def transform_v0(config):
""" https://www.kaggle.com/nroman/melanoma-pytorch-starter-efficientnet/data?scriptVersionId=35726268
Args:
config: CFG
Returns: train_tranforms, test_transforms
"""
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(size=224, scale=(0.7, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ColorJitter(brightness=32. / 255., saturation=0.5),
transforms.Cutout(scale=(0.05, 0.007), value=(0, 0)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
return train_transforms, test_transforms
def __init__(self, image_size):
self.data_transform = {
'train_transform':transforms.Compose([
transforms.RandomResizedCrop(size=image_size, scale=(0.7, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
#albumentatinoのNormalizeと同値
transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
]),
'test_transform': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
])}
def get_transforms():
train_transform = transforms.Compose(
[
AdvancedHairAugmentation(hairs_folder=f"{HAIRS}"),
transforms.RandomResizedCrop(size=SIZE, scale=(0.8, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
Microscope(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
test_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
return train_transform, test_transform
from siim_isic_melanoma_classification.config import get_config
from siim_isic_melanoma_classification.net import EfficientNetB5MLP
# %%
config = get_config()
# %%
util.initialize(config)
if util.is_kaggle():
import kaggle_timm_pretrained
kaggle_timm_pretrained.patch()
# %%
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=config.image_size, scale=(0.8, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
my_transforms.Microscope(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
])
# %%
all_source, _ = io.load_my_isic2020_csv(size=config.image_size,
is_sanity_check=config.sanity_check)
# %%
fold_index = int(os.environ["KAGGLE_TRAIN_FOLD_INDEX"])
n_fold = int(os.environ["KAGGLE_N_FOLD"])
import torchtoolbox.transform as transforms
from augmentation import *
"""
データセットに使うtransformer
"""
train_transform = transforms.Compose([
AdvancedHairAugmentation(hairs_folder = '/kaggle/input/melanoma-hairs'),
transforms.RandomResizedCrop(size = 256, scale = (0.8, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
Microscope(p = 0.5),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])
def __init__(self, df: pd.DataFrame,config, imfolder: str, split = 'train', meta_features = None):
"""
Class initialization
Args:
df (pd.DataFrame): DataFrame with data description
imfolder (str): folder with images
split : train ,val,test
transforms: image transformation method to be applied
meta_features (list): list of features with meta information, such as sex and age
"""
self.df = df
self.imfolder = imfolder
self.split = split
self.meta_features = meta_features
self.input_size = config['input_size']
self.same_sized_crop = config['same_sized_crop']
self.hair_aug = config['hair_aug']
self.microscope_aug = config['microscope_aug']
self.config = config
if split == 'train' or split == 'test':
all_transforms = []
if self.hair_aug :
all_transforms.append(AdvancedHairAugmentation(hairs_folder='melanoma_hair/'))
if self.same_sized_crop:
all_transforms.append(transforms.RandomCrop(self.input_size))
else:
all_transforms.append(transforms.RandomResizedCrop(self.input_size,scale=(config.get('scale_min',0.08),1.0)))
all_transforms.append(transforms.RandomHorizontalFlip())
all_transforms.append(transforms.RandomVerticalFlip())
#if config.get('full_rot',0) > 0:
# if config.get('scale',False):
# all_transforms.append(transforms.RandomChoice([transforms.RandomAffine(config['full_rot'], scale=config['scale'], shear=config.get('shear',0), resample=Image.NEAREST),
# transforms.RandomAffine(config['full_rot'],scale=config['scale'],shear=config.get('shear',0), resample=Image.BICUBIC),
# transforms.RandomAffine(config['full_rot'],scale=config['scale'],shear=config.get('shear',0), resample=Image.BILINEAR)]))
# else:
# all_transforms.append(transforms.RandomChoice([transforms.RandomRotation(config['full_rot'], resample=Image.NEAREST),
# transforms.RandomRotation(config['full_rot'], resample=Image.BICUBIC),
# transforms.RandomRotation(config['full_rot'], resample=Image.BILINEAR)]))
if config.get('full_rot',0) > 0:
if config.get('scale',False):
all_transforms.append(transforms.RandomAffine(config['full_rot'], scale=config['scale'], shear=config.get('shear',0)))
# transforms.RandomAffine(config['full_rot'],scale=config['scale'],shear=config.get('shear',0), resample=Image.BICUBIC),
# transforms.RandomAffine(config['full_rot'],scale=config['scale'],shear=config.get('shear',0), resample=Image.BILINEAR)]))
else:
all_transforms.append(transforms.RandomRotation(config['full_rot']))
# transforms.RandomRotation(config['full_rot'], resample=Image.BICUBIC),
# transforms.RandomRotation(config['full_rot'], resample=Image.BILINEAR)]))
all_transforms.append(transforms.ColorJitter(brightness=32. / 255.,saturation=0.5))
if self.microscope_aug:
all_transforms.append(Microscope(p=0.6))
if config['cutout']:
all_transforms.append(Cutout_v0(n_holes=1,length=config['cutout_length']))
#all_transforms.append(transforms.Cutout(scale=(0.05, 0.007), value=(0, 0)))
all_transforms.append(transforms.ToTensor())
all_transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]))
self.composed = transforms.Compose(all_transforms)
else:
self.composed = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
])