""" This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause. """ import os import pickle import sys import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from utils.mypath import MyPath from torchvision.datasets.utils import check_integrity, download_and_extract_archive class simclr_c10(Dataset): base_folder = 'D:/wjd/simclr_c10' filename = "simclr_c10" def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False): super('wjd/simclr_c10/', self).__init__() self.data = None self.root = root self.transform = transform self.train = train # training set or test set self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo'] def __getitem__(self, index): """ Args: index (int): Index Returns: dict: {'image': image, 'target': index of target class, 'meta': dict} """ img, target = self.data[index], self.targets[index] img_size = (img.shape[0], img.shape[1]) img = Image.fromarray(img) class_name = self.classes[target] if self.transform is not None: img = self.transform(img) out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}} return out def get_image(self, index): img = self.data[index] return img def __len__(self): return len(self.data) def extra_repr(self): return "Split: {}".format("Train" if self.train is True else "Test") return outside function，这段代码正确吗

这段代码有一些问题：
1. `super('wjd/simclr_c10/', self).__init__()` 这句话不正确，应该是 `super(simclr_c10, self).__init__()`
2. `self.targets` 在代码中没有定义，需要在 `__init__` 函数中将其定义并初始化
3. `return outside function` 这句话不应该出现在类定义中，需要将其删除
4. 另外，`check_integrity` 和 `download_and_extract_archive` 函数在代码中没有使用，可以删除

修正后的代码如下所示：

import os
import pickle
import sys
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from utils.mypath import MyPath

class simclr_c10(Dataset):
    base_folder = 'D:/wjd/simclr_c10'
    filename = "simclr_c10"

    def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False):
        super(simclr_c10, self).__init__()
        self.data = None
        self.targets = []
        self.root = root
        self.transform = transform
        self.train = train  # training set or test set
        self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo']
        self._load_data()

    def _load_data(self):
        if self.train:
            data_file = os.path.join(self.root, self.base_folder, 'train.pkl')
        else:
            data_file = os.path.join(self.root, self.base_folder, 'test.pkl')
        with open(data_file, 'rb') as f:
            data = pickle.load(f)
        self.data = data['data']
        self.targets = data['targets']

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            dict: {'image': image, 'target': index of target class, 'meta': dict}
        """
        img, target = self.data[index], self.targets[index]
        img_size = (img.shape[0], img.shape[1])
        img = Image.fromarray(img)
        class_name = self.classes[target]

        if self.transform is not None:
            img = self.transform(img)

        out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}}
        return out

    def get_image(self, index):
        img = self.data[index]
        return img

    def __len__(self):
        return len(self.data)

    def extra_repr(self):
        return "Split: {}".format("Train" if self.train is True else "Test")