models ...\")\n model = get_model(args).cuda()\n logger.info(model)\n\n ckpt_file = os.path.join(args.ckpt_dir, 'model_best.pth') if args.model_path is None else args.model_path\n if os.path.isfile(ckpt_file):\n logger.info(\"=> loading checkpoint '{}'\".format(ckpt_file))\n checkpoint = torch.load(ckpt_file)\n state_dict = checkpoint['state_dict']\n new_state_dict = collections.OrderedDict()\n for k, v in state_dict.items():\n name = k[7:]\n new_state_dict[name] = v.cpu()\n model.load_state_dict(new_state_dict, strict=True)\n logger.info(\"=> loaded checkpoint '{}'\".format(ckpt_file))\n else:\n raise RuntimeError(\"=> no checkpoint found at '{}'\".format(ckpt_file))\n\n test(model)\n\n\ndef data_prepare():\n \"\"\" Return area names of the test dataset \"\"\"\n data_list = sorted(os.listdir(args.data_dir))\n data_list = [item[:-4] for item in data_list if 'Area_{}'.format(args.test_area) in item]\n print(\"Totally {} samples in val set.\".format(len(data_list)))\n\n return data_list\n\n\ndef data_load(data_name):\n \"\"\" Load data by area name \"\"\"\n data_path = os.path.join(args.data_dir, data_name + '.npy')\n data = np.load(data_path) # xyzrgbl, N*7\n coord, feat, label = data[:, :3], data[:, 3:6], data[:, 6]\n\n idx_data = []\n if args.voxel_size:\n idx_sort, count = voxelize(coord - np.min(coord, 0), args.voxel_size, mode=1)\n for i in range(count.max()):\n idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + i % count\n idx_part = idx_sort[idx_select]\n idx_data.append(idx_part)\n else:\n idx_data.append(np.arange(label.shape[0]))\n\n return coord, feat, label, idx_data\n\n\ndef data_process(coord, feat, idx_data):\n \"\"\" Split points into batches by index \"\"\"\n idx_size = len(idx_data)\n idx_list, coord_list, feat_list, offset_list = [], [], [], []\n for i in range(idx_size):\n idx_part = idx_data[i]\n coord_part, feat_part = coord[idx_part], feat[idx_part]\n if args.voxel_max and coord_part.shape[0] > args.voxel_max:\n coord_p, idx_uni, cnt = np.random.rand(coord_part.shape[0]) * 1e-3, np.array([]), 0\n while idx_uni.size != idx_part.shape[0]:\n init_idx = np.argmin(coord_p)\n dist = np.sum(np.power(coord_part - coord_part[init_idx], 2), 1)\n idx_crop = np.argsort(dist)[:args.voxel_max]\n coord_sub, feat_sub, idx_sub = coord_part[idx_crop], feat_part[idx_crop], idx_part[idx_crop]\n dist = dist[idx_crop]\n delta = np.square(1 - dist / np.max(dist))\n coord_p[idx_crop] += delta\n coord_sub, feat_sub = input_normalize(coord_sub, feat_sub)\n idx_list.append(idx_sub), coord_list.append(coord_sub), feat_list.append(\n feat_sub), offset_list.append(idx_sub.size)\n idx_uni = np.unique(np.concatenate((idx_uni, idx_sub)))\n else:\n coord_part, feat_part = input_normalize(coord_part, feat_part)\n idx_list.append(idx_part), coord_list.append(coord_part), feat_list.append(\n feat_part), offset_list.append(idx_part.size)\n\n return idx_list, coord_list, feat_list, offset_list\n\n\ndef input_normalize(coord, feat):\n # normalize\n if args.data_norm == 'mean':\n coord -= np.mean(coord, 0)\n elif args.data_norm == 'min':\n coord -= np.min(coord, 0)\n else:\n raise Exception('No such data norm type')\n\n feat = feat / 255.\n if args.color_mean is not None and args.color_std is not None:\n feat = (feat - args.color_mean) / args.color_std\n return coord, feat\n\n\ndef visualize_scene(coord, pred, label, name):\n color_pred = PALETTE[pred.astype(np.int64)]\n color_gt = PALETTE[label.astype(np.int64)]\n pred_save_path = os.path.join(args.result_dir, '{}_pred.txt'.format(name))\n label_save_path = os.path.join(args.result_dir, '{}_label.txt'.format(name))\n np.savetxt(pred_save_path, np.hstack([coord, color_pred]), fmt=\"%f \" * 3 + \"%d \" * 3)\n np.savetxt(label_save_path, np.hstack([coord, color_gt]), fmt=\"%f \" * 3 + \"%d \" * 3)\n\n\ndef test(model):\n logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')\n batch_time = AverageMeter()\n model.eval()\n\n pred_list, label_list = [], []\n data_list = data_prepare()\n args.color_mean, args.color_std = get_rgb_stat(args)\n\n for idx_scene, scene_name in enumerate(data_list):\n end = time.time()\n coord, feat, label, idx_data = data_load(scene_name)\n idx_list, coord_list, feat_list, offset_list = data_process(coord, feat, idx_data)\n\n pred = torch.zeros((label.size, args.num_class)).cuda(non_blocking=True)\n pred_count = torch.zeros((label.size, args.num_class)).cuda(non_blocking=True)\n num_batch = int(np.ceil(len(idx_list) / args.batch_size_test))\n for idx_batch in range(num_batch):\n idx_start = idx_batch * args.batch_size_test\n idx_end = min((idx_batch + 1) * args.batch_size_test, len(idx_list))\n idx_part, coord_part, feat_part, offset_part = \\\n idx_list[idx_start:idx_end], coord_list[idx_start:idx_end], \\\n feat_list[idx_start:idx_end], offset_list[idx_start:idx_end]\n\n idx_part = np.concatenate(idx_part)\n coord_part = torch.FloatTensor(np.concatenate(coord_part)).cuda(non_blocking=True)\n feat_part = torch.FloatTensor(np.concatenate(feat_part)).cuda(non_blocking=True)\n offset_part = torch.IntTensor(np.cumsum(offset_part)).cuda(non_blocking=True)\n\n with torch.no_grad():\n pred_part = torch.nn.functional.softmax(model([coord_part, feat_part, offset_part]), dim=1) # (n, k)\n torch.cuda.empty_cache()\n\n pred[idx_part, :] += pred_part\n pred_count[idx_part, :] += 1.\n logger.info('Scene {}/{}, {}/{}, {}/{}'.format(idx_scene + 1, len(data_list), idx_end, len(idx_list), args.voxel_max, idx_part.shape[0]))\n\n # IoU per scene\n pred_choice = np.argmax((pred/pred_count).cpu().numpy(), 1)\n coord = coord\n label = label\n\n # median filter\n if args.filter:\n coord_gpu = torch.from_numpy(coord).float().cuda(non_blocking=True)\n pred_gpu = torch.from_numpy(pred_choice).int().cuda(non_blocking=True)\n pred_choice = pc_median_filter_gpu(coord_gpu, pred_gpu, 32)\n\n batch_time.update(time.time() - end)\n logger.info('Test: [{}/{}]-{} Batch {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(\n idx_scene + 1, len(data_list), label.size, batch_time=batch_time))\n pred_list.append(pred_choice)\n label_list.append(label)\n\n if args.visual:\n visualize_scene(coord, pred_choice, label, scene_name)\n\n # mIoU\n intersection, union, target = intersectionAndUnion(np.concatenate(pred_list), np.concatenate(label_list),\n args.num_class, args.ignore_label)\n iou_class = intersection / (union + 1e-10)\n accuracy_class = intersection / (target + 1e-10)\n mIoU = np.mean(iou_class)\n mAcc = np.mean(accuracy_class)\n allAcc = sum(intersection) / (sum(target) + 1e-10)\n logger.info('Val result: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}.'.format(mIoU * 100, mAcc * 100, allAcc * 100))\n\n for i in range(args.num_class):\n logger.info('Class_{} Result: IoU / Acc {:.2f} / {:.2f}, name: {}.'.format(\n i, iou_class[i] * 100, accuracy_class[i] * 100, LABEL2CLASS[i]))\n logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "segmentation/tool/train.py", "content": "\"\"\"\nAuthor: Haoxi Ran\nDate: 06/30/2022\n\"\"\"\n\nimport json\nimport os\nimport time\nfrom functools import partial\n\nimport numpy as np\nimport argparse\nfrom pathlib import Path\nimport datetime\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.optim\nimport torch.utils.data\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\n\nfrom tensorboardX import SummaryWriter\nfrom modules.aug_utils import transform_point_cloud_coord, transform_point_cloud_rgb\n\nfrom util.utils import AverageMeter, intersectionAndUnionGPU, find_free_port, get_dataset_description, get_optimizer, \\\n get_scheduler, get_loop, get_aug_args, get_loss, get_dataset_obj, get_rgb_stat, worker_init_fn\nfrom util.data_util import collate_fn\nfrom util.utils import get_model, get_class_weights, set_seed, main_process, get_logger\n\n\ndef parse_args():\n parser = argparse.ArgumentParser('Model')\n\n # Basic\n parser.add_argument('--log_dir', type=str, default=None, help='experiment root')\n parser.add_argument('--log_root', type=str, default='./log', help='log root dir')\n parser.add_argument('--dataset', type=str, default='S3DIS', help='dataset name')\n parser.add_argument('--model', default='pointnet_sem_seg', help='model name [default: pointnet_sem_seg]')\n parser.add_argument('--gpus', nargs='+', type=str, default=None)\n parser.add_argument('--seed', type=int, default=2000, help='Training Seed')\n parser.add_argument('--world_size', type=int, default=1, help='Number of processes participating in the job')\n parser.add_argument('--rank', type=int, default=0, help='Rank of the current process')\n parser.add_argument('--multiprocessing_distributed', action='store_false', default=True,\n help='Whether to use multiprocessing [default: True]')\n parser.add_argument('--sync_bn', action='store_true', default=False,\n help='Whether to use sync bn [default: False]')\n\n # Training\n parser.add_argument('--epoch', default=100, type=int, help='number of epoch in training [default: 100]')\n parser.add_argument('--batch_size', type=int, default=8, help='batch size in training [default: 32]')\n parser.add_argument('--workers', type=int, default=8, help='DataLoader Workers Number [default: 4]')\n parser.add_argument('--optimizer', type=str, default='AdamW', help='optimizer for training [SGD, AdamW]')\n parser.add_argument('--momentum', type=float, default=0.9, help='optimizer momentum [default: 0.9]')\n parser.add_argument('--weight_decay', type=float, default=1e-2, help='decay rate [default: 1e-2]')\n parser.add_argument('--scheduler', type=str, default='step', help='scheduler for training [step]')\n parser.add_argument('--learning_rate', default=0.006, type=float, help='init learning rate [default: 0.5]')\n parser.add_argument('--lr_decay', type=float, default=0.1, help='decay rate [default: 0.1]')\n parser.add_argument('--data_norm', type=str, default='mean', help='initializer for model [mean, min]')\n parser.add_argument('--lr_decay_epochs', type=int, default=[60, 80], nargs='+',\n help='for step scheduler. where to decay lr, can be a list')\n parser.add_argument('--start_epoch', type=int, default=0, help='Start Training Epoch [default: 0]')\n parser.add_argument('--train_freq', type=int, default=250, help='Training frequency [default: 250]')\n parser.add_argument('--resume', type=str, default=None, help='Trained checkpoint path')\n parser.add_argument('--pretrain', type=str, default=None, help='Pretrain model path')\n\n # Evaluation\n parser.add_argument('--batch_size_val', type=int, default=8, help='batch size in validation [default: 4]')\n parser.add_argument('--min_val', type=int, default=60, help='Min val epoch [default: 60]')\n parser.add_argument('--val_freq', type=int, default=1, help='Val frequency [default: 1]')\n parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test [default: 5]')\n\n # Augmentation\n parser.add_argument('--aug_scale', action='store_true', default=False,\n help='Whether to augment by scaling [default: False]')\n parser.add_argument('--aug_rotate', type=str, default=None,\n help='Type to augment by rotation [pert, pert_z, rot, rot_z]')\n parser.add_argument('--aug_jitter', action='store_true', default=False,\n help='Whether to augment by shifting [default: False]')\n parser.add_argument('--aug_flip', action='store_true', default=False,\n help='Whether to augment by flipping [default: False]')\n parser.add_argument('--aug_shift', action='store_true', default=False,\n help='Whether to augment by shifting [default: False]')\n parser.add_argument('--color_contrast', action='store_true', default=False,\n help='Whether to augment by RGB contrasting [default: False]')\n parser.add_argument('--color_shift', action='store_true', default=False,\n help='Whether to augment by RGB shifting [default: False]')\n parser.add_argument('--color_jitter', action='store_true', default=False,\n help='Whether to augment by RGB jittering [default: False]')\n parser.add_argument('--hs_shift', action='store_true', default=False,\n help='Whether to augment by HueSaturation shifting [default: False]')\n parser.add_argument('--color_drop', action='store_true', default=False,\n help='Whether to augment by RGB Dropout [default: False]')\n\n # RepSurf\n parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]')\n parser.add_argument('--return_polar', action='store_true', default=False,\n help='Whether to return polar coordinate in surface abstraction [default: False]')\n parser.add_argument('--freeze_epoch', default=1e6, type=int,\n help='number of epoch to freeze repsurf [default: 1e6]')\n\n return parser.parse_args()\n\n\ndef main_worker(gpu, ngpus_per_node, argss):\n global args, best_iou\n args, best_iou = argss, 0\n if args.distributed:\n if args.dist_url == \"env://\" and args.rank == -1:\n args.rank = int(os.environ[\"RANK\"])\n if args.multiprocessing_distributed:\n args.rank = args.rank * ngpus_per_node + gpu\n\n dist.init_process_group(backend='nccl', init_method=args.dist_url, world_size=args.world_size, rank=args.rank)\n\n set_seed(args.seed + args.rank)\n\n \"\"\"MODEL BUILDING\"\"\"\n # model\n model = get_model(args)\n\n if main_process(args):\n global logger, writer\n logger = get_logger(args.log_dir, args.model)\n writer = SummaryWriter(args.log_dir)\n logger.info(json.dumps(vars(args), indent=4, sort_keys=True)) # print args\n logger.info(\"=> creating models ...\")\n logger.info(\"Classes: {}\".format(args.num_class))\n logger.info(model)\n # print num of params\n num_param = sum(p.numel() for p in model.parameters() if p.requires_grad)\n logger.info('Total Number of Parameters: {} M'.format(str(float(num_param) / 1e6)[:5]))\n\n if args.distributed:\n torch.cuda.set_device(gpu)\n args.batch_size = int(args.batch_size / ngpus_per_node)\n args.batch_size_val = int(args.batch_size_val / ngpus_per_node)\n args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)\n\n if args.sync_bn:\n nn.SyncBatchNorm.convert_sync_batchnorm(model)\n model.cuda()\n # model parallel (Note: During DDP Training, enable 'find_unused_parameters' to freeze repsurf)\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu],\n find_unused_parameters='repsurf' in args.model)\n else:\n # model\n model.cuda()\n model = torch.nn.DataParallel(model)\n\n \"\"\"DATA LOADING\"\"\"\n coord_transform = transform_point_cloud_coord(args)\n rgb_transform = transform_point_cloud_rgb(args)\n\n dataset_obj = get_dataset_obj(args)\n rgb_mean, rgb_std = get_rgb_stat(args)\n if 'trainval' not in args.dataset:\n TRAIN_DATASET = dataset_obj(args, 'train', coord_transform, rgb_transform, rgb_mean, rgb_std, True)\n VAL_DATASET = dataset_obj(args, 'val', None, None, rgb_mean, rgb_std, False)\n VAL_DATASET.stop_aug = True\n if main_process(args):\n logger.info(\"Totally {} samples in {} set.\".format(len(TRAIN_DATASET) // args.loop, 'train'))\n logger.info(\"Totally {} samples in {} set.\".format(len(VAL_DATASET) // args.loop, 'val'))\n else:\n TRAIN_DATASET = dataset_obj(args, 'trainval', coord_transform, rgb_transform, rgb_mean, rgb_std, True)\n VAL_DATASET = None\n if main_process(args):\n logger.info(\"Totally {} samples in {} set.\".format(len(TRAIN_DATASET) // args.loop, 'trainval'))\n\n \"\"\"DATALOADER BUILDING\"\"\"\n train_sampler = torch.utils.data.distributed.DistributedSampler(TRAIN_DATASET) if args.distributed else None\n train_loader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=train_sampler is None,\n num_workers=args.workers, pin_memory=True, sampler=train_sampler,\n drop_last=True, collate_fn=collate_fn,\n worker_init_fn=partial(worker_init_fn, seed=args.seed))\n\n if VAL_DATASET is not None:\n val_sampler = torch.utils.data.distributed.DistributedSampler(VAL_DATASET) if args.distributed else None\n val_loader = torch.utils.data.DataLoader(VAL_DATASET, batch_size=args.batch_size_val, shuffle=False,\n num_workers=args.workers, pin_memory=True, sampler=val_sampler,\n collate_fn=collate_fn,\n worker_init_fn=partial(worker_init_fn, seed=args.seed + 100))\n\n \"\"\"TRAINING UTILS\"\"\"\n # loss\n label_weight = get_class_weights(args.description).cuda()\n criterion = get_loss(label_weight, args.ignore_label).cuda()\n # optimizer\n optimizer = get_optimizer(args, model)\n # scheduler\n scheduler = get_scheduler(args, optimizer)\n\n \"\"\"MODEL RESTORE\"\"\"\n if args.resume is not None:\n if os.path.isfile(args.resume):\n if main_process(args):\n logger.info(\"=> loading checkpoint '{}'\".format(args.resume))\n checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage.cuda())\n args.start_epoch = checkpoint['epoch']\n model.load_state_dict(checkpoint['state_dict'], strict=True)\n optimizer.load_state_dict(checkpoint['optimizer'])\n scheduler.load_state_dict(checkpoint['scheduler'])\n best_iou = checkpoint['best_iou']\n if main_process(args):\n logger.info(\"=> loaded checkpoint '{}' (epoch {})\".format(args.resume, checkpoint['epoch']))\n else:\n if main_process(args):\n logger.info(\"=> no checkpoint found at '{}'\".format(args.resume))\n if args.pretrain is not None:\n if os.path.isfile(args.pretrain):\n if main_process(args):\n logger.info(\"=> loading pretrained model '{}'\".format(args.pretrain))\n checkpoint = torch.load(args.pretrain, map_location=lambda storage, loc: storage.cuda())\n model.load_state_dict(checkpoint['state_dict'], strict=True)\n\n \"\"\"TRAINING\"\"\"\n for epoch in range(args.start_epoch, args.epoch):\n # train\n if args.distributed:\n train_sampler.set_epoch(epoch)\n loss_train, mIoU_train, mAcc_train, allAcc_train = train(train_loader, model, criterion, optimizer, epoch)\n scheduler.step()\n\n epoch_log = epoch + 1\n if main_process(args):\n writer.add_scalar('loss_train', loss_train, epoch_log)\n writer.add_scalar('mIoU_train', mIoU_train, epoch_log)\n writer.add_scalar('mAcc_train', mAcc_train, epoch_log)\n writer.add_scalar('allAcc_train', allAcc_train, epoch_log)\n\n # validate\n is_best = False\n if args.min_val < epoch_log and (epoch_log % args.val_freq == 0) and args.is_eval:\n loss_val, mIoU_val, mAcc_val, allAcc_val = validate(val_loader, model, criterion)\n\n if main_process(args):\n writer.add_scalar('loss_val', loss_val, epoch_log)\n writer.add_scalar('mIoU_val', mIoU_val, epoch_log)\n writer.add_scalar('mAcc_val', mAcc_val, epoch_log)\n writer.add_scalar('allAcc_val', allAcc_val, epoch_log)\n is_best = mIoU_val > best_iou\n best_iou = max(best_iou, mIoU_val)\n\n # save model\n if is_best and main_process(args):\n filename = args.ckpt_dir + '/model_best.pth'\n # save for publish\n torch.save({'state_dict': model.state_dict()}, filename)\n # save for training\n # torch.save({'epoch': epoch_log, 'state_dict': model.state_dict(),\n # 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(),\n # 'best_iou': best_iou, 'is_best': is_best}, filename)\n logger.info('Best validation mIoU updated to: {:.2f}'.format(best_iou * 100))\n\n if main_process(args):\n writer.close()\n logger.info('==>Training done!\\nBest Iou: {:.2f}'.format(best_iou * 100, ))\n\n\ndef train(train_loader, model, criterion, optimizer, epoch):\n batch_time = AverageMeter()\n data_time = AverageMeter()\n loss_meter = AverageMeter()\n intersection_meter = AverageMeter()\n union_meter = AverageMeter()\n target_meter = AverageMeter()\n\n model.train()\n\n # freeze weight\n if args.freeze_epoch < epoch + 1:\n # freeze params\n for n, p in model.module.named_parameters():\n if \"surface_constructor\" in n and p.requires_grad:\n p.requires_grad = False\n\n end = time.time()\n max_iter = args.epoch * len(train_loader)\n for i, (coord, feat, target, offset) in enumerate(train_loader): # [N, 3], [N, C], [N], [B]\n data_time.update(time.time() - end)\n coord, target, feat, offset = \\\n coord.cuda(non_blocking=True), target.cuda(non_blocking=True), feat.cuda(non_blocking=True), \\\n offset.cuda(non_blocking=True)\n\n output = model([coord, feat, offset])\n loss = criterion(output, target)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n n = coord.size(0)\n if args.multiprocessing_distributed:\n loss *= n\n count = target.new_tensor([n], dtype=torch.long)\n dist.all_reduce(loss), dist.all_reduce(count)\n n = count.item()\n loss /= n\n\n output = output[:, 1:].max(1)[1] + 1 if 'ScanNet' in args.dataset else output.max(1)[1] # remove unclassified label\n intersection, union, target = intersectionAndUnionGPU(output, target, args.num_class, args.ignore_label)\n if args.multiprocessing_distributed:\n dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target)\n intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()\n intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)\n\n accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)\n loss_meter.update(loss.item(), n)\n batch_time.update(time.time() - end)\n end = time.time()\n\n # calculate remain time\n current_iter = epoch * len(train_loader) + i + 1\n remain_iter = max_iter - current_iter\n remain_time = remain_iter * batch_time.avg\n t_m, t_s = divmod(remain_time, 60)\n t_h, t_m = divmod(t_m, 60)\n remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s))\n\n if (i + 1) % args.train_freq == 0 and main_process(args):\n logger.info('Epoch: [{}/{}][{}/{}] '\n 'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) '\n 'Remain {remain_time} '\n 'Loss {loss_meter.val:.4f} '\n 'Accuracy {accuracy:.2f}'.format(epoch + 1, args.epoch, i + 1, len(train_loader),\n batch_time=batch_time, remain_time=remain_time,\n loss_meter=loss_meter, accuracy=accuracy * 100))\n if main_process(args):\n writer.add_scalar('loss_train_batch', loss_meter.val, current_iter)\n writer.add_scalar('mIoU_train_batch', np.mean(intersection / (union + 1e-10)), current_iter)\n writer.add_scalar('mAcc_train_batch', np.mean(intersection / (target + 1e-10)), current_iter)\n writer.add_scalar('allAcc_train_batch', accuracy, current_iter)\n\n iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)\n accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)\n iou_class = iou_class[1:] if 'ScanNet' in args.dataset else iou_class\n accuracy_class = accuracy_class[1:] if 'ScanNet' in args.dataset else accuracy_class\n mIoU = np.mean(iou_class)\n mAcc = np.mean(accuracy_class)\n allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)\n if main_process(args):\n for class_idx, class_iou in enumerate(iou_class):\n writer.add_scalar(f'class_{class_idx}_train_iou', class_iou, epoch)\n if main_process(args):\n logger.info('Train result at epoch [{}/{}]: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}'.format(\n epoch + 1, args.epoch, mIoU * 100, mAcc * 100, allAcc * 100))\n return loss_meter.avg, mIoU, mAcc, allAcc\n\n\ndef validate(val_loader, model, criterion):\n if main_process(args):\n logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')\n batch_time = AverageMeter()\n data_time = AverageMeter()\n loss_meter = AverageMeter()\n intersection_meter = AverageMeter()\n union_meter = AverageMeter()\n target_meter = AverageMeter()\n\n model.eval()\n end = time.time()\n for i, (coord, feat, target, offset) in enumerate(val_loader):\n data_time.update(time.time() - end)\n coord, target, feat, offset = \\\n coord.cuda(non_blocking=True), target.cuda(non_blocking=True), feat.cuda(non_blocking=True), \\\n offset.cuda(non_blocking=True)\n\n with torch.no_grad():\n output = model([coord, feat, offset])\n\n loss = criterion(output, target)\n n = coord.size(0)\n if args.multiprocessing_distributed:\n loss *= n\n count = target.new_tensor([n], dtype=torch.long)\n dist.all_reduce(loss), dist.all_reduce(count)\n n = count.item()\n loss /= n\n\n choice = output[:, 1:].max(1)[1] + 1 if 'ScanNet' in args.dataset else output.max(1)[1] # remove unclassified label\n intersection, union, target = intersectionAndUnionGPU(choice, target, args.num_class, args.ignore_label)\n if args.multiprocessing_distributed:\n dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target)\n intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()\n intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)\n\n loss_meter.update(loss.item(), n)\n batch_time.update(time.time() - end)\n end = time.time()\n\n iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)\n accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)\n # remove unlabeled class\n iou_class = iou_class[1:] if 'ScanNet' in args.dataset else iou_class\n accuracy_class = accuracy_class[1:] if 'ScanNet' in args.dataset else accuracy_class\n mIoU = np.mean(iou_class)\n mAcc = np.mean(accuracy_class)\n allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)\n\n if main_process(args):\n logger.info('Val result: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}'.format(\n mIoU * 100, mAcc * 100, allAcc * 100))\n logger.info('Val loss: {:.4f}'.format(loss_meter.avg))\n for i in range(len(iou_class)):\n logger.info('Class_{} Result: IoU / Acc {:.2f}/{:.2f}'.format(\n i, iou_class[i] * 100, accuracy_class[i] * 100))\n logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')\n\n return loss_meter.avg, mIoU, mAcc, allAcc\n\n\nif __name__ == '__main__':\n import gc\n\n gc.collect()\n\n \"\"\"HYPER PARAMETER\"\"\"\n args = parse_args()\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = ','.join(args.gpus)\n if 'A6000' in torch.cuda.get_device_name(0):\n os.environ[\"NCCL_P2P_DISABLE\"] = '1'\n\n \"\"\"DDP SETTING\"\"\"\n args.dist_url = 'tcp://localhost:8888'\n if args.dist_url == \"env://\" and args.world_size == -1:\n args.world_size = int(os.environ[\"WORLD_SIZE\"])\n args.distributed = args.world_size > 1 or args.multiprocessing_distributed\n args.ngpus_per_node = len(args.gpus)\n if len(args.gpus) == 1:\n args.sync_bn = False\n args.distributed = False\n args.multiprocessing_distributed = False\n\n \"\"\"CREATE DIR\"\"\"\n experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log', args.dataset.split('_')[0]))\n if args.log_dir is None:\n timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))\n experiment_dir = experiment_dir.joinpath(timestr)\n else:\n experiment_dir = experiment_dir.joinpath(args.log_dir)\n experiment_dir.mkdir(exist_ok=True)\n checkpoints_dir = experiment_dir.joinpath('checkpoints/')\n checkpoints_dir.mkdir(exist_ok=True)\n args.ckpt_dir = str(checkpoints_dir)\n log_dir = experiment_dir.joinpath('logs/')\n log_dir.mkdir(exist_ok=True)\n args.log_dir = str(log_dir)\n\n \"\"\"DATASET INIT\"\"\"\n dataset_obj = get_dataset_obj(args)\n args.loop = get_loop(args)\n if args.dataset == 'S3DIS':\n args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \\\n 13, 80000, 0.04, 6, 255\n args.data_dir = './data/S3DIS/trainval_fullarea'\n dataset_obj(args, 'train')\n dataset_obj(args, 'val')\n elif args.dataset == 'ScanNet_train':\n args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \\\n 21, 120000, 0.02, 6, 0\n args.data_dir = './data/ScanNet'\n dataset_obj(args, 'train')\n dataset_obj(args, 'val')\n elif args.dataset == 'ScanNet_trainval':\n args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \\\n 21, 120000, 0.02, 6, 0\n args.data_dir = './data/ScanNet'\n dataset_obj(args, 'trainval')\n else:\n raise Exception('Not Impl. Dataset')\n\n args.is_eval = 'trainval' not in args.dataset\n args.aug_args = get_aug_args(args)\n args.description = get_dataset_description(args)\n print('Train Model on %s' % args.description)\n\n \"\"\"RUNNING\"\"\"\n if args.multiprocessing_distributed:\n port = find_free_port()\n args.dist_url = f\"tcp://localhost:{port}\"\n args.world_size = args.ngpus_per_node * args.world_size\n mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args.ngpus_per_node, args))\n else:\n main_worker(args.gpus, args.ngpus_per_node, args)\n" }, { "path": "segmentation/util/__init__.py", "content": "" }, { "path": "segmentation/util/data_util.py", "content": "import numpy as np\nimport SharedArray as SA\nimport torch\n\nfrom modules.voxelize_utils import voxelize\n\n\ndef sa_create(name, var):\n x = SA.create(name, var.shape, dtype=var.dtype)\n x[...] = var[...]\n x.flags.writeable = False\n return x\n\n\ndef collate_fn(batch):\n coord, feat, label = list(zip(*batch))\n offset, count = [], 0\n for item in coord:\n count += item.shape[0]\n offset.append(count)\n\n return torch.cat(coord), torch.cat(feat), torch.cat(label) if label[0] is not None else None, torch.IntTensor(\n offset)\n\n\ndef data_prepare(coord, feat, label, args, split, coord_transform, rgb_transform,\n rgb_mean=None, rgb_std=None, shuffle_index=True, stop_transform=False):\n dataset = args.dataset.split('_')[0]\n\n # coordinate augment\n if coord_transform and not stop_transform:\n coord, _, _ = coord_transform(coord, None, None)\n\n # rgb augment\n if rgb_transform and not stop_transform:\n _, feat, _ = rgb_transform(None, feat, None)\n\n # grid sampling\n if args.voxel_size:\n uniq_idx = voxelize(coord - np.min(coord, 0), args.voxel_size)\n coord, feat = coord[uniq_idx], feat[uniq_idx]\n if label is not None:\n label = label[uniq_idx]\n\n # drop points when overflow\n if split != 'val' and args.voxel_max and coord.shape[0] > args.voxel_max:\n init_idx = np.random.randint(coord.shape[0]) if 'train' in split else coord.shape[0] // 2\n crop_idx = np.argsort(np.sum(np.square(coord - coord[init_idx]), 1))[:args.voxel_max]\n coord, feat = coord[crop_idx], feat[crop_idx]\n if label is not None:\n label = label[crop_idx]\n\n # shuffle points\n if shuffle_index:\n shuf_idx = np.arange(coord.shape[0])\n np.random.shuffle(shuf_idx)\n coord, feat = coord[shuf_idx], feat[shuf_idx]\n if label is not None:\n label = label[shuf_idx]\n\n # coord norm\n if args.data_norm == 'mean':\n coord -= np.mean(coord, 0)\n elif args.data_norm == 'min':\n coord -= np.min(coord, 0)\n\n # rgb norm\n if dataset in ['S3DIS', 'ScanNet']:\n feat = feat / 255.\n if rgb_mean is not None and rgb_std is not None:\n feat = (feat - rgb_mean) / rgb_std\n\n return torch.FloatTensor(coord), torch.FloatTensor(feat), torch.LongTensor(label) if label is not None else None\n" }, { "path": "segmentation/util/utils.py", "content": "import importlib\nimport argparse\nimport random\nimport logging\nimport sys\n\nimport numpy as np\nimport torch\nfrom torch import nn\nimport torch.optim.lr_scheduler as lr_scheduler\n\nfrom dataset.S3DISDataLoader import S3DIS\nfrom modules.pointops.functions import pointops\n\n\ndef main_process(args):\n \"\"\"\n Determine whether the main process\n\n \"\"\"\n return not args.multiprocessing_distributed or (\n args.multiprocessing_distributed and args.rank % args.ngpus_per_node == 0)\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\ndef intersectionAndUnion(output, target, K, ignore_index=255):\n # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.\n assert (output.ndim in [1, 2, 3])\n assert output.shape == target.shape\n output = output.reshape(output.size).copy()\n target = target.reshape(target.size)\n output[np.where(target == ignore_index)[0]] = ignore_index\n intersection = output[np.where(output == target)[0]]\n area_intersection, _ = np.histogram(intersection, bins=np.arange(K + 1))\n area_output, _ = np.histogram(output, bins=np.arange(K + 1))\n area_target, _ = np.histogram(target, bins=np.arange(K + 1))\n area_union = area_output + area_target - area_intersection\n return area_intersection, area_union, area_target\n\n\ndef intersectionAndUnionGPU(output, target, K, ignore_index=255):\n # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.\n assert (output.dim() in [1, 2, 3])\n assert output.shape == target.shape\n output = output.view(-1)\n target = target.view(-1)\n output[target == ignore_index] = ignore_index\n intersection = output[output == target]\n area_intersection = torch.histc(intersection, bins=K, min=0, max=K - 1)\n area_output = torch.histc(output, bins=K, min=0, max=K - 1)\n area_target = torch.histc(target, bins=K, min=0, max=K - 1)\n area_union = area_output + area_target - area_intersection\n return area_intersection, area_union, area_target\n\n\ndef find_free_port():\n import socket\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # Binding to port 0 will cause the OS to find an available port for us\n sock.bind((\"\", 0))\n port = sock.getsockname()[1]\n sock.close()\n # NOTE: there is still a chance the port could be taken by other processes.\n return port\n\n\ndef set_seed(seed):\n \"\"\"\n Setting of Global Seed\n\n \"\"\"\n np.random.seed(seed)\n random.seed(seed)\n torch.manual_seed(seed) # cpu\n torch.cuda.manual_seed(seed)\n\n torch.backends.cudnn.deterministic = True # consistent results on the cpu and gpu\n torch.backends.cudnn.benchmark = False\n\n\ndef worker_init_fn(worker_id, seed=None):\n if seed is not None:\n random.seed(seed + worker_id)\n np.random.seed(seed + worker_id)\n torch.manual_seed(seed + worker_id)\n torch.cuda.manual_seed(seed + worker_id)\n torch.cuda.manual_seed_all(seed + worker_id)\n\n\ndef get_logger(log_dir, model):\n logger_name = \"main-logger\"\n logger = logging.getLogger(logger_name)\n logging.root.handlers = []\n\n logging.basicConfig(\n level=logging.INFO,\n format=\"[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s\",\n handlers=[\n logging.FileHandler('%s/%s.txt' % (log_dir, model)),\n logging.StreamHandler(sys.stdout)\n ]\n )\n\n return logger\n\n\ndef get_aug_args(args):\n dataset = args.dataset\n if 'S3DIS' in dataset:\n aug_args = {'scale_factor': 0.1, 'scale_ani': True, 'scale_prob': 1.,\n 'pert_factor': 0.03, 'pert_prob': 1., 'rot_prob': 0.5,\n 'shifts': [0.1, 0.1, 0.1], 'shift_prob': 1.}\n return aug_args\n else:\n raise Exception('No such dataset')\n\n\ndef get_dataset_obj(args):\n dataset_name = args.dataset\n if 'S3DIS' in dataset_name:\n return S3DIS\n\n\ndef get_dataset_description(args):\n dataset_name = args.dataset\n if 'S3DIS' in dataset_name:\n return '%s_A%d' % (dataset_name, args.test_area)\n if 'ScanNet' in dataset_name:\n return dataset_name\n\n\ndef get_loop(args):\n if 'S3DIS' in args.dataset:\n return 30\n if 'ScanNet' in args.dataset:\n return 6\n else:\n raise Exception('No Fixed Loop for the Dataset')\n\n\ndef get_class_weights(dataset_name):\n # pre-calculate the class weight\n if dataset_name == 'S3DIS_A1':\n num_per_class = [0.27362621, 0.3134626, 0.18798782, 1.38965602, 1.44210271, 0.86639497, 1.07227331,\n 1., 1.05912352, 1.92726327, 0.52329938, 2.04783419, 0.5104427]\n elif dataset_name == 'S3DIS_A2':\n num_per_class = [0.29036634, 0.34709631, 0.19514767, 1.20129272, 1.39663689, 0.87889087, 1.11586938,\n 1., 1.54599972, 1.87057415, 0.56458097, 1.87316536, 0.51576885]\n elif dataset_name == 'S3DIS_A3':\n num_per_class = [0.27578885, 0.32039725, 0.19055443, 1.14914046, 1.46885687, 0.85450877, 1.05414776,\n 1., 1.09680025, 2.09280004, 0.59355243, 1.95746691, 0.50429199]\n elif dataset_name == 'S3DIS_A4':\n num_per_class = [0.27667177, 0.32612854, 0.19886974, 1.18282174, 1.52145143, 0.8793782, 1.14202999,\n 1., 1.0857859, 1.89738584, 0.5964717, 1.95820557, 0.52113351]\n elif dataset_name == 'S3DIS_A5':\n num_per_class = [0.28459923, 0.32990557, 0.1999722, 1.20798185, 1.33784535, 1., 0.93323316, 1.0753585,\n 1.00199521, 1.53657772, 0.7987055, 1.82384844, 0.48565471]\n elif dataset_name == 'S3DIS_A6':\n num_per_class = [0.29442441, 0.37941846, 0.21360804, 0.9812721, 1.40968965, 0.88577139, 1.,\n 1.09387107, 1.53238009, 1.61365643, 1.15693894, 1.57821041, 0.47342451]\n elif dataset_name == 'ScanNet_train':\n num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985,\n 1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716,\n 1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497]\n elif dataset_name == 'ScanNet_trainval':\n num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985,\n 1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716,\n 1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497]\n else:\n raise Exception('No Prepared Class Weights of Dataset')\n return torch.FloatTensor(num_per_class)\n\n\ndef get_rgb_stat(args):\n if 'S3DIS' in args.dataset:\n mean, std = [0.52146571, 0.50457911, 0.44939377], [0.19645595, 0.19576158, 0.20104336]\n elif 'ScanNet' in args.dataset:\n mean, std = [0.08400667, 0.08400667, 0.08400667], [0.28983903, 0.28983903, 0.28983903]\n else:\n return None, None\n return np.array(mean, dtype=np.float32), np.array(std, dtype=np.float32)\n\n\ndef get_model(args):\n module = importlib.import_module('models.%s' % args.model)\n return module.Model(args)\n\n\ndef get_optimizer(args, model):\n param_dicts = model.parameters()\n if args.optimizer == 'SGD':\n optimizer = torch.optim.SGD(param_dicts, lr=args.learning_rate, momentum=args.momentum,\n weight_decay=args.weight_decay)\n elif args.optimizer == 'AdamW':\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)\n else:\n raise Exception('Not impl. such optimizer')\n return optimizer\n\n\ndef get_scheduler(args, optimizer):\n if args.scheduler == 'step':\n scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_decay_epochs, gamma=args.lr_decay)\n else:\n raise Exception('Not impl. such scheduler')\n return scheduler\n\n\ndef get_loss(weight=None, ignore_label=None):\n return nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_label)\n\n\ndef get_test_args():\n return argparse.Namespace()\n\n\ndef pc_median_filter_gpu(coord, label, group_size=16):\n \"\"\"\n :param coord: coordinates of a whole point cloud [N, 3]\n :param label: segmentation results of a whole point cloud [N,]\n :param group_size: num of neighbors for filtering\n \"\"\"\n offset = torch.IntTensor([coord.shape[0]]).to(coord.device)\n group_idx, _ = pointops.knnquery(group_size, coord, coord, offset, offset) # [N, group_size]\n group_label = label[group_idx.view(-1).long()].view(coord.shape[0], group_size) # [N, group_size]\n median_label = torch.median(group_label, 1)[0]\n return median_label.cpu().numpy()\n" }, { "path": "visualization/airplane_0001.txt", "content": "-0.098790,-0.182300,0.163800,0.829000,-0.557200,-0.048180\n0.994600,0.074420,0.010250,0.331800,-0.939500,0.085320\n0.189900,-0.292200,-0.926300,0.239000,-0.178100,-0.954500\n-0.989200,0.074610,-0.012350,-0.816500,-0.250800,-0.520100\n0.208700,0.221100,0.565600,0.837600,-0.019280,0.545900\n0.404700,0.080140,-0.002638,0.002952,0.973500,-0.228800\n-0.043890,-0.115500,-0.412900,-0.672800,0.679900,-0.291600\n-0.525000,0.158200,0.303300,0.000000,-0.413200,0.910600\n-0.191700,-0.160900,0.657400,0.228400,-0.071910,0.970900\n-0.267300,-0.265700,-0.804700,-0.992800,0.095000,0.073630\n0.694500,-0.135400,0.304400,0.023350,-0.973600,0.227200\n0.186300,-0.376900,0.426500,0.562700,-0.531300,0.633300\n-0.661600,-0.193600,0.102400,0.031270,0.768600,-0.638900\n0.082090,0.160900,0.201000,-0.021110,0.968000,-0.250000\n-0.320800,0.073440,-0.009860,-0.037590,-0.948400,-0.314900\n0.246400,-0.197800,0.130300,0.023760,-0.939300,0.342400\n0.001779,-0.038230,-0.784800,0.783800,0.607400,-0.129100\n0.061140,-0.017800,-0.094900,0.665400,0.715000,-0.214700\n0.550500,0.169500,0.301900,0.000000,0.702600,0.711600\n-0.196800,-0.387400,0.417100,-0.023430,-0.761800,0.647300\n-0.393700,-0.143600,0.305200,-0.031960,-0.971300,0.235800\n-0.000442,-0.374300,-0.645700,0.089020,-0.868000,0.488600\n0.092050,-0.059880,0.447800,-0.952200,0.271300,-0.140500\n-0.853100,0.171800,0.267000,-0.062610,0.997400,0.035810\n-0.218700,0.172400,0.294200,0.000000,0.991700,0.128500\n0.857200,0.171600,0.265400,0.192600,0.972600,0.130000\n-0.901500,-0.128400,0.273000,-0.896100,0.118600,0.427600\n-0.667200,0.078680,-0.011820,0.000351,0.989800,-0.142300\n0.701500,0.074850,-0.025130,-0.060230,0.016420,-0.998000\n0.913200,-0.187100,0.114700,0.022700,-0.935800,0.351700\n-0.007828,-0.264200,-0.185700,0.133700,0.984200,-0.115800\n0.514400,-0.198900,0.102600,-0.027860,0.730500,-0.682400\n0.224200,-0.229000,-0.645700,0.021850,0.981300,-0.191000\n-0.065370,-0.300100,-0.945700,-0.025270,-0.999000,-0.036020\n-0.031840,0.047050,0.632800,-0.539200,0.503100,0.675400\n0.306300,0.152500,0.294000,0.000000,-0.960800,0.277200\n0.393700,-0.139900,0.313100,0.021050,-0.824300,0.565800\n-0.092960,0.084370,0.056500,0.000003,-0.946600,0.322400\n-0.185900,-0.224500,-0.586600,-0.000442,-0.979000,0.204000\n-0.115300,-0.155400,0.391500,-0.757900,0.521700,-0.391700\n0.000016,-0.345800,0.298600,0.002517,0.674900,-0.737900\n-0.665700,0.008514,0.202400,-0.979200,0.016240,-0.202400\n0.103900,-0.056990,0.223300,0.998800,0.042160,0.022850\n-0.322500,-0.206400,0.107300,-0.032420,-0.940000,0.339700\n0.062460,-0.217700,0.026150,0.678400,-0.725800,0.114200\n-0.053780,0.081300,0.387300,-0.536600,0.843800,-0.004537\n0.180500,0.090560,0.030730,-0.005058,0.938000,-0.346500\n0.003237,-0.160900,-0.624400,1.000000,-0.007297,-0.004346\n-0.492200,0.097440,0.091620,0.000000,-0.945100,0.326800\n0.662400,0.035890,0.204400,0.922800,0.039630,-0.383300\n-0.099650,-0.126500,-0.049810,-0.994700,-0.097350,-0.033170\n0.711000,-0.193700,0.102800,-0.013360,0.733600,-0.679400\n-0.585100,-0.134500,0.312900,-0.025900,-0.784900,0.619100\n-0.825800,0.097440,0.091620,0.000000,-0.945100,0.326800\n-0.066960,-0.074600,-0.229900,-0.848300,0.488600,-0.204100\n0.049220,-0.269300,-0.799500,0.000000,-0.979100,0.203400\n0.881200,-0.131200,0.300200,0.415200,-0.750800,0.513700\n-0.946200,-0.183300,0.102500,0.023430,0.798800,-0.601100\n0.014360,-0.275600,-0.481700,-0.265400,0.964100,0.002391\n-0.100100,-0.014160,0.223500,-0.974100,0.221900,-0.042540\n0.546900,0.123800,0.100700,0.020250,0.905000,-0.424900\n0.825800,0.122000,0.097150,-0.015250,0.909000,-0.416600\n-0.317200,0.144400,0.151300,0.000030,0.951600,-0.307500\n0.134400,-0.216500,0.382100,0.932200,0.348600,-0.097240\n-0.492100,-0.187700,0.142300,-0.031760,-0.942200,0.333500\n0.080350,-0.150100,-0.296100,0.987100,0.124900,-0.100300\n0.122600,0.078230,0.347000,-0.566700,0.763100,0.310600\n0.057420,0.110700,0.505300,0.550600,0.789900,0.270100\n-0.235200,-0.141600,0.262200,0.037140,0.967600,-0.249900\n-0.683100,0.168400,0.235600,0.000000,0.987700,-0.156400\n-0.065070,-0.073350,0.523900,-0.709200,-0.608300,0.356300\n-0.738400,-0.126400,0.257000,0.026860,0.975800,-0.217000\n-0.103900,-0.250900,-0.749600,0.000000,0.978600,-0.205700\n0.399200,0.118200,0.164500,0.000000,-0.966800,0.255600\n0.047430,0.022240,0.097530,0.000143,0.211000,0.977500\n-0.158500,-0.275300,0.297400,0.863400,-0.500800,0.061880\n0.235600,-0.132400,0.295800,-0.029790,0.985900,-0.164700\n0.120300,-0.213900,0.222100,0.919200,0.385000,-0.082260\n-0.052810,-0.252200,-0.335200,-0.761800,-0.647800,-0.005893\n-0.003240,-0.155400,-0.892300,-1.000000,-0.000104,-0.000322\n-0.071080,0.170500,0.250900,0.015210,0.993500,-0.113100\n-0.371900,0.158200,0.303300,0.000000,-0.413200,0.910600\n0.546800,-0.153900,0.244800,0.023350,-0.965200,0.260300\n0.700000,0.171300,0.261700,0.035500,0.995100,-0.091960\n0.091220,-0.161500,-0.126800,-0.985800,0.165400,0.027450\n-0.010790,-0.192500,0.288600,-0.112100,-0.962700,0.246100\n0.251600,0.117200,0.160600,0.0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}, { "path": "visualization/table_0250.txt", "content": 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0.0587 0.2594\nv 0.2023 0.1324 0.1202\nv -0.0293 -0.1337 0.4505\nv 0.5091 0.1058 0.1175\nv 0.9300 -0.1885 0.1022\nf 1 901 711\nf 2 748 1024\nf 3 599 722\nf 4 959 564\nf 5 936 596\nf 6 923 926\nf 7 625 349\nf 8 608 550\nf 9 727 908\nf 10 985 314\nf 11 856 944\nf 12 811 291\nf 13 670 383\nf 14 917 807\nf 15 731 698\nf 16 684 614\nf 17 947 607\nf 18 937 820\nf 19 536 486\nf 20 1012 490\nf 21 974 893\nf 22 706 604\nf 23 991 397\nf 24 828 560\nf 25 570 834\nf 26 780 624\nf 27 778 532\nf 28 880 587\nf 29 921 960\nf 30 787 918\nf 31 728 889\nf 32 934 772\nf 33 812 696\nf 34 669 434\nf 35 988 796\nf 36 732 636\nf 37 957 480\nf 38 973 910\nf 39 883 340\nf 40 952 1018\nf 41 913 850\nf 42 694 969\nf 43 697 665\nf 44 870 886\nf 45 819 525\nf 46 824 333\nf 47 766 874\nf 48 680 488\nf 49 642 505\nf 50 657 673\nf 51 664 428\nf 52 753 508\nf 53 637 468\nf 54 615 436\nf 55 662 579\nf 56 546 786\nf 57 899 755\nf 58 878 765\nf 59 1003 900\nf 60 682 404\nf 61 928 551\nf 62 946 687\nf 63 986 785\nf 64 964 1007\nf 65 1016 661\nf 66 600 482\nf 67 864 767\nf 68 955 485\nf 69 997 710\nf 70 888 922\nf 71 842 1005\nf 72 848 540\nf 73 629 655\nf 74 879 653\nf 75 803 943\nf 76 806 634\nf 77 855 578\nf 78 769 757\nf 79 760 775\nf 80 715 717\nf 81 1010 593\nf 82 859 847\nf 83 941 595\nf 84 849 763\nf 85 885 792\nf 86 977 749\nf 87 808 594\nf 88 733 777\nf 89 866 667\nf 90 962 794\nf 91 1006 685\nf 92 678 484\nf 93 597 713\nf 94 1009 935\nf 95 965 537\nf 96 844 621\nf 97 869 603\nf 98 838 586\nf 99 956 649\nf 100 975 572\nf 101 961 948\nf 102 996 784\nf 103 951 840\nf 104 390 371\nf 105 970 916\nf 106 1002 930\nf 107 891 837\nf 108 736 716\nf 109 826 809\nf 110 708 832\nf 111 660 836\nf 112 822 770\nf 113 978 779\nf 114 858 862\nf 115 898 620\nf 116 725 689\nf 117 799 492\nf 118 853 827\nf 119 871 872\nf 120 730 905\nf 121 932 982\nf 122 810 487\nf 123 963 984\nf 124 752 745\nf 125 821 1017\nf 126 903 754\nf 127 813 887\nf 128 825 577\nf 129 990 995\nf 130 805 740\nf 131 793 881\nf 132 989 472\nf 133 692 529\nf 134 938 861\nf 135 719 703\nf 136 480 499\nf 137 702 471\nf 138 747 510\nf 139 758 676\nf 140 925 563\nf 141 1004 575\nf 142 816 909\nf 143 846 906\nf 144 971 818\nf 145 979 1001\nf 146 823 623\nf 147 953 737\nf 148 911 782\nf 149 992 550\nf 150 968 875\nf 151 966 507\nf 152 942 851\nf 153 798 454\nf 154 852 815\nf 155 693 473\nf 156 781 720\nf 157 621 502\nf 158 999 528\nf 159 945 724\nf 160 595 748\nf 161 695 646\nf 162 831 904\nf 163 768 786\nf 164 1023 521\nf 165 674 506\nf 166 624 620\nf 167 967 641\nf 168 517 415\nf 169 1015 654\nf 170 1021 746\nf 171 801 655\nf 172 1012 490\nf 173 622 596\nf 174 829 854\nf 175 585 659\nf 176 958 707\nf 177 1013 652\nf 178 954 800\nf 179 857 845\nf 180 684 424\nf 181 563 518\nf 182 841 665\nf 183 980 603\nf 184 675 766\nf 185 606 802\nf 186 983 789\nf 187 993 915\nf 188 1008 671\nf 189 922 919\nf 190 912 876\nf 191 672 485\nf 192 741 661\nf 193 817 656\nf 194 687 519\nf 195 504 552\nf 196 726 732\nf 197 1000 708\nf 198 694 418\nf 199 998 728\nf 200 893 678\nf 201 686 450\nf 202 605 718\nf 203 955 744\nf 204 725 682\nf 205 907 729\nf 206 668 698\nf 207 734 749\nf 208 1005 632\nf 209 751 897\nf 210 836 634\nf 211 882 710\nf 212 648 628\nf 213 902 881\nf 214 837 697\nf 215 807 637\nf 216 908 795\nf 217 1019 920\nf 218 840 662\nf 219 768 722\nf 220 884 699\nf 221 921 618\nf 222 805 681\nf 223 773 468\nf 224 560 471\nf 225 914 568\nf 226 909 939\nf 227 903 609\nf 228 977 631\nf 229 775 653\nf 230 745 895\nf 231 833 834\nf 232 892 594\nf 233 823 828\nf 234 931 531\nf 235 832 776\nf 236 994 803\nf 237 703 657\nf 238 813 729\nf 239 742 405\nf 240 930 835\nf 241 972 1011\nf 242 704 952\nf 243 794 934\nf 244 887 864\nf 245 812 646\nf 246 771 731\nf 247 959 685\nf 248 797 625\nf 249 839 943\nf 250 918 693\nf 251 783 733\nf 252 995 486\nf 253 956 799\nf 254 896 743\nf 255 849 455\nf 256 818 593\nf 257 937 933\nf 258 974 738\nf 259 601 753\nf 260 770 640\nf 261 764 769\nf 262 850 760\nf 263 898 666\nf 264 1022 736\nf 265 1016 951\nf 266 780 667\nf 267 859 900\nf 268 838 969\nf 269 897 895\nf 270 910 861\nf 271 916 814\nf 272 941 773\nf 273 964 953\nf 274 901 578\nf 275 990 593\nf 276 1024 611\nf 277 944 806\nf 278 779 873\nf 279 866 756\nf 280 982 725\nf 281 980 820\nf 282 499 491\nf 283 911 855\nf 284 843 940\nf 285 942 924\nf 286 785 950\nf 287 798 763\nf 288 740 630\nf 289 929 861\nf 290 1014 719\nf 291 1017 538\nf 292 735 702\nf 293 886 870\nf 294 868 856\nf 295 777 711\nf 296 830 845\nf 297 810 997\nf 298 949 948\nf 299 815 626\nf 300 547 461\nf 301 762 778\nf 302 882 709\nf 303 679 569\nf 304 842 701\nf 305 479 664\nf 306 782 911\nf 307 976 713\nf 308 696 797\nf 309 1003 1004\nf 310 831 726\nf 311 867 821\nf 312 931 571\nf 313 957 771\nf 314 1009 985\nf 315 750 435\nf 316 511 395\nf 317 828 632\nf 318 946 437\nf 319 774 515\nf 320 1011 553\nf 321 913 555\nf 322 695 485\nf 323 802 749\nf 324 708 690\nf 325 967 865\nf 326 926 790\nf 327 809 699\nf 328 815 607\nf 329 889 689\nf 330 809 13\nf 331 905 717\nf 332 927 748\nf 333 978 779\nf 334 987 647\nf 335 739 660\nf 336 881 816\nf 337 741 1016\nf 338 989 472\nf 339 852 410\nf 340 975 883\nf 341 900 497\nf 342 675 793\nf 343 1008 928\nf 344 728 612\nf 345 754 740\nf 346 1007 737\nf 347 654 618\nf 348 812 695\nf 349 649 499\nf 350 518 3\nf 351 932 889\nf 352 781 854\nf 353 980 677\nf 354 481 524\nf 355 885 528\nf 356 570 70\nf 357 1022 682\nf 358 757 642\nf 359 966 496\nf 360 834 792\nf 361 877 755\nf 362 936 605\nf 363 829 894\nf 364 984 869\nf 365 1002 835\nf 366 761 941\nf 367 758 636\nf 368 796 840\nf 369 983 961\nf 370 1020 902\nf 371 817 642\nf 372 901 652\nf 373 575 266\nf 374 576 443\nf 375 892 808\nf 376 904 899\nf 377 990 1010\nf 378 579 784\nf 379 850 582\nf 380 597 811\nf 381 811 650\nf 382 1020 723\nf 383 670 872\nf 384 858 738\nf 385 938 512\nf 386 851 692\nf 387 619 47\nf 388 945 933\nf 389 800 118\nf 390 923 592\nf 391 875 984\nf 392 1004 445\nf 393 874 619\nf 394 1005 736\nf 395 830 857\nf 396 973 513\nf 397 510 138\nf 398 796 724\nf 399 858 862\nf 400 1015 765\nf 401 841 760\nf 402 896 874\nf 403 891 907\nf 404 60 210\nf 405 620 624\nf 406 823 851\nf 407 641 155\nf 408 950 692\nf 409 947 660\nf 410 852 586\nf 411 890 832\nf 412 854 894\nf 413 994 853\nf 414 839 50\nf 415 946 62\nf 416 869 824\nf 417 932 735\nf 418 969 694\nf 419 843 871\nf 420 996 784\nf 421 981 804\nf 422 882 810\nf 423 774 752\nf 424 581 982\nf 425 754 172\nf 426 840 662\nf 427 844 868\nf 428 862 914\nf 429 837 697\nf 430 891 637\nf 431 970 916\nf 432 1018 952\nf 433 859 658\nf 434 954 34\nf 435 791 750\nf 436 54 132\nf 437 973 910\nf 438 792 669\nf 439 965 714\nf 440 767 864\nf 441 772 863\nf 442 906 746\nf 443 87 374\nf 444 993 689\nf 445 822 866\nf 446 877 711\nf 447 922 919\nf 448 930 860\nf 449 793 676\nf 450 123 325\nf 451 52 508\nf 452 881 751\nf 453 849 255\nf 454 798 35\nf 455 988 724\nf 456 879 825\nf 457 920 875\nf 458 790 326\nf 459 767 907\nf 460 1017 291\nf 461 939 719\nf 462 661 65\nf 463 853 827\nf 464 653 699\nf 465 674 702\nf 466 898 620\nf 467 924 567\nf 468 773 223\nf 469 764 645\nf 470 705 679\nf 471 691 224\nf 472 989 18\nf 473 918 787\nf 474 606 185\nf 475 1001 979\nf 476 1000 958\nf 477 942 700\nf 478 1003 681\nf 479 923 305\nf 480 37 136\nf 481 860 639\nf 482 995 704\nf 483 1019 616\nf 484 870 293\nf 485 68 191\nf 486 509 129\nf 487 960 921\nf 488 966 881\nf 489 1007 968\nf 490 1012 709\nf 491 957 282\nf 492 695 117\nf 493 819 911\nf 494 737 183\nf 495 823 623\nf 496 556 359\nf 497 900 742\nf 498 67 301\nf 499 649 282\nf 500 933 937\nf 501 1008 627\nf 502 157 279\nf 503 929 622\nf 504 953 195\nf 505 846 746\nf 506 998 735\nf 507 928 151\nf 508 753 52\nf 509 275 486\nf 510 963 397\nf 511 996 316\nf 512 202 5\nf 513 38 157\nf 514 878 765\nf 515 939 319\nf 516 940 884\nf 517 718 84\nf 518 786 163\nf 519 857 179\nf 520 997 813\nf 521 721 1023\nf 522 829 769\nf 523 304 1005\nf 524 1002 786\nf 525 744 565\nf 526 1018 821\nf 527 886 843\nf 528 999 944\nf 529 801 171\nf 530 238 643\nf 531 1021 931\nf 532 272 27\nf 533 909 700\nf 534 899 235\nf 535 833 633\nf 536 782 19\nf 537 804 342\nf 538 291 81\nf 539 816 774\nf 540 693 848\nf 541 945 753\nf 542 719 290\nf 543 803 716\nf 544 971 144\nf 545 893 200\nf 546 925 967\nf 547 300 59\nf 548 919 15\nf 549 186 544\nf 550 992 8\nf 551 1014 928\nf 552 147 52\nf 553 1011 717\nf 554 1013 819\nf 555 913 156\nf 556 236 496\nf 557 1019 598\nf 558 960 759\nf 559 166 830\nf 560 224 24\nf 561 863 912\nf 562 799 253\nf 563 140 181\nf 564 972 959\nf 565 983 186\nf 566 698 919\nf 567 683 467\nf 568 79 775\nf 569 683 431\nf 570 25 207\nf 571 145 312\nf 572 661 259\nf 573 883 457\nf 574 1023 926\nf 575 742 373\nf 576 374 187\nf 577 886 128\nf 578 1013 855\nf 579 378 55\nf 580 789 788\nf 581 902 213\nf 582 379 109\nf 583 842 993\nf 584 1006 972\nf 585 933 113\nf 586 852 739\nf 587 976 949\nf 588 787 925\nf 589 109 826\nf 590 734 189\nf 591 1 295\nf 592 894 363\nf 593 275 256\nf 594 808 232\nf 595 941 927\nf 596 936 929\nf 597 971 380\nf 598 873 980\nf 599 768 722\nf 600 66 242\nf 601 259 616\nf 602 1000 663\nf 603 875 97\nf 604 442 22\nf 605 202 362\nf 606 977 802\nf 607 660 328\nf 608 8 232\nf 609 1006 241\nf 610 964 273\nf 611 276 53\nf 612 344 31\nf 613 310 726\nf 614 776 16\nf 615 219 54\nf 616 483 601\nf 617 785 63\nf 618 221 635\nf 619 387 393\nf 620 898 405\nf 621 96 157\nf 622 503 173\nf 623 943 495\nf 624 166 296\nf 625 248 7\nf 626 299 607\nf 627 501 11\nf 628 212 11\nf 629 73 237\nf 630 38 658\nf 631 977 228\nf 632 394 208\nf 633 85 535\nf 634 210 76\nf 635 58 878\nf 636 36 367\nf 637 215 430\nf 638 375 915\nf 639 875 920\nf 640 939 539\nf 641 489 967\nf 642 817 358\nf 643 14 238\nf 644 926 781\nf 645 962 469\nf 646 245 161\nf 647 987 334\nf 648 943 212\nf 649 499 99\nf 650 144 818\nf 651 736 108\nf 652 961 372\nf 653 74 464\nf 654 1015 169\nf 655 441 171\nf 656 757 193\nf 657 50 237\nf 658 910 433\nf 659 103 175\nf 660 836 335\nf 661 65 741\nf 662 55 218\nf 663 948 176\nf 664 914 51\nf 665 182 43\nf 666 859 263\nf 667 266 780\nf 668 206 566\nf 669 438 34\nf 670 383 220\nf 671 188 866\nf 672 191 45\nf 673 987 345\nf 674 165 465\nf 675 342 184\nf 676 439 139\nf 677 945 353\nf 678 200 92\nf 679 431 303\nf 680 48 204\nf 681 1003 59\nf 682 204 357\nf 683 1020 902\nf 684 16 793\nf 685 972 247\nf 686 991 201\nf 687 194 62\nf 688 940 872\nf 689 329 116\nf 690 197 324\nf 691 986 471\nf 692 408 924\nf 693 540 155\nf 694 969 42\nf 695 161 348\nf 696 308 956\nf 697 43 429\nf 698 566 206\nf 699 884 220\nf 700 533 142\nf 701 375 304\nf 702 892 292\nf 703 237 719\nf 704 952 242\nf 705 121 735\nf 706 22 906\nf 707 176 446\nf 708 197 110\nf 709 302 169\nf 710 211 69\nf 711 251 295\nf 712 235 614\nf 713 880 307\nf 714 965 897\nf 715 80 317\nf 716 108 236\nf 717 553 331\nf 718 84 849\nf 719 939 290\nf 720 156 352\nf 721 1023 790\nf 722 219 599\nf 723 105 382\nf 724 455 398\nf 725 204 116\nf 726 310 732\nf 727 953 273\nf 728 344 199\nf 729 917 238\nf 730 268 120\nf 731 246 15\nf 732 36 726\nf 733 831 251\nf 734 922 207\nf 735 785 292\nf 736 108 1022\nf 737 494 346\nf 738 974 384\nf 739 335 586\nf 740 288 345\nf 741 192 337\nf 742 575 239\nf 743 254 774\nf 744 144 525\nf 745 230 124\nf 746 505 442\nf 747 824 138\nf 748 1024 2\nf 749 323 207\nf 750 435 862\nf 751 209 452\nf 752 269 124\nf 753 508 541\nf 754 345 425\nf 755 958 57\nf 756 279 294\nf 757 656 1021\nf 758 139 367\nf 759 29 244\nf 760 79 401\nf 761 366 272\nf 762 301 107\nf 763 287 84\nf 764 934 261\nf 765 514 58\nf 766 421 47\nf 767 459 67\nf 768 219 599\nf 769 78 261\nf 770 112 260\nf 771 313 246\nf 772 801 441\nf 773 468 223\nf 774 423 539\nf 775 229 79\nf 776 614 235\nf 777 295 251\nf 778 27 301\nf 779 873 278\nf 780 26 266\nf 781 352 156\nf 782 911 306\nf 783 251 162\nf 784 420 102\nf 785 814 735\nf 786 518 163\nf 787 588 473\nf 788 976 949\nf 789 976 580\nf 790 458 326\nf 791 435 738\nf 792 85 438\nf 793 131 449\nf 794 90 243\nf 795 216 273\nf 796 398 35\nf 797 248 308\nf 798 988 287\nf 799 253 562\nf 800 389 178\nf 801 772 529\nf 802 323 606\nf 803 543 994\nf 804 421 537\nf 805 222 130\nf 806 76 277\nf 807 14 215\nf 808 594 892\nf 809 330 327\nf 810 297 422\nf 811 12 381\nf 812 33 245\nf 813 238 127\nf 814 986 271\nf 815 328 299\nf 816 539 909\nf 817 371 193\nf 818 144 256\nf 819 554 283\nf 820 1019 281\nf 821 125 311\nf 822 112 445\nf 823 146 233\nf 824 747 46\nf 825 128 456\nf 826 109 582\nf 827 853 118\nf 828 24 317\nf 829 174 522\nf 830 296 102\nf 831 733 162\nf 832 235 411\nf 833 231 535\nf 834 360 231\nf 835 365 240\nf 836 210 660\nf 837 107 429\nf 838 98 268\nf 839 249 414\nf 840 218 426\nf 841 401 182\nf 842 993 583\nf 843 419 284\nf 844 427 96\nf 845 179 296\nf 846 505 143\nf 847 82 861\nf 848 72 540\nf 849 255 84\nf 850 379 41\nf 851 386 152\nf 852 154 410\nf 853 463 827\nf 854 412 174\nf 855 77 1013\nf 856 11 294\nf 857 179 395\nf 858 114 384\nf 859 82 267\nf 860 481 930\nf 861 289 938\nf 862 428 114\nf 863 931 561\nf 864 244 67\nf 865 325 391\nf 866 279 89\nf 867 1018 311\nf 868 294 427\nf 869 416 364\nf 870 484 886\nf 871 119 872\nf 872 688 119\nf 873 598 779\nf 874 402 47\nf 875 391 1007\nf 876 475 190\nf 877 361 446\nf 878 58 514\nf 879 456 74\nf 880 28 713\nf 881 336 902\nf 882 302 422\nf 883 573 340\nf 884 516 699\nf 885 85 355\nf 886 293 527\nf 887 244 759\nf 888 70 245\nf 889 329 351\nf 890 411 476\nf 891 107 403\nf 892 998 702\nf 893 200 545\nf 894 6 412\nf 895 269 897\nf 896 402 254\nf 897 269 714\nf 898 115 263\nf 899 376 57\nf 900 341 59\nf 901 1 372\nf 902 370 213\nf 903 126 227\nf 904 376 162\nf 905 331 120\nf 906 442 143\nf 907 205 403\nf 908 216 727\nf 909 226 816\nf 910 270 658\nf 911 148 283\nf 912 190 531\nf 913 41 555\nf 914 225 664\nf 915 187 583\nf 916 271 950\nf 917 14 729\nf 918 473 250\nf 919 548 189\nf 920 1019 217\nf 921 29 960\nf 922 189 447\nf 923 479 6\nf 924 467 285\nf 925 140 546\nf 926 644 6\nf 927 332 595\nf 928 61 507\nf 929 289 503\nf 930 448 240\nf 931 531 863\nf 932 121 417\nf 933 585 500\nf 934 764 32\nf 935 314 985\nf 936 362 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-0.1357 0.7158\nv -0.5393 -0.0934 -0.7696\nv -0.2615 0.2095 0.7437\nv 0.4596 0.2563 0.7422\nv 0.4574 0.2402 0.6837\nv -0.2255 0.0009 0.6105\nv 0.5417 -0.1236 0.5068\nv 0.0169 -0.0379 -0.7630\nv -0.5409 -0.1437 0.2192\nv 0.5440 -0.0853 -0.1106\nv 0.4753 0.1040 0.3263\nv -0.5288 -0.0104 0.2023\nv -0.5435 -0.0770 0.3968\nv 0.1692 -0.2033 0.6779\nv -0.3157 -0.0049 0.6618\nv 0.1353 -0.0173 0.7158\nv -0.2435 -0.1571 -0.7954\nv -0.3806 -0.1948 -0.7971\nv 0.2876 -0.0033 0.2277\nv 0.1015 0.0822 0.6127\nv 0.5416 -0.2287 -0.1106\nv -0.1438 -0.2541 0.6779\nv -0.3045 -0.3048 0.6779\nv 0.5756 0.3000 0.7051\nv -0.5074 -0.0033 0.4645\nv -0.4229 0.0665 0.2644\nv 0.3130 -0.3725 0.6779\nv 0.5263 -0.1821 0.4983\nv -0.3869 0.0941 0.6762\nv 0.5667 -0.3640 0.6780\nv 0.2960 0.1692 0.6775\nv 0.5243 -0.2341 -0.6350\nv 0.4144 0.2824 0.6938\nv -0.4889 0.0083 0.3799\nv -0.1015 0.2532 0.7442\nv -0.5703 -0.1228 0.7157\nv 0.5712 -0.1610 -0.4828\nv -0.3975 -0.1860 -0.5674\nv 0.3722 -0.1860 -0.5843\nv -0.1099 -0.0033 -0.4067\nv -0.4398 -0.1860 0.2023\nv -0.2368 -0.0033 0.1346\nv 0.4398 -0.1860 0.1685\nv -0.1607 -0.1860 -0.5082\nv -0.0761 -0.1860 -0.6519\nv 0.4906 -0.2879 0.6779\nv -0.3721 -0.1724 0.6705\nv 0.3214 -0.1860 -0.0599\nv 0.5285 -0.0104 -0.5251\nv 0.2030 -0.0033 -0.0937\nv -0.1522 -0.2964 0.7158\nv -0.2453 -0.1860 -0.0599\nv -0.2368 -0.1860 -0.6096\nv 0.2284 -0.0033 0.1431\nv -0.4736 -0.1860 -0.1022\nv 0.4144 -0.0033 0.1685\nv 0.2876 -0.1860 -0.3897\nv -0.0084 -0.1860 -0.1952\nv 0.0169 -0.0033 -0.5843\nv 0.0338 -0.1860 -0.1106\nv 0.1184 -0.2372 0.7162\nv -0.4229 -0.0680 0.7158\nv 0.3891 -0.1860 -0.4912\nv 0.2622 -0.1860 0.3461\nv 0.1353 0.1940 0.6776\nv -0.3975 0.3206 0.7399\nv -0.5074 0.3636 0.7196\nv 0.1607 -0.2402 -0.7725\nv 0.3214 0.1257 0.4391\nv 0.3017 -0.1536 -0.7892\nv -0.3890 0.1163 0.3379\nv 0.1776 -0.1779 -0.7971\nv -0.0423 0.3167 0.7019\nv 0.1523 0.0193 0.6120\nv 0.5705 -0.1864 -0.7196\nv 0.4933 0.0426 0.3207\nv -0.0796 -0.1818 -0.7576\nv 0.0684 -0.0043 0.6861\nv 0.4852 0.0886 0.4194\nv -0.4821 -0.0033 -0.3813\nv 0.5328 -0.0228 -0.1903\nv -0.5075 0.1276 0.7362\nv -0.4889 0.0163 0.5885\nv -0.5413 0.0668 0.6771\nv -0.1078 0.0940 0.6762\nv 0.1720 0.0288 0.2680\nv -0.1861 0.0422 0.2632\nv -0.5423 -0.2034 0.0416\nv -0.5440 -0.0853 -0.0430\nv 0.0677 -0.2034 -0.7680\nv -0.5715 -0.2118 0.1262\nv -0.3129 0.0422 0.2632\nv 0.3637 -0.0033 0.2530\nv 0.4933 0.0427 0.6146\nv -0.5497 0.2955 0.7422\nv -0.0592 0.0359 0.7166\nv -0.2622 0.0359 0.6777\nv -0.4821 0.1778 0.6781\nv -0.2030 0.1136 0.3215\nv 0.4821 0.1358 0.7377\nv 0.1438 0.1184 0.3434\nv 0.5409 -0.1294 0.6506\nv 0.5654 -0.1530 -0.0258\nv -0.4652 -0.0033 0.6421\nv -0.0155 0.0505 0.3207\nv -0.2622 -0.0033 -0.2121\nv 0.3975 -0.1860 -0.7281\nv 0.0126 0.0505 0.3799\nf 1 966 684\nf 2 1011 983\nf 3 779 643\nf 4 940 739\nf 5 872 595\nf 6 647 991\nf 7 1007 1004\nf 8 946 673\nf 9 975 490\nf 10 915 732\nf 11 786 731\nf 12 1015 563\nf 13 821 736\nf 14 678 641\nf 15 705 658\nf 16 985 689\nf 17 808 900\nf 18 968 604\nf 19 720 738\nf 20 904 715\nf 21 984 892\nf 22 785 743\nf 23 676 630\nf 24 737 560\nf 25 869 584\nf 26 801 637\nf 27 767 709\nf 28 887 910\nf 29 787 950\nf 30 943 543\nf 31 675 551\nf 32 947 845\nf 33 893 553\nf 34 870 597\nf 35 477 582\nf 36 960 762\nf 37 859 804\nf 38 897 903\nf 39 564 726\nf 40 487 465\nf 41 698 455\nf 42 756 740\nf 43 586 550\nf 44 882 701\nf 45 874 902\nf 46 992 780\nf 47 953 655\nf 48 1000 708\nf 49 937 609\nf 50 963 763\nf 51 817 877\nf 52 761 839\nf 53 713 777\nf 54 830 558\nf 55 1002 614\nf 56 749 669\nf 57 686 742\nf 58 864 682\nf 59 816 605\nf 60 790 854\nf 61 615 542\nf 62 973 886\nf 63 834 608\nf 64 923 944\nf 65 387 591\nf 66 690 687\nf 67 972 871\nf 68 768 245\nf 69 1006 789\nf 70 729 679\nf 71 901 764\nf 72 1023 883\nf 73 772 730\nf 74 880 596\nf 75 958 809\nf 76 917 680\nf 77 819 828\nf 78 918 776\nf 79 603 600\nf 80 445 671\nf 81 714 868\nf 82 990 936\nf 83 911 748\nf 84 863 533\nf 85 913 1022\nf 86 836 873\nf 87 934 525\nf 88 916 578\nf 89 746 815\nf 90 855 662\nf 91 803 783\nf 92 865 632\nf 93 791 1021\nf 94 851 727\nf 95 703 575\nf 96 977 745\nf 97 978 895\nf 98 530 355\nf 99 631 492\nf 100 982 653\nf 101 765 702\nf 102 987 942\nf 103 920 898\nf 104 766 488\nf 105 823 559\nf 106 994 747\nf 107 905 814\nf 108 989 833\nf 109 980 718\nf 110 961 907\nf 111 867 875\nf 112 568 754\nf 113 969 683\nf 114 552 648\nf 115 908 733\nf 116 884 508\nf 117 659 774\nf 118 773 890\nf 119 914 811\nf 120 909 534\nf 121 755 436\nf 122 795 494\nf 123 926 707\nf 124 970 506\nf 125 1009 935\nf 126 1013 922\nf 127 797 792\nf 128 721 413\nf 129 674 844\nf 130 697 758\nf 131 775 617\nf 132 840 533\nf 133 891 645\nf 134 1008 625\nf 135 769 689\nf 136 924 576\nf 137 974 862\nf 138 1003 719\nf 139 744 521\nf 140 566 843\nf 141 866 584\nf 142 650 771\nf 143 835 964\nf 144 886 507\nf 145 860 406\nf 146 760 458\nf 147 717 457\nf 148 995 927\nf 149 1024 657\nf 150 672 660\nf 151 628 781\nf 152 752 682\nf 153 813 528\nf 154 849 644\nf 155 832 825\nf 156 1010 573\nf 157 750 630\nf 158 667 666\nf 159 878 979\nf 160 532 555\nf 161 885 562\nf 162 740 565\nf 163 623 700\nf 164 812 500\nf 165 770 612\nf 166 1018 642\nf 167 1001 1012\nf 168 876 555\nf 169 959 757\nf 170 948 784\nf 171 853 658\nf 172 889 554\nf 173 828 664\nf 174 906 774\nf 175 734 919\nf 176 732 619\nf 177 1011 656\nf 178 888 551\nf 179 715 754\nf 180 678 492\nf 181 762 606\nf 182 1017 692\nf 183 786 803\nf 184 829 669\nf 185 663 685\nf 186 1014 708\nf 187 1019 757\nf 188 996 773\nf 189 722 640\nf 190 764 648\nf 191 1005 597\nf 192 755 718\nf 193 537 503\nf 194 834 604\nf 195 568 486\nf 196 810 852\nf 197 716 463\nf 198 794 695\nf 199 738 567\nf 200 891 857\nf 201 971 695\nf 202 548 516\nf 203 821 529\nf 204 848 713\nf 205 951 724\nf 206 993 578\nf 207 904 839\nf 208 502 663\nf 209 815 605\nf 210 698 657\nf 211 763 781\nf 212 802 688\nf 213 770 621\nf 214 954 714\nf 215 700 892\nf 216 661 540\nf 217 865 721\nf 218 928 679\nf 219 930 977\nf 220 759 607\nf 221 924 916\nf 222 909 819\nf 223 830 808\nf 224 679 820\nf 225 728 937\nf 226 983 982\nf 227 650 783\nf 228 557 608\nf 229 927 992\nf 230 507 630\nf 231 829 669\nf 232 907 969\nf 233 767 626\nf 234 867 761\nf 235 997 877\nf 236 939 903\nf 237 941 799\nf 238 890 756\nf 239 1007 925\nf 240 900 753\nf 241 974 676\nf 242 760 912\nf 243 816 639\nf 244 898 822\nf 245 784 768\nf 246 711 709\nf 247 981 592\nf 248 627 575\nf 249 806 902\nf 250 748 666\nf 251 936 619\nf 252 880 593\nf 253 766 637\nf 254 869 628\nf 255 881 978\nf 256 913 633\nf 257 831 584\nf 258 798 690\nf 259 688 570\nf 260 818 980\nf 261 880 752\nf 262 956 505\nf 263 854 929\nf 264 894 988\nf 265 807 687\nf 266 680 917\nf 267 932 782\nf 268 962 598\nf 269 952 837\nf 270 915 827\nf 271 788 696\nf 272 914 887\nf 273 968 697\nf 274 698 614\nf 275 985 744\nf 276 826 853\nf 277 953 568\nf 278 856 479\nf 279 823 1004\nf 280 958 719\nf 281 1004 665\nf 282 866 528\nf 283 873 759\nf 284 847 730\nf 285 1012 582\nf 286 779 643\nf 287 1019 861\nf 288 871 905\nf 289 921 949\nf 290 833 726\nf 291 845 792\nf 292 872 638\nf 293 851 854\nf 294 745 660\nf 295 925 787\nf 296 755 558\nf 297 741 896\nf 298 894 986\nf 299 972 856\nf 300 970 935\nf 301 975 596\nf 302 978 832\nf 303 885 882\nf 304 973 750\nf 305 865 762\nf 306 694 627\nf 307 940 739\nf 308 976 509\nf 309 841 585\nf 310 1006 984\nf 311 646 467\nf 312 689 744\nf 313 1020 999\nf 314 692 1021\nf 315 631 773\nf 316 749 721\nf 317 716 660\nf 318 844 674\nf 319 931 922\nf 320 860 624\nf 321 741 981\nf 322 982 796\nf 323 742 991\nf 324 998 800\nf 325 947 722\nf 326 644 775\nf 327 948 652\nf 328 1022 840\nf 329 956 457\nf 330 806 874\nf 331 863 641\nf 332 852 694\nf 333 859 769\nf 334 949 920\nf 335 612 512\nf 336 929 803\nf 337 933 737\nf 338 870 661\nf 339 786 837\nf 340 969 960\nf 341 736 609\nf 342 1002 802\nf 343 906 751\nf 344 960 599\nf 345 887 811\nf 346 557 83\nf 347 613 690\nf 348 946 659\nf 349 1003 791\nf 350 685 682\nf 351 667 487\nf 352 842 731\nf 353 919 726\nf 354 938 967\nf 355 897 530\nf 356 1004 823\nf 357 790 712\nf 358 683 954\nf 359 1008 1003\nf 360 933 612\nf 361 876 757\nf 362 950 850\nf 363 828 664\nf 364 899 602\nf 365 835 550\nf 366 830 558\nf 367 753 600\nf 368 857 779\nf 369 911 813\nf 370 622 479\nf 371 836 465\nf 372 916 88\nf 373 767 788\nf 374 990 782\nf 375 1008 942\nf 376 1024 804\nf 377 1017 518\nf 378 706 908\nf 379 763 498\nf 380 947 855\nf 381 831 851\nf 382 534 120\nf 383 986 1023\nf 384 1014 710\nf 385 877 707\nf 386 795 793\nf 387 591 133\nf 388 635 538\nf 389 801 766\nf 390 713 848\nf 391 1007 727\nf 392 903 706\nf 393 820 670\nf 394 913 464\nf 395 965 843\nf 396 897 825\nf 397 910 944\nf 398 843 742\nf 399 967 908\nf 400 745 544\nf 401 912 987\nf 402 961 993\nf 403 794 868\nf 404 818 536\nf 405 975 457\nf 406 846 995\nf 407 868 585\nf 408 934 737\nf 409 821 95\nf 410 893 559\nf 411 964 168\nf 412 839 875\nf 413 749 599\nf 414 735 993\nf 415 838 990\nf 416 730 756\nf 417 686 844\nf 418 924 789\nf 419 733 763\nf 420 852 642\nf 421 1000 895\nf 422 862 92\nf 423 952 731\nf 424 954 714\nf 425 966 663\nf 426 717 147\nf 427 670 597\nf 428 845 778\nf 429 769 567\nf 430 945 771\nf 431 888 996\nf 432 922 793\nf 433 875 752\nf 434 153 282\nf 435 814 107\nf 436 810 755\nf 437 932 782\nf 438 857 3\nf 439 983 1011\nf 440 31 675\nf 441 902 840\nf 442 904 20\nf 443 758 130\nf 444 738 611\nf 445 676 157\nf 446 955 72\nf 447 958 677\nf 448 965 754\nf 449 1001 824\nf 450 1016 709\nf 451 1006 69\nf 452 943 774\nf 453 805 569\nf 454 921 898\nf 455 41 554\nf 456 930 720\nf 457 956 147\nf 458 1015 629\nf 459 204 66\nf 460 919 807\nf 461 957 701\nf 462 980 718\nf 463 716 830\nf 464 870 394\nf 465 811 40\nf 466 774 659\nf 467 613 311\nf 468 991 784\nf 469 951 989\nf 470 879 183\nf 471 842 884\nf 472 740 714\nf 473 926 997\nf 474 951 205\nf 475 963 832\nf 476 998 1014\nf 477 126 386\nf 478 891 756\nf 479 821 622\nf 480 934 643\nf 481 1012 896\nf 482 861 937\nf 483 804 135\nf 484 930 977\nf 485 860 1010\nf 486 853 658\nf 487 40 818\nf 488 972 856\nf 489 918 213\nf 490 924 9\nf 491 981 792\nf 492 99 180\nf 493 981 579\nf 494 1001 690\nf 495 942 218\nf 496 878 953\nf 497 786 357\nf 498 931 1013\nf 499 683 337\nf 500 812 164\nf 501 56 507\nf 502 684 966\nf 503 889 124\nf 504 849 96\nf 505 262 84\nf 506 124 1002\nf 507 886 230\nf 508 254 116\nf 509 607 308\nf 510 665 815\nf 511 986 892\nf 512 770 335\nf 513 1016 662\nf 514 46 203\nf 515 794 118\nf 516 988 984\nf 517 1014 708\nf 518 1017 55\nf 519 845 947\nf 520 807 793\nf 521 899 744\nf 522 791 210\nf 523 579 321\nf 524 796 175\nf 525 770 649\nf 526 75 809\nf 527 955 979\nf 528 911 153\nf 529 914 821\nf 530 396 98\nf 531 921 626\nf 532 160 304\nf 533 863 132\nf 534 874 330\nf 535 957 925\nf 536 404 109\nf 537 905 425\nf 538 785 945\nf 539 915 651\nf 540 695 559\nf 541 854 60\nf 542 995 148\nf 543 775 943\nf 544 930 54\nf 545 734 824\nf 546 889 172\nf 547 817 350\nf 548 56 202\nf 549 814 877\nf 550 964 835\nf 551 31 812\nf 552 289 114\nf 553 794 33\nf 554 172 819\nf 555 168 160\nf 556 305 36\nf 557 228 130\nf 558 192 54\nf 559 893 540\nf 560 907 360\nf 561 925 851\nf 562 1007 1004\nf 563 912 12\nf 564 39 353\nf 565 162 890\nf 566 742 843\nf 567 640 199\nf 568 112 195\nf 569 453 728\nf 570 1009 229\nf 571 994 415\nf 572 942 625\nf 573 788 767\nf 574 883 623\nf 575 703 248\nf 576 894 52\nf 577 1018 64\nf 578 916 88\nf 579 176 493\nf 580 732 481\nf 581 129 674\nf 582 747 285\nf 583 883 72\nf 584 257 141\nf 585 81 309\nf 586 43 304\nf 587 72 892\nf 588 276 532\nf 589 992 856\nf 590 796 100\nf 591 643 645\nf 592 764 247\nf 593 58 252\nf 594 820 447\nf 595 872 292\nf 596 74 301\nf 597 870 34\nf 598 751 466\nf 599 413 344\nf 600 79 151\nf 601 1009 935\nf 602 788 271\nf 603 79 900\nf 604 194 18\nf 605 209 59\nf 606 181 92\nf 607 63 509\nf 608 63 228\nf 609 49 341\nf 610 70 191\nf 611 855 444\nf 612 360 165\nf 613 859 37\nf 614 55 657\nf 615 899 1010\nf 616 822 949\nf 617 906 343\nf 618 660 150\nf 619 994 936\nf 620 478 133\nf 621 213 78\nf 622 959 821\nf 623 163 574\nf 624 266 373\nf 625 572 134\nf 626 454 233\nf 627 944 923\nf 628 419 151\nf 629 798 376\nf 630 23 750\nf 631 315 99\nf 632 862 92\nf 633 256 85\nf 634 878 170\nf 635 945 388\nf 636 828 249\nf 637 253 288\nf 638 849 872\nf 639 243 209\nf 640 189 797\nf 641 331 14\nf 642 1018 917\nf 643 480 286\nf 644 326 154\nf 645 891 133\nf 646 1024 311\nf 647 323 6\nf 648 778 190\nf 649 934 337\nf 650 999 1020\nf 651 741 205\nf 652 858 47\nf 653 982 439\nf 654 1013 785\nf 655 953 47\nf 656 998 177\nf 657 1021 314\nf 658 15 171\nf 659 962 466\nf 660 618 150\nf 661 216 338\nf 662 380 822\nf 663 425 864\nf 664 173 809\nf 665 971 281\nf 666 158 250\nf 667 158 351\nf 668 961 184\nf 669 829 184\nf 670 393 224\nf 671 974 241\nf 672 781 150\nf 673 8 166\nf 674 844 318\nf 675 31 621\nf 676 241 23\nf 677 958 280\nf 678 14 180\nf 679 928 224\nf 680 917 76\nf 681 1011 439\nf 682 350 58\nf 683 358 499\nf 684 1 502\nf 685 864 350\nf 686 742 417\nf 687 265 319\nf 688 259 212\nf 689 312 135\nf 690 66 494\nf 691 929 941\nf 692 1024 314\nf 693 291 845\nf 694 306 332\nf 695 540 201\nf 696 271 76\nf 697 882 273\nf 698 274 210\nf 699 519 127\nf 700 215 163\nf 701 957 885\nf 702 951 290\nf 703 95 272\nf 704 148 259\nf 705 15 853\nf 706 392 378\nf 707 997 123\nf 708 1014 998\nf 709 450 246\nf 710 384 177\nf 711 307 103\nf 712 842 11\nf 713 53 390\nf 714 81 214\nf 715 179 20\nf 716 463 197\nf 717 147 164\nf 718 192 109\nf 719 280 138\nf 720 343 19\nf 721 217 128\nf 722 189 797\nf 723 1006 69\nf 724 833 290\nf 725 71 247\nf 726 290 353\nf 727 94 561\nf 728 225 15\nf 729 70 925\nf 730 416 73\nf 731 952 11\nf 732 580 896\nf 733 939 419\nf 734 175 545\nf 735 414 69\nf 736 13 341\nf 737 408 337\nf 738 374 444\nf 739 940 307\nf 740 472 162\nf 741 297 651\nf 742 323 566\nf 743 931 22\nf 744 521 275\nf 745 400 294\nf 746 89 303\nf 747 106 582\nf 748 250 83\nf 749 413 316\nf 750 157 304\nf 751 906 343\nf 752 433 261\nf 753 367 240\nf 754 448 179\nf 755 436 192\nf 756 238 478\nf 757 169 361\nf 758 443 273\nf 759 283 220\nf 760 242 146\nf 761 234 52\nf 762 36 181\nf 763 211 419\nf 764 71 190\nf 765 449 167\nf 766 253 389\nf 767 373 233\nf 768 68 245\nf 769 135 333\nf 770 512 165\nf 771 1020 430\nf 772 73 188\nf 773 188 890\nf 774 466 174\nf 775 131 543\nf 776 918 847\nf 777 401 799\nf 778 949 648\nf 779 368 3\nf 780 46 846\nf 781 672 211\nf 782 932 374\nf 783 91 999\nf 784 948 170\nf 785 22 538\nf 786 339 11\nf 787 29 295\nf 788 271 602\nf 789 993 69\nf 790 60 357\nf 791 93 522\nf 792 291 127\nf 793 386 1001\nf 794 553 198\nf 795 386 122\nf 796 590 524\nf 797 127 722\nf 798 376 629\nf 799 237 777\nf 800 1020 1000\nf 801 389 26\nf 802 125 935\nf 803 91 336\nf 804 376 37\nf 805 453 482\nf 806 874 330\nf 807 265 654\nf 808 223 17\nf 809 958 75\nf 810 436 196\nf 811 119 345\nf 812 500 164\nf 813 911 153\nf 814 549 435\nf 815 209 89\nf 816 59 243\nf 817 51 547\nf 818 260 404\nf 819 222 77\nf 820 594 393\nf 821 13 529\nf 822 947 380\nf 823 279 105\nf 824 449 205\nf 825 155 396\nf 826 979 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0.4746\nv -0.3830 -0.5218 -0.4091\nv 0.3679 -0.4610 -0.5513\nv -0.2996 0.7080 -0.4799\nv -0.3898 0.6767 -0.5420\nv -0.3218 0.3466 -0.6028\nv 0.7081 -0.4080 0.0330\nv 0.1568 -0.1071 -0.7167\nv 0.5489 0.1637 -0.1365\nv -0.4175 -0.0272 0.6964\nv 0.3902 -0.3068 -0.5711\nv -0.8792 -0.3959 0.0519\nv 0.3534 -0.2702 0.6160\nv 0.2999 0.7162 0.3537\nv -0.6548 0.4688 -0.3120\nv 0.5525 0.1905 -0.0015\nv -0.5335 -0.4187 -0.6280\nv 0.4374 0.3646 -0.2892\nv -0.3137 0.7161 0.5053\nv -0.3840 -0.5401 0.3046\nv -0.5182 0.1869 -0.5527\nv -0.0822 -0.1370 0.7695\nv -0.5884 -0.5183 0.1897\nv 0.4457 0.6082 -0.2180\nv 0.2889 0.3553 -0.4798\nv 0.8150 0.0826 -0.0669\nv -0.3461 -0.1062 0.7344\nv -0.2826 0.1727 0.6653\nv 0.3275 0.6082 -0.4077\nv 0.3814 -0.4884 -0.2839\nv 0.8325 0.2695 -0.0049\nv -0.5286 -0.5390 0.0584\nv -0.1090 0.7084 -0.5100\nv 0.0805 -0.4830 -0.7024\nv 0.6060 0.0427 -0.0585\nv 0.1576 0.5984 -0.5379\nv -0.5686 -0.2036 -0.6334\nv -0.8886 -0.2568 0.1379\nv 0.0673 0.3859 0.6001\nv -0.8425 -0.4969 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601\nf 54 883 721\nf 55 882 940\nf 56 824 694\nf 57 783 718\nf 58 828 823\nf 59 926 684\nf 60 839 644\nf 61 986 656\nf 62 699 696\nf 63 708 844\nf 64 687 682\nf 65 660 531\nf 66 880 1006\nf 67 613 534\nf 68 749 549\nf 69 853 912\nf 70 948 712\nf 71 792 753\nf 72 532 707\nf 73 909 650\nf 74 897 863\nf 75 906 627\nf 76 931 872\nf 77 723 551\nf 78 709 785\nf 79 911 693\nf 80 528 670\nf 81 703 605\nf 82 1012 787\nf 83 902 728\nf 84 970 877\nf 85 1020 624\nf 86 483 440\nf 87 932 761\nf 88 593 837\nf 89 925 596\nf 90 965 941\nf 91 879 953\nf 92 599 716\nf 93 797 561\nf 94 1010 584\nf 95 834 511\nf 96 822 813\nf 97 985 462\nf 98 659 575\nf 99 740 737\nf 100 917 840\nf 101 992 760\nf 102 975 628\nf 103 995 804\nf 104 807 617\nf 105 893 795\nf 106 778 806\nf 107 854 717\nf 108 794 497\nf 109 851 692\nf 110 963 493\nf 111 672 968\nf 112 404 432\nf 113 727 900\nf 114 899 581\nf 115 869 567\nf 116 991 936\nf 117 955 983\nf 118 713 664\nf 119 904 626\nf 120 974 513\nf 121 895 967\nf 122 762 817\nf 123 930 819\nf 124 857 378\nf 125 993 952\nf 126 1016 947\nf 127 679 630\nf 128 706 697\nf 129 846 784\nf 130 989 826\nf 131 562 522\nf 132 841 525\nf 133 510 582\nf 134 894 691\nf 135 1002 543\nf 136 798 614\nf 137 635 527\nf 138 690 512\nf 139 557 553\nf 140 746 738\nf 141 534 428\nf 142 622 470\nf 143 626 602\nf 144 881 588\nf 145 782 724\nf 146 448 472\nf 147 621 592\nf 148 866 803\nf 149 954 1019\nf 150 837 884\nf 151 1000 767\nf 152 927 854\nf 153 586 835\nf 154 781 652\nf 155 831 632\nf 156 805 544\nf 157 766 759\nf 158 630 550\nf 159 877 678\nf 160 633 718\nf 161 732 681\nf 162 670 506\nf 163 1014 606\nf 164 821 583\nf 165 695 839\nf 166 549 660\nf 167 1005 890\nf 168 942 629\nf 169 943 815\nf 170 755 1013\nf 171 548 802\nf 172 738 1007\nf 173 793 964\nf 174 896 776\nf 175 701 977\nf 176 962 659\nf 177 997 653\nf 178 801 479\nf 179 658 585\nf 180 1021 870\nf 181 747 631\nf 182 888 936\nf 183 708 692\nf 184 569 768\nf 185 861 819\nf 186 829 832\nf 187 974 980\nf 188 912 535\nf 189 843 920\nf 190 694 750\nf 191 1008 926\nf 192 944 673\nf 193 940 731\nf 194 702 711\nf 195 646 428\nf 196 916 862\nf 197 757 688\nf 198 775 897\nf 199 752 703\nf 200 967 657\nf 201 720 770\nf 202 887 585\nf 203 780 638\nf 204 816 639\nf 205 612 840\nf 206 934 874\nf 207 799 798\nf 208 1011 818\nf 209 817 533\nf 210 938 728\nf 211 1004 900\nf 212 705 623\nf 213 983 769\nf 214 867 683\nf 215 858 625\nf 216 849 745\nf 217 787 640\nf 218 942 663\nf 219 865 651\nf 220 811 981\nf 221 739 778\nf 222 971 650\nf 223 644 697\nf 224 796 764\nf 225 636 540\nf 226 1018 834\nf 227 937 984\nf 228 875 714\nf 229 603 645\nf 230 749 658\nf 231 910 857\nf 232 878 986\nf 233 914 737\nf 234 850 764\nf 235 649 605\nf 236 898 1006\nf 237 923 833\nf 238 859 684\nf 239 810 806\nf 240 766 1010\nf 241 773 631\nf 242 968 873\nf 243 915 459\nf 244 836 699\nf 245 963 743\nf 246 998 855\nf 247 949 844\nf 248 833 889\nf 249 627 608\nf 250 758 880\nf 251 716 700\nf 252 677 551\nf 253 950 812\nf 254 753 647\nf 255 999 584\nf 256 978 852\nf 257 907 579\nf 258 972 795\nf 259 871 779\nf 260 891 767\nf 261 1005 508\nf 262 600 582\nf 263 992 935\nf 264 905 751\nf 265 1012 750\nf 266 788 717\nf 267 849 745\nf 268 958 571\nf 269 918 672\nf 270 903 868\nf 271 804 956\nf 272 885 807\nf 273 617 554\nf 274 604 602\nf 275 1007 897\nf 276 987 953\nf 277 990 955\nf 278 892 765\nf 279 707 600\nf 280 866 637\nf 281 991 828\nf 282 956 808\nf 283 746 575\nf 284 925 685\nf 285 929 706\nf 286 939 628\nf 287 818 844\nf 288 689 545\nf 289 901 869\nf 290 597 471\nf 291 803 797\nf 292 965 648\nf 293 670 570\nf 294 996 932\nf 295 989 842\nf 296 570 621\nf 297 879 677\nf 298 656 458\nf 299 952 661\nf 300 864 568\nf 301 929 619\nf 302 748 694\nf 303 691 651\nf 304 946 449\nf 305 1020 921\nf 306 884 837\nf 307 671 488\nf 308 957 448\nf 309 995 686\nf 310 916 707\nf 311 696 727\nf 312 698 675\nf 313 835 586\nf 314 933 928\nf 315 970 901\nf 316 875 727\nf 317 1020 710\nf 318 471 525\nf 319 664 807\nf 320 845 774\nf 321 695 644\nf 322 511 431\nf 323 959 659\nf 324 961 789\nf 325 524 840\nf 326 1003 770\nf 327 876 187\nf 328 1001 984\nf 329 800 610\nf 330 519 20\nf 331 966 616\nf 332 810 575\nf 333 913 581\nf 334 930 440\nf 335 822 751\nf 336 1010 886\nf 337 903 115\nf 338 823 991\nf 339 762 926\nf 340 864 881\nf 341 805 1024\nf 342 782 733\nf 343 841 673\nf 344 896 931\nf 345 784 846\nf 346 904 560\nf 347 796 704\nf 348 1006 880\nf 349 543 432\nf 350 776 845\nf 351 860 592\nf 352 963 914\nf 353 1017 719\nf 354 962 615\nf 355 856 985\nf 356 662 160\nf 357 958 734\nf 358 843 491\nf 359 765 676\nf 360 954 928\nf 361 994 854\nf 362 887 747\nf 363 860 554\nf 364 752 703\nf 365 911 937\nf 366 722 653\nf 367 1019 821\nf 368 960 778\nf 369 832 760\nf 370 862 654\nf 371 1022 601\nf 372 1016 822\nf 373 859 620\nf 374 1014 802\nf 375 695 763\nf 376 1023 723\nf 377 609 331\nf 378 954 976\nf 379 1009 947\nf 380 1004 900\nf 381 820 806\nf 382 909 604\nf 383 939 653\nf 384 941 1008\nf 385 998 756\nf 386 497 622\nf 387 582 133\nf 388 988 918\nf 389 945 160\nf 390 769 847\nf 391 977 985\nf 392 632 801\nf 393 1007 863\nf 394 809 126\nf 395 705 153\nf 396 831 680\nf 397 996 652\nf 398 888 499\nf 399 787 878\nf 400 706 549\nf 401 889 876\nf 402 661 893\nf 403 826 989\nf 404 687 1002\nf 405 772 980\nf 406 755 349\nf 407 922 981\nf 408 754 127\nf 409 837 907\nf 410 919 1005\nf 411 682 547\nf 412 741 948\nf 413 784 613\nf 414 761 781\nf 415 973 827\nf 416 982 41\nf 417 701 642\nf 418 862 196\nf 419 969 110\nf 420 836 924\nf 421 734 709\nf 422 747 647\nf 423 920 758\nf 424 1000 767\nf 425 839 731\nf 426 940 882\nf 427 986 1012\nf 428 646 534\nf 429 776 726\nf 430 775 590\nf 431 794 322\nf 432 1002 349\nf 433 854 689\nf 434 903 296\nf 435 967 231\nf 436 975 266\nf 437 968 601\nf 438 779 781\nf 439 254 205\nf 440 334 86\nf 441 936 783\nf 442 805 712\nf 443 697 800\nf 444 972 917\nf 445 873 622\nf 446 825 817\nf 447 617 807\nf 448 667 14\nf 449 904 827\nf 450 532 916\nf 451 917 893\nf 452 925 838\nf 453 721 809\nf 454 867 771\nf 455 872 729\nf 456 801 193\nf 457 811 874\nf 458 899 606\nf 459 953 786\nf 460 790 957\nf 461 738 966\nf 462 977 774\nf 463 971 812\nf 464 568 10\nf 465 983 990\nf 466 244 311\nf 467 717 646\nf 468 886 25\nf 469 90 238\nf 470 756 142\nf 471 318 290\nf 472 310 146\nf 473 964 959\nf 474 919 167\nf 475 832 829\nf 476 995 686\nf 477 919 735\nf 478 841 791\nf 479 908 128\nf 480 974 120\nf 481 1019 976\nf 482 944 121\nf 483 927 86\nf 484 930 725\nf 485 967 200\nf 486 799 713\nf 487 962 960\nf 488 915 961\nf 489 167 261\nf 490 767 75\nf 491 895 358\nf 492 957 793\nf 493 870 674\nf 494 881 170\nf 495 946 777\nf 496 877 144\nf 497 1018 855\nf 498 1022 53\nf 499 538 398\nf 500 935 992\nf 501 852 979\nf 502 949 731\nf 503 792 225\nf 504 38 231\nf 505 999 760\nf 506 162 13\nf 507 874 934\nf 508 1005 883\nf 509 57 134\nf 510 848 942\nf 511 95 322\nf 512 698 816\nf 513 980 974\nf 514 1003 169\nf 515 947 905\nf 516 890 891\nf 517 742 642\nf 518 929 190\nf 519 1014 171\nf 520 884 4\nf 521 1011 7\nf 522 685 131\nf 523 722 366\nf 524 952 993\nf 525 944 132\nf 526 965 941\nf 527 887 137\nf 528 147 80\nf 529 923 759\nf 530 761 87\nf 531 65 212\nf 532 848 450\nf 533 912 535\nf 534 782 141\nf 535 1015 982\nf 536 128 60\nf 537 972 29\nf 538 182 552\nf 539 1021 50\nf 540 703 225\nf 541 106 313\nf 542 808 19\nf 543 135 349\nf 544 901 805\nf 545 288 1023\nf 546 711 699\nf 547 958 268\nf 548 882 171\nf 549 166 400\nf 550 868 33\nf 551 77 252\nf 552 936 22\nf 553 845 139\nf 554 363 273\nf 555 811 813\nf 556 950 589\nf 557 1008 139\nf 558 757 935\nf 559 715 48\nf 560 904 269\nf 561 93 803\nf 562 826 131\nf 563 774 175\nf 564 835 215\nf 565 1021 50\nf 566 997 898\nf 567 293 115\nf 568 932 464\nf 569 931 184\nf 570 293 296\nf 571 682 937\nf 572 770 1018\nf 573 880 250\nf 574 730 680\nf 575 332 98\nf 576 71 362\nf 577 815 1003\nf 578 858 39\nf 579 728 210\nf 580 6 402\nf 581 913 114\nf 582 262 387\nf 583 920 164\nf 584 759 766\nf 585 202 824\nf 586 153 50\nf 587 190 750\nf 588 1013 144\nf 589 812 556\nf 590 833 430\nf 591 745 216\nf 592 147 96\nf 593 88 306\nf 594 800 329\nf 595 798 207\nf 596 284 685\nf 597 290 83\nf 598 938 799\nf 599 681 92\nf 600 262 988\nf 601 53 437\nf 602 909 73\nf 603 229 76\nf 604 688 274\nf 605 81 235\nf 606 250 458\nf 607 916 196\nf 608 922 811\nf 609 720 377\nf 610 329 65\nf 611 345 145\nf 612 840 205\nf 613 67 413\nf 614 136 595\nf 615 969 354\nf 616 966 964\nf 617 447 273\nf 618 943 101\nf 619 824 301\nf 620 373 22\nf 621 147 33\nf 622 445 142\nf 623 749 212\nf 624 1020 85\nf 625 381 215\nf 626 827 415\nf 627 934 906\nf 628 975 939\nf 629 370 168\nf 630 158 127\nf 631 181 241\nf 632 802 155\nf 633 59 160\nf 634 996 154\nf 635 137 787\nf 636 225 81\nf 637 715 280\nf 638 203 264\nf 639 426 204\nf 640 785 217\nf 641 950 735\nf 642 417 517\nf 643 853 248\nf 644 839 60\nf 645 923 853\nf 646 31 195\nf 647 29 254\nf 648 292 38\nf 649 870 235\nf 650 909 971\nf 651 219 865\nf 652 154 45\nf 653 177 366\nf 654 370 952\nf 655 69 219\nf 656 61 298\nf 657 200 38\nf 658 230 179\nf 659 176 98\nf 660 65 166\nf 661 893 402\nf 662 762 356\nf 663 1013 218\nf 664 118 319\nf 665 294 145\nf 666 1007 25\nf 667 968 242\nf 668 224 78\nf 669 317 344\nf 670 162 293\nf 671 376 723\nf 672 269 111\nf 673 843 192\nf 674 739 180\nf 675 20 758\nf 676 846 45\nf 677 297 252\nf 678 159 341\nf 679 948 270\nf 680 750 265\nf 681 981 161\nf 682 984 571\nf 683 717 107\nf 684 926 59\nf 685 284 522\nf 686 772 32\nf 687 984 328\nf 688 197 604\nf 689 433 288\nf 690 882 426\nf 691 134 303\nf 692 772 109\nf 693 79 796\nf 694 56 190\nf 695 375 165\nf 696 62 311\nf 697 443 223\nf 698 512 312\nf 699 62 546\nf 700 1017 36\nf 701 977 51\nf 702 51 194\nf 703 199 81\nf 704 224 764\nf 705 9 212\nf 706 128 285\nf 707 279 310\nf 708 183 63\nf 709 78 421\nf 710 18 317\nf 711 546 18\nf 712 70 442\nf 713 799 118\nf 714 875 228\nf 715 48 291\nf 716 251 681\nf 717 107 467\nf 718 888 57\nf 719 885 353\nf 720 201 25\nf 721 1000 54\nf 722 366 523\nf 723 376 671\nf 724 145 345\nf 725 484 185\nf 726 931 429\nf 727 316 113\nf 728 579 210\nf 729 186 8\nf 730 908 16\nf 731 7 502\nf 732 216 161\nf 733 10 1001\nf 734 421 357\nf 735 253 641\nf 736 116 208\nf 737 99 233\nf 738 461 140\nf 739 221 180\nf 740 99 492\nf 741 412 276\nf 742 1004 517\nf 743 245 35\nf 744 336 201\nf 745 216 591\nf 746 978 140\nf 747 422 181\nf 748 302 56\nf 749 68 623\nf 750 265 680\nf 751 335 264\nf 752 364 199\nf 753 254 71\nf 754 961 5\nf 755 406 170\nf 756 998 385\nf 757 955 558\nf 758 423 250\nf 759 529 584\nf 760 101 505\nf 761 87 530\nf 762 122 662\nf 763 247 375\nf 764 224 234\nf 765 278 129\nf 766 240 584\nf 767 260 424\nf 768 46 305\nf 769 983 921\nf 770 572 201\nf 771 454 129\nf 772 405 686\nf 773 241 404\nf 774 320 4\nf 775 44 430\nf 776 429 350\nf 777 973 52\nf 778 106 221\nf 779 259 28\nf 780 27 203\nf 781 154 414\nf 782 342 145\nf 783 57 441\nf 784 413 129\nf 785 640 78\nf 786 459 91\nf 787 399 82\nf 788 266 107\nf 789 861 185\nf 790 914 460\nf 791 478 927\nf 792 71 753\nf 793 173 492\nf 794 108 226\nf 795 258 893\nf 796 224 347\nf 797 291 93\nf 798 136 595\nf 799 598 207\nf 800 594 329\nf 801 456 178\nf 802 831 632\nf 803 291 148\nf 804 271 103\nf 805 341 156\nf 806 239 381\nf 807 104 272\nf 808 282 542\nf 809 394 453\nf 810 239 820\nf 811 220 457\nf 812 253 589\nf 813 96 981\nf 814 13 315\nf 815 577 385\nf 816 204 828\nf 817 209 122\nf 818 287 208\nf 819 185 123\nf 820 381 39\nf 821 164 367\nf 822 96 335\nf 823 338 58\nf 824 619 56\nf 825 446 865\nf 826 562 403\nf 827 626 415\nf 828 58 338\nf 829 475 186\nf 830 348 997\nf 831 802 396\nf 832 475 186\nf 833 248 237\nf 834 95 226\nf 835 564 313\nf 836 420 244\nf 837 409 884\nf 838 32 271\nf 839 644 60\nf 840 612 100\nf 841 478 132\nf 842 19 295\nf 843 189 673\nf 844 247 287\nf 845 320 553\nf 846 676 129\nf 847 263 390\nf 848 532 510\nf 849 216 267\nf 850 234 785\nf 851 109 889\nf 852 501 256\nf 853 643 69\nf 854 433 361\nf 855 246 497\nf 856 355 896\nf 857 378 231\nf 858 578 39\nf 859 238 373\nf 860 363 96\nf 861 185 789\nf 862 370 196\nf 863 74 393\nf 864 300 34\nf 865 219 825\nf 866 970 280\nf 867 454 24\nf 868 550 270\nf 869 289 115\nf 870 493 649\nf 871 259 953\nf 872 455 76\nf 873 445 242\nf 874 507 206\nf 875 316 228\nf 876 327 401\nf 877 159 496\nf 878 232 1\nf 879 297 91\nf 880 348 573\nf 881 144 494\nf 882 690 55\nf 883 508 54\nf 884 520 306\nf 885 272 719\nf 886 336 25\nf 887 23 202\nf 888 182 718\nf 889 401 44\nf 890 1005 516\nf 891 260 54\nf 892 1023 278\nf 893 661 105\nf 894 134 287\nf 895 910 491\nf 896 344 856\nf 897 74 275\nf 898 236 478\nf 899 458 348\nf 900 1004 380\nf 901 544 289\nf 902 83 200\nf 903 434 270\nf 904 119 346\nf 905 27 515\nf 906 75 934\nf 907 257 409\nf 908 16 730\nf 909 382 650\nf 910 895 231\nf 911 365 79\nf 912 533 69\nf 913 333 581\nf 914 233 790\nf 915 243 488\nf 916 607 196\nf 917 451 100\nf 918 269 26\nf 919 474 410\nf 920 423 583\nf 921 305 769\nf 922 608 407\nf 923 237 645\nf 924 51 420\nf 925 89 284\nf 926 684 59\nf 927 152 483\nf 928 314 954\nf 929 285 301\nf 930 334 484\nf 931 76 726\nf 932 568 87\nf 933 314 116\nf 934 627 206\nf 935 500 263\nf 936 441 552\nf 937 227 365\nf 938 598 210\nf 939 383 286\nf 940 426 193\nf 941 384 526\nf 942 218 168\nf 943 618 169\nf 944 482 192\nf 945 389 15\nf 946 304 495\nf 947 379 515\nf 948 679 70\nf 949 502 247\nf 950 253 641\nf 951 21 368\nf 952 524 299\nf 953 459 276\nf 954 378 360\nf 955 117 277\nf 956 282 271\nf 957 492 308\nf 958 268 23\nf 959 323 42\nf 960 368 487\nf 961 324 754\nf 962 487 354\nf 963 245 352\nf 964 473 616\nf 965 526 292\nf 966 331 616\nf 967 200 485\nf 968 242 437\nf 969 419 615\nf 970 315 866\nf 971 222 463\nf 972 258 29\nf 973 777 415\nf 974 187 513\nf 975 628 436\nf 976 47 378\nf 977 701 391\nf 978 256 746\nf 979 3 501\nf 980 513 32\nf 981 681 220\nf 982 41 535\nf 983 213 465\nf 984 687 682\nf 985 97 355\nf 986 61 427\nf 987 276 70\nf 988 388 26\nf 989 130 295\nf 990 277 465\nf 991 281 116\nf 992 263 101\nf 993 125 524\nf 994 361 40\nf 995 103 476\nf 996 634 397\nf 997 177 566\nf 998 385 246\nf 999 505 255\nf 1000 424 151\nf 1001 10 328\nf 1002 135 432\nf 1003 326 514\nf 1004 380 900\nf 1005 508 890\nf 1006 348 236\nf 1007 666 275\nf 1008 191 384\nf 1009 27 379\nf 1010 336 240\nf 1011 208 949\nf 1012 265 82\nf 1013 663 588\nf 1014 163 374\nf 1015 535 41\nf 1016 126 372\nf 1017 36 353\nf 1018 226 497\nf 1019 481 367\nf 1020 305 85\nf 1021 180 565\nf 1022 53 498\nf 1023 892 24\nf 1024 28 341\n" }, { "path": "visualization/triangled_table.obj", "content": "# OBJ file\nv 0.3054 0.4966 -0.1096\nv -0.4233 -0.6658 0.6134\nv 0.4098 -0.6650 -0.6223\nv -0.5037 0.4803 0.7048\nv -0.5260 0.4696 -0.7036\nv 0.3923 -0.1745 0.5635\nv -0.3945 -0.3670 -0.5112\nv -0.3950 0.3760 -0.0054\nv 0.2003 0.4948 0.6841\nv 0.4122 0.0333 -0.6223\nv -0.3681 -0.0772 0.5103\nv 0.0548 0.4966 -0.6420\nv 0.4377 -0.6645 0.5825\nv 0.5089 0.4545 0.3210\nv -0.0078 0.4545 0.2662\nv 0.5120 0.4885 -0.5507\nv -0.3648 0.0849 -0.5240\nv -0.2662 0.4966 -0.3836\nv -0.3548 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0.4072\nv 0.1723 0.4966 -0.5480\nv -0.4852 0.4962 -0.3836\nv -0.3602 0.3848 -0.6278\nv -0.3753 0.3378 -0.6292\nv -0.4453 0.0397 -0.5720\nv -0.3895 -0.3043 0.5323\nv 0.3550 0.3704 0.1411\nv 0.5190 0.4816 0.1227\nv -0.3601 0.4545 0.6499\nv 0.4355 0.3326 0.5989\nv -0.3360 0.1729 0.5492\nv -0.3445 0.4966 0.4541\nv 0.0313 0.4545 -0.4854\nv 0.1566 0.4545 0.3680\nv 0.1488 0.4545 -0.2192\nf 1 856 876\nf 2 738 679\nf 3 922 780\nf 4 826 425\nf 5 942 490\nf 6 946 499\nf 7 742 441\nf 8 828 616\nf 9 748 681\nf 10 704 617\nf 11 893 665\nf 12 611 715\nf 13 924 562\nf 14 948 858\nf 15 691 796\nf 16 776 488\nf 17 902 421\nf 18 660 878\nf 19 887 885\nf 20 915 635\nf 21 980 882\nf 22 753 648\nf 23 837 859\nf 24 941 621\nf 25 949 906\nf 26 1003 773\nf 27 827 609\nf 28 849 985\nf 29 640 804\nf 30 883 354\nf 31 1004 921\nf 32 440 462\nf 33 718 724\nf 34 666 530\nf 35 673 1017\nf 36 943 914\nf 37 839 629\nf 38 600 831\nf 39 894 711\nf 40 968 492\nf 41 532 432\nf 42 897 998\nf 43 798 861\nf 44 965 651\nf 45 857 978\nf 46 668 511\nf 47 890 812\nf 48 913 891\nf 49 900 850\nf 50 646 707\nf 51 987 731\nf 52 944 496\nf 53 972 756\nf 54 631 671\nf 55 795 909\nf 56 817 615\nf 57 864 816\nf 58 990 735\nf 59 824 642\nf 60 903 747\nf 61 1018 645\nf 62 770 723\nf 63 969 637\nf 64 867 654\nf 65 898 740\nf 66 791 1024\nf 67 734 594\nf 68 1009 793\nf 69 786 761\nf 70 982 954\nf 71 870 994\nf 72 869 790\nf 73 772 689\nf 74 1023 879\nf 75 728 662\nf 76 744 755\nf 77 860 809\nf 78 866 525\nf 79 952 494\nf 80 775 465\nf 81 951 803\nf 82 863 491\nf 83 788 656\nf 84 745 647\nf 85 787 638\nf 86 993 820\nf 87 825 675\nf 88 919 865\nf 89 847 950\nf 90 910 655\nf 91 506 393\nf 92 515 633\nf 93 730 614\nf 94 722 697\nf 95 916 854\nf 96 981 686\nf 97 855 800\nf 98 963 813\nf 99 599 584\nf 100 538 345\nf 101 603 553\nf 102 1022 568\nf 103 741 526\nf 104 618 399\nf 105 702 495\nf 106 414 717\nf 107 777 852\nf 108 886 589\nf 109 938 783\nf 110 781 979\nf 111 834 767\nf 112 1005 848\nf 113 899 928\nf 114 907 822\nf 115 971 815\nf 116 895 871\nf 117 779 901\nf 118 1000 473\nf 119 810 774\nf 120 806 597\nf 121 743 1013\nf 122 960 610\nf 123 564 757\nf 124 719 768\nf 125 911 446\nf 126 966 408\nf 127 940 628\nf 128 818 710\nf 129 901 672\nf 130 821 507\nf 131 1020 433\nf 132 999 881\nf 133 819 582\nf 134 836 765\nf 135 639 620\nf 136 650 570\nf 137 873 874\nf 138 833 925\nf 139 975 549\nf 140 752 703\nf 141 814 632\nf 142 687 531\nf 143 1007 782\nf 144 991 842\nf 145 763 567\nf 146 598 962\nf 147 947 564\nf 148 807 758\nf 149 608 623\nf 150 958 935\nf 151 976 838\nf 152 636 619\nf 153 766 516\nf 154 771 579\nf 155 984 720\nf 156 695 705\nf 157 649 475\nf 158 652 945\nf 159 704 642\nf 160 995 992\nf 161 868 685\nf 162 979 1014\nf 163 1015 561\nf 164 1017 953\nf 165 801 605\nf 166 762 732\nf 167 931 651\nf 168 917 795\nf 169 970 973\nf 170 1001 641\nf 171 768 804\nf 172 819 725\nf 173 481 455\nf 174 686 809\nf 175 585 497\nf 176 677 487\nf 177 937 835\nf 178 1011 627\nf 179 740 805\nf 180 733 631\nf 181 997 764\nf 182 663 823\nf 183 786 648\nf 184 905 912\nf 185 852 831\nf 186 644 645\nf 187 964 682\nf 188 590 508\nf 189 707 753\nf 190 902 749\nf 191 920 592\nf 192 936 878\nf 193 778 727\nf 194 889 659\nf 195 829 669\nf 196 672 824\nf 197 692 488\nf 198 796 614\nf 199 843 735\nf 200 699 929\nf 201 961 839\nf 202 770 919\nf 203 908 601\nf 204 715 596\nf 205 893 389\nf 206 706 665\nf 207 725 744\nf 208 827 805\nf 209 996 1006\nf 210 745 726\nf 211 941 677\nf 212 802 647\nf 213 708 701\nf 214 877 785\nf 215 967 751\nf 216 864 752\nf 217 969 927\nf 218 700 721\nf 219 1020 688\nf 220 968 684\nf 221 800 860\nf 222 989 872\nf 223 840 504\nf 224 967 904\nf 225 1001 581\nf 226 666 474\nf 227 807 1019\nf 228 536 532\nf 229 926 897\nf 230 702 803\nf 231 789 661\nf 232 742 518\nf 233 982 509\nf 234 914 754\nf 235 923 724\nf 236 921 644\nf 237 637 653\nf 238 779 540\nf 239 943 741\nf 240 879 711\nf 241 736 680\nf 242 922 636\nf 243 911 808\nf 244 1003 773\nf 245 764 614\nf 246 862 622\nf 247 978 337\nf 248 884 730\nf 249 1011 722\nf 250 675 518\nf 251 898 769\nf 252 870 804\nf 253 676 396\nf 254 774 758\nf 255 947 909\nf 256 747 392\nf 257 875 611\nf 258 574 489\nf 259 924 853\nf 260 783 844\nf 261 792 551\nf 262 959 820\nf 263 1021 731\nf 264 966 748\nf 265 728 913\nf 266 964 946\nf 267 794 837\nf 268 976 577\nf 269 688 951\nf 270 1013 1012\nf 271 1000 714\nf 272 657 553\nf 273 876 766\nf 274 923 759\nf 275 915 550\nf 276 781 480\nf 277 977 972\nf 278 797 831\nf 279 998 928\nf 280 755 744\nf 281 1014 854\nf 282 844 664\nf 283 729 986\nf 284 545 681\nf 285 1019 866\nf 286 856 547\nf 287 799 912\nf 288 761 643\nf 289 679 738\nf 290 863 983\nf 291 960 715\nf 292 1010 593\nf 293 595 693\nf 294 1024 713\nf 295 906 825\nf 296 822 678\nf 297 717 978\nf 298 561 1015\nf 299 977 860\nf 300 832 839\nf 301 828 638\nf 302 1007 816\nf 303 900 577\nf 304 949 801\nf 305 985 849\nf 306 991 691\nf 307 916 190\nf 308 1012 521\nf 309 642 704\nf 310 749 979\nf 311 903 475\nf 312 861 460\nf 313 988 1021\nf 314 845 1010\nf 315 674 705\nf 316 512 894\nf 317 588 1017\nf 318 880 757\nf 319 892 527\nf 320 529 562\nf 321 1008 745\nf 322 824 565\nf 323 400 133\nf 324 956 835\nf 325 698 965\nf 326 993 708\nf 327 778 893\nf 328 929 829\nf 329 910 701\nf 330 945 815\nf 331 713 765\nf 332 992 933\nf 333 722 640\nf 334 506 12\nf 335 573 754\nf 336 942 553\nf 337 673 247\nf 338 882 527\nf 339 782 912\nf 340 950 737\nf 341 752 703\nf 342 901 672\nf 343 577 868\nf 344 570 756\nf 345 823 634\nf 346 988 987\nf 347 856 717\nf 348 932 990\nf 349 938 664\nf 350 893 495\nf 351 817 847\nf 352 939 559\nf 353 971 652\nf 354 30 272\nf 355 739 976\nf 356 901 533\nf 357 838 593\nf 358 792 961\nf 359 890 767\nf 360 781 668\nf 361 1008 1002\nf 362 851 987\nf 363 652 750\nf 364 774 254\nf 365 757 455\nf 366 784 633\nf 367 714 628\nf 368 142 312\nf 369 625 864\nf 370 932 990\nf 371 643 479\nf 372 785 859\nf 373 959 655\nf 374 981 585\nf 375 998 897\nf 376 800 983\nf 377 671 98\nf 378 1005 848\nf 379 973 787\nf 380 958 855\nf 381 1004 219\nf 382 173 123\nf 383 1007 799\nf 384 956 940\nf 385 610 12\nf 386 894 711\nf 387 869 710\nf 388 955 633\nf 389 665 205\nf 390 754 914\nf 391 925 847\nf 392 888 636\nf 393 225 581\nf 394 907 769\nf 395 814 558\nf 396 676 954\nf 397 883 797\nf 398 918 686\nf 399 946 682\nf 400 798 133\nf 401 828 700\nf 402 618 914\nf 403 841 962\nf 404 889 576\nf 405 966 907\nf 406 867 782\nf 407 598 930\nf 408 609 126\nf 409 890 973\nf 410 821 437\nf 411 999 341\nf 412 974 759\nf 413 701 637\nf 414 717 1\nf 415 852 808\nf 416 896 543\nf 417 87 605\nf 418 958 40\nf 419 623 724\nf 420 698 806\nf 421 777 472\nf 422 755 590\nf 423 810 147\nf 424 1015 591\nf 425 937 835\nf 426 760 805\nf 427 1023 879\nf 428 780 636\nf 429 617 540\nf 430 874 723\nf 431 989 766\nf 432 908 815\nf 433 646 939\nf 434 930 660\nf 435 788 904\nf 436 934 736\nf 437 821 507\nf 438 753 716\nf 439 775 1015\nf 440 32 326\nf 441 742 158\nf 442 949 906\nf 443 650 809\nf 444 855 958\nf 445 721 632\nf 446 1014 281\nf 447 743 852\nf 448 791 794\nf 449 992 877\nf 450 822 251\nf 451 731 694\nf 452 1009 552\nf 453 761 806\nf 454 881 651\nf 455 909 365\nf 456 943 335\nf 457 865 829\nf 458 917 795\nf 459 885 957\nf 460 861 687\nf 461 881 566\nf 462 708 701\nf 463 878 18\nf 464 850 49\nf 465 727 163\nf 466 54 194\nf 467 963 889\nf 468 888 747\nf 469 859 970\nf 470 715 257\nf 471 912 143\nf 472 310 110\nf 473 648 118\nf 474 809 226\nf 475 892 21\nf 476 917 135\nf 477 997 694\nf 478 145 210\nf 479 997 793\nf 480 1013 781\nf 481 946 880\nf 482 979 162\nf 483 1006 927\nf 484 984 709\nf 485 873 872\nf 486 750 675\nf 487 952 289\nf 488 983 197\nf 489 952 258\nf 490 705 942\nf 491 776 82\nf 492 802 122\nf 493 919 717\nf 494 289 79\nf 495 350 105\nf 496 944 881\nf 497 972 920\nf 498 982 233\nf 499 964 318\nf 500 1016 867\nf 501 840 18\nf 502 927 969\nf 503 726 569\nf 504 936 878\nf 505 794 661\nf 506 875 611\nf 507 734 995\nf 508 188 1004\nf 509 982 233\nf 510 716 753\nf 511 896 899\nf 512 894 316\nf 513 915 20\nf 514 975 1020\nf 515 693 595\nf 516 671 631\nf 517 991 658\nf 518 742 232\nf 519 774 746\nf 520 768 719\nf 521 831 743\nf 522 994 719\nf 523 744 644\nf 524 898 760\nf 525 712 907\nf 526 103 259\nf 527 882 319\nf 528 546 172\nf 529 924 853\nf 530 829 929\nf 531 937 1018\nf 532 41 971\nf 533 779 356\nf 534 926 697\nf 535 989 872\nf 536 902 854\nf 537 1000 1018\nf 538 993 100\nf 539 912 251\nf 540 10 238\nf 541 679 289\nf 542 695 728\nf 543 913 575\nf 544 734 201\nf 545 955 681\nf 546 798 528\nf 547 876 856\nf 548 1014 916\nf 549 645 139\nf 550 795 909\nf 551 67 372\nf 552 1009 452\nf 553 695 272\nf 554 94 333\nf 555 871 755\nf 556 787 812\nf 557 970 670\nf 558 814 395\nf 559 895 714\nf 560 301 663\nf 561 163 24\nf 562 13 320\nf 563 925 138\nf 564 947 123\nf 565 980 322\nf 566 515 240\nf 567 763 387\nf 568 1022 845\nf 569 680 843\nf 570 920 344\nf 571 730 884\nf 572 772 768\nf 573 943 239\nf 574 941 258\nf 575 928 543\nf 576 690 623\nf 577 935 900\nf 578 77 685\nf 579 905 858\nf 580 805 740\nf 581 883 225\nf 582 819 133\nf 583 904 724\nf 584 99 339\nf 585 704 374\nf 586 967 215\nf 587 957 459\nf 588 953 317\nf 589 687 384\nf 590 188 422\nf 591 424 24\nf 592 918 96\nf 593 838 357\nf 594 877 933\nf 595 842 693\nf 596 204 847\nf 597 793 120\nf 598 962 407\nf 599 99 149\nf 600 743 278\nf 601 854 908\nf 602 895 886\nf 603 1012 101\nf 604 801 25\nf 605 825 87\nf 606 951 269\nf 607 936 75\nf 608 690 149\nf 609 580 408\nf 610 122 385\nf 611 715 12\nf 612 818 273\nf 613 857 166\nf 614 93 198\nf 615 934 736\nf 616 217 8\nf 617 10 429\nf 618 402 104\nf 619 922 468\nf 620 341 135\nf 621 941 24\nf 622 246 862\nf 623 576 419\nf 624 171 626\nf 625 369 799\nf 626 830 772\nf 627 891 1011\nf 628 714 510\nf 629 1006 37\nf 630 169 787\nf 631 785 54\nf 632 986 890\nf 633 955 366\nf 634 994 823\nf 635 758 285\nf 636 888 242\nf 637 237 63\nf 638 996 301\nf 639 135 752\nf 640 29 333\nf 641 797 170\nf 642 309 59\nf 643 438 1005\nf 644 186 76\nf 645 61 186\nf 646 50 433\nf 647 960 212\nf 648 183 22\nf 649 903 747\nf 650 443 136\nf 651 965 167\nf 652 353 363\nf 653 887 19\nf 654 143 64\nf 655 884 248\nf 656 923 83\nf 657 272 397\nf 658 844 517\nf 659 109 194\nf 660 18 434\nf 661 231 505\nf 662 811 185\nf 663 86 301\nf 664 938 282\nf 665 11 389\nf 666 34 578\nf 667 957 725\nf 668 911 46\nf 669 195 347\nf 670 557 970\nf 671 516 377\nf 672 196 129\nf 673 874 35\nf 674 315 48\nf 675 87 486\nf 676 70 954\nf 677 952 176\nf 678 296 566\nf 679 738 2\nf 680 569 241\nf 681 9 545\nf 682 399 234\nf 683 1006 961\nf 684 855 863\nf 685 161 954\nf 686 398 174\nf 687 861 142\nf 688 951 269\nf 689 926 830\nf 690 608 576\nf 691 796 15\nf 692 77 197\nf 693 515 595\nf 694 477 451\nf 695 542 156\nf 696 961 839\nf 697 94 534\nf 698 806 420\nf 699 200 72\nf 700 218 124\nf 701 413 213\nf 702 230 105\nf 703 769 140\nf 704 585 10\nf 705 490 156\nf 706 206 951\nf 707 939 848\nf 708 213 462\nf 709 984 484\nf 710 790 387\nf 711 240 39\nf 712 907 251\nf 713 331 134\nf 714 1000 367\nf 715 291 611\nf 716 438 510\nf 717 347 762\nf 718 33 889\nf 719 520 124\nf 720 155 359\nf 721 812 445\nf 722 94 333\nf 723 846 62\nf 724 235 788\nf 725 886 172\nf 726 210 503\nf 727 465 193\nf 728 265 75\nf 729 283 626\nf 730 571 93\nf 731 51 940\nf 732 166 790\nf 733 180 837\nf 734 507 67\nf 735 58 199\nf 736 934 241\nf 737 340 84\nf 738 2 679\nf 739 355 97\nf 740 580 65\nf 741 103 239\nf 742 232 518\nf 743 831 121\nf 744 280 76\nf 745 84 210\nf 746 864 254\nf 747 649 256\nf 748 9 264\nf 749 979 310\nf 750 486 363\nf 751 1016 215\nf 752 341 140\nf 753 438 510\nf 754 390 234\nf 755 280 555\nf 756 53 221\nf 757 318 365\nf 758 254 635\nf 759 412 274\nf 760 524 771\nf 761 453 288\nf 762 166 717\nf 763 567 145\nf 764 997 181\nf 765 134 331\nf 766 273 431\nf 767 359 111\nf 768 520 171\nf 769 394 251\nf 770 62 723\nf 771 154 760\nf 772 73 572\nf 773 1003 244\nf 774 364 254\nf 775 80 439\nf 776 491 16\nf 777 911 107\nf 778 193 327\nf 779 238 117\nf 780 428 922\nf 781 110 276\nf 782 406 143\nf 783 260 109\nf 784 366 9\nf 785 877 631\nf 786 69 183\nf 787 85 556\nf 788 83 435\nf 789 348 231\nf 790 869 72\nf 791 66 448\nf 792 261 358\nf 793 68 479\nf 794 267 505\nf 795 550 55\nf 796 691 198\nf 797 641 278\nf 798 400 43\nf 799 287 383\nf 800 221 376\nf 801 295 604\nf 802 492 212\nf 803 81 230\nf 804 252 171\nf 805 580 208\nf 806 698 120\nf 807 227 148\nf 808 243 899\nf 809 77 443\nf 810 119 423\nf 811 928 662\nf 812 47 721\nf 813 963 98\nf 814 141 558\nf 815 330 432\nf 816 302 1007\nf 817 1001 351\nf 818 128 1024\nf 819 582 172\nf 820 262 86\nf 821 130 507\nf 822 450 769\nf 823 994 345\nf 824 322 59\nf 825 295 87\nf 826 4 937\nf 827 27 208\nf 828 301 8\nf 829 195 929\nf 830 626 689\nf 831 743 185\nf 832 300 730\nf 833 138 403\nf 834 348 111\nf 835 937 1018\nf 836 134 370\nf 837 23 733\nf 838 593 357\nf 839 696 201\nf 840 223 501\nf 841 403 840\nf 842 595 144\nf 843 199 569\nf 844 282 260\nf 845 568 314\nf 846 723 222\nf 847 89 351\nf 848 1005 378\nf 849 985 28\nf 850 464 577\nf 851 362 614\nf 852 185 415\nf 853 259 924\nf 854 601 95\nf 855 684 444\nf 856 347 286\nf 857 45 613\nf 858 14 579\nf 859 469 372\nf 860 77 221\nf 861 687 460\nf 862 246 721\nf 863 290 684\nf 864 57 746\nf 865 457 195\nf 866 285 227\nf 867 406 64\nf 868 1002 161\nf 869 72 790\nf 870 252 71\nf 871 116 555\nf 872 989 485\nf 873 485 137\nf 874 430 673\nf 875 257 506\nf 876 273 547\nf 877 594 785\nf 878 463 192\nf 879 1023 74\nf 880 318 481\nf 881 454 461\nf 882 527 21\nf 883 581 30\nf 884 248 655\nf 885 459 957\nf 886 108 725\nf 887 19 653\nf 888 636 468\nf 889 194 404\nf 890 359 409\nf 891 48 627\nf 892 319 475\nf 893 11 350\nf 894 512 39\nf 895 116 602\nf 896 416 511\nf 897 375 42\nf 898 65 251\nf 899 113 808\nf 900 1008 303\nf 901 342 129\nf 902 536 17\nf 903 60 649\nf 904 224 583\nf 905 184 912\nf 906 295 442\nf 907 394 712\nf 908 432 601\nf 909 255 455\nf 910 90 329\nf 911 243 668\nf 912 471 905\nf 913 48 543\nf 914 36 234\nf 915 20 275\nf 916 307 95\nf 917 168 476\nf 918 592 398\nf 919 493 202\nf 920 497 570\nf 921 236 31\nf 922 619 3\nf 923 656 235\nf 924 259 529\nf 925 563 391\nf 926 534 689\nf 927 502 483\nf 928 811 279\nf 929 328 829\nf 930 434 155\nf 931 167 545\nf 932 370 348\nf 933 160 992\nf 934 615 736\nf 935 150 577\nf 936 504 192\nf 937 835 531\nf 938 349 109\nf 939 352 707\nf 940 384 956\nf 941 24 574\nf 942 5 336\nf 943 36 239\nf 944 52 496\nf 945 330 353\nf 946 399 481\nf 947 255 564\nf 948 14 412\nf 949 25 442\nf 950 340 89\nf 951 81 688\nf 952 677 79\nf 953 588 1017\nf 954 70 685\nf 955 633 388\nf 956 324 384\nf 957 587 667\nf 958 150 380\nf 959 262 373\nf 960 291 122\nf 961 696 201\nf 962 598 403\nf 963 467 813\nf 964 499 187\nf 965 44 651\nf 966 126 264\nf 967 224 586\nf 968 220 40\nf 969 63 217\nf 970 169 557\nf 971 353 115\nf 972 497 977\nf 973 379 169\nf 974 412 953\nf 975 514 139\nf 976 151 268\nf 977 299 972\nf 978 247 297\nf 979 162 749\nf 980 565 21\nf 981 96 374\nf 982 70 498\nf 983 488 150\nf 984 155 484\nf 985 305 849\nf 986 632 283\nf 987 51 346\nf 988 346 987\nf 989 222 872\nf 990 58 348\nf 991 306 144\nf 992 160 332\nf 993 86 326\nf 994 823 522\nf 995 160 507\nf 996 209 638\nf 997 479 764\nf 998 279 375\nf 999 132 411\nf 1000 714 537\nf 1001 817 225\nf 1002 868 361\nf 1003 773 244\nf 1004 31 381\nf 1005 378 848\nf 1006 483 683\nf 1007 383 302\nf 1008 900 361\nf 1009 68 552\nf 1010 292 647\nf 1011 249 627\nf 1012 308 1013\nf 1013 270 1012\nf 1014 281 548\nf 1015 163 424\nf 1016 500 751\nf 1017 164 953\nf 1018 61 537\nf 1019 285 227\nf 1020 219 131\nf 1021 263 313\nf 1022 102 568\nf 1023 879 74\nf 1024 294 818\n" } ]