【小土堆】PyTorch深度学习入门个人笔记（一）

PyTorch深度学习入门个人笔记（一）

课程笔记（B站）：PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】 B站链接

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前言

随着人工智能的不断发展，深度学习这门技术日益重要。
本文用作深度学习个人笔记，帮助加深理解 。

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一 PyTorch加载数据

①pycharm可以申请学生认证，免费使用专业版；

②jupyter可以暂时先不安装

1. read_data.py

代码如下（示例）：

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)
        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/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
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2. rename_dataset.py

代码如下（示例）：

import os

root_dir = 'dataset/train'
target_dir = 'ants_image'
img_path = os.listdir(os.path.join(root_dir, target_dir))
label = target_dir.split('_')[0]
out_dir = 'ants_label'
for i in img_path:
    file_name = i.split('.jpg')[0]
    with open(os.path.join(root_dir, out_dir, "{}.txt".format(file_name)), 'w') as f:
        f.write(label)

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二 Tensorboard的使用

1. test_Tensorboard.py

代码如下（示例）：

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


writer = SummaryWriter("logs")
image_path = "dataset/train/ants_image/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')  # Note:从PIL到numpy需要在add_image()中指定shape中每一个数字/维表示的含义

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

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三 Transforms的使用

1.test_transforms.py

代码如下（示例）：

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 transforms.ToTensor去看两个问题

# 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")

# print(img)

# 1.transforms该如何使用（python）
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)

# print(tensor_img)


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

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2. UsefulTransforms.py

代码如下（示例）：

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

writer = SummaryWriter("logs")
img = Image.open("images/DSC_2258.jpg")
print(img)

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

# Normalize
print(img_tensor[0][0][0])
trans_norm = transforms.Normalize([2, 5, 2], [5, 3, 2])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm,1)

writer.close()

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总结

学会基本操作