使用MindTorch

使用MindTorch迁移PyTorch网络前，第一步是替换导入模块路径。

方式一：一行代码自动替换（推荐）

用户只需要在PyTorch源代码主入口调用torch系列相关的包导入部分之前调用from mindtorch.tools import mstorch_enable ，代码执行时torch同名的导入模块会自动被转换为mindtorch相应的模块（目前支持torch、torchvision、torchaudio相关模块的自动转换）。

from mindtorch.tools import mstorch_enable   # 需要在主入口文件导入torch相关模块的前面使用
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

# 1.Working with data
# Download training data from open datasets.
training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
# Download test data from open datasets.
test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())

# 2.Creating Models
class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

def train(dataloader, model, loss_fn, optimizer, device):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

def test(dataloader, model, loss_fn, device):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

if __name__ == '__main__':
    train_dataloader = DataLoader(training_data, batch_size=64)
    test_dataloader = DataLoader(test_data, batch_size=64)
    # Get cpu, gpu or mps device for training.
    device = (
        "cuda"
        if torch.cuda.is_available()
        else "mps"
        if torch.backends.mps.is_available()
        else "cpu"
    )
    model = NeuralNetwork().to(device)

    # 3.Optimizing the Model Parameters
    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

    epochs = 5
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer, device)
        test(test_dataloader, model, loss_fn, device)
    print("Done!")

    # 4.Saving Models
    torch.save(model.state_dict(), "model.pth")
    print("Saved PyTorch Model State to model.pth")

    # 5.Loading Models
    model = NeuralNetwork().to(device)
    model.load_state_dict(torch.load("model.pth"))

    classes = [
        "T-shirt/top",
        "Trouser",
        "Pullover",
        "Dress",
        "Coat",
        "Sandal",
        "Shirt",
        "Sneaker",
        "Bag",
        "Ankle boot",
    ]
    # 6.Predicted
    model.eval()
    x, y = test_data[0][0], test_data[0][1]
    with torch.no_grad():
        x = x.to(device)
        pred = model(x)
        predicted, actual = classes[pred[0].argmax(0)], classes[y]
        print(f'Predicted: "{predicted}", Actual: "{actual}"')

方式二：工具手动预先替换

替换代码中导入torch相关包的代码，可以利用mindtorch/tools下提供的replace_import_package工具可快速完成工程代码中torch及torchvision相关导入包的替换。

bash replace_import_package.sh [Project Path]

Project Path为需要进行替换的工程路经，默认为＂./＂。

或者，用户也可以逐文件手动的替换文件中的导入包部分代码，示例代码如下：

# 替换前
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# from torchvision import datasets, transforms

# 替换后
import mindtorch.torch as torch
import mindtorch.torch.nn as nn
import mindtorch.torch.nn.functional as F
from mindtorch.torchvision import datasets, transforms

MindTorch目前已支持大部分PyTorch和TorchVision的原生态表达接口，如果在import阶段遇到有不支持的模块或接口报错，可以通过创建ISSUE 向我们反馈，我们会优先支持。