PyTorch语法

张量的创建

import torch
a=[1,2,3.]
print(type(a))

b=torch.tensor(a)
print(b)
print(type(b))
print(b.dtype)

import numpy as np
c=np.random.normal((2,3))
d=torch.tensor(c)
print(d)

e=torch.ones_like(d)
print(e)
f=torch.zeros_like(d)
print(f)
g=torch.rand_like(d)
print(g)

print(torch.rand((2,2)))
print(torch.randn([2,2]))
print(torch.rand([2,2,]).dtype)

h=torch.rand([2,2,])
print(h.dtype)
print(h.shape)
print(h.device)
print(torch.is_tensor(h))

i=torch.tensor(0)
print(torch.is_nonzero(i))
print('-'*34)
print(torch.numel(h))
print(torch.zeros([5,5]))
a=torch.zeros([5,5],dtype=torch.int32)
print(a)
print(torch.zeros([5,5]).dtype)
print(torch.ones_like(a))
print('-'*34)
print(torch.arange(5))
print(torch.arange(0,5,2))
print(torch.range(0,5))
print(torch.range(0,2).dtype)
print(torch.arange(0,5).dtype)
for i in torch.arange(5):
    print('epoch:',i)
print(torch.eye(3))
print(torch.ones_like(a)*5)
print(torch.full([2,2],2))
print(torch.full_like(a,2))

a=torch.rand([3,2,])
print(a)
b=torch.rand([3,2,])
print(b)
print(torch.cat([a,b],dim=0))

张量的运算API(1)

import torch
b=torch.rand([3,2])
print(b)
c,d=torch.chunk(b,chunks=2,dim=1)
print(c)
print(d)
print(torch.reshape(torch.reshape(b,[2,3]),[-1]))
print('-'*34)
src = torch.tensor([[1,2],
                    [3,4],
                    [5,6]])

index = torch.tensor([[0,2],
                      [1,0],
                      [2,1]])

'''out = torch.zeros_like(src)
out.scatter_(dim=0, index=index, src=src)
#src [i][j] 这个数，要搬到 out 的 第 index [i][j] 行、第 j 列
print(out)
'''
a=torch.arange(10).reshape(5,2)
#print(a)
#print(torch.split(a,[1,4]))

'''
print(a.shape)
print(torch.squeeze(torch.reshape(a,[1,1,5,2]),dim=0).shape)
'''
b=torch.rand(5,2)
print(torch.stack([a,b],dim=0).shape)    #torch.Size([2, 5, 2])
print(torch.stack([a,b],dim=1).shape)    #torch.Size([5, 2, 2])
print(torch.cat([a,b],dim=0).shape)      #torch.Size([10, 2])
print(torch.cat([a,b],dim=1).shape)     #torch.Size([5, 4])

张量的运算API(2)

import torch
a=torch.rand([3,2])
print(a)
#print(torch.take(a,torch.tensor([0,2,4])))
'''
print(torch.tile(a,dims=[1,2]))
print(torch.tile(a,dims=[2,1]))
'''
'''
print(torch.transpose(a,0,1))
print('-'*34)
print(torch.unbind(a,dim=0))
print(torch.unbind(a,dim=1))
print('-'*34)
print(torch.unsqueeze(a,dim=0).shape)
print(torch.unsqueeze(a,dim=1).shape)
print(torch.unsqueeze(a,dim=-1).shape)
'''
b=torch.zeros_like(a)
print(torch.where(a>0.5,a,b))

dataset的基本代码实现

from torch.utils.data import Dataset
from PIL import Image
import os
class MyDataset(Dataset):
    def __init__(self,root_dir,label_dir):
        self.root_dir=root_dir
        self.label_dir=label_dir
        self.path=os.path.join(self.root_dir,self.label_dir)
        self.img_path=os.listdir(self.path)


    def __getitem__(self,index):
       img_name=self.img_path[index]
       img_item_path=os.path.join(self.root_dir,self.label_dir,img_name)
       img=Image.open(img_item_path)
       label=self.label_dir
       return img,label

    def __len__(self):
        return len(self.img_path)

root_dir='dataset'
ants_label_dir='ants'
bees_label_dir='bees'
ants_dataset=MyDataset(root_dir,ants_label_dir)
bees_dataset=MyDataset(root_dir,bees_label_dir)

dataset=ants_dataset+bees_dataset

如何用 SummaryWriter 记录图像和标量

import numpy
numpy.bool8 = bool

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer=SummaryWriter('logs')
image_path='dataset/ants/0013035.jpg'
img_PIL=Image.open(image_path)
img_array=np.array(img_PIL)
print(type(img_array))
print(img_array.shape)

writer.add_image("test",img_array,1,dataformats='HWC')
#y=2x
for i in range(100):
    writer.add_scalar('y=2x', 2*i, i)

writer.close()

使用transforms.ToTensor()将PIL图像转换为Tensor

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
img_path="dataset/ants/0013035.jpg"
img_PIL=Image.open(img_path)

writer=SummaryWriter('logs')

#1.使用transforms.ToTensor()将PIL图像转换为Tensor
transform=transforms.ToTensor()
img_tensor=transform(img_PIL)

writer.add_image('Tensor_img',img_tensor)
writer.close()

transform的常见图像预处理操作

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

img=Image.open("dataset/1E6B8C5DB9722D99D028F39265867A60.png")
print(img)
writer=SummaryWriter('logs')

# ToTensor
trans_totensor=transforms.ToTensor()
img_tensor=trans_totensor(img)
writer.add_image('ToTensor',img_tensor)

# Normalize
'''
将输入张量 img_tensor 的每个通道减去均值 mean，再除以标准差 std。
公式：output = (input - mean) / std'''
print(img_tensor[0][0][0])
transform_norm=transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])
img_norm=transform_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image('Normalize',img_norm,2)

#Resize
print(img.size)
#将输入图像 img（PIL 格式）缩放到 512×512 像素，得到 img_resize（依然是 PIL 图像）
transform_resize=transforms.Resize((512,512))
#img.PIL->resize->img_resize PIL
img_resize=transform_resize(img)
#img_resize PIL->to_tensor->img_resize_tensor Tensor
img_resize=trans_totensor(img_resize)
writer.add_image('Resize',img_resize,0)
print(img_resize)

#Compose
#Resize(512) 表示将图像的短边缩放到 512 像素，长边等比例缩放（保持宽高比）。
transforms_resize_2=transforms.Resize(512)
#PIL->PIL->Tensor
transform_compose=transforms.Compose([transform_resize,trans_totensor])
#先执行 transform_resize（对输入图像做尺寸调整），再执行 trans_totensor（将 PIL 图像转为 PyTorch 张量）
img_resize_2=transform_compose(img)
writer.add_image('Compose',img_resize_2,1)

#RandomCrop
#从同一张原始图像中随机裁剪出 10 个不同的 256×256 子图，并将它们全部转换为 PyTorch 张量。
transform_randomcrop=transforms.RandomCrop(256)
trans_compose_2=transforms.Compose([transform_randomcrop,trans_totensor])
#组合两个操作：先随机裁剪，再将裁剪后的 PIL 图像转换为张量（形状 (C, 256, 256)，值域 [0,1]）
for i in range(10):
    img_crop=trans_compose_2(img)
    writer.add_image('RandomCrop',img_crop,i)

writer.close()