def run_train(data_path: Path):
cfg = BasicConfig(
seed=444,
name='test_center_vgg_retrain2',
num_workers=4,
gpus=(0,),
batch_size=24,
num_epochs=15,
steps=(4, 9, np.inf),
warmup_epoch=1,
cooldown_epoch=2,
train_augmentations=alb.Compose([
alb.Resize(128, 128),
alb.OneOf([
alb.MotionBlur(blur_limit=5, p=0.2),
alb.MedianBlur(blur_limit=3, p=0.1),
alb.Blur(blur_limit=5, p=0.1)
], p=0.2),
alb.OneOf([
alb.ImageCompression(70, compression_type=alb.ImageCompression.ImageCompressionType.JPEG),
alb.ImageCompression(70, compression_type=alb.ImageCompression.ImageCompressionType.WEBP)
], p=0.2),
alb.OneOf([
alb.CLAHE(clip_limit=2),
alb.IAASharpen(),
alb.IAAEmboss(),
alb.RandomBrightnessContrast(),
], p=0.1),
alb.Rotate(5, border_mode=cv2.BORDER_REFLECT, p=0.2),
alb.OneOf([
alb.RandomResizedCrop(112, 112, scale=(0.9, 1.0), ratio=(0.8, 1.1), p=0.5),
alb.Resize(112, 112, p=0.5),
], p=1.0),
alb.HorizontalFlip(p=0.5),
alb.HueSaturationValue(p=0.7),
alb.ChannelShuffle(p=0.5)
]),
normalize=True,
weight_normalize=True,
uniform_subjects=True,
classifier_mult=3,
lr_factor=0.1,
initial_lr=1e-2,
# extra_rec=(Path('/run/media/andrey/Fast/FairFace/faces_emore/train.rec'),)
)
np.random.seed(cfg.seed)
mx.random.seed(cfg.seed)
train_df = load_info(data_path, Path('data/train_df.csv'))
lib.train.train(cfg, train_df)
def medium_3(image_size, p=1.0):
cutout_crop = int(0.25 * image_size)
return A.Compose(
[
# RandomCrop(input_size) / RandomResizedCrop (0.08, 1)
A.HorizontalFlip(p=0.5), # vflip
A.VerticalFlip(p=0.5), # hflip
A.ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.3),
A.OneOf([
A.RandomFog(
fog_coef_lower=0.3, fog_coef_upper=1.0, alpha_coef=.1),
A.ImageCompression(quality_lower=20, quality_upper=99),
],
p=0.3),
A.RandomBrightnessContrast(brightness_limit=0.125,
contrast_limit=0.2,
p=0.5), # contrast_limit=0.5
A.HueSaturationValue(hue_shift_limit=5,
sat_shift_limit=30,
val_shift_limit=20,
p=0.2),
A.GaussNoise(var_limit=(1, 50), p=0.4),
A.CoarseDropout(min_holes=1,
max_holes=2,
max_height=cutout_crop,
max_width=cutout_crop,
p=0.5),
],
p=p)
def __init__(self, p=0.1, quality_lower=40, quality_upper=90):
self.p = p
self.quality_lower = quality_lower
self.quality_upper = quality_upper
self.aug = A.ImageCompression(p=p,
quality_lower=quality_lower,
quality_upper=quality_upper)
def get_strong_train_transform():
return A.Compose(
[
#A.Resize(height=IMG_SIZE, width=IMG_SIZE, p=1),
A.RandomSizedBBoxSafeCrop(
IMG_SIZE, IMG_SIZE, interpolation=1, p=0.33),
A.HorizontalFlip(),
A.ShiftScaleRotate(shift_limit=0.05,
scale_limit=0.1,
rotate_limit=10,
interpolation=1,
p=0.5),
A.OneOf([
A.Blur(blur_limit=(1, 3), p=0.33),
A.MedianBlur(blur_limit=3, p=0.33),
A.ImageCompression(quality_lower=50, p=0.33),
],
p=0.5),
A.OneOf([
A.RandomGamma(gamma_limit=(85, 115), p=0.33),
A.RandomBrightnessContrast(brightness_limit=0.2, p=0.33),
A.HueSaturationValue(hue_shift_limit=25,
sat_shift_limit=25,
val_shift_limit=30,
p=0.5)
],
p=0.34),
A.CLAHE(clip_limit=2.5, p=0.5),
A.Normalize(always_apply=True, p=1.0),
ToTensorV2(p=1.0)
],
bbox_params=A.BboxParams(format='pascal_voc',
min_area=5,
min_visibility=0.1,
label_fields=['labels']))
def get_transforms(image_size):
transforms_train = albumentations.Compose([
albumentations.HorizontalFlip(p=0.5),
albumentations.ImageCompression(quality_lower=99, quality_upper=100),
albumentations.ShiftScaleRotate(shift_limit=0.2,
scale_limit=0.2,
rotate_limit=10,
border_mode=0,
p=0.7),
albumentations.Resize(image_size, image_size),
albumentations.Cutout(
max_h_size=int(image_size * 0.4),
max_w_size=int(image_size * 0.4),
num_holes=1,
p=0.5,
),
albumentations.Normalize(),
])
transforms_val = albumentations.Compose([
albumentations.Resize(image_size, image_size),
albumentations.Normalize()
])
return transforms_train, transforms_val
def __init__(self):
self.transform = None
try:
import albumentations as A
check_version(A.__version__, '1.0.3',
hard=True) # version requirement
self.transform = A.Compose([
A.Blur(p=0.01),
A.MedianBlur(p=0.01),
A.ToGray(p=0.01),
A.CLAHE(p=0.01),
A.RandomBrightnessContrast(p=0.0),
A.RandomGamma(p=0.0),
A.ImageCompression(quality_lower=75, p=0.0)
],
bbox_params=A.BboxParams(
format='yolo',
label_fields=['class_labels']))
LOGGER.info(
colorstr('albumentations: ') +
', '.join(f'{x}' for x in self.transform.transforms if x.p))
except ImportError: # package not installed, skip
pass
except Exception as e:
LOGGER.info(colorstr('albumentations: ') + f'{e}')
def __init__(self, root_dir, is_train):
super(FaceDataset, self).__init__()
#self.local_rank = local_rank
self.is_train = is_train
self.input_size = 256
self.num_kps = 68
transform_list = []
if is_train:
transform_list += \
[
A.ColorJitter(brightness=0.8, contrast=0.5, p=0.5),
A.ToGray(p=0.1),
A.ISONoise(p=0.1),
A.MedianBlur(blur_limit=(1,7), p=0.1),
A.GaussianBlur(blur_limit=(1,7), p=0.1),
A.MotionBlur(blur_limit=(5,12), p=0.1),
A.ImageCompression(quality_lower=50, quality_upper=90, p=0.05),
A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=40, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.8),
A.HorizontalFlip(p=0.5),
RectangleBorderAugmentation(limit=0.33, fill_value=0, p=0.2),
]
transform_list += \
[
A.geometric.resize.Resize(self.input_size, self.input_size, interpolation=cv2.INTER_LINEAR, always_apply=True),
A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
ToTensorV2(),
]
self.transform = A.ReplayCompose(transform_list,
keypoint_params=A.KeypointParams(
format='xy',
remove_invisible=False))
self.root_dir = root_dir
with open(osp.join(root_dir, 'annot.pkl'), 'rb') as f:
annot = pickle.load(f)
self.X, self.Y = annot
train_size = int(len(self.X) * 0.99)
if is_train:
self.X = self.X[:train_size]
self.Y = self.Y[:train_size]
else:
self.X = self.X[train_size:]
self.Y = self.Y[train_size:]
#if local_rank==0:
# logging.info('data_transform_list:%s'%transform_list)
flip_parts = ([1, 17], [2, 16], [3, 15], [4, 14], [5, 13], [6, 12],
[7, 11], [8, 10], [18, 27], [19, 26], [20, 25], [21, 24],
[22, 23], [32, 36], [33, 35], [37, 46], [38, 45],
[39, 44], [40, 43], [41, 48], [42, 47], [49,
55], [50, 54],
[51, 53], [62, 64], [61, 65], [68, 66], [59,
57], [60, 56])
self.flip_order = np.arange(self.num_kps)
for pair in flip_parts:
self.flip_order[pair[1] - 1] = pair[0] - 1
self.flip_order[pair[0] - 1] = pair[1] - 1
logging.info('len:%d' % len(self.X))
print('!!!len:%d' % len(self.X))
def get_transformer(face_policy: str, patch_size: int, net_normalizer: transforms.Normalize, train: bool):
# Transformers and traindb
if face_policy == 'scale':
# The loader crops the face isotropically then scales to a square of size patch_size_load
loading_transformations = [
A.PadIfNeeded(min_height=patch_size, min_width=patch_size,
border_mode=cv2.BORDER_CONSTANT, value=0,always_apply=True),
A.Resize(height=patch_size,width=patch_size,always_apply=True),
]
if train:
downsample_train_transformations = [
A.Downscale(scale_max=0.5, scale_min=0.5, p=0.5), # replaces scaled dataset
]
else:
downsample_train_transformations = []
elif face_policy == 'tight':
# The loader crops the face tightly without any scaling
loading_transformations = [
A.LongestMaxSize(max_size=patch_size, always_apply=True),
A.PadIfNeeded(min_height=patch_size, min_width=patch_size,
border_mode=cv2.BORDER_CONSTANT, value=0,always_apply=True),
]
if train:
downsample_train_transformations = [
A.Downscale(scale_max=0.5, scale_min=0.5, p=0.5), # replaces scaled dataset
]
else:
downsample_train_transformations = []
else:
raise ValueError('Unknown value for face_policy: {}'.format(face_policy))
if train:
aug_transformations = [
A.Compose([
A.HorizontalFlip(),
A.OneOf([
A.RandomBrightnessContrast(),
A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=30, val_shift_limit=20),
]),
A.OneOf([
A.ISONoise(),
A.IAAAdditiveGaussianNoise(scale=(0.01 * 255, 0.03 * 255)),
]),
A.Downscale(scale_min=0.7, scale_max=0.9, interpolation=cv2.INTER_LINEAR),
A.ImageCompression(quality_lower=50, quality_upper=99),
], )
]
else:
aug_transformations = []
# Common final transformations
final_transformations = [
A.Normalize(mean=net_normalizer.mean, std=net_normalizer.std, ),
ToTensorV2(),
]
transf = A.Compose(
loading_transformations + downsample_train_transformations + aug_transformations + final_transformations)
return transf
def get_obliterate_augs():
"""
Get the augmentation that can obliterate the hidden signal.
This is used as augmentation to create negative sample from positive one.
:return:
"""
return A.OneOf(
[
A.ImageCompression(quality_lower=70, quality_upper=95, p=1),
A.Downscale(p=1),
A.GaussianBlur(blur_limit=(5, 9), p=1),
],
p=1,
)
def get_train_transform(img_size):
return A.Compose(
[
A.Resize(height=img_size, width=img_size, p=1),
A.RandomSizedBBoxSafeCrop(
img_size, img_size, interpolation=1, p=0.33
),
A.Flip(),
A.ShiftScaleRotate(
shift_limit=0.05,
scale_limit=0.1,
rotate_limit=10,
interpolation=1,
border_mode=0,
value=0,
p=0.5,
),
A.OneOf(
[
A.Blur(blur_limit=(1, 3), p=0.33),
A.MedianBlur(blur_limit=3, p=0.33),
A.ImageCompression(quality_lower=50, p=0.33),
],
p=0.33,
),
A.OneOf(
[
A.RandomGamma(gamma_limit=(90, 110), p=0.2),
A.RandomBrightnessContrast(brightness_limit=0.2, p=0.4),
A.HueSaturationValue(
hue_shift_limit=25,
sat_shift_limit=25,
val_shift_limit=30,
p=0.5,
),
],
p=0.4,
),
A.CLAHE(clip_limit=2, p=0.2),
A.Normalize(always_apply=True, p=1.0),
ToTensorV2(p=1.0),
],
bbox_params=A.BboxParams(
format="pascal_voc",
min_area=64,
min_visibility=0.1,
label_fields=["labels"],
),
)
def setup_augmentors(self, augmentations):
self.augmentors = []
for aug_name, aug_config in augmentations.items():
aug = None
def get_albu(aug):
return albu.Compose(aug)
if aug_name == 'image_compression':
aug = get_albu([
albu.ImageCompression(
quality_lower=aug_config.get('quality_lower', 90),
quality_upper=aug_config.get('quality_upper'),
p=aug_config.get('probabilty', 0.5))
])
elif aug_name == 'posterize':
aug = get_albu([
albu.Posterize(num_bits=aug_config.get('num_bits', 4),
p=aug_config.get('probability', 0.5))
])
elif aug_name == 'blur':
aug = get_albu([
albu.Blur(blur_limit=aug_config.get('blur_limit', 7),
p=aug_config.get('probabilty', 0.5))
])
elif aug_name == 'median_blur':
aug = get_albu([
albu.MedianBlur(blur_limit=aug_config.get('blur_limit', 7),
p=aug_config.get('probabilty', 0.5))
])
elif aug_name == 'iso_noise':
aug = get_albu([
albu.ISONoise(
color_shift=(aug_config.get('min_color_shift', 0.01),
aug_config.get('max_color_shift', 0.05)),
intensity=(aug_config.get('min_intensity', 0.1),
aug_config.get('min_intensity', 0.5)),
p=aug_config.get('probabilty', 0.5))
])
if not aug:
continue
aug.name, aug.p, aug.base = aug_name, aug_config[
'probability'], self
self.augmentors.append(aug)
return
def aug_medium(prob=1):
return aug.Compose([
aug.HorizontalFlip(p=.5),
aug.OneOf([
aug.CLAHE(clip_limit=2, p=.5),
aug.IAASharpen(p=.25),
], p=0.35),
aug.RandomBrightnessContrast(p=.7),
aug.OneOf([
aug.GaussNoise(p=.35),
aug.ISONoise(p=.7),
aug.ImageCompression(quality_lower=70, quality_upper=100, p=.7)
], p=.6),
aug.RGBShift(p=.5),
aug.HueSaturationValue(hue_shift_limit=8, sat_shift_limit=12, val_shift_limit=8, p=.5),
aug.ToGray(p=.3)
], p=prob)
def hard_transforms(border_reflect=2):
result = [
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.ShiftScaleRotate(shift_limit=0.1,
scale_limit=0.1,
rotate_limit=15,
border_mode=border_reflect,
p=0.5),
albu.IAAPerspective(scale=(0.02, 0.05), p=0.3),
albu.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
p=0.3),
albu.RandomGamma(gamma_limit=(85, 115), p=0.3),
albu.HueSaturationValue(p=0.3),
albu.ImageCompression(quality_lower=80, p=0.5),
]
return result
def get_transform(img_size: int, darknet_pretrained: bool = False):
"""Initializes and returns data transformation pipeline
Args:
img_size (int): image size
darknet_pretrained (bool): if you use a pre-trained darknet model, you need to disable image normalization
"""
normalize = albu.Normalize(p=1)
if darknet_pretrained:
normalize = albu.Normalize(p=0)
train_transform = albu.Compose(
[
albu.RandomResizedCrop(
scale=(0.9, 1.0),
height=img_size,
width=img_size,
p=1,
),
albu.ShiftScaleRotate(
border_mode=cv2.BORDER_CONSTANT,
rotate_limit=10,
scale_limit=0,
p=0.5,
mask_value=255,
),
albu.ImageCompression(quality_lower=60, quality_upper=100, p=0.5),
normalize,
],
bbox_params=albu.BboxParams(format="pascal_voc",
label_fields=["category_id"]),
)
test_transform = albu.Compose(
[albu.Resize(height=img_size, width=img_size, p=1), normalize],
bbox_params=albu.BboxParams(format="pascal_voc",
label_fields=["category_id"]),
)
return train_transform, test_transform
def hard_transforms():
result = [
# Random shifts, stretches and turns with a 50% probability
albu.ShiftScaleRotate(shift_limit=0.1,
scale_limit=0.1,
rotate_limit=15,
border_mode=BORDER_REFLECT,
p=0.5),
albu.IAAPerspective(scale=(0.02, 0.05), p=0.3),
# Random brightness / contrast with a 30% probability
albu.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
p=0.3),
# Random gamma changes with a 30% probability
albu.RandomGamma(gamma_limit=(85, 115), p=0.3),
# Randomly changes the hue, saturation, and color value of the input image
albu.HueSaturationValue(p=0.3),
albu.ImageCompression(quality_lower=80),
]
return result
def __init__(self):
self.transform = None
try:
import albumentations as A
self.transform = A.Compose([
A.Blur(p=0.01),
A.MedianBlur(p=0.01),
A.ToGray(p=0.01),
A.CLAHE(p=0.01),
A.RandomBrightnessContrast(p=0.0),
A.RandomGamma(p=0.0),
A.ImageCompression(quality_lower=75, p=0.0)
],
bbox_params=A.BboxParams(
format='pascal_voc',
label_fields=['class_labels']))
except ImportError: # package not installed, skip
pass
except Exception as e:
print('albumentations: ' + f'{e}')
def get_image_augmentation():
""" Augmentations just for input and output images (not for masks) """
image_transform = [
albu.OneOf([
albu.Blur(p=0.2, blur_limit=(3, 5)),
albu.GaussNoise(p=0.2, var_limit=(10.0, 50.0)),
albu.ISONoise(p=0.2, intensity=(0.1, 0.5), color_shift=(0.01, 0.05)),
albu.ImageCompression(p=0.2, quality_lower=90, quality_upper=100, compression_type=0),
albu.MultiplicativeNoise(p=0.2, multiplier=(0.9, 1.1), per_channel=True, elementwise=True),
], p=1),
albu.OneOf([
albu.HueSaturationValue(p=0.2, hue_shift_limit=(-10, 10), sat_shift_limit=(-10, 10), val_shift_limit=(-10, 10)),
albu.RandomBrightness(p=0.3, limit=(-0.1, 0.1)),
albu.RandomGamma(p=0.3, gamma_limit=(80, 100), eps=1e-07),
albu.ToGray(p=0.1),
albu.ToSepia(p=0.1),
], p=1)
]
return albu.Compose(image_transform, additional_targets={
'image1': 'image',
'image2': 'image'
})
wandb.init(project='inception_v3', group=wandb.util.generate_id())
wandb.config.width_size = width_size
wandb.config.aspect_rate = 1
wandb.config.batch_size = batch_size
wandb.config.accumulation_step = accumulation_step
shutil.rmtree('tensorboard_runs', ignore_errors=True)
writer = SummaryWriter(log_dir='tensorboard_runs',
filename_suffix=str(time.time()))
ranzcr_df = pd.read_csv('train_folds.csv')
ranzcr_train_df = ranzcr_df[ranzcr_df['fold'] != 1]
chestx_df = pd.read_csv('chestx_pseudolabeled_data_lazy_balancing.csv')
train_image_transforms = alb.Compose([
alb.ImageCompression(quality_lower=65, p=0.5),
alb.HorizontalFlip(p=0.5),
alb.CLAHE(p=0.5),
alb.OneOf([
alb.GridDistortion(num_steps=8, distort_limit=0.5, p=1.0),
alb.OpticalDistortion(
distort_limit=0.5,
shift_limit=0.5,
p=1.0,
),
alb.ElasticTransform(alpha=3, p=1.0)
],
p=0.7),
alb.RandomResizedCrop(height=width_size,
width=width_size,
scale=(0.8, 1.2),
parser.add_argument('--verbose_eval', default=50, type=int)
parser.add_argument('--max_size', default=300, type=int)
parser.add_argument('--num_classes', default=1049, type=int)
parser.add_argument('--resume', default=None, type=str)
parser.add_argument('--depth', default=0, type=int)
args = parser.parse_args()
scaler = GradScaler()
writer = SummaryWriter()
fixed_seed(42)
device = 'cuda'
transforms_train = A.Compose([
A.HorizontalFlip(p=0.2),
A.ImageCompression(
quality_lower=99,
quality_upper=100),
A.ShiftScaleRotate(
shift_limit=0.2,
scale_limit=0.2,
rotate_limit=10,
border_mode=0, p=0.5),
A.ColorJitter(0.2, 0.2, 0.2, 0.2),
A.Resize(args.max_size, args.max_size),
A.OneOf([
A.RandomResizedCrop(args.max_size, args.max_size),
A.Cutout(
max_h_size=int(args.max_size*0.4),
max_w_size=int(args.max_size*0.4),
num_holes=1,
p=0.3),
int(0.5 * (train_parameters["height_crop_size"])),
int(2 * (train_parameters["height_crop_size"])),
),
height=train_parameters["height_crop_size"],
width=train_parameters["width_crop_size"],
w2h_ratio=1.0,
p=1,
),
albu.ShiftScaleRotate(border_mode=cv2.BORDER_CONSTANT,
rotate_limit=10,
scale_limit=0,
p=0.5,
mask_value=ignore_index),
albu.RandomBrightnessContrast(p=0.5),
albu.RandomGamma(p=0.5),
albu.ImageCompression(quality_lower=20, quality_upper=100, p=0.5),
albu.GaussNoise(p=0.5),
albu.Blur(p=0.5),
albu.CoarseDropout(p=0.5, max_height=26, max_width=16),
albu.OneOf([albu.HueSaturationValue(p=0.5),
albu.RGBShift(p=0.5)],
p=0.5),
normalization,
],
p=1,
)
val_augmentations = albu.Compose(
[
albu.PadIfNeeded(min_height=1024,
min_width=2048,
def train_function(gpu, world_size, node_rank, gpus):
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
torch.manual_seed(25)
np.random.seed(25)
rank = node_rank * gpus + gpu
dist.init_process_group(
backend='nccl',
init_method='env://',
world_size=world_size,
rank=rank
)
width_size = 512
batch_size = 32
accumulation_step = 5
device = torch.device("cuda:{}".format(gpu) if torch.cuda.is_available() else "cpu")
if rank == 0:
wandb.init(project='inception_v3', group=wandb.util.generate_id())
wandb.config.width_size = width_size
wandb.config.aspect_rate = 1
wandb.config.batch_size = batch_size
wandb.config.accumulation_step = accumulation_step
shutil.rmtree('tensorboard_runs', ignore_errors=True)
writer = SummaryWriter(log_dir='tensorboard_runs', filename_suffix=str(time.time()))
ranzcr_df = pd.read_csv('train_folds.csv')
ranzcr_train_df = ranzcr_df[ranzcr_df['fold'] != 1]
chestx_df = pd.read_csv('chestx_pseudolabeled_data_lazy_balancing.csv')
train_image_transforms = alb.Compose([
alb.ImageCompression(quality_lower=65, p=0.5),
alb.HorizontalFlip(p=0.5),
alb.CLAHE(p=0.5),
alb.OneOf([
alb.GridDistortion(
num_steps=8,
distort_limit=0.5,
p=1.0
),
alb.OpticalDistortion(
distort_limit=0.5,
shift_limit=0.5,
p=1.0,
),
alb.ElasticTransform(alpha=3, p=1.0)],
p=0.7
),
alb.RandomResizedCrop(
height=width_size,
width=width_size,
scale=(0.8, 1.2),
p=0.7
),
alb.RGBShift(p=0.5),
alb.RandomSunFlare(p=0.5),
alb.RandomFog(p=0.5),
alb.RandomBrightnessContrast(p=0.5),
alb.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=20,
val_shift_limit=20,
p=0.5
),
alb.ShiftScaleRotate(shift_limit=0.025, scale_limit=0.1, rotate_limit=20, p=0.5),
alb.CoarseDropout(
max_holes=12,
min_holes=6,
max_height=int(width_size / 6),
max_width=int(width_size / 6),
min_height=int(width_size / 6),
min_width=int(width_size / 20),
p=0.5
),
alb.IAAAdditiveGaussianNoise(loc=0, scale=(2.5500000000000003, 12.75), per_channel=False, p=0.5),
alb.IAAAffine(scale=1.0, translate_percent=None, translate_px=None, rotate=0.0, shear=0.0, order=1, cval=0,
mode='reflect', p=0.5),
alb.IAAAffine(rotate=90., p=0.5),
alb.IAAAffine(rotate=180., p=0.5),
alb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2()
])
train_set = NoisyStudentDataset(ranzcr_train_df, chestx_df, train_image_transforms,
'../ranzcr/train', '../data', width_size=width_size)
train_sampler = DistributedSampler(train_set, num_replicas=world_size, rank=rank, shuffle=True)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=False, num_workers=4, sampler=train_sampler)
ranzcr_valid_df = ranzcr_df[ranzcr_df['fold'] == 1]
valid_image_transforms = alb.Compose([
alb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2()
])
valid_set = ImageDataset(ranzcr_valid_df, valid_image_transforms, '../ranzcr/train', width_size=width_size)
valid_loader = DataLoader(valid_set, batch_size=batch_size, num_workers=4, pin_memory=False, drop_last=False)
# ranzcr_valid_df = ranzcr_df[ranzcr_df['fold'] == 1]
# valid_image_transforms = alb.Compose([
# alb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
# ToTensorV2()
# ])
# valid_set = ImageDataset(ranzcr_valid_df, valid_image_transforms, '../ranzcr/train', width_size=width_size)
# valid_sampler = DistributedSampler(valid_set, num_replicas=world_size, rank=rank)
# valid_loader = DataLoader(valid_set, batch_size=batch_size, num_workers=4, sampler=valid_sampler)
checkpoints_dir_name = 'inception_v3_noisy_student_{}'.format(width_size)
os.makedirs(checkpoints_dir_name, exist_ok=True)
# model = EfficientNetNoisyStudent(11, pretrained_backbone=True,
# mixed_precision=True, model_name='tf_efficientnet_b7_ns')
model = Inception(11, pretrained_backbone=True, mixed_precision=False, model_name='inception_v3')
model = SyncBatchNorm.convert_sync_batchnorm(model)
model.to(device)
model = DistributedDataParallel(model, device_ids=[gpu])
# class_weights = [354.625, 23.73913043478261, 2.777105767812362, 110.32608695652173,
# 52.679245283018865, 9.152656621728786, 4.7851333032083145,
# 8.437891632878731, 2.4620064899945917, 0.4034751151063363, 31.534942820838626]
class_names = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal',
'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal',
'CVC - Abnormal', 'CVC - Borderline', 'CVC - Normal', 'Swan Ganz Catheter Present']
scaler = GradScaler()
criterion = torch.nn.BCEWithLogitsLoss()
lr_start = 1e-4
lr_end = 1e-6
weight_decay = 0
epoch_num = 20
if rank == 0:
wandb.config.model_name = checkpoints_dir_name
wandb.config.lr_start = lr_start
wandb.config.lr_end = lr_end
wandb.config.weight_decay = weight_decay
wandb.config.epoch_num = epoch_num
wandb.config.optimizer = 'adam'
wandb.config.scheduler = 'CosineAnnealingLR'
wandb.config.is_loss_weights = 'no'
optimizer = Adam(model.parameters(), lr=lr_start, weight_decay=weight_decay)
scheduler = CosineAnnealingLR(optimizer, T_max=epoch_num, eta_min=lr_end, last_epoch=-1)
max_val_auc = 0
for epoch in range(epoch_num):
train_loss, train_avg_auc, train_auc, train_rocs, train_data_pr, train_duration = one_epoch_train(
model, train_loader, optimizer, criterion, device, scaler,
iters_to_accumulate=accumulation_step, clip_grads=False)
scheduler.step()
if rank == 0:
val_loss, val_avg_auc, val_auc, val_rocs, val_data_pr, val_duration = eval_model(
model, valid_loader, device, criterion, scaler)
wandb.log({'train_loss': train_loss, 'val_loss': val_loss,
'train_auc': train_avg_auc, 'val_auc': val_avg_auc, 'epoch': epoch})
for class_name, auc1, auc2 in zip(class_names, train_auc, val_auc):
wandb.log({'{} train auc'.format(class_name): auc1,
'{} val auc'.format(class_name): auc2, 'epoch': epoch})
if val_avg_auc > max_val_auc:
max_val_auc = val_avg_auc
wandb.run.summary["best_accuracy"] = val_avg_auc
print('EPOCH %d:\tTRAIN [duration %.3f sec, loss: %.3f, avg auc: %.3f]\t\t'
'VAL [duration %.3f sec, loss: %.3f, avg auc: %.3f]\tCurrent time %s' %
(epoch + 1, train_duration, train_loss, train_avg_auc,
val_duration, val_loss, val_avg_auc, str(datetime.now(timezone('Europe/Moscow')))))
torch.save(model.module.state_dict(),
os.path.join(checkpoints_dir_name, '{}_epoch{}_val_auc{}_loss{}_train_auc{}_loss{}.pth'.format(
checkpoints_dir_name, epoch + 1, round(val_avg_auc, 3), round(val_loss, 3),
round(train_avg_auc, 3), round(train_loss, 3))))
if rank == 0:
wandb.finish()
def load_train_transform(transform_type, patch_size=512):
if transform_type == 'basic':
transform = A.Compose([
A.RandomRotate90(p=0.75),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.PadIfNeeded(patch_size, patch_size)])
elif transform_type == 'light':
transform = A.Compose([
A.RandomRotate90(p=0.75),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Rotate(limit=90, interpolation=1, border_mode=0, value=0, mask_value=0, p=0.25),
A.HueSaturationValue(
hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=0,
p=0.5),
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.15, contrast_limit=0.0, p=1.0),
A.RandomBrightnessContrast(brightness_limit=0.0, contrast_limit=0.15, p=1.0),
A.RandomGamma(p=.0)], p=1.0),
A.PadIfNeeded(patch_size, patch_size)])
elif transform_type == 'moderate':
transform = A.Compose([
A.RandomRotate90(p=0.75),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Rotate(limit=360, interpolation=1, border_mode=0, value=0, mask_value=0, p=0.25),
A.transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2, p=0.1),
A.CoarseDropout(max_holes=12, max_height=12, max_width=12, fill_value=0, p=0.25),
A.RandomResizedCrop(patch_size, patch_size, scale=(0.5, 1.5), ratio=(0.5, 1.5), interpolation=1, p=0.5),
A.RGBShift(r_shift_limit=20, g_shift_limit=20, b_shift_limit=20, p=0.5),
A.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=20,
val_shift_limit=5,
p=0.5),
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.4, contrast_limit=0.0, p=0.75),
A.RandomBrightnessContrast(brightness_limit=0.0, contrast_limit=0.2, p=0.75),
A.RandomGamma(gamma_limit=(75, 125), p=0.5)], p=1.0),
A.OneOf([
A.Blur(blur_limit=1, p=0.75),
A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=0.75),
A.IAASharpen(alpha=(0.2, 0.5), lightness=(0.5, 1.0), p=0.75),
A.ImageCompression(quality_lower=60, quality_upper=100, compression_type=0, p=0.75)], p=1.0),
A.PadIfNeeded(patch_size, patch_size)])
elif transform_type == 'medium':
transform = A.Compose([
A.RandomRotate90(p=0.75),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Rotate(limit=90, interpolation=1, border_mode=0, value=0, mask_value=0, p=0.25),
A.HueSaturationValue(
hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=0,
p=0.5),
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.15, contrast_limit=0.0, p=1.0),
A.RandomBrightnessContrast(brightness_limit=0.0, contrast_limit=0.15, p=1.0),
A.RandomGamma(p=.0)], p=1.0),
A.OneOf([
A.ElasticTransform(
alpha=200,
sigma=20,
alpha_affine=20,
interpolation=1,
border_mode=0,
value=0,
mask_value=0,
p=1.0),
A.GridDistortion(
num_steps=10,
distort_limit=0.3,
interpolation=1,
border_mode=0,
value=0,
mask_value=0,
p=1.0)]),
A.PadIfNeeded(patch_size, patch_size)])
else:
transform = A.Compose([A.PadIfNeeded(patch_size, patch_size)])
return transform
def get_config(runner,
raw_uri,
processed_uri,
root_uri,
test=False,
external_model=False,
external_loss=False,
augment=False):
debug = False
train_scene_info = get_scene_info(join(processed_uri, 'train-scenes.csv'))
val_scene_info = get_scene_info(join(processed_uri, 'val-scenes.csv'))
log_tensorboard = True
run_tensorboard = True
class_config = ClassConfig(names=['no_building', 'building'])
if test:
debug = True
train_scene_info = train_scene_info[0:1]
val_scene_info = val_scene_info[0:1]
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
aoi_uri = join(raw_uri, aoi_path)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
label_crop_uri = join(processed_uri, 'crops',
os.path.basename(label_uri))
save_image_crop(raster_uri,
crop_uri,
label_uri=label_uri,
label_crop_uri=label_crop_uri,
size=600,
min_features=20,
class_config=class_config)
raster_uri = crop_uri
label_uri = label_crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(channel_order=[0, 1, 2],
uris=[raster_uri])
label_source = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=label_uri,
default_class_id=1,
ignore_crs_field=True),
ioa_thresh=0.5,
use_intersection_over_cell=False,
pick_min_class_id=False,
background_class_id=0,
infer_cells=True)
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source,
aoi_uris=[aoi_uri])
chip_sz = 200
train_scenes = [make_scene(info) for info in train_scene_info]
val_scenes = [make_scene(info) for info in val_scene_info]
dataset = DatasetConfig(class_config=class_config,
train_scenes=train_scenes,
validation_scenes=val_scenes)
if external_model:
model = ClassificationModelConfig(external_def=ExternalModuleConfig(
github_repo='lukemelas/EfficientNet-PyTorch',
# uri='s3://raster-vision-ahassan/models/EfficientNet-PyTorch.zip',
name='efficient_net',
entrypoint='efficientnet_b0',
force_reload=False,
entrypoint_kwargs={
'num_classes': len(class_config.names),
'pretrained': 'imagenet'
}))
else:
model = ClassificationModelConfig(backbone=Backbone.resnet50)
if external_loss:
external_loss_def = ExternalModuleConfig(
github_repo='AdeelH/pytorch-multi-class-focal-loss',
name='focal_loss',
entrypoint='focal_loss',
force_reload=False,
entrypoint_kwargs={
'alpha': [.75, .25],
'gamma': 2
})
else:
external_loss_def = None
solver = SolverConfig(lr=1e-4,
num_epochs=20,
test_num_epochs=4,
batch_sz=32,
one_cycle=True,
external_loss_def=external_loss_def)
if augment:
mu = np.array((0.485, 0.456, 0.406))
std = np.array((0.229, 0.224, 0.225))
aug_transform = A.Compose([
A.Flip(),
A.Transpose(),
A.RandomRotate90(),
A.ShiftScaleRotate(),
A.OneOf([
A.ChannelShuffle(),
A.CLAHE(),
A.FancyPCA(),
A.HueSaturationValue(),
A.RGBShift(),
A.ToGray(),
A.ToSepia(),
]),
A.OneOf([
A.RandomBrightness(),
A.RandomGamma(),
]),
A.OneOf([
A.GaussNoise(),
A.ISONoise(),
A.RandomFog(),
]),
A.OneOf([
A.Blur(),
A.MotionBlur(),
A.ImageCompression(),
A.Downscale(),
]),
A.CoarseDropout(max_height=32, max_width=32, max_holes=5)
])
base_transform = A.Normalize(mean=mu.tolist(), std=std.tolist())
plot_transform = A.Normalize(mean=(-mu / std).tolist(),
std=(1 / std).tolist(),
max_pixel_value=1.)
else:
aug_transform = None
base_transform = None
plot_transform = None
backend = PyTorchChipClassificationConfig(
model=model,
solver=solver,
log_tensorboard=log_tensorboard,
run_tensorboard=run_tensorboard,
test_mode=test,
base_transform=A.to_dict(base_transform),
aug_transform=A.to_dict(aug_transform),
plot_options=PlotOptions(transform=A.to_dict(plot_transform)))
config = ChipClassificationConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=chip_sz,
predict_chip_sz=chip_sz)
return config
def get_augmentations(name, img_size):
if name == 'training_none':
aug = A.Compose([
A.Resize(img_size, img_size),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
elif name == 'training_dropout':
aug = A.Compose([
A.Resize(img_size, img_size),
A.CoarseDropout(min_height=int(img_size * 0.05),
min_width=int(img_size * 0.05),
max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=1,
max_holes=20,
p=0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
elif name == 'training_1':
aug = A.Compose([
A.RandomResizedCrop(img_size, img_size, scale=(0.9, 1), p=1),
A.ShiftScaleRotate(p=0.5),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
p=0.7),
A.HueSaturationValue(hue_shift_limit=10,
val_shift_limit=10,
sat_shift_limit=10,
p=0.7),
A.CLAHE(clip_limit=(1, 4), p=0.5),
A.OneOf([
A.GaussNoise(var_limit=[10, 50]),
A.GaussianBlur(),
A.MotionBlur(),
A.MedianBlur(),
],
p=0.3),
A.OneOf([
A.OpticalDistortion(distort_limit=1.0),
A.GridDistortion(num_steps=5, distort_limit=1.),
A.ElasticTransform(alpha=3),
],
p=0.3),
A.OneOf([
A.ImageCompression(),
A.Downscale(scale_min=0.1, scale_max=0.15),
],
p=0.2),
A.IAAPiecewiseAffine(p=0.2),
A.IAASharpen(p=0.2),
A.CoarseDropout(max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=5,
max_holes=10,
p=0.5),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
elif name == 'training_2':
aug = A.Compose([
A.RandomResizedCrop(img_size, img_size, scale=(0.9, 1), p=1),
A.ShiftScaleRotate(p=0.5),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
p=0.7),
A.HueSaturationValue(hue_shift_limit=10,
val_shift_limit=10,
sat_shift_limit=10,
p=0.7),
A.CLAHE(clip_limit=(1, 4), p=0.5),
A.OneOf([
A.GaussNoise(var_limit=[10, 50]),
A.GaussianBlur(),
A.MotionBlur(),
A.MedianBlur(),
],
p=0.3),
A.OneOf([
A.OpticalDistortion(distort_limit=1.0),
A.GridDistortion(num_steps=5, distort_limit=1.),
A.ElasticTransform(alpha=3),
],
p=0.3),
A.OneOf([
A.ImageCompression(),
A.Downscale(scale_min=0.1, scale_max=0.15),
],
p=0.2),
A.IAAPiecewiseAffine(p=0.2),
A.IAASharpen(p=0.2),
A.CoarseDropout(max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=5,
max_holes=10,
p=0.5),
A.Normalize(),
ToTensorV2()
])
elif name == 'training_2_bis':
aug = A.Compose([
A.RandomResizedCrop(img_size, img_size, scale=(0.9, 1), p=1),
A.ShiftScaleRotate(rotate_limit=30, p=0.5),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
p=0.7),
A.HueSaturationValue(hue_shift_limit=10,
val_shift_limit=10,
sat_shift_limit=10,
p=0.7),
A.CLAHE(clip_limit=(1, 4), p=0.5),
A.OneOf([
A.GaussNoise(var_limit=[10, 50]),
A.GaussianBlur(),
A.MotionBlur(),
A.MedianBlur()
],
p=0.3),
#A.OneOf([A.OpticalDistortion(distort_limit=1.0), A.GridDistortion(num_steps=5, distort_limit=1.),
# A.ElasticTransform(alpha=3)], p=0.3),
A.OneOf([
A.ImageCompression(),
A.Downscale(scale_min=0.1, scale_max=0.15)
],
p=0.2),
#A.IAAPiecewiseAffine(p=0.2),
A.IAASharpen(p=0.2),
A.CoarseDropout(max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=5,
max_holes=10,
p=0.5),
A.Normalize(),
ToTensorV2()
])
elif name == 'training_3':
aug = A.Compose([
A.Rotate(limit=5),
A.RandomResizedCrop(img_size, img_size, scale=(0.9, 1), p=1),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.15,
contrast_limit=0.15,
p=0.5),
A.CoarseDropout(min_height=int(img_size * 0.05),
min_width=int(img_size * 0.05),
max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=1,
max_holes=10,
p=0.5),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
elif name == 'training_4':
aug = A.Compose([
A.Rotate(limit=5, p=1),
A.RandomResizedCrop(img_size, img_size, scale=(0.9, 1), p=1),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(brightness_limit=(-0.15, +0.25),
contrast_limit=(-0.15, +0.25),
p=1),
A.CLAHE(clip_limit=(1, 4), p=0.5),
A.OneOf([
A.GaussNoise(var_limit=(10, 50)),
A.GaussianBlur(),
A.MotionBlur(),
A.MedianBlur(),
],
p=1),
A.IAASharpen(p=0.3),
A.CoarseDropout(min_height=int(img_size * 0.05),
min_width=int(img_size * 0.05),
max_height=int(img_size * 0.1),
max_width=int(img_size * 0.1),
min_holes=1,
max_holes=20,
p=0),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
])
elif name == 'validation':
aug = A.Compose(
[A.Resize(img_size, img_size),
A.Normalize(),
ToTensorV2()])
elif name == 'none':
aug = A.Compose([A.Resize(img_size, img_size)])
else:
raise ValueError(f"{name} is not a valid augmentations name")
return aug
def get_transform_imagenet(use_albu_aug):
if use_albu_aug:
train_transform = al.Compose([
# al.Flip(p=0.5),
al.Resize(256, 256, interpolation=2),
al.RandomResizedCrop(224,
224,
scale=(0.08, 1.0),
ratio=(3. / 4., 4. / 3.),
interpolation=2),
al.HorizontalFlip(),
al.OneOf(
[
al.OneOf(
[
al.ShiftScaleRotate(
border_mode=cv2.BORDER_CONSTANT,
rotate_limit=30), # , p=0.05),
al.OpticalDistortion(
border_mode=cv2.BORDER_CONSTANT,
distort_limit=5.0,
shift_limit=0.1),
# , p=0.05),
al.GridDistortion(border_mode=cv2.BORDER_CONSTANT
), # , p=0.05),
al.ElasticTransform(
border_mode=cv2.BORDER_CONSTANT,
alpha_affine=15), # , p=0.05),
],
p=0.1),
al.OneOf(
[
al.RandomGamma(), # p=0.05),
al.HueSaturationValue(), # p=0.05),
al.RGBShift(), # p=0.05),
al.CLAHE(), # p=0.05),
al.ChannelShuffle(), # p=0.05),
al.InvertImg(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.RandomSnow(), # p=0.05),
al.RandomRain(), # p=0.05),
al.RandomFog(), # p=0.05),
al.RandomSunFlare(num_flare_circles_lower=1,
num_flare_circles_upper=2,
src_radius=110),
# p=0.05, ),
al.RandomShadow(), # p=0.05),
],
p=0.1),
al.RandomBrightnessContrast(p=0.1),
al.OneOf(
[
al.GaussNoise(), # p=0.05),
al.ISONoise(), # p=0.05),
al.MultiplicativeNoise(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.ToGray(), # p=0.05),
al.ToSepia(), # p=0.05),
al.Solarize(), # p=0.05),
al.Equalize(), # p=0.05),
al.Posterize(), # p=0.05),
al.FancyPCA(), # p=0.05),
],
p=0.1),
al.OneOf(
[
# al.MotionBlur(blur_limit=1),
al.Blur(blur_limit=[3, 5]),
al.MedianBlur(blur_limit=[3, 5]),
al.GaussianBlur(blur_limit=[3, 5]),
],
p=0.1),
al.OneOf(
[
al.CoarseDropout(), # p=0.05),
al.Cutout(), # p=0.05),
al.GridDropout(), # p=0.05),
al.ChannelDropout(), # p=0.05),
al.RandomGridShuffle(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.Downscale(), # p=0.1),
al.ImageCompression(quality_lower=60), # , p=0.1),
],
p=0.1),
],
p=0.5),
al.Normalize(),
ToTensorV2()
])
else:
train_transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
if use_albu_aug:
train_transform = MultiDataTransformAlbu(train_transform)
else:
train_transform = MultiDataTransform(train_transform)
return train_transform, test_transform
import os
from typing import Tuple, List
import albumentations as a
import cv2
import numpy as np
transform_distort = a.Compose([
a.ImageCompression(quality_lower=39, quality_upper=40, p=0.4),
a.MultiplicativeNoise(multiplier=(0.5, 1.5), per_channel=True, p=0.2),
a.RandomBrightnessContrast(p=0.3),
])
transform_beauty = a.Compose([
a.RandomBrightnessContrast(p=0.3),
])
def augment_distort(img: np.ndarray):
transformed = transform_distort(image=img)
transformed_image = transformed["image"]
return transformed_image
def augment_beauty(img: np.ndarray):
transformed = transform_beauty(image=img)
transformed_image = transformed["image"]
return transformed_image
def load_altered_dataset_beauty(
def experiment(device, args=None):
"""Train model.
Args:
device (str): device to use for training.
args (dict): experiment arguments.
"""
if args is None:
args = dict
train_config = args["train"]
train_augmentations = albu.Compose(
[
albu.OneOf([
albu.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=35,
val_shift_limit=25),
albu.RandomGamma(),
albu.CLAHE(),
]),
albu.RandomBrightnessContrast(brightness_limit=[-0.3, 0.3],
contrast_limit=[-0.3, 0.3],
p=0.5),
albu.OneOf([
albu.Blur(),
albu.MotionBlur(),
albu.GaussNoise(),
albu.ImageCompression(quality_lower=75)
]),
albu.ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.15,
rotate_limit=10,
border_mode=0,
p=0.5),
albu.Resize(300, 300),
albu.Normalize(),
ToTensorV2(),
],
bbox_params=albu.BboxParams(
"albumentations"
), # 'albumentations' because x1, y1, x2, y2 in range [0, 1]
)
train_dataset = COCOFileDataset(train_config["annotations"],
train_config["images_dir"],
transforms=train_augmentations)
train_loader = DataLoader(
train_dataset,
batch_size=train_config["batch_size"],
num_workers=train_config["num_workers"],
shuffle=True,
drop_last=True,
)
logger.info("Train dataset information:")
logger.info("\n" + train_dataset.info())
valid_config = args["validation"]
valid_augmentations = albu.Compose(
[
albu.Resize(300, 300),
albu.Normalize(),
ToTensorV2(),
],
bbox_params=albu.BboxParams(
format="albumentations"
), # 'albumentations' because x1, y1, x2, y2 in range [0, 1]
)
valid_dataset = COCOFileDataset(valid_config["annotations"],
valid_config["images_dir"],
transforms=valid_augmentations)
valid_loader = DataLoader(
valid_dataset,
batch_size=valid_config["batch_size"],
num_workers=valid_config["num_workers"],
shuffle=False,
drop_last=False,
)
logger.info("Validation dataset information:")
logger.info("\n" + valid_dataset.info())
model_config = args["model"]
num_classes = model_config["num_classes"] + 1 # +1 for background class
seed_all(42)
model = SSD300(model_config["backbone"], num_classes)
model = model.to(device)
optimizer = optim.AdamW(model.parameters(), lr=1e-3 / 2)
# optimizer = optim.SGD(model.parameters(), lr=2.6e-3, momentum=0.9, weight_decay=0.0005)
epoch_scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer, args["experiment"]["num_epochs"])
batch_scheduler = None
criterion = Loss(num_classes)
experiment_config = args["experiment"]
num_epochs = experiment_config["num_epochs"]
for epoch_idx in range(1, num_epochs + 1):
logger.info(f"Epoch: {epoch_idx}/{num_epochs}")
train_metrics = train_fn(train_loader,
model,
device,
criterion,
optimizer,
batch_scheduler,
verbose=False)
logger.info(f" Train: {train_metrics}")
# TODO: checkpoints
valid_metrics = valid_fn(valid_loader,
model,
device,
criterion,
verbose=False)
logger.info(f"Validation: {valid_metrics}")
epoch_scheduler.step()
export_to_onnx(model, torch.randn(1, 3, 300, 300),
experiment_config["onnx"])
logger.info("Exported ONNX model to '{}'".format(
experiment_config["onnx"]))
def train_process(data_path, config):
def _worker_init_fn_():
import random
import numpy as np
import torch
random_seed = config.random_seed
torch.manual_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(random_seed)
input_size = (config.img_height, config.img_width)
PAD_VALUE = (0, 0, 0)
IGNORE_INDEX = 255
transforms = [
abm.RandomResizedCrop(
scale=(0.7, 1),
ratio=(1.5, 2),
height=config.img_height,
width=config.img_width,
interpolation=cv2.INTER_NEAREST,
always_apply=True,
),
abm.OneOf([abm.IAAAdditiveGaussianNoise(),
abm.GaussNoise()], p=0.5),
abm.OneOf(
[
abm.MedianBlur(blur_limit=3),
abm.GaussianBlur(blur_limit=3),
abm.MotionBlur(blur_limit=3),
],
p=0.5,
),
abm.OneOf([
abm.ShiftScaleRotate(
rotate_limit=7,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_CONSTANT,
value=PAD_VALUE,
mask_value=IGNORE_INDEX,
p=1.0,
),
abm.ElasticTransform(
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_CONSTANT,
alpha_affine=30,
value=PAD_VALUE,
mask_value=IGNORE_INDEX,
p=1.0,
),
abm.Perspective(
scale=(0.05),
interpolation=cv2.INTER_NEAREST,
pad_mode=cv2.BORDER_CONSTANT,
pad_val=PAD_VALUE,
mask_pad_val=IGNORE_INDEX,
keep_size=True,
fit_output=True,
p=1.0,
),
]),
abm.RandomGamma(gamma_limit=(80, 120), p=0.5),
abm.RandomBrightnessContrast(brightness_limit=(-0.5, 0.5),
contrast_limit=(-0.5, 0.5),
p=0.5),
abm.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=0.5),
abm.RandomShadow(p=0.5),
abm.ChannelShuffle(p=0.5),
abm.ChannelDropout(p=0.5),
abm.HorizontalFlip(p=0.5),
abm.ImageCompression(quality_lower=50, p=0.5),
abm.Cutout(num_holes=100, max_w_size=8, max_h_size=8, p=0.5),
]
data_transform = DataTransformBase(transforms=transforms,
input_size=input_size,
normalize=True)
train_dataset = EgoRailDataset(data_path=data_path,
phase="train",
transform=data_transform)
val_dataset = EgoRailDataset(data_path=data_path,
phase="val",
transform=data_transform)
# train_dataset.weighted_class()
weighted_values = [8.90560578, 1.53155476]
train_data_loader = DataLoader(
train_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.num_workers,
drop_last=True,
worker_init_fn=_worker_init_fn_(),
)
val_data_loader = DataLoader(
val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers,
drop_last=True,
)
data_loaders_dict = {"train": train_data_loader, "val": val_data_loader}
model = BiSeNetV2(n_classes=config.num_classes)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
criterion = OHEMCELoss(thresh=config.ohem_ce_loss_thresh,
weighted_values=weighted_values)
base_lr_rate = config.lr_rate / (config.batch_size *
config.batch_multiplier)
base_weight_decay = config.weight_decay * (config.batch_size *
config.batch_multiplier)
def _lambda_epoch(epoch):
import math
max_epoch = config.num_epochs
return math.pow((1 - epoch * 1.0 / max_epoch), 0.9)
optimizer = torch.optim.SGD(
model.parameters(),
lr=base_lr_rate,
momentum=config.momentum,
weight_decay=base_weight_decay,
)
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=_lambda_epoch)
trainer = BiSeNetV2Trainer(
model=model,
criterion=criterion,
metric_func=None,
optimizer=optimizer,
data_loaders_dict=data_loaders_dict,
config=config,
scheduler=scheduler,
device=device,
)
if config.snapshot and os.path.isfile(config.snapshot):
trainer.resume_checkpoint(config.snapshot)
with torch.autograd.set_detect_anomaly(True):
trainer.train()
from transformers import PreTrainedTokenizerFast
from tqdm.auto import tqdm
train_transform = alb.Compose(
[
alb.Compose(
[alb.ShiftScaleRotate(shift_limit=0, scale_limit=(-.15, 0), rotate_limit=1, border_mode=0, interpolation=3,
value=[255, 255, 255], p=1),
alb.GridDistortion(distort_limit=0.1, border_mode=0, interpolation=3, value=[255, 255, 255], p=.5)], p=.15),
# alb.InvertImg(p=.15),
alb.RGBShift(r_shift_limit=15, g_shift_limit=15,
b_shift_limit=15, p=0.3),
alb.GaussNoise(10, p=.2),
alb.RandomBrightnessContrast(.05, (-.2, 0), True, p=0.2),
alb.ImageCompression(95, p=.3),
alb.ToGray(always_apply=True),
alb.Normalize((0.7931, 0.7931, 0.7931), (0.1738, 0.1738, 0.1738)),
# alb.Sharpen()
ToTensorV2(),
]
)
test_transform = alb.Compose(
[
alb.ToGray(always_apply=True),
alb.Normalize((0.7931, 0.7931, 0.7931), (0.1738, 0.1738, 0.1738)),
# alb.Sharpen()
ToTensorV2(),
]
)
def make_transform(args):
base_transform = [
A.Resize(args.img_size, args.img_size),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
]
train_transform = []
if args.Blur:
train_transform.append(A.Blur(p=args.Blur))
if args.Blur:
train_transform.append(A.ElasticTransform(p=args.Blur))
if args.CLAHE:
train_transform.append(
A.CLAHE(clip_limit=(1, 4), tile_grid_size=(8, 8), p=args.CLAHE))
if args.RandomBrightnessContrast:
train_transform.append(
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
brightness_by_max=True,
p=args.RandomBrightnessContrast))
if args.HueSaturationValue:
train_transform.append(
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=args.HueSaturationValue))
if args.RGBShift:
train_transform.append(
A.RGBShift(r_shift_limit=20,
g_shift_limit=20,
b_shift_limit=20,
p=args.RGBShift))
if args.RandomGamma:
train_transform.append(
A.RandomGamma(gamma_limit=(80, 120), p=args.RandomGamma))
if args.HorizontalFlip:
train_transform.append(A.HorizontalFlip(p=args.HorizontalFlip))
if args.VerticalFlip:
train_transform.append(A.VerticalFlip(p=args.VerticalFlip))
if args.ShiftScaleRotate:
train_transform.append(
A.ShiftScaleRotate(shift_limit=0.2,
scale_limit=0.2,
rotate_limit=10,
border_mode=args.ShiftScaleRotateMode,
p=args.ShiftScaleRotate))
if args.GridDistortion:
train_transform.append(
A.GridDistortion(num_steps=5,
distort_limit=(-0.3, 0.3),
p=args.GridDistortion))
if args.MotionBlur:
train_transform.append(
A.MotionBlur(blur_limit=(3, 7), p=args.MotionBlur))
if args.RandomResizedCrop:
train_transform.append(
A.RandomResizedCrop(height=args.img_size,
width=args.img_size,
scale=(-0.4, 1.0),
ratio=(0.75, 1.3333333333333333),
p=args.RandomResizedCrop))
if args.ImageCompression:
train_transform.append(
A.ImageCompression(quality_lower=99,
quality_upper=100,
p=args.ImageCompression))
train_transform.extend(base_transform)
train_transform = A.Compose(train_transform)
test_transform = A.Compose(base_transform)
return train_transform, test_transform
