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e5ccff5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 | from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from PIL import Image, ImageFilter, ImageOps
import logging
import numpy as np
import torch
from timm.data import create_transform
import random
from .randaugment import RandomAugment
class GaussianBlur(object):
"""
Apply Gaussian Blur to the PIL image.
"""
def __init__(self, p=0.1, radius_min=0.1, radius_max=2.):
self.prob = p
self.radius_min = radius_min
self.radius_max = radius_max
def __call__(self, img):
do_it = random.random() <= self.prob
if not do_it:
return img
img = img.filter(
ImageFilter.GaussianBlur(
radius=random.uniform(self.radius_min, self.radius_max)
)
)
return img
class Solarization(object):
"""
Apply Solarization to the PIL image.
"""
def __init__(self, p=0.2):
self.p = p
def __call__(self, img):
if random.random() < self.p:
return ImageOps.solarize(img)
else:
return img
class RandomGaussianNoise:
def __init__(self, noise_range=(0.05, 0.2)):
self.noise_range = noise_range
def __call__(self, img):
sig = float(torch.empty(1).uniform_(self.noise_range[0], self.noise_range[1]))
return add_gaussian_noise_pil(img, sig)
def add_gaussian_noise_pil(img, sig):
arr = np.asarray(img)
arr1 = arr + np.random.randn(*arr.shape) * sig * 255
arr1 = np.clip(arr1, 0, 255)
arr1 = arr1.astype(arr.dtype)
img = Image.fromarray(arr1)
return img
XRAY_MEAN = [0.4978]
XRAY_STD = [0.2449]
def get_mean_std(args):
if args.norm_type == 'default':
mean = IMAGENET_DEFAULT_MEAN
std = IMAGENET_DEFAULT_STD
elif args.norm_type == 'inception':
mean = IMAGENET_INCEPTION_MEAN
std = IMAGENET_INCEPTION_STD
elif args.norm_type == 'xray': # from medmae
mean = [0.5056, 0.5056, 0.5056]
std = [0.252, 0.252, 0.252]
else:
raise NotImplementedError
return mean, std
class Identity:
def __call__(self, x):
return x
def build_aug_trans(args):
aug_trans = []
if args.rot > 0:
logging.info(f'Rotation={args.rot}')
aug_trans.append(transforms.RandomRotation(degrees=args.rot, interpolation=3))
has_blur = 'blur' in args.aug_type
has_sol = 'sol' in args.aug_type
if has_blur or has_sol:
logging.info(f'blur {has_blur}, sol {has_sol}, p={args.iwm_blur_prob}')
if has_blur and has_sol:
t = transforms.RandomChoice([Solarization(p=1.0), GaussianBlur(p=1.0)])
elif has_blur:
t = GaussianBlur(p=1.0)
else:
t = Solarization(p=1.0)
aug_trans.append(transforms.RandomApply([t], p=args.iwm_blur_prob))
if 'jit' in args.aug_type:
logging.info(f'Jitter={args.color_jitter}')
aug_trans.append(transforms.RandomApply([transforms.ColorJitter(brightness=args.color_jitter, contrast=args.color_jitter)], p=1.0))
if 'noise' in args.aug_type:
aug_trans.append(transforms.RandomApply([RandomGaussianNoise(noise_range=args.iwm_noise_range)], p=args.iwm_noise_prob))
if 'ra' in args.aug_type:
logging.info(f'Random Augment (2,7)')
trans = RandomAugment(2,7, isPIL=True,augs=['Identity','AutoContrast','Equalize','Brightness','Sharpness',
'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate'])
aug_trans.append(trans)
aug_trans = transforms.Compose(aug_trans)
return aug_trans
def build_transform_crop(args, is_train=True):
img_size = args.input_size
resize_size = args.input_size * 512 // 448 if args.resize_size is None else args.resize_size
logging.info(f'resize size: {args.resize_size}, input size: {args.input_size}')
if is_train:
if args.crop_type == 'rc':
crop_trans = transforms.Compose([
transforms.Resize(resize_size, interpolation=Image.BICUBIC),
transforms.RandomCrop((img_size, img_size)),
transforms.RandomHorizontalFlip(),
])
elif args.crop_type == 'rrc':
logging.info(f'Scale Min: {args.scale_min}')
crop_trans = transforms.Compose([
transforms.RandomResizedCrop(img_size,scale=(args.scale_min, 1.0), interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(),
])
else:
raise NotImplementedError
else:
crop_trans = transforms.Compose([
transforms.Resize(resize_size, interpolation=Image.BICUBIC),
transforms.CenterCrop((img_size, img_size)),
])
return crop_trans
def build_transform_xray(args, is_train, ten_crop=False):
mean, std = get_mean_std(args)
post_trans = transforms.Compose([
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
transforms.Normalize(mean=mean,std=std)
])
if args.aug_type == 'timm' and is_train:
logging.info(f'Use timm transform with aa={args.aa}, scale_min={args.scale_min}')
transform = create_transform(
input_size=args.input_size,
is_training=True,
scale=(args.scale_min, 1.0),
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation='bicubic',
re_prob=0,
mean=mean,
std=std,
)
return transform
logging.info(f'crop type: {args.crop_type}, aug type: {args.aug_type}')
crop_trans = build_transform_crop(args, is_train)
if is_train:
aug_trans = build_aug_trans(args)
else:
aug_trans = Identity()
if ten_crop:
logging.info('Build TenCrop Transform!')
transform = transforms.Compose([
transforms.Resize(args.resize_size, interpolation=Image.BICUBIC),
transforms.TenCrop(args.input_size), # this is a tuple of PIL Images
transforms.Lambda(lambda crops: torch.stack([post_trans(crop) for crop in crops])) # returns a 4D tensor
])
else:
transform = transforms.Compose([crop_trans, aug_trans, post_trans])
return transform
def build_simclr_transform(args, img_size=224):
mean, std = get_mean_std(args)
transform = transforms.Compose([
transforms.RandomResizedCrop(size=img_size, scale=(0.2, 0.8)),
transforms.RandomAffine(degrees=(-20, 20)),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.4, 0.4),
transforms.RandomApply([transforms.GaussianBlur(kernel_size=9, sigma=(0.1, 2))], p=0.5),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
transforms.Normalize(mean=mean,std=std)
])
return transform
import albumentations
from albumentations.pytorch.transforms import ToTensorV2
def build_seg_transform(args, is_train):
mean, std = get_mean_std(args)
if is_train:
if args.aug_type == 'adam':
data_transforms = albumentations.Compose([
albumentations.Resize(224, 224),
albumentations.OneOf([
albumentations.RandomBrightnessContrast(),
albumentations.RandomGamma(),
], p=0.3),
albumentations.OneOf([
albumentations.ElasticTransform(alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03),
albumentations.GridDistortion(),
albumentations.OpticalDistortion(distort_limit=2, shift_limit=0.5),
], p=0.3),
albumentations.RandomSizedCrop(min_max_height=(156, 224), height=224, width=224,p=0.25),
albumentations.Normalize(mean, std),
ToTensorV2()
],p=1)
else:
jit_aug = albumentations.RandomBrightnessContrast(
brightness_limit=args.color_jitter,
contrast_limit=args.color_jitter,
p=args.iwm_jitter_prob
)
rot_aug = albumentations.ShiftScaleRotate(rotate_limit=args.rot, border_mode=0)
logging.info(f'Scale Aug Type: {args.aug_type}')
logging.info(f'Jitter: {args.color_jitter} (p={args.iwm_jitter_prob}), Rot: {args.rot}')
data_transforms = albumentations.Compose([
albumentations.Resize(args.input_size, args.input_size),
rot_aug,
jit_aug,
albumentations.HorizontalFlip(p=0.5),
albumentations.Normalize(mean, std),
ToTensorV2()
])
else:
data_transforms = albumentations.Compose([
albumentations.Resize(args.input_size, args.input_size),
albumentations.Normalize(mean, std),
ToTensorV2()
])
return data_transforms |