pytorch深度学习快速入门教程

文章目录

• ​​一、win10搭建pytorch环境​​
• ​​二、jupyter的基本使用​​

• ​​1. 特定环境使用jupyter​​

• ​​在环境中下载jupyter​​
• ​​在Jupyter导入环境​​

• ​​2. 两个帮助函数：dir()、help()​​
• ​​3. 三种运行方法对比​​

• ​​三、一些基本使用​​

• ​​from torch.utils.data import Dataset​​
• ​​TensofBoard的使用​​

• ​​图片类型转化为tensor​​
• ​​归一化​​

配套视频连接：

​​https://www.bilibili.com/video/BV1hE411t7RN?p=1​​

一、win10搭建pytorch环境

查看我的另一篇博客：​​博客链接​​

二、jupyter的基本使用

1. 特定环境使用jupyter

共有两种方法：

• 1.在环境中下载jupyter
• 2.在jupyter中导入环境

在环境中下载jupyter

下载jupyter，输入

pip install jupyter

或者

conda install jupyter

下载好之后，运行jupyter，输入

jupyter notebook

注意：如果打开报错的话，应该是路径没有添加到环境中，把anaconda3\lib添加到环境变量的环境中！

在Jupyter导入环境

​​参考链接​​

pip install ipykernel                  #将环境添加到ipython的kernel中
python -m ipykernel install --user --name tensorflow --display-name tf

使用shift+回车运行代码块，并跳转到下一个代码块

2. 两个帮助函数：dir()、help()

3. 三种运行方法对比

（1）Python文件运行：以整个文件运行，以大型项目为主

（2）Python控制台运行：以行运行，可以显示每个变量属性，不利于代码阅读和修改

（3）jupyter运行：以块运行

三、一些基本使用

from torch.utils.data import Dataset

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


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)   # dataset/train/ants
        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 = "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)

TensofBoard的使用

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

writer = SummaryWriter("logs")
image_path = "dataset/train/bees_image/16838648_415acd9e3f.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)

writer.add_image('test', img_array, 2, dataformats='HWC')


for i in range(100):
    writer.add_scalar('y=2x', 3*i, i)
writer.close()

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

# python的用法 -> tensor数据类型
# 通过transform.ToTensor去解决两个问题
# 1、transforms该如何使用（Python）
# 2、为什么我们需要Tensor数据类型

img_path = 'dataset/train/ants_image/0013035.jpg'
img = Image.open(img_path)

writer = SummaryWriter('logs')

tensor_trans = transforms.ToTensor()
tensor_image = tensor_trans(img)

writer.add_image("Tensor_img", tensor_image)
writer.close()

print(img)
print(tensor_image)

图片类型转化为tensor

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

writer = SummaryWriter("logs")
img = Image.open("dataset/train/ants_image/0013035.jpg")
print(img)

trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
writer.close()

归一化

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

writer = SummaryWriter("logs")
img = Image.open("dataset/train/ants_image/0013035.jpg")
print(img)

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


# Normalize   归一化
print(img_tensor[0][0][0])
# input = (input - mean) / std
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)

writer.close()

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

writer = SummaryWriter("logs")
img = Image.open("dataset/train/ants_image/0013035.jpg")
print(img)

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


# Normalize   归一化
print(img_tensor[0][0][0])
# input = (input - mean) / std
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
print(img.size)
trans_resize = transforms.Resize((512, 512))
# img PIL -> totensor -> img_resize
img_resize = trans_resize(img)
# img_resize -> totensor -> img_resize tensor
img_resize = trans_totensor(img_resize)
writer.add_image("Resize", img_resize, 0)
print(img_resize)


writer.close()

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

writer = SummaryWriter("logs")
img = Image.open("dataset/train/ants_image/0013035.jpg")
print(img)

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


# Normalize   归一化
print(img_tensor[0][0][0])
# input = (input - mean) / std
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
print(img.size)
trans_resize = transforms.Resize((512, 512))
# img PIL -> totensor -> img_resize
img_resize = trans_resize(img)
# img_resize -> 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)
trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Resize", img_resize_2, 1)

writer.close()