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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.
import torch
in PyTorch source code with import ms_adapter.pytorch
to realize that the model can support training on ascending. The support status of the higher-order APIs used in the model can be found here Supported ListPlease install MindSpore version 2.0.0Nightly according to the Installation Guide on MindSpore official website.
pip install ms_adapter
git clone https://git.openi.org.cn/OpenI/MSAdapter.git
cd MSAdapter
python setup.py install
If there is an insufficient permissions message, install as follows
python setup.py install --user || exit 1
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.
from ms_adapter.pytorch.utils.data import DataLoader
from ms_adapter.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)
from ms_adapter.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
import ms_adapter.pytorch as torch
import ms_adapter.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")
Developers are welcome to contribute. For more details, please see our Contribution Guidelines.
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