数据增强可以增加训练集的样本数量，缓解过拟合，并提高模型的泛化能力，从而有效提升算法的性能

1、数据预处理

2、使用节点

2、transform.RandomResizedCrop 随机尺寸裁剪缩放

3、水平翻转 与 垂直翻转

4、ColorJitter变换

5、ToTensor

6、Normalization 归一化

7、transforms.Compose

8、重写transforms

1、分类任务

2、目标检测任务

重写 transforms 的目的，接受多个参数，并对图像 和 标注做同步处理
下面以SSD目标检测项目中的重写transforms方法为例：

重写transforms.py代码文件

import randomimport torch
import torchvision.transforms as t
from torchvision.transforms import functional as Ffrom src import dboxes300_coco, calc_iou_tensor, Encoderclass Compose(object):"""组合多个transform函数"""def __init__(self, transforms):self.transforms = transformsdef __call__(self, image, target=None):for trans in self.transforms:image, target = trans(image, target)return image, targetclass ToTensor(object):"""将PIL图像转为Tensor"""def __call__(self, image, target):image = F.to_tensor(image).contiguous()return image, targetclass RandomHorizontalFlip(object):"""随机水平翻转图像以及bboxes,该方法应放在ToTensor后"""def __init__(self, prob=0.5):self.prob = probdef __call__(self, image, target):if random.random() < self.prob:# height, width = image.shape[-2:]image = image.flip(-1)  # 水平翻转图片bbox = target["boxes"]# bbox: xmin, ymin, xmax, ymax# bbox[:, [0, 2]] = width - bbox[:, [2, 0]]  # 翻转对应bbox坐标信息bbox[:, [0, 2]] = 1.0 - bbox[:, [2, 0]]  # 翻转对应bbox坐标信息target["boxes"] = bboxreturn image, target# This function is from https://github.com/chauhan-utk/ssd.DomainAdaptation.
class SSDCropping(object):"""根据原文，对图像进行裁剪,该方法应放在ToTensor前Cropping for SSD, according to original paperChoose between following 3 conditions:1. Preserve the original image2. Random crop minimum IoU is among 0.1, 0.3, 0.5, 0.7, 0.93. Random cropReference to https://github.com/chauhan-utk/src.DomainAdaptation"""def __init__(self):self.sample_options = (# Do nothingNone,# min IoU, max IoU(0.1, None),(0.3, None),(0.5, None),(0.7, None),(0.9, None),# no IoU requirements(None, None),)self.dboxes = dboxes300_coco()def __call__(self, image, target):# Ensure always return cropped imagewhile True:mode = random.choice(self.sample_options)if mode is None:  # 不做随机裁剪处理return image, targethtot, wtot = target['height_width']min_iou, max_iou = modemin_iou = float('-inf') if min_iou is None else min_ioumax_iou = float('+inf') if max_iou is None else max_iou# Implementation use 5 iteration to find possible candidatefor _ in range(5):# 0.3*0.3 approx. 0.1w = random.uniform(0.3, 1.0)h = random.uniform(0.3, 1.0)if w/h < 0.5 or w/h > 2:  # 保证宽高比例在0.5-2之间continue# left 0 ~ wtot - w, top 0 ~ htot - hleft = random.uniform(0, 1.0 - w)top = random.uniform(0, 1.0 - h)right = left + wbottom = top + h# boxes的坐标是在0-1之间的bboxes = target["boxes"]ious = calc_iou_tensor(bboxes, torch.tensor([[left, top, right, bottom]]))# tailor all the bboxes and return# all(): Returns True if all elements in the tensor are True, False otherwise.if not ((ious > min_iou) & (ious < max_iou)).all():continue# discard any bboxes whose center not in the cropped imagexc = 0.5 * (bboxes[:, 0] + bboxes[:, 2])yc = 0.5 * (bboxes[:, 1] + bboxes[:, 3])# 查找所有的gt box的中心点有没有在采样patch中的masks = (xc > left) & (xc < right) & (yc > top) & (yc < bottom)# if no such boxes, continue searching again# 如果所有的gt box的中心点都不在采样的patch中，则重新找if not masks.any():continue# 修改采样patch中的所有gt box的坐标（防止出现越界的情况）bboxes[bboxes[:, 0] < left, 0] = leftbboxes[bboxes[:, 1] < top, 1] = topbboxes[bboxes[:, 2] > right, 2] = rightbboxes[bboxes[:, 3] > bottom, 3] = bottom# 虑除不在采样patch中的gt boxbboxes = bboxes[masks, :]# 获取在采样patch中的gt box的标签labels = target['labels']labels = labels[masks]# 裁剪patchleft_idx = int(left * wtot)top_idx = int(top * htot)right_idx = int(right * wtot)bottom_idx = int(bottom * htot)image = image.crop((left_idx, top_idx, right_idx, bottom_idx))# 调整裁剪后的bboxes坐标信息bboxes[:, 0] = (bboxes[:, 0] - left) / wbboxes[:, 1] = (bboxes[:, 1] - top) / hbboxes[:, 2] = (bboxes[:, 2] - left) / wbboxes[:, 3] = (bboxes[:, 3] - top) / h# 更新crop后的gt box坐标信息以及标签信息target['boxes'] = bboxestarget['labels'] = labelsreturn image, targetclass Resize(object):"""对图像进行resize处理,该方法应放在ToTensor前"""def __init__(self, size=(300, 300)):self.resize = t.Resize(size)def __call__(self, image, target):image = self.resize(image)return image, targetclass ColorJitter(object):"""对图像颜色信息进行随机调整,该方法应放在ToTensor前"""def __init__(self, brightness=0.125, contrast=0.5, saturation=0.5, hue=0.05):self.trans = t.ColorJitter(brightness, contrast, saturation, hue)def __call__(self, image, target):image = self.trans(image)return image, targetclass Normalization(object):"""对图像标准化处理,该方法应放在ToTensor后"""def __init__(self, mean=None, std=None):if mean is None:mean = [0.485, 0.456, 0.406]if std is None:std = [0.229, 0.224, 0.225]self.normalize = t.Normalize(mean=mean, std=std)def __call__(self, image, target):image = self.normalize(image)return image, targetclass AssignGTtoDefaultBox(object):"""将DefaultBox与GT进行匹配"""def __init__(self):self.default_box = dboxes300_coco()self.encoder = Encoder(self.default_box)def __call__(self, image, target):boxes = target['boxes']labels = target["labels"]# bboxes_out (Tensor 8732 x 4), labels_out (Tensor 8732)bboxes_out, labels_out = self.encoder.encode(boxes, labels)target['boxes'] = bboxes_outtarget['labels'] = labels_outreturn image, target

重写的dataset类文件代码如下：

from torch.utils.data import Dataset
import os
import torch
import json
from PIL import Image
from lxml import etreeclass VOCDataSet(Dataset):"""读取解析PASCAL VOC2007/2012数据集"""def __init__(self, voc_root, year="2012", transforms=None, train_set='train.txt'):assert year in ["2007", "2012"], "year must be in ['2007', '2012']"# 增加容错能力if "VOCdevkit" in voc_root:self.root = os.path.join(voc_root, f"VOC{year}")else:self.root = os.path.join(voc_root, "VOCdevkit", f"VOC{year}")self.img_root = os.path.join(self.root, "JPEGImages")self.annotations_root = os.path.join(self.root, "Annotations")txt_list = os.path.join(self.root, "ImageSets", "Main", train_set)with open(txt_list) as read:self.xml_list = [os.path.join(self.annotations_root, line.strip() + ".xml")for line in read.readlines() if len(line.strip()) > 0]# read class_indictjson_file = "./pascal_voc_classes.json"assert os.path.exists(json_file), "{} file not exist.".format(json_file)with open(json_file, 'r') as f:self.class_dict = json.load(f)self.transforms = transformsdef __len__(self):return len(self.xml_list)def __getitem__(self, idx):# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]data_height = int(data["size"]["height"])data_width = int(data["size"]["width"])height_width = [data_height, data_width]img_path = os.path.join(self.img_root, data["filename"])image = Image.open(img_path)if image.format != "JPEG":raise ValueError("Image '{}' format not JPEG".format(img_path))assert "object" in data, "{} lack of object information.".format(xml_path)boxes = []labels = []iscrowd = []for obj in data["object"]:# 将所有的gt box信息转换成相对值0-1之间xmin = float(obj["bndbox"]["xmin"]) / data_widthxmax = float(obj["bndbox"]["xmax"]) / data_widthymin = float(obj["bndbox"]["ymin"]) / data_heightymax = float(obj["bndbox"]["ymax"]) / data_height# 进一步检查数据，有的标注信息中可能有w或h为0的情况，这样的数据会导致计算回归loss为nanif xmax <= xmin or ymax <= ymin:print("Warning: in '{}' xml, there are some bbox w/h <=0".format(xml_path))continueboxes.append([xmin, ymin, xmax, ymax])labels.append(self.class_dict[obj["name"]])if "difficult" in obj:iscrowd.append(int(obj["difficult"]))else:iscrowd.append(0)# convert everything into a torch.Tensorboxes = torch.as_tensor(boxes, dtype=torch.float32)labels = torch.as_tensor(labels, dtype=torch.int64)iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64)height_width = torch.as_tensor(height_width, dtype=torch.int64)image_id = torch.tensor([idx])area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])target = {}target["boxes"] = boxestarget["labels"] = labelstarget["image_id"] = image_idtarget["area"] = areatarget["iscrowd"] = iscrowdtarget["height_width"] = height_widthif self.transforms is not None:image, target = self.transforms(image, target)return image, targetdef get_height_and_width(self, idx):# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]data_height = int(data["size"]["height"])data_width = int(data["size"]["width"])return data_height, data_widthdef parse_xml_to_dict(self, xml):"""将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dictArgs：xml: xml tree obtained by parsing XML file contents using lxml.etreeReturns:Python dictionary holding XML contents."""if len(xml) == 0:  # 遍历到底层，直接返回tag对应的信息return {xml.tag: xml.text}result = {}for child in xml:child_result = self.parse_xml_to_dict(child)  # 递归遍历标签信息if child.tag != 'object':result[child.tag] = child_result[child.tag]else:if child.tag not in result:  # 因为object可能有多个，所以需要放入列表里result[child.tag] = []result[child.tag].append(child_result[child.tag])return {xml.tag: result}def coco_index(self, idx):"""该方法是专门为pycocotools统计标签信息准备，不对图像和标签作任何处理由于不用去读取图片，可大幅缩减统计时间Args:idx: 输入需要获取图像的索引"""# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]data_height = int(data["size"]["height"])data_width = int(data["size"]["width"])height_width = [data_height, data_width]# img_path = os.path.join(self.img_root, data["filename"])# image = Image.open(img_path)# if image.format != "JPEG":#     raise ValueError("Image format not JPEG")boxes = []labels = []iscrowd = []for obj in data["object"]:# 将所有的gt box信息转换成相对值0-1之间xmin = float(obj["bndbox"]["xmin"]) / data_widthxmax = float(obj["bndbox"]["xmax"]) / data_widthymin = float(obj["bndbox"]["ymin"]) / data_heightymax = float(obj["bndbox"]["ymax"]) / data_heightboxes.append([xmin, ymin, xmax, ymax])labels.append(self.class_dict[obj["name"]])iscrowd.append(int(obj["difficult"]))# convert everything into a torch.Tensorboxes = torch.as_tensor(boxes, dtype=torch.float32)labels = torch.as_tensor(labels, dtype=torch.int64)iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64)height_width = torch.as_tensor(height_width, dtype=torch.int64)image_id = torch.tensor([idx])area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])target = {}target["boxes"] = boxestarget["labels"] = labelstarget["image_id"] = image_idtarget["area"] = areatarget["iscrowd"] = iscrowdtarget["height_width"] = height_widthreturn target@staticmethoddef collate_fn(batch):images, targets = tuple(zip(*batch))# images = torch.stack(images, dim=0)## boxes = []# labels = []# img_id = []# for t in targets:#     boxes.append(t['boxes'])#     labels.append(t['labels'])#     img_id.append(t["image_id"])# targets = {"boxes": torch.stack(boxes, dim=0),#            "labels": torch.stack(labels, dim=0),#            "image_id": torch.as_tensor(img_id)}return images, targets

3、分割任务

定义VOCSegmentation类的py文件

import osimport torch.utils.data as data
from PIL import Imageclass VOCSegmentation(data.Dataset):def __init__(self, voc_root, year="2012", transforms=None, txt_name: str = "train.txt"):super(VOCSegmentation, self).__init__()assert year in ["2007", "2012"], "year must be in ['2007', '2012']"root = os.path.join(voc_root, "VOCdevkit", f"VOC{year}")assert os.path.exists(root), "path '{}' does not exist.".format(root)image_dir = os.path.join(root, 'JPEGImages')mask_dir = os.path.join(root, 'SegmentationClass')txt_path = os.path.join(root, "ImageSets", "Segmentation", txt_name)assert os.path.exists(txt_path), "file '{}' does not exist.".format(txt_path)with open(os.path.join(txt_path), "r") as f:file_names = [x.strip() for x in f.readlines() if len(x.strip()) > 0]self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]assert (len(self.images) == len(self.masks))self.transforms = transformsdef __getitem__(self, index):"""Args:index (int): IndexReturns:tuple: (image, target) where target is the image segmentation."""img = Image.open(self.images[index]).convert('RGB')target = Image.open(self.masks[index])    # gqr：读取的mask文件是一个单通道的掩膜数据if self.transforms is not None:img, target = self.transforms(img, target)return img, targetdef __len__(self):return len(self.images)@staticmethoddef collate_fn(batch):images, targets = list(zip(*batch))batched_imgs = cat_list(images, fill_value=0)   # gqr:将不同尺寸的数据打包桶相同尺寸大小的tensor，有利于加速训练batched_targets = cat_list(targets, fill_value=255)   # gqr:将不同尺寸的数据打包桶相同尺寸大小的tensor，有利于加速训练return batched_imgs, batched_targetsdef cat_list(images, fill_value=0):# 计算该batch数据中，channel, h, w的最大值max_size = tuple(max(s) for s in zip(*[img.shape for img in images]))batch_shape = (len(images),) + max_sizebatched_imgs = images[0].new(*batch_shape).fill_(fill_value)for img, pad_img in zip(images, batched_imgs):pad_img[..., :img.shape[-2], :img.shape[-1]].copy_(img)return batched_imgs# dataset = VOCSegmentation(voc_root="/data/", transforms=get_transform(train=True))
# d1 = dataset[0]
# print(d1)

重写的transforms代码：

import numpy as np
import randomimport torch
from torchvision import transforms as T
from torchvision.transforms import functional as Fdef pad_if_smaller(img, size, fill=0):# 如果图像最小边长小于给定size，则用数值fill进行paddingmin_size = min(img.size)if min_size < size:ow, oh = img.sizepadh = size - oh if oh < size else 0padw = size - ow if ow < size else 0img = F.pad(img, (0, 0, padw, padh), fill=fill)return imgclass Compose(object):def __init__(self, transforms):self.transforms = transformsdef __call__(self, image, target):for t in self.transforms:image, target = t(image, target)return image, targetclass RandomResize(object):def __init__(self, min_size, max_size=None):self.min_size = min_sizeif max_size is None:max_size = min_sizeself.max_size = max_sizedef __call__(self, image, target):size = random.randint(self.min_size, self.max_size)# 这里size传入的是int类型，所以是将图像的最小边长缩放到size大小image = F.resize(image, size)# 这里的interpolation注意下，在torchvision(0.9.0)以后才有InterpolationMode.NEAREST# 如果是之前的版本需要使用PIL.Image.NEARESTtarget = F.resize(target, size, interpolation=T.InterpolationMode.NEAREST)return image, targetclass RandomHorizontalFlip(object):def __init__(self, flip_prob):self.flip_prob = flip_probdef __call__(self, image, target):if random.random() < self.flip_prob:image = F.hflip(image)target = F.hflip(target)return image, targetclass RandomCrop(object):def __init__(self, size):self.size = sizedef __call__(self, image, target):image = pad_if_smaller(image, self.size)target = pad_if_smaller(target, self.size, fill=255)crop_params = T.RandomCrop.get_params(image, (self.size, self.size))image = F.crop(image, *crop_params)target = F.crop(target, *crop_params)return image, targetclass CenterCrop(object):def __init__(self, size):self.size = sizedef __call__(self, image, target):image = F.center_crop(image, self.size)target = F.center_crop(target, self.size)return image, targetclass ToTensor(object):def __call__(self, image, target):image = F.to_tensor(image)target = torch.as_tensor(np.array(target), dtype=torch.int64)return image, targetclass Normalize(object):def __init__(self, mean, std):self.mean = meanself.std = stddef __call__(self, image, target):image = F.normalize(image, mean=self.mean, std=self.std)return image, target