gotch
1.0.0
gotch为 Pytorch C++ API (Libtorch) 创建了一个薄包装器，以利用其已经优化的 C++ 张量 API (3039) 和支持 CUDA 的动态图计算，并提供惯用的 Go API 用于在 Go 中开发和实现深度学习。
一些功能是
gotch处于活跃开发模式，可能会有 API 重大更改。请随意提出请求、报告问题或讨论任何问题。欢迎所有贡献。
gotch当前版本是v0.9.1
11.8 ，否则使用 CPU 版本。2.1.0注意： libtorch将安装在/usr/local/lib
wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-libtorch.sh
chmod +x setup-libtorch.sh
export CUDA_VER=cpu && bash setup-libtorch.sh更新环境：在 Debian/Ubuntu 中，将以下行添加/更新到.bashrc文件
export GOTCH_LIBTORCH= " /usr/local/lib/libtorch "
export LIBRARY_PATH= " $LIBRARY_PATH : $GOTCH_LIBTORCH /lib "
export CPATH= " $CPATH : $GOTCH_LIBTORCH /lib: $GOTCH_LIBTORCH /include: $GOTCH_LIBTORCH /include/torch/csrc/api/include "
export LD_LIBRARY_PATH= " $LD_LIBRARY_PATH : $GOTCH_LIBTORCH /lib " wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-gotch.sh
chmod +x setup-gotch.sh
export CUDA_VER=cpu && export GOTCH_VER=v0.9.1 && bash setup-gotch.sh注意：确保您的机器有可用的 CUDA。
nvidia-smi wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-libtorch.sh
chmod +x setup-libtorch.sh
export CUDA_VER=11.8 && bash setup-libtorch.sh更新环境：在 Debian/Ubuntu 中，将以下行添加/更新到.bashrc文件
export GOTCH_LIBTORCH= " /usr/local/lib/libtorch "
export LIBRARY_PATH= " $LIBRARY_PATH : $GOTCH_LIBTORCH /lib "
export CPATH= " $CPATH : $GOTCH_LIBTORCH /lib: $GOTCH_LIBTORCH /include: $GOTCH_LIBTORCH /include/torch/csrc/api/include "
LD_LIBRARY_PATH= " $LD_LIBRARY_PATH : $GOTCH_LIBTORCH /lib:/usr/lib64-nvidia:/usr/local/cuda- ${CUDA_VERSION} /lib64 " wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-gotch.sh
chmod +x setup-gotch.sh
export CUDA_VER=11.8 && export GOTCH_VER=v0.9.1 && bash setup-gotch.sh import (
"fmt"
"github.com/sugarme/gotch"
"github.com/sugarme/gotch/ts"
)
func basicOps () {
xs := ts . MustRand ([] int64 { 3 , 5 , 6 }, gotch . Float , gotch . CPU )
fmt . Printf ( "%8.3f n " , xs )
fmt . Printf ( "%i" , xs )
/*
(1,.,.) =
0.391 0.055 0.638 0.514 0.757 0.446
0.817 0.075 0.437 0.452 0.077 0.492
0.504 0.945 0.863 0.243 0.254 0.640
0.850 0.132 0.763 0.572 0.216 0.116
0.410 0.660 0.156 0.336 0.885 0.391
(2,.,.) =
0.952 0.731 0.380 0.390 0.374 0.001
0.455 0.142 0.088 0.039 0.862 0.939
0.621 0.198 0.728 0.914 0.168 0.057
0.655 0.231 0.680 0.069 0.803 0.243
0.853 0.729 0.983 0.534 0.749 0.624
(3,.,.) =
0.734 0.447 0.914 0.956 0.269 0.000
0.427 0.034 0.477 0.535 0.440 0.972
0.407 0.945 0.099 0.184 0.778 0.058
0.482 0.996 0.085 0.605 0.282 0.671
0.887 0.029 0.005 0.216 0.354 0.262
TENSOR INFO:
Shape: [3 5 6]
DType: float32
Device: {CPU 1}
Defined: true
*/
// Basic tensor operations
ts1 := ts . MustArange ( ts . IntScalar ( 6 ), gotch . Int64 , gotch . CPU ). MustView ([] int64 { 2 , 3 }, true )
defer ts1 . MustDrop ()
ts2 := ts . MustOnes ([] int64 { 3 , 4 }, gotch . Int64 , gotch . CPU )
defer ts2 . MustDrop ()
mul := ts1 . MustMatmul ( ts2 , false )
defer mul . MustDrop ()
fmt . Printf ( "ts1: n %2d" , ts1 )
fmt . Printf ( "ts2: n %2d" , ts2 )
fmt . Printf ( "mul tensor (ts1 x ts2): n %2d" , mul )
/*
ts1:
0 1 2
3 4 5
ts2:
1 1 1 1
1 1 1 1
1 1 1 1
mul tensor (ts1 x ts2):
3 3 3 3
12 12 12 12
*/
// In-place operation
ts3 := ts . MustOnes ([] int64 { 2 , 3 }, gotch . Float , gotch . CPU )
fmt . Printf ( "Before: n %v" , ts3 )
ts3 . MustAddScalar_ ( ts . FloatScalar ( 2.0 ))
fmt . Printf ( "After (ts3 + 2.0): n %v" , ts3 )
/*
Before:
1 1 1
1 1 1
After (ts3 + 2.0):
3 3 3
3 3 3
*/
} import (
"fmt"
"github.com/sugarme/gotch"
"github.com/sugarme/gotch/nn"
"github.com/sugarme/gotch/ts"
)
type Net struct {
conv1 * nn. Conv2D
conv2 * nn. Conv2D
fc * nn. Linear
}
func newNet ( vs * nn. Path ) * Net {
conv1 := nn . NewConv2D ( vs , 1 , 16 , 2 , nn . DefaultConv2DConfig ())
conv2 := nn . NewConv2D ( vs , 16 , 10 , 2 , nn . DefaultConv2DConfig ())
fc := nn . NewLinear ( vs , 10 , 10 , nn . DefaultLinearConfig ())
return & Net {
conv1 ,
conv2 ,
fc ,
}
}
func ( n Net ) ForwardT ( xs * ts. Tensor , train bool ) * ts. Tensor {
xs = xs . MustView ([] int64 { - 1 , 1 , 8 , 8 }, false )
outC1 := xs . Apply ( n . conv1 )
outMP1 := outC1 . MaxPool2DDefault ( 2 , true )
defer outMP1 . MustDrop ()
outC2 := outMP1 . Apply ( n . conv2 )
outMP2 := outC2 . MaxPool2DDefault ( 2 , true )
outView2 := outMP2 . MustView ([] int64 { - 1 , 10 }, true )
defer outView2 . MustDrop ()
outFC := outView2 . Apply ( n . fc )
return outFC . MustRelu ( true )
}
func main () {
vs := nn . NewVarStore ( gotch . CPU )
net := newNet ( vs . Root ())
xs := ts . MustOnes ([] int64 { 8 , 8 }, gotch . Float , gotch . CPU )
logits := net . ForwardT ( xs , false )
fmt . Printf ( "Logits: %0.3f" , logits )
}
//Logits: 0.000 0.000 0.000 0.225 0.321 0.147 0.000 0.207 0.000 0.000 gotchgotch已获得 Apache 2.0 许可。
