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{
"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
}
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