Pytorch (environment, tensorboard, transforms, torchvision, dataloader)

Software use anacoder and pycharm

Environment configuration and installation

solve pip The problem of slow installation

install anaconda

install anaconda

conda create -n pytorch python=3.6

Active version

conda activate pytorch

View Toolkit

install pytorch

pytorch Official website

Installation instructions

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

Install the appropriate version according to the official website
CommandNotFoundError: No command ‘conda creat’. Did you mean ‘conda create’?

Get into python Environmental Science

Test local gpu Whether it can be pytorch Use

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

pycharm install

Reference resources

To configure anaconda

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

jupyter

Get into Anaconda Prompt terminal

conda activate pytorch

jupyter notebook

python Study

python Learn two magic weapons

dir() function , It can let us know what is in the toolbox and the partition in the toolbox .
help() function , Let us know how each tool is used , How to use the tool .

pycharm

data

Download datasets

Dataset Links
Dataset Links

jupyvter Use

pycharm

image Use

example

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")

Dataset addition

On the basis of the above

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

establish 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 It was Google TensorFlow Visual tools , It can be used to record training data 、 Evaluation data 、 Network structure 、 Image, etc , And you can web On display , It is very helpful to observe the process of neural network .
install

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)#  In turn, is 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')#  Mark , picture , step , Format 
for i in range(100):
    writer.add_scalar("y=2x",2*i,i)#  In turn, is tag,y,x
writer.close()

transforms

transforms.ToTensor Picture format conversion tensor type

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)

install 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
Mirror image

torch.Tensor Exhibition 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()

Add __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")

Easy to use

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 normalization 
print(img_tensor[0][0][0])
# output = (input - mean) / std
# mean: The mean value of each channel 
# std： Standard deviation of each channel  rgb Three 
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. Then cut 
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 Data sets using

Dataset Links

import torchvision
# Download datasets 
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)

#  A category of a dataset 
print(train_set[0])
# // The category name attribute in the dataset 
print(train_set.classes)
# Return picture and target value  
img,target=train_set[0]
print(img)
print(target)
print(train_set.classes[target])
img.show()

Transforming data tensor type

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
# / Type conversion 
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)
# View the type conversion results 
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 Use

import torchvision
from torch.utils.data import DataLoader
#  Prepared dataset 
test_data=torchvision.datasets.CIFAR10("./data_set_test",train=False,transform=torchvision.transforms.ToTensor())
#  Test the first picture and 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 Look at the dataset

import torchvision
from torch.utils.data import DataLoader
#  Prepared dataset 
from torch.utils.tensorboard import SummaryWriter

test_data=torchvision.datasets.CIFAR10("./data_set_test",train=False,transform=torchvision.transforms.ToTensor())
#  Test the first picture and target, Parameters ： Data sets , Quantity per piece , Shuffle or not ,0, Do you want the last remainder 
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()

Next