I have attached dataloader that I have written for Imagenet. I have saved the paths to images with their respective classname in .npy file and load it from there. Hierarchy of file is same as provided in Kaggle dataset for Imagenet. I am not sure if it is correct. It seems to return image tensor and label fine, but model accuracy is very low - so I was wondering if I did any mistake in this part. Any feedback will be highly appreciated! Thank you!

```
# Custom Dataloader
class CustomDataset(Dataset):
def __init__(self,train=True,transform=None):
self.data = []
self.transform = transform
# Class to Label Encoding Loading
class_to_label_loading = np.load("~/model_implementation/AlexNet/class_to_label.npy",allow_pickle=True)
self.class_to_label = class_to_label_loading.item()
if not train:
self.data = np.load("~/model_implementation/AlexNet/val_data.npy")
else:
self.data = np.load("~/model_implementation/AlexNet/train_data.npy")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
img_path, class_name = self.data[idx]
img = Image.open(img_path)
rgb_img = img.convert('RGB')
label = self.class_to_label[class_name]
if self.transform is not None:
img_tensor = self.transform(rgb_img)
label_tensor = torch.tensor([label])
return img_tensor, label_tensor
```

and transform is pretty standard one for Imagenet dataset like this:

```
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
```