wehub-resource-sync
170 lines
4.3 KiB
Plaintext
170 lines
4.3 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "561ef1d6-e8fc-431f-9c58-4c862b2813ec",
|
|
"metadata": {},
|
|
"source": [
|
|
"# PyTorch DataLoader State and Nested Iterations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "37d57e04-facc-4543-9939-c724a57ce9c6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2.1.0'"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import torch\n",
|
|
"torch.__version__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9fb9f618-f274-4f76-951f-40508cb85c1d",
|
|
"metadata": {},
|
|
"source": [
|
|
"Iterating over a dataloader in a separate function will not affect its state in the main training loop. In PyTorch, a DataLoader is typically an iterable that can be iterated over multiple times independently. Each iteration over the DataLoader starts from the beginning and goes through the dataset in a fresh sequence (if shuffle is true, the sequence will be different each time).\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "1813f041-0366-4be3-890f-99c1a9c9d831",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"main loop: 1\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 2\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 3\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 4\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 5\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 6\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 7\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 8\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 9\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n",
|
|
"main loop: 10\n",
|
|
"nested loop: 1\n",
|
|
"nested loop: 2\n",
|
|
"nested loop: 3\n",
|
|
"nested loop: 4\n",
|
|
"nested loop: 5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from torch.utils.data import Dataset, DataLoader\n",
|
|
"\n",
|
|
"# Custom Dataset class\n",
|
|
"class IntegerDataset(Dataset):\n",
|
|
" def __init__(self, start, end):\n",
|
|
" self.data = list(range(start, end + 1))\n",
|
|
"\n",
|
|
" def __len__(self):\n",
|
|
" return len(self.data)\n",
|
|
"\n",
|
|
" def __getitem__(self, idx):\n",
|
|
" return self.data[idx]\n",
|
|
"\n",
|
|
"# Create a Dataset for integers 1 to 10\n",
|
|
"integer_dataset = IntegerDataset(1, 10)\n",
|
|
"\n",
|
|
"# Create a DataLoader\n",
|
|
"integer_loader = DataLoader(integer_dataset, batch_size=1, shuffle=False)\n",
|
|
"\n",
|
|
"# A function to estimate the loss based on a subset of training examples\n",
|
|
"def calc_loss(data_loader, iters):\n",
|
|
" for j in integer_loader:\n",
|
|
" print(\"nested loop:\", j.item())\n",
|
|
" if j >= iters: \n",
|
|
" break\n",
|
|
"\n",
|
|
"# Example: Iterate over the DataLoader\n",
|
|
"for i in integer_loader:\n",
|
|
" print(\"main loop:\", i.item())\n",
|
|
" calc_loss(integer_loader, iters=5)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|