Repository: hancyran/RepSurf Branch: main Commit: 29bacd52fe61 Files: 115 Total size: 2.6 MB Directory structure: gitextract_ziqlnw07/ ├── .gitignore ├── LICENSE.txt ├── README.md ├── classification/ │ ├── README.md │ ├── dataset/ │ │ ├── ScanObjectNNDataLoader.py │ │ └── __init__.py │ ├── init.sh │ ├── models/ │ │ ├── __init__.py │ │ └── repsurf/ │ │ ├── __init__.py │ │ ├── repsurf_ssg_umb.py │ │ └── repsurf_ssg_umb_2x.py │ ├── modules/ │ │ ├── __init__.py │ │ ├── pointnet2_utils.py │ │ ├── pointops/ │ │ │ ├── __init__.py │ │ │ ├── functions/ │ │ │ │ ├── __init__.py │ │ │ │ └── pointops.py │ │ │ ├── setup.py │ │ │ └── src/ │ │ │ ├── __init__.py │ │ │ ├── ballquery/ │ │ │ │ ├── ballquery_cuda.cpp │ │ │ │ ├── ballquery_cuda_kernel.cu │ │ │ │ └── ballquery_cuda_kernel.h │ │ │ ├── cuda_utils.h │ │ │ ├── grouping/ │ │ │ │ ├── grouping_cuda.cpp │ │ │ │ ├── grouping_cuda_kernel.cu │ │ │ │ └── grouping_cuda_kernel.h │ │ │ ├── grouping_int/ │ │ │ │ ├── grouping_int_cuda.cpp │ │ │ │ ├── grouping_int_cuda_kernel.cu │ │ │ │ └── grouping_int_cuda_kernel.h │ │ │ ├── interpolation/ │ │ │ │ ├── interpolation_cuda.cpp │ │ │ │ ├── interpolation_cuda_kernel.cu │ │ │ │ └── interpolation_cuda_kernel.h │ │ │ ├── knnquery/ │ │ │ │ ├── __init__.py │ │ │ │ ├── knnquery_cuda.cpp │ │ │ │ ├── knnquery_cuda_kernel.cu │ │ │ │ └── knnquery_cuda_kernel.h │ │ │ ├── knnquery_heap/ │ │ │ │ ├── __init__.py │ │ │ │ ├── knnquery_heap_cuda.cpp │ │ │ │ ├── knnquery_heap_cuda_kernel.cu │ │ │ │ └── knnquery_heap_cuda_kernel.h │ │ │ ├── pointops_api.cpp │ │ │ └── sampling/ │ │ │ ├── sampling_cuda.cpp │ │ │ ├── sampling_cuda_kernel.cu │ │ │ └── sampling_cuda_kernel.h │ │ ├── polar_utils.py │ │ ├── ptaug_utils.py │ │ ├── recons_utils.py │ │ └── repsurface_utils.py │ ├── scripts/ │ │ └── scanobjectnn/ │ │ ├── repsurf_ssg_umb.sh │ │ └── repsurf_ssg_umb_2x.sh │ ├── tool/ │ │ └── train_cls_scanobjectnn.py │ └── util/ │ ├── __init__.py │ └── utils.py ├── segmentation/ │ ├── README.md │ ├── dataset/ │ │ ├── S3DISDataLoader.py │ │ └── __init__.py │ ├── init.sh │ ├── models/ │ │ ├── __init__.py │ │ ├── pointnet2/ │ │ │ ├── __init__.py │ │ │ └── pointnet2_ssg.py │ │ ├── pointtransformer/ │ │ │ ├── __init__.py │ │ │ └── pointtransformer.py │ │ └── repsurf/ │ │ ├── __init__.py │ │ └── repsurf_umb_ssg.py │ ├── modules/ │ │ ├── __init__.py │ │ ├── aug_utils.py │ │ ├── pointnet2_utils.py │ │ ├── pointops/ │ │ │ ├── __init__.py │ │ │ ├── functions/ │ │ │ │ ├── __init__.py │ │ │ │ └── pointops.py │ │ │ ├── setup.py │ │ │ └── src/ │ │ │ ├── __init__.py │ │ │ ├── aggregation/ │ │ │ │ ├── aggregation_cuda.cpp │ │ │ │ ├── aggregation_cuda_kernel.cu │ │ │ │ └── aggregation_cuda_kernel.h │ │ │ ├── cuda_utils.h │ │ │ ├── grouping/ │ │ │ │ ├── grouping_cuda.cpp │ │ │ │ ├── grouping_cuda_kernel.cu │ │ │ │ └── grouping_cuda_kernel.h │ │ │ ├── interpolation/ │ │ │ │ ├── interpolation_cuda.cpp │ │ │ │ ├── interpolation_cuda_kernel.cu │ │ │ │ └── interpolation_cuda_kernel.h │ │ │ ├── knnquery/ │ │ │ │ ├── knnquery_cuda.cpp │ │ │ │ ├── knnquery_cuda_kernel.cu │ │ │ │ └── knnquery_cuda_kernel.h │ │ │ ├── pointops_api.cpp │ │ │ ├── sampling/ │ │ │ │ ├── sampling_cuda.cpp │ │ │ │ ├── sampling_cuda_kernel.cu │ │ │ │ └── sampling_cuda_kernel.h │ │ │ └── subtraction/ │ │ │ ├── subtraction_cuda.cpp │ │ │ ├── subtraction_cuda_kernel.cu │ │ │ └── subtraction_cuda_kernel.h │ │ ├── pointtransformer_utils.py │ │ ├── polar_utils.py │ │ ├── recons_utils.py │ │ ├── repsurface_utils.py │ │ └── voxelize_utils.py │ ├── scripts/ │ │ └── s3dis/ │ │ ├── test_pointnet2.sh │ │ ├── test_pointtransformer.sh │ │ ├── test_repsurf_umb.sh │ │ ├── train_pointnet2.sh │ │ ├── train_pointtransformer.sh │ │ └── train_repsurf_umb.sh │ ├── tool/ │ │ ├── test_s3dis.py │ │ └── train.py │ └── util/ │ ├── __init__.py │ ├── data_util.py │ └── utils.py └── visualization/ ├── airplane_0001.txt ├── bed_0001.txt ├── cup_0001.txt ├── table_0250.txt ├── triangled_airplane.obj ├── triangled_bed.obj ├── triangled_cup.obj └── triangled_table.obj ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2022 Haoxi Ran. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # RepSurf - Surface Representation for Point Clouds
[CVPR 2022 Oral] By *[Haoxi Ran\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact) [![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)
[![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)
[![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)
[![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)
[![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)
[![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) ### The pytorch official implementation of "[Surface Representation for Point Clouds](http://arxiv.org/abs/2205.05740)" ### [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)
## News: - (**Sep 10** NEW :fire:) We have uploaded the implementation of RepSurf on S3DIS along with its training log and pretrained weights. - (**June 24** :fire:) We sucessfully finished our Oral presentation at CVPR 2022! - (**May 11**) We have uploaded the implementation of RepSurf on ScanObjectNN along with its training log and pretrained weights. ## Tasks: ### We conduct experiments of different tasks on different codebases: > Classification: **[3D Object Classification](./classification)**
> Segmentation: **[3D Semantic Segmentation](./segmentation)** ## Visualization We provide several visualization results in the folder **./visualization** for a closer look at the construction of RepSurf. ## License RepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for commercial use. ================================================ FILE: classification/README.md ================================================ # RepSurf for Classification
By *[Haoxi Ran\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact) ### [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) ## Preparation ### Environment We tested under the environment: * python 3.7 * pytorch 1.6.0 * cuda 10.1 * gcc 7.2.0 * h5py For anaconda user, initialize the conda environment **repsurf-cls** by: ``` sh init.sh ``` ## Experiments ### ScanObjectNN (Data & Logs: [Google Drive](https://drive.google.com/drive/folders/1DGWT9W46MKVI0-lu18hJhB-R3BFVWuCs?usp=sharing)) * Performance:
Model Accuracy #Params Augment Code Log Checkpoint
MVTN 82.8 4.24M None link N/A link
PointMLP 85.7 12.6M Scale, Shift link link link
PointNet++ SSG 77.9 1.475M Rotate, Jitter link N/A N/A
Umbrella RepSurf (PointNet++ SSG) 84.87 1.483M None link google drive google drive (6MB)
Umbrella RepSurf (PointNet++ SSG, 2x) 86.05 6.806M None link google drive google drive (27MB)

* To download dataset: ``` wget https://download.cs.stanford.edu/orion/scanobjectnn/h5_files.zip unzip h5_files.zip ln -s [PATH]/h5_files data/ScanObjectNN ``` **Note**: We conduct all experiments on the hardest variant of ScanObjectNN (**PB_T50_RS**).
* To train **Umbrella RepSurf** on ScanObjectNN: ``` sh scripts/scanobjectnn/repsurf_ssg_umb.sh ``` * To train **Umbrella RepSurf (2x setting)** on ScanObjectNN: ``` sh scripts/scanobjectnn/repsurf_ssg_umb_2x.sh ``` ## Acknowledgment We use part of the library [pointops](https://github.com/hszhao/PointWeb/tree/master/lib/pointops) from [PointWeb](https://github.com/hszhao/PointWeb). ## License RepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for commercial use. ================================================ FILE: classification/dataset/ScanObjectNNDataLoader.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import h5py import warnings from torch.utils.data import Dataset warnings.filterwarnings('ignore') class ScanObjectNNDataLoader(Dataset): def __init__(self, root, split='training', bg=True): self.root = root assert (split == 'training' or split == 'test') if bg: print('Use data with background points') dir_name = 'main_split' else: print('Use data without background points') dir_name = 'main_split_nobg' file_name = '_objectdataset_augmentedrot_scale75.h5' h5_name = '{}/{}/{}'.format(self.root, dir_name, split + file_name) with h5py.File(h5_name, mode="r") as f: self.data = f['data'][:].astype('float32') self.label = f['label'][:].astype('int64') print('The size of %s data is %d' % (split, self.data.shape[0])) def __len__(self): return self.data.shape[0] def __getitem__(self, index): return self.data[index].T, self.label[index] ================================================ FILE: classification/dataset/__init__.py ================================================ ================================================ FILE: classification/init.sh ================================================ #!/bin/sh mkdir -p log/PointAnalysis/log/ScanObjectNN mkdir -p data/ conda create -n repsurf-cls python=3.7 -y conda activate repsurf-cls conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch -c conda-forge -y conda install -c anaconda h5py -y cd modules/pointops python3 setup.py install cd - ================================================ FILE: classification/models/__init__.py ================================================ ================================================ FILE: classification/models/repsurf/__init__.py ================================================ ================================================ FILE: classification/models/repsurf/repsurf_ssg_umb.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch.nn as nn import torch.nn.functional as F from modules.repsurface_utils import SurfaceAbstractionCD, UmbrellaSurfaceConstructor class Model(nn.Module): def __init__(self, args): super(Model, self).__init__() center_channel = 0 if not args.return_center else (6 if args.return_polar else 3) repsurf_channel = 10 self.init_nsample = args.num_point self.return_dist = args.return_dist self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_channel, return_dist=args.return_dist, aggr_type=args.umb_pool, cuda=args.cuda_ops) self.sa1 = SurfaceAbstractionCD(npoint=512, radius=0.2, nsample=32, feat_channel=repsurf_channel, pos_channel=center_channel, mlp=[64, 64, 128], group_all=False, return_polar=args.return_polar, cuda=args.cuda_ops) self.sa2 = SurfaceAbstractionCD(npoint=128, radius=0.4, nsample=64, feat_channel=128 + repsurf_channel, pos_channel=center_channel, mlp=[128, 128, 256], group_all=False, return_polar=args.return_polar, cuda=args.cuda_ops) self.sa3 = SurfaceAbstractionCD(npoint=None, radius=None, nsample=None, feat_channel=256 + repsurf_channel, pos_channel=center_channel, mlp=[256, 512, 1024], group_all=True, return_polar=args.return_polar, cuda=args.cuda_ops) # modelnet40 self.classfier = nn.Sequential( nn.Linear(1024, 512), nn.BatchNorm1d(512), nn.ReLU(True), nn.Dropout(0.4), nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(True), nn.Dropout(0.4), nn.Linear(256, args.num_class)) def forward(self, points): # init center = points[:, :3, :] normal = self.surface_constructor(center) center, normal, feature = self.sa1(center, normal, None) center, normal, feature = self.sa2(center, normal, feature) center, normal, feature = self.sa3(center, normal, feature) feature = feature.view(-1, 1024) feature = self.classfier(feature) feature = F.log_softmax(feature, -1) return feature ================================================ FILE: classification/models/repsurf/repsurf_ssg_umb_2x.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch.nn as nn import torch.nn.functional as F from modules.repsurface_utils import SurfaceAbstractionCD, UmbrellaSurfaceConstructor class Model(nn.Module): def __init__(self, args): super(Model, self).__init__() center_channel = 0 if not args.return_center else (6 if args.return_polar else 3) repsurf_channel = 10 self.init_nsample = args.num_point self.return_dist = args.return_dist self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_channel, return_dist=args.return_dist, aggr_type=args.umb_pool, cuda=args.cuda_ops) self.sa1 = SurfaceAbstractionCD(npoint=512, radius=0.1, nsample=24, feat_channel=repsurf_channel, pos_channel=center_channel, mlp=[128, 128, 256], group_all=False, return_polar=args.return_polar, cuda=args.cuda_ops) self.sa2 = SurfaceAbstractionCD(npoint=128, radius=0.2, nsample=24, feat_channel=256 + repsurf_channel, pos_channel=center_channel, mlp=[256, 256, 512], group_all=False, return_polar=args.return_polar, cuda=args.cuda_ops) self.sa3 = SurfaceAbstractionCD(npoint=32, radius=0.4, nsample=24, feat_channel=512 + repsurf_channel, pos_channel=center_channel, mlp=[512, 512, 1024], group_all=False, return_polar=args.return_polar, cuda=args.cuda_ops) self.sa4 = SurfaceAbstractionCD(npoint=None, radius=None, nsample=None, feat_channel=1024 + repsurf_channel, pos_channel=center_channel, mlp=[1024, 1024, 2048], group_all=True, return_polar=args.return_polar, cuda=args.cuda_ops) # modelnet40 self.classfier = nn.Sequential( nn.Linear(2048, 512), nn.BatchNorm1d(512), nn.ReLU(True), nn.Dropout(0.4), nn.Linear(512, 256), nn.BatchNorm1d(256), nn.ReLU(True), nn.Dropout(0.4), nn.Linear(256, args.num_class)) def forward(self, points): # init center = points[:, :3, :] normal = self.surface_constructor(center) center, normal, feature = self.sa1(center, normal, None) center, normal, feature = self.sa2(center, normal, feature) center, normal, feature = self.sa3(center, normal, feature) center, normal, feature = self.sa4(center, normal, feature) feature = feature.view(-1, 2048) feature = self.classfier(feature) feature = F.log_softmax(feature, -1) return feature ================================================ FILE: classification/modules/__init__.py ================================================ ================================================ FILE: classification/modules/pointnet2_utils.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch try: from modules.pointops.functions.pointops import furthestsampling, gathering, ballquery, knnquery, \ grouping, interpolation, nearestneighbor except: raise Exception('Failed to load pointops') def square_distance(src, dst): """ Calculate Squared distance between each two points. """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def index_points(points, idx, cuda=False, is_group=False): if cuda: if is_group: points = grouping(points.transpose(1, 2).contiguous(), idx) return points.permute(0, 2, 3, 1).contiguous() else: points = gathering(points.transpose(1, 2).contiguous(), idx) return points.permute(0, 2, 1).contiguous() device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def farthest_point_sample(xyz, npoint, cuda=False): """ Input: xyz: pointcloud data, [B, N, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npoint] FLOPs: S * (3 + 3 + 2) """ if cuda: if not xyz.is_contiguous(): xyz = xyz.contiguous() return furthestsampling(xyz, npoint) device = xyz.device B, N, C = xyz.shape centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) distance = torch.ones(B, N).to(device) * 1e10 farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) batch_indices = torch.arange(B, dtype=torch.long).to(device) for i in range(npoint): centroids[:, i] = farthest centroid = xyz[batch_indices, farthest, :].view(B, 1, 3) dist = torch.sum((xyz - centroid) ** 2, -1) mask = dist < distance distance[mask] = dist[mask] farthest = torch.max(distance, -1)[1] return centroids def query_ball_point(radius, nsample, xyz, new_xyz, debug=False, cuda=False): if cuda: if not xyz.is_contiguous(): xyz = xyz.contiguous() if not new_xyz.is_contiguous(): new_xyz = new_xyz.contiguous() return ballquery(radius, nsample, xyz, new_xyz) device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] if debug: num_miss = torch.sum(mask) num_over = torch.sum(torch.clamp(torch.sum(sqrdists < radius ** 2, dim=2) - nsample, min=0)) return num_miss, num_over return group_idx def query_knn_point(k, xyz, new_xyz, cuda=False): if cuda: if not xyz.is_contiguous(): xyz = xyz.contiguous() if not new_xyz.is_contiguous(): new_xyz = new_xyz.contiguous() return knnquery(k, xyz, new_xyz) dist = square_distance(new_xyz, xyz) group_idx = dist.sort(descending=False, dim=-1)[1][:, :, :k] return group_idx def sample(nsample, feature, cuda=False): feature = feature.permute(0, 2, 1) xyz = feature[:, :, :3] fps_idx = farthest_point_sample(xyz, nsample, cuda=cuda) # [B, npoint, C] torch.cuda.empty_cache() feature = index_points(feature, fps_idx, cuda=cuda, is_group=False) torch.cuda.empty_cache() feature = feature.permute(0, 2, 1) return feature ================================================ FILE: classification/modules/pointops/__init__.py ================================================ ================================================ FILE: classification/modules/pointops/functions/__init__.py ================================================ from .pointops import * ================================================ FILE: classification/modules/pointops/functions/pointops.py ================================================ from typing import Tuple import numpy as np import torch from torch.autograd import Function import torch.nn as nn try: import pointops_cuda except ImportError: import warnings import os from torch.utils.cpp_extension import load warnings.warn("Unable to load pointops_cuda cpp extension.") pointops_cuda_src = os.path.join(os.path.dirname(__file__), "../src") pointops_cuda = load('pointops_cuda', [ pointops_cuda_src + '/pointops_api.cpp', pointops_cuda_src + '/ballquery/ballquery_cuda.cpp', pointops_cuda_src + '/ballquery/ballquery_cuda_kernel.cu', pointops_cuda_src + '/knnquery/knnquery_cuda.cpp', pointops_cuda_src + '/knnquery/knnquery_cuda_kernel.cu', pointops_cuda_src + '/knnquery_heap/knnquery_heap_cuda.cpp', pointops_cuda_src + '/knnquery_heap/knnquery_heap_cuda_kernel.cu', pointops_cuda_src + '/grouping/grouping_cuda.cpp', pointops_cuda_src + '/grouping/grouping_cuda_kernel.cu', pointops_cuda_src + '/grouping_int/grouping_int_cuda.cpp', pointops_cuda_src + '/grouping_int/grouping_int_cuda_kernel.cu', pointops_cuda_src + '/interpolation/interpolation_cuda.cpp', pointops_cuda_src + '/interpolation/interpolation_cuda_kernel.cu', pointops_cuda_src + '/sampling/sampling_cuda.cpp', pointops_cuda_src + '/sampling/sampling_cuda_kernel.cu' ], build_directory=pointops_cuda_src, verbose=False) class FurthestSampling(Function): @staticmethod def forward(ctx, xyz, m): """ input: xyz: (b, n, 3) and n > m, m: int32 output: idx: (b, m) """ assert xyz.is_contiguous() b, n, _ = xyz.size() idx = torch.cuda.IntTensor(b, m) temp = torch.cuda.FloatTensor(b, n).fill_(1e10) pointops_cuda.furthestsampling_cuda(b, n, m, xyz, temp, idx) return idx @staticmethod def backward(xyz, a=None): return None, None furthestsampling = FurthestSampling.apply class Gathering(Function): @staticmethod def forward(ctx, features, idx): """ input: features: (b, c, n), idx : (b, m) tensor output: (b, c, m) """ assert features.is_contiguous() assert idx.is_contiguous() b, c, n = features.size() m = idx.size(1) output = torch.cuda.FloatTensor(b, c, m) pointops_cuda.gathering_forward_cuda(b, c, n, m, features, idx, output) ctx.for_backwards = (idx, c, n) return output @staticmethod def backward(ctx, grad_out): idx, c, n = ctx.for_backwards b, m = idx.size() grad_features = torch.cuda.FloatTensor(b, c, n).zero_() grad_out_data = grad_out.data.contiguous() pointops_cuda.gathering_backward_cuda(b, c, n, m, grad_out_data, idx, grad_features.data) return grad_features, None gathering = Gathering.apply class NearestNeighbor(Function): @staticmethod def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Find the three nearest neighbors of unknown in known input: unknown: (b, n, 3), known: (b, m, 3) output: dist2: (b, n, 3) l2 distance to the three nearest neighbors idx: (b, n, 3) index of 3 nearest neighbors """ assert unknown.is_contiguous() assert known.is_contiguous() b, n, _ = unknown.size() m = known.size(1) dist2 = torch.cuda.FloatTensor(b, n, 3) idx = torch.cuda.IntTensor(b, n, 3) pointops_cuda.nearestneighbor_cuda(b, n, m, unknown, known, dist2, idx) return torch.sqrt(dist2), idx @staticmethod def backward(ctx, a=None, b=None): return None, None nearestneighbor = NearestNeighbor.apply class Interpolation(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor: """ Performs weight linear interpolation on 3 features input: features: (b, c, m) features descriptors to be interpolated from idx: (b, n, 3) three nearest neighbors of the target features in features weight: (b, n, 3) weights output: (b, c, n) tensor of the interpolated features """ features = features.contiguous() assert features.is_contiguous() assert idx.is_contiguous() assert weight.is_contiguous() b, c, m = features.size() n = idx.size(1) ctx.interpolation_for_backward = (idx, weight, m) output = torch.cuda.FloatTensor(b, c, n) pointops_cuda.interpolation_forward_cuda(b, c, m, n, features, idx, weight, output) return output @staticmethod def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ input: grad_out: (b, c, n) output: grad_features: (b, c, m), None, None """ idx, weight, m = ctx.interpolation_for_backward b, c, n = grad_out.size() grad_features = torch.cuda.FloatTensor(b, c, m).zero_() grad_out_data = grad_out.data.contiguous() pointops_cuda.interpolation_backward_cuda(b, c, n, m, grad_out_data, idx, weight, grad_features.data) return grad_features, None, None interpolation = Interpolation.apply class Grouping(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: """ input: features: (b, c, n), idx : (b, m, nsample) containing the indicies of features to group with output: (b, c, m, nsample) """ assert features.is_contiguous() assert idx.is_contiguous() b, c, n = features.size() _, m, nsample = idx.size() output = torch.cuda.FloatTensor(b, c, m, nsample) pointops_cuda.grouping_forward_cuda(b, c, n, m, nsample, features, idx, output) ctx.for_backwards = (idx, n) return output @staticmethod def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ input: grad_out: (b, c, m, nsample) output: (b, c, n), None """ idx, n = ctx.for_backwards b, c, m, nsample = grad_out.size() grad_features = torch.cuda.FloatTensor(b, c, n).zero_() grad_out_data = grad_out.data.contiguous() pointops_cuda.grouping_backward_cuda(b, c, n, m, nsample, grad_out_data, idx, grad_features.data) return grad_features, None grouping = Grouping.apply class GroupingInt(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: """ input: features: (b, c, n), idx : (b, m, nsample) containing the indicies of features to group with output: (b, c, m, nsample) """ assert features.is_contiguous() assert idx.is_contiguous() b, c, n = features.size() _, m, nsample = idx.size() output = torch.cuda.LongTensor(b, c, m, nsample) pointops_cuda.grouping_int_forward_cuda(b, c, n, m, nsample, features, idx, output) return output @staticmethod def backward(ctx, a=None): return None, None grouping_int = GroupingInt.apply class BallQuery(Function): @staticmethod def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor: """ input: radius: float, radius of the balls nsample: int, maximum number of features in the balls xyz: torch.Tensor, (b, n, 3) xyz coordinates of the features new_xyz: torch.Tensor, (b, m, 3) centers of the ball query output: (b, m, nsample) tensor with the indicies of the features that form the query balls """ assert xyz.is_contiguous() assert new_xyz.is_contiguous() b, n, _ = xyz.size() m = new_xyz.size(1) idx = torch.cuda.IntTensor(b, m, nsample).zero_() pointops_cuda.ballquery_cuda(b, n, m, radius, nsample, new_xyz, xyz, idx) return idx @staticmethod def backward(ctx, a=None): return None, None, None, None ballquery = BallQuery.apply def pairwise_distances(x, y=None): ''' Input: x is a Nxd matrix y is an optional Mxd matirx Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:] if y is not given then use 'y=x'. i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2 ''' x_norm = (x ** 2).sum(1).view(-1, 1) if y is not None: y_t = torch.transpose(y, 0, 1) y_norm = (y ** 2).sum(1).view(1, -1) else: y_t = torch.transpose(x, 0, 1) y_norm = x_norm.view(1, -1) dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t) import numpy as np return torch.clamp(dist, 0.0, np.inf) class KNNQueryNaive(Function): @staticmethod def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]: """ KNN Indexing input: nsample: int32, Number of neighbor xyz: (b, n, 3) coordinates of the features new_xyz: (b, m, 3) centriods output: idx: (b, m, nsample) """ if new_xyz is None: new_xyz = xyz b, m, _ = new_xyz.size() n = xyz.size(1) ''' idx = torch.zeros(b, m, nsample).int().cuda() for i in range(b): dist = pairwise_distances(new_xyz[i, :, :], xyz[i, :, :]) [_, idxs] = torch.sort(dist, dim=1) idx[i, :, :] = idxs[:, 0:nsample] ''' # ''' # new_xyz_repeat = new_xyz.repeat(1, 1, n).view(b, m * n, 3) # xyz_repeat = xyz.repeat(1, m, 1).view(b, m * n, 3) # dist = (new_xyz_repeat - xyz_repeat).pow(2).sum(dim=2).view(b, m, 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( dim=2).view(b, m, n) [_, idxs] = torch.sort(dist, dim=2) idx = idxs[:, :, 0:nsample].int() # ''' return idx @staticmethod def backward(ctx): return None, None, None knnquery_naive = KNNQueryNaive.apply class KNNQuery(Function): @staticmethod def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]: """ KNN Indexing input: nsample: int32, Number of neighbor xyz: (b, n, 3) coordinates of the features new_xyz: (b, m, 3) centriods output: idx: (b, m, nsample) ( dist2: (b, m, nsample) ) """ if new_xyz is None: new_xyz = xyz xyz = xyz.contiguous() new_xyz = new_xyz.contiguous() assert xyz.is_contiguous() assert new_xyz.is_contiguous() b, m, _ = new_xyz.size() n = xyz.size(1) idx = torch.cuda.IntTensor(b, m, nsample).zero_() dist2 = torch.cuda.FloatTensor(b, m, nsample).zero_() pointops_cuda.knnquery_cuda(b, n, m, nsample, xyz, new_xyz, idx, dist2) return idx @staticmethod def backward(ctx, a=None): return None, None, None knnquery = KNNQuery.apply class KNNQuery_Heap(Function): @staticmethod def forward(ctx, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor = None) -> Tuple[torch.Tensor]: """ KNN Indexing input: nsample: int32, Number of neighbor xyz: (b, n, 3) coordinates of the features new_xyz: (b, m, 3) centriods output: idx: (b, m, nsample) ( dist2: (b, m, nsample) ) """ if new_xyz is None: new_xyz = xyz assert xyz.is_contiguous() assert new_xyz.is_contiguous() b, m, _ = new_xyz.size() n = xyz.size(1) idx = torch.cuda.IntTensor(b, m, nsample).zero_() dist2 = torch.cuda.FloatTensor(b, m, nsample).zero_() pointops_cuda.knnquery_heap_cuda(b, n, m, nsample, xyz, new_xyz, idx, dist2) ctx.mark_non_differentiable(idx) return idx @staticmethod def backward(ctx, a=None): return None, None, None knnquery_heap = KNNQuery_Heap.apply class QueryAndGroup(nn.Module): """ Groups with a ball query of radius parameters: radius: float32, Radius of ball nsample: int32, Maximum number of features to gather in the ball """ def __init__(self, radius=None, nsample=32, use_xyz=True, return_idx=False): super(QueryAndGroup, self).__init__() self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz self.return_idx = return_idx def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor = None, features: torch.Tensor = None, idx: torch.Tensor = None) -> torch.Tensor: """ input: xyz: (b, n, 3) coordinates of the features new_xyz: (b, m, 3) centriods features: (b, c, n) idx: idx of neighbors # idxs: (b, n) output: new_features: (b, c+3, m, nsample) # grouped_idxs: (b, m, nsample) """ if new_xyz is None: new_xyz = xyz if idx is None: if self.radius is not None: idx = ballquery(self.radius, self.nsample, xyz, new_xyz) else: # idx = knnquery_naive(self.nsample, xyz, new_xyz) # (b, m, nsample) # idx = knnquery(self.nsample, xyz, new_xyz) # (b, m, nsample) idx = knnquery_heap(self.nsample, xyz, new_xyz) # (b, m, nsample) xyz_trans = xyz.transpose(1, 2).contiguous() grouped_xyz = grouping(xyz_trans, idx) # (b, 3, m, nsample) # grouped_idxs = grouping(idxs.unsqueeze(1).float(), idx).squeeze(1).int() # (b, m, nsample) grouped_xyz_diff = grouped_xyz - new_xyz.transpose(1, 2).unsqueeze(-1) if features is not None: grouped_features = grouping(features, idx) if self.use_xyz: new_features = torch.cat([grouped_xyz_diff, grouped_features], dim=1) # (b, 3+c, m, nsample) else: new_features = grouped_features else: assert self.use_xyz, "Cannot have not features and not use xyz as a feature!" new_features = grouped_xyz_diff if self.return_idx: return new_features, grouped_xyz, idx.long() # (b,c,m,k), (b,3,m,k), (b,m,k) else: return new_features, grouped_xyz ================================================ FILE: classification/modules/pointops/setup.py ================================================ #python3 setup.py install from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension import os from distutils.sysconfig import get_config_vars (opt,) = get_config_vars('OPT') os.environ['OPT'] = " ".join( flag for flag in opt.split() if flag != '-Wstrict-prototypes' ) setup( name='pointops', ext_modules=[ CUDAExtension('pointops_cuda', [ 'src/pointops_api.cpp', 'src/ballquery/ballquery_cuda.cpp', 'src/ballquery/ballquery_cuda_kernel.cu', 'src/knnquery/knnquery_cuda.cpp', 'src/knnquery/knnquery_cuda_kernel.cu', 'src/knnquery_heap/knnquery_heap_cuda.cpp', 'src/knnquery_heap/knnquery_heap_cuda_kernel.cu', 'src/grouping/grouping_cuda.cpp', 'src/grouping/grouping_cuda_kernel.cu', 'src/grouping_int/grouping_int_cuda.cpp', 'src/grouping_int/grouping_int_cuda_kernel.cu', 'src/interpolation/interpolation_cuda.cpp', 'src/interpolation/interpolation_cuda_kernel.cu', 'src/sampling/sampling_cuda.cpp', 'src/sampling/sampling_cuda_kernel.cu', ], extra_compile_args={'cxx': ['-g'], 'nvcc': ['-O2']}) ], cmdclass={'build_ext': BuildExtension}) ================================================ FILE: classification/modules/pointops/src/__init__.py ================================================ ================================================ FILE: classification/modules/pointops/src/ballquery/ballquery_cuda.cpp ================================================ #include #include #include #include #include "ballquery_cuda_kernel.h" extern THCState *state; #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, " must be a CUDAtensor ") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) void 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) { const float *new_xyz = new_xyz_tensor.data_ptr(); const float *xyz = xyz_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); ballquery_cuda_launcher(b, n, m, radius, nsample, new_xyz, xyz, idx); } void 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) { CHECK_INPUT(new_xyz_tensor); CHECK_INPUT(xyz_tensor); const float *new_xyz = new_xyz_tensor.data_ptr(); const float *xyz = xyz_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); ballquery_cuda_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx, stream); } ================================================ FILE: classification/modules/pointops/src/ballquery/ballquery_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "ballquery_cuda_kernel.h" // input: new_xyz(b, m, 3) xyz(b, n, 3) // output: idx(b, m, nsample) __global__ void ballquery_cuda_kernel(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx) { int batch_index = blockIdx.x; xyz += batch_index * n * 3; new_xyz += batch_index * m * 3; idx += m * nsample * batch_index; int index = threadIdx.x; int stride = blockDim.x; float radius2 = radius * radius; for (int j = index; j < m; j += stride) { float new_x = new_xyz[j * 3 + 0]; float new_y = new_xyz[j * 3 + 1]; float new_z = new_xyz[j * 3 + 2]; for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) { float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < radius2) { if (cnt == 0) { for (int l = 0; l < nsample; ++l) idx[j * nsample + l] = k; } idx[j * nsample + cnt] = k; ++cnt; } } } } void ballquery_cuda_launcher(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx) { ballquery_cuda_kernel<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx); } __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) { int bs_idx = blockIdx.y; int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (bs_idx >= b || pt_idx >= m) return; new_xyz += bs_idx * m * 3 + pt_idx * 3; xyz += bs_idx * n * 3; idx += bs_idx * m * nsample + pt_idx * nsample; float radius2 = radius * radius; float new_x = new_xyz[0]; float new_y = new_xyz[1]; float new_z = new_xyz[2]; int cnt = 0; for (int k = 0; k < n; ++k) { float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < radius2){ if (cnt == 0){ for (int l = 0; l < nsample; ++l) { idx[l] = k; } } idx[cnt] = k; ++cnt; if (cnt >= nsample){ break; } } } } void 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) { // param new_xyz: (B, m, 3) // param xyz: (B, n, 3) // param idx: (B, m, nsample) cudaError_t err; dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); ballquery_cuda_kernel_fast<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx); // cudaDeviceSynchronize(); // for using printf in kernel function err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: classification/modules/pointops/src/ballquery/ballquery_cuda_kernel.h ================================================ #ifndef _BALLQUERY_CUDA_KERNEL #define _BALLQUERY_CUDA_KERNEL #include #include #include void 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); void 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); #ifdef __cplusplus extern "C" { #endif void ballquery_cuda_launcher(int b, int n, int m, float radius, int nsample, const float *xyz, const float *new_xyz, int *idx); void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/cuda_utils.h ================================================ #ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #define TOTAL_THREADS 1024 #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, " must be a CUDAtensor ") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) #define THREADS_PER_BLOCK 256 #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) inline int opt_n_threads(int work_size) { const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return max(min(1 << pow_2, TOTAL_THREADS), 1); } inline dim3 opt_block_config(int x, int y) { const int x_threads = opt_n_threads(x); const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); dim3 block_config(x_threads, y_threads, 1); return block_config; } #endif ================================================ FILE: classification/modules/pointops/src/grouping/grouping_cuda.cpp ================================================ #include #include #include #include #include "grouping_cuda_kernel.h" extern THCState *state; void 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) { const float *points = points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *out = out_tensor.data_ptr(); grouping_forward_cuda_launcher(b, c, n, m, nsample, points, idx, out); } void 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) { float *grad_points = grad_points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); const float *grad_out = grad_out_tensor.data_ptr(); grouping_backward_cuda_launcher(b, c, n, m, nsample, grad_out, idx, grad_points); } void 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) { const float *points = points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *out = out_tensor.data_ptr(); grouping_forward_cuda_launcher_fast(b, c, n, npoints, nsample, points, idx, out); } ================================================ FILE: classification/modules/pointops/src/grouping/grouping_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "grouping_cuda_kernel.h" // input: points(b, c, n) idx(b, m, nsample) // output: out(b, c, m, nsample) __global__ void grouping_forward_cuda_kernel(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out) { int batch_index = blockIdx.x; points += batch_index * n * c; idx += batch_index * m * nsample; out += batch_index * m * nsample * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * m; i += stride) { const int l = i / m; const int j = i % m; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; out[(l * m + j) * nsample + k] = points[l * n + ii]; } } } // input: grad_out(b, c, m, nsample), idx(b, m, nsample) // output: grad_points(b, c, 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) { int batch_index = blockIdx.x; grad_out += batch_index * m * nsample * c; idx += batch_index * m * nsample; grad_points += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * m; i += stride) { const int l = i / m; const int j = i % m; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; atomicAdd(grad_points + l * n + ii, grad_out[(l * m + j) * nsample + k]); } } } void grouping_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out) { grouping_forward_cuda_kernel<<>>(b, c, n, m, nsample, points, idx, out); } void grouping_backward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *grad_out, const int *idx, float *grad_points) { grouping_backward_cuda_kernel<<>>(b, c, n, m, nsample, grad_out, idx, grad_points); } // input: points(b, c, n) idx(b, npoints, nsample) // output: out(b, c, npoints, nsample) __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) { int bs_idx = blockIdx.z; int c_idx = blockIdx.y; int index = blockIdx.x * blockDim.x + threadIdx.x; int pt_idx = index / nsample; if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return; int sample_idx = index % nsample; idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; int in_idx = bs_idx * c * n + c_idx * n + idx[0]; int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx; out[out_idx] = points[in_idx]; } // input: points(b, c, n) idx(b, npoints, nsample) // output: out(b, c, npoints, nsample) void grouping_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out) { cudaError_t err; dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); grouping_forward_cuda_kernel_fast<<>>(b, c, n, npoints, nsample, points, idx, out); // cudaDeviceSynchronize(); // for using printf in kernel function err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: classification/modules/pointops/src/grouping/grouping_cuda_kernel.h ================================================ #ifndef _GROUPING_CUDA_KERNEL #define _GROUPING_CUDA_KERNEL #include #include #include void grouping_forward_cuda(int b, int c, int n, int m, int nsample, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out); void 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); void 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); #ifdef __cplusplus extern "C" { #endif void grouping_forward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *points, const int *idx, float *out); void grouping_backward_cuda_launcher(int b, int c, int n, int m, int nsample, const float *grad_out, const int *idx, float *grad_points); void grouping_forward_cuda_launcher_fast(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/grouping_int/grouping_int_cuda.cpp ================================================ #include #include #include #include #include "grouping_int_cuda_kernel.h" extern THCState *state; void 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) { const long int *points = points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); long int *out = out_tensor.data_ptr(); grouping_int_forward_cuda_launcher(b, c, n, m, nsample, points, idx, out); } void 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) { const long int *points = points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); long int *out = out_tensor.data_ptr(); grouping_int_forward_cuda_launcher_fast(b, c, n, m, nsample, points, idx, out); } ================================================ FILE: classification/modules/pointops/src/grouping_int/grouping_int_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "grouping_int_cuda_kernel.h" // input: points(b, c, n) idx(b, m, nsample) // output: out(b, c, m, nsample) __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) { int batch_index = blockIdx.x; points += batch_index * n * c; idx += batch_index * m * nsample; out += batch_index * m * nsample * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * m; i += stride) { const int l = i / m; const int j = i % m; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; out[(l * m + j) * nsample + k] = points[l * n + ii]; } } } void 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) { grouping_int_forward_cuda_kernel<<>>(b, c, n, m, nsample, points, idx, out); } __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) { int bs_idx = blockIdx.z; int c_idx = blockIdx.y; int index = blockIdx.x * blockDim.x + threadIdx.x; int pt_idx = index / nsample; if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return; int sample_idx = index % nsample; idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; int in_idx = bs_idx * c * n + c_idx * n + idx[0]; int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx; out[out_idx] = points[in_idx]; } void 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) { cudaError_t err; dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); grouping_int_forward_cuda_kernel_fast<<>>(b, c, n, npoints, nsample, points, idx, out); // cudaDeviceSynchronize(); // for using printf in kernel function err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: classification/modules/pointops/src/grouping_int/grouping_int_cuda_kernel.h ================================================ #ifndef _GROUPING_INT_CUDA_KERNEL #define _GROUPING_INT_CUDA_KERNEL #include #include #include void 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); void 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); #ifdef __cplusplus extern "C" { #endif void 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); void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/interpolation/interpolation_cuda.cpp ================================================ #include #include #include #include #include "interpolation_cuda_kernel.h" extern THCState *state; void nearestneighbor_cuda(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) { const float *unknown = unknown_tensor.data_ptr(); const float *known = known_tensor.data_ptr(); float *dist2 = dist2_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); nearestneighbor_cuda_launcher(b, n, m, unknown, known, dist2, idx); } void 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) { const float *points = points_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); float *out = out_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); interpolation_forward_cuda_launcher(b, c, m, n, points, idx, weight, out); } void 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) { const float *grad_out = grad_out_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); float *grad_points = grad_points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); interpolation_backward_cuda_launcher(b, c, n, m, grad_out, idx, weight, grad_points); } void 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) { const float *unknown = unknown_tensor.data_ptr(); const float *known = known_tensor.data_ptr(); float *dist2 = dist2_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); nearestneighbor_cuda_launcher_fast(b, n, m, unknown, known, dist2, idx); } void 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) { const float *points = points_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); float *out = out_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); interpolation_forward_cuda_launcher_fast(b, c, m, n, points, idx, weight, out); } ================================================ FILE: classification/modules/pointops/src/interpolation/interpolation_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "interpolation_cuda_kernel.h" // input: unknown(b, n, 3) known(b, m, 3) // output: dist2(b, n, 3), idx(b, n, 3) __global__ void nearestneighbor_cuda_kernel(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx) { int batch_index = blockIdx.x; unknown += batch_index * n * 3; known += batch_index * m * 3; dist2 += batch_index * n * 3; idx += batch_index * n * 3; int index = threadIdx.x; int stride = blockDim.x; for (int j = index; j < n; j += stride) { float ux = unknown[j * 3 + 0]; float uy = unknown[j * 3 + 1]; float uz = unknown[j * 3 + 2]; double best1 = 1e40, best2 = 1e40, best3 = 1e40; int besti1 = 0, besti2 = 0, besti3 = 0; for (int k = 0; k < m; ++k) { float x = known[k * 3 + 0]; float y = known[k * 3 + 1]; float z = known[k * 3 + 2]; float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); if (d < best1) { best3 = best2; besti3 = besti2; best2 = best1; besti2 = besti1; best1 = d; besti1 = k; } else if (d < best2) { best3 = best2; besti3 = besti2; best2 = d; besti2 = k; } else if (d < best3) { best3 = d; besti3 = k; } } dist2[j * 3 + 0] = best1; dist2[j * 3 + 1] = best2; dist2[j * 3 + 2] = best3; idx[j * 3 + 0] = besti1; idx[j * 3 + 1] = besti2; idx[j * 3 + 2] = besti3; } } // input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) // output: out(b, c, 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) { int batch_index = blockIdx.x; points += batch_index * m * c; idx += batch_index * n * 3; weight += batch_index * n * 3; out += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + points[l * m + i3] * w3; } } // input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3) // output: grad_points(b, c, m) __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) { int batch_index = blockIdx.x; grad_out += batch_index * n * c; idx += batch_index * n * 3; weight += batch_index * n * 3; grad_points += batch_index * m * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; atomicAdd(grad_points + l * m + i1, grad_out[i] * w1); atomicAdd(grad_points + l * m + i2, grad_out[i] * w2); atomicAdd(grad_points + l * m + i3, grad_out[i] * w3); } } void nearestneighbor_cuda_launcher(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx) { nearestneighbor_cuda_kernel<<>>(b, n, m, unknown, known, dist2, idx); } void interpolation_forward_cuda_launcher(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out) { interpolation_forward_cuda_kernel<<>>(b, c, m, n, points, idx, weight, out); } void 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) { interpolation_backward_cuda_kernel<<>>(b, n, c, m, grad_out, idx, weight, grad_points); } // input: unknown(b, n, 3) known(b, m, 3) // output: dist2(b, n, 3), idx(b, n, 3) __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) { int bs_idx = blockIdx.y; int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (bs_idx >= b || pt_idx >= n) return; unknown += bs_idx * n * 3 + pt_idx * 3; known += bs_idx * m * 3; dist2 += bs_idx * n * 3 + pt_idx * 3; idx += bs_idx * n * 3 + pt_idx * 3; float ux = unknown[0]; float uy = unknown[1]; float uz = unknown[2]; double best1 = 1e40, best2 = 1e40, best3 = 1e40; int besti1 = 0, besti2 = 0, besti3 = 0; for (int k = 0; k < m; ++k) { float x = known[k * 3 + 0]; float y = known[k * 3 + 1]; float z = known[k * 3 + 2]; float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); if (d < best1) { best3 = best2; besti3 = besti2; best2 = best1; besti2 = besti1; best1 = d; besti1 = k; } else if (d < best2) { best3 = best2; besti3 = besti2; best2 = d; besti2 = k; } else if (d < best3) { best3 = d; besti3 = k; } } dist2[0] = best1; dist2[1] = best2; dist2[2] = best3; idx[0] = besti1; idx[1] = besti2; idx[2] = besti3; } // input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) // output: out(b, c, 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) { int bs_idx = blockIdx.z; int c_idx = blockIdx.y; int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (bs_idx >= b || c_idx >= c || pt_idx >= n) return; weight += bs_idx * n * 3 + pt_idx * 3; points += bs_idx * c * m + c_idx * m; idx += bs_idx * n * 3 + pt_idx * 3; out += bs_idx * c * n + c_idx * n; out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]]; } void nearestneighbor_cuda_launcher_fast(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx) { cudaError_t err; dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); nearestneighbor_cuda_kernel_fast<<>>(b, n, m, unknown, known, dist2, idx); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } void interpolation_forward_cuda_launcher_fast(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out) { cudaError_t err; dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); interpolation_forward_cuda_kernel_fast<<>>(b, c, m, n, points, idx, weight, out); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: classification/modules/pointops/src/interpolation/interpolation_cuda_kernel.h ================================================ #ifndef _INTERPOLATION_CUDA_KERNEL #define _INTERPOLATION_CUDA_KERNEL #include #include #include void nearestneighbor_cuda(int b, int n, int m, at::Tensor unknown_tensor, at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor); void 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); void 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); void 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); void 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); #ifdef __cplusplus extern "C" { #endif void nearestneighbor_cuda_launcher(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx); void interpolation_forward_cuda_launcher(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out); void 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); void nearestneighbor_cuda_launcher_fast(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx); void interpolation_forward_cuda_launcher_fast(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/knnquery/__init__.py ================================================ ================================================ FILE: classification/modules/pointops/src/knnquery/knnquery_cuda.cpp ================================================ #include #include #include #include #include "knnquery_cuda_kernel.h" extern THCState *state; #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, " must be a CUDAtensor ") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) void 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) { CHECK_INPUT(new_xyz_tensor); CHECK_INPUT(xyz_tensor); const float *new_xyz = new_xyz_tensor.data_ptr(); const float *xyz = xyz_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); float *dist2 = dist2_tensor.data_ptr(); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); knnquery_cuda_launcher(b, n, m, nsample, xyz, new_xyz, idx, dist2, stream); } ================================================ FILE: classification/modules/pointops/src/knnquery/knnquery_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "knnquery_cuda_kernel.h" // input: xyz (b, n, 3) new_xyz (b, m, 3) // output: idx (b, m, nsample) dist2 (b, m, nsample) __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) { int bs_idx = blockIdx.y; int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (bs_idx >= b || pt_idx >= m) return; new_xyz += bs_idx * m * 3 + pt_idx * 3; xyz += bs_idx * n * 3; idx += bs_idx * m * nsample + pt_idx * nsample; float new_x = new_xyz[0]; float new_y = new_xyz[1]; float new_z = new_xyz[2]; //double* best = new double[nsample]; //int* besti = new int[nsample]; double best[200]; int besti[200]; for(int i = 0; i < nsample; i++){ best[i] = 1e40; besti[i] = 0; } for(int k = 0; k < n; k++){ float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); for(int j = 0; j < nsample; j++){ if(d2 < best[j]){ for(int i = nsample - 1; i > j; i--){ best[i] = best[i - 1]; besti[i] = besti[i - 1]; } best[j] = d2; besti[j] = k; break; } } } for(int i = 0; i < nsample; i++){ idx[i] = besti[i]; dist2[i] = best[i]; } //delete []best; //delete []besti; } void 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) { // param new_xyz: (B, m, 3) // param xyz: (B, n, 3) // param idx: (B, m, nsample) cudaError_t err; dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); // fprintf('%d, %d', blocks, threads); knnquery_cuda_kernel<<>>(b, n, m, nsample, xyz, new_xyz, idx, dist2); // cudaDeviceSynchronize(); // for using printf in kernel function // err = cudaGetLastError(); // if (cudaSuccess != err) { // fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); // exit(-1); // } } ================================================ FILE: classification/modules/pointops/src/knnquery/knnquery_cuda_kernel.h ================================================ #ifndef _KNNQUERY_CUDA_KERNEL #define _KNNQUERY_CUDA_KERNEL #include #include #include void 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); #ifdef __cplusplus extern "C" { #endif void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/knnquery_heap/__init__.py ================================================ ================================================ FILE: classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda.cpp ================================================ #include #include #include #include #include "knnquery_heap_cuda_kernel.h" extern THCState *state; #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x, " must be a CUDAtensor ") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) void 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) { CHECK_INPUT(new_xyz_tensor); CHECK_INPUT(xyz_tensor); const float *new_xyz = new_xyz_tensor.data_ptr(); const float *xyz = xyz_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); float *dist2 = dist2_tensor.data_ptr(); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); knnquery_heap_cuda_launcher(b, n, m, nsample, xyz, new_xyz, idx, dist2, stream); } ================================================ FILE: classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "knnquery_heap_cuda_kernel.h" __device__ void swap_float(float *x, float *y) { float tmp = *x; *x = *y; *y = tmp; } __device__ void swap_int(int *x, int *y) { int tmp = *x; *x = *y; *y = tmp; } __device__ void reheap(float *dist, int *idx, int k) { int root = 0; int child = root * 2 + 1; while (child < k) { if(child + 1 < k && dist[child+1] > dist[child]) child++; if(dist[root] > dist[child]) return; swap_float(&dist[root], &dist[child]); swap_int(&idx[root], &idx[child]); root = child; child = root * 2 + 1; } } __device__ void heap_sort(float *dist, int *idx, int k) { int i; for (i = k - 1; i > 0; i--) { swap_float(&dist[0], &dist[i]); swap_int(&idx[0], &idx[i]); reheap(dist, idx, i); } } // input: xyz (b, n, 3) new_xyz (b, m, 3) // output: idx (b, m, nsample) dist2 (b, m, nsample) __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) { int bs_idx = blockIdx.y; int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (bs_idx >= b || pt_idx >= m) return; new_xyz += bs_idx * m * 3 + pt_idx * 3; xyz += bs_idx * n * 3; idx += bs_idx * m * nsample + pt_idx * nsample; dist2 += bs_idx * m * nsample + pt_idx * nsample; float new_x = new_xyz[0]; float new_y = new_xyz[1]; float new_z = new_xyz[2]; float best_dist[100]; int best_idx[100]; for(int i = 0; i < nsample; i++){ best_dist[i] = 1e10; best_idx[i] = 0; } for(int i = 0; i < n; i++){ float x = xyz[i * 3 + 0]; float y = xyz[i * 3 + 1]; float z = xyz[i * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < best_dist[0]){ best_dist[0] = d2; best_idx[0] = i; reheap(best_dist, best_idx, nsample); } } heap_sort(best_dist, best_idx, nsample); for(int i = 0; i < nsample; i++){ idx[i] = best_idx[i]; dist2[i] = best_dist[i]; } } void 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) { // param new_xyz: (B, m, 3) // param xyz: (B, n, 3) // param idx: (B, m, nsample) cudaError_t err; dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) dim3 threads(THREADS_PER_BLOCK); knnquery_heap_cuda_kernel<<>>(b, n, m, nsample, xyz, new_xyz, idx, dist2); // cudaDeviceSynchronize(); // for using printf in kernel function err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: classification/modules/pointops/src/knnquery_heap/knnquery_heap_cuda_kernel.h ================================================ #ifndef _KNNQUERY_HEAP_CUDA_KERNEL #define _KNNQUERY_HEAP_CUDA_KERNEL #include #include #include void 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); #ifdef __cplusplus extern "C" { #endif void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/pointops/src/pointops_api.cpp ================================================ #include #include #include "ballquery/ballquery_cuda_kernel.h" #include "grouping/grouping_cuda_kernel.h" #include "grouping_int/grouping_int_cuda_kernel.h" #include "sampling/sampling_cuda_kernel.h" #include "interpolation/interpolation_cuda_kernel.h" #include "knnquery/knnquery_cuda_kernel.h" #include "knnquery_heap/knnquery_heap_cuda_kernel.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("ballquery_cuda", &ballquery_cuda_fast, "ballquery_cuda_fast"); // name in python, cpp function address, docs m.def("knnquery_cuda", &knnquery_cuda, "knnquery_cuda"); m.def("knnquery_heap_cuda", &knnquery_heap_cuda, "knnquery_heap_cuda"); m.def("grouping_forward_cuda", &grouping_forward_cuda_fast, "grouping_forward_cuda_fast"); m.def("grouping_backward_cuda", &grouping_backward_cuda, "grouping_backward_cuda"); m.def("grouping_int_forward_cuda", &grouping_int_forward_cuda_fast, "grouping_int_forward_cuda_fast"); m.def("gathering_forward_cuda", &gathering_forward_cuda, "gathering_forward_cuda"); m.def("gathering_backward_cuda", &gathering_backward_cuda, "gathering_backward_cuda"); m.def("furthestsampling_cuda", &furthestsampling_cuda, "furthestsampling_cuda"); m.def("nearestneighbor_cuda", &nearestneighbor_cuda_fast, "nearestneighbor_cuda_fast"); m.def("interpolation_forward_cuda", &interpolation_forward_cuda_fast, "interpolation_forward_cuda_fast"); m.def("interpolation_backward_cuda", &interpolation_backward_cuda, "interpolation_backward_cuda"); } ================================================ FILE: classification/modules/pointops/src/sampling/sampling_cuda.cpp ================================================ #include #include #include #include #include "sampling_cuda_kernel.h" extern THCState *state; void gathering_forward_cuda(int b, int c, int n, int m, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) { const float *points = points_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *out = out_tensor.data_ptr(); gathering_forward_cuda_launcher(b, c, n, m, points, idx, out); } void 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) { const float *grad_out = grad_out_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *grad_points = grad_points_tensor.data_ptr(); gathering_backward_cuda_launcher(b, c, n, m, grad_out, idx, grad_points); } void furthestsampling_cuda(int b, int n, int m, at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) { const float *points = points_tensor.data_ptr(); float *temp = temp_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); furthestsampling_cuda_launcher(b, n, m, points, temp, idx); } ================================================ FILE: classification/modules/pointops/src/sampling/sampling_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "sampling_cuda_kernel.h" // input: points(b, c, n) idx(b, m) // output: out(b, c, m) __global__ void gathering_forward_cuda_kernel(int b, int c, int n, int m, const float *points, const int *idx, float *out) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; out[(i * c + l) * m + j] = points[(i * c + l) * n + a]; } } } } // input: grad_out(b, c, m) idx(b, m) // output: grad_points(b, c, 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) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; atomicAdd(grad_points + (i * c + l) * n + a, grad_out[(i * c + l) * m + j]); } } } } void gathering_forward_cuda_launcher(int b, int c, int n, int m, const float *points, const int *idx, float *out) { gathering_forward_cuda_kernel<<>>(b, c, n, m, points, idx, out); } void gathering_backward_cuda_launcher(int b, int c, int n, int m, const float *grad_out, const int *idx, float *grad_points) { gathering_backward_cuda_kernel<<>>(b, c, n, m, grad_out, idx, grad_points); } __device__ void __update(float *dists, int *dists_i, int idx1, int idx2) { const float v1 = dists[idx1], v2 = dists[idx2]; const int i1 = dists_i[idx1], i2 = dists_i[idx2]; dists[idx1] = max(v1, v2); dists_i[idx1] = v2 > v1 ? i2 : i1; } // Input dataset: (b, n, 3), tmp: (b, n) // Ouput idxs (b, m) template __global__ void furthestsampling_cuda_kernel(int b, int n, int m, const float *dataset, float *temp, int *idxs) { if (m <= 0) return; __shared__ float dists[block_size]; __shared__ int dists_i[block_size]; int batch_index = blockIdx.x; dataset += batch_index * n * 3; temp += batch_index * n; idxs += batch_index * m; int tid = threadIdx.x; const int stride = block_size; int old = 0; if (threadIdx.x == 0) idxs[0] = old; __syncthreads(); for (int j = 1; j < m; j++) { int besti = 0; float best = -1; float x1 = dataset[old * 3 + 0]; float y1 = dataset[old * 3 + 1]; float z1 = dataset[old * 3 + 2]; for (int k = tid; k < n; k += stride) { float x2, y2, z2; x2 = dataset[k * 3 + 0]; y2 = dataset[k * 3 + 1]; z2 = dataset[k * 3 + 2]; //float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); //if (mag <= 1e-3) // continue; float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); float d2 = min(d, temp[k]); temp[k] = d2; besti = d2 > best ? k : besti; best = d2 > best ? d2 : best; } dists[tid] = best; dists_i[tid] = besti; __syncthreads(); if (block_size >= 1024) { if (tid < 512) { __update(dists, dists_i, tid, tid + 512); } __syncthreads(); } if (block_size >= 512) { if (tid < 256) { __update(dists, dists_i, tid, tid + 256); } __syncthreads(); } if (block_size >= 256) { if (tid < 128) { __update(dists, dists_i, tid, tid + 128); } __syncthreads(); } if (block_size >= 128) { if (tid < 64) { __update(dists, dists_i, tid, tid + 64); } __syncthreads(); } if (block_size >= 64) { if (tid < 32) { __update(dists, dists_i, tid, tid + 32); } __syncthreads(); } if (block_size >= 32) { if (tid < 16) { __update(dists, dists_i, tid, tid + 16); } __syncthreads(); } if (block_size >= 16) { if (tid < 8) { __update(dists, dists_i, tid, tid + 8); } __syncthreads(); } if (block_size >= 8) { if (tid < 4) { __update(dists, dists_i, tid, tid + 4); } __syncthreads(); } if (block_size >= 4) { if (tid < 2) { __update(dists, dists_i, tid, tid + 2); } __syncthreads(); } if (block_size >= 2) { if (tid < 1) { __update(dists, dists_i, tid, tid + 1); } __syncthreads(); } old = dists_i[0]; if (tid == 0) idxs[j] = old; } } void furthestsampling_cuda_launcher(int b, int n, int m, const float *dataset, float *temp, int *idxs) { unsigned int n_threads = opt_n_threads(n); switch (n_threads) { case 1024: furthestsampling_cuda_kernel<1024><<>>(b, n, m, dataset, temp, idxs); break; case 512: furthestsampling_cuda_kernel<512><<>>(b, n, m, dataset, temp, idxs); break; case 256: furthestsampling_cuda_kernel<256><<>>(b, n, m, dataset, temp, idxs); break; case 128: furthestsampling_cuda_kernel<128><<>>(b, n, m, dataset, temp, idxs); break; case 64: furthestsampling_cuda_kernel<64><<>>(b, n, m, dataset, temp, idxs); break; case 32: furthestsampling_cuda_kernel<32><<>>(b, n, m, dataset, temp, idxs); break; case 16: furthestsampling_cuda_kernel<16><<>>(b, n, m, dataset, temp, idxs); break; case 8: furthestsampling_cuda_kernel<8><<>>(b, n, m, dataset, temp, idxs); break; case 4: furthestsampling_cuda_kernel<4><<>>(b, n, m, dataset, temp, idxs); break; case 2: furthestsampling_cuda_kernel<2><<>>(b, n, m, dataset, temp, idxs); break; case 1: furthestsampling_cuda_kernel<1><<>>(b, n, m, dataset, temp, idxs); break; default: furthestsampling_cuda_kernel<512><<>>(b, n, m, dataset, temp, idxs); } } ================================================ FILE: classification/modules/pointops/src/sampling/sampling_cuda_kernel.h ================================================ #ifndef _SAMPLING_CUDA_KERNEL #define _SAMPLING_CUDA_KERNEL #include #include #include void gathering_forward_cuda(int b, int c, int n, int m, at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor); void 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); void furthestsampling_cuda(int b, int n, int m, at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor); #ifdef __cplusplus extern "C" { #endif void gathering_forward_cuda_launcher(int b, int c, int n, int m, const float *points, const int *idx, float *out); void gathering_backward_cuda_launcher(int b, int c, int n, int m, const float *grad_out, const int *idx, float *grad_points); void furthestsampling_cuda_launcher(int b, int n, int m, const float *dataset, float *temp, int *idxs); #ifdef __cplusplus } #endif #endif ================================================ FILE: classification/modules/polar_utils.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch import numpy as np def xyz2sphere(xyz, normalize=True): """ Convert XYZ to Spherical Coordinate reference: https://en.wikipedia.org/wiki/Spherical_coordinate_system :param xyz: [B, N, 3] / [B, N, G, 3] :return: (rho, theta, phi) [B, N, 3] / [B, N, G, 3] """ rho = torch.sqrt(torch.sum(torch.pow(xyz, 2), dim=-1, keepdim=True)) rho = torch.clamp(rho, min=0) # range: [0, inf] theta = torch.acos(xyz[..., 2, None] / rho) # range: [0, pi] phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi] # check nan idx = rho == 0 theta[idx] = 0 if normalize: theta = theta / np.pi # [0, 1] phi = phi / (2 * np.pi) + .5 # [0, 1] out = torch.cat([rho, theta, phi], dim=-1) return out def xyz2cylind(xyz, normalize=True): """ Convert XYZ to Cylindrical Coordinate reference: https://en.wikipedia.org/wiki/Cylindrical_coordinate_system :param normalize: Normalize phi & z :param xyz: [B, N, 3] / [B, N, G, 3] :return: (rho, phi, z) [B, N, 3] """ rho = torch.sqrt(torch.sum(torch.pow(xyz[..., :2], 2), dim=-1, keepdim=True)) rho = torch.clamp(rho, 0, 1) # range: [0, 1] phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi] z = xyz[..., 2, None] z = torch.clamp(z, -1, 1) # range: [-1, 1] if normalize: phi = phi / (2 * np.pi) + .5 z = (z + 1.) / 2. out = torch.cat([rho, phi, z], dim=-1) return out ================================================ FILE: classification/modules/ptaug_utils.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch ################# # MAIN ################# def get_aug_args(args): dataset = args.dataset if dataset == 'ScanObjectNN': aug_args = {'scale_factor': 0.5, 'shift_factor': 0.3} return aug_args else: raise Exception('No such dataset') def transform_point_cloud(batch, args, aug_args, train=True, label=None): """batch: B x 3/6 x N""" if args.aug_scale: batch[:, 0:3] = scale_point_cloud(batch[:, 0:3], aug_args['scale_factor']) if args.aug_shift: batch[:, 0:3] = shift_point_cloud(batch[:, 0:3], shift_range=aug_args['shift_factor']) if label is not None: return batch, label return batch ################# # Shift ################# def shift_point_cloud(batch_data, shift_range=0.2): """ Randomly shift point cloud. Shift is per point cloud. Input: B x C x N array, original batch of point clouds Return: B x C x N array, shifted batch of point clouds """ shifts = (torch.rand(batch_data.shape[0], 3, 1, device=batch_data.device) * 2. - 1.) * shift_range batch_data += shifts return batch_data ################# # Scale ################# def scale_point_cloud(batch_data, scale_range=0.2): """ Randomly scale the point cloud. Scale is per point cloud. Input: B x C x N array, original batch of point clouds Return: B x C x N array, scaled batch of point clouds """ scales = (torch.rand(batch_data.shape[0], 3, 1, device=batch_data.device) * 2. - 1.) * scale_range + 1. batch_data *= scales return batch_data ================================================ FILE: classification/modules/recons_utils.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch from torch import nn from modules.pointnet2_utils import query_knn_point, index_points def _recons_factory(type): if type == 'knn': return knn_recons else: raise Exception('Not Implemented Reconstruction Type') def knn_recons(k, center, context, cuda=False): idx = query_knn_point(k, context, center, cuda=cuda) torch.cuda.empty_cache() group_xyz = index_points(context, idx, cuda=cuda, is_group=True) # [B, N, K, C] torch.cuda.empty_cache() return group_xyz def cal_normal(group_xyz, random_inv=False, is_group=False): """ Calculate Normal Vector (Unit Form + First Term Positive) :param group_xyz: [B, N, K=3, 3] / [B, N, G, K=3, 3] :param random_inv: :param return_intersect: :param return_const: :return: [B, N, 3] """ edge_vec1 = group_xyz[..., 1, :] - group_xyz[..., 0, :] # [B, N, 3] edge_vec2 = group_xyz[..., 2, :] - group_xyz[..., 0, :] # [B, N, 3] nor = torch.cross(edge_vec1, edge_vec2, dim=-1) unit_nor = nor / torch.norm(nor, dim=-1, keepdim=True) # [B, N, 3] / [B, N, G, 3] if not is_group: pos_mask = (unit_nor[..., 0] > 0).float() * 2. - 1. # keep x_n positive else: pos_mask = (unit_nor[..., 0:1, 0] > 0).float() * 2. - 1. unit_nor = unit_nor * pos_mask.unsqueeze(-1) # batch-wise random inverse normal vector (prob: 0.5) if random_inv: random_mask = torch.randint(0, 2, (group_xyz.size(0), 1, 1)).float() * 2. - 1. random_mask = random_mask.to(unit_nor.device) if not is_group: unit_nor = unit_nor * random_mask else: unit_nor = unit_nor * random_mask.unsqueeze(-1) return unit_nor def pca(X, k, center=True): """ Principal Components Analysis impl. with SVD function :param X: :param k: :param center: :return: """ n = X.size()[0] ones = torch.ones(n).view([n, 1]) h = ((1 / n) * torch.mm(ones, ones.t())) if center else torch.zeros(n * n).view([n, n]) H = torch.eye(n) - h X_center = torch.mm(H.double(), X.double()) u, s, v = torch.svd(X_center) components = v[:k].t() explained_variance = torch.mul(s[:k], s[:k]) / (n - 1) return {'X': X, 'k': k, 'components': components, 'explained_variance': explained_variance} def cal_center(group_xyz): """ Calculate Global Coordinates of the Center of Triangle :param group_xyz: [B, N, K, 3] / [B, N, G, K, 3]; K >= 3 :return: [B, N, 3] / [B, N, G, 3] """ center = torch.mean(group_xyz, dim=-2) return center def cal_area(group_xyz): """ Calculate Area of Triangle :param group_xyz: [B, N, K, 3] / [B, N, G, K, 3]; K = 3 :return: [B, N, 1] / [B, N, G, 1] """ pad_shape = group_xyz[..., 0, None].shape det_xy = torch.det(torch.cat([group_xyz[..., 0, None], group_xyz[..., 1, None], torch.ones(pad_shape)], dim=-1)) det_yz = torch.det(torch.cat([group_xyz[..., 1, None], group_xyz[..., 2, None], torch.ones(pad_shape)], dim=-1)) det_zx = torch.det(torch.cat([group_xyz[..., 2, None], group_xyz[..., 0, None], torch.ones(pad_shape)], dim=-1)) area = torch.sqrt(det_xy ** 2 + det_yz ** 2 + det_zx ** 2).unsqueeze(-1) return area def cal_const(normal, center, is_normalize=True): """ Calculate Constant Term (Standard Version, with x_normal to be 1) math:: const = x_nor * x_0 + y_nor * y_0 + z_nor * z_0 :param is_normalize: :param normal: [B, N, 3] / [B, N, G, 3] :param center: [B, N, 3] / [B, N, G, 3] :return: [B, N, 1] / [B, N, G, 1] """ const = torch.sum(normal * center, dim=-1, keepdim=True) factor = torch.sqrt(torch.Tensor([3])).to(normal.device) const = const / factor if is_normalize else const return const def check_nan(normal, center, pos=None): """ Check & Remove NaN in normal tensor :param pos: [B, N, 1] :param center: [B, N, 3] :param normal: [B, N, 3] :return: """ B, N, _ = normal.shape mask = torch.sum(torch.isnan(normal), dim=-1) > 0 mask_first = torch.argmax((~mask).int(), dim=-1) normal_first = normal[torch.arange(B), None, mask_first].repeat([1, N, 1]) normal[mask] = normal_first[mask] center_first = center[torch.arange(B), None, mask_first].repeat([1, N, 1]) center[mask] = center_first[mask] if pos is not None: pos_first = pos[torch.arange(B), None, mask_first].repeat([1, N, 1]) pos[mask] = pos_first[mask] return normal, center, pos return normal, center def check_nan_umb(normal, center, pos=None): """ Check & Remove NaN in normal tensor :param pos: [B, N, G, 1] :param center: [B, N, G, 3] :param normal: [B, N, G, 3] :return: """ B, N, G, _ = normal.shape mask = torch.sum(torch.isnan(normal), dim=-1) > 0 mask_first = torch.argmax((~mask).int(), dim=-1) b_idx = torch.arange(B).unsqueeze(1).repeat([1, N]) n_idx = torch.arange(N).unsqueeze(0).repeat([B, 1]) normal_first = normal[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1]) normal[mask] = normal_first[mask] center_first = center[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1]) center[mask] = center_first[mask] if pos is not None: pos_first = pos[b_idx, n_idx, None, mask_first].repeat([1, 1, G, 1]) pos[mask] = pos_first[mask] return normal, center, pos return normal, center class SurfaceConstructor(nn.Module): """ Surface Constructor for Point Clouds Formulation of A Surface: A * (x - x_0) + B * (y - y_0) + C * (z - z_0) = 0, where A^2 + B^2 + C^2 = 1 & A > 0 """ def __init__(self, r=None, k=3, recons_type='knn', return_dist=False, random_inv=True, cuda=False): super(SurfaceConstructor, self).__init__() self.K = k self.R = r self.recons = _recons_factory(recons_type) self.cuda = cuda self.return_dist = return_dist self.random_inv = random_inv def forward(self, center, context): """ Input: center: input points position as centroid points, [B, 3, N] context: input points position as context points, [B, 3, N'] Output: normal: normals of constructed triangles, [B, 3, N] center: centroids of constructed triangles, [B, 3, N] pos: position info of constructed triangles, [B, 1, N] """ center = center.permute(0, 2, 1) context = context.permute(0, 2, 1) group_xyz = self.recons(self.K, center, context, cuda=self.cuda) normal = cal_normal(group_xyz, random_inv=self.random_inv) center = cal_center(group_xyz) if self.return_dist: pos = cal_const(normal, center) normal, center, pos = check_nan(normal, center, pos) normal = normal.permute(0, 2, 1) center = center.permute(0, 2, 1) pos = pos.permute(0, 2, 1) return normal, center, pos normal, center = check_nan(normal, center) normal = normal.permute(0, 2, 1) center = center.permute(0, 2, 1) return normal, center if __name__ == '__main__': xyz = torch.rand(1, 3, 1024) * 2. - 1. constructor = SurfaceConstructor(return_dist=True) normal, center, pos = constructor(xyz, xyz) print(normal.shape) print(center.shape) ================================================ FILE: classification/modules/repsurface_utils.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ import torch import torch.nn as nn import torch.nn.functional as F from modules.pointnet2_utils import farthest_point_sample, index_points, query_knn_point, query_ball_point from modules.polar_utils import xyz2sphere from modules.recons_utils import cal_const, cal_normal, cal_center, check_nan_umb def sample_and_group(npoint, radius, nsample, center, normal, feature, return_normal=True, return_polar=False, cuda=False): """ Input: center: input points position data normal: input points normal data feature: input points feature Return: new_center: sampled points position data new_normal: sampled points normal data new_feature: sampled points feature """ # sample fps_idx = farthest_point_sample(center, npoint, cuda=cuda) # [B, npoint, A] torch.cuda.empty_cache() # sample center new_center = index_points(center, fps_idx, cuda=cuda, is_group=False) torch.cuda.empty_cache() # sample normal new_normal = index_points(normal, fps_idx, cuda=cuda, is_group=False) torch.cuda.empty_cache() # group idx = query_ball_point(radius, nsample, center, new_center, cuda=cuda) torch.cuda.empty_cache() # group normal group_normal = index_points(normal, idx, cuda=cuda, is_group=True) # [B, npoint, nsample, B] torch.cuda.empty_cache() # group center group_center = index_points(center, idx, cuda=cuda, is_group=True) # [B, npoint, nsample, A] torch.cuda.empty_cache() group_center_norm = group_center - new_center.unsqueeze(2) torch.cuda.empty_cache() # group polar if return_polar: group_polar = xyz2sphere(group_center_norm) group_center_norm = torch.cat([group_center_norm, group_polar], dim=-1) if feature is not None: group_feature = index_points(feature, idx, cuda=cuda, is_group=True) new_feature = torch.cat([group_center_norm, group_normal, group_feature], dim=-1) if return_normal \ else torch.cat([group_center_norm, group_feature], dim=-1) else: new_feature = torch.cat([group_center_norm, group_normal], dim=-1) return new_center, new_normal, new_feature def sample_and_group_all(center, normal, feature, return_normal=True, return_polar=False): """ Input: center: input centroid position data normal: input normal data feature: input feature data Return: new_center: sampled points position data new_normal: sampled points position data new_feature: sampled points data """ device = center.device B, N, C = normal.shape new_center = torch.zeros(B, 1, 3).to(device) new_normal = new_center group_normal = normal.view(B, 1, N, C) group_center = center.view(B, 1, N, 3) if return_polar: group_polar = xyz2sphere(group_center) group_center = torch.cat([group_center, group_polar], dim=-1) new_feature = torch.cat([group_center, group_normal, feature.view(B, 1, N, -1)], dim=-1) if return_normal \ else torch.cat([group_center, feature.view(B, 1, N, -1)], dim=-1) return new_center, new_normal, new_feature def resort_points(points, idx): """ Resort Set of points along G dim """ device = points.device B, N, G, _ = points.shape view_shape = [B, 1, 1] repeat_shape = [1, N, G] b_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) view_shape = [1, N, 1] repeat_shape = [B, 1, G] n_indices = torch.arange(N, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[b_indices, n_indices, idx, :] return new_points def group_by_umbrella(xyz, new_xyz, k=9, cuda=False): """ Group a set of points into umbrella surfaces """ idx = query_knn_point(k, xyz, new_xyz, cuda=cuda) torch.cuda.empty_cache() group_xyz = index_points(xyz, idx, cuda=cuda, is_group=True)[:, :, 1:] # [B, N', K-1, 3] torch.cuda.empty_cache() group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2) group_phi = xyz2sphere(group_xyz_norm)[..., 2] # [B, N', K-1] sort_idx = group_phi.argsort(dim=-1) # [B, N', K-1] # [B, N', K-1, 1, 3] sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2) sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3) group_centriod = torch.zeros_like(sorted_group_xyz) umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2) return umbrella_group_xyz class SurfaceAbstraction(nn.Module): """ Surface Abstraction Module """ def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all, return_polar=True, return_normal=True, cuda=False): super(SurfaceAbstraction, self).__init__() self.npoint = npoint self.radius = radius self.nsample = nsample self.return_normal = return_normal self.return_polar = return_polar self.cuda = cuda self.group_all = group_all self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel for out_channel in mlp: self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm2d(out_channel)) last_channel = out_channel def forward(self, center, normal, feature): normal = normal.permute(0, 2, 1) center = center.permute(0, 2, 1) if feature is not None: feature = feature.permute(0, 2, 1) if self.group_all: new_center, new_normal, new_feature = sample_and_group_all(center, normal, feature, return_polar=self.return_polar, return_normal=self.return_normal) else: new_center, new_normal, new_feature = sample_and_group(self.npoint, self.radius, self.nsample, center, normal, feature, return_polar=self.return_polar, return_normal=self.return_normal, cuda=self.cuda) new_feature = new_feature.permute(0, 3, 2, 1) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_feature = F.relu(bn(conv(new_feature))) new_feature = torch.max(new_feature, 2)[0] new_center = new_center.permute(0, 2, 1) new_normal = new_normal.permute(0, 2, 1) return new_center, new_normal, new_feature class SurfaceAbstractionCD(nn.Module): """ Surface Abstraction Module """ def __init__(self, npoint, radius, nsample, feat_channel, pos_channel, mlp, group_all, return_normal=True, return_polar=False, cuda=False): super(SurfaceAbstractionCD, self).__init__() self.npoint = npoint self.radius = radius self.nsample = nsample self.return_normal = return_normal self.return_polar = return_polar self.cuda = cuda self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() self.pos_channel = pos_channel self.group_all = group_all self.mlp_l0 = nn.Conv2d(self.pos_channel, mlp[0], 1) self.mlp_f0 = nn.Conv2d(feat_channel, mlp[0], 1) self.bn_l0 = nn.BatchNorm2d(mlp[0]) self.bn_f0 = nn.BatchNorm2d(mlp[0]) # mlp_l0+mlp_f0 can be considered as the first layer of mlp_convs last_channel = mlp[0] for out_channel in mlp[1:]: self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm2d(out_channel)) last_channel = out_channel def forward(self, center, normal, feature): normal = normal.permute(0, 2, 1) center = center.permute(0, 2, 1) if feature is not None: feature = feature.permute(0, 2, 1) if self.group_all: new_center, new_normal, new_feature = sample_and_group_all(center, normal, feature, return_normal=self.return_normal, return_polar=self.return_polar) else: new_center, new_normal, new_feature = sample_and_group(self.npoint, self.radius, self.nsample, center, normal, feature, return_normal=self.return_normal, return_polar=self.return_polar, cuda=self.cuda) new_feature = new_feature.permute(0, 3, 2, 1) # init layer loc = self.bn_l0(self.mlp_l0(new_feature[:, :self.pos_channel])) feat = self.bn_f0(self.mlp_f0(new_feature[:, self.pos_channel:])) new_feature = loc + feat new_feature = F.relu(new_feature) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_feature = F.relu(bn(conv(new_feature))) new_feature = torch.max(new_feature, 2)[0] new_center = new_center.permute(0, 2, 1) new_normal = new_normal.permute(0, 2, 1) return new_center, new_normal, new_feature class UmbrellaSurfaceConstructor(nn.Module): """ Umbrella-based Surface Abstraction Module """ def __init__(self, k, in_channel, aggr_type='sum', return_dist=False, random_inv=True, cuda=False): super(UmbrellaSurfaceConstructor, self).__init__() self.k = k self.return_dist = return_dist self.random_inv = random_inv self.aggr_type = aggr_type self.cuda = cuda self.mlps = nn.Sequential( nn.Conv2d(in_channel, in_channel, 1, bias=False), nn.BatchNorm2d(in_channel), nn.ReLU(True), nn.Conv2d(in_channel, in_channel, 1, bias=True), nn.BatchNorm2d(in_channel), nn.ReLU(True), nn.Conv2d(in_channel, in_channel, 1, bias=True), ) def forward(self, center): center = center.permute(0, 2, 1) # surface construction group_xyz = group_by_umbrella(center, center, k=self.k, cuda=self.cuda) # [B, N, K-1, 3 (points), 3 (coord.)] # normal group_normal = cal_normal(group_xyz, random_inv=self.random_inv, is_group=True) # coordinate group_center = cal_center(group_xyz) # polar group_polar = xyz2sphere(group_center) if self.return_dist: group_pos = cal_const(group_normal, group_center) group_normal, group_center, group_pos = check_nan_umb(group_normal, group_center, group_pos) new_feature = torch.cat([group_center, group_polar, group_normal, group_pos], dim=-1) # N+P+CP: 10 else: group_normal, group_center = check_nan_umb(group_normal, group_center) new_feature = torch.cat([group_center, group_polar, group_normal], dim=-1) new_feature = new_feature.permute(0, 3, 2, 1) # [B, C, G, N] # mapping new_feature = self.mlps(new_feature) # aggregation if self.aggr_type == 'max': new_feature = torch.max(new_feature, 2)[0] elif self.aggr_type == 'avg': new_feature = torch.mean(new_feature, 2) else: new_feature = torch.sum(new_feature, 2) return new_feature ================================================ FILE: classification/scripts/scanobjectnn/repsurf_ssg_umb.sh ================================================ #!/usr/bin/env bash set -v python3 tool/train_cls_scanobjectnn.py \ --cuda_ops \ --batch_size 64 \ --model repsurf.repsurf_ssg_umb \ --epoch 250 \ --log_dir repsurf_cls_ssg_umb \ --gpus 0 \ --n_workers 12 \ --return_center \ --return_dist \ --return_polar \ --group_size 8 \ --umb_pool sum \ --num_point 1024 ================================================ FILE: classification/scripts/scanobjectnn/repsurf_ssg_umb_2x.sh ================================================ #!/usr/bin/env bash set -v python3 tool/train_cls_scanobjectnn.py \ --cuda_ops \ --batch_size 64 \ --model repsurf.repsurf_ssg_umb_2x \ --epoch 250 \ --log_dir repsurf_cls_ssg_umb_2x \ --gpus 0 \ --n_workers 12 \ --return_center \ --return_dist \ --return_polar \ --group_size 8 \ --umb_pool sum \ --num_point 1024 ================================================ FILE: classification/tool/train_cls_scanobjectnn.py ================================================ """ Author: Haoxi Ran Date: 05/10/2022 """ from functools import partial import argparse import numpy as np import os import torch import datetime import logging from pathlib import Path from dataset.ScanObjectNNDataLoader import ScanObjectNNDataLoader from modules.ptaug_utils import transform_point_cloud, scale_point_cloud, get_aug_args from modules.pointnet2_utils import sample from util.utils import get_model, get_loss, set_seed, weight_init def parse_args(): """PARAMETERS""" parser = argparse.ArgumentParser('RepSurf') # Basic parser.add_argument('--log_dir', type=str, default=None, help='experiment root') parser.add_argument('--data_dir', type=str, default='./data', help='data dir') parser.add_argument('--log_root', type=str, default='./log', help='log root dir') parser.add_argument('--model', default='repsurf.scanobjectnn.repsurf_ssg_umb', help='model file name [default: repsurf_ssg_umb]') parser.add_argument('--gpus', nargs='+', type=str, default=None) parser.add_argument('--seed', type=int, default=2800, help='Training Seed') parser.add_argument('--cuda_ops', action='store_true', default=False, help='Whether to use cuda version operations [default: False]') # Training parser.add_argument('--batch_size', type=int, default=64, help='batch size in training [default: 64]') parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training [Adam, SGD]') parser.add_argument('--scheduler', type=str, default='step', help='scheduler for training') parser.add_argument('--epoch', default=500, type=int, help='number of epoch in training [default: 500]') parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training [default: 0.001]') parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate [default: 1e-4]') parser.add_argument('--decay_step', default=20, type=int, help='number of epoch per decay [default: 20]') parser.add_argument('--n_workers', type=int, default=4, help='DataLoader Workers Number [default: 4]') parser.add_argument('--init', type=str, default=None, help='initializer for model [kaiming, xavier]') # Evaluation parser.add_argument('--min_val', type=int, default=100, help='Min val epoch [default: 100]') # Augmentation parser.add_argument('--aug_scale', action='store_true', default=False, help='Whether to augment by scaling [default: False]') parser.add_argument('--aug_shift', action='store_true', default=False, help='Whether to augment by shifting [default: False]') # Modeling parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') parser.add_argument('--return_dist', action='store_true', default=False, help='Whether to use signed distance [default: False]') parser.add_argument('--return_center', action='store_true', default=False, help='Whether to return center in surface abstraction [default: False]') parser.add_argument('--return_polar', action='store_true', default=False, help='Whether to return polar coordinate in surface abstraction [default: False]') parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]') parser.add_argument('--umb_pool', type=str, default='sum', help='pooling for umbrella repsurf [sum, mean, max]') return parser.parse_args() def test(model, loader, num_class=15, num_point=1024, num_votes=10, total_num=1): vote_correct = 0 sing_correct = 0 classifier = model.eval() for j, data in enumerate(loader): points, target = data points, target = points.cuda(), target.cuda() # preprocess points = sample(num_point, points) # vote vote_pool = torch.zeros(target.shape[0], num_class).cuda() for i in range(num_votes): new_points = points.clone() # scale if i > 0: new_points[:, :3] = scale_point_cloud(new_points[:, :3]) # predict pred = classifier(new_points) # single if i == 0: sing_pred = pred # vote vote_pool += pred vote_pred = vote_pool / num_votes # single pred sing_pred_choice = sing_pred.data.max(1)[1] sing_correct += sing_pred_choice.eq(target.long().data).cpu().sum() # vote pred vote_pred_choice = vote_pred.data.max(1)[1] vote_correct += vote_pred_choice.eq(target.long().data).cpu().sum() sing_acc = sing_correct.item() / total_num vote_acc = vote_correct.item() / total_num return sing_acc, vote_acc def main(args): def log_string(s): logger.info(s) print(s) '''HYPER PARAMETER''' os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(args.gpus) set_seed(args.seed) '''CREATE DIR''' experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log')) experiment_dir.mkdir(exist_ok=True) experiment_dir = experiment_dir.joinpath('ScanObjectNN') experiment_dir.mkdir(exist_ok=True) if args.log_dir is None: timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) experiment_dir = experiment_dir.joinpath(timestr) else: experiment_dir = experiment_dir.joinpath(args.log_dir) experiment_dir.mkdir(exist_ok=True) checkpoints_dir = experiment_dir.joinpath('checkpoints/') checkpoints_dir.mkdir(exist_ok=True) log_dir = experiment_dir.joinpath('logs/') log_dir.mkdir(exist_ok=True) '''LOG''' args = parse_args() logger = logging.getLogger("Model") logger.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model)) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) log_string('PARAMETER ...') log_string(args) '''DATA LOADING''' log_string('Load dataset ...') args.num_class = 15 args.dataset = 'ScanObjectNN' args.normal = False aug_args = get_aug_args(args) DATA_PATH = os.path.join(args.data_dir, 'ScanObjectNN') TRAIN_DATASET = ScanObjectNNDataLoader(root=DATA_PATH, split='training') TEST_DATASET = ScanObjectNNDataLoader(root=DATA_PATH, split='test') trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=args.n_workers, drop_last=True) testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=args.n_workers) '''MODEL BUILDING''' classifier = torch.nn.DataParallel(get_model(args)).cuda() criterion = get_loss().cuda() try: checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') start_epoch = checkpoint['epoch'] classifier.load_state_dict(checkpoint['model_state_dict']) log_string('Use pretrain model') except: log_string('No existing model, starting training from scratch...') start_epoch = 0 if args.init: init_func = partial(weight_init, init_type=args.init) classifier = classifier.apply(init_func) '''OPTIMIZER''' if args.optimizer == 'Adam': optimizer = torch.optim.Adam( classifier.parameters(), lr=args.learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=args.decay_rate) elif args.optimizer == 'SGD': optimizer = torch.optim.SGD( classifier.parameters(), lr=args.learning_rate, momentum=0.9) '''LR SCHEDULER''' if args.scheduler == 'step': scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.decay_step, gamma=0.7) else: raise Exception('No Such Scheduler') global_epoch = 0 global_step = 0 best_sing_acc = 0.0 best_vote_acc = 0.0 loader_len = len(trainDataLoader) '''TRANING''' logger.info('Start training...') for epoch in range(start_epoch, args.epoch): log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch)) train_loss = [] train_correct = 0 scheduler.step() for batch_id, data in enumerate(trainDataLoader): '''INPUT''' points, target = data points, target = points.cuda(), target.cuda() '''PREPROCESS''' points = sample(args.num_point, points) points = transform_point_cloud(points, args, aug_args) '''FORWARD''' optimizer.zero_grad() lr = optimizer.state_dict()['param_groups'][0]['lr'] classifier = classifier.train() pred = classifier(points) loss = criterion(pred, target.long()) pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.long().data).cpu().sum() train_correct += correct train_loss.append(loss.item()) '''BACKWARD''' loss.backward() optimizer.step() global_step += 1 if batch_id % 80 == 0: print('Epoch: [{0}][{1}/{2}] lr {lr:.6f} loss {loss:.4f}'. format(epoch, batch_id, len(trainDataLoader), lr=lr, loss=loss.item())) train_instance_acc = train_correct.item() / (loader_len * args.batch_size) train_mean_loss = np.mean(train_loss) log_string('Train Instance Accuracy: %.2f, Loss: %f' % (train_instance_acc * 100, train_mean_loss)) if epoch >= args.min_val: with torch.no_grad(): sing_acc, vote_acc = test(classifier.eval(), testDataLoader, num_point=args.num_point, total_num=len(TEST_DATASET)) if sing_acc >= best_sing_acc: best_sing_acc = sing_acc if vote_acc >= best_vote_acc: best_vote_acc = vote_acc best_epoch = epoch + 1 log_string('Test Single Accuracy: %.2f' % (sing_acc * 100)) log_string('Best Single Accuracy: %.2f' % (best_sing_acc * 100)) log_string('Test Vote Accuracy: %.2f' % (vote_acc * 100)) log_string('Best Vote Accuracy: %.2f' % (best_vote_acc * 100)) if vote_acc >= best_vote_acc: logger.info('Save model...') savepath = str(checkpoints_dir) + '/best_model.pth' log_string('Saving at %s' % savepath) state = { 'epoch': best_epoch, 'vote_acc': vote_acc, 'model_state_dict': classifier.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), } torch.save(state, savepath) global_epoch += 1 logger.info('End of training...') if __name__ == '__main__': args = parse_args() main(args) ================================================ FILE: classification/util/__init__.py ================================================ ================================================ FILE: classification/util/utils.py ================================================ import importlib import argparse import random import numpy as np import torch from torch import nn import torch.nn.functional as F def set_seed(seed): """ Setting of Global Seed """ torch.backends.cudnn.enabled = True torch.backends.cudnn.deterministic = True # consistent results on the cpu and gpu torch.backends.cudnn.benchmark = True np.random.seed(seed) random.seed(seed) torch.manual_seed(seed) # cpu torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # gpu def weight_init(m, init_type): if init_type == 'xavier': init_func = torch.nn.init.xavier_normal_ elif init_type == 'kaiming': init_func = torch.nn.init.kaiming_normal_ else: raise Exception('No such init type') if isinstance(m, (torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv1d)): init_func(m.weight) if m.bias is not None: torch.nn.init.constant_(m.bias, 0) elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm1d)): torch.nn.init.constant_(m.weight, 1) # constant # torch.nn.init.normal_(m.weight, 1.0, 0.02) # normal torch.nn.init.constant_(m.bias, 0) class ClsLoss(nn.Module): def __init__(self): super(ClsLoss, self).__init__() def forward(self, pred, target): total_loss = F.nll_loss(pred, target) return total_loss class SmoothClsLoss(nn.Module): def __init__(self, smoothing_ratio=0.1): super(SmoothClsLoss, self).__init__() self.smoothing_ratio = smoothing_ratio def forward(self, pred, target): eps = self.smoothing_ratio n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, target.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) # log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * pred).sum(dim=1).mean() return loss def get_model(args): module = importlib.import_module('models.%s' % args.model) return module.Model(args) def get_loss(): return SmoothClsLoss() def get_test_args(): return argparse.Namespace() ================================================ FILE: segmentation/README.md ================================================ # RepSurf for Segmentation
By *[Haoxi Ran\*](https://hancyran.github.io/) , Jun Liu, Chengjie Wang* ( * : corresponding contact) ### [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) ## Preparation ### Environment We tested under the environment: * python 3.7 * pytorch 1.6.0 / 1.8.0 * cuda 10.1 / 11.1 * gcc 7.2.0 * h5py * sharedarray * tensorboardx For anaconda user, initialize the conda environment **repsurf-seg** by: ``` sh init.sh ``` ## Experiments ### S3DIS Area-5 (Data & Logs: [Google Drive](https://drive.google.com/drive/folders/1jIZuy4RPFJ4YHAE8ScVQgwtBwNGgfKnv?usp=sharing)) * Performance using the same settings:
Model mIoU mAcc OA #Params Training Time Code Training Log Test Log Checkpoint
Point Transformer
(our settings)		 70.37 (official: 70.4) 77.02 (official: 76.5) 90.80 (official: 90.8) 7.767M 19.91h pointtransformer.py google drive google drive google drive
(30 MB)
PointNet++ SSG (our settings) 64.05 71.52 87.92 0.968M 9.08h pointnet2_ssg.py google drive google drive google drive
(4 MB)
PointNet++ SSG w/ Umbrella RepSurf (ours) 68.86 76.54 90.22 0.976M 9.18h repsurf_umb_ssg.py google drive google drive google drive
(4 MB)

**Note**: 1. 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. 2. 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. 3. 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. 4. 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). * To (firstly install gdown by **pip install gdown** and) download dataset: ``` cd ./data/S3DIS gdown https://drive.google.com/u/1/uc?id=1UDM-bjrtqoIR9FWoIRyqLUJGyKEs22fP tar zxf s3dis.tar.gz && rm s3dis.tar.gz && cd - ``` * To train one model (**Umbrella RepSurf, Point Transformer, PointNet2**) for S3DIS Area-5: ``` sh scripts/s3dis/train_[MODEL].sh # MODEL: repsurf_umb, pointnet2, pointtransformer ``` * To test one model (**Our Umbrella RepSurf, Point Transformer, PointNet2**) for S3DIS Area-5 on whole scenes: ``` sh scripts/s3dis/test_[MODEL].sh # MODEL: repsurf_umb, pointnet2, pointtransformer ``` ## Acknowledgment We thank the [Point Transformer Implementation](https://github.com/POSTECH-CVLab/point-transformer) for the library pointops. ## License RepSurf is under the Apache-2.0 license. Please contact the primary author **Haoxi Ran (ranhaoxi@gmail.com)** for commercial use. ================================================ FILE: segmentation/dataset/S3DISDataLoader.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import os import numpy as np import SharedArray as SA from torch.utils.data import Dataset from util.data_util import sa_create, data_prepare NUM_CLASS = 13 class S3DIS(Dataset): def __init__(self, args, split, coord_transform=None, rgb_transform=None, rgb_mean=None, rgb_std=None, shuffle_index=False): super().__init__() self.args, self.split, self.coord_transform, self.rgb_transform, self.rgb_mean, self.rgb_std, self.shuffle_index = \ args, split, coord_transform, rgb_transform, rgb_mean, rgb_std, shuffle_index self.stop_aug = False data_list = sorted(os.listdir(args.data_dir)) data_list = [item[:-4] for item in data_list if 'Area_' in item] if split == 'train': self.data_list = [item for item in data_list if not 'Area_{}'.format(args.test_area) in item] else: self.data_list = [item for item in data_list if 'Area_{}'.format(args.test_area) in item] self.data_idx = np.arange(len(self.data_list)) for item in self.data_list: if not os.path.exists("/dev/shm/s3dis_{}".format(item)): data_path = os.path.join(args.data_dir, item + '.npy') data = np.load(data_path).astype(np.float32) # xyzrgbl, N*7 sa_create("shm://s3dis_{}".format(item), data) def __getitem__(self, idx): data_idx = self.data_idx[idx % len(self.data_idx)] data = SA.attach("shm://s3dis_{}".format(self.data_list[data_idx])).copy() coord, feat, label = data[:, 0:3], data[:, 3:6], data[:, 6] coord, feat, label = \ data_prepare(coord, feat, label, self.args, self.split, self.coord_transform, self.rgb_transform, self.rgb_mean, self.rgb_std, self.shuffle_index, self.stop_aug) return coord, feat, label def __len__(self): return len(self.data_idx) * self.args.loop @staticmethod def print_weight(data_root, data_list): print('Computing label weight...') num_point_list = [] label_freq = np.zeros(NUM_CLASS) label_total = np.zeros(NUM_CLASS) # load data for idx, item in enumerate(data_list): data_path = os.path.join(data_root, item + '.npy') data = np.load(data_path) labels = data[:, 6] freq = np.histogram(labels, range(NUM_CLASS + 1))[0] label_freq += freq label_total += (freq > 0).astype(np.float) * labels.size num_point_list.append(labels.size) # label weight label_freq = label_freq / label_total label_weight = np.median(label_freq) / label_freq print(label_weight) @staticmethod def print_mean_std(data_root, data_list): print('Computing color mean & std...') point_list = [] for idx, item in enumerate(data_list): data_path = os.path.join(data_root, item + '.npy') data = np.load(data_path) point_list.append(data[:, 3:6]) points = np.vstack(point_list) / 255. mean = np.mean(points, 0) std = np.std(points, 0) print(f'mean: {mean}, std:{std}') ================================================ FILE: segmentation/dataset/__init__.py ================================================ ================================================ FILE: segmentation/init.sh ================================================ #!/bin/sh mkdir -p log/PointAnalysis/log/S3DIS mkdir -p log/PointAnalysis/log/ScanNet mkdir -p data/S3DIS mkdir -p data/ScanNet conda create -n repsurf-seg python=3.7 -y conda activate repsurf-seg #conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch -c conda-forge -y pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html conda install -c anaconda h5py pyyaml -y conda install -c conda-forge sharedarray tensorboardx -y cd modules/pointops python3 setup.py install cd - ================================================ FILE: segmentation/models/__init__.py ================================================ ================================================ FILE: segmentation/models/pointnet2/__init__.py ================================================ ================================================ FILE: segmentation/models/pointnet2/pointnet2_ssg.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import torch.nn as nn from modules.pointnet2_utils import PointNetSetAbstraction, PointNetFeaturePropagation class Model(nn.Module): def __init__(self, args): super(Model, self).__init__() self.sa1 = PointNetSetAbstraction(4, 32, 6 + 3, [32, 32, 64], num_sector=4) self.sa2 = PointNetSetAbstraction(4, 32, 64 + 3, [64, 64, 128]) self.sa3 = PointNetSetAbstraction(4, 32, 128 + 3, [128, 128, 256]) self.sa4 = PointNetSetAbstraction(4, 32, 256 + 3, [256, 256, 512]) self.fp4 = PointNetFeaturePropagation(768, [256, 256]) self.fp3 = PointNetFeaturePropagation(384, [256, 256]) self.fp2 = PointNetFeaturePropagation(320, [256, 128]) self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128]) self.classifier = nn.Sequential( nn.Linear(128, 128), nn.BatchNorm1d(128), nn.ReLU(True), nn.Dropout(0.5), nn.Linear(128, args.num_class), ) def forward(self, pos_feat_off0): pos_feat_off0[1] = torch.cat([pos_feat_off0[0], pos_feat_off0[1]], 1) pos_feat_off1 = self.sa1(pos_feat_off0) pos_feat_off2 = self.sa2(pos_feat_off1) pos_feat_off3 = self.sa3(pos_feat_off2) pos_feat_off4 = self.sa4(pos_feat_off3) pos_feat_off3[1] = self.fp4(pos_feat_off3, pos_feat_off4) pos_feat_off2[1] = self.fp3(pos_feat_off2, pos_feat_off3) pos_feat_off1[1] = self.fp2(pos_feat_off1, pos_feat_off2) pos_feat_off0[1] = self.fp1([pos_feat_off0[0], None, pos_feat_off0[2]], pos_feat_off1) feature = self.classifier(pos_feat_off0[1]) return feature ================================================ FILE: segmentation/models/pointtransformer/__init__.py ================================================ ================================================ FILE: segmentation/models/pointtransformer/pointtransformer.py ================================================ import torch import torch.nn as nn from modules.pointtransformer_utils import PointTransformerBlock, TransitionDown, TransitionUp class Model(nn.Module): def __init__(self, args): super().__init__() block = PointTransformerBlock num_block = [2, 3, 4, 6, 3] self.in_c = args.in_channel self.in_planes, planes = self.in_c, [32, 64, 128, 256, 512] fpn_planes, fpnhead_planes, share_planes = 128, 64, 8 stride, nsample = [1, 4, 4, 4, 4], [16, 16, 16, 16, 16] self.enc1 = self._make_enc(block, planes[0], num_block[0], share_planes, stride=stride[0], nsample=nsample[0]) # N/1 self.enc2 = self._make_enc(block, planes[1], num_block[1], share_planes, stride=stride[1], nsample=nsample[1], num_sector=4) # N/4 self.enc3 = self._make_enc(block, planes[2], num_block[2], share_planes, stride=stride[2], nsample=nsample[2]) # N/16 self.enc4 = self._make_enc(block, planes[3], num_block[3], share_planes, stride=stride[3], nsample=nsample[3]) # N/64 self.enc5 = self._make_enc(block, planes[4], num_block[4], share_planes, stride=stride[4], nsample=nsample[4]) # N/256 self.dec5 = self._make_dec(block, planes[4], 2, share_planes, nsample=nsample[4], is_head=True) # transform p5 self.dec4 = self._make_dec(block, planes[3], 2, share_planes, nsample=nsample[3]) # fusion p5 and p4 self.dec3 = self._make_dec(block, planes[2], 2, share_planes, nsample=nsample[2]) # fusion p4 and p3 self.dec2 = self._make_dec(block, planes[1], 2, share_planes, nsample=nsample[1]) # fusion p3 and p2 self.dec1 = self._make_dec(block, planes[0], 2, share_planes, nsample=nsample[0]) # fusion p2 and p1 self.cls = nn.Sequential(nn.Linear(planes[0], planes[0]), nn.BatchNorm1d(planes[0]), nn.ReLU(inplace=True), nn.Linear(planes[0], args.num_class)) def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16, num_sector=1): layers = [TransitionDown(self.in_planes, planes * block.expansion, stride, nsample, num_sector)] self.in_planes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.in_planes, self.in_planes, share_planes, nsample=nsample)) return nn.Sequential(*layers) def _make_dec(self, block, planes, blocks, share_planes=8, nsample=16, is_head=False): layers = [TransitionUp(self.in_planes, None if is_head else planes * block.expansion)] self.in_planes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.in_planes, self.in_planes, share_planes, nsample=nsample)) return nn.Sequential(*layers) def forward(self, pxo, *args): p0, x0, o0 = pxo # (n, 3), (n, c), (b) x0 = p0 if self.in_c == 3 else torch.cat((p0, x0), 1) p1, x1, o1 = self.enc1([p0, x0, o0]) p2, x2, o2 = self.enc2([p1, x1, o1]) p3, x3, o3 = self.enc3([p2, x2, o2]) p4, x4, o4 = self.enc4([p3, x3, o3]) p5, x5, o5 = self.enc5([p4, x4, o4]) x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1] x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1] x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1] x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1] x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1] x = self.cls(x1) return x ================================================ FILE: segmentation/models/repsurf/__init__.py ================================================ ================================================ FILE: segmentation/models/repsurf/repsurf_umb_ssg.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import torch.nn as nn from modules.repsurface_utils import UmbrellaSurfaceConstructor, SurfaceAbstractionCD, SurfaceFeaturePropagationCD class Model(nn.Module): def __init__(self, args): super(Model, self).__init__() center_channel = 6 if args.return_polar else 3 repsurf_in_channel = 10 repsurf_out_channel = 10 self.sa1 = SurfaceAbstractionCD(4, 32, args.in_channel + repsurf_out_channel, center_channel, [32, 32, 64], True, args.return_polar, num_sector=4) self.sa2 = SurfaceAbstractionCD(4, 32, 64 + repsurf_out_channel, center_channel, [64, 64, 128], True, args.return_polar) self.sa3 = SurfaceAbstractionCD(4, 32, 128 + repsurf_out_channel, center_channel, [128, 128, 256], True, args.return_polar) self.sa4 = SurfaceAbstractionCD(4, 32, 256 + repsurf_out_channel, center_channel, [256, 256, 512], True, args.return_polar) self.fp4 = SurfaceFeaturePropagationCD(512, 256, [256, 256]) self.fp3 = SurfaceFeaturePropagationCD(256, 128, [256, 256]) self.fp2 = SurfaceFeaturePropagationCD(256, 64, [256, 128]) self.fp1 = SurfaceFeaturePropagationCD(128, None, [128, 128, 128]) self.classifier = nn.Sequential( nn.Linear(128, 128), nn.BatchNorm1d(128), nn.ReLU(True), nn.Dropout(0.5), nn.Linear(128, args.num_class), ) self.surface_constructor = UmbrellaSurfaceConstructor(args.group_size + 1, repsurf_in_channel, repsurf_out_channel) def forward(self, pos_feat_off0): pos_nor_feat_off0 = [ pos_feat_off0[0], self.surface_constructor(pos_feat_off0[0], pos_feat_off0[2]), torch.cat([pos_feat_off0[0], pos_feat_off0[1]], 1), pos_feat_off0[2] ] pos_nor_feat_off1 = self.sa1(pos_nor_feat_off0) pos_nor_feat_off2 = self.sa2(pos_nor_feat_off1) pos_nor_feat_off3 = self.sa3(pos_nor_feat_off2) pos_nor_feat_off4 = self.sa4(pos_nor_feat_off3) 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] pos_nor_feat_off3[1] = self.fp4(pos_nor_feat_off3, pos_nor_feat_off4) pos_nor_feat_off2[1] = self.fp3(pos_nor_feat_off2, pos_nor_feat_off3) pos_nor_feat_off1[1] = self.fp2(pos_nor_feat_off1, pos_nor_feat_off2) pos_nor_feat_off0[1] = self.fp1([pos_nor_feat_off0[0], None, pos_nor_feat_off0[2]], pos_nor_feat_off1) feature = self.classifier(pos_nor_feat_off0[1]) return feature ================================================ FILE: segmentation/modules/__init__.py ================================================ ================================================ FILE: segmentation/modules/aug_utils.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import numpy as np def transform_point_cloud_coord(args): transform_list = [] aug_args = args.aug_args if args.aug_scale: transform_list.append( RandomScale(aug_args['scale_factor'], aug_args['scale_ani'], aug_args['scale_prob'])) if args.aug_rotate: if args.aug_rotate == 'pert': transform_list.append( RandomRotatePerturb(aug_args['pert_factor'], 3 * aug_args['pert_factor'], aug_args['pert_prob'])) elif args.aug_rotate == 'pert_z': transform_list.append( RandomRotatePerturbAligned(aug_args['pert_factor'], 3 * aug_args['pert_factor'], aug_args['pert_prob'])) elif args.aug_rotate == 'rot': transform_list.append( RandomRotate(prob=aug_args['rot_prob'])) elif args.aug_rotate == 'rot_z': transform_list.append( RandomRotateAligned(prob=aug_args['rot_prob'])) if args.aug_jitter: transform_list.append( RandomJitter(aug_args['jitter_factor'], 5 * aug_args['jitter_factor'], aug_args['jitter_prob'], args.lidar)) if args.aug_flip: transform_list.append(RandomFlip()) if args.aug_shift: transform_list.append(RandomShift(aug_args['shifts'], aug_args['shift_prob'])) return Compose(transform_list) if len(transform_list) > 0 else None def transform_point_cloud_rgb(args): transform_list = [] aug_args = args.aug_args if args.color_contrast: transform_list.append(ChromaticAutoContrast()) if args.color_shift: transform_list.append(ChromaticTranslation()) if args.color_jitter: transform_list.append(ChromaticJitter()) if args.hs_shift: transform_list.append(HueSaturationTranslation()) if args.color_drop: transform_list.append(RandomDropColor()) return Compose(transform_list) if len(transform_list) > 0 else None class Compose(object): def __init__(self, transforms): self.transforms = transforms def __call__(self, coord, feat, label, mask=None): for t in self.transforms: coord, feat, label = t(coord, feat, label, mask) return coord, feat, label class RandomRotate(object): def __init__(self, rot=(np.pi/24, np.pi/24, np.pi/4), prob=1.): self.rot = rot self.prob = prob def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.prob: angle_x = np.random.uniform(-self.rot[0], self.rot[0]) angle_y = np.random.uniform(-self.rot[1], self.rot[1]) angle_z = np.random.uniform(-self.rot[2], self.rot[2]) cos_x, sin_x = np.cos(angle_x), np.sin(angle_x) cos_y, sin_y = np.cos(angle_y), np.sin(angle_y) cos_z, sin_z = np.cos(angle_z), np.sin(angle_z) R_x = np.array([[1, 0, 0], [0, cos_x, -sin_x], [0, sin_x, cos_x]]) R_y = np.array([[cos_y, 0, sin_y], [0, 1, 0], [-sin_y, 0, cos_y]]) R_z = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]]) R = np.dot(R_z, np.dot(R_y, R_x)) coord = np.dot(coord, np.transpose(R)) return coord, feat, label class RandomRotateAligned(object): def __init__(self, rot=np.pi, prob=1.): self.rot = rot self.prob = prob def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.prob: angle_z = np.random.uniform(-self.rot, self.rot) cos_z, sin_z = np.cos(angle_z), np.sin(angle_z) R = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]]) coord = np.dot(coord, R) return coord, feat, label class RandomRotatePerturb(object): def __init__(self, sigma=0.03, clip=0.09, prob=1.): self.sigma = sigma self.clip = clip self.prob = prob def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.prob: angle_x = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip) angle_y = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip) angle_z = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip) cos_x, sin_x = np.cos(angle_x), np.sin(angle_x) cos_y, sin_y = np.cos(angle_y), np.sin(angle_y) cos_z, sin_z = np.cos(angle_z), np.sin(angle_z) R_x = np.array([[1, 0, 0], [0, cos_x, -sin_x], [0, sin_x, cos_x]]) R_y = np.array([[cos_y, 0, sin_y], [0, 1, 0], [-sin_y, 0, cos_y]]) R_z = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]]) R = np.dot(R_z, np.dot(R_y, R_x)) coord = np.dot(coord, np.transpose(R)) return coord, feat, label class RandomRotatePerturbAligned(object): def __init__(self, sigma=0.03, clip=0.09, prob=1.): self.sigma = sigma self.clip = clip self.prob = prob def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.prob: angle_z = np.clip(np.random.normal() * self.sigma, -self.clip, self.clip) cos_z, sin_z = np.cos(angle_z), np.sin(angle_z) R = np.array([[cos_z, -sin_z, 0], [sin_z, cos_z, 0], [0, 0, 1]]) coord = np.dot(coord, R) return coord, feat, label class RandomScale(object): def __init__(self, scale=0.1, anisotropic=False, prob=1.): self.scale = scale self.anisotropic = anisotropic self.prob = prob def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.prob: scale = np.random.uniform(1 - self.scale, 1 + self.scale, 3 if self.anisotropic else 1) coord *= scale return coord, feat, label class RandomShift(object): def __init__(self, shift=(0.2, 0.2, 0), p=0.95): self.shift = shift self.p = p def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: shift_x = np.random.uniform(-self.shift[0], self.shift[0]) shift_y = np.random.uniform(-self.shift[1], self.shift[1]) shift_z = np.random.uniform(-self.shift[2], self.shift[2]) coord += [shift_x, shift_y, shift_z] return coord, feat, label class RandomFlip(object): def __init__(self, p=1.): self.p = p def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: if np.random.rand() < 0.5: coord[:, 0] = -coord[:, 0] if np.random.rand() < 0.5: coord[:, 1] = -coord[:, 1] return coord, feat, label class RandomJitter(object): def __init__(self, sigma=0.01, clip=0.05, p=1., is_lidar=False): self.sigma = sigma self.clip = clip self.p = p self.is_lidar = is_lidar def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: assert (self.clip > 0) jitter = np.clip(self.sigma * np.random.randn(coord.shape[0], 3), -1 * self.clip, self.clip) if self.is_lidar: jitter[:, 2] *= 0.1 # re-scale z-axis jitter coord += jitter return coord, feat, label class ChromaticAutoContrast(object): def __init__(self, p=0.2, blend_factor=None): self.p = p self.blend_factor = blend_factor def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: tmp_feat = feat[mask] if mask is not None else feat lo = np.min(tmp_feat, 0, keepdims=True) hi = np.max(tmp_feat, 0, keepdims=True) scale = 255 / (hi - lo) contrast_feat = (tmp_feat[:, :3] - lo) * scale blend_factor = np.random.rand() if self.blend_factor is None else self.blend_factor tmp_feat[:, :3] = (1 - blend_factor) * tmp_feat[:, :3] + blend_factor * contrast_feat if mask is not None: feat[mask] = tmp_feat else: feat = tmp_feat return coord, feat, label class ChromaticTranslation(object): def __init__(self, p=0.95, ratio=0.05): self.p = p self.ratio = ratio def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: tr = (np.random.rand(1, feat.shape[1]) - 0.5) * 255 * 2 * self.ratio feat[:, :3] = np.clip(tr + feat[:, :3], 0, 255) if mask is not None: feat[:, :3][~mask] = 0. return coord, feat, label class ChromaticJitter(object): def __init__(self, p=0.95, std=0.005): self.p = p self.std = std def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: noise = np.random.randn(*feat.shape) noise *= self.std * 255 feat[:, :3] = np.clip(noise + feat[:, :3], 0, 255) if mask is not None: feat[:, :3][~mask] = 0. return coord, feat, label class HueSaturationTranslation(object): @staticmethod def rgb_to_hsv(rgb): # Translated from source of colorsys.rgb_to_hsv # r,g,b should be a numpy arrays with values between 0 and 255 # rgb_to_hsv returns an array of floats between 0.0 and 1.0. rgb = rgb.astype('float') hsv = np.zeros_like(rgb) # in case an RGBA array was passed, just copy the A channel hsv[..., 3:] = rgb[..., 3:] r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2] maxc = np.max(rgb[..., :3], axis=-1) minc = np.min(rgb[..., :3], axis=-1) hsv[..., 2] = maxc mask = maxc != minc hsv[mask, 1] = (maxc - minc)[mask] / maxc[mask] rc = np.zeros_like(r) gc = np.zeros_like(g) bc = np.zeros_like(b) rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask] gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask] bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask] hsv[..., 0] = np.select([r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc) hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0 return hsv @staticmethod def hsv_to_rgb(hsv): # Translated from source of colorsys.hsv_to_rgb # h,s should be a numpy arrays with values between 0.0 and 1.0 # v should be a numpy array with values between 0.0 and 255.0 # hsv_to_rgb returns an array of uints between 0 and 255. rgb = np.empty_like(hsv) rgb[..., 3:] = hsv[..., 3:] h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2] i = (h * 6.0).astype('uint8') f = (h * 6.0) - i p = v * (1.0 - s) q = v * (1.0 - s * f) t = v * (1.0 - s * (1.0 - f)) i = i % 6 conditions = [s == 0.0, i == 1, i == 2, i == 3, i == 4, i == 5] rgb[..., 0] = np.select(conditions, [v, q, p, p, t, v], default=v) rgb[..., 1] = np.select(conditions, [v, v, v, q, p, p], default=t) rgb[..., 2] = np.select(conditions, [v, p, t, v, v, q], default=p) return rgb.astype('uint8') def __init__(self, hue_max=0.5, saturation_max=0.2, p=1.): self.hue_max = hue_max self.saturation_max = saturation_max self.p = p def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: # Assume feat[:, :3] is rgb tmp_feat = feat[mask] if mask is not None else feat hsv = HueSaturationTranslation.rgb_to_hsv(tmp_feat[:, :3]) hue_val = (np.random.rand() - 0.5) * 2 * self.hue_max sat_ratio = 1 + (np.random.rand() - 0.5) * 2 * self.saturation_max hsv[..., 0] = np.remainder(hue_val + hsv[..., 0] + 1, 1) hsv[..., 1] = np.clip(sat_ratio * hsv[..., 1], 0, 1) tmp_feat[:, :3] = np.clip(HueSaturationTranslation.hsv_to_rgb(hsv), 0, 255) if mask is not None: feat[mask] = tmp_feat else: feat = tmp_feat return coord, feat, label class RandomDropColor(object): def __init__(self, p=0.2): self.p = p def __call__(self, coord, feat, label, mask=None): if np.random.rand() < self.p: feat[:, :3] = 0 return coord, feat, label ================================================ FILE: segmentation/modules/pointnet2_utils.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import torch.nn as nn import torch.nn.functional as F from modules.pointops.functions import pointops def sample_and_group(stride, nsample, xyz, points, offset, return_idx=False, num_sector=1): # sample if stride > 1: new_offset, sample_idx = [offset[0].item() // stride], offset[0].item() // stride for i in range(1, offset.shape[0]): sample_idx += (offset[i].item() - offset[i - 1].item()) // stride new_offset.append(sample_idx) new_offset = torch.cuda.IntTensor(new_offset) if num_sector > 1: fps_idx = pointops.sectorized_fps(xyz, offset, new_offset, num_sector) # [M] else: fps_idx = pointops.furthestsampling(xyz, offset, new_offset) # [M] new_xyz = xyz[fps_idx.long(), :] # [M, 3] else: new_xyz = xyz new_offset = offset # group N, M = xyz.shape[0], new_xyz.shape[0] group_idx, _ = pointops.knnquery(nsample, xyz, new_xyz, offset, new_offset) # [M, nsample] group_xyz = xyz[group_idx.view(-1).long(), :].view(M, nsample, 3) # [M, nsample, 3] group_xyz_norm = group_xyz - new_xyz.unsqueeze(1) if points is not None and not return_idx: C = points.shape[1] group_points = points[group_idx.view(-1).long(), :].view(M, nsample, C) new_points = torch.cat([group_xyz_norm, group_points], dim=-1) # [M, nsample, 3/6+C] else: new_points = group_xyz_norm if return_idx: return new_xyz, new_points, new_offset, group_idx else: return new_xyz, new_points, new_offset class PointNetSetAbstraction(nn.Module): """ PointNet2 SA Module """ def __init__(self, stride, nsample, in_channel, mlp, num_sector=1): super(PointNetSetAbstraction, self).__init__() self.stride = stride self.nsample = nsample self.num_sector = num_sector self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel for out_channel in mlp: self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, pos_feat_off): xyz, points, offset = pos_feat_off # [N, 3], [N, C], [B] new_xyz, new_points, new_offset = sample_and_group(self.stride, self.nsample, xyz, points, offset, num_sector=self.num_sector) # new_xyz: sampled points position data, [M, 3] # new_points: sampled points data, [M, nsample, 3+C] new_points = new_points.transpose(1, 2).contiguous() # [M, 3+C, nsample] for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) new_points = torch.max(new_points, 2)[0] return [new_xyz, new_points, new_offset] class PointNetFeaturePropagation(nn.Module): """ PointNet2 FP Module """ def __init__(self, in_channel, mlp): super(PointNetFeaturePropagation, self).__init__() self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel for out_channel in mlp: self.mlp_convs.append(nn.Linear(last_channel, out_channel)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, pos_feat_off1, pos_feat_off2): xyz1, points1, offset1 = pos_feat_off1 # [N, 3], [N, C], [B] xyz2, points2, offset2 = pos_feat_off2 # [M, 3], [M, C], [B] idx, dist = pointops.knnquery(3, xyz2, xyz1, offset2, offset1) # [M, 3], [M, 3] dist_recip = 1.0 / (dist + 1e-8) # [M, 3] norm = torch.sum(dist_recip, dim=1, keepdim=True) weight = dist_recip / norm # [M, 3] interpolated_points = torch.cuda.FloatTensor(xyz1.shape[0], points2.shape[1]).zero_() for i in range(3): interpolated_points += points2[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1) # skip connection if points1 is not None: new_points = torch.cat([points1, interpolated_points], dim=1) # [M, C1+C2] else: new_points = interpolated_points # mlp for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) return new_points ================================================ FILE: segmentation/modules/pointops/__init__.py ================================================ ================================================ FILE: segmentation/modules/pointops/functions/__init__.py ================================================ ================================================ FILE: segmentation/modules/pointops/functions/pointops.py ================================================ from typing import Tuple import torch from torch.autograd import Function import torch.nn as nn try: import pointops_cuda except ImportError: import warnings import os from torch.utils.cpp_extension import load warnings.warn("Unable to load pointops_cuda cpp extension.") pointops_cuda_src = os.path.join(os.path.dirname(__file__), "../src") pointops_cuda = load('pointops_cuda', [ pointops_cuda_src + '/pointops_api.cpp', pointops_cuda_src + '/knnquery/knnquery_cuda.cpp', pointops_cuda_src + '/knnquery/knnquery_cuda_kernel.cu', pointops_cuda_src + '/interpolation/interpolation_cuda.cpp', pointops_cuda_src + '/interpolation/interpolation_cuda_kernel.cu', pointops_cuda_src + '/sampling/sampling_cuda.cpp', pointops_cuda_src + '/sampling/sampling_cuda_kernel.cu', pointops_cuda_src + '/subtraction/subtraction_cuda.cpp', pointops_cuda_src + '/subtraction/subtraction_cuda_kernel.cu', pointops_cuda_src + '/aggregation/aggregation_cuda.cpp', pointops_cuda_src + '/aggregation/aggregation_cuda_kernel.cu', ], build_directory=pointops_cuda_src, verbose=False) class FurthestSampling(Function): @staticmethod def forward(ctx, xyz, offset, new_offset): """ input: xyz: (n, 3), offset: (b), new_offset: (b) output: idx: (m) """ assert xyz.is_contiguous() n, b, n_max = xyz.shape[0], offset.shape[0], offset[0] for i in range(1, b): n_max = max(offset[i] - offset[i - 1], n_max) idx = torch.cuda.IntTensor(new_offset[b - 1].item()).zero_() tmp = torch.cuda.FloatTensor(n).fill_(1e10) pointops_cuda.furthestsampling_cuda(b, n_max, xyz, offset, new_offset, tmp, idx) del tmp return idx furthestsampling = FurthestSampling.apply class SectorizedFurthestSampling(Function): @staticmethod def forward(ctx, xyz, offset, new_offset, num_sectors, min_points=10000): """ input: xyz: (n, 3), offset: (b), new_offset: (b) output: idx: (m) """ assert xyz.is_contiguous() # cut into batches last_offset = 0 sizes = [] new_sizes = [] indices = [] for i in range(offset.shape[0]): size = offset[i] - last_offset if size < min_points: tmp_num_sectors = 1 else: tmp_num_sectors = num_sectors batch_xyz = xyz[last_offset:last_offset + size] angle = torch.atan2(batch_xyz[:, 0], batch_xyz[:, 1]) # [0, 2*pi] sector_range = torch.linspace(angle.min(), angle.max() + 1e-4, tmp_num_sectors + 1) for s in range(tmp_num_sectors): indices.append( torch.where((angle >= sector_range[s]) & (angle < sector_range[s + 1]))[0] + last_offset ) sizes.append(indices[-1].shape[0]) if i > 0: new_size = (new_offset[i] - new_offset[i - 1]).item() else: new_size = new_offset[i].item() new_sizes_this_batch = [new_size // tmp_num_sectors for i in range(tmp_num_sectors)] new_sizes_this_batch[-1] += new_size % tmp_num_sectors new_sizes += new_sizes_this_batch last_offset = offset[i] sizes = torch.tensor(sizes, dtype=torch.long).to(offset) sector_offset = sizes.cumsum(dim=0) new_sizes = torch.tensor(new_sizes, dtype=torch.long).to(offset) new_sector_offset = new_sizes.cumsum(dim=0) indices = torch.cat(indices).long().to(offset.device) sector_xyz = xyz[indices].contiguous() # transform to sectors new_xyz = [] n, b, n_max = sector_xyz.shape[0], sector_offset.shape[0], sector_offset[0] for i in range(1, b): n_max = max(sector_offset[i] - sector_offset[i - 1], n_max) idx = torch.cuda.IntTensor(new_sector_offset[b - 1].item()).zero_() tmp = torch.cuda.FloatTensor(n).fill_(1e10) pointops_cuda.furthestsampling_cuda(b, n_max, sector_xyz, sector_offset.int(), new_sector_offset.int(), tmp, idx) idx = indices[idx.long()] del tmp del sector_xyz return idx sectorized_fps = SectorizedFurthestSampling.apply class KNNQuery(Function): @staticmethod def forward(ctx, nsample, xyz, new_xyz, offset, new_offset): """ input: xyz: (n, 3), new_xyz: (m, 3), offset: (b), new_offset: (b) output: idx: (m, nsample), dist2: (m, nsample) """ if new_xyz is None: new_xyz = xyz assert xyz.is_contiguous() and new_xyz.is_contiguous() m = new_xyz.shape[0] idx = torch.cuda.IntTensor(m, nsample).zero_() dist2 = torch.cuda.FloatTensor(m, nsample).zero_() pointops_cuda.knnquery_cuda(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2) return idx, torch.sqrt(dist2) knnquery = KNNQuery.apply class Grouping(Function): @staticmethod def forward(ctx, input, idx): """ input: input: (n, c), idx : (m, nsample) output: (m, nsample, c) """ assert input.is_contiguous() and idx.is_contiguous() m, nsample, n, c = idx.shape[0], idx.shape[1], input.shape[0], input.shape[1] output = torch.cuda.FloatTensor(m, nsample, c) pointops_cuda.grouping_forward_cuda(m, nsample, c, input, idx, output) ctx.n = n ctx.save_for_backward(idx) return output @staticmethod def backward(ctx, grad_output): """ input: grad_out: (m, c, nsample) output: (n, c), None """ n = ctx.n idx, = ctx.saved_tensors m, nsample, c = grad_output.shape grad_input = torch.cuda.FloatTensor(n, c).zero_() pointops_cuda.grouping_backward_cuda(m, nsample, c, grad_output, idx, grad_input) return grad_input, None grouping = Grouping.apply def queryandgroup(nsample, xyz, new_xyz, feat, idx, offset, new_offset, use_xyz=True): """ input: xyz: (n, 3), new_xyz: (m, 3), feat: (n, c), idx: (m, nsample), offset: (b), new_offset: (b) output: new_feat: (m, c+3, nsample), grouped_idx: (m, nsample) """ assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous() if new_xyz is None: new_xyz = xyz if idx is None: idx, _ = knnquery(nsample, xyz, new_xyz, offset, new_offset) # (m, nsample) n, m, c = xyz.shape[0], new_xyz.shape[0], feat.shape[1] grouped_xyz = xyz[idx.view(-1).long(), :].view(m, nsample, 3) # (m, nsample, 3) # grouped_xyz = grouping(xyz, idx) # (m, nsample, 3) grouped_xyz -= new_xyz.unsqueeze(1) # (m, nsample, 3) grouped_feat = feat[idx.view(-1).long(), :].view(m, nsample, c) # (m, nsample, c) # grouped_feat = grouping(feat, idx) # (m, nsample, c) if use_xyz: return torch.cat((grouped_xyz, grouped_feat), -1) # (m, nsample, 3+c) else: return grouped_feat class Subtraction(Function): @staticmethod def forward(ctx, input1, input2, idx): """ input: input1: (n, c), input2: (n, c), idx: (n, nsample) output: (n, nsample, c) """ assert input1.is_contiguous() and input2.is_contiguous() n, c = input1.shape; nsample = idx.shape[-1] output = torch.cuda.FloatTensor(n, nsample, c).zero_() pointops_cuda.subtraction_forward_cuda(n, nsample, c, input1, input2, idx, output) ctx.save_for_backward(idx) return output @staticmethod def backward(ctx, grad_output): """ input: grad_out: (n, nsample, c) output: grad_input1: (n, c), grad_input2: (n, c) """ idx, = ctx.saved_tensors n, nsample, c = grad_output.shape grad_input1 = torch.cuda.FloatTensor(n, c).zero_() grad_input2 = torch.cuda.FloatTensor(n, c).zero_() pointops_cuda.subtraction_backward_cuda(n, nsample, c, idx, grad_output, grad_input1, grad_input2) return grad_input1, grad_input2, None subtraction = Subtraction.apply class Aggregation(Function): @staticmethod def forward(ctx, input, position, weight, idx): """ input: input: (n, c), position: (n, nsample, c), weight : (n, nsample, c'), idx: (n, nsample) output: (n, c) """ assert input.is_contiguous() and position.is_contiguous() and weight.is_contiguous() n, nsample, c = position.shape; w_c = weight.shape[-1] output = torch.cuda.FloatTensor(n, c).zero_() pointops_cuda.aggregation_forward_cuda(n, nsample, c, w_c, input, position, weight, idx, output) ctx.save_for_backward(input, position, weight, idx) return output @staticmethod def backward(ctx, grad_output): """ input: grad_out: (n, c) output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight : (n, nsample, c') """ input, position, weight, idx = ctx.saved_tensors n, nsample, c = position.shape; w_c = weight.shape[-1] grad_input = torch.cuda.FloatTensor(n, c).zero_() grad_position = torch.cuda.FloatTensor(n, nsample, c).zero_() grad_weight = torch.cuda.FloatTensor(n, nsample, w_c).zero_() pointops_cuda.aggregation_backward_cuda(n, nsample, c, w_c, input, position, weight, idx, grad_output, grad_input, grad_position, grad_weight) return grad_input, grad_position, grad_weight, None aggregation = Aggregation.apply def interpolation(xyz, new_xyz, feat, offset, new_offset, k=3): """ input: xyz: (m, 3), new_xyz: (n, 3), feat: (m, c), offset: (b), new_offset: (b) output: (n, c) """ assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous() idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, 3), (n, 3) dist_recip = 1.0 / (dist + 1e-8) # (n, 3) norm = torch.sum(dist_recip, dim=1, keepdim=True) weight = dist_recip / norm # (n, 3) new_feat = torch.cuda.FloatTensor(new_xyz.shape[0], feat.shape[1]).zero_() for i in range(k): new_feat += feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1) return new_feat class Interpolation(Function): @staticmethod def forward(ctx, xyz, new_xyz, input, offset, new_offset, k=3): """ input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c) """ assert xyz.is_contiguous() and new_xyz.is_contiguous() and input.is_contiguous() idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, k), (n, k) dist_recip = 1.0 / (dist + 1e-8) # (n, k) norm = torch.sum(dist_recip, dim=1, keepdim=True) weight = dist_recip / norm # (n, k) n, c, m = new_xyz.shape[0], input.shape[1], input.shape[0] output = torch.cuda.FloatTensor(n, c).zero_() pointops_cuda.interpolation_forward_cuda(n, c, k, input, idx, weight, output) ctx.m, ctx.k = m, k ctx.save_for_backward(idx, weight) return output @staticmethod def backward(ctx, grad_output): """ input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c) """ m, k = ctx.m, ctx.k idx, weight = ctx.saved_tensors n, c = grad_output.shape grad_input = torch.cuda.FloatTensor(m, c).zero_() pointops_cuda.interpolation_backward_cuda(n, c, k, grad_output, idx, weight, grad_input) return None, None, grad_input, None, None, None interpolation2 = Interpolation.apply ================================================ FILE: segmentation/modules/pointops/setup.py ================================================ #python3 setup.py install from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension import os from distutils.sysconfig import get_config_vars (opt,) = get_config_vars('OPT') os.environ['OPT'] = " ".join( flag for flag in opt.split() if flag != '-Wstrict-prototypes' ) setup( name='pointops_cuda', author='Hengshuang Zhao', ext_modules=[ CUDAExtension('pointops_cuda', [ 'src/pointops_api.cpp', 'src/knnquery/knnquery_cuda.cpp', 'src/knnquery/knnquery_cuda_kernel.cu', 'src/sampling/sampling_cuda.cpp', 'src/sampling/sampling_cuda_kernel.cu', 'src/grouping/grouping_cuda.cpp', 'src/grouping/grouping_cuda_kernel.cu', 'src/interpolation/interpolation_cuda.cpp', 'src/interpolation/interpolation_cuda_kernel.cu', 'src/subtraction/subtraction_cuda.cpp', 'src/subtraction/subtraction_cuda_kernel.cu', 'src/aggregation/aggregation_cuda.cpp', 'src/aggregation/aggregation_cuda_kernel.cu', ], extra_compile_args={'cxx': ['-g'], 'nvcc': ['-O2']} ) ], cmdclass={'build_ext': BuildExtension} ) ================================================ FILE: segmentation/modules/pointops/src/__init__.py ================================================ ================================================ FILE: segmentation/modules/pointops/src/aggregation/aggregation_cuda.cpp ================================================ #include #include #include #include #include "aggregation_cuda_kernel.h" void 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) { const float *input = input_tensor.data_ptr(); const float *position = position_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *output = output_tensor.data_ptr(); aggregation_forward_cuda_launcher(n, nsample, c, w_c, input, position, weight, idx, output); } void 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) { const float *input = input_tensor.data_ptr(); const float *position = position_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); const float *grad_output = grad_output_tensor.data_ptr(); float *grad_input = grad_input_tensor.data_ptr(); float *grad_position = grad_position_tensor.data_ptr(); float *grad_weight = grad_weight_tensor.data_ptr(); aggregation_backward_cuda_launcher(n, nsample, c, w_c, input, position, weight, idx, grad_output, grad_input, grad_position, grad_weight); } ================================================ FILE: segmentation/modules/pointops/src/aggregation/aggregation_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "aggregation_cuda_kernel.h" __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) { // input: input: (n, c), position: (n, nsample, c), weight: (n, nsample, w_c), idx: (n, nsample), output: (n, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * c) return; const int c_idx = index % c; const int n_idx = index / c; const int w_c_idx = c_idx % w_c; for (int nsample_idx = 0; nsample_idx < nsample; nsample_idx++) { int idx_idx = n_idx * nsample + nsample_idx; int input_idx = idx[idx_idx] * c + c_idx; int position_idx = n_idx * nsample * c + nsample_idx * c + c_idx; int weight_idx = n_idx * nsample * w_c + nsample_idx * w_c + w_c_idx; output[index] += (input[input_idx] + position[position_idx]) * weight[weight_idx]; } } __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) { // input: grad_output: (n, c), output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight: (n, nsample, w_c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * c) return; const int c_idx = index % c; const int n_idx = index / c; const int w_c_idx = c_idx % w_c; for (int nsample_idx = 0; nsample_idx < nsample; nsample_idx++) { int idx_idx = n_idx * nsample + nsample_idx; int input_idx = idx[idx_idx] * c + c_idx; int position_idx = n_idx * nsample * c + nsample_idx * c + c_idx; int weight_idx = n_idx * nsample * w_c + nsample_idx * w_c + w_c_idx; atomicAdd(grad_input + input_idx, grad_output[index] * weight[weight_idx]); grad_position[position_idx] = grad_output[index] * weight[weight_idx]; atomicAdd(grad_weight + weight_idx, grad_output[index] * (input[input_idx] + position[position_idx])); } } void 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) { // input: input: (n, c), position: (n, nsample, c), weight: (n, nsample, w_c), idx: (n, nsample), output: (n, c) dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); aggregation_forward_cuda_kernel<<>>(n, nsample, c, w_c, input, position, weight, idx, output); } void 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) { // input: grad_output: (n, c), output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight: (n, nsample, w_c) dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); aggregation_backward_cuda_kernel<<>>(n, nsample, c, w_c, input, position, weight, idx, grad_output, grad_input, grad_position, grad_weight); } ================================================ FILE: segmentation/modules/pointops/src/aggregation/aggregation_cuda_kernel.h ================================================ #ifndef _AGGREGATION_CUDA_KERNEL #define _AGGREGATION_CUDA_KERNEL #include #include #include void 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); void 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); #ifdef __cplusplus extern "C" { #endif void 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); void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointops/src/cuda_utils.h ================================================ #ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #include #define TOTAL_THREADS 1024 #define THREADS_PER_BLOCK 256 #define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0)) inline int opt_n_threads(int work_size) { const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return std::max(std::min(1 << pow_2, TOTAL_THREADS), 1); } inline dim3 opt_block_config(int x, int y) { const int x_threads = opt_n_threads(x); const int y_threads = std::max(std::min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); dim3 block_config(x_threads, y_threads, 1); return block_config; } #endif ================================================ FILE: segmentation/modules/pointops/src/grouping/grouping_cuda.cpp ================================================ #include #include #include #include #include "grouping_cuda_kernel.h" void grouping_forward_cuda(int m, int nsample, int c, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor output_tensor) { const float *input = input_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *output = output_tensor.data_ptr(); grouping_forward_cuda_launcher(m, nsample, c, input, idx, output); } void grouping_backward_cuda(int m, int nsample, int c, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor grad_input_tensor) { const float *grad_output = grad_output_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *grad_input = grad_input_tensor.data_ptr(); grouping_backward_cuda_launcher(m, nsample, c, grad_output, idx, grad_input); } ================================================ FILE: segmentation/modules/pointops/src/grouping/grouping_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "grouping_cuda_kernel.h" __global__ void grouping_forward_cuda_kernel(int m, int nsample, int c, const float *__restrict__ input, const int *__restrict__ idx, float *__restrict__ output) { // input: input: (n, c), idx: (m, nsample), output: (m, nsample, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= m * nsample * c) return; const int c_idx = index % c; const int nsample_idx = (index / c) % nsample; const int m_idx = index / nsample / c; const int input_idx = idx[m_idx * nsample + nsample_idx] * c + c_idx; output[index] = input[input_idx]; } __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) { // input: grad_output: (m, nsample, c), idx: (m, nsample), output: grad_input: (n, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= m * nsample * c) return; const int c_idx = index % c; const int nsample_idx = (index / c) % nsample; const int m_idx = index / nsample / c; const int input_idx = idx[m_idx * nsample + nsample_idx] * c + c_idx; atomicAdd(grad_input + input_idx, grad_output[index]); } void grouping_forward_cuda_launcher(int m, int nsample, int c, const float *input, const int *idx, float *output) { // input: input: (n, c), idx: (m, nsample), output: (m, nsample, c) dim3 blocks(DIVUP(m * nsample * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); grouping_forward_cuda_kernel<<>>(m, nsample, c, input, idx, output); } void grouping_backward_cuda_launcher(int m, int nsample, int c, const float *grad_output, const int *idx, float *grad_input) { // input: grad_output: (m, nsample, c), idx: (m, nsample), output: grad_input: (n, c) dim3 blocks(DIVUP(m * nsample * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); grouping_backward_cuda_kernel<<>>(m, nsample, c, grad_output, idx, grad_input); } ================================================ FILE: segmentation/modules/pointops/src/grouping/grouping_cuda_kernel.h ================================================ #ifndef _GROUPING_CUDA_KERNEL #define _GROUPING_CUDA_KERNEL #include #include #include void grouping_forward_cuda(int m, int nsample, int c, at::Tensor input_tensor, at::Tensor idx_tensor, at::Tensor output_tensor); void grouping_backward_cuda(int m, int nsample, int c, at::Tensor grad_output_tensor, at::Tensor idx_tensor, at::Tensor grad_input_tensor); #ifdef __cplusplus extern "C" { #endif void grouping_forward_cuda_launcher(int m, int nsample, int c, const float *input, const int *idx, float *output); void grouping_backward_cuda_launcher(int m, int nsample, int c, const float *grad_output, const int *idx, float *grad_input); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointops/src/interpolation/interpolation_cuda.cpp ================================================ #include #include #include #include #include "interpolation_cuda_kernel.h" void 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) { const float *input = input_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); float *output = output_tensor.data_ptr(); interpolation_forward_cuda_launcher(n, c, k, input, idx, weight, output); } void 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) { const float *grad_output = grad_output_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); const float *weight = weight_tensor.data_ptr(); float *grad_input = grad_input_tensor.data_ptr(); interpolation_backward_cuda_launcher(n, c, k, grad_output, idx, weight, grad_input); } ================================================ FILE: segmentation/modules/pointops/src/interpolation/interpolation_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "interpolation_cuda_kernel.h" __global__ void interpolation_forward_cuda_kernel(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output) { // input: input: (m, c), idx: (n, k), weight: (n, k), output: output (n, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * c) return; int c_idx = index % c; int n_idx = index / c; for (int i = 0; i < k; i++) { int idx_idx = n_idx * k + i; int input_idx = idx[idx_idx] * c + c_idx; output[index] += input[input_idx] * weight[idx_idx]; } } __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) { // input: grad_output: (n, c), idx: (n, k), weight: (n, k), output: grad_input (m, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * c) return; int c_idx = index % c; int n_idx = index / c; for (int i = 0; i < k; i++) { int idx_idx = n_idx * k + i; int input_idx = idx[idx_idx] * c + c_idx; atomicAdd(grad_input + input_idx, grad_output[index] * weight[idx_idx]); } } void interpolation_forward_cuda_launcher(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output) { // input: input: (m, c), idx: (n, k), weight: (n, k), output: output (n, c) dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); interpolation_forward_cuda_kernel<<>>(n, c, k, input, idx, weight, output); } void interpolation_backward_cuda_launcher(int n, int c, int k, const float *grad_output, const int *idx, const float *weight, float *grad_input) { // input: grad_output: (n, c), idx: (n, k), weight: (n, k), output: grad_input (m, c) dim3 blocks(DIVUP(n * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); interpolation_backward_cuda_kernel<<>>(n, c, k, grad_output, idx, weight, grad_input); } ================================================ FILE: segmentation/modules/pointops/src/interpolation/interpolation_cuda_kernel.h ================================================ #ifndef _INTERPOLATION_CUDA_KERNEL #define _INTERPOLATION_CUDA_KERNEL #include #include #include void 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); void 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); #ifdef __cplusplus extern "C" { #endif void interpolation_forward_cuda_launcher(int n, int c, int k, const float *input, const int *idx, const float *weight, float *output); void interpolation_backward_cuda_launcher(int n, int c, int k, const float *grad_output, const int *idx, const float *weight, float *grad_input); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointops/src/knnquery/knnquery_cuda.cpp ================================================ #include #include #include #include #include "knnquery_cuda_kernel.h" void 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) { const float *xyz = xyz_tensor.data_ptr(); const float *new_xyz = new_xyz_tensor.data_ptr(); const int *offset = offset_tensor.data_ptr(); const int *new_offset = new_offset_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); float *dist2 = dist2_tensor.data_ptr(); knnquery_cuda_launcher(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2); } ================================================ FILE: segmentation/modules/pointops/src/knnquery/knnquery_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "knnquery_cuda_kernel.h" __device__ void swap_float(float *x, float *y) { float tmp = *x; *x = *y; *y = tmp; } __device__ void swap_int(int *x, int *y) { int tmp = *x; *x = *y; *y = tmp; } __device__ void reheap(float *dist, int *idx, int k) { int root = 0; int child = root * 2 + 1; while (child < k) { if(child + 1 < k && dist[child+1] > dist[child]) child++; if(dist[root] > dist[child]) return; swap_float(&dist[root], &dist[child]); swap_int(&idx[root], &idx[child]); root = child; child = root * 2 + 1; } } __device__ void heap_sort(float *dist, int *idx, int k) { int i; for (i = k - 1; i > 0; i--) { swap_float(&dist[0], &dist[i]); swap_int(&idx[0], &idx[i]); reheap(dist, idx, i); } } __device__ int get_bt_idx(int idx, const int *offset) { int i = 0; while (1) { if (idx < offset[i]) break; else i++; } return i; } __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) { // input: xyz (n, 3) new_xyz (m, 3) // output: idx (m, nsample) dist2 (m, nsample) int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; if (pt_idx >= m) return; new_xyz += pt_idx * 3; idx += pt_idx * nsample; dist2 += pt_idx * nsample; int bt_idx = get_bt_idx(pt_idx, new_offset); int start; if (bt_idx == 0) start = 0; else start = offset[bt_idx - 1]; int end = offset[bt_idx]; float new_x = new_xyz[0]; float new_y = new_xyz[1]; float new_z = new_xyz[2]; float best_dist[100]; int best_idx[100]; for(int i = 0; i < nsample; i++){ best_dist[i] = 1e10; best_idx[i] = start; } for(int i = start; i < end; i++){ float x = xyz[i * 3 + 0]; float y = xyz[i * 3 + 1]; float z = xyz[i * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < best_dist[0]){ best_dist[0] = d2; best_idx[0] = i; reheap(best_dist, best_idx, nsample); } } heap_sort(best_dist, best_idx, nsample); for(int i = 0; i < nsample; i++){ idx[i] = best_idx[i]; dist2[i] = best_dist[i]; } } void 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) { // input: new_xyz: (m, 3), xyz: (n, 3), idx: (m, nsample) dim3 blocks(DIVUP(m, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); knnquery_cuda_kernel<<>>(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2); } ================================================ FILE: segmentation/modules/pointops/src/knnquery/knnquery_cuda_kernel.h ================================================ #ifndef _KNNQUERY_CUDA_KERNEL #define _KNNQUERY_CUDA_KERNEL #include #include #include void 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); #ifdef __cplusplus extern "C" { #endif void 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); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointops/src/pointops_api.cpp ================================================ #include #include #include "knnquery/knnquery_cuda_kernel.h" #include "sampling/sampling_cuda_kernel.h" #include "grouping/grouping_cuda_kernel.h" #include "interpolation/interpolation_cuda_kernel.h" #include "aggregation/aggregation_cuda_kernel.h" #include "subtraction/subtraction_cuda_kernel.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("knnquery_cuda", &knnquery_cuda, "knnquery_cuda"); m.def("furthestsampling_cuda", &furthestsampling_cuda, "furthestsampling_cuda"); m.def("grouping_forward_cuda", &grouping_forward_cuda, "grouping_forward_cuda"); m.def("grouping_backward_cuda", &grouping_backward_cuda, "grouping_backward_cuda"); m.def("interpolation_forward_cuda", &interpolation_forward_cuda, "interpolation_forward_cuda"); m.def("interpolation_backward_cuda", &interpolation_backward_cuda, "interpolation_backward_cuda"); m.def("subtraction_forward_cuda", &subtraction_forward_cuda, "subtraction_forward_cuda"); m.def("subtraction_backward_cuda", &subtraction_backward_cuda, "subtraction_backward_cuda"); m.def("aggregation_forward_cuda", &aggregation_forward_cuda, "aggregation_forward_cuda"); m.def("aggregation_backward_cuda", &aggregation_backward_cuda, "aggregation_backward_cuda"); } ================================================ FILE: segmentation/modules/pointops/src/sampling/sampling_cuda.cpp ================================================ #include #include #include #include #include "sampling_cuda_kernel.h" void 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) { const float *xyz = xyz_tensor.data_ptr(); const int *offset = offset_tensor.data_ptr(); const int *new_offset = new_offset_tensor.data_ptr(); float *tmp = tmp_tensor.data_ptr(); int *idx = idx_tensor.data_ptr(); furthestsampling_cuda_launcher(b, n, xyz, offset, new_offset, tmp, idx); } ================================================ FILE: segmentation/modules/pointops/src/sampling/sampling_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "sampling_cuda_kernel.h" __device__ void __update(float *dists, int *dists_i, int idx1, int idx2) { const float v1 = dists[idx1], v2 = dists[idx2]; const int i1 = dists_i[idx1], i2 = dists_i[idx2]; dists[idx1] = max(v1, v2); dists_i[idx1] = v2 > v1 ? i2 : i1; } // input xyz: (n, 3), tmp: (b, n_max) // ouput idx (m) template __global__ void furthestsampling_cuda_kernel(const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx) { __shared__ float dists[block_size]; __shared__ int dists_i[block_size]; int bid = blockIdx.x; int start_n, end_n, start_m, end_m, old; if (bid == 0) { start_n = 0; end_n = offset[0]; start_m = 0; end_m = new_offset[0]; old = 0; } else { start_n = offset[bid - 1]; end_n = offset[bid]; start_m = new_offset[bid - 1]; end_m = new_offset[bid]; old = offset[bid - 1]; } const int stride = block_size; int tid = threadIdx.x; if (tid == 0) idx[start_m] = start_n; __syncthreads(); for (int j = start_m + 1; j < end_m; j++) { int besti = start_n; float best = -1; float x1 = xyz[old * 3 + 0]; float y1 = xyz[old * 3 + 1]; float z1 = xyz[old * 3 + 2]; for (int k = start_n + tid; k < end_n; k += stride) { float x2 = xyz[k * 3 + 0]; float y2 = xyz[k * 3 + 1]; float z2 = xyz[k * 3 + 2]; float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); float d2 = min(d, tmp[k]); tmp[k] = d2; besti = d2 > best ? k : besti; best = d2 > best ? d2 : best; } dists[tid] = best; dists_i[tid] = besti; __syncthreads(); if (block_size >= 1024) { if (tid < 512) { __update(dists, dists_i, tid, tid + 512); } __syncthreads(); } if (block_size >= 512) { if (tid < 256) { __update(dists, dists_i, tid, tid + 256); } __syncthreads(); } if (block_size >= 256) { if (tid < 128) { __update(dists, dists_i, tid, tid + 128); } __syncthreads(); } if (block_size >= 128) { if (tid < 64) { __update(dists, dists_i, tid, tid + 64); } __syncthreads(); } if (block_size >= 64) { if (tid < 32) { __update(dists, dists_i, tid, tid + 32); } __syncthreads(); } if (block_size >= 32) { if (tid < 16) { __update(dists, dists_i, tid, tid + 16); } __syncthreads(); } if (block_size >= 16) { if (tid < 8) { __update(dists, dists_i, tid, tid + 8); } __syncthreads(); } if (block_size >= 8) { if (tid < 4) { __update(dists, dists_i, tid, tid + 4); } __syncthreads(); } if (block_size >= 4) { if (tid < 2) { __update(dists, dists_i, tid, tid + 2); } __syncthreads(); } if (block_size >= 2) { if (tid < 1) { __update(dists, dists_i, tid, tid + 1); } __syncthreads(); } old = dists_i[0]; if (tid == 0) idx[j] = old; } } void furthestsampling_cuda_launcher(int b, int n, const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx) { unsigned int n_threads = opt_n_threads(n); switch (n_threads) { case 1024: furthestsampling_cuda_kernel<1024><<>>(xyz, offset, new_offset, tmp, idx); break; case 512: furthestsampling_cuda_kernel<512><<>>(xyz, offset, new_offset, tmp, idx); break; case 256: furthestsampling_cuda_kernel<256><<>>(xyz, offset, new_offset, tmp, idx); break; case 128: furthestsampling_cuda_kernel<128><<>>(xyz, offset, new_offset, tmp, idx); break; case 64: furthestsampling_cuda_kernel<64><<>>(xyz, offset, new_offset, tmp, idx); break; case 32: furthestsampling_cuda_kernel<32><<>>(xyz, offset, new_offset, tmp, idx); break; case 16: furthestsampling_cuda_kernel<16><<>>(xyz, offset, new_offset, tmp, idx); break; case 8: furthestsampling_cuda_kernel<8><<>>(xyz, offset, new_offset, tmp, idx); break; case 4: furthestsampling_cuda_kernel<4><<>>(xyz, offset, new_offset, tmp, idx); break; case 2: furthestsampling_cuda_kernel<2><<>>(xyz, offset, new_offset, tmp, idx); break; case 1: furthestsampling_cuda_kernel<1><<>>(xyz, offset, new_offset, tmp, idx); break; default: furthestsampling_cuda_kernel<512><<>>(xyz, offset, new_offset, tmp, idx); } } ================================================ FILE: segmentation/modules/pointops/src/sampling/sampling_cuda_kernel.h ================================================ #ifndef _SAMPLING_CUDA_KERNEL #define _SAMPLING_CUDA_KERNEL #include #include #include void 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); #ifdef __cplusplus extern "C" { #endif void furthestsampling_cuda_launcher(int b, int n, const float *xyz, const int *offset, const int *new_offset, float *tmp, int *idx); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointops/src/subtraction/subtraction_cuda.cpp ================================================ #include #include #include #include #include "subtraction_cuda_kernel.h" void 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) { const float *input1 = input1_tensor.data_ptr(); const float *input2 = input2_tensor.data_ptr(); const int *idx = idx_tensor.data_ptr(); float *output = output_tensor.data_ptr(); subtraction_forward_cuda_launcher(n, nsample, c, input1, input2, idx, output); } void 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) { const int *idx = idx_tensor.data_ptr(); const float *grad_output = grad_output_tensor.data_ptr(); float *grad_input1 = grad_input1_tensor.data_ptr(); float *grad_input2 = grad_input2_tensor.data_ptr(); subtraction_backward_cuda_launcher(n, nsample, c, idx, grad_output, grad_input1, grad_input2); } ================================================ FILE: segmentation/modules/pointops/src/subtraction/subtraction_cuda_kernel.cu ================================================ #include "../cuda_utils.h" #include "subtraction_cuda_kernel.h" __global__ void subtraction_forward_cuda_kernel(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output) { // input: input1: (n, c), input2: (n, c), idx: (n, nsample), output: (n, nsample, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * nsample * c) return; const int c_idx = index % c; const int nsample_idx = (index / c) % nsample; const int n_idx = index / nsample / c; const int idx_idx = n_idx * nsample + nsample_idx; const int input1_idx = n_idx * c + c_idx; const int input2_idx = idx[idx_idx] * c + c_idx; output[index] = input1[input1_idx] - input2[input2_idx]; } __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) { // input: grad_output: (n, nsample, c), output: grad_input1: (n, c), grad_input2: (n, c) int index = blockIdx.x * blockDim.x + threadIdx.x; if (index >= n * nsample * c) return; const int c_idx = index % c; const int nsample_idx = (index / c) % nsample; const int n_idx = index / nsample / c; const int idx_idx = n_idx * nsample + nsample_idx; const int input1_idx = n_idx * c + c_idx; const int input2_idx = idx[idx_idx] * c + c_idx; atomicAdd(grad_input1 + input1_idx, grad_output[index]); atomicAdd(grad_input2 + input2_idx, -grad_output[index]); } void subtraction_forward_cuda_launcher(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output) { // input: input1: (n, c), input2: (n, c), idx: (n, nsample), output: (n, nsample, c) dim3 blocks(DIVUP(n * nsample * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); subtraction_forward_cuda_kernel<<>>(n, nsample, c, input1, input2, idx, output); } void subtraction_backward_cuda_launcher(int n, int nsample, int c, const int *idx, const float *grad_output, float *grad_input1, float *grad_input2) { // input: grad_output: (n, nsample, c), output: grad_input1: (n, c), grad_input2: (n, c) dim3 blocks(DIVUP(n * nsample * c, THREADS_PER_BLOCK)); dim3 threads(THREADS_PER_BLOCK); subtraction_backward_cuda_kernel<<>>(n, nsample, c, idx, grad_output, grad_input1, grad_input2); } ================================================ FILE: segmentation/modules/pointops/src/subtraction/subtraction_cuda_kernel.h ================================================ #ifndef _SUBTRACTION_CUDA_KERNEL #define _SUBTRACTION_CUDA_KERNEL #include #include #include void 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); void 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); #ifdef __cplusplus extern "C" { #endif void subtraction_forward_cuda_launcher(int n, int nsample, int c, const float *input1, const float *input2, const int *idx, float *output); void subtraction_backward_cuda_launcher(int n, int nsample, int c, const int *idx, const float *grad_output, float *grad_input1, float *grad_input2); #ifdef __cplusplus } #endif #endif ================================================ FILE: segmentation/modules/pointtransformer_utils.py ================================================ import torch import torch.nn as nn from modules.pointops.functions import pointops class PointTransformerLayer(nn.Module): def __init__(self, in_planes, out_planes, share_planes=8, nsample=16): super().__init__() self.mid_planes = mid_planes = out_planes // 1 self.out_planes = out_planes self.share_planes = share_planes self.nsample = nsample self.linear_q = nn.Linear(in_planes, mid_planes) self.linear_k = nn.Linear(in_planes, mid_planes) self.linear_v = nn.Linear(in_planes, out_planes) self.linear_p = nn.Sequential(nn.Linear(3, 3), nn.BatchNorm1d(3), nn.ReLU(inplace=True), nn.Linear(3, out_planes)) self.linear_w = nn.Sequential(nn.BatchNorm1d(mid_planes), nn.ReLU(inplace=True), nn.Linear(mid_planes, mid_planes // share_planes), nn.BatchNorm1d(mid_planes // share_planes), nn.ReLU(inplace=True), nn.Linear(out_planes // share_planes, out_planes // share_planes)) self.softmax = nn.Softmax(dim=1) def forward(self, pxo) -> torch.Tensor: p, x, o = pxo # (n, 3), (n, c), (b) x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x) # (n, c) x_k = pointops.queryandgroup(self.nsample, p, p, x_k, None, o, o, use_xyz=True) # (n, nsample, 3+c) x_v = pointops.queryandgroup(self.nsample, p, p, x_v, None, o, o, use_xyz=False) # (n, nsample, c) p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:] for i, layer in enumerate(self.linear_p): # (n, nsample, c) p_r = layer(p_r.transpose(1, 2).contiguous()).transpose(1, 2).contiguous() if i == 1 else layer(p_r) w = x_k - x_q.unsqueeze(1) + p_r.view(p_r.shape[0], p_r.shape[1], self.out_planes // self.mid_planes, self.mid_planes).sum(2) # (n, nsample, c) for i, layer in enumerate(self.linear_w): w = layer(w.transpose(1, 2).contiguous()).transpose(1, 2).contiguous() if i % 3 == 0 else layer(w) w = self.softmax(w) # (n, nsample, c) n, nsample, c = x_v.shape s = self.share_planes x = ((x_v + p_r).view(n, nsample, s, c // s) * w.unsqueeze(2)).sum(1).view(n, c) return x class TransitionDown(nn.Module): def __init__(self, in_planes, out_planes, stride=1, nsample=16, num_sector=1): super().__init__() self.stride, self.nsample, self.num_sector = stride, nsample, num_sector if stride != 1: self.linear = nn.Linear(3 + in_planes, out_planes, bias=False) self.pool = nn.MaxPool1d(nsample) else: self.linear = nn.Linear(in_planes, out_planes, bias=False) self.bn = nn.BatchNorm1d(out_planes) self.relu = nn.ReLU(inplace=True) def forward(self, pxo): p, x, o = pxo # (n, 3), (n, c), (b) if self.stride != 1: n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride for i in range(1, o.shape[0]): count += (o[i].item() - o[i - 1].item()) // self.stride n_o.append(count) n_o = torch.cuda.IntTensor(n_o) if self.num_sector > 1 and self.training: idx = pointops.sectorized_fps(p, o, n_o, self.num_sector) # [M] else: idx = pointops.furthestsampling(p, o, n_o) # [M] n_p = p[idx.long(), :] # (m, 3) x = pointops.queryandgroup(self.nsample, p, n_p, x, None, o, n_o, use_xyz=True) # (m, 3+c, nsample) x = self.relu(self.bn(self.linear(x).transpose(1, 2).contiguous())) # (m, c, nsample) x = self.pool(x).squeeze(-1) # (m, c) p, o = n_p, n_o else: x = self.relu(self.bn(self.linear(x))) # (n, c) return [p, x, o] class TransitionUp(nn.Module): def __init__(self, in_planes, out_planes=None): super().__init__() if out_planes is None: self.linear1 = nn.Sequential(nn.Linear(2 * in_planes, in_planes), nn.BatchNorm1d(in_planes), nn.ReLU(inplace=True)) self.linear2 = nn.Sequential(nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True)) else: self.linear1 = nn.Sequential(nn.Linear(out_planes, out_planes), nn.BatchNorm1d(out_planes), nn.ReLU(inplace=True)) self.linear2 = nn.Sequential(nn.Linear(in_planes, out_planes), nn.BatchNorm1d(out_planes), nn.ReLU(inplace=True)) def forward(self, pxo1, pxo2=None): if pxo2 is None: _, x, o = pxo1 # (n, 3), (n, c), (b) x_tmp = [] for i in range(o.shape[0]): if i == 0: s_i, e_i, cnt = 0, o[0], o[0] else: s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1] x_b = x[s_i:e_i, :] x_b = torch.cat((x_b, self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1)), 1) x_tmp.append(x_b) x = torch.cat(x_tmp, 0) x = self.linear1(x) else: p1, x1, o1 = pxo1 p2, x2, o2 = pxo2 x = self.linear1(x1) + pointops.interpolation(p2, p1, self.linear2(x2), o2, o1) return x class PointTransformerBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, share_planes=8, nsample=16): super(PointTransformerBlock, self).__init__() self.linear1 = nn.Linear(in_planes, planes, bias=False) self.bn1 = nn.BatchNorm1d(planes) self.transformer2 = PointTransformerLayer(planes, planes, share_planes, nsample) self.bn2 = nn.BatchNorm1d(planes) self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False) self.bn3 = nn.BatchNorm1d(planes * self.expansion) self.relu = nn.ReLU(inplace=True) def forward(self, pxo): p, x, o = pxo # (n, 3), (n, c), (b) identity = x x = self.relu(self.bn1(self.linear1(x))) x = self.relu(self.bn2(self.transformer2([p, x, o]))) x = self.bn3(self.linear3(x)) x += identity x = self.relu(x) return [p, x, o] ================================================ FILE: segmentation/modules/polar_utils.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import numpy as np def xyz2sphere(xyz, normalize=True): """ Convert XYZ to Spherical Coordinate reference: https://en.wikipedia.org/wiki/Spherical_coordinate_system :param xyz: [N, 3] / [N, G, 3] :return: (rho, theta, phi) [N, 3] / [N, G, 3] """ rho = torch.sqrt(torch.sum(torch.pow(xyz, 2), dim=-1, keepdim=True)) rho = torch.clamp(rho, min=0) # range: [0, inf] theta = torch.acos(xyz[..., 2, None] / rho) # range: [0, pi] phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi] # check nan idx = rho == 0 theta[idx] = 0 if normalize: theta = theta / np.pi # [0, 1] phi = phi / (2 * np.pi) + .5 # [0, 1] out = torch.cat([rho, theta, phi], dim=-1) return out def xyz2cylind(xyz, normalize=True): """ Convert XYZ to Cylindrical Coordinate reference: https://en.wikipedia.org/wiki/Cylindrical_coordinate_system :param normalize: Normalize phi & z :param xyz: [N, 3] / [N, G, 3] :return: (rho, phi, z) [N, 3] / [N, G, 3] """ rho = torch.sqrt(torch.sum(torch.pow(xyz[..., :2], 2), dim=-1, keepdim=True)) rho = torch.clamp(rho, 0, 1) # range: [0, 1] phi = torch.atan2(xyz[..., 1, None], xyz[..., 0, None]) # range: [-pi, pi] z = xyz[..., 2, None] z = torch.clamp(z, -1, 1) # range: [-1, 1] if normalize: phi = phi / (2 * np.pi) + .5 z = (z + 1.) / 2. out = torch.cat([rho, phi, z], dim=-1) return out ================================================ FILE: segmentation/modules/recons_utils.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import numpy as np def cal_normal(group_xyz, offset, random_inv=False, is_group=False): """ Calculate Normal Vector (Unit Form + First Term Positive) :param group_xyz: [N, K=3, 3] / [N, G, K=3, 3] """ edge_vec1 = group_xyz[..., 1, :] - group_xyz[..., 0, :] # [N, 3] edge_vec2 = group_xyz[..., 2, :] - group_xyz[..., 0, :] # [N, 3] nor = torch.cross(edge_vec1, edge_vec2, dim=-1) unit_nor = nor / torch.norm(nor, dim=-1, keepdim=True) # [B, N, 3] / [B, N, G, 3] if not is_group: pos_mask = (unit_nor[..., 0] > 0).float() * 2. - 1. # keep x_n positive else: pos_mask = (unit_nor[..., 0:1, 0] > 0).float() * 2. - 1. unit_nor = unit_nor * pos_mask.unsqueeze(-1) # batch-wise random inverse normal vector (prob: 0.5) if random_inv: batch_prob = np.random.rand(offset.shape[0]) < 0.5 random_mask = [] sample_offset = [0] + list(offset.cpu().numpy()) for idx in range(len(sample_offset) - 1): sample_mask = torch.ones((sample_offset[idx+1] - sample_offset[idx], 1), dtype=torch.float32) if not batch_prob[idx]: sample_mask *= -1 random_mask.append(sample_mask) random_mask = torch.cat(random_mask, dim=0).to(unit_nor.device) # random_mask = torch.randint(0, 2, (group_xyz.size(0), 1)).float() * 2. - 1. # random_mask = random_mask.to(unit_nor.device) if not is_group: unit_nor = unit_nor * random_mask else: unit_nor = unit_nor * random_mask.unsqueeze(-1) return unit_nor def cal_center(group_xyz): """ Calculate Global Coordinates of the Center of Triangle :param group_xyz: [N, K, 3] / [N, G, K, 3]; K >= 3 :return: [N, 3] / [N, G, 3] """ center = torch.mean(group_xyz, dim=-2) return center def cal_area(group_xyz): """ Calculate Area of Triangle :param group_xyz: [N, K, 3] / [N, G, K, 3]; K = 3 :return: [N, 1] / [N, G, 1] """ pad_shape = group_xyz[..., 0, None].shape det_xy = torch.det(torch.cat([group_xyz[..., 0, None], group_xyz[..., 1, None], torch.ones(pad_shape)], dim=-1)) det_yz = torch.det(torch.cat([group_xyz[..., 1, None], group_xyz[..., 2, None], torch.ones(pad_shape)], dim=-1)) det_zx = torch.det(torch.cat([group_xyz[..., 2, None], group_xyz[..., 0, None], torch.ones(pad_shape)], dim=-1)) area = torch.sqrt(det_xy ** 2 + det_yz ** 2 + det_zx ** 2).unsqueeze(-1) return area def cal_const(normal, center, is_normalize=True): """ Calculate Constant Term (Standard Version, with x_normal to be 1) math:: const = x_nor * x_0 + y_nor * y_0 + z_nor * z_0 :param is_normalize: :param normal: [N, 3] / [N, G, 3] :param center: [N, 3] / [N, G, 3] :return: [N, 1] / [N, G, 1] """ const = torch.sum(normal * center, dim=-1, keepdim=True) factor = torch.sqrt(torch.Tensor([3])).to(normal.device) const = const / factor if is_normalize else const return const def check_nan(normal, center, pos=None): """ Check & Remove NaN in normal tensor :param pos: [N, 1] :param center: [N, 3] :param normal: [N, 3] """ N, _ = normal.shape mask = torch.sum(torch.isnan(normal), dim=-1) > 0 mask_first = torch.argmax((~mask).int(), dim=-1) normal_first = normal[None, mask_first].repeat([N, 1]) normal[mask] = normal_first[mask] center_first = center[None, mask_first].repeat([N, 1]) center[mask] = center_first[mask] if pos is not None: pos_first = pos[None, mask_first].repeat([N, 1]) pos[mask] = pos_first[mask] return normal, center, pos return normal, center def check_nan_umb(normal, center, pos=None): """ Check & Remove NaN in normal tensor :param pos: [N, G, 1] :param center: [N, G, 3] :param normal: [N, G, 3] """ N, G, _ = normal.shape mask = torch.sum(torch.isnan(normal), dim=-1) > 0 mask_first = torch.argmax((~mask).int(), dim=-1) normal_first = normal[torch.arange(N), None, mask_first].repeat([1, G, 1]) normal[mask] = normal_first[mask] center_first = center[torch.arange(N), None, mask_first].repeat([1, G, 1]) center[mask] = center_first[mask] if pos is not None: pos_first = pos[torch.arange(N), None, mask_first].repeat([1, G, 1]) pos[mask] = pos_first[mask] return normal, center, pos return normal, center ================================================ FILE: segmentation/modules/repsurface_utils.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import torch import torch.nn as nn import torch.nn.functional as F from modules.polar_utils import xyz2sphere from modules.recons_utils import cal_const, cal_normal, cal_center, check_nan_umb from modules.pointops.functions import pointops def sample_and_group(stride, nsample, center, normal, feature, offset, return_polar=False, num_sector=1, training=True): # sample if stride > 1: new_offset, sample_idx = [offset[0].item() // stride], offset[0].item() // stride for i in range(1, offset.shape[0]): sample_idx += (offset[i].item() - offset[i - 1].item()) // stride new_offset.append(sample_idx) new_offset = torch.cuda.IntTensor(new_offset) if num_sector > 1 and training: fps_idx = pointops.sectorized_fps(center, offset, new_offset, num_sector) # [M] else: fps_idx = pointops.furthestsampling(center, offset, new_offset) # [M] new_center = center[fps_idx.long(), :] # [M, 3] new_normal = normal[fps_idx.long(), :] # [M, 3] else: new_center = center new_normal = normal new_offset = offset # group N, M, D = center.shape[0], new_center.shape[0], normal.shape[1] group_idx, _ = pointops.knnquery(nsample, center, new_center, offset, new_offset) # [M, nsample] group_center = center[group_idx.view(-1).long(), :].view(M, nsample, 3) # [M, nsample, 3] group_normal = normal[group_idx.view(-1).long(), :].view(M, nsample, D) # [M, nsample, 10] group_center_norm = group_center - new_center.unsqueeze(1) if return_polar: group_polar = xyz2sphere(group_center_norm) group_center_norm = torch.cat([group_center_norm, group_polar], dim=-1) if feature is not None: C = feature.shape[1] group_feature = feature[group_idx.view(-1).long(), :].view(M, nsample, C) new_feature = torch.cat([group_center_norm, group_normal, group_feature], dim=-1) # [npoint, nsample, C+D] else: new_feature = torch.cat([group_center_norm, group_normal], dim=-1) return new_center, new_normal, new_feature, new_offset def resort_points(points, idx): """ Resort Set of points along G dim :param points: [N, G, 3] :param idx: [N, G] :return: [N, G, 3] """ device = points.device N, G, _ = points.shape n_indices = torch.arange(N, dtype=torch.long).to(device).view([N, 1]).repeat([1, G]) new_points = points[n_indices, idx, :] return new_points def _fixed_rotate(xyz): # y-axis:45deg -> z-axis:45deg rot_mat = torch.FloatTensor([[0.5, -0.5, 0.7071], [0.7071, 0.7071, 0.], [-0.5, 0.5, 0.7071]]).to(xyz.device) return xyz @ rot_mat def group_by_umbrella_v2(xyz, new_xyz, offset, new_offset, k=9): """ Group a set of points into umbrella surfaces :param xyz: [N, 3] :param new_xyz: [N', 3] :param k: number of homogenous neighbors :return: [N', K-1, 3, 3] """ group_idx, _ = pointops.knnquery(k, xyz, new_xyz, offset, new_offset) # [M, K] group_xyz = xyz[group_idx.view(-1).long(), :].view(new_xyz.shape[0], k, 3) # [M, K, 3] group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2) group_phi = xyz2sphere(_fixed_rotate(group_xyz_norm))[..., 2] # [M, K-1] sort_idx = group_phi.argsort(dim=-1) # [M, K-1] # [M, K-1, 1, 3] sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2) sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3) group_centriod = torch.zeros_like(sorted_group_xyz) umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2) return umbrella_group_xyz def group_by_umbrella(xyz, new_xyz, offset, new_offset, k=9): """ Group a set of points into umbrella surfaces :param xyz: [N, 3] :param new_xyz: [N', 3] :param k: number of homogenous neighbors :return: [N', K-1, 3, 3] """ group_idx, _ = pointops.knnquery(k, xyz, new_xyz, offset, new_offset) # [M, K] group_xyz = xyz[group_idx.view(-1).long(), :].view(new_xyz.shape[0], k, 3) # [M, K, 3] group_xyz_norm = group_xyz - new_xyz.unsqueeze(-2) group_phi = xyz2sphere(group_xyz_norm)[..., 2] # [M, K-1] sort_idx = group_phi.argsort(dim=-1) # [M, K-1] # [M, K-1, 1, 3] sorted_group_xyz = resort_points(group_xyz_norm, sort_idx).unsqueeze(-2) sorted_group_xyz_roll = torch.roll(sorted_group_xyz, -1, dims=-3) group_centriod = torch.zeros_like(sorted_group_xyz) umbrella_group_xyz = torch.cat([group_centriod, sorted_group_xyz, sorted_group_xyz_roll], dim=-2) return umbrella_group_xyz def sort_factory(s_type): if s_type is None: return group_by_umbrella elif s_type == 'fix': return group_by_umbrella_v2 else: raise Exception('No such sorting method') class SurfaceAbstraction(nn.Module): """ Surface Abstraction Module """ def __init__(self, stride, nsample, in_channel, mlp, return_polar=True, num_sector=1): super(SurfaceAbstraction, self).__init__() self.stride = stride self.nsample = nsample self.num_sector = num_sector self.return_polar = return_polar self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel for out_channel in mlp: self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, pos_nor_feat_off): center, normal, feature, offset = pos_nor_feat_off # [N, 3], [N, 10], [N, C], [B] new_center, new_normal, new_feature, new_offset = sample_and_group(self.stride, self.nsample, center, normal, feature, offset, return_polar=self.return_polar, num_sector=self.num_sector, training=self.training) # new_center: sampled points position data, [M, 3] # new_normal: sampled normal feature data, [M, 3/10] # new_feature: sampled feature, [M, nsample, 3+3/10+C] new_feature = new_feature.transpose(1, 2).contiguous() # [M, 3+C, nsample] for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_feature = F.relu(bn(conv(new_feature))) new_feature = torch.max(new_feature, 2)[0] return [new_center, new_normal, new_feature, new_offset] class SurfaceAbstractionCD(nn.Module): """ Surface Abstraction Module w/ Channel De-differentiation """ def __init__(self, stride, nsample, feat_channel, pos_channel, mlp, return_normal=True, return_polar=False, num_sector=1): super(SurfaceAbstractionCD, self).__init__() self.stride = stride self.nsample = nsample self.return_normal = return_normal self.return_polar = return_polar self.num_sector = num_sector self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() self.pos_channel = pos_channel self.mlp_l0 = nn.Conv1d(self.pos_channel, mlp[0], 1) self.mlp_f0 = nn.Conv1d(feat_channel, mlp[0], 1) self.bn_l0 = nn.BatchNorm1d(mlp[0]) self.bn_f0 = nn.BatchNorm1d(mlp[0]) last_channel = mlp[0] for out_channel in mlp[1:]: self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, pos_nor_feat_off): center, normal, feature, offset = pos_nor_feat_off # [N, 3], [N, 10], [N, C], [B] new_center, new_normal, new_feature, new_offset = sample_and_group(self.stride, self.nsample, center, normal, feature, offset, return_polar=self.return_polar, num_sector=self.num_sector, training=self.training) # new_center: sampled points position data, [M, 3] # new_normal: sampled normal feature data, [M, 3/10] # new_feature: sampled feature, [M, nsample, 3+3/10+C] new_feature = new_feature.transpose(1, 2).contiguous() # [M, 3+C, nsample] # init layer loc = self.bn_l0(self.mlp_l0(new_feature[:, :self.pos_channel])) feat = self.bn_f0(self.mlp_f0(new_feature[:, self.pos_channel:])) new_feature = loc + feat new_feature = F.relu(new_feature) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_feature = F.relu(bn(conv(new_feature))) new_feature = torch.max(new_feature, 2)[0] return [new_center, new_normal, new_feature, new_offset] class SurfaceFeaturePropagationCD(nn.Module): """ Surface FP Module w/ Channel De-differentiation """ def __init__(self, prev_channel, skip_channel, mlp): super(SurfaceFeaturePropagationCD, self).__init__() self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() self.skip = skip_channel is not None self.mlp_f0 = nn.Linear(prev_channel, mlp[0]) self.norm_f0 = nn.BatchNorm1d(mlp[0]) if skip_channel is not None: self.mlp_s0 = nn.Linear(skip_channel, mlp[0]) self.norm_s0 = nn.BatchNorm1d(mlp[0]) last_channel = mlp[0] for out_channel in mlp[1:]: self.mlp_convs.append(nn.Linear(last_channel, out_channel)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, pos_feat_off1, pos_feat_off2): xyz1, points1, offset1 = pos_feat_off1 # [N, 3], [N, C], [B] xyz2, points2, offset2 = pos_feat_off2 # [M, 3], [M, C], [B] # interpolation idx, dist = pointops.knnquery(3, xyz2, xyz1, offset2, offset1) # [M, 3], [M, 3] dist_recip = 1.0 / (dist + 1e-8) # [M, 3] norm = torch.sum(dist_recip, dim=1, keepdim=True) weight = dist_recip / norm # [M, 3] points2 = self.norm_f0(self.mlp_f0(points2)) interpolated_points = torch.cuda.FloatTensor(xyz1.shape[0], points2.shape[1]).zero_() for i in range(3): interpolated_points += points2[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1) # init layer if self.skip: skip = self.norm_s0(self.mlp_s0(points1)) new_points = F.relu(interpolated_points + skip) else: new_points = F.relu(interpolated_points) # mlp for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) return new_points class UmbrellaSurfaceConstructor(nn.Module): """ Umbrella Surface Representation Constructor """ def __init__(self, k, in_channel, out_channel, random_inv=True, sort='fix'): super(UmbrellaSurfaceConstructor, self).__init__() self.k = k self.random_inv = random_inv self.mlps = nn.Sequential( nn.Conv1d(in_channel, out_channel, 1, bias=True), nn.BatchNorm1d(out_channel), nn.ReLU(True), nn.Conv1d(out_channel, out_channel, 1, bias=True), ) self.sort_func = sort_factory(sort) def forward(self, center, offset): # umbrella surface reconstruction group_xyz = self.sort_func(center, center, offset, offset, k=self.k) # [N, K-1, 3 (points), 3 (coord.)] # normal group_normal = cal_normal(group_xyz, offset, random_inv=self.random_inv, is_group=True) # coordinate group_center = cal_center(group_xyz) # polar group_polar = xyz2sphere(group_center) # surface position group_pos = cal_const(group_normal, group_center) group_normal, group_center, group_pos = check_nan_umb(group_normal, group_center, group_pos) new_feature = torch.cat([group_polar, group_normal, group_pos, group_center], dim=-1) # P+N+SP+C: 10 new_feature = new_feature.transpose(1, 2).contiguous() # [N, C, G] # mapping new_feature = self.mlps(new_feature) # aggregation new_feature = torch.sum(new_feature, 2) return new_feature ================================================ FILE: segmentation/modules/voxelize_utils.py ================================================ import numpy as np def fnv_hash_vec(arr): """ FNV64-1A """ assert arr.ndim == 2 # Floor first for negative coordinates arr = arr.copy() arr = arr.astype(np.uint64, copy=False) hashed_arr = np.uint64(14695981039346656037) * np.ones(arr.shape[0], dtype=np.uint64) for j in range(arr.shape[1]): hashed_arr *= np.uint64(1099511628211) hashed_arr = np.bitwise_xor(hashed_arr, arr[:, j]) return hashed_arr def ravel_hash_vec(arr): """ Ravel the coordinates after subtracting the min coordinates. """ assert arr.ndim == 2 arr = arr.copy() arr -= arr.min(0) arr = arr.astype(np.uint64, copy=False) arr_max = arr.max(0).astype(np.uint64) + 1 keys = np.zeros(arr.shape[0], dtype=np.uint64) # Fortran style indexing for j in range(arr.shape[1] - 1): keys += arr[:, j] keys *= arr_max[j + 1] keys += arr[:, -1] return keys def voxelize(coord, voxel_size=0.05, hash_type='fnv', mode=0): # voxelize coordinates discrete_coord = np.floor(coord / np.array(voxel_size)) # shuffle coordinates if hash_type == 'ravel': key = ravel_hash_vec(discrete_coord) else: key = fnv_hash_vec(discrete_coord) idx_sort = np.argsort(key) key_sort = key[idx_sort] _, count = np.unique(key_sort, return_counts=True) if mode == 0: # train mode idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + np.random.randint(0, count.max(), count.size) % count idx_unique = idx_sort[idx_select] return idx_unique else: # val mode return idx_sort, count ================================================ FILE: segmentation/scripts/s3dis/test_pointnet2.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='pointnet2_A5' python3 tool/test_s3dis.py --log_dir ${log_dir} \ --batch_size_test 12 \ --gpu_id 0 \ --model pointnet2.pointnet2_ssg \ --test_area 5 \ --filter ================================================ FILE: segmentation/scripts/s3dis/test_pointtransformer.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='pointtransformer_A5' python3 tool/test_s3dis.py --log_dir ${log_dir} \ --batch_size_test 12 \ --gpu_id 0 \ --model pointtransformer.pointtransformer \ --test_area 5 \ --filter ================================================ FILE: segmentation/scripts/s3dis/test_repsurf_umb.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='repsurf_umb_A5' python3 tool/test_s3dis.py --log_dir ${log_dir} \ --batch_size_test 12 \ --gpu_id 0 \ --model repsurf.repsurf_umb_ssg \ --test_area 5 \ --filter ================================================ FILE: segmentation/scripts/s3dis/train_pointnet2.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='pointnet2_A5' python3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \ --batch_size 8 \ --batch_size_val 24 \ --workers 24 \ --gpus 0 1 2 3 \ --model pointnet2.pointnet2_ssg \ --optimizer AdamW \ --min_val 60 \ --epoch 100 \ --lr_decay_epochs 60 80 \ --test_area 5 \ --learning_rate 0.006 \ --lr_decay 0.1 \ --weight_decay 1e-2 \ --aug_scale \ --color_contrast \ --color_shift \ --color_jitter \ --hs_shift ================================================ FILE: segmentation/scripts/s3dis/train_pointtransformer.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='pointtransformer_A5' python3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \ --batch_size 8 \ --batch_size_val 24 \ --workers 24 \ --gpus 0 1 2 3 \ --model pointtransformer.pointtransformer \ --optimizer AdamW \ --min_val 60 \ --epoch 100 \ --lr_decay_epochs 60 80 \ --test_area 5 \ --learning_rate 0.006 \ --lr_decay 0.1 \ --weight_decay 1e-2 \ --aug_scale \ --color_contrast \ --color_shift \ --color_jitter \ --hs_shift ================================================ FILE: segmentation/scripts/s3dis/train_repsurf_umb.sh ================================================ #!/bin/bash export PYTHONPATH=./ log_dir='repsurf_umb_A5' python3 tool/train.py --log_dir ${log_dir} --dataset S3DIS \ --batch_size 8 \ --batch_size_val 24 \ --workers 24 \ --gpus 0 1 2 3 \ --model repsurf.repsurf_umb_ssg \ --optimizer AdamW \ --min_val 60 \ --epoch 100 \ --lr_decay_epochs 60 80 \ --test_area 5 \ --learning_rate 0.006 \ --lr_decay 0.1 \ --weight_decay 1e-2 \ --freeze_epoch 10 \ --color_contrast \ --color_shift \ --color_jitter \ --hs_shift ================================================ FILE: segmentation/tool/test_s3dis.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import json import os import time import random import numpy as np import argparse import collections from pathlib import Path import torch import torch.nn.parallel import torch.optim import torch.utils.data from util.utils import AverageMeter, intersectionAndUnion, get_rgb_stat, pc_median_filter_gpu from util.utils import get_model, get_logger from modules.voxelize_utils import voxelize LABEL2COLOR = collections.OrderedDict([ ('ceiling', [0, 255, 0]), ('floor', [0, 0, 255]), ('wall', [0, 255, 255]), ('beam', [255, 255, 0]), ('column', [255, 0, 255]), ('window', [100, 100, 255]), ('door', [200, 200, 100]), ('chair', [170, 120, 200]), ('table', [255, 0, 0]), ('bookcase', [200, 100, 100]), ('sofa', [10, 200, 100]), ('board', [200, 200, 200]), ('clutter', [50, 50, 50])]) LABEL2CLASS = list(LABEL2COLOR.keys()) PALETTE = np.array(list(LABEL2COLOR.values()), dtype=np.int64) def parse_args(): parser = argparse.ArgumentParser('Model') # Basic parser.add_argument('--log_dir', type=str, default=None, help='experiment root') parser.add_argument('--data_dir', type=str, default='./data/S3DIS/trainval_fullarea', help='data dir') parser.add_argument('--log_root', type=str, default='./log', help='log root dir') parser.add_argument('--model_path', type=str, default=None, help='saved model weight') parser.add_argument('--model', default='pointnet_sem_seg', help='model name [default: pointnet_sem_seg]') parser.add_argument('--gpu_id', type=str, default='0') parser.add_argument('--seed', type=int, default=1000, help='Test Seed') # Test parser.add_argument('--batch_size_test', type=int, default=12, help='batch size in test [default: 24]') parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test [default: 5]') parser.add_argument('--filter', action='store_true', default=False, help='Apply median filter [default: False]') parser.add_argument('--data_norm', type=str, default='mean', help='initializer for model [mean, min, z_min]') parser.add_argument('--visual', action='store_true', default=False, help='Output visual results [default: False]') # Modeling parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]') parser.add_argument('--return_polar', action='store_true', default=False, help='Whether to return polar coordinate in surface abstraction [default: False]') return parser.parse_args() def main(): global args, logger args = parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id random.seed(args.seed) np.random.seed(args.seed) args.dataset, args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \ 'S3DIS', 13, 80000, 0.04, 6, 255 experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log', 'S3DIS')) experiment_dir = experiment_dir.joinpath(args.log_dir) checkpoints_dir = experiment_dir.joinpath('checkpoints/') args.ckpt_dir = str(checkpoints_dir) log_dir = experiment_dir.joinpath('logs/') args.log_dir = str(log_dir) result_dir = experiment_dir.joinpath('visual/') result_dir.mkdir(exist_ok=True) args.result_dir = str(result_dir) logger = get_logger(args.log_dir, 'test_%s' % args.model) logger.info(json.dumps(vars(args), indent=4, sort_keys=True)) logger.info("=> creating models ...") model = get_model(args).cuda() logger.info(model) ckpt_file = os.path.join(args.ckpt_dir, 'model_best.pth') if args.model_path is None else args.model_path if os.path.isfile(ckpt_file): logger.info("=> loading checkpoint '{}'".format(ckpt_file)) checkpoint = torch.load(ckpt_file) state_dict = checkpoint['state_dict'] new_state_dict = collections.OrderedDict() for k, v in state_dict.items(): name = k[7:] new_state_dict[name] = v.cpu() model.load_state_dict(new_state_dict, strict=True) logger.info("=> loaded checkpoint '{}'".format(ckpt_file)) else: raise RuntimeError("=> no checkpoint found at '{}'".format(ckpt_file)) test(model) def data_prepare(): """ Return area names of the test dataset """ data_list = sorted(os.listdir(args.data_dir)) data_list = [item[:-4] for item in data_list if 'Area_{}'.format(args.test_area) in item] print("Totally {} samples in val set.".format(len(data_list))) return data_list def data_load(data_name): """ Load data by area name """ data_path = os.path.join(args.data_dir, data_name + '.npy') data = np.load(data_path) # xyzrgbl, N*7 coord, feat, label = data[:, :3], data[:, 3:6], data[:, 6] idx_data = [] if args.voxel_size: idx_sort, count = voxelize(coord - np.min(coord, 0), args.voxel_size, mode=1) for i in range(count.max()): idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + i % count idx_part = idx_sort[idx_select] idx_data.append(idx_part) else: idx_data.append(np.arange(label.shape[0])) return coord, feat, label, idx_data def data_process(coord, feat, idx_data): """ Split points into batches by index """ idx_size = len(idx_data) idx_list, coord_list, feat_list, offset_list = [], [], [], [] for i in range(idx_size): idx_part = idx_data[i] coord_part, feat_part = coord[idx_part], feat[idx_part] if args.voxel_max and coord_part.shape[0] > args.voxel_max: coord_p, idx_uni, cnt = np.random.rand(coord_part.shape[0]) * 1e-3, np.array([]), 0 while idx_uni.size != idx_part.shape[0]: init_idx = np.argmin(coord_p) dist = np.sum(np.power(coord_part - coord_part[init_idx], 2), 1) idx_crop = np.argsort(dist)[:args.voxel_max] coord_sub, feat_sub, idx_sub = coord_part[idx_crop], feat_part[idx_crop], idx_part[idx_crop] dist = dist[idx_crop] delta = np.square(1 - dist / np.max(dist)) coord_p[idx_crop] += delta coord_sub, feat_sub = input_normalize(coord_sub, feat_sub) idx_list.append(idx_sub), coord_list.append(coord_sub), feat_list.append( feat_sub), offset_list.append(idx_sub.size) idx_uni = np.unique(np.concatenate((idx_uni, idx_sub))) else: coord_part, feat_part = input_normalize(coord_part, feat_part) idx_list.append(idx_part), coord_list.append(coord_part), feat_list.append( feat_part), offset_list.append(idx_part.size) return idx_list, coord_list, feat_list, offset_list def input_normalize(coord, feat): # normalize if args.data_norm == 'mean': coord -= np.mean(coord, 0) elif args.data_norm == 'min': coord -= np.min(coord, 0) else: raise Exception('No such data norm type') feat = feat / 255. if args.color_mean is not None and args.color_std is not None: feat = (feat - args.color_mean) / args.color_std return coord, feat def visualize_scene(coord, pred, label, name): color_pred = PALETTE[pred.astype(np.int64)] color_gt = PALETTE[label.astype(np.int64)] pred_save_path = os.path.join(args.result_dir, '{}_pred.txt'.format(name)) label_save_path = os.path.join(args.result_dir, '{}_label.txt'.format(name)) np.savetxt(pred_save_path, np.hstack([coord, color_pred]), fmt="%f " * 3 + "%d " * 3) np.savetxt(label_save_path, np.hstack([coord, color_gt]), fmt="%f " * 3 + "%d " * 3) def test(model): logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>') batch_time = AverageMeter() model.eval() pred_list, label_list = [], [] data_list = data_prepare() args.color_mean, args.color_std = get_rgb_stat(args) for idx_scene, scene_name in enumerate(data_list): end = time.time() coord, feat, label, idx_data = data_load(scene_name) idx_list, coord_list, feat_list, offset_list = data_process(coord, feat, idx_data) pred = torch.zeros((label.size, args.num_class)).cuda(non_blocking=True) pred_count = torch.zeros((label.size, args.num_class)).cuda(non_blocking=True) num_batch = int(np.ceil(len(idx_list) / args.batch_size_test)) for idx_batch in range(num_batch): idx_start = idx_batch * args.batch_size_test idx_end = min((idx_batch + 1) * args.batch_size_test, len(idx_list)) idx_part, coord_part, feat_part, offset_part = \ idx_list[idx_start:idx_end], coord_list[idx_start:idx_end], \ feat_list[idx_start:idx_end], offset_list[idx_start:idx_end] idx_part = np.concatenate(idx_part) coord_part = torch.FloatTensor(np.concatenate(coord_part)).cuda(non_blocking=True) feat_part = torch.FloatTensor(np.concatenate(feat_part)).cuda(non_blocking=True) offset_part = torch.IntTensor(np.cumsum(offset_part)).cuda(non_blocking=True) with torch.no_grad(): pred_part = torch.nn.functional.softmax(model([coord_part, feat_part, offset_part]), dim=1) # (n, k) torch.cuda.empty_cache() pred[idx_part, :] += pred_part pred_count[idx_part, :] += 1. logger.info('Scene {}/{}, {}/{}, {}/{}'.format(idx_scene + 1, len(data_list), idx_end, len(idx_list), args.voxel_max, idx_part.shape[0])) # IoU per scene pred_choice = np.argmax((pred/pred_count).cpu().numpy(), 1) coord = coord label = label # median filter if args.filter: coord_gpu = torch.from_numpy(coord).float().cuda(non_blocking=True) pred_gpu = torch.from_numpy(pred_choice).int().cuda(non_blocking=True) pred_choice = pc_median_filter_gpu(coord_gpu, pred_gpu, 32) batch_time.update(time.time() - end) logger.info('Test: [{}/{}]-{} Batch {batch_time.val:.3f} ({batch_time.avg:.3f})'.format( idx_scene + 1, len(data_list), label.size, batch_time=batch_time)) pred_list.append(pred_choice) label_list.append(label) if args.visual: visualize_scene(coord, pred_choice, label, scene_name) # mIoU intersection, union, target = intersectionAndUnion(np.concatenate(pred_list), np.concatenate(label_list), args.num_class, args.ignore_label) iou_class = intersection / (union + 1e-10) accuracy_class = intersection / (target + 1e-10) mIoU = np.mean(iou_class) mAcc = np.mean(accuracy_class) allAcc = sum(intersection) / (sum(target) + 1e-10) logger.info('Val result: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}.'.format(mIoU * 100, mAcc * 100, allAcc * 100)) for i in range(args.num_class): logger.info('Class_{} Result: IoU / Acc {:.2f} / {:.2f}, name: {}.'.format( i, iou_class[i] * 100, accuracy_class[i] * 100, LABEL2CLASS[i])) logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<') if __name__ == '__main__': main() ================================================ FILE: segmentation/tool/train.py ================================================ """ Author: Haoxi Ran Date: 06/30/2022 """ import json import os import time from functools import partial import numpy as np import argparse from pathlib import Path import datetime import torch import torch.nn as nn import torch.nn.parallel import torch.optim import torch.utils.data import torch.multiprocessing as mp import torch.distributed as dist from tensorboardX import SummaryWriter from modules.aug_utils import transform_point_cloud_coord, transform_point_cloud_rgb from util.utils import AverageMeter, intersectionAndUnionGPU, find_free_port, get_dataset_description, get_optimizer, \ get_scheduler, get_loop, get_aug_args, get_loss, get_dataset_obj, get_rgb_stat, worker_init_fn from util.data_util import collate_fn from util.utils import get_model, get_class_weights, set_seed, main_process, get_logger def parse_args(): parser = argparse.ArgumentParser('Model') # Basic parser.add_argument('--log_dir', type=str, default=None, help='experiment root') parser.add_argument('--log_root', type=str, default='./log', help='log root dir') parser.add_argument('--dataset', type=str, default='S3DIS', help='dataset name') parser.add_argument('--model', default='pointnet_sem_seg', help='model name [default: pointnet_sem_seg]') parser.add_argument('--gpus', nargs='+', type=str, default=None) parser.add_argument('--seed', type=int, default=2000, help='Training Seed') parser.add_argument('--world_size', type=int, default=1, help='Number of processes participating in the job') parser.add_argument('--rank', type=int, default=0, help='Rank of the current process') parser.add_argument('--multiprocessing_distributed', action='store_false', default=True, help='Whether to use multiprocessing [default: True]') parser.add_argument('--sync_bn', action='store_true', default=False, help='Whether to use sync bn [default: False]') # Training parser.add_argument('--epoch', default=100, type=int, help='number of epoch in training [default: 100]') parser.add_argument('--batch_size', type=int, default=8, help='batch size in training [default: 32]') parser.add_argument('--workers', type=int, default=8, help='DataLoader Workers Number [default: 4]') parser.add_argument('--optimizer', type=str, default='AdamW', help='optimizer for training [SGD, AdamW]') parser.add_argument('--momentum', type=float, default=0.9, help='optimizer momentum [default: 0.9]') parser.add_argument('--weight_decay', type=float, default=1e-2, help='decay rate [default: 1e-2]') parser.add_argument('--scheduler', type=str, default='step', help='scheduler for training [step]') parser.add_argument('--learning_rate', default=0.006, type=float, help='init learning rate [default: 0.5]') parser.add_argument('--lr_decay', type=float, default=0.1, help='decay rate [default: 0.1]') parser.add_argument('--data_norm', type=str, default='mean', help='initializer for model [mean, min]') parser.add_argument('--lr_decay_epochs', type=int, default=[60, 80], nargs='+', help='for step scheduler. where to decay lr, can be a list') parser.add_argument('--start_epoch', type=int, default=0, help='Start Training Epoch [default: 0]') parser.add_argument('--train_freq', type=int, default=250, help='Training frequency [default: 250]') parser.add_argument('--resume', type=str, default=None, help='Trained checkpoint path') parser.add_argument('--pretrain', type=str, default=None, help='Pretrain model path') # Evaluation parser.add_argument('--batch_size_val', type=int, default=8, help='batch size in validation [default: 4]') parser.add_argument('--min_val', type=int, default=60, help='Min val epoch [default: 60]') parser.add_argument('--val_freq', type=int, default=1, help='Val frequency [default: 1]') parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test [default: 5]') # Augmentation parser.add_argument('--aug_scale', action='store_true', default=False, help='Whether to augment by scaling [default: False]') parser.add_argument('--aug_rotate', type=str, default=None, help='Type to augment by rotation [pert, pert_z, rot, rot_z]') parser.add_argument('--aug_jitter', action='store_true', default=False, help='Whether to augment by shifting [default: False]') parser.add_argument('--aug_flip', action='store_true', default=False, help='Whether to augment by flipping [default: False]') parser.add_argument('--aug_shift', action='store_true', default=False, help='Whether to augment by shifting [default: False]') parser.add_argument('--color_contrast', action='store_true', default=False, help='Whether to augment by RGB contrasting [default: False]') parser.add_argument('--color_shift', action='store_true', default=False, help='Whether to augment by RGB shifting [default: False]') parser.add_argument('--color_jitter', action='store_true', default=False, help='Whether to augment by RGB jittering [default: False]') parser.add_argument('--hs_shift', action='store_true', default=False, help='Whether to augment by HueSaturation shifting [default: False]') parser.add_argument('--color_drop', action='store_true', default=False, help='Whether to augment by RGB Dropout [default: False]') # RepSurf parser.add_argument('--group_size', type=int, default=8, help='Size of umbrella group [default: 8]') parser.add_argument('--return_polar', action='store_true', default=False, help='Whether to return polar coordinate in surface abstraction [default: False]') parser.add_argument('--freeze_epoch', default=1e6, type=int, help='number of epoch to freeze repsurf [default: 1e6]') return parser.parse_args() def main_worker(gpu, ngpus_per_node, argss): global args, best_iou args, best_iou = argss, 0 if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: args.rank = args.rank * ngpus_per_node + gpu dist.init_process_group(backend='nccl', init_method=args.dist_url, world_size=args.world_size, rank=args.rank) set_seed(args.seed + args.rank) """MODEL BUILDING""" # model model = get_model(args) if main_process(args): global logger, writer logger = get_logger(args.log_dir, args.model) writer = SummaryWriter(args.log_dir) logger.info(json.dumps(vars(args), indent=4, sort_keys=True)) # print args logger.info("=> creating models ...") logger.info("Classes: {}".format(args.num_class)) logger.info(model) # print num of params num_param = sum(p.numel() for p in model.parameters() if p.requires_grad) logger.info('Total Number of Parameters: {} M'.format(str(float(num_param) / 1e6)[:5])) if args.distributed: torch.cuda.set_device(gpu) args.batch_size = int(args.batch_size / ngpus_per_node) args.batch_size_val = int(args.batch_size_val / ngpus_per_node) args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) if args.sync_bn: nn.SyncBatchNorm.convert_sync_batchnorm(model) model.cuda() # model parallel (Note: During DDP Training, enable 'find_unused_parameters' to freeze repsurf) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu], find_unused_parameters='repsurf' in args.model) else: # model model.cuda() model = torch.nn.DataParallel(model) """DATA LOADING""" coord_transform = transform_point_cloud_coord(args) rgb_transform = transform_point_cloud_rgb(args) dataset_obj = get_dataset_obj(args) rgb_mean, rgb_std = get_rgb_stat(args) if 'trainval' not in args.dataset: TRAIN_DATASET = dataset_obj(args, 'train', coord_transform, rgb_transform, rgb_mean, rgb_std, True) VAL_DATASET = dataset_obj(args, 'val', None, None, rgb_mean, rgb_std, False) VAL_DATASET.stop_aug = True if main_process(args): logger.info("Totally {} samples in {} set.".format(len(TRAIN_DATASET) // args.loop, 'train')) logger.info("Totally {} samples in {} set.".format(len(VAL_DATASET) // args.loop, 'val')) else: TRAIN_DATASET = dataset_obj(args, 'trainval', coord_transform, rgb_transform, rgb_mean, rgb_std, True) VAL_DATASET = None if main_process(args): logger.info("Totally {} samples in {} set.".format(len(TRAIN_DATASET) // args.loop, 'trainval')) """DATALOADER BUILDING""" train_sampler = torch.utils.data.distributed.DistributedSampler(TRAIN_DATASET) if args.distributed else None train_loader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=train_sampler is None, num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True, collate_fn=collate_fn, worker_init_fn=partial(worker_init_fn, seed=args.seed)) if VAL_DATASET is not None: val_sampler = torch.utils.data.distributed.DistributedSampler(VAL_DATASET) if args.distributed else None val_loader = torch.utils.data.DataLoader(VAL_DATASET, batch_size=args.batch_size_val, shuffle=False, num_workers=args.workers, pin_memory=True, sampler=val_sampler, collate_fn=collate_fn, worker_init_fn=partial(worker_init_fn, seed=args.seed + 100)) """TRAINING UTILS""" # loss label_weight = get_class_weights(args.description).cuda() criterion = get_loss(label_weight, args.ignore_label).cuda() # optimizer optimizer = get_optimizer(args, model) # scheduler scheduler = get_scheduler(args, optimizer) """MODEL RESTORE""" if args.resume is not None: if os.path.isfile(args.resume): if main_process(args): logger.info("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage.cuda()) args.start_epoch = checkpoint['epoch'] model.load_state_dict(checkpoint['state_dict'], strict=True) optimizer.load_state_dict(checkpoint['optimizer']) scheduler.load_state_dict(checkpoint['scheduler']) best_iou = checkpoint['best_iou'] if main_process(args): logger.info("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) else: if main_process(args): logger.info("=> no checkpoint found at '{}'".format(args.resume)) if args.pretrain is not None: if os.path.isfile(args.pretrain): if main_process(args): logger.info("=> loading pretrained model '{}'".format(args.pretrain)) checkpoint = torch.load(args.pretrain, map_location=lambda storage, loc: storage.cuda()) model.load_state_dict(checkpoint['state_dict'], strict=True) """TRAINING""" for epoch in range(args.start_epoch, args.epoch): # train if args.distributed: train_sampler.set_epoch(epoch) loss_train, mIoU_train, mAcc_train, allAcc_train = train(train_loader, model, criterion, optimizer, epoch) scheduler.step() epoch_log = epoch + 1 if main_process(args): writer.add_scalar('loss_train', loss_train, epoch_log) writer.add_scalar('mIoU_train', mIoU_train, epoch_log) writer.add_scalar('mAcc_train', mAcc_train, epoch_log) writer.add_scalar('allAcc_train', allAcc_train, epoch_log) # validate is_best = False if args.min_val < epoch_log and (epoch_log % args.val_freq == 0) and args.is_eval: loss_val, mIoU_val, mAcc_val, allAcc_val = validate(val_loader, model, criterion) if main_process(args): writer.add_scalar('loss_val', loss_val, epoch_log) writer.add_scalar('mIoU_val', mIoU_val, epoch_log) writer.add_scalar('mAcc_val', mAcc_val, epoch_log) writer.add_scalar('allAcc_val', allAcc_val, epoch_log) is_best = mIoU_val > best_iou best_iou = max(best_iou, mIoU_val) # save model if is_best and main_process(args): filename = args.ckpt_dir + '/model_best.pth' # save for publish torch.save({'state_dict': model.state_dict()}, filename) # save for training # torch.save({'epoch': epoch_log, 'state_dict': model.state_dict(), # 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), # 'best_iou': best_iou, 'is_best': is_best}, filename) logger.info('Best validation mIoU updated to: {:.2f}'.format(best_iou * 100)) if main_process(args): writer.close() logger.info('==>Training done!\nBest Iou: {:.2f}'.format(best_iou * 100, )) def train(train_loader, model, criterion, optimizer, epoch): batch_time = AverageMeter() data_time = AverageMeter() loss_meter = AverageMeter() intersection_meter = AverageMeter() union_meter = AverageMeter() target_meter = AverageMeter() model.train() # freeze weight if args.freeze_epoch < epoch + 1: # freeze params for n, p in model.module.named_parameters(): if "surface_constructor" in n and p.requires_grad: p.requires_grad = False end = time.time() max_iter = args.epoch * len(train_loader) for i, (coord, feat, target, offset) in enumerate(train_loader): # [N, 3], [N, C], [N], [B] data_time.update(time.time() - end) coord, target, feat, offset = \ coord.cuda(non_blocking=True), target.cuda(non_blocking=True), feat.cuda(non_blocking=True), \ offset.cuda(non_blocking=True) output = model([coord, feat, offset]) loss = criterion(output, target) optimizer.zero_grad() loss.backward() optimizer.step() n = coord.size(0) if args.multiprocessing_distributed: loss *= n count = target.new_tensor([n], dtype=torch.long) dist.all_reduce(loss), dist.all_reduce(count) n = count.item() loss /= n output = output[:, 1:].max(1)[1] + 1 if 'ScanNet' in args.dataset else output.max(1)[1] # remove unclassified label intersection, union, target = intersectionAndUnionGPU(output, target, args.num_class, args.ignore_label) if args.multiprocessing_distributed: dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target) intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy() intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target) accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10) loss_meter.update(loss.item(), n) batch_time.update(time.time() - end) end = time.time() # calculate remain time current_iter = epoch * len(train_loader) + i + 1 remain_iter = max_iter - current_iter remain_time = remain_iter * batch_time.avg t_m, t_s = divmod(remain_time, 60) t_h, t_m = divmod(t_m, 60) remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s)) if (i + 1) % args.train_freq == 0 and main_process(args): logger.info('Epoch: [{}/{}][{}/{}] ' 'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) ' 'Remain {remain_time} ' 'Loss {loss_meter.val:.4f} ' 'Accuracy {accuracy:.2f}'.format(epoch + 1, args.epoch, i + 1, len(train_loader), batch_time=batch_time, remain_time=remain_time, loss_meter=loss_meter, accuracy=accuracy * 100)) if main_process(args): writer.add_scalar('loss_train_batch', loss_meter.val, current_iter) writer.add_scalar('mIoU_train_batch', np.mean(intersection / (union + 1e-10)), current_iter) writer.add_scalar('mAcc_train_batch', np.mean(intersection / (target + 1e-10)), current_iter) writer.add_scalar('allAcc_train_batch', accuracy, current_iter) iou_class = intersection_meter.sum / (union_meter.sum + 1e-10) accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10) iou_class = iou_class[1:] if 'ScanNet' in args.dataset else iou_class accuracy_class = accuracy_class[1:] if 'ScanNet' in args.dataset else accuracy_class mIoU = np.mean(iou_class) mAcc = np.mean(accuracy_class) allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10) if main_process(args): for class_idx, class_iou in enumerate(iou_class): writer.add_scalar(f'class_{class_idx}_train_iou', class_iou, epoch) if main_process(args): logger.info('Train result at epoch [{}/{}]: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}'.format( epoch + 1, args.epoch, mIoU * 100, mAcc * 100, allAcc * 100)) return loss_meter.avg, mIoU, mAcc, allAcc def validate(val_loader, model, criterion): if main_process(args): logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>') batch_time = AverageMeter() data_time = AverageMeter() loss_meter = AverageMeter() intersection_meter = AverageMeter() union_meter = AverageMeter() target_meter = AverageMeter() model.eval() end = time.time() for i, (coord, feat, target, offset) in enumerate(val_loader): data_time.update(time.time() - end) coord, target, feat, offset = \ coord.cuda(non_blocking=True), target.cuda(non_blocking=True), feat.cuda(non_blocking=True), \ offset.cuda(non_blocking=True) with torch.no_grad(): output = model([coord, feat, offset]) loss = criterion(output, target) n = coord.size(0) if args.multiprocessing_distributed: loss *= n count = target.new_tensor([n], dtype=torch.long) dist.all_reduce(loss), dist.all_reduce(count) n = count.item() loss /= n choice = output[:, 1:].max(1)[1] + 1 if 'ScanNet' in args.dataset else output.max(1)[1] # remove unclassified label intersection, union, target = intersectionAndUnionGPU(choice, target, args.num_class, args.ignore_label) if args.multiprocessing_distributed: dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target) intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy() intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target) loss_meter.update(loss.item(), n) batch_time.update(time.time() - end) end = time.time() iou_class = intersection_meter.sum / (union_meter.sum + 1e-10) accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10) # remove unlabeled class iou_class = iou_class[1:] if 'ScanNet' in args.dataset else iou_class accuracy_class = accuracy_class[1:] if 'ScanNet' in args.dataset else accuracy_class mIoU = np.mean(iou_class) mAcc = np.mean(accuracy_class) allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10) if main_process(args): logger.info('Val result: mIoU / mAcc / OA {:.2f} / {:.2f} / {:.2f}'.format( mIoU * 100, mAcc * 100, allAcc * 100)) logger.info('Val loss: {:.4f}'.format(loss_meter.avg)) for i in range(len(iou_class)): logger.info('Class_{} Result: IoU / Acc {:.2f}/{:.2f}'.format( i, iou_class[i] * 100, accuracy_class[i] * 100)) logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<') return loss_meter.avg, mIoU, mAcc, allAcc if __name__ == '__main__': import gc gc.collect() """HYPER PARAMETER""" args = parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(args.gpus) if 'A6000' in torch.cuda.get_device_name(0): os.environ["NCCL_P2P_DISABLE"] = '1' """DDP SETTING""" args.dist_url = 'tcp://localhost:8888' if args.dist_url == "env://" and args.world_size == -1: args.world_size = int(os.environ["WORLD_SIZE"]) args.distributed = args.world_size > 1 or args.multiprocessing_distributed args.ngpus_per_node = len(args.gpus) if len(args.gpus) == 1: args.sync_bn = False args.distributed = False args.multiprocessing_distributed = False """CREATE DIR""" experiment_dir = Path(os.path.join(args.log_root, 'PointAnalysis', 'log', args.dataset.split('_')[0])) if args.log_dir is None: timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) experiment_dir = experiment_dir.joinpath(timestr) else: experiment_dir = experiment_dir.joinpath(args.log_dir) experiment_dir.mkdir(exist_ok=True) checkpoints_dir = experiment_dir.joinpath('checkpoints/') checkpoints_dir.mkdir(exist_ok=True) args.ckpt_dir = str(checkpoints_dir) log_dir = experiment_dir.joinpath('logs/') log_dir.mkdir(exist_ok=True) args.log_dir = str(log_dir) """DATASET INIT""" dataset_obj = get_dataset_obj(args) args.loop = get_loop(args) if args.dataset == 'S3DIS': args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \ 13, 80000, 0.04, 6, 255 args.data_dir = './data/S3DIS/trainval_fullarea' dataset_obj(args, 'train') dataset_obj(args, 'val') elif args.dataset == 'ScanNet_train': args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \ 21, 120000, 0.02, 6, 0 args.data_dir = './data/ScanNet' dataset_obj(args, 'train') dataset_obj(args, 'val') elif args.dataset == 'ScanNet_trainval': args.num_class, args.voxel_max, args.voxel_size, args.in_channel, args.ignore_label = \ 21, 120000, 0.02, 6, 0 args.data_dir = './data/ScanNet' dataset_obj(args, 'trainval') else: raise Exception('Not Impl. Dataset') args.is_eval = 'trainval' not in args.dataset args.aug_args = get_aug_args(args) args.description = get_dataset_description(args) print('Train Model on %s' % args.description) """RUNNING""" if args.multiprocessing_distributed: port = find_free_port() args.dist_url = f"tcp://localhost:{port}" args.world_size = args.ngpus_per_node * args.world_size mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args.ngpus_per_node, args)) else: main_worker(args.gpus, args.ngpus_per_node, args) ================================================ FILE: segmentation/util/__init__.py ================================================ ================================================ FILE: segmentation/util/data_util.py ================================================ import numpy as np import SharedArray as SA import torch from modules.voxelize_utils import voxelize def sa_create(name, var): x = SA.create(name, var.shape, dtype=var.dtype) x[...] = var[...] x.flags.writeable = False return x def collate_fn(batch): coord, feat, label = list(zip(*batch)) offset, count = [], 0 for item in coord: count += item.shape[0] offset.append(count) return torch.cat(coord), torch.cat(feat), torch.cat(label) if label[0] is not None else None, torch.IntTensor( offset) def data_prepare(coord, feat, label, args, split, coord_transform, rgb_transform, rgb_mean=None, rgb_std=None, shuffle_index=True, stop_transform=False): dataset = args.dataset.split('_')[0] # coordinate augment if coord_transform and not stop_transform: coord, _, _ = coord_transform(coord, None, None) # rgb augment if rgb_transform and not stop_transform: _, feat, _ = rgb_transform(None, feat, None) # grid sampling if args.voxel_size: uniq_idx = voxelize(coord - np.min(coord, 0), args.voxel_size) coord, feat = coord[uniq_idx], feat[uniq_idx] if label is not None: label = label[uniq_idx] # drop points when overflow if split != 'val' and args.voxel_max and coord.shape[0] > args.voxel_max: init_idx = np.random.randint(coord.shape[0]) if 'train' in split else coord.shape[0] // 2 crop_idx = np.argsort(np.sum(np.square(coord - coord[init_idx]), 1))[:args.voxel_max] coord, feat = coord[crop_idx], feat[crop_idx] if label is not None: label = label[crop_idx] # shuffle points if shuffle_index: shuf_idx = np.arange(coord.shape[0]) np.random.shuffle(shuf_idx) coord, feat = coord[shuf_idx], feat[shuf_idx] if label is not None: label = label[shuf_idx] # coord norm if args.data_norm == 'mean': coord -= np.mean(coord, 0) elif args.data_norm == 'min': coord -= np.min(coord, 0) # rgb norm if dataset in ['S3DIS', 'ScanNet']: feat = feat / 255. if rgb_mean is not None and rgb_std is not None: feat = (feat - rgb_mean) / rgb_std return torch.FloatTensor(coord), torch.FloatTensor(feat), torch.LongTensor(label) if label is not None else None ================================================ FILE: segmentation/util/utils.py ================================================ import importlib import argparse import random import logging import sys import numpy as np import torch from torch import nn import torch.optim.lr_scheduler as lr_scheduler from dataset.S3DISDataLoader import S3DIS from modules.pointops.functions import pointops def main_process(args): """ Determine whether the main process """ return not args.multiprocessing_distributed or ( args.multiprocessing_distributed and args.rank % args.ngpus_per_node == 0) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def intersectionAndUnion(output, target, K, ignore_index=255): # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. assert (output.ndim in [1, 2, 3]) assert output.shape == target.shape output = output.reshape(output.size).copy() target = target.reshape(target.size) output[np.where(target == ignore_index)[0]] = ignore_index intersection = output[np.where(output == target)[0]] area_intersection, _ = np.histogram(intersection, bins=np.arange(K + 1)) area_output, _ = np.histogram(output, bins=np.arange(K + 1)) area_target, _ = np.histogram(target, bins=np.arange(K + 1)) area_union = area_output + area_target - area_intersection return area_intersection, area_union, area_target def intersectionAndUnionGPU(output, target, K, ignore_index=255): # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. assert (output.dim() in [1, 2, 3]) assert output.shape == target.shape output = output.view(-1) target = target.view(-1) output[target == ignore_index] = ignore_index intersection = output[output == target] area_intersection = torch.histc(intersection, bins=K, min=0, max=K - 1) area_output = torch.histc(output, bins=K, min=0, max=K - 1) area_target = torch.histc(target, bins=K, min=0, max=K - 1) area_union = area_output + area_target - area_intersection return area_intersection, area_union, area_target def find_free_port(): import socket sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Binding to port 0 will cause the OS to find an available port for us sock.bind(("", 0)) port = sock.getsockname()[1] sock.close() # NOTE: there is still a chance the port could be taken by other processes. return port def set_seed(seed): """ Setting of Global Seed """ np.random.seed(seed) random.seed(seed) torch.manual_seed(seed) # cpu torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True # consistent results on the cpu and gpu torch.backends.cudnn.benchmark = False def worker_init_fn(worker_id, seed=None): if seed is not None: random.seed(seed + worker_id) np.random.seed(seed + worker_id) torch.manual_seed(seed + worker_id) torch.cuda.manual_seed(seed + worker_id) torch.cuda.manual_seed_all(seed + worker_id) def get_logger(log_dir, model): logger_name = "main-logger" logger = logging.getLogger(logger_name) logging.root.handlers = [] logging.basicConfig( level=logging.INFO, format="[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s", handlers=[ logging.FileHandler('%s/%s.txt' % (log_dir, model)), logging.StreamHandler(sys.stdout) ] ) return logger def get_aug_args(args): dataset = args.dataset if 'S3DIS' in dataset: aug_args = {'scale_factor': 0.1, 'scale_ani': True, 'scale_prob': 1., 'pert_factor': 0.03, 'pert_prob': 1., 'rot_prob': 0.5, 'shifts': [0.1, 0.1, 0.1], 'shift_prob': 1.} return aug_args else: raise Exception('No such dataset') def get_dataset_obj(args): dataset_name = args.dataset if 'S3DIS' in dataset_name: return S3DIS def get_dataset_description(args): dataset_name = args.dataset if 'S3DIS' in dataset_name: return '%s_A%d' % (dataset_name, args.test_area) if 'ScanNet' in dataset_name: return dataset_name def get_loop(args): if 'S3DIS' in args.dataset: return 30 if 'ScanNet' in args.dataset: return 6 else: raise Exception('No Fixed Loop for the Dataset') def get_class_weights(dataset_name): # pre-calculate the class weight if dataset_name == 'S3DIS_A1': num_per_class = [0.27362621, 0.3134626, 0.18798782, 1.38965602, 1.44210271, 0.86639497, 1.07227331, 1., 1.05912352, 1.92726327, 0.52329938, 2.04783419, 0.5104427] elif dataset_name == 'S3DIS_A2': num_per_class = [0.29036634, 0.34709631, 0.19514767, 1.20129272, 1.39663689, 0.87889087, 1.11586938, 1., 1.54599972, 1.87057415, 0.56458097, 1.87316536, 0.51576885] elif dataset_name == 'S3DIS_A3': num_per_class = [0.27578885, 0.32039725, 0.19055443, 1.14914046, 1.46885687, 0.85450877, 1.05414776, 1., 1.09680025, 2.09280004, 0.59355243, 1.95746691, 0.50429199] elif dataset_name == 'S3DIS_A4': num_per_class = [0.27667177, 0.32612854, 0.19886974, 1.18282174, 1.52145143, 0.8793782, 1.14202999, 1., 1.0857859, 1.89738584, 0.5964717, 1.95820557, 0.52113351] elif dataset_name == 'S3DIS_A5': num_per_class = [0.28459923, 0.32990557, 0.1999722, 1.20798185, 1.33784535, 1., 0.93323316, 1.0753585, 1.00199521, 1.53657772, 0.7987055, 1.82384844, 0.48565471] elif dataset_name == 'S3DIS_A6': num_per_class = [0.29442441, 0.37941846, 0.21360804, 0.9812721, 1.40968965, 0.88577139, 1., 1.09387107, 1.53238009, 1.61365643, 1.15693894, 1.57821041, 0.47342451] elif dataset_name == 'ScanNet_train': num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985, 1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716, 1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497] elif dataset_name == 'ScanNet_trainval': num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985, 1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716, 1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497] else: raise Exception('No Prepared Class Weights of Dataset') return torch.FloatTensor(num_per_class) def get_rgb_stat(args): if 'S3DIS' in args.dataset: mean, std = [0.52146571, 0.50457911, 0.44939377], [0.19645595, 0.19576158, 0.20104336] elif 'ScanNet' in args.dataset: mean, std = [0.08400667, 0.08400667, 0.08400667], [0.28983903, 0.28983903, 0.28983903] else: return None, None return np.array(mean, dtype=np.float32), np.array(std, dtype=np.float32) def get_model(args): module = importlib.import_module('models.%s' % args.model) return module.Model(args) def get_optimizer(args, model): param_dicts = model.parameters() if args.optimizer == 'SGD': optimizer = torch.optim.SGD(param_dicts, lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) elif args.optimizer == 'AdamW': optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay) else: raise Exception('Not impl. such optimizer') return optimizer def get_scheduler(args, optimizer): if args.scheduler == 'step': scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_decay_epochs, gamma=args.lr_decay) else: raise Exception('Not impl. such scheduler') return scheduler def get_loss(weight=None, ignore_label=None): return nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_label) def get_test_args(): return argparse.Namespace() def pc_median_filter_gpu(coord, label, group_size=16): """ :param coord: coordinates of a whole point cloud [N, 3] :param label: segmentation results of a whole point cloud [N,] :param group_size: num of neighbors for filtering """ offset = torch.IntTensor([coord.shape[0]]).to(coord.device) group_idx, _ = pointops.knnquery(group_size, coord, coord, offset, offset) # [N, group_size] group_label = label[group_idx.view(-1).long()].view(coord.shape[0], group_size) # [N, group_size] median_label = torch.median(group_label, 1)[0] return median_label.cpu().numpy() ================================================ FILE: visualization/airplane_0001.txt 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