Metadata-Version: 2.1
Name: msadapter
Version: 0.1.0b0
Summary: MSAdapter is a toolkit for support the PyTorch model running on Ascend.
Home-page: https://openi.pcl.ac.cn/OpenI/MSAdapter 
Author: Peng Cheng Lab, HUAWEI
Author-email: pcl.openi@pcl.ac.cn
License: Apache 2.0
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: OS Independent
Description-Content-Type: text/plain
License-File: LICENSE

Introduction

=============

MSAdapter is MindSpore tool for adapting the PyTorch interface, which is designed to make PyTorch code perform efficiently on Ascend without changing the habits of the original PyTorch users.



|MSAdapter-architecture|



Install

=======



MSAdapter has some prerequisites that need to be installed first, including MindSpore, PIL, NumPy.



.. code:: bash



    # for last stable version

    pip install msadapter



    # for latest release candidate

    pip install --upgrade --pre msadapter



Alternatively, you can install the latest or development version by directly pulling from OpenI:



.. code:: bash



    pip3 install git+https://openi.pcl.ac.cn/OpenI/MSAdapter.git



User guide

===========

For data processing and model building, MSAdapter can be used in the same way as PyTorch, while the model training part of the code needs to be customized, as shown in the following example.



1. Data processing (only modify the import package)



.. code:: python



    from msadapter.pytorch.utils.data import DataLoader

    from msadapter.torchvision import datasets, transforms



    transform = transforms.Compose([transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),

                                    transforms.ToTensor(),

                                    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.247, 0.2435, 0.2616])

                                   ])

    train_images = datasets.CIFAR10('./', train=True, download=True, transform=transform)

    train_data = DataLoader(train_images, batch_size=128, shuffle=True, num_workers=2, drop_last=True)



2. Model construction (modify import package only)



.. code:: python



    from msadapter.pytorch.nn import Module, Linear, Flatten



    class MLP(Module):

        def __init__(self):

            super(MLP, self).__init__()

            self.flatten = Flatten()

            self.line1 = Linear(in_features=1024, out_features=64)

            self.line2 = Linear(in_features=64, out_features=128, bias=False)

            self.line3 = Linear(in_features=128, out_features=10)



        def forward(self, inputs):

            x = self.flatten(inputs)

            x = self.line1(x)

            x = self.line2(x)

            x = self.line3(x)

            return x



3.Model training (custom training)



.. code:: python



    import msadapter.pytorch as torch

    import msadapter.pytorch.nn as nn

    import mindspore as ms



    net = MLP()

    net.train()

    epochs = 500

    criterion = nn.CrossEntropyLoss()

    optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0005)



    # Define the training process

    loss_net = ms.nn.WithLossCell(net, criterion)

    train_net = ms.nn.TrainOneStepCell(loss_net, optimizer)



    for i in range(epochs):

        for X, y in train_data:

            res = train_net(X, y)

            print("epoch:{}, loss:{:.6f}".format(i, res.asnumpy()))

    # Save model

    ms.save_checkpoint(net, "save_path.ckpt")





License

=======



MSAdapter is released under the Apache 2.0 license.



.. |MSAdapter-architecture| image:: https://openi.pcl.ac.cn/laich/pose_data/raw/branch/master/MSA_F.png

