Create a dataloader using the ImageNet dataset in starter python code below

import json from glob import glob from PIL import Image

import torch import torch.nn as nn import torch.utils.data as data from torchvision.models import alexnet import torchvision.transforms as transforms

class ImageNet(data.Dataset): def __init__(self, path): self.path = path self.folder_paths = glob("{}/*/".format(self.path)) self.json_path = "{}/imagenet_class_index.json".format(self.path)

with open("{}/imagenet_class_index.json".format(self.path), "r") as f: self.lbl_dic = json.load(f) self.lbl_dic = {v[0]: int(k) for k, v in self.lbl_dic.items()}

self.img_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ])

self.imgs = [] self.lbls = [] for folder_path in self.folder_paths: image_paths = glob("{}/*".format(folder_path)) self.imgs += image_paths self.lbls += [self.lbl_dic[folder_path.split("/")[-2]]] * len(image_paths)

def __getitem__(self, index): img = Image.open(self.imgs[index]).convert("RGB") img = self.img_transforms(img) lbl = self.lbls[index] return img, lbl

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

if __name__ == "__main__": # Your code goes here pass