Upload Load_Datasets_Example.ipynb

Load_Datasets_Example.ipynb

@@ -0,0 +1,104 @@
+{
+ "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
+}