Pytorch学习

配置环境

python3.9+anaconda+pytorch

查看cuda版本号:WIN+S搜NVIDIA控制面板,帮助—系统信息-组件 查看

image-20220815221841700

或者控制台输入nvidia-smi

image-20220815222002069.png

创建新的conda虚拟环境:

conda create -n pytorch python=3.9

查看已安装的虚拟环境

conda env list

删除环境

conda remove -n pytorch --all

进入虚拟环境

conda activate pytorch

打开Pytorch官网,按照需求选择安装对应的Pytorch,输入对应命令行

下载过慢:https://blog.csdn.net/watermelon1123/article/details/88122020

测试

import torch
torch.cuda.is_available()

out

True

补充:

dir(): 函数不带参数时,返回当前范围内的变量、方法和定义的类型列表;带参数时,返回参数的属性、方法列表

image-20220816141315388.png

help():类似说明书

image-20220816141423004.png


虚拟anaconda环境中安装jupyter notebook https://blog.inkuang.com/2019/319/

pytorch加载数据

数据集:

  • 提供一种方式取获取数据及其标签
  • 如何获取每一个数据及其标签
  • 告诉我们总共有多少的数据

eg:

获取数据集图片信息

from PIL import Image
img_path = "./hymenoptera_data/train/ants/0013035.jpg"
img = Image.open(img_path)
img.size
# Out[]: (768, 512)

获取数据集目录列表

dir_path = "hymenoptera_data/train/ants"
import os
img_path_list = os.listdir(dir_path)
img_path_list[0]
# Out[]: '0013035.jpg'

拼接路径:

image-20220816151330511.png

import os
root_dir = "hymenoptera_data/train"
label_dir = "ants"
path = os.path.join(root_dir, label_dir)

最终查看

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, 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"
label_dir = "ants"
ants_dataset = MyData(root_dir, label_dir)

查看单个图片

img, label = ants_dataset[0]
img.show()

可以拼接两个数据集

train_dataset = ants_dataset + bees_dataset

tensorboard的使用

导入包

from torch.utils.tensorboard import SummaryWriter

1.writer.add_scalar()
功能:将标量添加到 summary
参数:
tag (string):数据标识符
scalar_value (float or string/blobname):要保存的数值
global_step (int):全局步值
walltime (float):可选参数,用于记录发生的时间,默认为 time.time()

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("logs")

for i in range(100):
    writer.add_scalar("y=x", i, i)

writer.close()

控制台打开tensorboard:tensorboard --logdir=logs

image-20220816172808324.png

2.writer.add_image()

add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats=‘CHW’):

绘制图片,可用于检查模型的输入,监测 feature map 的变化,或是观察 weight。
tag:图的名称
img_tensor:图片的类型要是torch.Tensor, numpy.array, string这三种
global_step:第几张图片
dataformats=‘CHW’,默认CHW,tensor是CHW,numpy是HWC

image-20220816175620437.png

Transforms

ToTensor():将PIL或np形式图片转换为tensor形式

from PIL import Image
from torchvision import transforms

image_path = "hymenoptera_data/train/ants/0013035.jpg"
img = Image.open(image_path)
print(img)

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

print(tensor_img)

输出:

<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x512 at 0x150B5D38EE0>
tensor([[[0.3137, 0.3137, 0.3137,  ..., 0.3176, 0.3098, 0.2980],
         [0.3176, 0.3176, 0.3176,  ..., 0.3176, 0.3098, 0.2980],
         [0.3216, 0.3216, 0.3216,  ..., 0.3137, 0.3098, 0.3020],
         ...,
         [0.3412, 0.3412, 0.3373,  ..., 0.1725, 0.3725, 0.3529],
         [0.3412, 0.3412, 0.3373,  ..., 0.3294, 0.3529, 0.3294],
         [0.3412, 0.3412, 0.3373,  ..., 0.3098, 0.3059, 0.3294]],

        [[0.5922, 0.5922, 0.5922,  ..., 0.5961, 0.5882, 0.5765],
         [0.5961, 0.5961, 0.5961,  ..., 0.5961, 0.5882, 0.5765],
         [0.6000, 0.6000, 0.6000,  ..., 0.5922, 0.5882, 0.5804],
         ...,
         [0.6275, 0.6275, 0.6235,  ..., 0.3608, 0.6196, 0.6157],
         [0.6275, 0.6275, 0.6235,  ..., 0.5765, 0.6275, 0.5961],
         [0.6275, 0.6275, 0.6235,  ..., 0.6275, 0.6235, 0.6314]],

        [[0.9137, 0.9137, 0.9137,  ..., 0.9176, 0.9098, 0.8980],
         [0.9176, 0.9176, 0.9176,  ..., 0.9176, 0.9098, 0.8980],
         [0.9216, 0.9216, 0.9216,  ..., 0.9137, 0.9098, 0.9020],
         ...,
         [0.9294, 0.9294, 0.9255,  ..., 0.5529, 0.9216, 0.8941],
         [0.9294, 0.9294, 0.9255,  ..., 0.8863, 1.0000, 0.9137],
         [0.9294, 0.9294, 0.9255,  ..., 0.9490, 0.9804, 0.9137]]])

Process finished with exit code 0

image-20220816213946449

最后修改:2022 年 08 月 19 日
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