data_augmentation.md

Data Augmentation

AutoAugment

timm AutoAugment	Help	plsc TimmAutoAugment
aa	Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)	config_str
	Interpolation mode	interpolation
	Image size to apply resize	img_size
	fillcolor	mean

Mixup and Cutmix

timm Mixup	help	plsc Mixup	plsc Cutmix
mixup_alpha	mixup alpha value, mixup is active if > 0.	alpha
cutmix_alpha	cutmix alpha value, cutmix is active if > 0.		alpha
cutmix_minmax	cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.		does not support
prob	probability of applying mixup or cutmix per batch or element		Does not need, always 1.0
switch_prob	probability of switching to cutmix instead of mixup when both are active	prob	prob
mode	how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)		Does not need, always 'batch'
correct_lam	apply lambda correction when cutmix bbox clipped by image borders		Does not need, always True
label_smoothing	apply label smoothing to the mixed target tensor	epsilon	epsilon
num_classes	number of classes for target	class_num	class_num

RandomErasing

timm RandomErasing	help	plsc RandomErasing
probability	Probability that the Random Erasing operation will be performed.	EPSILON
min_area	Minimum percentage of erased area wrt input image area.	sl
max_area	Maximum percentage of erased area wrt input image area.	sh
min_aspect	Minimum aspect ratio of erased area.	r1
mode	pixel color mode, one of 'const', 'rand', or 'pixel'	mode
min_count	minimum number of erasing blocks per image, area per box is scaled by count.	Does not need, always 1
max_count	maximum number of erasing blocks per image, area per box is scaled by count. per-image count is randomly chosen between 1 and this value.	Does not need, always 1
	the number of try to random erase	attempt

Example configuration in yaml

DataLoader:
  Train:
    dataset:
      name: ImageNetDataset
      image_root: ./dataset/ILSVRC2012/
      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
            interpolation: bicubic
            backend: pil
        - RandFlipImage:
            flip_code: 1
        - TimmAutoAugment:
            config_str: rand-m9-mstd0.5-inc1
            interpolation: bicubic
            img_size: 224
            mean: [0.485, 0.456, 0.406]
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - RandomErasing:
            EPSILON: 0.25
            sl: 0.02
            sh: 1.0/3.0
            r1: 0.3
            attempt: 10
            use_log_aspect: True
            mode: pixel
        - ToCHWImage:
      batch_transform_ops:
        - TransformOpSampler:
            Mixup:
              alpha: 0.8
              prob: 0.5
              epsilon: 0.1
              class_num: 1000
            Cutmix:
              alpha: 1.0
              prob: 0.5
              epsilon: 0.1
              class_num: 1000
    sampler:
      name: RepeatedAugSampler
      batch_size: 64
      drop_last: False
      shuffle: True
    loader:
      num_workers: 8
      use_shared_memory: True

Example code in py

transform_train = transforms.Compose([
        transforms.DecodeImage(to_rgb=True, channel_first=False),
        transforms.RandCropImage(args.input_size, interpolation="bicubic"),  # 3 is bicubic
        transforms.RandFlipImage(),
        transforms.TimmAutoAugment(config_str=args.aa, interpolation="bicubic", img_size=args.input_size, mean=[0.485, 0.456, 0.406]),
        transforms.NormalizeImage(scale=1.0/255.0, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], order='hwc'),
        transforms.RandomErasing(EPSILON=args.reprob, sl=0.02, sh=1.0/3.0, r1=0.3, attempt=10, use_log_aspect=True, mode=args.remode),
        transforms.ToCHWImage()])


batch_transform_ops = {}
batch_transform_ops['Mixup'] = {"alpha": args.mixup, "prob": args.mixup_switch_prob, "epsilon": args.smoothing, "class_num": args.nb_classes}
batch_transform_ops['Cutmix'] = {"alpha": args.cutmix, "prob": args.mixup_switch_prob, "epsilon": args.smoothing, "class_num": args.nb_classes}
mixup_fn = transforms.TransformOpSampler(**batch_transform_ops)

def mixup_collate_fn(batch):
    batch = mixup_fn(batch)
    batch = collate_fn(batch)
    return batch
    
data_loader_train = paddle.io.DataLoader(
    dataset_train, batch_sampler=sampler_train,
    num_workers=8,
    collate_fn=mixup_collate_fn,
)