一、数据增强

数据增强又称为数据增广,数据扩增,它是对训练集进行变换,使训练集更丰富,从而让模型更具泛化能力

在这里插入图片描述

二、transforms——裁剪

2.1. transforms.CenterCrop

transforms.CenterCrop(size)

功能: 从图像中心裁剪图片

size: 所需裁剪图片尺寸

2.2 transforms.RandomCrop

transforms.RandomCrop(size,
				      padding=None,
					  pad_if_needed=False,
					  fill=0,
					  padding_mode=' constant')

功能: 从图片中随机裁剪出尺寸为size的图片

size: 所需裁剪图片尺寸
padding: 设置填充大小
- 当为a时,上下左右均填充a个像素
- 当为(a, b)时,上下填充b个像素,左右填充a个像素
- 当为(a, b, c, d)时,左,上,右,下分别填充a, b, c, d
pad-if-need: 若图像小于设定size ,则填充
padding_mode: 填充模式,有4种模式
- constant: 像素值由fill设定
- edge: 像素值由图像边缘像素决定
- reflect: 镜像填充,最后一个像素不镜像, eg: [1,2,3,4] → [3,2,1,2,3,4,3,2]
- symmetric: 镜像填充,最后一个像素镜像, eg: [1.2.3.4] → [2,1,1,2,3,4,4,3]
fill: constant时,设置填充的像素值，即填充颜色

2.3. RandomResizedCrop

RandomResizedCrop (size,scale=(0.08, 1.0),ratio=(3/4, 4/3),interpolation)

功能: 随机大小、长宽比栽剪图片

size: 所需裁剪图片尺寸
scale: 随机裁剪面积比例,默认(0.08, 1)
ratio: 随机长宽比,默认(3/4, 4/3)
interpolation: 插值方法
- PIL.Image.NEAREST
- PIL.Image.BILINEAR
- PIL.Image.BICUBIC

2.4 FiveCrop

transforms.FiveCrop(size)

功能: 在图像的上下左右以及中心裁剪出尺寸为size的5张图片

2.5 TenCrop

transforms.TenCrop(size,vertical_flip=False)

功能：先获得FiveCrop处理的5张图片，对这5张图片进行水平或者垂直镜像获得10张图片

size: 所需裁剪图片尺寸
vertical_flip: 是否垂直翻转

三、transforms——翻转和旋转

3.1 RandomHorizontalFlip

RandomHorizontalFlip(p=0.5)

功能: 依概率垂直(上下)翻转图片

p: 翻转概率

3.2 RandomVerticalFlip

RandomVerticalFlip(p=0.5)

功能: 依概率水平(左右)翻转图片

p: 翻转概率

3.3 RandomRotation

RandomRotation(degrees,resample=False,expand=False,center=None)

功能: 随机旋转图片

degrees: 旋转角度
- 当为a时,在（-a, a)之间选择旋转角度
- 当为(a, b)时，在(a, b)之间随机选择一个旋转角度
resample: 重采样方法
expand: 是否扩大图片, 以保持原图信息
center: 旋转点设置, 默认中心旋转

四、代码实践

基于前面的人民币二分类模型的训练过程，数据增强部分

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# -*- coding:utf-8 -*-
import os
import numpy as np
import torch
import random
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from tools.my_dataset import RMBDataset
from PIL import Image
from matplotlib import pyplot as plt


def set_seed(seed=1):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


set_seed(1)  # 设置随机种子

# 参数设置
MAX_EPOCH = 10
BATCH_SIZE = 1
LR = 0.01
log_interval = 10
val_interval = 1
rmb_label = {"1": 0, "100": 1}


def transform_invert(img_, transform_train):
    """
    将data 进行反transfrom操作
    :param img_: tensor
    :param transform_train: torchvision.transforms
    :return: PIL image
    """
    if 'Normalize' in str(transform_train):
        norm_transform = list(filter(lambda x: isinstance(x, transforms.Normalize), transform_train.transforms))
        mean = torch.tensor(norm_transform[0].mean, dtype=img_.dtype, device=img_.device)
        std = torch.tensor(norm_transform[0].std, dtype=img_.dtype, device=img_.device)
        img_.mul_(std[:, None, None]).add_(mean[:, None, None])

    img_ = img_.transpose(0, 2).transpose(0, 1)  # C*H*W --> H*W*C
    img_ = np.array(img_) * 255

    if img_.shape[2] == 3:
        img_ = Image.fromarray(img_.astype('uint8')).convert('RGB')
    elif img_.shape[2] == 1:
        img_ = Image.fromarray(img_.astype('uint8').squeeze())
    else:
        raise Exception("Invalid img shape, expected 1 or 3 in axis 2, but got {}!".format(img_.shape[2]) )

    return img_


# ============================ step 1/5 数据 ============================
split_dir = os.path.join("..", "data", "rmb_split")
train_dir = os.path.join(split_dir, "train")
valid_dir = os.path.join(split_dir, "valid")

norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]


train_transform = transforms.Compose([
    transforms.Resize((224, 224)),

    # 1 CenterCrop
    transforms.CenterCrop(512),     # 512

    # 2 RandomCrop
    # transforms.RandomCrop(224, padding=16),
    # transforms.RandomCrop(224, padding=(16, 64)),
    # transforms.RandomCrop(224, padding=16, fill=(255, 0, 0)),
    # transforms.RandomCrop(512, pad_if_needed=True),   # pad_if_needed=True
    # transforms.RandomCrop(224, padding=64, padding_mode='edge'),
    # transforms.RandomCrop(224, padding=64, padding_mode='reflect'),
    # transforms.RandomCrop(1024, padding=1024, padding_mode='symmetric'),

    # 3 RandomResizedCrop
    # transforms.RandomResizedCrop(size=224, scale=(0.5, 0.5)),

    # 4 FiveCrop
    # transforms.FiveCrop(112),
    # transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])),

    # 5 TenCrop
    # transforms.TenCrop(112, vertical_flip=False),
    # transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])),

    # 1 Horizontal Flip
    # transforms.RandomHorizontalFlip(p=1),

    # 2 Vertical Flip
    # transforms.RandomVerticalFlip(p=0.5),

    # 3 RandomRotation
    # transforms.RandomRotation(90),
    # transforms.RandomRotation((90), expand=True),
    # transforms.RandomRotation(30, center=(0, 0)),
    # transforms.RandomRotation(30, center=(0, 0), expand=True),   # expand only for center rotation

    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std),
])

valid_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std)
])

# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)

# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)


# ============================ step 5/5 训练 ============================
for epoch in range(MAX_EPOCH):
    for i, data in enumerate(train_loader):

        inputs, labels = data   # B C H W

        img_tensor = inputs[0, ...]     # C H W
        img = transform_invert(img_tensor, train_transform)
        plt.imshow(img)
        plt.show()
        plt.pause(0.5)
        plt.close()

        # bs, ncrops, c, h, w = inputs.shape
        # for n in range(ncrops):
        #     img_tensor = inputs[0, n, ...]  # C H W
        #     img = transform_invert(img_tensor, train_transform)
        #     plt.imshow(img)
        #     plt.show()
        #     plt.pause(1)