Delete Load_Datasets_Example.ipynb

Load_Datasets_Example.ipynb

@@ -1,104 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "from torchvision import datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_path = '/ssd/Datasets/I2E-ImageNet/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class I2E_NpzFolder(datasets.DatasetFolder):\n",
-    "    def __init__(self, root, loader=None, extensions=['npz'], transform=None, target_transform=None, is_valid_file=None, allow_empty=False):\n",
-    "        super(I2E_NpzFolder, self).__init__(root, loader, extensions, transform, target_transform, is_valid_file, allow_empty)\n",
-    "\n",
-    "    def __getitem__(self, index):\n",
-    "        path, target = self.samples[index]\n",
-    "        sample = torch.from_numpy(np.load(path)['arr_0']).float()\n",
-    "        if self.transform is not None:\n",
-    "            sample = self.transform(sample)\n",
-    "        if self.target_transform is not None:\n",
-    "            target = self.target_transform(target)\n",
-    "\n",
-    "        return sample, target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "len(train_dataset): 1281167, len(val_dataset): 50000\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_dataset = I2E_NpzFolder(root=os.path.join(dataset_path, 'train'))\n",
-    "val_dataset = I2E_NpzFolder(root=os.path.join(dataset_path, 'val'))\n",
-    "print(f'len(train_dataset): {len(train_dataset)}, len(val_dataset): {len(val_dataset)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "img.shape: torch.Size([8, 2, 224, 224]), label: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "img, label = train_dataset[0]\n",
-    "print(f'img.shape: {img.shape}, label: {label}')        # [T=8, p=2, H, W]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pytorch291",
-   "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
-}