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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import timm
from timm.data import create_transform
from yacs.config import CfgNode as CN
from PIL import ImageFilter
import logging
import random
import torch
import torchvision.transforms as T
from .autoaugment import AutoAugmentPolicy
from .autoaugment import AutoAugment
from .autoaugment import RandAugment
from .autoaugment import TrivialAugmentWide
from .threeaugment import deitIII_Solarization
from .threeaugment import deitIII_gray_scale
from .threeaugment import deitIII_GaussianBlur
from PIL import ImageOps
from timm.data.transforms import RandomResizedCropAndInterpolation
logger = logging.getLogger(__name__)
class GaussianBlur(object):
"""Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
sigma = random.uniform(self.sigma[0], self.sigma[1])
x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
return x
def get_resolution(original_resolution):
"""Takes (H,W) and returns (precrop, crop)."""
area = original_resolution[0] * original_resolution[1]
return (160, 128) if area < 96*96 else (512, 480)
INTERPOLATION_MODES = {
'bilinear': T.InterpolationMode.BILINEAR,
'bicubic': T.InterpolationMode.BICUBIC,
'nearest': T.InterpolationMode.NEAREST,
}
def build_transforms(cfg, is_train=True):
# assert isinstance(cfg.DATASET.OUTPUT_SIZE, (list, tuple)), 'DATASET.OUTPUT_SIZE should be list or tuple'
normalize = T.Normalize(
mean=cfg['IMAGE_ENCODER']['IMAGE_MEAN'],
std=cfg['IMAGE_ENCODER']['IMAGE_STD']
)
transforms = None
if is_train:
if 'THREE_AUG' in cfg['AUG']:
img_size = cfg['IMAGE_ENCODER']['IMAGE_SIZE']
remove_random_resized_crop = cfg['AUG']['THREE_AUG']['SRC']
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
primary_tfl = []
scale=(0.08, 1.0)
interpolation='bicubic'
if remove_random_resized_crop:
primary_tfl = [
T.Resize(img_size, interpolation=3),
T.RandomCrop(img_size, padding=4,padding_mode='reflect'),
T.RandomHorizontalFlip()
]
else:
primary_tfl = [
RandomResizedCropAndInterpolation(
img_size, scale=scale, interpolation=interpolation),
T.RandomHorizontalFlip()
]
secondary_tfl = [T.RandomChoice([gray_scale(p=1.0),
Solarization(p=1.0),
GaussianBlurDeiTv3(p=1.0)])]
color_jitter = cfg['AUG']['THREE_AUG']['COLOR_JITTER']
if color_jitter is not None and not color_jitter==0:
secondary_tfl.append(T.ColorJitter(color_jitter, color_jitter, color_jitter))
final_tfl = [
T.ToTensor(),
T.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
]
return T.Compose(primary_tfl+secondary_tfl+final_tfl)
elif 'TIMM_AUG' in cfg['AUG'] and cfg['AUG']['TIMM_AUG']['USE_TRANSFORM']:
logger.info('=> use timm transform for training')
timm_cfg = cfg['AUG']['TIMM_AUG']
transforms = create_transform(
input_size=cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0],
is_training=True,
use_prefetcher=False,
no_aug=False,
re_prob=timm_cfg.get('RE_PROB', 0.),
re_mode=timm_cfg.get('RE_MODE', 'const'),
re_count=timm_cfg.get('RE_COUNT', 1),
re_num_splits= 0 if not timm_cfg.get('RE_SPLITS', False) else timm_cfg['RE_SPLITS'], # if false or 0, return 0
scale=cfg['AUG'].get('SCALE', None),
ratio=cfg['AUG'].get('RATIO', None),
hflip=timm_cfg.get('HFLIP', 0.5),
vflip=timm_cfg.get('VFLIP', 0.),
color_jitter=timm_cfg.get('COLOR_JITTER', 0.4),
auto_augment=timm_cfg.get('AUTO_AUGMENT', None),
interpolation=cfg['AUG']['INTERPOLATION'],
mean=cfg['IMAGE_ENCODER']['IMAGE_MEAN'],
std=cfg['IMAGE_ENCODER']['IMAGE_STD'],
)
elif 'TORCHVISION_AUG' in cfg['AUG']:
logger.info('=> use torchvision transform fro training')
crop_size = cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0]
interpolation = INTERPOLATION_MODES[cfg['AUG']['INTERPOLATION']]
trans = [
T.RandomResizedCrop(
crop_size, scale=cfg['AUG']['SCALE'], ratio=cfg['AUG']['RATIO'],
interpolation=interpolation
)
]
hflip_prob = cfg['AUG']['TORCHVISION_AUG']['HFLIP']
auto_augment_policy = cfg['AUG']['TORCHVISION_AUG'].get('AUTO_AUGMENT', None)
if hflip_prob > 0:
trans.append(T.RandomHorizontalFlip(hflip_prob))
if auto_augment_policy is not None:
if auto_augment_policy == "ra":
trans.append(RandAugment(interpolation=interpolation))
elif auto_augment_policy == "ta_wide":
trans.append(TrivialAugmentWide(interpolation=interpolation))
else:
aa_policy = AutoAugmentPolicy(auto_augment_policy)
trans.append(AutoAugment(policy=aa_policy, interpolation=interpolation))
trans.extend(
[
T.ToTensor(),
normalize,
]
)
random_erase_prob = cfg['AUG']['TORCHVISION_AUG']['RE_PROB']
random_erase_scale = cfg['AUG']['TORCHVISION_AUG'].get('RE_SCALE', 0.33)
if random_erase_prob > 0:
# NCFC (4/26/2023): Added scale parameter to random erasing for medical imaging
trans.append(T.RandomErasing(p=random_erase_prob, scale = (0.02, random_erase_scale)))
from torchvision.transforms import InterpolationMode
rotation = cfg['AUG']['TORCHVISION_AUG'].get('ROTATION', 0.0)
if (rotation > 0.0):
trans.append(T.RandomRotation(rotation, interpolation=InterpolationMode.BILINEAR))
logger.info(" TORCH AUG: Rotation: " + str(rotation))
transforms = T.Compose(trans)
elif cfg['AUG'].get('RANDOM_CENTER_CROP', False):
logger.info('=> use random center crop data augmenation')
# precrop, crop = get_resolution(cfg.TRAIN.IMAGE_SIZE)
crop = cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0]
padding = cfg['AUG'].get('RANDOM_CENTER_CROP_PADDING', 32)
precrop = crop + padding
mode = INTERPOLATION_MODES[cfg['AUG']['INTERPOLATION']]
transforms = T.Compose([
T.Resize(
(precrop, precrop),
interpolation=mode
),
T.RandomCrop((crop, crop)),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize,
])
elif cfg['AUG'].get('MAE_FINETUNE_AUG', False):
mean = cfg['IMAGE_ENCODER']['IMAGE_MEAN']
std = cfg['IMAGE_ENCODER']['IMAGE_STD']
transforms = create_transform(
input_size=cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0],
is_training=True,
color_jitter=cfg['AUG'].get('COLOR_JITTER', None),
auto_augment=cfg['AUG'].get('AUTO_AUGMENT', 'rand-m9-mstd0.5-inc1'),
interpolation='bicubic',
re_prob=cfg['AUG'].get('RE_PROB', 0.25),
re_mode=cfg['AUG'].get('RE_MODE', "pixel"),
re_count=cfg['AUG'].get('RE_COUNT', 1),
mean=mean,
std=std,
)
elif cfg['AUG'].get('MAE_PRETRAIN_AUG', False):
mean = cfg['IMAGE_ENCODER']['IMAGE_MEAN']
std = cfg['IMAGE_ENCODER']['IMAGE_STD']
transforms = T.Compose([
T.RandomResizedCrop(cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0], scale=tuple(cfg['AUG']['SCALE']), interpolation=INTERPOLATION_MODES["bicubic"]), # 3 is bicubic
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=mean, std=std)])
elif cfg['AUG'].get('ThreeAugment', False): # from DeiT III
mean = cfg['IMAGE_ENCODER']['IMAGE_MEAN']
std = cfg['IMAGE_ENCODER']['IMAGE_STD']
img_size = cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0]
remove_random_resized_crop = cfg['AUG'].get('src', False)
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
primary_tfl = []
scale=(0.08, 1.0)
interpolation='bicubic'
if remove_random_resized_crop:
primary_tfl = [
T.Resize(img_size, interpolation=3), # bicubic
T.RandomCrop(img_size, padding=4,padding_mode='reflect'),
T.RandomHorizontalFlip()
]
else:
primary_tfl = [
timm.data.transforms.RandomResizedCropAndInterpolation(
img_size, scale=scale, interpolation=interpolation),
T.RandomHorizontalFlip()
]
secondary_tfl = [T.RandomChoice([deitIII_gray_scale(p=1.0),
deitIII_Solarization(p=1.0),
deitIII_GaussianBlur(p=1.0)])]
color_jitter = cfg['AUG']['COLOR_JITTER']
secondary_tfl.append(T.ColorJitter(color_jitter, color_jitter, color_jitter))
final_tfl = [
T.ToTensor(),
T.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
]
transforms = T.Compose(primary_tfl+secondary_tfl+final_tfl)
logger.info('=> training transformers: {}'.format(transforms))
else:
mode = INTERPOLATION_MODES[cfg['AUG']['INTERPOLATION']]
if cfg['TEST']['CENTER_CROP']:
transforms = T.Compose([
T.Resize(
int(cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0] / 0.875),
# the same behavior as in deit: size = int((256 / 224) * args.input_size)
# 224 / 256 = 0.875
interpolation=mode
),
T.CenterCrop(cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0]),
T.ToTensor(),
normalize,
])
else:
transforms = T.Compose([
T.Resize(
(cfg['IMAGE_ENCODER']['IMAGE_SIZE'][1], cfg['IMAGE_ENCODER']['IMAGE_SIZE'][0]),
interpolation=mode
),
T.ToTensor(),
normalize,
])
logger.info('=> testing transformers: {}'.format(transforms))
return transforms
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