File size: 1,799 Bytes
ac9c669 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
{
"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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pytorch291n",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|