3、PyTorch张量的运算API（下）

1. 数值转换

12.34 = 16# 4145 7074
16# 4145 7074 = 12.34

import struct


class IEEE754Converter:
    @staticmethod
    def float_to_hex(f):
        """
        将浮点数转换为IEEE 754格式的16进制表示
        """
        binary_representation = struct.pack('>f', f)
        hex_representation = ''.join(f'{byte:02X}' for byte in binary_representation)
        return f"16#{hex_representation}"

    @staticmethod
    def hex_to_float(hex_str):
        """
        将IEEE 754格式的16进制表示转换为浮点数
        """
        if not hex_str.startswith("16#"):
            raise ValueError("输入必须以 '16#' 开头")
        hex_value = hex_str[3:]  # 去掉 "16#"
        if len(hex_value) != 8:
            raise ValueError("无效的16进制格式（需要8位字符）")
        binary_representation = bytes.fromhex(hex_value)
        return struct.unpack('>f', binary_representation)[0]


# 测试
converter = IEEE754Converter()

# 浮点数转16进制
# input_float = 50.8
input_float = 12.34
hex_result = converter.float_to_hex(input_float)
print(f"输入: {input_float}, 转换为: {hex_result}")

# 16进制转浮点数
input_hex = hex_result
float_result = converter.hex_to_float(input_hex)
print(f"输入: {input_hex}, 转换为: {float_result}")

• 结果：

输入: 12.34, 转换为: 16#414570A4
输入: 16#414570A4, 转换为: 12.34000015258789

2. 相关API

• 重点：torch.take,torch.where,torch.randperm
• python

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :pytorch_learn3.py
# @Time      :2024/11/17 16:08
# @Author    :Jason Zhang
import torch

torch.manual_seed(12232)

if __name__ == "__main__":
    run_code = 0
    test_a = torch.randint(1, 20, (4, 5))
    index_a = torch.randint(1, 10, (1, 10))
    take_a = torch.take(input=test_a, index=index_a)
    print(f"test_a=\n{test_a}")
    print(f"index_a={index_a}")
    print(f"take_a=\n{take_a}")
    test_b = torch.arange(12).reshape(3, 4)
    index_b = [2, 3]
    tile_b = torch.tile(test_b, index_b)
    print(f"test_b=\n{test_b}")
    print(f"index_b=\n{index_b}")
    print(f"tile_b=\n{tile_b}")
    test_c = torch.arange(24).reshape(2, 3, 4)
    transpose_01_c = torch.transpose(test_c, 0, 1)
    transpose_02_c = torch.transpose(test_c, 0, 2)
    transpose_12_c = torch.transpose(test_c, 1, 2)
    print(f"shape={test_c.shape};test_c=\n{test_c}")
    print(f"shape={transpose_01_c.shape};transpose_01_c=\n{transpose_01_c}")
    print(f"transpose01 from {test_c.shape} -->{transpose_01_c.shape}")
    print(f"transpose02 from {test_c.shape} -->{transpose_02_c.shape}")
    print(f"transpose12 from {test_c.shape} -->{transpose_12_c.shape}")
    test_d = torch.arange(12).reshape(3, 4)
    unbind_d0 = torch.unbind(test_d)
    unbind_d1 = torch.unbind(test_d, dim=1)
    print(f"test_d=\n{test_d}")
    print(f"unbind_d0=\n{unbind_d0}")
    print(f"unbind_d1=\n{unbind_d1}")
    test_e = torch.arange(12).reshape(3, 4)
    un_squeeze_d = torch.unsqueeze(test_e, dim=0)
    print(f"test_e=\n{test_e}")
    print(f"un_squeeze_d=\n{un_squeeze_d}")
    print(f"un_squeeze_0 from {test_e.shape} to --> {un_squeeze_d.shape}")
    test_f = torch.randn(3, 4)
    condition_f = torch.zeros_like(test_f)
    where_f = torch.where(test_f > 0, test_f, condition_f)
    print(f"test_f=\n{test_f}")
    print(f"condition_f=\n{condition_f}")
    print(f"where_f=\n{where_f}")
    test_uniform = torch.empty(3, 3).uniform_(0, 1)
    test_bernoulli = torch.bernoulli(test_uniform)
    print(f"test_uniform=\n{test_uniform}")
    print(f"test_bernoulli=\n{test_bernoulli}")
    test_normal = torch.normal(mean=0, std=3, size=(1, 4))
    print(f"test_normal=\n{test_normal}")
    test_rand = torch.rand((3, 4))
    print(f"test_rand=\n{test_rand}")
    test_ones = torch.ones((4, 5))
    test_rand_like = torch.rand_like(test_ones)
    print(f"test_ones=\n{test_ones}")
    print(f"test_rand_like=\n{test_rand_like}")
    test_randint = torch.randint(1, 10, (3, 4))
    print(f"test_randint=\n{test_randint}")
    test_randn = torch.randn((3,4))
    print(f"test_randn=\n{test_randn}")
    test_randperm = torch.randperm(8)
    print(f"test_randperm={test_randperm}")

• 结果：

test_a=
tensor([[10, 10, 13,  9, 13],
        [18,  3, 18, 13, 14],
        [12,  3,  2,  6, 15],
        [17, 10,  8,  3, 19]])
index_a=tensor([[3, 8, 1, 3, 4, 7, 3, 4, 3, 9]])
take_a=
tensor([[ 9, 13, 10,  9, 13, 18,  9, 13,  9, 14]])
test_b=
tensor([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
index_b=
[2, 3]
tile_b=
tensor([[ 0,  1,  2,  3,  0,  1,  2,  3,  0,  1,  2,  3],
        [ 4,  5,  6,  7,  4,  5,  6,  7,  4,  5,  6,  7],
        [ 8,  9, 10, 11,  8,  9, 10, 11,  8,  9, 10, 11],
        [ 0,  1,  2,  3,  0,  1,  2,  3,  0,  1,  2,  3],
        [ 4,  5,  6,  7,  4,  5,  6,  7,  4,  5,  6,  7],
        [ 8,  9, 10, 11,  8,  9, 10, 11,  8,  9, 10, 11]])
shape=torch.Size([2, 3, 4]);test_c=
tensor([[[ 0,  1,  2,  3],
         [ 4,  5,  6,  7],
         [ 8,  9, 10, 11]],

        [[12, 13, 14, 15],
         [16, 17, 18, 19],
         [20, 21, 22, 23]]])
shape=torch.Size([3, 2, 4]);transpose_01_c=
tensor([[[ 0,  1,  2,  3],
         [12, 13, 14, 15]],

        [[ 4,  5,  6,  7],
         [16, 17, 18, 19]],

        [[ 8,  9, 10, 11],
         [20, 21, 22, 23]]])
transpose01 from torch.Size([2, 3, 4]) -->torch.Size([3, 2, 4])
transpose02 from torch.Size([2, 3, 4]) -->torch.Size([4, 3, 2])
transpose12 from torch.Size([2, 3, 4]) -->torch.Size([2, 4, 3])
test_d=
tensor([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
unbind_d0=
(tensor([0, 1, 2, 3]), tensor([4, 5, 6, 7]), tensor([ 8,  9, 10, 11]))
unbind_d1=
(tensor([0, 4, 8]), tensor([1, 5, 9]), tensor([ 2,  6, 10]), tensor([ 3,  7, 11]))
test_e=
tensor([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
un_squeeze_d=
tensor([[[ 0,  1,  2,  3],
         [ 4,  5,  6,  7],
         [ 8,  9, 10, 11]]])
un_squeeze_0 from torch.Size([3, 4]) to --> torch.Size([1, 3, 4])
test_f=
tensor([[-1.0091, -1.6478,  0.2227, -0.6253],
        [-1.1560, -0.3321,  0.6481, -0.6534],
        [-0.0812,  0.3430, -0.5654,  1.3285]])
condition_f=
tensor([[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]])
where_f=
tensor([[0.0000, 0.0000, 0.2227, 0.0000],
        [0.0000, 0.0000, 0.6481, 0.0000],
        [0.0000, 0.3430, 0.0000, 1.3285]])
test_uniform=
tensor([[0.8787, 0.1452, 0.5137],
        [0.3538, 0.1417, 0.2804],
        [0.5722, 0.0614, 0.8160]])
test_bernoulli=
tensor([[1., 0., 1.],
        [0., 0., 0.],
        [0., 0., 1.]])
test_normal=
tensor([[ 1.4836,  3.7629, -0.9947,  3.2829]])
test_rand=
tensor([[0.8395, 0.9317, 0.4699, 0.1299],
        [0.6146, 0.6594, 0.6898, 0.4271],
        [0.1025, 0.0930, 0.0631, 0.8740]])
test_ones=
tensor([[1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.]])
test_rand_like=
tensor([[0.2708, 0.1703, 0.9583, 0.3686, 0.9401],
        [0.9255, 0.0381, 0.8322, 0.0164, 0.3185],
        [0.9103, 0.2022, 0.8393, 0.8391, 0.3060],
        [0.9032, 0.6207, 0.4548, 0.8162, 0.6557]])
test_randint=
tensor([[5, 2, 3, 2],
        [7, 5, 4, 8],
        [5, 6, 4, 3]])
test_randn=
tensor([[ 2.5142, -0.9164,  0.9966, -0.3689],
        [ 1.4050,  1.1789, -0.0524, -0.4698],
        [ 0.5387, -1.1596,  0.1878, -0.7700]])
test_randperm=tensor([3, 5, 1, 4, 6, 0, 2, 7])