libtorch 常用api函数示例（史上最全、最详细）
转载：https://www.cnblogs.com/yanghailin/p/12901586.html
pytorch/libtorch qq群： 1041467052
其实pytorch的函数libtorch都有，只是写法上有些出入。
libtorch的官方文档链接
class tensor
只是官方文档只是类似与函数申明，没有告诉干嘛的，只能通过函数名字猜了。比如我要一个一个函数和已知的一个torch::Tensor变量形状一样，只是填充指定的数值，我记得在哪里看到过的有个full开头的函数，然后我就搜素full，然后找到一个函数full_like好像是我需要的。（见0）
- pytorch/libtorch qq群： 1041467052
- 调试技巧：
- CMakeLists.txt
- 0.torch::full_like
- 1.创建与初始化tensor 1.1 torch::rand 1.2 torch::empty 1.3 torch::ones 1.4 torch::Tensor keep = torch::zeros({scores.size(0)}).to(torch::kLong).to(scores.device()); 1.5 torch::Tensor num_out = torch::full({ 2,3 }, -2, torch::dtype(torch::kLong));torch::full创建tensor指定形 1.6 torch::Tensor a = torch::ones({3,2}).fill_(-8).to(torch::kCUDA); 1.7. torch::full_like（见0）创建一个和已知tensor形状一样的 状并填充指定val的
- 2.拼接tensor torch::cat 以及vector 和cat的融合操作
- 3.torch的切片操作 【select（浅拷贝）】【index_select 深拷贝）】【index 深拷贝】【slice 浅拷贝】 narrow，narrow_copy
- 4.squeeze() unsqueeze()
- 5.torch::nonzero 输出非0的坐标
- 6.访问tensor值 a.item()就把1*1 的 tensor的a转为float
- 7.opencv Mat类型转tensor 或者其他的vector或者数组数据转tensor
- 8.tensor 的size sizes() numel()
- 9.torch::sort
- 10.clamp 把数值控制在min max之间，小于min的就为min，大于max的就为max
- 11.大于> 小于< 运算
- 12.转置Tensor::transpose
- 13.expand_as
- 14.乘 mul_ 除div 减sub_
- 15.加载模型
- 16.模型forward出来的结果
- 17.resize_ zero_
- 18.meshgrid 把tens变成方阵
- 19.flatten 展平tensor
- 20.fill_ tensor填充某个值 就地操作，填充当前tensor
- 21.torch::stack
- 22.reshape
- 23. view
- 24.argmax argmin
- 25.where
- 26.accessor
- 27. torch::max torch::min 同max
- 28.masked_select 与 masked_fill
- 29.libtorch综合操作1
- 30.pytorch nms <---------> libtorch nms
- 31.数据类型很重要！ .to(torch::kByte);
- 32.指针访问Tensor数据
- 33 PyTorch内Tensor按索引赋值的方法比较
- 44 输出多个tensor（pytorch端）以及取出多个tensor（libtorch端）
- 45. torch::Tensor作为函数参数，不管是引用还是不引用，函数内部对形参操作都会影响本来的tensor，即都是引用
- 46. 实现pytorch下标神操作
- 47.pytorch libtorch的tensor验证精度
- 48. 其他--颜色映射
- 49.torch.gather
- 50. torch::argsort(libtorch1.0没有这个函数) torch::sort
- 51. 判断tensor是否为空 ind_mask.sizes().empty()
调试技巧：
torch::Tensor box_1 = torch::rand({5,4});
std::cout<<box_1<<std::endl; //可以打印出数值
box_1.print();//可以打印形状
CMakeLists.txt
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(main)
SET(CMAKE_BUILD_TYPE "Debug")
set(CMAKE_PREFIX_PATH "/data_2/everyday/0429/pytorch/torch")
find_package(Torch REQUIRED)
set(CMAKE_PREFIX_PATH "/home/yhl/software_install/opencv3.2")
find_package(OpenCV REQUIRED)
add_executable(main main.cpp)
target_link_libraries(main "${TORCH_LIBRARIES}")
target_link_libraries(main ${OpenCV_LIBS})
set_property(TARGET main PROPERTY CXX_STANDARD 11)
0.torch::full_like
static Tensor at::full_like(const Tensor &self, Scalar fill_value, const TensorOptions &options = {}, c10::optional memory_format = c10::nullopt)
然后就自己试：
#include <iostream>
#include "torch/script.h"
#include "torch/torch.h"
using namespace std;
int main() {
torch::Tensor tmp_1 = torch::rand({2,3});
torch::Tensor tmp_2 = torch::full_like(tmp_1,1);
cout<<tmp_1<<endl;
cout<<tmp_2<<endl;
}
打印的结果如下：
0.8465 0.5771 0.4404
0.9805 0.8665 0.7807
[ Variable[CPUFloatType]{2,3} ]
1 1 1
1 1 1
[ Variable[CPUFloatType]{2,3} ]
1.创建与初始化tensor 1.1 torch::rand 1.2 torch::empty 1.3 torch::ones 1.4 torch::Tensor keep = torch::zeros({scores.size(0)}).to(torch::kLong).to(scores.device()); 1.5 torch::Tensor num_out = torch::full({ 2,3 }, -2, torch::dtype(torch::kLong));torch::full创建tensor指定形 1.6 torch::Tensor a = torch::ones({3,2}).fill_(-8).to(torch::kCUDA); 1.7. torch::full_like（见0）创建一个和已知tensor形状一样的 状并填充指定val的
1.1 torch::rand
torch::Tensor input = torch::rand({ 1,3,2,3 });
(1,1,.,.) =
0.5943 0.4822 0.6663
0.7099 0.0374 0.9833
(1,2,.,.) =
0.4384 0.4567 0.2143
0.3967 0.4999 0.9196
(1,3,.,.) =
0.2467 0.5066 0.8654
0.7873 0.4758 0.3718
[ Variable[CPUFloatType]{1,3,2,3} ]
1.2 torch::empty
torch::Tensor a = torch::empty({2, 4});
std::cout << a << std::endl;
7.0374e+22 5.7886e+22 6.7120e+22 6.7331e+22
6.7120e+22 1.8515e+28 7.3867e+20 9.2358e-01
[ Variable[CPUFloatType]{2,4} ]
1.3 torch::ones
torch::Tensor a = torch::ones({2, 4});
std::cout << a<< std::endl;
1 1 1 1
1 1 1 1
[ Variable[CPUFloatType]{2,4} ]
1.4 torch::zeros
torch::Tensor scores；
torch::Tensor keep = torch::zeros({scores.size(0)}).to(torch::kLong).to(scores.device());
1.5 torch::full
inline at::Tensor full(at::IntArrayRef size, at::Scalar fill_value, c10::optional names, const at::TensorOptions & options = {})
inline at::Tensor full(at::IntArrayRef size, at::Scalar fill_value, const at::TensorOptions & options = {})
torch::Tensor num_out = torch::full({ 2,3 }, -2, torch::dtype(torch::kLong));
std::cout<<num_out<<std::endl;
1.6 torch::Tensor a = torch::ones({3,2}).fill_(-8).to(torch::kCUDA);
torch::Tensor a = torch::ones({3,2}).fill_(-8).to(torch::kCUDA);
std::cout<<a<<std::endl;
-8 -8
-8 -8
-8 -8
[ Variable[CUDAFloatType]{3,2} ]
2.拼接tensor torch::cat 以及vector 和cat的融合操作
2.1 按列拼接
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = torch::rand({2,1});
torch::Tensor cat_1 = torch::cat({a,b},1);//按列拼接--》》前提是行数需要一样
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<cat_1<<std::endl;
0.3551 0.7215 0.3603
0.1188 0.4577 0.2201
[ Variable[CPUFloatType]{2,3} ]
0.5876
0.3040
[ Variable[CPUFloatType]{2,1} ]
0.3551 0.7215 0.3603 0.5876
0.1188 0.4577 0.2201 0.3040
[ Variable[CPUFloatType]{2,4} ]
注意：如果行数不一样会报如下错误
terminate called after throwing an instance of 'std::runtime_error'
what(): invalid argument 0: Sizes of tensors must match except in dimension 1. Got 2 and 4 in dimension 0 at /data_2/everyday/0429/pytorch/aten/src/TH/generic/THTensor.cpp:689
2.2 按行拼接
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = torch::rand({1,3});
torch::Tensor cat_1 = torch::cat({a,b},0);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<cat_1<<std::endl;
0.0004 0.7852 0.4586
0.1612 0.6524 0.7655
[ Variable[CPUFloatType]{2,3} ]
0.5999 0.5445 0.2152
[ Variable[CPUFloatType]{1,3} ]
0.0004 0.7852 0.4586
0.1612 0.6524 0.7655
0.5999 0.5445 0.2152
[ Variable[CPUFloatType]{3,3} ]
2.3 其他例子
torch::Tensor box_1 = torch::rand({5,4});
torch::Tensor score_1 = torch::rand({5,1});
torch::Tensor label_1 = torch::rand({5,1});
torch::Tensor result_1 = torch::cat({box_1,score_1,label_1},1);
result_1.print();
[Variable[CPUFloatType] [5, 6]]
2.4 vector 和cat的融合操作
torch::Tensor xs_t0 = xs - wh_0 / 2;
torch::Tensor ys_t0 = ys - wh_1 / 2;
torch::Tensor xs_t1 = xs + wh_0 / 2;
torch::Tensor ys_t1 = ys + wh_1 / 2;
xs_t0.print();
ys_t0.print();
xs_t1.print();
ys_t1.print();
vector<torch::Tensor> abce = {xs_t0,ys_t0,xs_t1,ys_t1};
torch::Tensor bboxes = torch::cat(abce,2);
std::cout<<"-----cat shape---"<<std::endl;
bboxes.print();
while(1);
打印如下：
[Variable[CUDAType] [1, 100, 1]]
[Variable[CUDAType] [1, 100, 1]]
[Variable[CUDAType] [1, 100, 1]]
[Variable[CUDAType] [1, 100, 1]]
[Variable[CUDAType] [1, 100, 4]]
-----cat shape---
也可以一句话搞定：
torch::Tensor bboxes = torch::cat({xs_t0,ys_t0,xs_t1,ys_t1},2);
3.torch的切片操作 【select（浅拷贝）】【index_select 深拷贝）】【index 深拷贝】【slice 浅拷贝】 narrow，narrow_copy
select【浅拷贝】只能指定取某一行或某一列
index【深拷贝】只能指定取某一行
index_select【深拷贝】可以按行或按列，指定多行或多列
slice【浅拷贝】 连续的行或列
narrow，narrow_copy
当是浅拷贝，又不想影响之前的结果的时候，可以加个clone（），比如：
torch::Tensor x1 = boxes.select(1,0).clone();
3.1 inline Tensor Tensor::select(int64_t dim, int64_t index) ；好像只能整2维的。第一个参数是维度，0是取行，1是取 列，第二个参数是索引的序号
3.1.1 select//按行取
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.select(0,1);//按行取
std::cout<<b<<std::endl;
0.6201 0.7021 0.1975
0.3080 0.6304 0.1558
[ Variable[CPUFloatType]{2,3} ]
0.3080
0.6304
0.1558
[ Variable[CPUFloatType]{3} ]
3.1.2 select//按列取
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.select(1,1);
std::cout<<b<<std::endl;
0.8295 0.9871 0.1287
0.8466 0.7719 0.2354
[ Variable[CPUFloatType]{2,3} ]
0.9871
0.7719
[ Variable[CPUFloatType]{2} ]
注意：这里是浅拷贝，就是改变b，同时a的值也会同样的改变
3.1.3 select浅拷贝
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.select(1,1);
std::cout<<b<<std::endl;
b[0] = 0.0;
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.0938 0.2861 0.0089
0.3481 0.5806 0.3711
[ Variable[CPUFloatType]{2,3} ]
0.2861
0.5806
[ Variable[CPUFloatType]{2} ]
0.0938 0.0000 0.0089
0.3481 0.5806 0.3711
[ Variable[CPUFloatType]{2,3} ]
0.0000
0.5806
[ Variable[CPUFloatType]{2} ]
可以看到，b[0] = 0.0;然后a，b的对应位置都为0了。浅拷贝！！
3.2 inline Tensor Tensor::index_select(Dimname dim, const Tensor & index) //同样的，dim0表示按行，1表示按列 index表示取的行号或者列号，这里
比较奇怪，index一定要是toType(torch::kLong)这种类型的。还有一个奇怪的地方是我准备用数组导入tensor的，发现idx全是0，原因未知
torch::Tensor a = torch::rand({2,6});
std::cout<<a<<std::endl;
slice
torch::Tensor idx = torch::empty({4}).toType(torch::kLong);
idx[0]=0;
idx[1]=2;
idx[2]=4;
idx[3]=1;
// int idx_data[4] = {1,3,2,4};
// torch::Tensor idx = torch::from_blob(idx_data,{4}).toType(torch::kLong);//idx全是0 ?????????????????
std::cout<<idx<<std::endl;
torch::Tensor b = a.index_select(1,idx);
std::cout<<b<<std::endl;
0.4956 0.5028 0.0863 0.9464 0.6714 0.5348
0.3523 0.2245 0.0924 0.7088 0.6913 0.2237
[ Variable[CPUFloatType]{2,6} ]
0
2
4
1
[ Variable[CPULongType]{4} ]
0.4956 0.0863 0.6714 0.5028
0.3523 0.0924 0.6913 0.2245
[ Variable[CPUFloatType]{2,4} ]
3.2.2 index_select深拷贝
torch::Tensor a = torch::rand({2,6});
std::cout<<a<<std::endl;
torch::Tensor idx = torch::empty({4}).toType(torch::kLong);
idx[0]=0;
idx[1]=2;
idx[2]=4;
idx[3]=1;
// int idx_data[4] = {1,3,2,4};
// torch::Tensor idx = torch::from_blob(idx_data,{4}).toType(torch::kLong);
std::cout<<idx<<std::endl;
torch::Tensor b = a.index_select(1,idx);
std::cout<<b<<std::endl;
b[0][0]=0.0;
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.6118 0.6078 0.5052 0.9489 0.6201 0.8975
0.0901 0.2040 0.1452 0.6452 0.9593 0.7454
[ Variable[CPUFloatType]{2,6} ]
0
2
4
1
[ Variable[CPULongType]{4} ]
0.6118 0.5052 0.6201 0.6078
0.0901 0.1452 0.9593 0.2040
[ Variable[CPUFloatType]{2,4} ]
0.6118 0.6078 0.5052 0.9489 0.6201 0.8975
0.0901 0.2040 0.1452 0.6452 0.9593 0.7454
[ Variable[CPUFloatType]{2,6} ]
0.0000 0.5052 0.6201 0.6078
0.0901 0.1452 0.9593 0.2040
[ Variable[CPUFloatType]{2,4} ]
3.3 index inline Tensor Tensor::index(TensorList indices)
这个函数实验下来，只能按行取，且是深拷贝
torch::Tensor a = torch::rand({2,6});
std::cout<<a<<std::endl;
torch::Tensor idx_1 = torch::empty({2}).toType(torch::kLong);
idx_1[0]=0;
idx_1[1]=1;
torch::Tensor bb = a.index(idx_1);
bb[0][0]=0;
std::cout<<bb<<std::endl;
std::cout<<a<<std::endl;
0.1349 0.8087 0.2659 0.3364 0.0202 0.4498
0.4785 0.4274 0.9348 0.0437 0.6732 0.3174
[ Variable[CPUFloatType]{2,6} ]
0.0000 0.8087 0.2659 0.3364 0.0202 0.4498
0.4785 0.4274 0.9348 0.0437 0.6732 0.3174
[ Variable[CPUFloatType]{2,6} ]
0.1349 0.8087 0.2659 0.3364 0.0202 0.4498
0.4785 0.4274 0.9348 0.0437 0.6732 0.3174
[ Variable[CPUFloatType]{2,6} ]
3.4 slice inline Tensor Tensor::slice(int64_t dim, int64_t start, int64_t end, int64_t step) //dim0表示按行取，1表示按列取，从start开始，到end（不含）结束
可以看到结果，是浅拷贝！！！
torch::Tensor a = torch::rand({2,6});
std::cout<<a<<std::endl;
torch::Tensor b = a.slice(0,0,1);
torch::Tensor c = a.slice(1,0,3);
b[0][0]=0.0;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
std::cout<<a<<std::endl;
0.8270 0.7952 0.3743 0.7992 0.9093 0.5945
0.3764 0.8419 0.7977 0.4150 0.8531 0.9207
[ Variable[CPUFloatType]{2,6} ]
0.0000 0.7952 0.3743 0.7992 0.9093 0.5945
[ Variable[CPUFloatType]{1,6} ]
0.0000 0.7952 0.3743
0.3764 0.8419 0.7977
[ Variable[CPUFloatType]{2,3} ]
0.0000 0.7952 0.3743 0.7992 0.9093 0.5945
0.3764 0.8419 0.7977 0.4150 0.8531 0.9207
[ Variable[CPUFloatType]{2,6} ]
3.5 narrow narrow_copy
inline Tensor Tensor::narrow(int64_t dim, int64_t start, int64_t length) const
inline Tensor Tensor::narrow_copy(int64_t dim, int64_t start, int64_t length) const
torch::Tensor a = torch::rand({4,6});
torch::Tensor b = a.narrow(0,1,2);
torch::Tensor c = a.narrow_copy(0,1,2);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
0.9812 0.4205 0.4169 0.2412 0.8769 0.9873
0.8052 0.0312 0.9901 0.5065 0.6344 0.3408
0.0182 0.6933 0.9375 0.8675 0.5201 0.9521
0.5119 0.3880 0.1117 0.5413 0.8203 0.4163
[ Variable[CPUFloatType]{4,6} ]
0.8052 0.0312 0.9901 0.5065 0.6344 0.3408
0.0182 0.6933 0.9375 0.8675 0.5201 0.9521
[ Variable[CPUFloatType]{2,6} ]
0.8052 0.0312 0.9901 0.5065 0.6344 0.3408
0.0182 0.6933 0.9375 0.8675 0.5201 0.9521
[ Variable[CPUFloatType]{2,6} ]
4.squeeze() unsqueeze()
inline Tensor Tensor::squeeze() const//不加参数的，把所有为1的维度都压缩
inline Tensor Tensor::squeeze(int64_t dim)const//加参数的，指定哪个维度压缩
inline Tensor & Tensor::squeeze_() const //暂时不知道啥区别
inline Tensor & Tensor::squeeze_(int64_t dim) const //暂时不知道啥区别
4.1 squeeze()
(1,.,.) =
0.5516 0.6561 0.3603
0.7555 0.1048 0.2016
[ Variable[CPUFloatType]{1,2,3} ]
0.5516 0.6561 0.3603
0.7555 0.1048 0.2016
[ Variable[CPUFloatType]{2,3} ]
(1,.,.) =
0.7675 0.5439 0.5162
(2,.,.) =
0.6103 0.1925 0.1222
[ Variable[CPUFloatType]{2,1,3} ]
0.7675 0.5439 0.5162
0.6103 0.1925 0.1222
[ Variable[CPUFloatType]{2,3} ]
(1,1,.,.) =
0.9875
0.1980
(2,1,.,.) =
0.6973
0.3272
[ Variable[CPUFloatType]{2,1,2,1} ]
0.9875 0.1980
0.6973 0.3272
[ Variable[CPUFloatType]{2,2} ]
4.2 squeeze(int64_t dim) 指定压缩哪个维度
torch::Tensor a = torch::rand({1,1,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.squeeze();
std::cout<<b<<std::endl;
torch::Tensor c = a.squeeze(0);
std::cout<<c<<std::endl;
torch::Tensor d = a.squeeze(1);
std::cout<<d<<std::endl;
torch::Tensor e = a.squeeze(2);
std::cout<<e<<std::endl;
(1,.,.) =
0.8065 0.1287 0.8073
[ Variable[CPUFloatType]{1,1,3} ]
0.8065
0.1287
0.8073
[ Variable[CPUFloatType]{3} ]
0.8065 0.1287 0.8073
[ Variable[CPUFloatType]{1,3} ]
0.8065 0.1287 0.8073
[ Variable[CPUFloatType]{1,3} ]
(1,.,.) =
0.8065 0.1287 0.8073
[ Variable[CPUFloatType]{1,1,3} ]
4.3. unsqueeze
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.unsqueeze(0);
std::cout<<b<<std::endl;
torch::Tensor bb = a.unsqueeze(1);
std::cout<<bb<<std::endl;
torch::Tensor bbb = a.unsqueeze(2);
std::cout<<bbb<<std::endl;
0.7945 0.0331 0.1666
0.7821 0.3359 0.0663
[ Variable[CPUFloatType]{2,3} ]
(1,.,.) =
0.7945 0.0331 0.1666
0.7821 0.3359 0.0663
[ Variable[CPUFloatType]{1,2,3} ]
(1,.,.) =
0.7945 0.0331 0.1666
(2,.,.) =
0.7821 0.3359 0.0663
[ Variable[CPUFloatType]{2,1,3} ]
(1,.,.) =
0.7945
0.0331
0.1666
(2,.,.) =
0.7821
0.3359
0.0663
[ Variable[CPUFloatType]{2,3,1} ]
5.torch::nonzero 输出非0的坐标
torch::Tensor a = torch::rand({2,3});
a[0][1] = 0;
a[1][2] = 0;
std::cout<<a<<std::endl;
torch::Tensor b = torch::nonzero(a);
std::cout<<b<<std::endl;
0.4671 0.0000 0.3360
0.9320 0.9246 0.0000
[ Variable[CPUFloatType]{2,3} ]
0 0
0 2
1 0
1 1
[ Variable[CPULongType]{4,2} ]
6.访问tensor值 a.item()就把1*1 的 tensor的a转为float
取出tensor的某个值 为int或者float ===》》》auto bbb = a[1][1].item().toFloat();
一般情况下取出tensor某个值可以直接下标索引即可。比如a[0][1],但是这个值还是tensor类型的，要想为c++的int或者float的，如下：
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
auto bbb = a[1][1].item().toFloat();
std::cout<<bbb<<std::endl;
0.7303 0.6608 0.0024
0.5917 0.0145 0.6472
[ Variable[CPUFloatType]{2,3} ]
0.014509
[ Variable[CPUFloatType]{} ]
0.014509
另外的例子：
torch::Tensor scores = torch::rand({10});
std::tuple<torch::Tensor,torch::Tensor> sort_ret = torch::sort(scores.unsqueeze(1), 0, 1);
torch::Tensor v = std::get<0>(sort_ret).squeeze(1).to(scores.device());
torch::Tensor idx = std::get<1>(sort_ret).squeeze(1).to(scores.device());
std::cout<<scores<<std::endl;
std::cout<<v<<std::endl;
std::cout<<idx<<std::endl;
for(int i=0;i<10;i++)
{
int idx_1 = idx[i].item<int>();
float s = v[i].item<float>();
std::cout<<idx_1<<" "<<s<<std::endl;
}
0.1125
0.9524
0.7033
0.3204
0.7907
0.8486
0.7783
0.3215
0.0378
0.7512
[ Variable[CPUFloatType]{10} ]
0.9524
0.8486
0.7907
0.7783
0.7512
0.7033
0.3215
0.3204
0.1125
0.0378
[ Variable[CPUFloatType]{10} ]
1
5
4
6
9
2
7
3
0
8
[ Variable[CPULongType]{10} ]
1 0.952351
5 0.848641
4 0.790685
6 0.778329
9 0.751163
2 0.703278
7 0.32146
3 0.320435
0 0.112517
8 0.0378203
7.opencv Mat类型转tensor 或者其他的vector或者数组数据转tensor
7.1
Mat m_out = imread(path);
//[320,320,3]
input_tensor = torch::from_blob(
m_out.data, {m_SIZE_IMAGE, m_SIZE_IMAGE, 3}).toType(torch::kFloat32);//torch::kByte //大坑
//[3,320,320]
input_tensor = input_tensor.permute({2,0,1});
input_tensor = input_tensor.unsqueeze(0);
input_tensor = input_tensor.to(torch::kFloat).to(m_device);
这里需要注意，因为上面图片被我预处理减均值过的，导致m_out像素值有负数，如果torch::kByte这种格式，会把负数变成正数，所以需要torch::kFloat32类型的。
permute({2,0,1});
之前是opencv Mat是
0 1 2
[320,320,3]
经过了permute({2,0,1})，表示把对应位置换一下，就变成了[3,320,320]
7.2
std::vector<float> region_priors;
//region_priors.push_back(num) region_priors的size是6375 × 4
torch::Tensor m_prior = torch::from_blob(region_priors.data(),{6375,4}).cuda();
8.tensor 的size sizes() numel()
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
auto aa = a.size(0);
auto bb = a.size(1);
auto a_size = a.sizes();
std::cout<<aa<<std::endl;
std::cout<<bb<<std::endl;
std::cout<<a_size<<std::endl;
int num_ = a.numel();
std::cout<<num_<<std::endl;
0.6522 0.0480 0.0009
0.1185 0.4639 0.0386
[ Variable[CPUFloatType]{2,3} ]
2
3
[2, 3]
6
8.2
有个问题就是当torch::Tensor a;直接定义一个tensor的时候，再访问
torch::Tensor a;
auto a_size = a.sizes();
就会报错
terminate called after throwing an instance of 'c10::Error'
what(): sizes() called on undefined Tensor (sizes at /data_2/everyday/0429/pytorch/c10/core/UndefinedTensorImpl.cpp:12)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&) + 0x6a (0x7f83b563f0aa in /data_2/everyday/0429/pytorch/torch/lib/libc10.so)
frame #1: c10::UndefinedTensorImpl::sizes() const + 0x258 (0x7f83b56362b8 in /data_2/everyday/0429/pytorch/torch/lib/libc10.so)
frame #2: at::Tensor::sizes() const + 0x27 (0x405fc9 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #3: main + 0x30 (0x405d06 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #4: __libc_start_main + 0xf0 (0x7f83b4d12830 in /lib/x86_64-linux-gnu/libc.so.6)
frame #5: _start + 0x29 (0x405c09 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
程序异常结束。
用numel()就没有问题
torch::Tensor a;
int num_ = a.numel();
std::cout<<num_<<std::endl;
8.3 获取维度大小，比如[1,5,8,2],我需要得到维度4
auto aaa = img_poly.sizes();
int len_ = aaa.size();
9.torch::sort
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, Dimname dim, bool descending)
dim0表示按行，1表示按列
descending=false表示升序，true表示降序
返回的是元组，第一个表示排序后的值，第二个表示排序之后对应之前的索引。
torch::Tensor scores = torch::rand({10});
std::tuple<torch::Tensor,torch::Tensor> sort_ret = torch::sort(scores.unsqueeze(1), 0, 1);
torch::Tensor v = std::get<0>(sort_ret).squeeze(1).to(scores.device());
torch::Tensor idx = std::get<1>(sort_ret).squeeze(1).to(scores.device());
std::cout<<scores<<std::endl;
std::cout<<v<<std::endl;
std::cout<<idx<<std::endl;
0.8355
0.1386
0.7910
0.0988
0.2607
0.7810
0.7855
0.5529
0.5846
0.1403
[ Variable[CPUFloatType]{10} ]
0.8355
0.7910
0.7855
0.7810
0.5846
0.5529
0.2607
0.1403
0.1386
0.0988
[ Variable[CPUFloatType]{10} ]
0
2
6
5
8
7
4
9
1
3
[ Variable[CPULongType]{10} ]
10.clamp 把数值控制在min max之间，小于min的就为min，大于max的就为max
inline Tensor Tensor::clamp(c10::optional min, c10::optional max) const
torch::Tensor a = torch::rand({2,3});
a[0][0] = 20;
a[0][1] = 21;
a[0][2] = 22;
a[1][0] = 23;
a[1][1] = 24;
std::cout<<a<<std::endl;
torch::Tensor b = a.clamp(21,22);
std::cout<<b<<std::endl;
20.0000 21.0000 22.0000
23.0000 24.0000 0.4792
[ Variable[CPUFloatType]{2,3} ]
21 21 22
22 22 21
[ Variable[CPUFloatType]{2,3} ]
在工程中，一般要取tensor里面的值，还有有时候就只限制一边，比如只限制min，如下：
xx1 = xx1.clamp(x1[i].item().toFloat(),INT_MAX*1.0);
11.大于> 小于< 运算
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a > 0.5;
std::cout<<b<<std::endl;
0.3526 0.0321 0.7098
0.9794 0.6531 0.9410
[ Variable[CPUFloatType]{2,3} ]
0 0 1
1 1 1
[ Variable[CPUBoolType]{2,3} ]
12.转置Tensor::transpose
inline Tensor Tensor::transpose(Dimname dim0, Dimname dim1) const
torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.transpose(1,0);
std::cout<<b<<std::endl;
0.4039 0.3568 0.9978
0.6895 0.7258 0.5576
[ Variable[CPUFloatType]{2,3} ]
0.4039 0.6895
0.3568 0.7258
0.9978 0.5576
[ Variable[CPUFloatType]{3,2} ]
13.expand_as
inline Tensor Tensor::expand_as(const Tensor & other) const
torch::Tensor a = torch::rand({2,3});;
// torch::Tensor b = torch::ones({2,2});
torch::Tensor b = torch::ones({2,1});
torch::Tensor c = b.expand_as(a);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
0.6063 0.4150 0.7665
0.8663 0.9563 0.7461
[ Variable[CPUFloatType]{2,3} ]
1
1
[ Variable[CPUFloatType]{2,1} ]
1 1 1
1 1 1
[ Variable[CPUFloatType]{2,3} ]
注意维度有一定要求，我这么写torch::Tensor b = torch::ones({2,2});torch::Tensor b = torch::ones({2});都会报错：
terminate called after throwing an instance of 'c10::Error'
what(): The expanded size of the tensor (3) must match the existing size (2) at non-singleton dimension 1. Target sizes: [2, 3]. Tensor sizes: [2, 2] (inferExpandGeometry at /data_2/everyday/0429/pytorch/aten/src/ATen/ExpandUtils.cpp:76)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&) + 0x6a (0x7f6a488150aa in /data_2/everyday/0429/pytorch/torch/lib/libc10.so)
frame #1: at::inferExpandGeometry(c10::ArrayRef, c10::ArrayRef, c10::ArrayRef) + 0x76b (0x7f6a49df7a4b in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #2: at::native::expand(at::Tensor const&, c10::ArrayRef, bool) + 0x84 (0x7f6a4a1e4324 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #3: + 0x1aeb9e1 (0x7f6a4a5189e1 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #4: + 0x19e8a2e (0x7f6a4a415a2e in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #5: + 0x3509dee (0x7f6a4bf36dee in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #6: + 0x19e8a2e (0x7f6a4a415a2e in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #7: + 0x14e8a61 (0x7f6a49f15a61 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #8: at::native::expand_as(at::Tensor const&, at::Tensor const&) + 0x39 (0x7f6a4a1e4d49 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #9: + 0x1aece9f (0x7f6a4a519e9f in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #10: + 0x3680543 (0x7f6a4c0ad543 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #11: + 0x19e6bb4 (0x7f6a4a413bb4 in /data_2/everyday/0429/pytorch/torch/lib/libtorch.so)
frame #12: at::Tensor c10::KernelFunction::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(at::Tensor const&, at::Tensor const&) const + 0xb0 (0x433e06 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #13: at::Tensor c10::impl::OperatorEntry::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const::{lambda(c10::DispatchTable const&)#1}::operator()(c10::DispatchTable const&) const + 0x79 (0x432525 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #14: std::result_of<at::Tensor c10::impl::OperatorEntry::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const::{lambda(c10::DispatchTable const&)#1} (c10::DispatchTable const&)>::type c10::LeftRightc10::DispatchTable::read<at::Tensor c10::impl::OperatorEntry::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const::{lambda(c10::DispatchTable const&)#1}>(at::Tensor c10::impl::OperatorEntry::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const::{lambda(c10::DispatchTable const&)#1}&&) const + 0x11c (0x4340ba in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #15: at::Tensor c10::impl::OperatorEntry::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const + 0x5f (0x4325a5 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #16: at::Tensor c10::Dispatcher::callUnboxed<at::Tensor, at::Tensor const&, at::Tensor const&>(c10::OperatorHandle const&, c10::TensorTypeId, at::Tensor const&, at::Tensor const&) const + 0x85 (0x42fd5d in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #17: at::Tensor::expand_as(at::Tensor const&) const + 0x1a5 (0x42ba47 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #18: main + 0xbd (0x427c97 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #19: __libc_start_main + 0xf0 (0x7f6a47ee8830 in /lib/x86_64-linux-gnu/libc.so.6)
frame #20: _start + 0x29 (0x426999 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
14.乘 mul_ 除div 减sub_
boxes_my.select(1,0).mul_(width);
boxes_my.select(1,1).mul_(height);
boxes_my.select(1,2).mul_(width);
boxes_my.select(1,3).mul_(height);
prediction.select(2, 3).div(2);
input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);
15.加载模型
torch::Device m_device(torch::kCUDA);
torch::jit::script::Module m_model = torch::jit::load(path_pt);
m_model.to(m_device);
m_model.eval();
16.模型forward出来的结果
当模型有几个东东输出来的时候
auto output = m_model.forward({input_tensor});
auto tpl = output.toTuple();
auto arm_loc = tpl->elements()[0].toTensor();
// arm_loc.print();
// std::cout<<arm_loc[0]<<std::endl;
auto arm_conf = tpl->elements()[1].toTensor();
//arm_conf.print();
auto odm_loc = tpl->elements()[2].toTensor();
//odm_loc.print();
// std::cout<<odm_loc[0]<<std::endl;
auto odm_conf = tpl->elements()[3].toTensor();
// odm_conf.print();
17.resize_ zero_
Tensor & resize_(IntArrayRef size) const;
Tensor & zero_() const;
torch::Tensor a = torch::rand({1,3,2,2});
const int batch_size = a.size(0);
const int depth = a.size(1);
const int image_height = a.size(2);
const int image_width = a.size(3);
torch::Tensor crops = torch::rand({1,3,2,2});
// torch::Tensor crops;
crops.resize_({ batch_size, depth, image_height, image_width });
crops.zero_();
std::cout<<a<<std::endl;
std::cout<<crops<<std::endl;
(1,1,.,.) =
0.7889 0.3291
0.2541 0.8283
(1,2,.,.) =
0.0209 0.1846
0.2528 0.2755
(1,3,.,.) =
0.0294 0.6623
0.2736 0.3376
[ Variable[CPUFloatType]{1,3,2,2} ]
(1,1,.,.) =
0 0
0 0
(1,2,.,.) =
0 0
0 0
(1,3,.,.) =
0 0
0 0
[ Variable[CPUFloatType]{1,3,2,2} ]
注意：这里如果只定义 torch::Tensor crops;//torch::Tensor crops = torch::rand({1,3,2,2})；就会报错，感觉还是要先初始化一下才会分配内存，要不然就会报错！
terminate called after throwing an instance of '
c10::Error'
what(): There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::resize_. This usually means that this function requires a non-empty list of Tensors. Available functions are [CUDATensorId, QuantizedCPUTensorId, CPUTensorId, VariableTensorId] (lookup_ at /data_2/everyday/0429/pytorch/torch/include/ATen/core/dispatch/DispatchTable.h:243)
frame #0: c10::Error::Error(c10::SourceLocation, std::cxx11::basic_string<char, std::char_traits, std::allocator > const&) + 0x6a (0x7fa2f5f450aa in /data_2/everyday/0429/pytorch/torch/lib/libc10.so)
frame #1: c10::KernelFunction const& c10::DispatchTable::lookup<c10::DispatchTable::lookup(c10::TensorTypeId) const::{lambda()#1}>(c10::DispatchTable::lookup(c10::TensorTypeId) const::{lambda()#1} const&) const + 0x1da (0x42eaa8 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #2: c10::DispatchTable::lookup(c10::TensorTypeId) const + 0x3a (0x42acf4 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #3: at::Tensor& c10::impl::OperatorEntry::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const::{lambda(c10::DispatchTable const&)#1}::operator()(c10::DispatchTable const&) const + 0x51 (0x431543 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #4: std::result_of<at::Tensor& c10::impl::OperatorEntry::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const::{lambda(c10::DispatchTable const&)#1} (c10::DispatchTable const&)>::type c10::LeftRightc10::DispatchTable::read<at::Tensor& c10::impl::OperatorEntry::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const::{lambda(c10::DispatchTable const&)#1}>(at::Tensor& c10::impl::OperatorEntry::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const::{lambda(c10::DispatchTable const&)#1}&&) const + 0x114 (0x4333c6 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #5: at::Tensor& c10::impl::OperatorEntry::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const + 0x63 (0x4315c7 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #6: at::Tensor& c10::Dispatcher::callUnboxedOnly<at::Tensor&, at::Tensor&, c10::ArrayRef >(c10::OperatorHandle const&, c10::TensorTypeId, at::Tensor&, c10::ArrayRef) const + 0x7b (0x42eff5 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #7: at::Tensor::resize(c10::ArrayRef) const + 0x1a1 (0x42af3f in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #8: main + 0x134 (0x42798f in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
frame #9: __libc_start_main + 0xf0 (0x7fa2f5618830 in /lib/x86_64-linux-gnu/libc.so.6)
frame #10: _start + 0x29 (0x426719 in /data_2/everyday/0516/build-libtorch- syntax-unknown-Default/main)
18.meshgrid 把tens变成方阵
static inline std::vector meshgrid(TensorList tensors)
torch::Tensor scales = torch::ones({2});
torch::Tensor ratios = torch::ones({2});
ratios += 2;
std::cout<<scales<<std::endl;
std::cout<<ratios<<std::endl;
std::vector<torch::Tensor> mesh = torch::meshgrid({ scales, ratios });
torch::Tensor scales_1 = mesh[0];
torch::Tensor ratios_1 = mesh[1];
std::cout<<scales_1<<std::endl;
std::cout<<ratios_1<<std::endl;
1
1
[ Variable[CPUFloatType]{2} ]
3
3
[ Variable[CPUFloatType]{2} ]
1 1
1 1
[ Variable[CPUFloatType]{2,2} ]
3 3
3 3
[ Variable[CPUFloatType]{2,2} ]
19.flatten 展平tensor
Tensor flatten(int64_t start_dim=0, int64_t end_dim=-1) const;
Tensor flatten(int64_t start_dim, int64_t end_dim, Dimname out_dim) const;
Tensor flatten(Dimname start_dim, Dimname end_dim, Dimname out_dim) const;
Tensor flatten(DimnameList dims, Dimname out_dim) const;
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = a.flatten();
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.9953 0.1461 0.0084
0.6169 0.4037 0.7685
[ Variable[CPUFloatType]{2,3} ]
0.9953
0.1461
0.0084
0.6169
0.4037
0.7685
20.fill_ tensor填充某个值 就地操作，填充当前tensor
Tensor & fill_(Scalar value) const;
Tensor & fill_(const Tensor & value) const;
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = a.fill_(4);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
4 4 4
4 4 4
[ Variable[CPUFloatType]{2,3} ]
4 4 4
4 4 4
[ Variable[CPUFloatType]{2,3} ]
21.torch::stack
static inline Tensor stack(TensorList tensors, int64_t dim)
torch::Tensor a = torch::rand({3});
torch::Tensor b = torch::rand({3});
torch::Tensor c = torch::stack({a,b},1);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
0.6776
0.5610
0.2835
[ Variable[CPUFloatType]{3} ]
0.6846
0.3753
0.3873
[ Variable[CPUFloatType]{3} ]
0.6776 0.6846
0.5610 0.3753
0.2835 0.3873
[ Variable[CPUFloatType]{3,2} ]
torch::Tensor a = torch::rand({3});
torch::Tensor b = torch::rand({3});
torch::Tensor c = torch::stack({a,b},0);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
0.7129
0.1650
0.6764
[ Variable[CPUFloatType]{3} ]
0.8035
0.1807
0.8100
[ Variable[CPUFloatType]{3} ]
0.7129 0.1650 0.6764
0.8035 0.1807 0.8100
[ Variable[CPUFloatType]{2,3} ]
22.reshape
inline Tensor Tensor::reshape(IntArrayRef shape) const
torch::Tensor a = torch::rand({2,4});
torch::Tensor b = a.reshape({-1,2});
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.3782 0.6390 0.6919 0.8298
0.3872 0.5923 0.4337 0.9634
[ Variable[CPUFloatType]{2,4} ]
0.3782 0.6390
0.6919 0.8298
0.3872 0.5923
0.4337 0.9634
[ Variable[CPUFloatType]{4,2} ]
23. view
inline Tensor Tensor::view(IntArrayRef size) const
需要先contiguous
a.contiguous().view({-1, 4});
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = a.contiguous().view({ -1, 6 });
torch::Tensor c = a.contiguous().view({ 3, 2 });
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
0.2069 0.8814 0.8506
0.6451 0.0107 0.7591
[ Variable[CPUFloatType]{2,3} ]
0.2069 0.8814 0.8506 0.6451 0.0107 0.7591
[ Variable[CPUFloatType]{1,6} ]
0.2069 0.8814
0.8506 0.6451
0.0107 0.7591
[ Variable[CPUFloatType]{3,2} ]
注意这里和转置不一样
24.argmax argmin
static inline Tensor argmax(const Tensor & self, c10::optional<int64_t> dim=c10::nullopt, bool keepdim=false);
static inline Tensor argmin(const Tensor & self, c10::optional<int64_t> dim=c10::nullopt, bool keepdim=false);
torch::Tensor a = torch::rand({2,3});
auto b = torch::argmax(a, 0);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.9337 0.7443 0.1323
0.6514 0.5068 0.5052
[ Variable[CPUFloatType]{2,3} ]
0
0
1
[ Variable[CPULongType]{3} ]
torch::Tensor a = torch::rand({2,3});
auto b = torch::argmax(a, 1);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.0062 0.3846 0.4844
0.9555 0.2844 0.4025
[ Variable[CPUFloatType]{2,3} ]
2
0
[ Variable[CPULongType]{2} ]
25.where
static inline Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other);
static inline std::vector where(const Tensor & condition);
torch::Tensor d = torch::where(a>0.5,b,c);
说明：在a大于0.5的位置设为pos，d的pos位置上用b的pos位置上面值填充，其余的位置上值是c的值
torch::Tensor a = torch::rand({2,3});
torch::Tensor b = torch::ones({2,3});
torch::Tensor c = torch::zeros({2,3});
torch::Tensor d = torch::where(a>0.5,b,c);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
std::cout<<c<<std::endl;
std::cout<<d<<std::endl;
0.7301 0.8926 0.9570
0.0979 0.5679 0.4473
[ Variable[CPUFloatType]{2,3} ]
1 1 1
1 1 1
[ Variable[CPUFloatType]{2,3} ]
0 0 0
0 0 0
[ Variable[CPUFloatType]{2,3} ]
1 1 1
0 1 0
[ Variable[CPUFloatType]{2,3} ]
另外的例子：
auto b = torch::where(a>0.5);
torch::Tensor a = torch::rand({2,3});
auto b = torch::where(a>0.5);
std::cout<<a<<std::endl;
std::cout<<b<<std::endl;
0.3439 0.1622 0.7149
0.4845 0.5982 0.9443
[ Variable[CPUFloatType]{2,3} ]
0
1
1
[ Variable[CPULongType]{3} ]
2
1
2
[ Variable[CPULongType]{3} ]
26.accessor
TensorAccessor<T,N> accessor() const&
auto result_data = result.accessor<float, 2>(); //2代表二维
示例1：
torch::Tensor one = torch::randn({9,6});
auto foo_one=one.accessor<float,2>();
for(int i=0,sum=0;i<foo_one.size(0);i++)
for(int j=0;j<foo_one.size(1);j++)
sum+=foo_one[i][j];
示例2：
torch::Tensor result;
for(int i=1;i<m_num_class;i++)
{
//...
if(0 == result.numel())
{
result = result_.clone();
}else
{
result = torch::cat({result,result_},0);//按行拼接
}
}
result =result.cpu();
auto result_data = result.accessor<float, 2>();
cv::Mat img_draw = img.clone();
for(int i=0;i<result_data.size(0);i++)
{
float score = result_data[i][4];
if(score < 0.4) { continue;}
int x1 = result_data[i][0];
int y1 = result_data[i][1];
int x2 = result_data[i][2];
int y2 = result_data[i][3];
int id_label = result_data[i][5];
cv::rectangle(img_draw,cv::Point(x1,y1),cv::Point(x2,y2),cv::Scalar(255,0,0),3);
cv::putText(img_draw,label_map[id_label],cv::Point(x1,y2),CV_FONT_HERSHEY_SIMPLEX,1,cv::Scalar(255,0,55));
}
27. torch::max torch::min 同max
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, Dimname dim, bool keepdim=false);
static inline Tensor max(const Tensor & self);
torch::Tensor a = torch::rand({4,2});
std::tuple<torch::Tensor, torch::Tensor> max_test = torch::max(a,1);
auto max_val = std::get<0>(max_test);
// index
auto index = std::get<1>(max_test);
std::cout<<a<<std::endl;
std::cout<<max_val<<std::endl;
std::cout<<index<<std::endl;
0.1082 0.7954
0.3099 0.4507
0.2447 0.5169
0.8210 0.3141
[ Variable[CPUFloatType]{4,2} ]
0.7954
0.4507
0.5169
0.8210
[ Variable[CPUFloatType]{4} ]
1
1
1
0
[ Variable[CPULongType]{4} ]
另外一个例子：全局max
torch::Tensor a = torch::rand({4,2});
torch::Tensor max_test = torch::max(a);
std::cout<<a<<std::endl;
std::cout<<max_test<<std::endl;
0.1904 0.9493
0.6521 0.5788
0.9216 0.5997
0.1758 0.7384
[ Variable[CPUFloatType]{4,2} ]
0.94929
[ Variable[CPUFloatType]{} ]
28.masked_select 与 masked_fill
28.1 Tensor masked_select(const Tensor & mask) const;
torch::Tensor a = torch::rand({2,3});
torch::Tensor c = (a>0.25);
torch::Tensor d = a.masked_select(c);
std::cout<<a<<std::endl;
std::cout<<c<<std::endl;
std::cout<<d<<std::endl;
0.0667 0.3812 0.3810
0.3558 0.8628 0.6329
[ Variable[CPUFloatType]{2,3} ]
0 1 1
1 1 1
[ Variable[CPUBoolType]{2,3} ]
0.3812
0.3810
0.3558
0.8628
0.6329
[ Variable[CPUFloatType]{5} ]
28.2 Tensor masked_fill(const Tensor & mask, Scalar value) const;
Tensor & masked_fill_(const Tensor & mask, const Tensor & value) const;
Tensor masked_fill(const Tensor & mask, const Tensor & value) const;
torch::Tensor a = torch::rand({2,3});
torch::Tensor aa = a.clone();
aa.masked_fill_(aa>0.5,-2);
std::cout<<a<<std::endl;
std::cout<<aa<<std::endl;
0.8803 0.2387 0.8577
0.8166 0.0730 0.4682
[ Variable[CPUFloatType]{2,3} ]
-2.0000 0.2387 -2.0000
-2.0000 0.0730 0.4682
[ Variable[CPUFloatType]{2,3} ]
28.3 masked_fill_ 带下划线的都是就地操作
有个需求是Tensor score表示得分，Tensor label表示标签，他们都是同大小的。后处理就是当label=26并且label=26的分数小于0.5，那么就把label相应位置置1
float index[] = {3,2,3,3,5,6,7,8,9,10,11,12,13,14,15,16};
float score[] = {0.1,0.1,0.9,0.9,0.9,0.1,0.1,0.1,0.1,0.1,0.8,0.8,0.8,0.8,0.8,0.8};
torch::Tensor aa = torch::from_blob(index, {4,4}).toType(torch::kFloat32);
torch::Tensor bb = torch::from_blob(score, {4,4}).toType(torch::kFloat32);
std::cout<<aa<<std::endl;
std::cout<<bb<<std::endl;
torch::Tensor tmp = (aa == 3);
torch::Tensor tmp_2 = (bb >= 0.9);
std::cout<<tmp<<std::endl;
std::cout<<tmp_2<<std::endl;
torch::Tensor condition_111 = tmp * tmp_2;
std::cout<<condition_111<<std::endl;
aa.masked_fill_(condition_111,-1);
std::cout<<aa<<std::endl;
输出如下：
3 2 3 3
5 6 7 8
9 10 11 12
13 14 15 16
[ Variable[CPUFloatType]{4,4} ]
0.1000 0.1000 0.9000 0.9000
0.9000 0.1000 0.1000 0.1000
0.1000 0.1000 0.8000 0.8000
0.8000 0.8000 0.8000 0.8000
[ Variable[CPUFloatType]{4,4} ]
1 0 1 1
0 0 0 0
0 0 0 0
0 0 0 0
[ Variable[CPUByteType]{4,4} ]
0 0 1 1
1 0 0 0
0 0 0 0
0 0 0 0
[ Variable[CPUByteType]{4,4} ]
0 0 1 1
0 0 0 0
0 0 0 0
0 0 0 0
[ Variable[CPUByteType]{4,4} ]
3 2 -1 -1
5 6 7 8
9 10 11 12
13 14 15 16
[ Variable[CPUFloatType]{4,4} ]
29.libtorch综合操作1
torch::jit::script::Module module = torch::jit::load(argv[1]);
std::cout << "== Switch to GPU mode" << std::endl;
// to GPU
module.to(at::kCUDA);
if (LoadImage(file_name, image)) {
auto input_tensor = torch::from_blob(
image.data, {1, kIMAGE_SIZE, kIMAGE_SIZE, kCHANNELS});
input_tensor = input_tensor.permute({0, 3, 1, 2});
input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);
// to GPU
input_tensor = input_tensor.to(at::kCUDA);
torch::Tensor out_tensor = module.forward({input_tensor}).toTensor();
auto results = out_tensor.sort(-1, true);
auto softmaxs = std::get<0>(results)[0].softmax(0);
auto indexs = std::get<1>(results)[0];
for (int i = 0; i < kTOP_K; ++i) {
auto idx = indexs[i].item<int>();
std::cout << " ============= Top-" << i + 1
<< " =============" << std::endl;
std::cout << " Label: " << labels[idx] << std::endl;
std::cout << " With Probability: "
<< softmaxs[i].item<float>() * 100.0f << "%" << std::endl;
}
}
30.pytorch nms <---------> libtorch nms
pytorch nms
比如：
boxes [1742,4]
scores [1742]
def nms(boxes, scores, overlap=0.5, top_k=200):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for suppressing unnecessary boxes.
top_k: (int) The Maximum number of box preds to consider.
Return:
The indices of the kept boxes with respect to num_priors.
"""
keep = scores.new(scores.size(0)).zero_().long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
return keep, count
libtorch nms
bool nms(const torch::Tensor& boxes, const torch::Tensor& scores, torch::Tensor &keep, int &count,float overlap, int top_k)
{
count =0;
keep = torch::zeros({scores.size(0)}).to(torch::kLong).to(scores.device());
if(0 == boxes.numel())
{
return false;
}
torch::Tensor x1 = boxes.select(1,0).clone();
torch::Tensor y1 = boxes.select(1,1).clone();
torch::Tensor x2 = boxes.select(1,2).clone();
torch::Tensor y2 = boxes.select(1,3).clone();
torch::Tensor area = (x2-x1)*(y2-y1);
// std::cout<<area<<std::endl;
std::tuple<torch::Tensor,torch::Tensor> sort_ret = torch::sort(scores.unsqueeze(1), 0, 0);
torch::Tensor v = std::get<0>(sort_ret).squeeze(1).to(scores.device());
torch::Tensor idx = std::get<1>(sort_ret).squeeze(1).to(scores.device());
int num_ = idx.size(0);
if(num_ > top_k) //python:idx = idx[-top_k:]
{
idx = idx.slice(0,num_-top_k,num_).clone();
}
torch::Tensor xx1,yy1,xx2,yy2,w,h;
while(idx.numel() > 0)
{
auto i = idx[-1];
keep[count] = i;
count += 1;
if(1 == idx.size(0))
{
break;
}
idx = idx.slice(0,0,idx.size(0)-1).clone();
xx1 = x1.index_select(0,idx);
yy1 = y1.index_select(0,idx);
xx2 = x2.index_select(0,idx);
yy2 = y2.index_select(0,idx);
xx1 = xx1.clamp(x1[i].item().toFloat(),INT_MAX*1.0);
yy1 = yy1.clamp(y1[i].item().toFloat(),INT_MAX*1.0);
xx2 = xx2.clamp(INT_MIN*1.0,x2[i].item().toFloat());
yy2 = yy2.clamp(INT_MIN*1.0,y2[i].item().toFloat());
w = xx2 - xx1;
h = yy2 - yy1;
w = w.clamp(0,INT_MAX);
h = h.clamp(0,INT_MAX);
torch::Tensor inter = w * h;
torch::Tensor rem_areas = area.index_select(0,idx);
torch::Tensor union_ = (rem_areas - inter) + area[i];
torch::Tensor Iou = inter * 1.0 / union_;
torch::Tensor index_small = Iou < overlap;
auto mask_idx = torch::nonzero(index_small).squeeze();
idx = idx.index_select(0,mask_idx);//pthon: idx = idx[IoU.le(overlap)]
}
return true;
}
31.数据类型很重要！ .to(torch::kByte);
31.1
//[128,512]
torch::Tensor b = torch::argmax(output_1, 2).cpu();
// std::cout<<b<<std::endl;
b.print();
cv::Mat mask(T_height, T_width, CV_8UC1, (uchar*)b.data_ptr());
imshow("mask",mask*255);
waitKey(0);
[Variable[CPULongType] [128, 512]]
如上！得到的b是分割图[128, 512]。可是死活不能显示！！然后我检测b的值和pytorch的对比，发现是一致的。可是上面的就是死活得不到想要的分割图，全是黑的，为0.可是我把值打出来有不为0的啊！
之前工程也是这么写的啊，哎。。。然后我就github上面找psenet libtorch的实现，发现人家也是类似的写法
cv::Mat tempImg = Mat::zeros(T_height, T_width, CV_8UC1);
memcpy((void *) tempImg.data, b.data_ptr(), sizeof(torch::kU8) * b.numel());
我也这么写，发现还是不行！！！2个小时过去了，没有办法，我准备把128*512的数据保存在els里面查看。漫无目的的实验了一下
cout<<b[0][0].item().toFloat()<<endl;
这样可以打印出值，一定要加.toFloat()才行。漫无目的的编写循环
for(int i=0;i<128;i++)
for(int j=0;j<512;j++)
{
}
可是不服啊！哪里有问题呢，值都是对的就是显示不出来？
发现刚刚上面b[0][0].item().toFloat()必须加.toFloat()，那么我的b是什么类型的呢，是tensor类型的，具体什么类型呢，看到打印的[Variable[CPULongType] [128, 512]]，long类型的。
哦，那我转一下类型看看。翻看之前的转类型的，发现只需要在tensor后面加.to(torch::kFloat32);类似的
因为我需要int的，我就先int一下，
torch::Tensor b = torch::argmax(output_1, 2).cpu().to(torch::kInt);
试了一下还是不行，
.to(torch::kFloat32); 试了一下还是不行，
我在敲torch::k的时候编译器会自动弹出k开头的东西。其中第一个就是kByte.然后试了下：
torch::Tensor b = torch::argmax(output_1, 2).cpu().to(torch::kByte);
！！！！
可以了！出来了我想要的分割图。
搞死我了，数据类型的问题。至少整了2个小时！
31.2
要把中间处理的图片转为tensor
Mat m_tmp = grayMat.clone();
torch::Tensor label_deal = torch::from_blob(
m_tmp.data, {grayMat.rows, grayMat.cols}).toType(torch::kByte).to(m_device);
// label_deal = label_deal.to(m_device);
auto aaa = torch::max(label_deal);
std::cout<<label_deal<<std::endl;
std::cout<<aaa<<std::endl;
while(1);
又是一个大坑啊！！！一开始认为就这么就ok了，然后后面的处理结果不对，就一步步排查哪里出问题，然后定位到这里，m_tmp的像素值在tensor里面压根就对不上啊！！！我知道m_tmp最大像素值34，可是打出来的tensor最大255！！！哎，是torch::kByte类型啊！没办法，再换成kFloat32还是不行，值更离谱还有nan的。。呃呃呃。然后发现.toType(torch::kByte)还有.to(torch::kByte)这个写法的，到底用哪个还是一样？然后继续实验还是一样有问题，然后把.to(m_device);单独拎出来还是不行，因为根据之前的经验，torch::Tensor tmp = tmp.cpu();好像是需要单独写，要不然会有问题。那这边啥问题呢？像素值就是不能正确放到tensor！！！咋回事呢？？？
然后郁闷良久，那么Mat的类型是不是也要转。
Mat m_tmp = grayMat.clone();
m_tmp.convertTo(m_tmp,CV_32FC1);/////又是个大坑 图片要先转float32啊
torch::Tensor label_deal = torch::from_blob(
m_tmp.data, {grayMat.rows, grayMat.cols}).toType(torch::kByte).to(m_device);
这样就可以了！！！呃呃呃，一定要转CV_32FC1吗？可能是吧！
32.指针访问Tensor数据
torch::Tensor output = m_model->forward({input_tensor}).toTensor()[0];
torch::Tensor output_cpu = output.cpu();
//output_cpu Variable[CPUFloatType] [26, 480, 480]]
output_cpu.print();
void *ptr = output_cpu.data_ptr();
//std::cout<<(float*)ptr[0]<<std::endl;
只能用void 或者auto来定义，否则会报错。比如我用float ptr = output_cpu.data_ptr();会报错：
error: invalid conversion from ‘void’ to ‘float’ [-fpermissive]
float *ptr = output_cpu.data_ptr();
那么void *编译通过了，我需要用指针访问tensor里面的数据啊！
torch::Tensor output = m_model->forward({input_tensor}).toTensor()[0];
torch::Tensor output_cpu = output.cpu();
//output_cpu Variable[CPUFloatType] [26, 480, 480]]
output_cpu.print();
void *ptr = output_cpu.data_ptr();
std::cout<<(float*)ptr<<std::endl;
如上这么写，输出：
[Variable[CPUFloatType] [26, 480, 480]]
0x7fab195ee040
输出来的是个地址，那怎么访问数据呢，自然而然的就这么写：
std::cout<<(float)ptr[0]<<std::endl;
这么写又报错！！！！
: error: ‘void’ is not a pointer-to-object type，然后又这么写：
std::cout<<(float*)ptr[0][0][0]<<std::endl;还是报一样的错误！。没有办法，然后Google了一下，发现有报错和我一样的，以及解决方案：
果真！解决了！
void *ptr = output_cpu.data_ptr();
// std::cout<<*((float*)ptr[0][0][0])<<std::endl;
// std::cout<<(float*)ptr[0][0][0]<<std::endl;
std::cout<<*((float*)(ptr+2))<<std::endl;
还有一种写法：
const float* result = reinterpret_cast<const float *>(output_cpu.data_ptr());
还有刚刚的那种写法：
void *ptr = output_cpu.data_ptr();
const float* result = (float*)ptr;
33 PyTorch内Tensor按索引赋值的方法比较
PyTorch内Tensor按索引赋值的方法比较[https://www.jianshu.com/p/e568213c8501]
44 输出多个tensor（pytorch端）以及取出多个tensor（libtorch端）
pytorch端的输出：
def forward(self, x, batch=None):
output, cnn_feature = self.dla(x)
return (output['ct_hm'],output['wh'],cnn_feature)
对应的libtorch端
auto out = m_model->forward({input_tensor});
auto tpl = out.toTuple();
auto out_ct_hm = tpl->elements()[0].toTensor();
out_ct_hm.print();
auto out_wh = tpl->elements()[1].toTensor();
out_wh.print();
auto out_cnn_feature = tpl->elements()[2].toTensor();
out_cnn_feature.print();
如果输出单个tensor，就是
at::Tensor output = module->forward(inputs).toTensor();
45. torch::Tensor作为函数参数，不管是引用还是不引用，函数内部对形参操作都会影响本来的tensor，即都是引用
void test_tensor(torch::Tensor a)
{
a[0][0] = -100;
}
int main(int argc, const char* argv[])
{
torch::Tensor p = torch::rand({2,2});
std::cout<<p<<std::endl;
std::cout<<"~~~~#########~~~~~~~~~~~~~~~~~~~~~~~~~~"<<std::endl;
test_tensor(p);
std::cout<<p<<std::endl;
while (1);
}
输出如下：
0.0509 0.3509
0.8019 0.1350
[ Variable[CPUType]{2,2} ]
~~~~#########~~~~~~~~~~~~~~~~~~~~~~~~~~
-100.0000 0.3509
0.8019 0.1350
[ Variable[CPUType]{2,2} ]
可以看出，函数void test_tensor(torch::Tensor a)，虽然不是引用，但是经过了这个函数之后值改变了！
46. 实现pytorch下标神操作
比如在pytorch端，写法如下：
c=b[a]
其中，a的形状是[1,100], b的形状是[1,100,40,2]，所以，大家猜c的形状是什么。。哦，还有一个已知条件是a相当于一个掩模，a里面的值只有0或者1，假设a的前5个值是1，其余为0
得到的c的形状是[5,40,2],大概也能猜到就是把为1的那些行取出，其余的不要！ 那么，libtorch端如何优雅的实现呢？
呃呃呃，暂时没有想到什么好法子，因为libtorch端不支持下标操作。。很麻烦。。。然后自己写的循环实现的：
为了方便看数值，只假设10个。
// aim [1,10,2,2] ind_mask_ [1,10] 比如前5个是1余都是0 得到的结果形状是[5,40,2] 即pytorch里面的操作 aim = aim[ind_mask]
torch::Tensor deal_mask_index22(torch::Tensor aim_,torch::Tensor ind_mask_)
{
torch::Tensor aim = aim_.clone().squeeze(0);//[1,100,40,2] -->> [100,40,2]
torch::Tensor ind_mask = ind_mask_.clone().squeeze(0);////[1,100] -->> [100]
int row = ind_mask.size(0);
int cnt = 0;
for(int i=0;i<row;i++)
{
if(ind_mask[i].item().toInt())
{
cnt += 1;
}
}
torch::Tensor out = torch::zeros({cnt,aim.size(1),aim.size(2)});
int index_ = 0;
for(int i=0;i<row;i++)
{
if(ind_mask[i].item().toInt())
{
out[index_++] = aim[i];
// std::cout<<i<<std::endl;
}
}
std::cout<<"##############################################"<<std::endl;
std::cout<<out<<std::endl;
return out;
}
int main(int argc, const char* argv[])
{
torch::Tensor ind_mask = torch::ones({1,10});
ind_mask[0][0] = 0;
ind_mask[0][1] = 0;
ind_mask[0][2] = 0;
ind_mask[0][4] = 0;
torch::Tensor aim = torch::rand({1,10,2,2});
std::cout<<aim<<std::endl;
deal_mask_index22(aim,ind_mask);
while (1);
}
47.pytorch libtorch的tensor验证精度
[pytorch libtorch的tensor验证精度](pytorch libtorch的tensor验证精度)
https://www.cnblogs.com/yanghailin/p/13669046.html
48. 其他--颜色映射
/////////////////////////////////////////////////////////////////////////////////////////////////
auto t1 = std::chrono::steady_clock::now();
// static torch::Tensor tensor_m0 = torch::zeros({m_height,m_width}).to(torch::kByte).to(torch::kCPU);
// static torch::Tensor tensor_m1 = torch::zeros({m_height,m_width}).to(torch::kByte).to(torch::kCPU);
// static torch::Tensor tensor_m2 = torch::zeros({m_height,m_width}).to(torch::kByte).to(torch::kCPU);
static torch::Tensor tensor_m0 = torch::zeros({m_height,m_width}).to(torch::kByte);
static torch::Tensor tensor_m1 = torch::zeros({m_height,m_width}).to(torch::kByte);
static torch::Tensor tensor_m2 = torch::zeros({m_height,m_width}).to(torch::kByte);
tensor_m0 = tensor_m0.to(torch::kCUDA);
tensor_m1 = tensor_m1.to(torch::kCUDA);
tensor_m2 = tensor_m2.to(torch::kCUDA);
for(int i=1;i<m_color_cnt;i++)
{
tensor_m0.masked_fill_(index==i,colormap[i * 3]);
tensor_m1.masked_fill_(index==i,colormap[i * 3 + 1]);
tensor_m2.masked_fill_(index==i,colormap[i * 3 + 2]);
}
torch::Tensor tensor_m00 = tensor_m0.cpu();
torch::Tensor tensor_m11 = tensor_m1.cpu();
torch::Tensor tensor_m22 = tensor_m2.cpu();
cv::Mat m0 = cv::Mat(m_height, m_width, CV_8UC1, (uchar*)tensor_m00.data_ptr());
cv::Mat m1 = cv::Mat(m_height, m_width, CV_8UC1, (uchar*)tensor_m11.data_ptr());
cv::Mat m2 = cv::Mat(m_height, m_width, CV_8UC1, (uchar*)tensor_m22.data_ptr());
std::vector<cv::Mat> channels = {m0,m1,m2};
cv::Mat mergeImg;
cv::merge(channels, mergeImg);
mergeImg = mergeImg.clone();
auto ttt1 = std::chrono::duration_cast<std::chrono::milliseconds>
(std::chrono::steady_clock::now() - t1).count();
std::cout << "merge time="<<ttt1<<"ms"<<std::endl;
/////////////////////////////////////////////////////////////////////////////////////////////
用cpu需要３５ｍｓ左右,ｇｐｕ２－３ｍｓ,下面的代码实现功能一样也是２－３ｍｓ
auto t0 = std::chrono::steady_clock::now();
for (int i = 0; i<labelMat.rows; i++)
{
for (int j = 0; j<labelMat.cols; j++)
{
int id = labelMat.at<uchar>(i,j);
if(0 == id)
{
continue;
}
colorMat.at<cv::Vec3b>(i, j)[0] = colormap[id * 3];
colorMat.at<cv::Vec3b>(i, j)[1] = colormap[id * 3 + 1];
colorMat.at<cv::Vec3b>(i, j)[2] = colormap[id * 3 + 2];
}
}
auto ttt = std::chrono::duration_cast<std::chrono::milliseconds>
(std::chrono::steady_clock::now() - t0).count();
std::cout << "consume time="<<ttt<<"ms"<<std::endl;
49.torch.gather
纯pytorch端的： （转载于https://www.jianshu.com/p/5d1f8cd5fe31）
torch.gather(input, dim, index, out=None) → Tensor
沿给定轴 dim ,将输入索引张量 index 指定位置的值进行聚合.
对一个 3 维张量,输出可以定义为:
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
Parameters:
input (Tensor) – 源张量
dim (int) – 索引的轴
index (LongTensor) – 聚合元素的下标(index需要是torch.longTensor类型)
out (Tensor, optional) – 目标张量
例子：
dim = 1
import torch
a = torch.randint(0, 30, (2, 3, 5))
print(a)
#tensor([[[ 18., 5., 7., 1., 1.],
# [ 3., 26., 9., 7., 9.],
# [ 10., 28., 22., 27., 0.]],
# [[ 26., 10., 20., 29., 18.],
# [ 5., 24., 26., 21., 3.],
# [ 10., 29., 10., 0., 22.]]])
index = torch.LongTensor([[[0,1,2,0,2],
[0,0,0,0,0],
[1,1,1,1,1]],
[[1,2,2,2,2],
[0,0,0,0,0],
[2,2,2,2,2]]])
print(a.size()==index.size())
b = torch.gather(a, 1,index)
print(b)
#True
#tensor([[[ 18., 26., 22., 1., 0.],
# [ 18., 5., 7., 1., 1.],
# [ 3., 26., 9., 7., 9.]],
# [[ 5., 29., 10., 0., 22.],
# [ 26., 10., 20., 29., 18.],
# [ 10., 29., 10., 0., 22.]]])
dim =2
c = torch.gather(a, 2,index)
print(c)
#tensor([[[ 18., 5., 7., 18., 7.],
# [ 3., 3., 3., 3., 3.],
# [ 28., 28., 28., 28., 28.]],
# [[ 10., 20., 20., 20., 20.],
# [ 5., 5., 5., 5., 5.],
# [ 10., 10., 10., 10., 10.]]])
dim = 0
index2 = torch.LongTensor([[[0,1,1,0,1],
[0,1,1,1,1],
[1,1,1,1,1]],
[[1,0,0,0,0],
[0,0,0,0,0],
[1,1,0,0,0]]])
d = torch.gather(a, 0,index2)
print(d)
#tensor([[[ 18., 10., 20., 1., 18.],
# [ 3., 24., 26., 21., 3.],
# [ 10., 29., 10., 0., 22.]],
# [[ 26., 5., 7., 1., 1.],
# [ 3., 26., 9., 7., 9.],
# [ 10., 29., 22., 27., 0.]]])
这里我之前看过然后再看到的时候又是一头雾水，然后记录在此！主要是这个
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
可是这个gather函数可以干什么呢？直观上就是output和input的形状是一样的，自己推导一两个看看，比如dim=1
output[0][0][0] = input[0] [index[0][0][0]] [0],然后先查找index找到index[0][0][0]=0，然后再查找input[0][0][0]
流程就是这样，所以，index是下标索引，其值不能超过dim的维度！
直观上就是在某个维度整了个新的映射规则得到output，关键还在于index！这个就是规则。
50. torch::argsort(libtorch1.0没有这个函数) torch::sort
用1.1写好的一个libtorch工程，由于项目是用1.0的，然后把写好的1.1转1.0.然后提示说：
error: ‘argsort’ is not a member of ‘torch’
恩，我知道了，就是由于版本问题导致函数名对不上，可是我去哪里找argsort啊，然后，看到之前的max好像有记录索引的，然后又看到sort，然后实验了一下，和argsort结果一样！
//pytorch1.1
torch::Tensor edge_idx_sort2 = torch::argsort(edge_num, 2, true);
//pytorch1.0
std::tupletorch::Tensor,torch::Tensor sort_ret = torch::sort(edge_num, 2, true);
// torch::Tensor v = std::get<0>(sort_ret);
torch::Tensor edge_idx_sort = std::get<1>(sort_ret);
51. 判断tensor是否为空 ind_mask.sizes().empty()
int row = ind_mask.size(0);
如果ind_mask是空代码就会奔溃报错，
terminate called after throwing an instance of 'c10::Error'
what(): dimension specified as 0 but tensor has no dimensions (maybe_wrap_dim at /data_1/leon_develop/pytorch/aten/src/ATen/core/WrapDimMinimal.h:9)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x6a (0x7f4cf0a4af5a in /data_2/project_202009/chejian/3rdparty/libtorch/lib/libc10.so)
frame #1: <unknown function> + 0x48a74f (0x7f4d010af74f in /data_2/project_202009/chejian/3rdparty/libtorch/lib/libcaffe2.so)
frame #2: at::native::size(at::Tensor const&, long) + 0x20 (0x7f4d010afac0 in /data_2/project_202009/chejian/3rdparty/libtorch/lib/libcaffe2.so)
frame #3: at::Tensor::size(long) const + 0x36 (0x467fba in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #4: deal_mask_index(at::Tensor, at::Tensor) + 0x1a7 (0x45a83e in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #5: get_gcn_feature(at::Tensor, at::Tensor, at::Tensor, int, int) + 0x4f3 (0x45e092 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #6: init_poly(std::shared_ptr<torch::jit::script::Module> const&, std::shared_ptr<torch::jit::script::Module> const&, at::Tensor const&, std::tuple<at::Tensor, at::Tensor, at::Tensor> const&) + 0x168 (0x45e777 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #7: main + 0xaee (0x463ab5 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #8: __libc_start_main + 0xf0 (0x7f4ced29c840 in /lib/x86_64-linux-gnu/libc.so.6)
frame #9: _start + 0x29 (0x456b89 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
所以，有必要判断tensor是否为空，可是：
ind_mask.numel() //返回总个数，但是为空的时候返回1
ind_mask.sizes()// 返回类似python list的东东，[1, 100, 40, 2] [1, 40, 2]
ind_mask.sizes()然后我跟到sizes()libtorch函数定义里面是IntList类型的，然后再跟踪，using IntList = ArrayRef<int64_t>;然后再跟踪，ArrayRef，然后看这个类，找到
/// empty - Check if the array is empty.
constexpr bool empty() const {
return Length == 0;
}
所以，说明有判断为空的成员函数可以调用！
if(ind_mask.sizes().empty())
{
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
我也太难了吧！
本来以为搞定了的！
if(ind_mask.sizes().empty())
{
torch::Tensor tmp;
return tmp;
}
当判断一个tensor为空，我就创建一个tensor退出，因为函数返回是torch::Tensor类型的。
但是直接创建的这个tensor访问sizes也会报错！！！
如下：
torch::Tensor tmp;
tmp.print(); //打印[UndefinedTensor]
if(tmp.sizes().empty())
{
}
[UndefinedTensor]
terminate called after throwing an instance of 'c10::Error'
what(): sizes() called on undefined Tensor (sizes at /data_1/leon_develop/pytorch/aten/src/ATen/core/UndefinedTensorImpl.cpp:12)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x6a (0x7f35f1b21f5a in /data_2/project_202009/chejian/3rdparty/libtorch/lib/libc10.so)
frame #1: at::UndefinedTensorImpl::sizes() const + 0x77 (0x7f360217d6b7 in /data_2/project_202009/chejian/3rdparty/libtorch/lib/libcaffe2.so)
frame #2: at::Tensor::sizes() const + 0x27 (0x45e921 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #3: main + 0x55 (0x45bcaa in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
frame #4: __libc_start_main + 0xf0 (0x7f35ee373840 in /lib/x86_64-linux-gnu/libc.so.6)
frame #5: _start + 0x29 (0x44f889 in /data_2/project_202009/libtorch/snake_libtorch_cuda8/cmake-build-debug/example-app)
但是这个时候：
torch::Tensor tmp;
tmp.print();
std::cout<<tmp.numel()<<std::endl; // 输出为0
！！！！
所以，直接定义tensor，这个时候的.numel()为0.
小弟不才，同时谢谢友情赞助！
每一个不曾起舞的日子，都是对生命的辜负。
But it is the same with man as with the tree. The more he seeks to rise into the height and light, the more vigorously do his roots struggle earthward, downward, into the dark, the deep - into evil.
其实人跟树是一样的，越是向往高处的阳光，它的根就越要伸向黑暗的地底。----尼采
