PyTorch-Lightning执行trainer.predict()时出现AttributeError错误求助

基于PyTorch-Lightning官方MNIST训练示例添加预测步骤后，执行trainer.predict(model)时触发如下错误：

AttributeError: 'list' object has no attribute 'flatten'

已按照教程添加predict_step、predict_dataloader方法，并在setup函数中配置了predict阶段，但问题仍未解决，完整运行代码如下：

import os

import torch
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from pytorch_lightning.loggers import CSVLogger
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision import transforms
from torchvision.datasets import MNIST

PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
BATCH_SIZE = 256 if torch.cuda.is_available() else 64


class LitMNIST(LightningModule):
    def __init__(self, data_dir=PATH_DATASETS, hidden_size=64, learning_rate=2e-4):

        super().__init__()

        # Set our init args as class attributes
        self.data_dir = data_dir
        self.hidden_size = hidden_size
        self.learning_rate = learning_rate

        # Hardcode some dataset specific attributes
        self.num_classes = 10
        self.dims = (1, 28, 28)
        channels, width, height = self.dims
        self.transform = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
            ]
        )

        # Define PyTorch model
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(channels * width * height, hidden_size),
            nn.ReLU(),
            nn.Dropout(p=0.9),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_size, self.num_classes),
        )

        self.val_accuracy = Accuracy(task='multiclass', num_classes=10)  # I fixed this since the code from the tutorial didn't work
        self.test_accuracy = Accuracy(task='multiclass', num_classes=10)  # I fixed this since the code from the tutorial didn't work

    def forward(self, x):
        x = self.model(x)
        return F.log_softmax(x, dim=1)

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        self.val_accuracy.update(preds, y)

        # Calling self.log will surface up scalars for you in TensorBoard
        self.log("val_loss", loss, prog_bar=True)
        self.log("val_acc", self.val_accuracy, prog_bar=True)

    def test_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        self.test_accuracy.update(preds, y)

        # Calling self.log will surface up scalars for you in TensorBoard
        self.log("test_loss", loss, prog_bar=True)
        self.log("test_acc", self.test_accuracy, prog_bar=True)

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
        return optimizer

    ####################
    # DATA RELATED HOOKS
    ####################

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage=None):
        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)

        if stage == "predict":
            self.mnist_predict = MNIST(self.data_dir, train=False, transform=self.transform)

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)

    def predict_dataloader(self):
        return DataLoader(self.mnist_predict, batch_size=BATCH_SIZE)

model = LitMNIST(hidden_size=2)
trainer = Trainer(
    accelerator="auto",
    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
    max_epochs=3,
    callbacks=[TQDMProgressBar(refresh_rate=20)],
    logger=CSVLogger(save_dir="logs/"),
)
trainer.fit(model)
predict = trainer.predict(model)

错误根源 #

MNIST数据集的DataLoader返回的batch是**(图像张量, 标签)**的元组，而当前predict_step中直接将整个batch传入self()（即forward方法），导致forward接收的是一个元组而非图像张量。模型中的nn.Flatten()层只能处理张量，无法处理元组/列表，因此触发'list' object has no attribute 'flatten'错误。

修复方法 #

修改predict_step方法，从batch中提取图像张量部分，忽略标签后再传入模型：

def predict_step(self, batch, batch_idx, dataloader_idx=0):
    x, _ = batch  # 提取图像张量x，丢弃标签
    return self(x)

修改后，forward方法将接收到正确的图像张量，模型即可正常执行预测步骤。

内容的提问来源于stack exchange，提问作者yshicht