深度学习(1)

一、torch的安装

基于直接设备情况，选择合适的torch版本，有显卡的建议安装GPU版本，可以通过nvidia-smi命令来查看显卡驱动的版本，在官网中根据cuda版本，选择合适的版本号，下面是安装示例代码
GPU：

pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu124

CPU：

pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cpu

！！！，因为torch包很大，有3个G左右，下载时请选择稳定的网络环境，预防因网络波动导致安装失败。

二、tensor简述

PyTorch会将数据封装成张量（Tensor）进行计算，所谓张量就是元素为相同类型的多维矩阵。张量可以在 GPU 上加速运行。

2.1、概述

张量是一个多维数组，通俗来说可以看作是扩展了标量、向量、矩阵的更高维度的数组。张量的维度决定了它的形状（Shape），常见的张量有：

标量：0阶张量；
向量：1阶张量；
矩阵：2阶张量；
其他：例如高阶张量、图像、视频等复杂数据结构。

2.2、特点

动态计算图
GPU支持
自动微分

2.3、数据类型

PyTorch中有3种数据类型：浮点数、整数、布尔。其中，浮点数和整数又分为8位、16位、32位、64位，加起来共9种。

三、tensor创建---- torch.tensor：根据指定的数据创建张量。

3.1、标量

python">def th_touchs():# ? 创建一个一阶张量t1 = torch.tensor(5)print(t1.shape) # 打印张量的形状print(t1.dtype) # 打印张量的数据类型print(type(t1)) # 打印张量的类型

3.2、数组

python"># 数组
data = np.array([1, 2, 3, 4])
x = torch.tensor(data)
print(x)

3.3、列表

python"># 列表
data = [1,2,3,4]
x = torch.tensor(data)
print(x)

3.4、指定张量的数据类型

python">data = np.array([1, 2, 3, 4],dtype = int)
x = torch.tensor(data)
print(x)

3.5、指定张量的执行设备

python">data = np.array([1, 2, 3, 4],divice = "cuda")
x = torch.tensor(data)
print(x)

3.6、其他指定类型张量

python">"""
创建张量
"""
import torch# ! 创建线性张量
def test_linear():# 左闭右开区间，步长为2x = torch.arange(1, 10, 2)# print(x)#闭区间，自动等差步长y = torch.linspace(1, 10, 4)# print(y)# 等比数列，base表示指数，表示3^1~3^10取5个数z = torch.logspace(1, 10, 10,base=2)# print(z)# ! 创建随机张量
def test_tensor():# 设置随机数种子torch.manual_seed(123)# 获取当前随机数种子# print(torch.initial_seed())# 生成随机张量x = torch.rand(10, 5)# print(x)# 生成正太分布的随机张量y = torch.randn(2, 10)# print(y)# 自定义方差和均值z = torch.normal(mean=10, std=2, size=(2, 2))print(z)# ! 创建0/1张量
def test_zeros():# 创建指定形状的张量x = torch.zeros((4, 4), dtype=torch.int64)# print(x)# 从数据中构建张量,传入的容器只能是tensorx = torch.tensor([[1, 2, 3],[4, 5, 6]])x = torch.rand(3,3)x = torch.zeros_like(x)print(x)def test_ones():# 创建指定形状的张量x = torch.ones((4, 4), dtype=torch.int64)# print(x)# 从数据中构建张量,传入的容器只能是tensorx = torch.tensor([[1, 2, 3],[4, 5, 6]])x = torch.rand(3,3)x = torch.ones_like(x)print(x)# ! 创建指定值张量
def test_full():# 创建指定形状的张量x = torch.full((4, 4), fill_value=7)# print(x)# 从数据中构建张量,传入的容器只能是tensorx = torch.tensor([[1, 2, 3],[4, 5, 6]])x = torch.full_like(x, 11)print(x)# ! 创建单位矩阵张量
def test_eye():# 创建指定形状的张量x = torch.eye(4, 4)print(x)if __name__ == '__main__':test_linear()test_tensor()test_zeros()test_ones()test_full()test_eye()

tensor常用属性

python">"""
常见属性
"""import torchdef th_tensor():x = torch.tensor([1, 2, 3],device='cuda') # 指定创建到cuda/cpu的tensorprint(x.dtype) # 数据类型print(x.device)# tensor所在的设备，默认是cpuprint(x.shape) # 形状# 设备切换
def device_change():# 方式1# 将tensor创建在cuda设备上x = torch.tensor([1, 2, 3], device='cuda')# print(1,x.device)# 方式2# 先创建一个cpu上的tensorx = torch.tensor([1, 2, 3])# 将tensor移动到cuda设备上x = x.to('cuda:0')# print(2,x.device)# 通过api获取设备是否有cuda# 检查CUDA是否可用res = torch.cuda.is_available()print(res)# 条件判断c = 1 if 100>10 else 0print(c)# 根据CUDA可用性将tensor移动到cuda或cpu设备上x.to("cuda" if torch.cuda.is_available() else "cpu")print(x.device)# 方式3# 创建一个cpu上的tensorx = torch.tensor([1, 2, 3])print(x.device)# 把tensor移动到cuda上y = x.cuda()  # 把tensor移动到cuda上print(y.device)# 根据CUDA可用性将tensor移动到cuda或cpu设备上x = x.cuda() if torch.cuda.is_available() else x.cpu()# 类型转换
def type_convert():# 直接指定tensor的数据类型x = torch.tensor([1, 2, 3], dtype=torch.float64)print(x.dtype)# 通过type方法转换tensor的数据类型x  = x.type(torch.int8)print(x.dtype)# 通过half方法转换tensor的数据类型x = x.half()print(x.dtype)# 通过double方法转换tensor的数据类型x = x.double()print(x.dtype)# 通过float方法转换tensor的数据类型x = x.float()print(x.dtype)# 通过int方法转换tensor的数据类型x = x.int()print(x.dtype)if __name__ == '__main__':# print(th_tensor())# device_change()type_convert()

五、tensor数据类型转换

5.1、常规数据类型转换

python">import numpy as np
import torchdef th_data_np():x = torch.tensor([1, 2, 3])print(x)# 把Tensor转换为numpy数组x1 = x.numpy()print(x1)print(type(x1))# x 和x1 是两个不同的对象，但它们都指向同一个数据存储空间x1[0] = 100print(x)def th_data_copy():x = torch.tensor([1, 2, 3])print(x)# 把Tensor转换为numpy数组并copy,copy()不会改变原来的数据x1 = x.numpy().copy() # 注意这里是copy（深拷贝）,还有一个view（浅拷贝）print(x1)print(type(x1))x1[0] = 100print(x)print(x1)def th_data_np_tensor():# np转tensor，不共用内存(浅拷贝)x = np.array([1, 2, 3])print(x)# 把numpy数组转换为Tensorx1 = torch.tensor(x)x[0] = 100x1[0] = 200print(x)print(x1)# from_numpy()会和原来的数组共享内存(深拷贝)x2 = np.array([1, 2, 3])x3 = torch.from_numpy(x2)x2[0] = 100x3[1] = 200print(x2)print(x3)if __name__ == '__main__':# th_data_np()# th_data_copy()th_data_np_tensor()

5.2、图片数据类型转换

python">
from PIL import Image
import torch
from torchvision import transformsdef tensor_to_img():pass# ? 将图像转换为张量
def img_to_tensor():path = './data/1.png'img = Image.open(path)print(img)transfer = transforms.ToTensor()img_tensor = transfer(img)print(img_tensor.shape)# ? 将张量转换为图像
def test():# r = torch.rand(315,315)# g = torch.rand(315,315)# b = torch.rand(315,315)img_tensor = torch.rand(4,315,315)# print(img_tensor)# print(img_tensor.shape)# tensor转PIL对象transfer = transforms.ToPILImage()img = transfer(img_tensor)img.show()def test2():prth = r"./data/1.png"img = Image.open(prth)print(img)transfer = transforms.ToTensor()img_tensor = transfer(img)img_tensor[0] = 255tensor2pil = transforms.ToPILImage()img_pil = tensor2pil(img_tensor)img_pil.show()img_pil.save('./data/2.png')if __name__ == "__main__":# img_to_tensor(# test()test2()

六、tensor常见操作

6.1、获取元素

python">"""
从tensor中获取元素
"""import torchdef th_items():# 标量x = torch.tensor(1)print(x.item())# 一阶x = torch.tensor([100])print(x.item())# ! 如果输入的数据大于一个，会报错x = torch.tensor([1, 2])# print(x.item())if __name__ == '__main__':th_items()

6.2、元素值运算

python">"""
元素值运算
"""
import torchdef th_cction():torch.manual_seed(666)x = torch.randint(1, 10, (3, 3))print(x)# ? 加x1 = x.add(100)print(x1)# !带_结尾的函数，基本上都是在原数据进行操作x.add_(200)print(x)# ? 减x2 = x.sub(50)print(x2)x.sub_(40)print(x)# ? 乘x3 = x.mul(2)print(x3)x.mul_(2)print(x)# ? 除x4 = x.div(2)print(x4)x.div_(2)print(x)# ? 幂x5 = x.pow(2)print(x5)x.pow_(2)print(x)# ?x6 = x**2print(x6)if __name__ == '__main__':th_cction()

6.3、阿达玛积

python">"""
计算阿达玛积"""
import torchdef adama():x1 = torch.tensor([[1,2],[3,4]])x2 = torch.tensor([[1,2],[3,4]])#! Adama积矩阵形状必须相同x3 = x1 * x2print(x3)if __name__ == '__main__':adama()

6.4、相乘

python">"""
矩阵相乘
"""import torchdef dot():x1 = torch.tensor([[1,2],[3,4]])x2 = torch.tensor([[1,2],[3,4]])x3 = torch.matmul(x1, x2)x3 = x1.matmul(x2)x3 = x1 @ x2x3 = x1.mm(x2)print(x3)if __name__ == '__main__':dot()

6.5、索引

python">"""
索引
"""import torchdef index():# tensor 的布尔运算# torch.manual_seed(66)# x = torch.randint(0, 10, (5, 5))# print(x)# x1 = x > 7# print(x1)# x3 = x[x1]# print(x3)# print(x[x % 2 == 0])# 创建一个5x5的tensor作为示例数据x = torch.tensor([[2, 3, 2000, 10, 20],  # 满足条件：偶数，奇数，闰年[1, 2, 2001, 30, 40],  # 不满足条件：第一列是奇数[4, 5, 2004, 50, 60],  # 满足条件：偶数，奇数，闰年[3, 7, 1900, 70, 80],  # 不满足条件：第三列不是闰年（虽然能被4整除，但也能被100整除且不能被400整除）[6, 9, 1600, 90, 100]  # 满足条件：偶数，奇数，闰年])# 找出第一列是偶数，第二列是奇数，第三列是闰年的行中的第四列和第五列的数据print(x[(x[:, 0] % 2 == 0) & (x[:, 1] % 2 != 0) & ((x[:, 2] % 4 == 0) | (x[:, 2] % 400 == 0))][:, [3, 4]])# ? 索引赋值
def index_ass():torch.manual_seed(66)x = torch.randint(1,10,(5, 5))print(x)x1 = x[1,1]print(x1)x[1,1] = 100print(x)x[:,3] = 200print(x)x[:,:] = 300print(x)x.fill_(400)print(x)if __name__ == "__main__":# index()index_ass()

6.6、拼接

python">"""
拼接
"""import torch
from PIL import Image
from torchvision import transformsdef montage_cat():x = torch.randint(1, 10, (3,3))y = torch.randint(1, 10, (3,3))print(x)print(y)# cat()：不会增加维度# 拼接,dim=0:按行拼接，dim=1:按列拼接z = torch.cat([x, y], dim=0)print(z)def montage_stack():torch.manual_seed(66)x = torch.randint(1, 10, (3,3))y = torch.randint(1, 10, (3,3))# print(x)# print(y)# ! stack()：会增加维度（张量级别）# ! 堆叠，dim=0:按行堆叠，dim=1:按列堆叠# ! 要堆叠的张量必须具有相同的形状z = torch.stack([x, y],dim=1)print(z)def montage_img():# 加载本地图像为PILimg = Image.open('./data/1.png')transfer = transforms.ToTensor()img_tensor = transfer(img)# print(img_tensor)# print(img_tensor.shape)res = torch.stack([img_tensor[0], img_tensor[1],img_tensor[2]], dim=0)if __name__ == '__main__':# montage_cat()# montage_stack()montage_img()

6.7、形状

python">"""
形状操作
"""import torchdef th_reshape():x = torch.reshape(x, (-1, 5))x1 = torch.reshape(x, (2, 6))print(x)x2 = torch.reshape(x, (2, 3, 2))print(x2)def th_view():x = torch.randint(1, 10,(4, 3))# print(x)# !改变形状，由于没有改变原x中的内存空间，因此改变形状操作比reshape快x1 = x.view((2, 6))# print(x1)# x2 = torch.randit(1, 10,(4, 3))# # x3 = torch.reshape(x2, (2, 6))# x4 = x2.t() # 转置矩阵# print(x4)# # !改变形状，在内存中不连续的数据不能通过view转换# x5 = x4.view(2,6)# print(x5)# ! 改变形状后，是否共享数据内存x6 = torch.randint(1, 10, (4, 3))x7 = x6.view(2, 6)x6[1,1] = 999print(x6)print(x7)# ? 改变维度0
def rh_transpose():x1 = torch.randint(1, 10, (4, 3))print(x1,x1.shape)# ! transpose(x, dim0, dim1), 交换两个维度x2 = torch.transpose(x1, 0, 1)print(x2,x2.shape)# ? 改变维度1
def th_permute():x1 = torch.randint(0, 255, (3, 512,360))print(x1.shape)x2 = x1.permute(1, 2, 0)print(x2.shape)# ? 改变为1维
def th_flatten():x1 = torch.randint(0, 255, (4, 3))# print(x1)x2 = x1.flatten()# print(x2)x3 = torch.randint(0, 255, (3, 4, 2, 2))print(x3)x4 = x3.flatten(start_dim=1, end_dim=-1)print(x4)# ? 数据升维
def th_unsqueeze():x1 = torch.randint(0, 255, (4, 3))print(x1)# ! 0,表示在0处插入一个维度x2 = x1.unsqueeze(0)print(x2.shape)# ? 数据降维
def th_squeeze():x1 = torch.randint(0, 255, (1, 4, 3, 1))print(x1)x2 = x1.squeeze()print(x2)x3 = x1.squeeze(0).squeeze(-1)print(x3)# ? 数据分割
def th_split():x1 = torch.randint(0, 255, (4, 3))print(x1)# ! split(),表示每个tensor有2行x2, x3 = torch.split(x1, 2)# print(x2, x3)# ! chunk(),表示将数据分割成多少份x4 = torch.chunk(x1, 4)print(x4)# ? 广播
def th_broadcast():a = torch.arange(1, 13).reshape(3, 4)b = torch.arange(1, 5)c = a + bif __name__ == '__main__':# th_reshape()# th_view()# rh_transpose()# th_permute()# th_flatten()# th_unsqueeze()# th_squeeze()# th_split()th_broadcast()
```