mmdetection源码剖析(1)--NMS
2024-08-30 10:39:16
mmdetection源码剖析(1)--NMS
熟悉目标检测的应该都清楚NMS是什么算法，但是如果我们要与C++和cuda结合直接写成Pytorch的操作你们清楚怎么写吗？最近在看mmdetection的源码，发现其实原来写C++和cuda的扩展也不难，下面给大家讲一下。
C ++的扩展是允许用户来创建自定义PyTorch框架外的操作（operators ）的，即从PyTorch后端分离。此方法与实现本地PyTorch操作的方式不同。C ++扩展旨在为您节省大量与将操作与PyTorch后端集成在一起相关的样板，同时为基于PyTorch的项目提供高度的灵活性。
官方给出了一个LLTM的例子，大家也可以看一下。
NMS算法
先复习一下NMS的算法：
- （1）将所有框的得分排序，选中最高分及其对应的框
- （2）遍历其余的框，如果和当前最高分框的重叠面积(IOU)大于一定阈值，我们就将框删除。
- （3）从未处理的框中继续选一个得分最高的，重复上述过程。
这里我给出一份纯numpy的实现：
def nms(bounding_boxes, Nt):
if len(bounding_boxes) == 0:
return [], []
bboxes = np.array(bounding_boxes)
x1 = bboxes[:, 0]
y1 = bboxes[:, 1]
x2 = bboxes[:, 2]
y2 = bboxes[:, 3]
scores = bboxes[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = np.argsort(scores)
picked_boxes = []
while order.size > 0:
index = order[-1]
picked_boxes.append(bounding_boxes[index])
x11 = np.maximum(x1[index], x1[order[:-1]])
y11 = np.maximum(y1[index], y1[order[:-1]])
x22 = np.minimum(x2[index], x2[order[:-1]])
y22 = np.minimum(y2[index], y2[order[:-1]])
w = np.maximum(0.0, x22 - x11 + 1)
h = np.maximum(0.0, y22 - y11 + 1)
intersection = w * h
ious = intersection / (areas[index] + areas[order[:-1]] - intersection)
left = np.where(ious < Nt)
order = order[left]
return picked_boxes
编写Pytorch C++扩展的步骤
需要编写下面5个文件：
- nms_kernel.cu
- nms_cuda.cpp
- nms_ext.cpp
- setup.py
- nms_wrapper.py
(1) nms_kernel.cu 主要使用ATen和THC库编写nms_cuda_forward的函数，使用C++编写，涉及一些lazyInitCUDA，THCudaFree，THCCeilDiv 的操作，算法跟我们前面写的numpy差不太多。
(2) nms_cuda.cpp 是调用了nms_kernel.cu文件的nms_cuda_forward封装了一下变成nms_cuda函数。
(3) nms_ext.cpp 进一步封装nms_cuda函数为nms，并且通过PYBIND11_MODULE绑定成python可调用的函数。
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("nms", &nms, "non-maximum suppression");
}
通过上面那样就相当于告诉python函数名定义为nms了。
(4) setup.py 就是编译一遍nms_ext，至此你就可以通过nms_ext.nms调用cpp extension作为pytorch的操作了
make_cuda_ext(
name='nms_ext',
module='mmdet.ops.nms',
sources=['src/nms_ext.cpp', 'src/cpu/nms_cpu.cpp'],
sources_cuda=[
'src/cuda/nms_cuda.cpp', 'src/cuda/nms_kernel.cu'
]),
(5) nms_wrapper.py 再次封装 nms_ext.nms，方便使用，使用实例：
from . import nms_ext
inds = nms_ext.nms(dets_th, iou_thr)
稍微完整的代码如下，但是我也删减了一些，只剩下nms相关的代码，想要看完整代码可以点击下面的文件名。
nms_kernel.cu (这个估计有部分是Facebook写的)
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/DeviceGuard.h>
#include <THC/THC.h>
#include <THC/THCDeviceUtils.cuh>
#include <vector>
#include <iostream>
int const threadsPerBlock = sizeof(unsigned long long) * 8;
__device__ inline float devIoU(float const * const a, float const * const b) {
float left = max(a[0], b[0]), right = min(a[2], b[2]);
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
float width = max(right - left, 0.f), height = max(bottom - top, 0.f);
float interS = width * height;
float Sa = (a[2] - a[0]) * (a[3] - a[1]);
float Sb = (b[2] - b[0]) * (b[3] - b[1]);
return interS / (Sa + Sb - interS);
}
__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
const float *dev_boxes, unsigned long long *dev_mask) {
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
// if (row_start > col_start) return;
const int row_size =
min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
const int col_size =
min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
__shared__ float block_boxes[threadsPerBlock * 5];
if (threadIdx.x < col_size) {
block_boxes[threadIdx.x * 5 + 0] =
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
block_boxes[threadIdx.x * 5 + 1] =
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
block_boxes[threadIdx.x * 5 + 2] =
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
block_boxes[threadIdx.x * 5 + 3] =
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
block_boxes[threadIdx.x * 5 + 4] =
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
}
__syncthreads();
if (threadIdx.x < row_size) {
const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
const float *cur_box = dev_boxes + cur_box_idx * 5;
int i = 0;
unsigned long long t = 0;
int start = 0;
if (row_start == col_start) {
start = threadIdx.x + 1;
}
for (i = start; i < col_size; i++) {
if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
t |= 1ULL << i;
}
}
const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock);
dev_mask[cur_box_idx * col_blocks + col_start] = t;
}
}
// boxes is a N x 5 tensor
at::Tensor nms_cuda_forward(const at::Tensor boxes, float nms_overlap_thresh) {
// Ensure CUDA uses the input tensor device.
at::DeviceGuard guard(boxes.device());
using scalar_t = float;
AT_ASSERTM(boxes.device().is_cuda(), "boxes must be a CUDA tensor");
auto scores = boxes.select(1, 4);
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
auto boxes_sorted = boxes.index_select(0, order_t);
int boxes_num = boxes.size(0);
const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock);
scalar_t* boxes_dev = boxes_sorted.data_ptr<scalar_t>();
THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
unsigned long long* mask_dev = NULL;
//THCudaCheck(THCudaMalloc(state, (void**) &mask_dev,
// boxes_num * col_blocks * sizeof(unsigned long long)));
mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long));
dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock),
THCCeilDiv(boxes_num, threadsPerBlock));
dim3 threads(threadsPerBlock);
nms_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(boxes_num,
nms_overlap_thresh,
boxes_dev,
mask_dev);
std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
THCudaCheck(cudaMemcpyAsync(
&mask_host[0],
mask_dev,
sizeof(unsigned long long) * boxes_num * col_blocks,
cudaMemcpyDeviceToHost,
at::cuda::getCurrentCUDAStream()
));
std::vector<unsigned long long> remv(col_blocks);
memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU));
int64_t* keep_out = keep.data_ptr<int64_t>();
int num_to_keep = 0;
for (int i = 0; i < boxes_num; i++) {
int nblock = i / threadsPerBlock;
int inblock = i % threadsPerBlock;
if (!(remv[nblock] & (1ULL << inblock))) {
keep_out[num_to_keep++] = i;
unsigned long long *p = &mask_host[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv[j] |= p[j];
}
}
}
THCudaFree(state, mask_dev);
// TODO improve this part
return order_t.index({
keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to(
order_t.device(), keep.scalar_type())});
}
#include <torch/extension.h>
#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ")
at::Tensor nms_cuda_forward(const at::Tensor boxes, float nms_overlap_thresh);
at::Tensor nms_cuda(const at::Tensor& dets, const float threshold) {
CHECK_CUDA(dets);
if (dets.numel() == 0)
return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
return nms_cuda_forward(dets, threshold);
}
#include <torch/extension.h>
#ifdef WITH_CUDA
at::Tensor nms_cuda(const at::Tensor& dets, const float threshold);
#endif
at::Tensor nms(const at::Tensor& dets, const float threshold){
if (dets.device().is_cuda()) {
#ifdef WITH_CUDA
return nms_cuda(dets, threshold);
#else
AT_ERROR("nms is not compiled with GPU support");
#endif
}
# return nms_cpu(dets, threshold);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("nms", &nms, "non-maximum suppression");
}
def make_cuda_ext(name, module, sources, sources_cuda=[]):
define_macros = []
extra_compile_args = {'cxx': []}
if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
define_macros += [('WITH_CUDA', None)]
extension = CUDAExtension
extra_compile_args['nvcc'] = [
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
]
sources += sources_cuda
else:
print(f'Compiling {name} without CUDA')
extension = CppExtension
# raise EnvironmentError('CUDA is required to compile MMDetection!')
return extension(
name=f'{module}.{name}',
sources=[os.path.join(*module.split('.'), p) for p in sources],
define_macros=define_macros,
extra_compile_args=extra_compile_args)
if __name__ == '__main__':
write_version_py()
setup(
name='mmdet',
version=get_version(),
description='Open MMLab Detection Toolbox and Benchmark',
long_description=readme(),
author='OpenMMLab',
author_email='chenkaidev@gmail.com',
keywords='computer vision, object detection',
url='https://github.com/open-mmlab/mmdetection',
packages=find_packages(exclude=('configs', 'tools', 'demo')),
package_data={'mmdet.ops': ['*/*.so']},
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
],
license='Apache License 2.0',
setup_requires=parse_requirements('requirements/build.txt'),
tests_require=parse_requirements('requirements/tests.txt'),
install_requires=parse_requirements('requirements/runtime.txt'),
extras_require={
'all': parse_requirements('requirements.txt'),
'tests': parse_requirements('requirements/tests.txt'),
'build': parse_requirements('requirements/build.txt'),
'optional': parse_requirements('requirements/optional.txt'),
},
ext_modules=[
make_cuda_ext(
name='compiling_info',
module='mmdet.ops.utils',
sources=['src/compiling_info.cpp']),
make_cuda_ext(
name='nms_ext',
module='mmdet.ops.nms',
sources=['src/nms_ext.cpp', 'src/cpu/nms_cpu.cpp'],
sources_cuda=[
'src/cuda/nms_cuda.cpp', 'src/cuda/nms_kernel.cu'
]),
],
cmdclass={'build_ext': BuildExtension},
zip_safe=False)
from . import nms_ext
def nms(dets, iou_thr, device_id=None):
# convert dets (tensor or numpy array) to tensor
if isinstance(dets, torch.Tensor):
is_numpy = False
dets_th = dets
elif isinstance(dets, np.ndarray):
is_numpy = True
device = 'cpu' if device_id is None else f'cuda:{device_id}'
dets_th = torch.from_numpy(dets).to(device)
else:
raise TypeError('dets must be either a Tensor or numpy array, '
f'but got {type(dets)}')
# execute cpu or cuda nms
if dets_th.shape[0] == 0:
inds = dets_th.new_zeros(0, dtype=torch.long)
else:
if dets_th.is_cuda:
inds = nms_ext.nms(dets_th, iou_thr)
else:
inds = nms_ext.nms(dets_th, iou_thr)
if is_numpy:
inds = inds.cpu().numpy()
return dets[inds, :], inds
总结
想要编写一个c++和cuda的扩展给Pytorch使用，其实主要就4步：
- 使用ATEN和THC编写前向代码cu文件A
- 封装成一个cpp文件B
- 再把B封装一遍并且使用PYBIND11_MODULE绑定函数名function
- 通过make_cuda_ext（这个是mmdetection自定义的函数）把组件setup一遍
通过绑定的函数名function就可以在Pytorch中调用了。
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