Pytorch_Dart #

Pytorch_Dart is a dart wrapper for Libtorch,striving to provide an experience identical to PyTorch. You can use it as an alternative to Numpy in your Dart/Flutter projects.

This package is experimental and APIs may change in the future.

Theoretically you can run pytorch_dart on MacOS by simply replace /libtorch-linux/libtorch with libtorch for MacOS.

Getting Started #

Add pytorch_dart to your pubspec.yaml #

    pytorch_dart:^0.0.7

Setup #

dart run pytorch_dart:setup --platform <your_platform>

<your_platform> only support linux , android and windows now.

Enjoy it! #

    import 'package:pytorch_dart/pytorch_dart.dart' as torch;

For Android developers #

libtorch for android needs a specific version of NDK,so install NDK and choose version 21.4.7075529

Then add a row in android/local.properties in your project

ndk.dir=<path_to_your_ndk>/21.4.7075529

After adding a column,your local.properties should look like this:

flutter.sdk=/home/pc/flutter
    sdk.dir=/home/pc/Android/Sdk
    flutter.buildMode=debug
    ndk.dir=/home/pc/Android/Sdk/ndk/21.4.7075529

For windows developers #

Troubleshooting

Launching lib\main.dart on Windows in debug mode...
√  Built build\windows\x64\runner\Debug\example.exe.
Error waiting for a debug connection: The log reader stopped unexpectedly, or never started.
Error launching application on Windows.

Solutions:

1. Download libtorch from here
2. Unzip it
3. copy all the files from libtorch\lib\ to build\windows\x64\runner\Debug\

Usage #

Brief Introduction #

1. It include some basic functions in torch now.
2. Support for torch.nn is coming soon.
3. Almost all function usages remain consistent with PyTorch.
4. Example

var d=torch.eye(3,2);
print(d);

Result:

flutter:
 1  0
 0  1
 0  0
[ CPUFloatType{3,2} ]

Operator overloading #

Attention:Dart has no magic functions(like _radd_ in python).Therefore, in binary operators, tensor can only be on the left side.

Example

import 'package:pytorch_dart/pytorch_dart.dart' as torch;
...

var c=torch.DoubleTensor([[1.0,2.0,3.0],[4.0,5.0,6.0]]);
var d=c+10;// no errors
var e=10+c;//cause errors

Other binary operators (-,*,/)are just like +

For operator [] ,you can use it just like in Pytorch.

Example

import 'package:pytorch_dart/pytorch_dart.dart' as torch;
...

var c=torch.DoubleTensor([[1.0,2.0,3.0],[4.0,5.0,6.0]]);
print(c[0][0]);

Result

flutter: 1
[ CPUDoubleType{} ]

torch #

1. torch.tensor() is not supported in pytorch_dart,use torch.IntTensor(),torch.FloatTensor or torch.DoubleTensor to create tensors.

2. Functions avaliable now: torch.empty() torch.eye() torch.ones() torch.IntTensor(List<int> list) torch.FloatTensor(List<double> list) torch.DoubleTensor(List<double> list) torch.arange(double start, double end, double step,{bool requiresGrad = false}) torch.linspace(double start, double end, int steps,{bool requiresGrad = false}) torch.logspace(double start, double end, int steps, double base,{bool requiresGrad = false}) torch.equal(Tensor a,Tensor b) torch.add(Tensor a, tensor b,{double alpha=1}) torch.sub(Tensor a, tensor b,{double alpha=1}) torch.mul(Tensor a, tensor b) torch.div(Tensor a, tensor b) torch.add_(Tensor a, tensor b,{double alpha=1}) torch.sub_(Tensor a, tensor b,{double alpha=1}) torch.mul_(Tensor a, tensor b) torch.div_(Tensor a, tensor b) torch.sum(Tensor a) torch.mm(Tensor a, Tensor b) torch.transpose(Tensor a,int dim0,int dim1) torch.permute(Tensor a,List <int> permute_list)

3. Almost all function usages remain consistent with PyTorch.

4. Broadcasting also works for pytorch_dart.

5. Some in-place operation are supported,such as torch.add_()

6. Example

   import 'package:pytorch_dart/pytorch_dart.dart' as torch;
   ...
   
   var c=torch.DoubleTensor([[1.0,2.0,3.0],[4.0,5.0,6.0]]);
   var d=torch.add(10,c)
   print(d)
   

   Result:

   flutter:
    11  12  13
    14  15  16
   [ CPUDoubleType{2,3} ]
   

torch.tensor #

1. Functions avaliable now: .dim() .dtype() .shape() .size() .detach() .add_() .sub_() .mul_() .div_()

2. .dtype() is different from its implementation in Pytorch. In Pytorch,.dtype returns an object represents the data type of a tensor But in pytorch_dart,.dtype() returns a number represents the data type of a tensor.(maybe I will rewrite it later) Example

   import 'package:pytorch_dart/pytorch_dart.dart' as torch;
   ...
   
   var c=torch.DoubleTensor([[1.0,2.0,3.0],[4.0,5.0,6.0]]);
   print(c.dtype())
   

   Result

   flutter: 7
   

   7 represents torch.float64.

3. Other function usages remain consistent with PyTorch.

Roadmap #

1. Add support for iOS and MacOS.
2. Add support for other functions,such as torch.nn

Acknowledgement #

This project uses pytorch-flutter-FFI-example and gotorch