深度学习相关课题

pytorch简单了解 读取数据

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(root_dir,label_dir)
        self.img_path=os.listdir(self.path)

    def __getitem__(self, idx):
        imag_name=self.img_path[idx]
        imag_item_path=os.path.join(self.root_dir,self.label_dir,imag_name)
        img=Image.open(imag_item_path)
        label=self.label_dir
        return img,label
    def __len__(self):
        return len(self.img_path)

root_dir='dataset\\hymenoptera_data\\train'
ants_dir='ants'
ants=mydata(root_dir,ants_dir)

tensorboard使用

from torch.utils.tensorboard import SummaryWriter

import numpy as np
from PIL import Image
writer=SummaryWriter("logs")

imagepath=r'dataset\hymenoptera_data\train\ants\0013035.jpg'

imag_PIL=Image.open(imagepath)
ima_array=np.array(imag_PIL)
writer.add_image('test',ima_array,1,dataformats='HWC')

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

writer.close()

transform常用用法

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

writer=SummaryWriter('logs')
img=Image.open(r'dataset\hymenoptera_data\train\ants\5650366_e22b7e1065.jpg')
print(img)  #pil数据

trans_totensor=transforms.ToTensor()
img_totensor=trans_totensor(img)  #转化成tensor数据
writer.add_image('totensor1',img_totensor)

print(img_totensor[0][0][0])
transnorm=transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])
imagnorm=transnorm(img_totensor)  #对tensor数据进行规划
print(imagnorm[0][0][0])


writer.add_image('normal1',imagnorm)


print(img.size)
tran_resize=transforms.Resize((512,512))  #创建类
imgresize=trans_totensor(img)
writer.add_image('resize',imgresize,0)
print(imgresize)


#compose


writer.close()