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August 30, 2024 18:01
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Shape annotation in Python
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from typing import TypeVar, Generic, Protocol, runtime_checkable\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import tensorflow as tf\n", | |
| "import torch\n", | |
| "\n", | |
| "DType = TypeVar('DType')\n", | |
| "Shape = TypeVar('Shape')\n", | |
| "\n", | |
| "@runtime_checkable\n", | |
| "class NDArray(Generic[DType, Shape], Protocol):\n", | |
| " def __len__(self) -> int: ...\n", | |
| " def __abs__(self) -> \"NDArray[DType, Shape]\": ...\n", | |
| "\n", | |
| "a: NDArray[float, (3, 2)] = np.ndarray((3, 2), dtype=float)\n", | |
| "b: NDArray[float, (3, 2)] = tf.constant([[1., 2.], [3., 4.], [5., 6.]])\n", | |
| "c: NDArray[float, (3, 2)] = torch.tensor([[1., 2.], [3., 4.], [5., 6.]])\n", | |
| "d: NDArray[float, (3, 2)] = [[1., 2.], [3., 4.], [5., 6.]]\n", | |
| "\n", | |
| "assert isinstance(a, np.ndarray)\n", | |
| "assert isinstance(a, NDArray)\n", | |
| "\n", | |
| "assert isinstance(b, tf.Tensor)\n", | |
| "assert isinstance(b, NDArray)\n", | |
| "\n", | |
| "assert isinstance(c, torch.Tensor)\n", | |
| "assert isinstance(c, NDArray)\n", | |
| "\n", | |
| "assert isinstance(d, list)\n", | |
| "assert not isinstance(d, NDArray)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from typing import TypeVar, TypeVarTuple, Generic, Protocol, runtime_checkable\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import tensorflow as tf\n", | |
| "import torch\n", | |
| "\n", | |
| "DType = TypeVar('DType')\n", | |
| "Shape = TypeVarTuple('Shape')\n", | |
| "\n", | |
| "@runtime_checkable\n", | |
| "class NDArray(Generic[DType, *Shape], Protocol):\n", | |
| " def __len__(self) -> int: ...\n", | |
| " def __abs__(self) -> \"NDArray[DType, *Shape]\": ...\n", | |
| "\n", | |
| "a: NDArray[float, 3, 2] = np.ndarray((3, 2), dtype=float)\n", | |
| "b: NDArray[float, 3, 2] = tf.constant([[1., 2.], [3., 4.], [5., 6.]])\n", | |
| "c: NDArray[float, 3, 2] = torch.tensor([[1., 2.], [3., 4.], [5., 6.]])\n", | |
| "d: NDArray[float, 3, 2] = [[1., 2.], [3., 4.], [5., 6.]]\n", | |
| "\n", | |
| "assert isinstance(a, np.ndarray)\n", | |
| "assert isinstance(a, NDArray)\n", | |
| "\n", | |
| "assert isinstance(b, tf.Tensor)\n", | |
| "assert isinstance(b, NDArray)\n", | |
| "\n", | |
| "assert isinstance(c, torch.Tensor)\n", | |
| "assert isinstance(c, NDArray)\n", | |
| "\n", | |
| "assert isinstance(d, list)\n", | |
| "assert not isinstance(d, NDArray)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": ".venv", | |
| "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": "3.12.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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