pytorch复习

Posted 2022-12-30 钟钟终

基本函数:

dir()函数：让我们知道此工具箱以及此工具箱中有什么东西
help()函数：能让我们知道每个工具的使用方法

蚂蚁、蜜蜂数据集
目录操作，以及基本数据集的制作

from torch.utils.data import Dataset
import cv2
import os
from PIL import Image


class MyData(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, idx):
        img_name = self.img_path[idx]
        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 = "hymenoptera_data/train"
ants_label_dir = "ants"
bees_label_dir = "bees"
ants_dataset = MyData(root_dir, ants_label_dir)
bees_dataset = MyData(root_dir, bees_label_dir)

train_dataset=ants_dataset + bees_dataset

DataSet

使用官方数据集进行下载

import torchvision
from torch.utils.tensorboard import SummaryWriter

dataset_transform=torchvision.transforms.Compose(
    torchvision.transforms.ToTensor()
)
train_set=torchvision.datasets.CIFAR10(root="./dataset",transform=dataset_transform,train=True,download=True)
test_set=torchvision.datasets.CIFAR10(root="./dataset",transform=dataset_transform,train=False,download=True)

writer=SummaryWriter("p10")
for i in range(10):
    img,target=test_set[i]
    writer.add_image("test_set",img,i)

writer.close()

Dataloader

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("loader")

test_set = torchvision.datasets.CIFAR10(root="./dataset",
                                        transform=torchvision.transforms.ToTensor(),
                                        train=False, download=True)
test_loader = DataLoader(dataset=test_set, batch_size=64,
                         shuffle=False, num_workers=0,
                         drop_last=True)

# 测试数据集中第一张图片以及target
img, target = test_set[0]
print(img.shape)
print(target)
# 训练
for epoch in range(2):
    step = 0
    for data in test_loader:
        imgs, targets = data
        writer.add_images(f"Epoch:epoch", imgs, step)
        step += 1
writer.close()

神经网络

eg：

import torch
import torch.nn as nn

class Model(nn.Module):

    def __init__(self) -> None:
        super().__init__()

    def forward(self,input):
        output=input+1
        return output

model=Model()
x=torch.tensor(1.0)
output=model(x)
print(output)

卷积核:

import torch
import torch.nn as nn
import torch.nn.functional as F
input=torch.tensor([[1,2,0,3,1],
                    [0,1,2,3,1],
                    [1,2,1,0,0],
                    [5,2,3,1,1],
                    [2,1,0,1,1]])
# 卷积核
kernel=torch.tensor([[1,2,1],
                     [0,1,0],
                     [2,1,0]])
# 调整格式
input=torch.reshape(input,(1,1,5,5))
kernel=torch.reshape(kernel,(1,1,3,3))

print(input.shape)
print(kernel.shape)

output=F.conv2d(input,kernel,stride=1)
print(output)

output=F.conv2d(input,kernel,stride=1,padding=1)
print(output)

nn.Conv2d:

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

test_dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                            download=True)
test_load = DataLoader(test_dataset, batch_size=64)


# 模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)  # 初始化中，有了一个卷积层

    def forward(self, x):
        x = self.conv1(x)
        return x


model = Model()
writer=SummaryWriter("logs")
step = 0
for data in test_load:
    imgs, targets = data
    output = model(imgs)
    print(imgs.shape)
    print(output.shape)
    # torch.Size([64, 3, 32, 32])
    writer.add_images("input",imgs,step)

    # torch.Size([64, 6, 30, 30]) -> [xxx,3,30,30]
    output=torch.reshape(output,(-1,3,30,30))
    print(output.shape)
    print("----------------------")
    writer.add_images("output",output,step)
    step+=1

最大池化：

import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# input=torch.tensor([[1,2,0,3,1],
#                     [0,1,2,3,1],
#                     [1,2,1,0,0],
#                     [5,2,3,1,1],
#                     [2,1,0,1,1]],dtype=torch.float32)
# input=torch.reshape(input,(-1,1,5,5))
# print(input.shape)

dataset=torchvision.datasets.CIFAR10("./dataset",train=False,download=True,
                                     transform=torchvision.transforms.ToTensor())
dataloader=DataLoader(dataset,batch_size=64)

writer=SummaryWriter("logs_maxpool")

class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.maxpool1=MaxPool2d(kernel_size=3,ceil_mode=True)

    def forward(self,input):
        output=self.maxpool1(input)
        return output

model=Model()
step=0
for data in dataloader:
    imgs,targets=data
    writer.add_images("input",imgs,step)
    output=model(imgs)
    writer.add_images("output",output,step)
    step+=1

writer.close()

非线性：

ReLU()、Sigmoid()函数

import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d, ReLU,Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("./dataset",train=False,download=True,
                                     transform=torchvision.transforms.ToTensor())
dataloader=DataLoader(dataset,batch_size=64)

writer=SummaryWriter("logs_relu")

class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.relu=ReLU()
        self.sigmoid=Sigmoid()

    def forward(self,input):
        output=self.sigmoid(input)
        return output

model=Model()
step=0
for data in dataloader:
    imgs,targets=data
    writer.add_images("input",imgs,step)
    output=model(imgs)
    writer.add_images("output",output,step)
    step+=1

writer.close()

线性层以及其他层的介绍

import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d, ReLU,Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("./dataset",
                                     train=False,
                                     download=True,
                                     transform=torchvision.transforms.ToTensor())
dataloader=DataLoader(dataset,batch_size=64,drop_last=True)

class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear=nn.Linear(196608,10)

    def forward(self,input):
        output=self.linear(input)
        return output

model=Model()
for data in dataloader:
    imgs,targets=data
    print(imgs.shape)
    output=torch.flatten(imgs)
    print(output.shape)
    output=model(output)
    print(output.shape)