Pytorch.transforms 数据处理

RandomErasing数据处理

我因为是做图像修复领域的，所以需要对数据进行随机擦除的预处理操作。

RandomErasing函数解析

    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False):
        assert isinstance(value, (numbers.Number, str, tuple, list))
        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
            warnings.warn("range should be of kind (min, max)")
        if scale[0] < 0 or scale[1] > 1:
            raise ValueError("range of scale should be between 0 and 1")
        if p < 0 or p > 1:
            raise ValueError("range of random erasing probability should be between 0 and 1")

        self.p = p
        self.scale = scale
        self.ratio = ratio
        self.value = value
        self.inplace = inplace

其中
p=概率，scale=面积，ratio=长宽比，value=默认就行（代表RGB或者灰度图）
如是你想将所有图片都进行erase，可以直接将P设为1.

transforms.RandomErasing(1)

transform模块的使用

模板官网上有相关教程，这里我就写一个我自己使用的模板

my_trans = transforms.Compose([
	transforms.Resize((512,512)),
	#resize 图片的大小
	transforms.RandomHorizontalFlip(),
	#随机翻转
	transforms.ToTensor(),
	#归一化，一定要加的 防止各种小bug
	transforms.RandomErasing(1,(0.02,0.03),value=1),
	#这里就是随机擦除了，我要每张图都擦除，并且面积不要太大，所以就设置了以上的数据。
])

相关代码

给出完整代码可训练自己的数据集：

import torch
from torchvision import transforms, utils
from torchvision.datasets import ImageFolder
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader

"""
	数据预处理
"""
def detection_collate(batch):
    """Custom collate fn for dealing with batches of images that have a different
    number of associated object annotations (bounding boxes).
    Arguments:
        batch: (tuple) A tuple of tensor images and lists of annotations
    Return:
        A tuple containing:
            1) (tensor) batch of images stacked on their 0 dim
            2) (list of tensors) annotations for a given image are stacked on 0 dim
    """
    targets = []
    imgs = []
    for sample in batch:
        imgs.append(sample[0])
        targets.append(torch.FloatTensor(sample[1]))
    return torch.stack(imgs, 0), targets

hy_trans = transforms.Compose([
	transforms.Resize((512,512)),
	transforms.ToTensor(),
	transforms.RandomErasing(1,(0.02,0.03),value=1),
])

"""
	生成batch_size迭代器，用于加载数据
"""
train_data = ImageFolder(r'E:\pycharm\dataset\shujuzengqiang\test', transform=hy_trans)
#图片的真实路径其实是E:\pycharm\dataset\shujuzengqiang\test\test 不知道为啥写完整反而识别出错。
train_loader = DataLoader(train_data, batch_size=1, shuffle=True )
for i_batch, img in enumerate(train_loader):
		#  img[0] ->图像， img[1] -> label
		fig = plt.figure()
		#  img 格式 {batch,  channel,  height ,  weight}, 和make_grid要求一样；
		# make_grid 批量显示多幅画面   ->  输出 {channel,  height ,  weight}
		grid = utils.make_grid(img[0], padding=0)
		#  tensor -> numpy； chw -> hwc,  plt才能显示
		plt.imshow(grid.numpy().transpose((1, 2, 0)))
		utils.save_image(grid,'%d.jpg'%(i_batch))
		if i_batch==10:
			break

print("computer finision")