Pytorch personal learning record summary 03

Table of contents

Use of Transeforms

Common transforms

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Use of Transeforms

The transeforms in torchvision mainly transform (preprocess) the image.from torchvision import transforms

transeforms中常用的就是以下几种方法：（Alt+7可唤出左侧的Structure结构）
“Compose”, “ToTensor”, “PILToTensor”, “ConvertImageDtype”, “ToPILImage”, “Normalize”, “Resize”, “Scale”,“CenterCrop”
 

Compose: Composes several transforms together. Args: list of transforms to compose. Combine several transforms together. Parameters: [Transform object list], for example transforms.Compose([transforms.CenterCrop(10),transforms.ToTensor(),…])

ToTensor: Convert a PIL Image or numpy.ndarray to tensor.

ToPILImage: Convert a tensor or a graph to a PIL Image.

Normalize(torch.nn.Module): Normalize a tensor image with mean and standard deviation. This transform does not support PIL Image. Normalize a tensor image with mean and standard deviation. This conversion does not support PIL images. (Given mean:(mean[1],…,mean[n]) and std:(std[1],…,std[n]) for n dimensions, this transformation will normalize each channel )

Resize(torch.nn.Module): Resize the input image (PIL Image or Tensor) to the given size. Return PIL Image or Tensor: Rescaled image. Resize the input image (PIL Image or Tensor) to the given size size. size (sequence or int), when it is a sequence, adjust it to the specified (h, w); when it is an int, adjust the min(h, w) of the original image to the size, and then make the other side proportional zoom.

RandomCrop(torch.nn.Module): Crop the given image (PIL Image or Tensor) at a random location. Crop an image of a given size at a random location (the input requirements for size are the same as Resize).

Use ToTensor() to convert PIL Image to tensor

You can also use ToTensor() to convert numpy.ndarray to tensor ( the data type read by opencv is numpy.ndarray)

import numpy as np
from torchvision import transforms
from PIL import Image

image_path = 'hymenoptera_data/train/ants/0013035.jpg'
image = Image.open(image_path)

# 1.transforms该如何使用(python)
tensor_trans = transforms.ToTensor()	# ToTensor()中不带参数
tensor_img = tensor_trans(image)		# 不能直接写成transforms.ToTensor(image)

print(np.array(image).shape)	# (512, 768, 3)
print(tensor_img.shape)			# torch.Size([3, 512, 768])，通道数变到第0维了

ToTensor works with Tensorboard

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

image_path = 'hymenoptera_data/train/ants/0013035.jpg'
image = Image.open(image_path)

# 1.transforms该如何使用(python)
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(image)

print(np.array(image).shape)
print(tensor_img.shape)

# 写入tensorboard
writer = SummaryWriter('logs')
writer.add_image('tag', tensor_img, 1)
writer.close()

Common transforms

The data type of the image is often different in different scenarios, it is easy to make mistakes, and it needs to be converted to a specific format before it can be used!

• __call__()The role of the method: to turn an instantiated object of a class into a callable object . Calling the instance object is to execute __call__()the code in the method.
• You can use the built-in function callableto determine whether it is a callable object. For example, to judge pwhether it is a callable object: print(callable(p))return True or False.

  class Person:
      def __call__(self, name):
          print('__call__' + ' Hello ' + name)
  
      def hello(self, name):
          print('hello ' + name)
  
  
  person = Person()               # 实例化一个对象person
  person('zhangsan')              # 像调用函数一样调用person对象
  person.__call__('zhangshan_2')  # 也可像调用类函数调用
  person.hello('wangwu')          # 调用类函数person
  
  # __call__ Hello zhangsan
  # __call__ Hello zhangshan_2
  # hello wangwu
  

  from torch.utils.tensorboard import SummaryWriter
  from torchvision import transforms
  from PIL import Image
  
  image_path = 'hymenoptera_data/train/ants/0013035.jpg'
  image = Image.open(image_path)
  
  writer = SummaryWriter('logs')
  
  # 1.Totensor
  trans_totensor = transforms.ToTensor()
  img_tensor = trans_totensor(image)
  writer.add_image('ToTensor', img_tensor)  # 这里只传入了tag和image_tensor，没有写入第3个参数global_step，则会默认是第0步
  
  # 2.Normalize 可以改变色调
  trans_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
  img_norm = trans_norm(img_tensor)
  writer.add_image('Normalize', img_norm)
  
  trans_norm = transforms.Normalize([1, 3, 5], [3, 2, 1])
  img_norm_2 = trans_norm(img_tensor)
  writer.add_image('Normalize', img_norm_2, 1)
  
  trans_norm = transforms.Normalize([2, 0.5, 3], [5, 2.6, 1.5])
  img_norm_3 = trans_norm(img_tensor)
  writer.add_image('Normalize', img_norm_3, 2)
  
  # 3.Resize 将PIL或者tensor缩放为指定大小然后输出PIL或者tensor
  w, h = image.size   # PIL.Image的size先表示的宽再表示的高
  
  trans_resize = transforms.Resize(min(w, h) // 2)    # 缩放为原来的1/2
  img_resize = trans_resize(image)  # 对PIL进行缩放
  writer.add_image('Resize', trans_totensor(img_resize))  # 因为在tensorboard中显示，所以需要转换为tensor或numpy类型
  
  trans_resize = transforms.Resize(min(w, h) // 4)    # 缩放为原来的1/4
  img_resize_tensor = trans_resize(img_tensor)
  writer.add_image('Resize', img_resize_tensor, 1)
  
  # 4.compose 组合这些操作
  trans_compose = transforms.Compose(
      [transforms.Resize(min(w, h) // 2), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
  img_campose = trans_compose(image)  # image是PIL.Image格式
  writer.add_image('Compose', img_campose)
  
  # 5.Randomcrop 随机裁剪
  trans_randomcrop = transforms.RandomCrop(min(w, h) // 4)    # 从原图中任意位置裁剪1/4
  # img_ranomcrop = trans_randomcrop(img_tensor)
  for i in range(10):
      img_ranomcrop = trans_randomcrop(img_tensor)
      writer.add_image('RandomCrop', img_ranomcrop, i)
  
  # close()一定要记得写啊！
  writer.close()