使用ray tunes可训练的API训练pytorch模型时，类型'NoneType'不会出现迭代错误

如何解决使用ray tunes可训练的API训练pytorch模型时，类型'NoneType'不会出现迭代错误

我写了一个简单的pytorch脚本来训练MNIST，它运行良好。我重新实现了与Trainable类一起使用的脚本：

import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torchvision import datasets,transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
import ray

from ray import tune

# Change these values if you want the training to run quicker or slower.
EPOCH_SIZE = 512
TEST_SIZE = 256


class ConvNet(nn.Module):

    def __init__(self):
        super(ConvNet,self).__init__()
        # In this example,we don't change the model architecture
        # due to simplicity.
        self.conv1 = nn.Conv2d(1,3,kernel_size=3)
        self.fc = nn.Linear(192,10)

    def forward(self,x):
        x = F.relu(F.max_pool2d(self.conv1(x),3))
        x = x.view(-1,192)
        x = self.fc(x)
        return F.log_softmax(x,dim=1)


class AlexTrainer(tune.Trainable):

    def setup(self,config):

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        # Data Setup
        mnist_transforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])

        self.train_loader = DataLoader(
            datasets.MNIST("~/data",train=True,download=True,transform=mnist_transforms),batch_size=64,shuffle=True)
        self.test_loader = DataLoader(
            datasets.MNIST("~/data",train=False,shuffle=True)

        self.model = ConvNet()
        self.optimizer = optim.SGD(self.model.parameters(),lr=config["lr"],momentum=config["momentum"])

        print('finished setup')

    def step(self):

        self.train()
        print("after train")
        acc = self.test()

        return {'acc': acc}

    def train(self):

        print("in train")

        self.model.train()
        for batch_idx,(data,target) in enumerate(self.train_loader):

            # We set this just for the example to run quickly.
            if batch_idx * len(data) > EPOCH_SIZE:
                return

            data,target = data.to(self.device),target.to(self.device)
            self.optimizer.zero_grad()

            print(type(data))

            output = self.model(data)
            loss = F.nll_loss(output,target)
            loss.backward()

            self.optimizer.step()

    def test(self):
        self.model.eval()

        correct = 0
        total = 0
        with torch.no_grad():
            for batch_idx,target) in enumerate(self.test_loader):
                # We set this just for the example to run quickly.
                if batch_idx * len(data) > TEST_SIZE:
                    break
                data,target.to(self.device)
                outputs = self.model(data)
                _,predicted = torch.max(outputs.data,1)
                total += target.size(0)
                correct += (predicted == target).sum().item()

        return correct / total


if __name__ == '__main__':
    ray.init()
    analysis = tune.run(
        AlexTrainer,stop={"training_iteration": 2},# verbose=1,config={
            "lr": tune.sample_from(lambda spec: 10 ** (-10 * np.random.rand())),"momentum": tune.uniform(0.1,0.9)
        }
    )

但是，当我尝试运行时，这次却失败了：

Traceback (most recent call last):
  File "/hdd/raytune/venv/lib/python3.6/site-packages/ray/tune/trial_runner.py",line 473,in _process_trial
    is_duplicate = RESULT_DUPLICATE in result
TypeError: argument of type 'NoneType' is not iterable
Traceback (most recent call last):
  File "/hdd/raytune/test_3.py",line 116,in <module>
    "momentum": tune.uniform(0.1,0.9)
  File "/hdd/raytune/venv/lib/python3.6/site-packages/ray/tune/tune.py",line 356,in run
    raise TuneError("Trials did not complete",incomplete_trials)
ray.tune.error.TuneError: ('Trials did not complete',[AlexTrainer_9b3cd_00000])

这可能是什么原因？

解决方法

这是因为您实际上是在覆盖train中现有的Trainable方法。如果您将train方法重命名为其他方法，则应该可以正常使用。