Ray框架下pytorch模型训练（图像分类）_ray datasets

最近学习Ray框架进行分布式模型训练，Ray框架下的pytorch模型与普通的pytorch框架还是有一定区别，记录一下留做笔记。

这里没有用官网文档给的数据集，在上一篇写了如何加载自己的pytorch数据集，在定义训练模型时，在TorchTrainer中不指定数据集参数，在训练模型中直接加载自己的数据集，就可以实现训练自己的数据集

import torch
import torch.nn as nn
import torch.nn.functional as F
from ray import train
import torch.optim as optim
from torch.utils.data import DataLoader

from picread_data import generate_map, MyDatasets


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1)  # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


def train_loop_per_worker(config):
    generate_map(config["put_in"], 2)
    # 数据加载
    trainloader = DataLoader(MyDatasets('D:/tmp/photo', 'trainmap.txt'), batch_size=config["batch_size"], shuffle=True)

    testloader = DataLoader(MyDatasets('D:/tmp/photo', 'testmap.txt'), batch_size=config["batch_size"], shuffle=True)

    model = train.torch.prepare_model(Net())

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

    for epoch in range(2):
        running_loss = 0.0
        for i, data in enumerate(trainloader):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print(f"[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}")
                running_loss = 0.0

        train.save_checkpoint(model=model.module.state_dict())


from ray.ml.train.integrations.torch.torch_trainer import TorchTrainer

trainer = TorchTrainer(
    train_loop_per_worker=train_loop_per_worker,
    train_loop_config={"batch_size": 2,
                       "put_in": "D:/tmp/photo"},
    # datasets={"train": train_dataset},
    scaling_config={"num_workers": 2}
)
result = trainer.fit()
latest_checkpoint = result.checkpoint

scaling_config={"num_workers": 2}这里可以指定训练资源