pytorch（环境、tensorboard、transforms、torchvision、dataloader）

软件使用anacoder和pycharm

环境配置和安装

解决pip安装时速度慢的问题

安装anaconda

安装anaconda

conda create -n pytorch python=3.6

激活版本

conda activate pytorch

查看工具包

安装pytorch

pytorch官网

安装指令

conda install pytorch torchvision cudatoolkit=9.2 -c pytorch -c defaults -c numba/1abe1/dev

根据官网安装合适版本
CommandNotFoundError: No command ‘conda creat’. Did you mean ‘conda create’?

进入python环境

检测本地gpu是否可被pytorch使用

>>> import torch
>>> torch.cuda.is_available()

pycharm安装

参考

配置anaconda

错误Cannot run program “D:…\venv\Scripts\python.exe“ (in directory ): CreateProcess error=2

jupyter

进入Anaconda Prompt终端

conda activate pytorch

jupyter notebook

python学习

python学习两法宝

dir()函数，能让我们知道工具箱以及工具箱中的分隔区有什么东西。
help()函数，能让我们知道每个工具是如何使用的，工具的使用方法。

pycharm

数据

下载数据集

数据集链接
数据集链接

jupyvter使用

pycharm

image的使用

实例

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, 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)

ants_dataset=MyData("dataset/train","ants")

数据集相加

上面的基础上

ants_dataset=MyData("dataset/train","ants")
bees_dataset=MyData("dataset/train","bees")
train_dataset=ants_dataset+bees_dataset

创建label

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)

tensorboard

Tensorboard原本是Google TensorFlow的可视化工具，可以用于记录训练数据、评估数据、网络结构、图像等，并且可以在web上展示，对于观察神经网络的过程非常有帮助。
安装

pip install tensorboard

SummaryWriter

add_scalar

from torch.utils.tensorboard import SummaryWriter
writer=SummaryWriter("logs")
for i in range(100):
    writer.add_scalar("y=2x",2*i,i)# 依次是tag，y，x
writer.close()

AttributeError:module ‘distutils‘ has no attribute ‘version

add_image

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

writer=SummaryWriter("logs")
image_path='dataset/train/bees_image/17209602_fe5a5a746f.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,3,dataformats='HWC')# 标记，图片，步长，格式
for i in range(100):
    writer.add_scalar("y=2x",2*i,i)# 依次是tag，y，x
writer.close()

transforms

transforms.ToTensor图片格式转换tensor类型

from torchvision import transforms
from PIL import Image

img_path="dataset/train/ants_image/7759525_1363d24e88.jpg"
img=Image.open(img_path)
print(img,"\n")

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

安装 opencv-python

pip --default-timeout=300 install opencv-python  -i https://pypi.douban.com/simple

pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host=‘files.pythonhosted.org’, port=443): Read timed out
镜像

torch.Tensor展示add_image

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

img_path="dataset/train/ants_image/7759525_1363d24e88.jpg"
img=Image.open(img_path)

writer=SummaryWriter("logs")

tensor_trans=transforms.ToTensor()
tensor_img=tensor_trans(img)
writer.add_image("Tensor_img",tensor_img)
writer.close()

补充__call__

class person:
    def __call__(self,name):
        print( " __call__"+" Hello " + name)
    def hello(self,name):
        print( "hello"+ name )
person = person( )
person("zhangsan")
person.hello("lisi")

简单使用

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

writer=SummaryWriter("logs")
img=Image.open("dataset/train/ants_image/5650366_e22b7e1065.jpg")
print(img)

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

# 2.Normalize归一化
print(img_tensor[0][0][0])
# output = (input - mean) / std
# mean:各通道的均值
# std：各通道的标准差 rgb三种
trans_norm=transforms.Normalize([2,6,5],[3,2,1])
img_norm=trans_norm(img_tensor)
writer.add_image("Normalize",img_norm,2)
print(img_norm[0][0][0])

#3.resize
print(img.size)
trans_resize=transforms.Resize((512,512))
#img PIL->resize->img_resize PIL
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)


#4. compose resize-2
trans_resize_2=transforms.Resize(256)
#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)

#5.随即裁剪
trans_random=transforms.RandomCrop((100,200))
trans_compose_2=transforms.Compose([trans_random,trans_totensor])
for i in range(10):
    writer.add_image("RandomCrop",trans_compose_2(img),i)

writer.close()

torchvision的数据集使用

数据集链接

import torchvision
#下载数据集
train_set=torchvision.datasets.CIFAR10(root="./data_set_train",train=True,download=True)
test_set=torchvision.datasets.CIFAR10(root="./data_set_test",train=False,download=True)

# 数据集的某一个类别
print(train_set[0])
# //数据集中的类别名称属性
print(train_set.classes)
#返回图片和目标值 
img,target=train_set[0]
print(img)
print(target)
print(train_set.classes[target])
img.show()

将数据转化tensor类型

import torchvision
dataset_tansform=torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
train_set=torchvision.datasets.CIFAR10(root="./data_set_train",train=True,transform=dataset_tansform,download=True)
test_set=torchvision.datasets.CIFAR10(root="./data_set_test",train=False,transform=dataset_tansform,download=True)
print(test_set[0])

import torchvision
from torch.utils.tensorboard import  SummaryWriter
# /类型转换
dataset_tansform=torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
train_set=torchvision.datasets.CIFAR10(root="./data_set_train",train=True,transform=dataset_tansform,download=True)
test_set=torchvision.datasets.CIFAR10(root="./data_set_test",train=False,transform=dataset_tansform,download=True)
#类型转换结果查看
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()

dataloader的使用

import torchvision
from torch.utils.data import DataLoader
# 准备的数据集
test_data=torchvision.datasets.CIFAR10("./data_set_test",train=False,transform=torchvision.transforms.ToTensor())
# 测试数据集中第一张图片和target
test_loader=DataLoader(dataset=test_data,batch_size=4,shuffle=True,num_workers=0,drop_last=False)


img,target= test_data[0]
print(img.shape)
print(target)

for data in test_loader:
    imggs,targets=data
    print(imggs.shape)
    print(targets)

SummaryWriter查看数据集

import torchvision
from torch.utils.data import DataLoader
# 准备的数据集
from torch.utils.tensorboard import SummaryWriter

test_data=torchvision.datasets.CIFAR10("./data_set_test",train=False,transform=torchvision.transforms.ToTensor())
# 测试数据集中第一张图片和target,参数：数据集，每份数量，是否洗牌，0，是否要最后余数
test_loader=DataLoader(dataset=test_data,batch_size=4,shuffle=True,num_workers=0,drop_last=False)


img,target= test_data[0]
print(img.shape)
print(target)

writer=SummaryWriter("dataloader")
step=0
for data in test_loader:
    imgs,targets=data
    print(imgs.shape)
    print(targets)
    writer.add_images("test_data",imgs,step)
    step+=1
writer.close()

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