Блог

Я написал простой скрипт pytorch для обучения MNIST, и он отлично работал. Я переопределил свой скрипт для использования с обучаемым классом:

 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, transform=mnist_transforms),
            batch_size=64,
            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, (data, 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 = data.to(self.device), 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)
        }
    )
  

Однако, когда я пытаюсь запустить, на этот раз это не удается:

 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])
  

В чем может быть причина этого?

Это потому, что вы фактически переопределяете существующий train метод в Trainable . Если вы переименуете свой train метод во что-то другое, он должен работать так, как ожидалось.