msadapter 0.1.0

简介

MSAdapter是MindSpore工具，用于适配PyTorch接口，旨在使PyTorch代码在Ascend上高效运行，同时不改变原始PyTorch用户的使用习惯。

安装

MSAdapter有一些先决条件需要安装，包括MindSpore、PIL、NumPy。

# for last stable version
pip install msadapter

# for latest release candidate
pip install --upgrade --pre msadapter

或者，您可以直接从OpenI拉取最新或开发版本进行安装

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

用户指南

对于数据处理和模型构建，MSAdapter可以使用与PyTorch相同的方式，而代码中的模型训练部分需要定制，如下面的示例所示。

1. 数据处理（仅修改导入包）

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. 模型构建（仅修改导入包）

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. 模型训练（自定义训练）

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 = ms.nn.SGD(net.trainable_params(), learning_rate=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")

许可证

MSAdapter采用Apache 2.0许可证发布。