{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "from load_i2e_datasets import I2E_Dataset\n",
    "os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'            # Use HF mirror server in some regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_cache_path = 'Your path to model cache'\n",
    "load_datasets = 'I2E-CIFAR10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = I2E_Dataset(model_cache_path, load_datasets, split='train', transform=None, target_transform=None)\n",
    "val_dataset = I2E_Dataset(model_cache_path, load_datasets, split='validation', transform=None, target_transform=None)\n",
    "print(f\"Train samples: {len(train_dataset)}, Validation samples: {len(val_dataset)}\")\n",
    "\n",
    "# You can create dataloaders according to your needs\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=256, shuffle=False, num_workers=32, pin_memory=False)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=256, shuffle=False, num_workers=32, pin_memory=False)"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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