pytorch：tensorboard和transforms学习

tensorboard:可视化数据

在anaconda安装：

pip install tensorboard==2.12.0

最好使用这个版本 不然后面调用会报错 因为版本过高的原因

然后还碰到了安装的时候 安装到C盘去了 但是我用的虚拟环境是在E盘：此时去C盘把那些新安装的复制过来就好了 附录我C盘的路径：C:\Users\asus\AppData\Roaming\Python\Python38\Scripts

（搜索了一下有大佬说可能是python版本的问题 以后pip/conda install的时候指定一下python版本 可以先在虚拟环境中python --version查看一下）

transforms:数据增强（数据预处理）

transforms是个工具箱 里面有很多工具 可以处理图片得到一个结果

tool = transforms.totensor()

result = tool(input)

为什么非要转化成tensor型？因为它里面有很多神经网络深度学习的参数 想要训练就需要tensor型

常见的transforms工具(按住ctrl可以进入源码)

1. ToTensor()

   code：

   from PIL import Image
   from torch.utils.tensorboard import SummaryWriter
   from torchvision import transforms
   
   writer = SummaryWriter('../logs')
   #tensorboard --logdir=../logs
   img = Image.open('../images/1.jpg')
   print(img)
   
   # use TOTensor()
   trans_tensor = transforms.ToTensor()
   img_tensor = trans_tensor(img)
   writer.add_image('totensor',img_tensor,dataformats='CHW')
   
   writer.close()
   

   logs:

   

2. Normalize() 标准化

   设置Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]) （这样设置可以实现归一化）

   input[channel] = (input[channel] - mean[channel]) / std[channel]

   =(input - 0.5) / 0.5

   =2 * input - 1

   input [0,1]

   result [-1,1]

   code:(在writer.close()之前加上)

   # use Normalize()
   print(img_tensor[0][0][0])
   trans_norm = transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])
   #trans_norm = transforms.Normalize([1,2,3],[3,2,1])
   img_norm = trans_norm(img_tensor)
   print(img_norm[0][0][0])
   writer.add_image('normalize',img_norm)
   #writer.add_image('normalize',img_norm,1)
   

   logs:

   

   可以换几个数据试一下：（冬天寒冷小苏 变成夏天红温小苏了）

   

3. Resize()

   改变图片大小

   code：

   #use Resize()
   print(img.size)
   trans_resize = transforms.Resize((512,512))
   img_resize = trans_resize(img)
   img_resize = trans_totensor(img_resize)
   print(img_resize)
   writer.add_image('resize',img_resize,0)
   

   好吧：是拉伸小饼

   

4. Compose()

   串联多个图片变换的操作

   code:

   #use Compose() - Resize() - 2
   trans_resize_2 = transforms.Resize(512)
   trans_compose = transforms.Compose([trans_resize_2,trans_totensor])
   img_resize_2 = trans_compose(img)
   writer.add_image('resize',img_resize_2,1)
   

   

5. RandomCrop()

   随机裁剪

   code：

   #use RandomCrop()
   trans_random = transforms.RandomCrop(512)
   trans_compose_2 = transforms.Compose([trans_random,trans_totensor])
   #随机裁剪十次
   for i in range(10):
       img_crop = trans_compose_2(img)
       writer.add_image('random_crop',img_crop,i)
   

   

   

   还可以根据宽和高裁剪的

   code：

   trans_random = transforms.RandomCrop((500,1000))
   trans_compose_3 = transforms.Compose([trans_random,trans_totensor])
   for i in range(10):
       img_crop = trans_compose_3(img)
       writer.add_image('random_crophw',img_crop,i)
   

   

6. 以上完整代码

   先展示目录结构：

   

   code：

   from PIL import Image
   from torch.utils.tensorboard import SummaryWriter
   from torchvision import transforms
   import cv2
   import numpy as np
   
   writer = SummaryWriter('../logs')
   #tensorboard --logdir=../logs
   img = Image.open('../images/1.jpg')
   print(img)
   
   # use TOTensor()
   trans_totensor = transforms.ToTensor()
   img_tensor = trans_totensor(img)
   writer.add_image('totensor',img_tensor,dataformats='CHW')
   
   # use Normalize()
   print(img_tensor[0][0][0])
   trans_norm = transforms.Normalize([1,2,3],[3,2,1])
   img_norm = trans_norm(img_tensor)
   print(img_norm[0][0][0])
   writer.add_image('normalize',img_norm,1)
   
   #use Resize()
   print(img.size)
   trans_resize = transforms.Resize((512,512))
   img_resize = trans_resize(img)
   img_resize = trans_totensor(img_resize)
   print(img_resize)
   writer.add_image('resize',img_resize,0)
   
   #use Compose() - Resize() - 2
   trans_resize_2 = transforms.Resize(512)
   trans_compose = transforms.Compose([trans_resize_2,trans_totensor])
   img_resize_2 = trans_compose(img)
   writer.add_image('resize',img_resize_2,1)
   
   #use RandomCrop()
   trans_random = transforms.RandomCrop(512)
   trans_compose_2 = transforms.Compose([trans_random,trans_totensor])
   for i in range(10):
       img_crop = trans_compose_2(img)
       writer.add_image('random_crop',img_crop,i)
   
   trans_random = transforms.RandomCrop((500,1000))
   trans_compose_3 = transforms.Compose([trans_random,trans_totensor])
   for i in range(10):
       img_crop = trans_compose_3(img)
       writer.add_image('random_crophw',img_crop,i)
   
   writer.close()