PyTorch深度学习快速入门—

PyTorch深度学习快速入门——P1-P13

环境配置

Anaconda，创建conda create -n pytorch python=3.12，使用conda activate pytorch切换到环境。
安装pytorch，conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia，使用import torch，torch.cuda.is_available()查看cuda是否可用。

编辑器选择

之前一直用的vscode写python，但看了视频之后发现pycharm真好用：
python console这里可以直接查看变量及其属性
structure这里能查看方法
terminal是终端

快捷键

当报错的时候移到那里，alt+enter快捷修复，没导入包的时候好用
想查看属性和方法的时候移过去按住ctrl，点击，即可跳转
想查看方法要输入什么参数的时候ctrl+P

两大法宝函数dir和help

dir：列出所有属性和方法
help：查看具体用法（也可以用??）

jupyter notebook

在pytorch环境中pip install jupyter notebook安装了jupyter notebook，但没有添加环境变量不一定找得到，使用python -m jupyter notebook自动找到适合当前环境的notebook，细粒化程度高。

Dataset

使用PTL读取图片，演示了一下Dataset的做法，继承了Dataset之后实现__getitem__和__len__方法，图片文件加目录为
在这里插入图片描述

from torch.utils.data import Dataset
from PIL import Image
import osclass MyData(Dataset):def __init__(self, root_dir, label_dir):self.root_dir = root_dirself.label_dir = label_dirself.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_dirreturn img, labeldef __len__(self):return len(self.img_path)root_dir = ("dataset/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

Tensorboard的使用

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Imagewriter = SummaryWriter("logs")
image_path = "data/train/bees_image/85112639_6e860b0469.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, 2, dataformats='HWC')
# y = x
for i in range(100):writer.add_scalar("y=2x", 3 * i, i)writer.close()

在terminal输入以下指令查看tensorboard，可以自己定义文件夹名和端口号：
tensorboard --logdir=logs --port=6007
在这里插入图片描述

Transforms的一些用法

P9.transforms.py

from PIL import Image
from torchvision import transforms
from torch.utils.tensorboard import SummaryWriter# python的用法 -》 tensor数据类型
# 通过 transforms.ToTensor去解决两个问题
# 2. 为什么我们需要Tensor数据类型img_path = "data/train/ants_image/5650366_e22b7e1065.jpg"
img = Image.open(img_path)writer = SummaryWriter("logs")# 1. transforms该如何使用(python)
# 实例化ToTensor
tensor_trans = transforms.ToTensor()
# 调用实例，transforms.ToTensor的call方法
tensor_img = tensor_trans(img)print(tensor_img)writer.add_image("tensor_img", tensor_img)writer.close()# 使用opencv读图片
# import cv2
# cv_img = cv2.imread(img_path)

P10.UsefulTransforms.py

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transformswriter = SummaryWriter("logs")
img = Image.open("data/train/bees_image/16838648_415acd9e3f.jpg")
print(img)# ToTensor使用
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("Totensor", img_tensor)# Normalize使用
print(img_tensor[0][0][0])
trans_norm = transforms.Normalize ([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm)# Resize使用
print(img.size)
trans_resize = transforms.Resize((512, 512))
img_resize = trans_resize(img)
# img_resize PIL -> totensor -> img_resize tensor
img_resize = trans_totensor(img_resize)
writer.add_image("Resize", img_resize, 0)
print(img_resize)# Compose - resize - 2
trans_resize_2 = transforms.Resize(512)
# PIL -> PIL -> tensor
trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Resize", img_resize_2, 1)# RandomCrop
trans_random = transforms.RandomCrop((200, 300))
trans_compose_2 = transforms.Compose([trans_random, trans_totensor])
for i in range(10):img_crop = trans_compose_2(img)writer.add_image("RandomCrop", img_crop, i)writer.close()

均是在利用transforms处理图片，然后用TensorBoard查看中间结果。

一些数据集的使用代码

import torchvision
from torch.utils.tensorboard import SummaryWriterdataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()
])
train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=dataset_transform, download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=dataset_transform, download=True)# print(test_set[0])
# print(test_set.classes)
#
# img, target = test_set[0]
# print(img)
# print(target)
# print(test_set.classes[target])
# img.show()# print(test_set[0])writer = SummaryWriter("p10")
for i in range(10):img, target = test_set[i]writer.add_image("test_set", img, i)writer.close()